{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.insert(0,\"..\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# print(sys.path)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [], "source": [ "from optimus import Optimus" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\numpy\\_distributor_init.py:32: UserWarning: loaded more than 1 DLL from .libs:\n", "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.NOIJJG62EMASZI6NYURL6JBKM4EVBGM7.gfortran-win_amd64.dll\n", "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dll\n", " stacklevel=1)\n" ] } ], "source": [ "# op = Optimus(engine=\"dask\",verbose=True)\n", "op = Optimus(\"dask\", n_workers=1, threads_per_worker=8, processes=False, memory_limit=\"3G\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# df = op.load.file(\"data/Meteorite_Landings.csv\")\n", "# df = op.load.file(\"https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv\")\n", "df = op.load.file(\"data/crime.csv\").cache()\n", "# df = op.load.file(\"data/crime.csv\")\n", "# df= df.to_optimus_pandas()\n", "\n", "# op.load.csv(\"data/airline-safety_csv.csv\", sep=\",\", error_bad_lines=False, header=True, null_value=\"null\",infer_schema=\"true\").ext.cache()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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INCIDENT_NUMBEROFFENSE_CODEOFFENSE_CODE_GROUPOFFENSE_DESCRIPTIONDISTRICTREPORTING_AREASHOOTINGOCCURRED_ON_DATEYEARMONTHDAY_OF_WEEKHOURUCR_PARTSTREETLatLongLocation
0I18207094500619LarcenyLARCENY ALL OTHERSD148082018-09-02 13:00:0020189Sunday13Part OneLINCOLN ST42.35779134-71.13937053(42.35779134, -71.13937053)
1I18207094301402VandalismVANDALISMC113472018-08-21 00:00:0020188Tuesday0Part TwoHECLA ST42.30682138-71.06030035(42.30682138, -71.06030035)
2I18207094103410TowedTOWED MOTOR VEHICLED41512018-09-03 19:27:0020189Monday19Part ThreeCAZENOVE ST42.34658879-71.07242943(42.34658879, -71.07242943)
3I18207094003114Investigate PropertyINVESTIGATE PROPERTYD42722018-09-03 21:16:0020189Monday21Part ThreeNEWCOMB ST42.33418175-71.07866441(42.33418175, -71.07866441)
4I18207093803114Investigate PropertyINVESTIGATE PROPERTYB34212018-09-03 21:05:0020189Monday21Part ThreeDELHI ST42.27536542-71.09036101(42.27536542, -71.09036101)
5I18207093603820Motor Vehicle Accident ResponseM/V ACCIDENT INVOLVING PEDESTRIAN - INJURYC113982018-09-03 21:09:0020189Monday21Part ThreeTALBOT AVE42.29019621-71.07159012(42.29019621, -71.07159012)
6I18207093300724Auto TheftAUTO THEFTB23302018-09-03 21:25:0020189Monday21Part OneNORMANDY ST42.30607218-71.08273260(42.30607218, -71.08273260)
7I18207093203301Verbal DisputesVERBAL DISPUTEB25842018-09-03 20:39:3720189Monday20Part ThreeLAWN ST42.32701648-71.10555088(42.32701648, -71.10555088)
8I18207093100301RobberyROBBERY - STREETC61772018-09-03 20:48:0020189Monday20Part OneMASSACHUSETTS AVE42.33152148-71.07085307(42.33152148, -71.07085307)
9I18207092903301Verbal DisputesVERBAL DISPUTEC113642018-09-03 20:38:0020189Monday20Part ThreeLESLIE ST42.29514664-71.05860832(42.29514664, -71.05860832)
\n", "
" ], "text/plain": [ " INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP \\\n", "0 I182070945 00619 Larceny \n", "1 I182070943 01402 Vandalism \n", "2 I182070941 03410 Towed \n", "3 I182070940 03114 Investigate Property \n", "4 I182070938 03114 Investigate Property \n", "5 I182070936 03820 Motor Vehicle Accident Response \n", "6 I182070933 00724 Auto Theft \n", "7 I182070932 03301 Verbal Disputes \n", "8 I182070931 00301 Robbery \n", "9 I182070929 03301 Verbal Disputes \n", "\n", " OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA \\\n", "0 LARCENY ALL OTHERS D14 808 \n", "1 VANDALISM C11 347 \n", "2 TOWED MOTOR VEHICLE D4 151 \n", "3 INVESTIGATE PROPERTY D4 272 \n", "4 INVESTIGATE PROPERTY B3 421 \n", "5 M/V ACCIDENT INVOLVING PEDESTRIAN - INJURY C11 398 \n", "6 AUTO THEFT B2 330 \n", "7 VERBAL DISPUTE B2 584 \n", "8 ROBBERY - STREET C6 177 \n", "9 VERBAL DISPUTE C11 364 \n", "\n", " SHOOTING OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART \\\n", "0 2018-09-02 13:00:00 2018 9 Sunday 13 Part One \n", "1 2018-08-21 00:00:00 2018 8 Tuesday 0 Part Two \n", "2 2018-09-03 19:27:00 2018 9 Monday 19 Part Three \n", "3 2018-09-03 21:16:00 2018 9 Monday 21 Part Three \n", "4 2018-09-03 21:05:00 2018 9 Monday 21 Part Three \n", "5 2018-09-03 21:09:00 2018 9 Monday 21 Part Three \n", "6 2018-09-03 21:25:00 2018 9 Monday 21 Part One \n", "7 2018-09-03 20:39:37 2018 9 Monday 20 Part Three \n", "8 2018-09-03 20:48:00 2018 9 Monday 20 Part One \n", "9 2018-09-03 20:38:00 2018 9 Monday 20 Part Three \n", "\n", " STREET Lat Long Location \n", "0 LINCOLN ST 42.35779134 -71.13937053 (42.35779134, -71.13937053) \n", "1 HECLA ST 42.30682138 -71.06030035 (42.30682138, -71.06030035) \n", "2 CAZENOVE ST 42.34658879 -71.07242943 (42.34658879, -71.07242943) \n", "3 NEWCOMB ST 42.33418175 -71.07866441 (42.33418175, -71.07866441) \n", "4 DELHI ST 42.27536542 -71.09036101 (42.27536542, -71.09036101) \n", "5 TALBOT AVE 42.29019621 -71.07159012 (42.29019621, -71.07159012) \n", "6 NORMANDY ST 42.30607218 -71.08273260 (42.30607218, -71.08273260) \n", "7 LAWN ST 42.32701648 -71.10555088 (42.32701648, -71.10555088) \n", "8 MASSACHUSETTS AVE 42.33152148 -71.07085307 (42.33152148, -71.07085307) \n", "9 LESLIE ST 42.29514664 -71.05860832 (42.29514664, -71.05860832) " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "df1 = df.cols.lower(\"INCIDENT_NUMBER\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'file_name': 'data/crime.csv',\n", " 'name': 'crime.csv',\n", " 'transformations': {'actions': [{'lower': ['INCIDENT_NUMBER']}]}}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.meta" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 319073 rows / 1 columns
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1 partition(s)
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INCIDENT_NUMBER
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1 (object)
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Viewing 10 of 319073 rows / 1 columns
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1 partition(s) <class 'optimus.engines.dask.dataframe.DaskDataFrame'>
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INCIDENT_NUMBEROFFENSE_CODEOFFENSE_CODE_GROUPOFFENSE_DESCRIPTIONDISTRICTREPORTING_AREASHOOTINGOCCURRED_ON_DATEYEARMONTHDAY_OF_WEEKHOURUCR_PARTSTREETLatLongLocation
0larceny00619LarcenyLARCENY ALL OTHERSD148082018-09-02 13:00:0020189Sunday13Part OneLINCOLN ST42.35779134-71.13937053(42.35779134, -71.13937053)
1I18207094301402VandalismVANDALISMC113472018-08-21 00:00:0020188Tuesday0Part TwoHECLA ST42.30682138-71.06030035(42.30682138, -71.06030035)
2I18207094103410TowedTOWED MOTOR VEHICLED41512018-09-03 19:27:0020189Monday19Part ThreeCAZENOVE ST42.34658879-71.07242943(42.34658879, -71.07242943)
3I18207094003114Investigate PropertyINVESTIGATE PROPERTYD42722018-09-03 21:16:0020189Monday21Part ThreeNEWCOMB ST42.33418175-71.07866441(42.33418175, -71.07866441)
4I18207093803114Investigate PropertyINVESTIGATE PROPERTYB34212018-09-03 21:05:0020189Monday21Part ThreeDELHI ST42.27536542-71.09036101(42.27536542, -71.09036101)
......................................................
319068I050310906-0003125Warrant ArrestsWARRANT ARRESTD42852016-06-05 17:25:0020166Sunday17Part ThreeCOVENTRY ST42.33695098-71.08574813(42.33695098, -71.08574813)
319069I030217815-0800111HomicideMURDER, NON-NEGLIGIENT MANSLAUGHTERE185202015-07-09 13:38:0020157Thursday13Part OneRIVER ST42.25592648-71.12317207(42.25592648, -71.12317207)
319070I030217815-0803125Warrant ArrestsWARRANT ARRESTE185202015-07-09 13:38:0020157Thursday13Part ThreeRIVER ST42.25592648-71.12317207(42.25592648, -71.12317207)
319071I010370257-0003125Warrant ArrestsWARRANT ARRESTE135692016-05-31 19:35:0020165Tuesday19Part ThreeNEW WASHINGTON ST42.30233307-71.11156487(42.30233307, -71.11156487)
31907214205255003125Warrant ArrestsWARRANT ARRESTD49032015-06-22 00:12:0020156Monday0Part ThreeWASHINGTON ST42.33383935-71.08029038(42.33383935, -71.08029038)
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319073 rows × 17 columns

\n", "
" ], "text/plain": [ " INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP \\\n", "0 larceny 00619 Larceny \n", "1 I182070943 01402 Vandalism \n", "2 I182070941 03410 Towed \n", "3 I182070940 03114 Investigate Property \n", "4 I182070938 03114 Investigate Property \n", "... ... ... ... \n", "319068 I050310906-00 03125 Warrant Arrests \n", "319069 I030217815-08 00111 Homicide \n", "319070 I030217815-08 03125 Warrant Arrests \n", "319071 I010370257-00 03125 Warrant Arrests \n", "319072 142052550 03125 Warrant Arrests \n", "\n", " OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING \\\n", "0 LARCENY ALL OTHERS D14 808 \n", "1 VANDALISM C11 347 \n", "2 TOWED MOTOR VEHICLE D4 151 \n", "3 INVESTIGATE PROPERTY D4 272 \n", "4 INVESTIGATE PROPERTY B3 421 \n", "... ... ... ... ... \n", "319068 WARRANT ARREST D4 285 \n", "319069 MURDER, NON-NEGLIGIENT MANSLAUGHTER E18 520 \n", "319070 WARRANT ARREST E18 520 \n", "319071 WARRANT ARREST E13 569 \n", "319072 WARRANT ARREST D4 903 \n", "\n", " OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART \\\n", "0 2018-09-02 13:00:00 2018 9 Sunday 13 Part One \n", "1 2018-08-21 00:00:00 2018 8 Tuesday 0 Part Two \n", "2 2018-09-03 19:27:00 2018 9 Monday 19 Part Three \n", "3 2018-09-03 21:16:00 2018 9 Monday 21 Part Three \n", "4 2018-09-03 21:05:00 2018 9 Monday 21 Part Three \n", "... ... ... ... ... ... ... \n", "319068 2016-06-05 17:25:00 2016 6 Sunday 17 Part Three \n", "319069 2015-07-09 13:38:00 2015 7 Thursday 13 Part One \n", "319070 2015-07-09 13:38:00 2015 7 Thursday 13 Part Three \n", "319071 2016-05-31 19:35:00 2016 5 Tuesday 19 Part Three \n", "319072 2015-06-22 00:12:00 2015 6 Monday 0 Part Three \n", "\n", " STREET Lat Long \\\n", "0 LINCOLN ST 42.35779134 -71.13937053 \n", "1 HECLA ST 42.30682138 -71.06030035 \n", "2 CAZENOVE ST 42.34658879 -71.07242943 \n", "3 NEWCOMB ST 42.33418175 -71.07866441 \n", "4 DELHI ST 42.27536542 -71.09036101 \n", "... ... ... ... \n", "319068 COVENTRY ST 42.33695098 -71.08574813 \n", "319069 RIVER ST 42.25592648 -71.12317207 \n", "319070 RIVER ST 42.25592648 -71.12317207 \n", "319071 NEW WASHINGTON ST 42.30233307 -71.11156487 \n", "319072 WASHINGTON ST 42.33383935 -71.08029038 \n", "\n", " Location \n", "0 (42.35779134, -71.13937053) \n", "1 (42.30682138, -71.06030035) \n", "2 (42.34658879, -71.07242943) \n", "3 (42.33418175, -71.07866441) \n", "4 (42.27536542, -71.09036101) \n", "... ... \n", "319068 (42.33695098, -71.08574813) \n", "319069 (42.25592648, -71.12317207) \n", "319070 (42.25592648, -71.12317207) \n", "319071 (42.30233307, -71.11156487) \n", "319072 (42.33383935, -71.08029038) \n", "\n", "[319073 rows x 17 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import dask\n", "from dask.dataframe import from_delayed\n", "\n", "\n", "def oset(col_name, expr, where):\n", " \n", " where = (where.data[where.cols.names(0)[0]]).to_delayed()\n", " ddf = df.to_delayed()\n", " \n", " def func(_df, _col_name, _mask):\n", " \n", " _df.loc[_mask,[_col_name]]=expr(_df)\n", " return _df\n", "\n", " for _part,_mask in zip(ddf, where):\n", " delayed_parts = dask.delayed(func)(_part, col_name, _mask)\n", "\n", " return from_delayed(delayed_parts)\n", "\n", "col_name=\"INCIDENT_NUMBER\"\n", "where= ((df[col_name]==\"I182070945\") | (df[\"DISTRICT\"]==\"I182070945\"))\n", "F = df.functions\n", "\n", "def expr(series):\n", " return F.lower(series[\"OFFENSE_CODE_GROUP\"])\n", "\n", "\n", "oset(col_name, expr, where).compute()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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INCIDENT_NUMBEROFFENSE_CODEOFFENSE_CODE_GROUPOFFENSE_DESCRIPTIONDISTRICTREPORTING_AREASHOOTINGOCCURRED_ON_DATEYEARMONTHDAY_OF_WEEKHOURUCR_PARTSTREETLatLongLocation
0larceny00619LarcenyLARCENY ALL OTHERSD148082018-09-02 13:00:0020189Sunday13Part OneLINCOLN ST42.35779134-71.13937053(42.35779134, -71.13937053)
1I18207094301402VandalismVANDALISMC113472018-08-21 00:00:0020188Tuesday0Part TwoHECLA ST42.30682138-71.06030035(42.30682138, -71.06030035)
2I18207094103410TowedTOWED MOTOR VEHICLED41512018-09-03 19:27:0020189Monday19Part ThreeCAZENOVE ST42.34658879-71.07242943(42.34658879, -71.07242943)
3I18207094003114Investigate PropertyINVESTIGATE PROPERTYD42722018-09-03 21:16:0020189Monday21Part ThreeNEWCOMB ST42.33418175-71.07866441(42.33418175, -71.07866441)
4I18207093803114Investigate PropertyINVESTIGATE PROPERTYB34212018-09-03 21:05:0020189Monday21Part ThreeDELHI ST42.27536542-71.09036101(42.27536542, -71.09036101)
5I18207093603820Motor Vehicle Accident ResponseM/V ACCIDENT INVOLVING PEDESTRIAN - INJURYC113982018-09-03 21:09:0020189Monday21Part ThreeTALBOT AVE42.29019621-71.07159012(42.29019621, -71.07159012)
6I18207093300724Auto TheftAUTO THEFTB23302018-09-03 21:25:0020189Monday21Part OneNORMANDY ST42.30607218-71.08273260(42.30607218, -71.08273260)
7I18207093203301Verbal DisputesVERBAL DISPUTEB25842018-09-03 20:39:3720189Monday20Part ThreeLAWN ST42.32701648-71.10555088(42.32701648, -71.10555088)
8I18207093100301RobberyROBBERY - STREETC61772018-09-03 20:48:0020189Monday20Part OneMASSACHUSETTS AVE42.33152148-71.07085307(42.33152148, -71.07085307)
9I18207092903301Verbal DisputesVERBAL DISPUTEC113642018-09-03 20:38:0020189Monday20Part ThreeLESLIE ST42.29514664-71.05860832(42.29514664, -71.05860832)
\n", "
" ], "text/plain": [ " INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP \\\n", "0 larceny 00619 Larceny \n", "1 I182070943 01402 Vandalism \n", "2 I182070941 03410 Towed \n", "3 I182070940 03114 Investigate Property \n", "4 I182070938 03114 Investigate Property \n", "5 I182070936 03820 Motor Vehicle Accident Response \n", "6 I182070933 00724 Auto Theft \n", "7 I182070932 03301 Verbal Disputes \n", "8 I182070931 00301 Robbery \n", "9 I182070929 03301 Verbal Disputes \n", "\n", " OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA \\\n", "0 LARCENY ALL OTHERS D14 808 \n", "1 VANDALISM C11 347 \n", "2 TOWED MOTOR VEHICLE D4 151 \n", "3 INVESTIGATE PROPERTY D4 272 \n", "4 INVESTIGATE PROPERTY B3 421 \n", "5 M/V ACCIDENT INVOLVING PEDESTRIAN - INJURY C11 398 \n", "6 AUTO THEFT B2 330 \n", "7 VERBAL DISPUTE B2 584 \n", "8 ROBBERY - STREET C6 177 \n", "9 VERBAL DISPUTE C11 364 \n", "\n", " SHOOTING OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART \\\n", "0 2018-09-02 13:00:00 2018 9 Sunday 13 Part One \n", "1 2018-08-21 00:00:00 2018 8 Tuesday 0 Part Two \n", "2 2018-09-03 19:27:00 2018 9 Monday 19 Part Three \n", "3 2018-09-03 21:16:00 2018 9 Monday 21 Part Three \n", "4 2018-09-03 21:05:00 2018 9 Monday 21 Part Three \n", "5 2018-09-03 21:09:00 2018 9 Monday 21 Part Three \n", "6 2018-09-03 21:25:00 2018 9 Monday 21 Part One \n", "7 2018-09-03 20:39:37 2018 9 Monday 20 Part Three \n", "8 2018-09-03 20:48:00 2018 9 Monday 20 Part One \n", "9 2018-09-03 20:38:00 2018 9 Monday 20 Part Three \n", "\n", " STREET Lat Long Location \n", "0 LINCOLN ST 42.35779134 -71.13937053 (42.35779134, -71.13937053) \n", "1 HECLA ST 42.30682138 -71.06030035 (42.30682138, -71.06030035) \n", "2 CAZENOVE ST 42.34658879 -71.07242943 (42.34658879, -71.07242943) \n", "3 NEWCOMB ST 42.33418175 -71.07866441 (42.33418175, -71.07866441) \n", "4 DELHI ST 42.27536542 -71.09036101 (42.27536542, -71.09036101) \n", "5 TALBOT AVE 42.29019621 -71.07159012 (42.29019621, -71.07159012) \n", "6 NORMANDY ST 42.30607218 -71.08273260 (42.30607218, -71.08273260) \n", "7 LAWN ST 42.32701648 -71.10555088 (42.32701648, -71.10555088) \n", "8 MASSACHUSETTS AVE 42.33152148 -71.07085307 (42.33152148, -71.07085307) \n", "9 LESLIE ST 42.29514664 -71.05860832 (42.29514664, -71.05860832) " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['INCIDENT_NUMBER']" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"INCIDENT_NUMBER\"].cols.names(0)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", " ... \n", "319068 False\n", "319069 False\n", "319070 False\n", "319071 False\n", "319072 False\n", "Length: 319073, dtype: bool" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "((df[\"INCIDENT_NUMBER\"].data[\"INCIDENT_NUMBER\"]==\"i182070945\") | (df[\"DISTRICT\"].data[\"DISTRICT\"]==\"I182070945\")).compute()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['INCIDENT_NUMBER',\n", " 'OFFENSE_CODE',\n", " 'OFFENSE_CODE_GROUP',\n", " 'OFFENSE_DESCRIPTION',\n", " 'DISTRICT',\n", " 'REPORTING_AREA',\n", " 'SHOOTING',\n", " 'OCCURRED_ON_DATE',\n", " 'YEAR',\n", " 'MONTH',\n", " 'DAY_OF_WEEK',\n", " 'HOUR',\n", " 'UCR_PART',\n", " 'STREET',\n", " 'Lat',\n", " 'Long',\n", " 'Location']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.names()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df.set_buffer()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 1.67 s\n" ] }, { "data": { "text/plain": [ "{'columns': {'INCIDENT_NUMBER': {'profiler_dtype': {'dtype': 'string'},\n", " 'stats': {'match': 319073,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'I162030584', 'count': 13},\n", " {'value': 'I152080623', 'count': 11},\n", " {'value': 'I172013170', 'count': 10},\n", " {'value': 'I182065208', 'count': 10},\n", " {'value': 'I172096394', 'count': 10},\n", " {'value': 'I162071327', 'count': 9},\n", " {'value': 'I162001871', 'count': 9},\n", " {'value': 'I172056883', 'count': 9},\n", " {'value': 'I172054429', 'count': 9},\n", " {'value': 'I162098170', 'count': 9},\n", " {'value': 'I172022524', 'count': 9},\n", " {'value': 'I152076465', 'count': 8},\n", " {'value': 'I172053616', 'count': 8},\n", " {'value': 'I152105431', 'count': 8},\n", " {'value': 'I162082917', 'count': 8},\n", " {'value': 'I162064331', 'count': 8},\n", " {'value': 'I162056703', 'count': 8},\n", " {'value': 'I130041200-00', 'count': 8},\n", " {'value': 'I162087224', 'count': 8},\n", " {'value': 'I162022140', 'count': 8},\n", " {'value': 'I172069723', 'count': 8},\n", " {'value': 'I162090278', 'count': 8},\n", " {'value': 'I162074826', 'count': 8},\n", " {'value': 'I162078338', 'count': 8},\n", " {'value': 'I152101399', 'count': 7},\n", " {'value': 'I152095733', 'count': 7},\n", " {'value': 'I162054378', 'count': 7},\n", " {'value': 'I152067057', 'count': 7},\n", " {'value': 'I152096998', 'count': 7},\n", " {'value': 'I172018004', 'count': 7},\n", " {'value': 'I162083089', 'count': 7},\n", " {'value': 'I162045680', 'count': 7},\n", " {'value': 'I152071480', 'count': 7}],\n", " 'count_uniques': 282517},\n", " 'dtype': 'object'}},\n", " 'name': None,\n", " 'file_name': None,\n", " 'summary': {'cols_count': 17,\n", " 'rows_count': 319073,\n", " 'dtypes_list': ['object'],\n", " 'total_count_dtypes': 1,\n", " 'missing_count': 0,\n", " 'p_missing': 0.0}}" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "df.profile(\"INCIDENT_NUMBER\")" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'profile': {'columns': {'INCIDENT_NUMBER': {'profiler_dtype': {'dtype': 'string'},\n", " 'stats': {'match': 319073,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'I162030584', 'count': 13},\n", " {'value': 'I152080623', 'count': 11},\n", " {'value': 'I172013170', 'count': 10},\n", " {'value': 'I182065208', 'count': 10},\n", " {'value': 'I172096394', 'count': 10},\n", " {'value': 'I162071327', 'count': 9},\n", " {'value': 'I162001871', 'count': 9},\n", " {'value': 'I172056883', 'count': 9},\n", " {'value': 'I172054429', 'count': 9},\n", " {'value': 'I162098170', 'count': 9},\n", " {'value': 'I172022524', 'count': 9},\n", " {'value': 'I152076465', 'count': 8},\n", " {'value': 'I172053616', 'count': 8},\n", " {'value': 'I152105431', 'count': 8},\n", " {'value': 'I162082917', 'count': 8},\n", " {'value': 'I162064331', 'count': 8},\n", " {'value': 'I162056703', 'count': 8},\n", " {'value': 'I130041200-00', 'count': 8},\n", " {'value': 'I162087224', 'count': 8},\n", " {'value': 'I162022140', 'count': 8},\n", " {'value': 'I172069723', 'count': 8},\n", " {'value': 'I162090278', 'count': 8},\n", " {'value': 'I162074826', 'count': 8},\n", " {'value': 'I162078338', 'count': 8},\n", " {'value': 'I152101399', 'count': 7},\n", " {'value': 'I152095733', 'count': 7},\n", " {'value': 'I162054378', 'count': 7},\n", " {'value': 'I152067057', 'count': 7},\n", " {'value': 'I152096998', 'count': 7},\n", " {'value': 'I172018004', 'count': 7},\n", " {'value': 'I162083089', 'count': 7},\n", " {'value': 'I162045680', 'count': 7},\n", " {'value': 'I152071480', 'count': 7}],\n", " 'count_uniques': 282517},\n", " 'dtype': 'object'}},\n", " 'name': None,\n", " 'file_name': None,\n", " 'summary': {'cols_count': 17,\n", " 'rows_count': 319073,\n", " 'dtypes_list': ['object'],\n", " 'total_count_dtypes': 1,\n", " 'missing_count': 0,\n", " 'p_missing': 0.0}},\n", " 'transformations': {'actions': []}}" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.meta" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "df = df.cols.cast(\"OFFENSE_CODE\", \"int\").cache()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 319073 rows / 17 columns
\n", "
1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
INCIDENT_NUMBER
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
OFFENSE_CODE
\n", "
2 (int64)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
OFFENSE_CODE_GROUP
\n", "
3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
OFFENSE_DESCRIPTION
\n", "
4 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
DISTRICT
\n", "
5 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
REPORTING_AREA
\n", "
6 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
SHOOTING
\n", "
7 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
OCCURRED_ON_DATE
\n", "
8 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
YEAR
\n", "
9 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
MONTH
\n", "
10 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
DAY_OF_WEEK
\n", "
11 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
HOUR
\n", "
12 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
UCR_PART
\n", "
13 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
STREET
\n", "
14 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Lat
\n", "
15 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Long
\n", "
16 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Location
\n", "
17 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " I172015220\n", " \n", "
\n", "
\n", "
\n", " \n", " 3301\n", " \n", "
\n", "
\n", "
\n", " \n", " Verbal⋅Disputes\n", " \n", "
\n", "
\n", "
\n", " \n", " VERBAL⋅DISPUTE\n", " \n", "
\n", "
\n", "
\n", " \n", " C6\n", " \n", "
\n", "
\n", "
\n", " \n", " 186\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " 2017-02-24⋅19:17:00\n", " \n", "
\n", "
\n", "
\n", " \n", " 2017\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " Friday\n", " \n", "
\n", "
\n", "
\n", " \n", " 19\n", " \n", "
\n", "
\n", "
\n", " \n", " Part⋅Three\n", " \n", "
\n", "
\n", "
\n", " \n", " ALLSTATE⋅RD\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " (0.00000000,⋅0.00000000)\n", " \n", "
\n", "
\n", "
\n", " \n", " I172080021\n", " \n", "
\n", "
\n", "
\n", " \n", " 3831\n", " \n", "
\n", "
\n", "
\n", " \n", " Motor⋅Vehicle⋅Accident⋅Response\n", " \n", "
\n", "
\n", "
\n", " \n", " M/V⋅-⋅LEAVING⋅SCENE⋅-⋅PROPERTY⋅DAMAGE\n", " \n", "
\n", "
\n", "
\n", " \n", " C11\n", " \n", "
\n", "
\n", "
\n", " \n", " 351\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " 2017-09-23⋅12:00:00\n", " \n", "
\n", "
\n", "
\n", " \n", " 2017\n", " \n", "
\n", "
\n", "
\n", " \n", " 9\n", " \n", "
\n", "
\n", "
\n", " \n", " Saturday\n", " \n", "
\n", "
\n", "
\n", " \n", " 12\n", " \n", "
\n", "
\n", "
\n", " \n", " Part⋅Three\n", " \n", "
\n", "
\n", "
\n", " \n", " ADAMS⋅ST\n", " \n", "
\n", "
\n", "
\n", " \n", " 42.30060526\n", " \n", "
\n", "
\n", "
\n", " \n", " -71.05923027\n", " \n", "
\n", "
\n", "
\n", " \n", " (42.30060526,⋅-71.05923027)\n", " \n", "
\n", "
\n", "
\n", " \n", " I172002366\n", " \n", "
\n", "
\n", "
\n", " \n", " 3831\n", " \n", "
\n", "
\n", "
\n", " \n", " Motor⋅Vehicle⋅Accident⋅Response\n", " \n", "
\n", "
\n", "
\n", " \n", " M/V⋅-⋅LEAVING⋅SCENE⋅-⋅PROPERTY⋅DAMAGE\n", " \n", "
\n", "
\n", "
\n", " \n", " B2\n", " \n", "
\n", "
\n", "
\n", " \n", " 183\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " 2017-01-06⋅16:40:00\n", " \n", "
\n", "
\n", "
\n", " \n", " 2017\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Friday\n", " \n", "
\n", "
\n", "
\n", " \n", " 16\n", " \n", "
\n", "
\n", "
\n", " \n", " Part⋅Three\n", " \n", "
\n", "
\n", "
\n", " \n", " E⋅COTTAGE⋅ST\n", " \n", "
\n", "
\n", "
\n", " \n", " 42.32150685\n", " \n", "
\n", "
\n", "
\n", " \n", " -71.07076276\n", " \n", "
\n", "
\n", "
\n", " \n", " (42.32150685,⋅-71.07076276)\n", " \n", "
\n", "
\n", "
\n", " \n", " I172025121\n", " \n", "
\n", "
\n", "
\n", " \n", " 3301\n", " \n", "
\n", "
\n", "
\n", " \n", " Verbal⋅Disputes\n", " \n", "
\n", "
\n", "
\n", " \n", " VERBAL⋅DISPUTE\n", " \n", "
\n", "
\n", "
\n", " \n", " A7\n", " \n", "
\n", "
\n", "
\n", " \n", " 30\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " 2017-03-31⋅22:32:00\n", " \n", "
\n", "
\n", "
\n", " \n", " 2017\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " Friday\n", " \n", "
\n", "
\n", "
\n", " \n", " 22\n", " \n", "
\n", "
\n", "
\n", " \n", " Part⋅Three\n", " \n", "
\n", "
\n", "
\n", " \n", " FRANKFORT⋅ST\n", " \n", "
\n", "
\n", "
\n", " \n", " 42.37083684\n", " \n", "
\n", "
\n", "
\n", " \n", " -71.03397707\n", " \n", "
\n", "
\n", "
\n", " \n", " (42.37083684,⋅-71.03397707)\n", " \n", "
\n", "
\n", "
\n", " \n", " I172096396\n", " \n", "
\n", "
\n", "
\n", " \n", " 3820\n", " \n", "
\n", "
\n", "
\n", " \n", " Motor⋅Vehicle⋅Accident⋅Response\n", " \n", "
\n", "
\n", "
\n", " \n", " M/V⋅ACCIDENT⋅INVOLVING⋅PEDESTRIAN⋅-⋅INJURY\n", " \n", "
\n", "
\n", "
\n", " \n", " A15\n", " \n", "
\n", "
\n", "
\n", " \n", " 41\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " 2017-11-18⋅19:55:00\n", " \n", "
\n", "
\n", "
\n", " \n", " 2017\n", " \n", "
\n", "
\n", "
\n", " \n", " 11\n", " \n", "
\n", "
\n", "
\n", " \n", " Saturday\n", " \n", "
\n", "
\n", "
\n", " \n", " 19\n", " \n", "
\n", "
\n", "
\n", " \n", " Part⋅Three\n", " \n", "
\n", "
\n", "
\n", " \n", " CHELSEA⋅ST\n", " \n", "
\n", "
\n", "
\n", " \n", " 42.37530654\n", " \n", "
\n", "
\n", "
\n", " \n", " -71.05628689\n", " \n", "
\n", "
\n", "
\n", " \n", " (42.37530654,⋅-71.05628689)\n", " \n", "
\n", "
\n", "
\n", " \n", " I182008862\n", " \n", "
\n", "
\n", "
\n", " \n", " 802\n", " \n", "
\n", "
\n", "
\n", " \n", " Simple⋅Assault\n", " \n", "
\n", "
\n", "
\n", " \n", " ASSAULT⋅SIMPLE⋅-⋅BATTERY\n", " \n", "
\n", "
\n", "
\n", " \n", " C6\n", " \n", "
\n", "
\n", "
\n", " \n", " 936\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " 2018-02-02⋅23:24:00\n", " \n", "
\n", "
\n", "
\n", " \n", " 2018\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " Friday\n", " \n", "
\n", "
\n", "
\n", " \n", " 23\n", " \n", "
\n", "
\n", "
\n", " \n", " Part⋅Two\n", " \n", "
\n", "
\n", "
\n", " \n", " B⋅ST\n", " \n", "
\n", "
\n", "
\n", " \n", " 42.34061082\n", " \n", "
\n", "
\n", "
\n", " \n", " -71.05414345\n", " \n", "
\n", "
\n", "
\n", " \n", " (42.34061082,⋅-71.05414345)\n", " \n", "
\n", "
\n", "
\n", " \n", " I152052685\n", " \n", "
\n", "
\n", "
\n", " \n", " 3810\n", " \n", "
\n", "
\n", "
\n", " \n", " Motor⋅Vehicle⋅Accident⋅Response\n", " \n", "
\n", "
\n", "
\n", " \n", " M/V⋅ACCIDENT⋅-⋅INVOLVING⋅ BICYCLE⋅-⋅INJURY\n", " \n", "
\n", "
\n", "
\n", " \n", " D14\n", " \n", "
\n", "
\n", "
\n", " \n", " 772\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " 2015-06-25⋅19:10:00\n", " \n", "
\n", "
\n", "
\n", " \n", " 2015\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Thursday\n", " \n", "
\n", "
\n", "
\n", " \n", " 19\n", " \n", "
\n", "
\n", "
\n", " \n", " Part⋅Three\n", " \n", "
\n", "
\n", "
\n", " \n", " CHESTNUT⋅HILL⋅AVE\n", " \n", "
\n", "
\n", "
\n", " \n", " 42.34420182\n", " \n", "
\n", "
\n", "
\n", " \n", " -71.15346093\n", " \n", "
\n", "
\n", "
\n", " \n", " (42.34420182,⋅-71.15346093)\n", " \n", "
\n", "
\n", "
\n", " \n", " I162041872\n", " \n", "
\n", "
\n", "
\n", " \n", " 3112\n", " \n", "
\n", "
\n", "
\n", " \n", " Landlord/Tenant⋅Disputes\n", " \n", "
\n", "
\n", "
\n", " \n", " LANDLORD⋅-⋅TENANT⋅SERVICE\n", " \n", "
\n", "
\n", "
\n", " \n", " C11\n", " \n", "
\n", "
\n", "
\n", " \n", " 333\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " 2016-05-28⋅16:53:00\n", " \n", "
\n", "
\n", "
\n", " \n", " 2016\n", " \n", "
\n", "
\n", "
\n", " \n", " 5\n", " \n", "
\n", "
\n", "
\n", " \n", " Saturday\n", " \n", "
\n", "
\n", "
\n", " \n", " 16\n", " \n", "
\n", "
\n", "
\n", " \n", " Part⋅Three\n", " \n", "
\n", "
\n", "
\n", " \n", " STANLEY⋅ST\n", " \n", "
\n", "
\n", "
\n", " \n", " 42.30942585\n", " \n", "
\n", "
\n", "
\n", " \n", " -71.06956549\n", " \n", "
\n", "
\n", "
\n", " \n", " (42.30942585,⋅-71.06956549)\n", " \n", "
\n", "
\n", "
\n", " \n", " I172005594\n", " \n", "
\n", "
\n", "
\n", " \n", " 3410\n", " \n", "
\n", "
\n", "
\n", " \n", " Towed\n", " \n", "
\n", "
\n", "
\n", " \n", " TOWED⋅MOTOR⋅VEHICLE\n", " \n", "
\n", "
\n", "
\n", " \n", " A1\n", " \n", "
\n", "
\n", "
\n", " \n", " 97\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " 2017-01-21⋅15:12:00\n", " \n", "
\n", "
\n", "
\n", " \n", " 2017\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Saturday\n", " \n", "
\n", "
\n", "
\n", " \n", " 15\n", " \n", "
\n", "
\n", "
\n", " \n", " Part⋅Three\n", " \n", "
\n", "
\n", "
\n", " \n", " SCHOOL⋅ST\n", " \n", "
\n", "
\n", "
\n", " \n", " 42.35800156\n", " \n", "
\n", "
\n", "
\n", " \n", " -71.06044213\n", " \n", "
\n", "
\n", "
\n", " \n", " (42.35800156,⋅-71.06044213)\n", " \n", "
\n", "
\n", "
\n", " \n", " I172004587\n", " \n", "
\n", "
\n", "
\n", " \n", " 3807\n", " \n", "
\n", "
\n", "
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INCIDENT_NUMBEROFFENSE_CODEOFFENSE_CODE_GROUPOFFENSE_DESCRIPTIONDISTRICTREPORTING_AREASHOOTINGOCCURRED_ON_DATEYEARMONTHDAY_OF_WEEKHOURUCR_PARTSTREETLatLongLocation
0i182070945619LarcenyLARCENY ALL OTHERSD148082018-09-02 13:00:0020189Sunday13Part OneLINCOLN ST42.35779134-71.13937053(42.35779134, -71.13937053)
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2i1820709413410TowedTOWED MOTOR VEHICLED41512018-09-03 19:27:0020189Monday19Part ThreeCAZENOVE ST42.34658879-71.07242943(42.34658879, -71.07242943)
3i1820709403114Investigate PropertyINVESTIGATE PROPERTYD42722018-09-03 21:16:0020189Monday21Part ThreeNEWCOMB ST42.33418175-71.07866441(42.33418175, -71.07866441)
4i1820709383114Investigate PropertyINVESTIGATE PROPERTYB34212018-09-03 21:05:0020189Monday21Part ThreeDELHI ST42.27536542-71.09036101(42.27536542, -71.09036101)
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" ], "text/plain": [ " INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP OFFENSE_DESCRIPTION \\\n", "0 i182070945 619 Larceny LARCENY ALL OTHERS \n", "1 i182070943 1402 Vandalism VANDALISM \n", "2 i182070941 3410 Towed TOWED MOTOR VEHICLE \n", "3 i182070940 3114 Investigate Property INVESTIGATE PROPERTY \n", "4 i182070938 3114 Investigate Property INVESTIGATE PROPERTY \n", "\n", " DISTRICT REPORTING_AREA SHOOTING OCCURRED_ON_DATE YEAR MONTH \\\n", "0 D14 808 2018-09-02 13:00:00 2018 9 \n", "1 C11 347 2018-08-21 00:00:00 2018 8 \n", "2 D4 151 2018-09-03 19:27:00 2018 9 \n", "3 D4 272 2018-09-03 21:16:00 2018 9 \n", "4 B3 421 2018-09-03 21:05:00 2018 9 \n", "\n", " DAY_OF_WEEK HOUR UCR_PART STREET Lat Long \\\n", "0 Sunday 13 Part One LINCOLN ST 42.35779134 -71.13937053 \n", "1 Tuesday 0 Part Two HECLA ST 42.30682138 -71.06030035 \n", "2 Monday 19 Part Three CAZENOVE ST 42.34658879 -71.07242943 \n", "3 Monday 21 Part Three NEWCOMB ST 42.33418175 -71.07866441 \n", "4 Monday 21 Part Three DELHI ST 42.27536542 -71.09036101 \n", "\n", " Location \n", "0 (42.35779134, -71.13937053) \n", "1 (42.30682138, -71.06030035) \n", "2 (42.34658879, -71.07242943) \n", "3 (42.33418175, -71.07866441) \n", "4 (42.27536542, -71.09036101) " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.lower(\"INCIDENT_NUMBER\").data.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# import pandas as pd\n", "# df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],\n", "# index=['cobra', 'viper', 'sidewinder'],\n", "# columns=['max_speed', 'shield'])\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0I182070945619LarcenyLARCENY ALL OTHERSD148082018-09-02 13:00:0020189Sunday13Part OneLINCOLN ST42.35779134-71.13937053(42.35779134, -71.13937053)
1I1820709431402VandalismVANDALISMC113472018-08-21 00:00:0020188Tuesday0Part TwoHECLA ST42.30682138-71.06030035(42.30682138, -71.06030035)
2I1820709413410TowedTOWED MOTOR VEHICLED41512018-09-03 19:27:0020189Monday19Part ThreeCAZENOVE ST42.34658879-71.07242943(42.34658879, -71.07242943)
3I1820709403114Investigate PropertyINVESTIGATE PROPERTYD42722018-09-03 21:16:0020189Monday21Part ThreeNEWCOMB ST42.33418175-71.07866441(42.33418175, -71.07866441)
4I1820709383114Investigate PropertyINVESTIGATE PROPERTYB34212018-09-03 21:05:0020189Monday21Part ThreeDELHI ST42.27536542-71.09036101(42.27536542, -71.09036101)
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" ], "text/plain": [ " INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP OFFENSE_DESCRIPTION \\\n", "0 I182070945 619 Larceny LARCENY ALL OTHERS \n", "1 I182070943 1402 Vandalism VANDALISM \n", "2 I182070941 3410 Towed TOWED MOTOR VEHICLE \n", "3 I182070940 3114 Investigate Property INVESTIGATE PROPERTY \n", "4 I182070938 3114 Investigate Property INVESTIGATE PROPERTY \n", "\n", " DISTRICT REPORTING_AREA SHOOTING OCCURRED_ON_DATE YEAR MONTH \\\n", "0 D14 808 2018-09-02 13:00:00 2018 9 \n", "1 C11 347 2018-08-21 00:00:00 2018 8 \n", "2 D4 151 2018-09-03 19:27:00 2018 9 \n", "3 D4 272 2018-09-03 21:16:00 2018 9 \n", "4 B3 421 2018-09-03 21:05:00 2018 9 \n", "\n", " DAY_OF_WEEK HOUR UCR_PART STREET Lat Long \\\n", "0 Sunday 13 Part One LINCOLN ST 42.35779134 -71.13937053 \n", "1 Tuesday 0 Part Two HECLA ST 42.30682138 -71.06030035 \n", "2 Monday 19 Part Three CAZENOVE ST 42.34658879 -71.07242943 \n", "3 Monday 21 Part Three NEWCOMB ST 42.33418175 -71.07866441 \n", "4 Monday 21 Part Three DELHI ST 42.27536542 -71.09036101 \n", "\n", " Location \n", "0 (42.35779134, -71.13937053) \n", "1 (42.30682138, -71.06030035) \n", "2 (42.34658879, -71.07242943) \n", "3 (42.33418175, -71.07866441) \n", "4 (42.27536542, -71.09036101) " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.data.head()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'Rows' object has no attribute 'is_profiler_dtype'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_profiler_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"INCIDENT_NUMBER\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"int\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: 'Rows' object has no attribute 'is_profiler_dtype'" ] } ], "source": [ "df.rows.is_profiler_dtype(\"INCIDENT_NUMBER\", \"int\").head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.cols.match(\"INCIDENT_NUMBER\",\"I\").head()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['INCIDENT_NUMBER',\n", " 'OFFENSE_CODE',\n", " 'OFFENSE_CODE_GROUP',\n", " 'OFFENSE_DESCRIPTION',\n", " 'DISTRICT',\n", " 'REPORTING_AREA',\n", " 'SHOOTING',\n", " 'OCCURRED_ON_DATE',\n", " 'YEAR',\n", " 'MONTH',\n", " 'DAY_OF_WEEK',\n", " 'HOUR',\n", " 'UCR_PART',\n", " 'STREET',\n", " 'Lat',\n", " 'Long',\n", " 'Location']" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.names()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['INCIDENT_NUMBER']" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list((\"INCIDENT_NUMBER\",))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# df.cols.select((\"INCIDENT_NUMBER\",))\n", "df.cols.select(\"INCIDENT_NUMBER\")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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INCIDENT_NUMBEROFFENSE_CODEOFFENSE_CODE_GROUPOFFENSE_DESCRIPTIONDISTRICTREPORTING_AREASHOOTINGOCCURRED_ON_DATEYEARMONTHDAY_OF_WEEKHOURUCR_PARTSTREETLatLongLocation
0I182070945619LarcenyLARCENY ALL OTHERSD148082018-09-02 13:00:0020189Sunday13Part OneLINCOLN ST42.35779134-71.13937053(42.35779134, -71.13937053)
1I1820709431402VandalismVANDALISMC113472018-08-21 00:00:0020188Tuesday0Part TwoHECLA ST42.30682138-71.06030035(42.30682138, -71.06030035)
2I1820709413410TowedTOWED MOTOR VEHICLED41512018-09-03 19:27:0020189Monday19Part ThreeCAZENOVE ST42.34658879-71.07242943(42.34658879, -71.07242943)
3I1820709403114Investigate PropertyINVESTIGATE PROPERTYD42722018-09-03 21:16:0020189Monday21Part ThreeNEWCOMB ST42.33418175-71.07866441(42.33418175, -71.07866441)
4I1820709383114Investigate PropertyINVESTIGATE PROPERTYB34212018-09-03 21:05:0020189Monday21Part ThreeDELHI ST42.27536542-71.09036101(42.27536542, -71.09036101)
5I1820709363820Motor Vehicle Accident ResponseM/V ACCIDENT INVOLVING PEDESTRIAN - INJURYC113982018-09-03 21:09:0020189Monday21Part ThreeTALBOT AVE42.29019621-71.07159012(42.29019621, -71.07159012)
6I182070933724Auto TheftAUTO THEFTB23302018-09-03 21:25:0020189Monday21Part OneNORMANDY ST42.30607218-71.08273260(42.30607218, -71.08273260)
7I1820709323301Verbal DisputesVERBAL DISPUTEB25842018-09-03 20:39:3720189Monday20Part ThreeLAWN ST42.32701648-71.10555088(42.32701648, -71.10555088)
8I182070931301RobberyROBBERY - STREETC61772018-09-03 20:48:0020189Monday20Part OneMASSACHUSETTS AVE42.33152148-71.07085307(42.33152148, -71.07085307)
9I1820709293301Verbal DisputesVERBAL DISPUTEC113642018-09-03 20:38:0020189Monday20Part ThreeLESLIE ST42.29514664-71.05860832(42.29514664, -71.05860832)
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" ], "text/plain": [ " INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP \\\n", "0 I182070945 619 Larceny \n", "1 I182070943 1402 Vandalism \n", "2 I182070941 3410 Towed \n", "3 I182070940 3114 Investigate Property \n", "4 I182070938 3114 Investigate Property \n", "5 I182070936 3820 Motor Vehicle Accident Response \n", "6 I182070933 724 Auto Theft \n", "7 I182070932 3301 Verbal Disputes \n", "8 I182070931 301 Robbery \n", "9 I182070929 3301 Verbal Disputes \n", "\n", " OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA \\\n", "0 LARCENY ALL OTHERS D14 808 \n", "1 VANDALISM C11 347 \n", "2 TOWED MOTOR VEHICLE D4 151 \n", "3 INVESTIGATE PROPERTY D4 272 \n", "4 INVESTIGATE PROPERTY B3 421 \n", "5 M/V ACCIDENT INVOLVING PEDESTRIAN - INJURY C11 398 \n", "6 AUTO THEFT B2 330 \n", "7 VERBAL DISPUTE B2 584 \n", "8 ROBBERY - STREET C6 177 \n", "9 VERBAL DISPUTE C11 364 \n", "\n", " SHOOTING OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART \\\n", "0 2018-09-02 13:00:00 2018 9 Sunday 13 Part One \n", "1 2018-08-21 00:00:00 2018 8 Tuesday 0 Part Two \n", "2 2018-09-03 19:27:00 2018 9 Monday 19 Part Three \n", "3 2018-09-03 21:16:00 2018 9 Monday 21 Part Three \n", "4 2018-09-03 21:05:00 2018 9 Monday 21 Part Three \n", "5 2018-09-03 21:09:00 2018 9 Monday 21 Part Three \n", "6 2018-09-03 21:25:00 2018 9 Monday 21 Part One \n", "7 2018-09-03 20:39:37 2018 9 Monday 20 Part Three \n", "8 2018-09-03 20:48:00 2018 9 Monday 20 Part One \n", "9 2018-09-03 20:38:00 2018 9 Monday 20 Part Three \n", "\n", " STREET Lat Long Location \n", "0 LINCOLN ST 42.35779134 -71.13937053 (42.35779134, -71.13937053) \n", "1 HECLA ST 42.30682138 -71.06030035 (42.30682138, -71.06030035) \n", "2 CAZENOVE ST 42.34658879 -71.07242943 (42.34658879, -71.07242943) \n", "3 NEWCOMB ST 42.33418175 -71.07866441 (42.33418175, -71.07866441) \n", "4 DELHI ST 42.27536542 -71.09036101 (42.27536542, -71.09036101) \n", "5 TALBOT AVE 42.29019621 -71.07159012 (42.29019621, -71.07159012) \n", "6 NORMANDY ST 42.30607218 -71.08273260 (42.30607218, -71.08273260) \n", "7 LAWN ST 42.32701648 -71.10555088 (42.32701648, -71.10555088) \n", "8 MASSACHUSETTS AVE 42.33152148 -71.07085307 (42.33152148, -71.07085307) \n", "9 LESLIE ST 42.29514664 -71.05860832 (42.29514664, -71.05860832) " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 319073 rows / 1 columns
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1 partition(s)
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Viewing 10 of 319073 rows / 1 columns
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1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[\"INCIDENT_NUMBER\"].mask.equal(\"INCIDENT_NUMBER\",\"I182070945\").display()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.mask.match(\"INCIDENT_NUMBER\",\"int\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "\n", "import dask\n", "from dask.dataframe import from_delayed\n", "\n", "\n", "def oset(col_name, expr, where):\n", " \n", " where = (where.data[where.cols.names(0)[0]]).to_delayed()\n", " ddf = df.to_delayed()\n", " \n", " def func(_df, _col_name, _mask):\n", " \n", "# _df.loc[_mask,[_col_name]]=expr(_df)\n", " return _df\n", "\n", " for _part,_mask in zip(ddf, where):\n", " delayed_parts = dask.delayed(func)(_part, col_name, _mask)\n", "\n", " return from_delayed(delayed_parts)\n", "\n", "col_name=\"INCIDENT_NUMBER\"\n", "where= ((df[col_name]==\"i182070945\") | (df[\"DISTRICT\"]==\"I182070945\"))\n", "\n", "F = df.functions\n", "\n", "def expr(series):\n", " return F.lower(series[\"OFFENSE_CODE_GROUP\"])\n", "\n", "\n", "oset(col_name, expr, where).compute()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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INCIDENT_NUMBEROFFENSE_CODEOFFENSE_CODE_GROUPOFFENSE_DESCRIPTIONDISTRICTREPORTING_AREASHOOTINGOCCURRED_ON_DATEYEARMONTHDAY_OF_WEEKHOURUCR_PARTSTREETLatLongLocation
0i18207094500619LarcenyLARCENY ALL OTHERSD148082018-09-02 13:00:0020189Sunday13Part OneLINCOLN ST42.35779134-71.13937053(42.35779134, -71.13937053)
1I18207094301402VandalismVANDALISMC113472018-08-21 00:00:0020188Tuesday0Part TwoHECLA ST42.30682138-71.06030035(42.30682138, -71.06030035)
2I18207094103410TowedTOWED MOTOR VEHICLED41512018-09-03 19:27:0020189Monday19Part ThreeCAZENOVE ST42.34658879-71.07242943(42.34658879, -71.07242943)
3I18207094003114Investigate PropertyINVESTIGATE PROPERTYD42722018-09-03 21:16:0020189Monday21Part ThreeNEWCOMB ST42.33418175-71.07866441(42.33418175, -71.07866441)
4I18207093803114Investigate PropertyINVESTIGATE PROPERTYB34212018-09-03 21:05:0020189Monday21Part ThreeDELHI ST42.27536542-71.09036101(42.27536542, -71.09036101)
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" ], "text/plain": [ " INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP OFFENSE_DESCRIPTION \\\n", "0 i182070945 00619 Larceny LARCENY ALL OTHERS \n", "1 I182070943 01402 Vandalism VANDALISM \n", "2 I182070941 03410 Towed TOWED MOTOR VEHICLE \n", "3 I182070940 03114 Investigate Property INVESTIGATE PROPERTY \n", "4 I182070938 03114 Investigate Property INVESTIGATE PROPERTY \n", "\n", " DISTRICT REPORTING_AREA SHOOTING OCCURRED_ON_DATE YEAR MONTH \\\n", "0 D14 808 2018-09-02 13:00:00 2018 9 \n", "1 C11 347 2018-08-21 00:00:00 2018 8 \n", "2 D4 151 2018-09-03 19:27:00 2018 9 \n", "3 D4 272 2018-09-03 21:16:00 2018 9 \n", "4 B3 421 2018-09-03 21:05:00 2018 9 \n", "\n", " DAY_OF_WEEK HOUR UCR_PART STREET Lat Long \\\n", "0 Sunday 13 Part One LINCOLN ST 42.35779134 -71.13937053 \n", "1 Tuesday 0 Part Two HECLA ST 42.30682138 -71.06030035 \n", "2 Monday 19 Part Three CAZENOVE ST 42.34658879 -71.07242943 \n", "3 Monday 21 Part Three NEWCOMB ST 42.33418175 -71.07866441 \n", "4 Monday 21 Part Three DELHI ST 42.27536542 -71.09036101 \n", "\n", " Location \n", "0 (42.35779134, -71.13937053) \n", "1 (42.30682138, -71.06030035) \n", "2 (42.34658879, -71.07242943) \n", "3 (42.33418175, -71.07866441) \n", "4 (42.27536542, -71.09036101) " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# col_name = \"INCIDENT_NUMBER\"\n", "def func(pdf):\n", " mask = pdf[\"INCIDENT_NUMBER\"]==\"I182070945\"\n", " pdf.loc[mask,[\"INCIDENT_NUMBER\"]]=pdf[\"INCIDENT_NUMBER\"].str.lower()\n", " return pdf\n", "\n", "df.data.map_partitions(func).head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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INCIDENT_NUMBEROFFENSE_CODEOFFENSE_CODE_GROUPOFFENSE_DESCRIPTIONDISTRICTREPORTING_AREASHOOTINGOCCURRED_ON_DATEYEARMONTHDAY_OF_WEEKHOURUCR_PARTSTREETLatLongLocation
0i18207094500619LarcenyLARCENY ALL OTHERSD148082018-09-02 13:00:0020189Sunday13Part OneLINCOLN ST42.35779134-71.13937053(42.35779134, -71.13937053)
1I18207094301402VandalismVANDALISMC113472018-08-21 00:00:0020188Tuesday0Part TwoHECLA ST42.30682138-71.06030035(42.30682138, -71.06030035)
2I18207094103410TowedTOWED MOTOR VEHICLED41512018-09-03 19:27:0020189Monday19Part ThreeCAZENOVE ST42.34658879-71.07242943(42.34658879, -71.07242943)
3I18207094003114Investigate PropertyINVESTIGATE PROPERTYD42722018-09-03 21:16:0020189Monday21Part ThreeNEWCOMB ST42.33418175-71.07866441(42.33418175, -71.07866441)
4I18207093803114Investigate PropertyINVESTIGATE PROPERTYB34212018-09-03 21:05:0020189Monday21Part ThreeDELHI ST42.27536542-71.09036101(42.27536542, -71.09036101)
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" ], "text/plain": [ " INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP OFFENSE_DESCRIPTION \\\n", "0 i182070945 00619 Larceny LARCENY ALL OTHERS \n", "1 I182070943 01402 Vandalism VANDALISM \n", "2 I182070941 03410 Towed TOWED MOTOR VEHICLE \n", "3 I182070940 03114 Investigate Property INVESTIGATE PROPERTY \n", "4 I182070938 03114 Investigate Property INVESTIGATE PROPERTY \n", "\n", " DISTRICT REPORTING_AREA SHOOTING OCCURRED_ON_DATE YEAR MONTH \\\n", "0 D14 808 2018-09-02 13:00:00 2018 9 \n", "1 C11 347 2018-08-21 00:00:00 2018 8 \n", "2 D4 151 2018-09-03 19:27:00 2018 9 \n", "3 D4 272 2018-09-03 21:16:00 2018 9 \n", "4 B3 421 2018-09-03 21:05:00 2018 9 \n", "\n", " DAY_OF_WEEK HOUR UCR_PART STREET Lat Long \\\n", "0 Sunday 13 Part One LINCOLN ST 42.35779134 -71.13937053 \n", "1 Tuesday 0 Part Two HECLA ST 42.30682138 -71.06030035 \n", "2 Monday 19 Part Three CAZENOVE ST 42.34658879 -71.07242943 \n", "3 Monday 21 Part Three NEWCOMB ST 42.33418175 -71.07866441 \n", "4 Monday 21 Part Three DELHI ST 42.27536542 -71.09036101 \n", "\n", " Location \n", "0 (42.35779134, -71.13937053) \n", "1 (42.30682138, -71.06030035) \n", "2 (42.34658879, -71.07242943) \n", "3 (42.33418175, -71.07866441) \n", "4 (42.27536542, -71.09036101) " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.data.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "op.client" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 6.96 s\n" ] }, { "data": { "text/plain": [ "{'columns': {'INCIDENT_NUMBER': {'stats': {'match': 319073,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'I162030584', 'count': 13},\n", " {'value': 'I152080623', 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'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': '(0.00000000, 0.00000000)', 'count': 19999},\n", " {'value': '(42.34862382, -71.08277637)', 'count': 1243},\n", " {'value': '(42.36183857, -71.05976489)', 'count': 1208},\n", " {'value': '(42.28482577, -71.09137369)', 'count': 1121},\n", " {'value': '(42.32866284, -71.08563401)', 'count': 1042},\n", " {'value': '(42.25621592, -71.12401947)', 'count': 898},\n", " {'value': '(42.29755533, -71.05970910)', 'count': 783},\n", " {'value': '(42.34128751, -71.05467933)', 'count': 773},\n", " {'value': '(-1.00000000, -1.00000000)', 'count': 745},\n", " {'value': '(42.33152148, -71.07085307)', 'count': 735},\n", " {'value': '(42.35231190, -71.06370510)', 'count': 688},\n", " {'value': '(42.33954199, -71.06940877)', 'count': 655},\n", " {'value': '(42.32696647, -71.06198607)', 'count': 652},\n", " {'value': '(42.35512339, -71.06087980)', 'count': 584},\n", " {'value': '(42.30971857, -71.10429432)', 'count': 573},\n", " {'value': '(42.29848866, -71.06313294)', 'count': 562},\n", " {'value': '(42.33401829, -71.07638124)', 'count': 561},\n", " {'value': '(42.33367922, -71.09187755)', 'count': 550},\n", " {'value': '(42.31043400, -71.06134010)', 'count': 523},\n", " {'value': '(42.35095909, -71.07412780)', 'count': 523},\n", " {'value': '(42.35241815, -71.06525499)', 'count': 515},\n", " {'value': '(42.37081805, -71.03929078)', 'count': 507},\n", " {'value': '(42.33428841, -71.07239518)', 'count': 504},\n", " {'value': '(42.32696802, -71.08051941)', 'count': 472},\n", " {'value': '(42.34980175, -71.07840978)', 'count': 472},\n", " {'value': '(42.33511904, -71.07491710)', 'count': 455},\n", " {'value': '(42.35037870, -71.07626098)', 'count': 445},\n", " {'value': '(42.34653820, -71.09880598)', 'count': 444},\n", " {'value': '(42.36643546, -71.06135413)', 'count': 440},\n", " {'value': '(42.28709355, -71.14822128)', 'count': 438},\n", " {'value': '(42.35602373, -71.06177615)', 'count': 436},\n", " {'value': '(42.34840576, -71.08688339)', 'count': 432},\n", " {'value': '(42.34905600, -71.15049850)', 'count': 427}],\n", " 'count_uniques': 18194},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}}},\n", " 'name': None,\n", " 'file_name': ('data/crime.csv', 'crime.csv'),\n", " 'summary': {'cols_count': 17,\n", " 'rows_count': 319073,\n", " 'dtypes_list': ['object'],\n", " 'total_count_dtypes': 1,\n", " 'missing_count': 0,\n", " 'p_missing': 0.0}}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "df.profile()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 6 of 6 rows / 3 columns
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1 partition(s)
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cedula
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edad
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apellido
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3 (object)
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Viewing 6 of 6 rows / 3 columns
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1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'cedula': 17484892.0, 'edad': 20.0, 'apellido': nan}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.min(\"*\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'columns': {'cedula': {'stats': {'match': 5,\n", " 'missing': 0,\n", " 'mismatch': 1,\n", " 'profiler_dtype': {'dtype': 'int'},\n", " 'frequency': [{'value': 'aaa', 'count': 1},\n", " {'value': '28479393', 'count': 1},\n", " {'value': '23760628', 'count': 1},\n", " {'value': '21857839', 'count': 1},\n", " {'value': '19748383', 'count': 1},\n", " {'value': '17484892', 'count': 1}],\n", " 'count_uniques': 6},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'int'}},\n", " 'edad': {'stats': {'match': 6,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'int'},\n", " 'hist': [{'lower': 20.0, 'upper': 20.25, 'count': 1},\n", " {'lower': 20.25, 'upper': 20.5, 'count': 0},\n", " {'lower': 20.5, 'upper': 20.75, 'count': 0},\n", " {'lower': 20.75, 'upper': 21.0, 'count': 0},\n", " {'lower': 21.0, 'upper': 21.25, 'count': 0},\n", " {'lower': 21.25, 'upper': 21.5, 'count': 0},\n", " {'lower': 21.5, 'upper': 21.75, 'count': 0},\n", " {'lower': 21.75, 'upper': 22.0, 'count': 0},\n", " {'lower': 22.0, 'upper': 22.25, 'count': 0},\n", " {'lower': 22.25, 'upper': 22.5, 'count': 0},\n", " {'lower': 22.5, 'upper': 22.75, 'count': 0},\n", " {'lower': 22.75, 'upper': 23.0, 'count': 0},\n", " {'lower': 23.0, 'upper': 23.25, 'count': 0},\n", " {'lower': 23.25, 'upper': 23.5, 'count': 0},\n", " {'lower': 23.5, 'upper': 23.75, 'count': 0},\n", " {'lower': 23.75, 'upper': 24.0, 'count': 0},\n", " {'lower': 24.0, 'upper': 24.25, 'count': 1},\n", " {'lower': 24.25, 'upper': 24.5, 'count': 0},\n", " {'lower': 24.5, 'upper': 24.75, 'count': 0},\n", " {'lower': 24.75, 'upper': 25.0, 'count': 0},\n", " {'lower': 25.0, 'upper': 25.25, 'count': 0},\n", " {'lower': 25.25, 'upper': 25.5, 'count': 0},\n", " {'lower': 25.5, 'upper': 25.75, 'count': 0},\n", " {'lower': 25.75, 'upper': 26.0, 'count': 0},\n", " {'lower': 26.0, 'upper': 26.25, 'count': 1},\n", " {'lower': 26.25, 'upper': 26.5, 'count': 0},\n", " {'lower': 26.5, 'upper': 26.75, 'count': 0},\n", " {'lower': 26.75, 'upper': 27.0, 'count': 0},\n", " {'lower': 27.0, 'upper': 27.25, 'count': 1},\n", " {'lower': 27.25, 'upper': 27.5, 'count': 0},\n", " {'lower': 27.5, 'upper': 27.75, 'count': 0},\n", " {'lower': 27.75, 'upper': 28.0, 'count': 2}],\n", " 'count_uniques': 5},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'int'}},\n", " 'apellido': {'stats': {'match': 6,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'null', 'count': 4},\n", " {'value': 'martinez', 'count': 1},\n", " {'value': 'contreras', 'count': 1}],\n", " 'count_uniques': 3},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}}},\n", " 'name': 'edad.csv',\n", " 'file_name': 'data/edad.csv',\n", " 'summary': {'cols_count': 3,\n", " 'rows_count': 6,\n", " 'dtypes_list': ['object'],\n", " 'total_count_dtypes': 1,\n", " 'missing_count': 0,\n", " 'p_missing': 0.0}}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.profile(flush = False)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 6 of 6 rows / 3 columns
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1 partition(s)
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\n", "
cedula
\n", "
1 (object)
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\n", " \n", " not nullable\n", " \n", "
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edad
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2 (int64)
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apellido
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3 (object)
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Viewing 6 of 6 rows / 3 columns
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1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.to_integer(\"edad\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'cedula': 17484892.0, 'edad': 20.0, 'apellido': nan}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.min(\"*\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# df[\"edad\"].data = df[\"edad\"].astype(\"int\")\n", "df = df.cols.cast(\"edad\", \"int\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 6 of 6 rows / 3 columns
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1 partition(s)
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\n", "
cedula
\n", "
1 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
edad
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2 (object)
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apellido
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3 (object)
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Viewing 6 of 6 rows / 3 columns
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1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "tornado.application - ERROR - Uncaught exception GET /individual-workers/ws (127.0.0.1)\n", "HTTPServerRequest(protocol='http', host='127.0.0.1:8787', method='GET', uri='/individual-workers/ws', version='HTTP/1.1', remote_ip='127.0.0.1')\n", "Traceback (most recent call last):\n", " File \"C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\tornado\\websocket.py\", line 956, in _accept_connection\n", " open_result = handler.open(*handler.open_args, **handler.open_kwargs)\n", " File \"C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\bokeh\\server\\views\\ws.py\", line 135, in open\n", " raise ProtocolError(\"Token is expired.\")\n", "bokeh.protocol.exceptions.ProtocolError: Token is expired.\n" ] } ], "source": [ "df.sample()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "_df= df.data" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " cedula edad apellido\n", "0 28479393 20 null\n", "1 17484892 28 martinez\n", "2 23760628 24 contreras\n", "3 21857839 26 null\n", "4 19748383 27 null" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_df.head()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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cedulaedadapellido
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" ], "text/plain": [ " cedula edad apellido\n", "1 17484892 28 martinez\n", "2 23760628 24 contreras\n", "3 21857839 26 null\n", "4 19748383 27 null\n", "5 aaa 28 null" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_df[_df[\"edad\"]>20].head()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py:6181: UserWarning: Insufficient elements for `head`. 10 elements requested, only 6 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "data": { "text/html": [ "
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cedulaedadapellido
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" ], "text/plain": [ " cedula edad apellido\n", "0 28479393 20 null\n", "1 17484892 28 martinez\n", "2 23760628 24 contreras\n", "3 21857839 26 null\n", "4 19748383 27 null\n", "5 aaa 28 null" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_df.head(10)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'col_name' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"str\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mInfer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mProfilerDataTypesFunctions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"int\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'col_name' is not defined" ] } ], "source": [ "df[col_name].astype(\"str\").str.match(Infer.ProfilerDataTypesFunctions[\"int\"]).head()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'cedula': '28479393', 'edad': 20, 'apellido': 'null'},\n", " {'cedula': '17484892', 'edad': 28, 'apellido': 'martinez'},\n", " {'cedula': '23760628', 'edad': 24, 'apellido': 'contreras'},\n", " {'cedula': '21857839', 'edad': 26, 'apellido': 'null'},\n", " {'cedula': '19748383', 'edad': 27, 'apellido': 'null'},\n", " {'cedula': 'aaa', 'edad': 28, 'apellido': 'null'}]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 1 of 1 rows / 3 columns
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1 partition(s)
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cedula
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1 (object)
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edad
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2 (int64)
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Viewing 1 of 1 rows / 3 columns
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1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# df.rows.match(\"cedula\",\"int\").ext.display()\n", "df.rows.mismatch(\"cedula\",\"int\").ext.display()\n", "# df.data.head(10)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " cedula edad apellido\n", "0 28479393 20 null\n", "1 17484892 28 martinez\n", "2 23760628 24 contreras\n", "3 21857839 26 null\n", "4 19748383 27 null\n", " cedula edad apellido\n", "0 28479393 20 null\n", "1 17484892 28 martinez\n", "2 23760628 24 contreras\n", "3 21857839 26 null\n", "4 19748383 27 null\n", "5 aaa 28 null\n", " cedula edad apellido\n", "0 28479393 20 null\n", "1 17484892 28 martinez\n", "2 23760628 24 contreras\n", "3 21857839 26 null\n", "4 19748383 27 null\n", "5 aaa 28 null\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py:6181: UserWarning: Insufficient elements for `head`. 10 elements requested, only 5 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n", "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py:6181: UserWarning: Insufficient elements for `head`. 10 elements requested, only 6 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n", "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py:6181: UserWarning: Insufficient elements for `head`. 10 elements requested, only 6 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] } ], "source": [ "from optimus.infer import is_dict, Infer\n", "\n", "cols_and_inferred_dtype = df.cols.infer_profiler_dtypes(\"*\")\n", "\n", "col_dtypes = {}\n", "for col_name,j in cols_and_inferred_dtype.items():\n", " dtype = list(j.values())[0]\n", " col_dtypes[col_name] = Infer.ProfilerDataTypesFunctions[dtype]\n", "\n", " mask = _df[col_name].astype(\"str\").str.match(Infer.ProfilerDataTypesFunctions[dtype])\n", " print(df.data[mask].head(10))\n", " \n", "\n", "# print(col_dtypes)\n", "# df.cols.match(\"*\", ProfilerDataTypesFunctions[dtype]).cols.frequency()" ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'Rows' object has no attribute 'mismatch'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[1;31m# df.rows.not_equal(\"edad\",24)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmismatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: 'Rows' object has no attribute 'mismatch'" ] } ], "source": [ "# df.rows.greater_than(\"edad\",20)\n", "# df.rows.greater_than_equal(\"edad\",20)\n", "\n", "# df.rows.less_than(\"edad\",24)\n", "# df.rows.less_than_equal(\"edad\",24)\n", "\n", "# df.rows.between(\"edad\",20, 25)\n", "\n", "# df.rows.equal(\"edad\",24)\n", "# df.rows.not_equal(\"edad\",24)\n", "\n", "df.rows.mismatch()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 4 of 4 rows / 3 columns
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1 partition(s)
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cedula
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1 (object)
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edad
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2 (int64)
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apellido
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3 (object)
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Viewing 4 of 4 rows / 3 columns
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1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.rows.select(df[\"edad\"]>20)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "a= df.data" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'Rows' object has no attribute 'greater_than'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# df.rows.is_in(\"apellido\",[\"contreras\",\"2\"], output_cols=\"new\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# df.rows.is_in(\"apellido\",[\"contreras\",\"2\"], output_cols=\"new\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgreater_than\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cedula\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;31m# df.greather_than_equal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# df.less_than\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'Rows' object has no attribute 'greater_than'" ] } ], "source": [ "# df.rows.is_in(\"apellido\",[\"contreras\",\"2\"], output_cols=\"new\")\n", "# df.rows.is_in(\"apellido\",[\"contreras\",\"2\"], output_cols=\"new\")\n", "df.rows.greater_than(\"cedula\",2)\n", "# df.greather_than_equal\n", "# df.less_than\n", "# df.less_than_equal\n", "# df.between\n", "\n", "\n", "df.rows.is_in(\"apellido\",[\"contreras\",\"2\"], output_cols=\"new\")\n", "# a.rows.select(a['new']==True)\n", "\n", "# is_greater\n", "# is_betweent\n", "# is_match\n", "\n", "# is_null\n", "# endswith\n", "\n" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 5 of 5 rows / 3 columns
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1 partition(s)
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cedula
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1 (object)
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edad
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2 (object)
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apellido
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3 (object)
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Viewing 5 of 5 rows / 3 columns
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1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'df' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"apellido\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpattern\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined" ] } ], "source": [ "a = df.cols.select(\"apellido\").cols.pattern()" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dask Series Structure:\n", "npartitions=1\n", " bool\n", " ...\n", "Name: apellido, dtype: bool\n", "Dask Name: eq, 3 tasks" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.data[\"apellido\"]==\"lllllllll\"" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 5 of 5 rows / 3 columns
\n", "
1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
cedula
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
edad
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
apellido
\n", "
3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " 23760628\n", " \n", "
\n", "
\n", "
\n", " \n", " 24\n", " \n", "
\n", "
\n", "
\n", " \n", " contreras\n", " \n", "
\n", "
\n", "
\n", " \n", " 28479393\n", " \n", "
\n", "
\n", "
\n", " \n", " 20\n", " \n", "
\n", "
\n", "
\n", " \n", " null\n", " \n", "
\n", "
\n", "
\n", " \n", " 21857839\n", " \n", "
\n", "
\n", "
\n", " \n", " 26\n", " \n", "
\n", "
\n", "
\n", " \n", " null\n", " \n", "
\n", "
\n", "
\n", " \n", " 17484892\n", " \n", "
\n", "
\n", "
\n", " \n", " 28\n", " \n", "
\n", "
\n", "
\n", " \n", " martinez\n", " \n", "
\n", "
\n", "
\n", " \n", " 19748383\n", " \n", "
\n", "
\n", "
\n", " \n", " 27\n", " \n", "
\n", "
\n", "
\n", " \n", " null\n", " \n", "
\n", "
\n", "\n", "
Viewing 5 of 5 rows / 3 columns
\n", "
1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display(10)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 1 of 1 rows / 1 columns
\n", "
1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
apellido
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " llllllll\n", " \n", "
\n", "
\n", "\n", "
Viewing 1 of 1 rows / 1 columns
\n", "
1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.pattern(\"\").rows.select(a==\"llllllll\")\n", "# a= df.cols.select(\"apellido\").cols.pattern().data[\"apellido\"]\n", "# # a.head()\n", "# (df.data[a==\"llllllll\"]).head()\n", "\n", "# a.cols.match(a[\"apellido\"]==\"llllllll\").head()" ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 5 of 5 rows / 3 columns
\n", "
None partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
apellido
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
__output__
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
__match__
\n", "
3 (bool)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " martinez\n", " \n", "
\n", "
\n", "
\n", " \n", " llllllll\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " null\n", " \n", "
\n", "
\n", "
\n", " \n", " llll\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " contreras\n", " \n", "
\n", "
\n", "
\n", " \n", " lllllllll\n", " \n", "
\n", "
\n", "
\n", " \n", " True\n", " \n", "
\n", "
\n", "
\n", " \n", " null\n", " \n", "
\n", "
\n", "
\n", " \n", " llll\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " null\n", " \n", "
\n", "
\n", "
\n", " \n", " llll\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "\n", "
Viewing 5 of 5 rows / 3 columns
\n", "
None partition(s) <class 'optimus.new_optimus.PandasDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 154, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.buffer_window(\"*\", 0, 5)[\"apellido\"].cols.pattern(\"apellido\",\"__output__\").rows.find('df[\"__output__\"]==\"lllllllll\"')" ] }, { "cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 5 of 5 rows / 4 columns
\n", "
1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
cedula
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
edad
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
apellido
\n", "
3 (bool)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
new
\n", "
4 (bool)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " 17484892\n", " \n", "
\n", "
\n", "
\n", " \n", " 28\n", " \n", "
\n", "
\n", "
\n", " \n", " True\n", " \n", "
\n", "
\n", "
\n", " \n", " True\n", " \n", "
\n", "
\n", "
\n", " \n", " 21857839\n", " \n", "
\n", "
\n", "
\n", " \n", " 26\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " 23760628\n", " \n", "
\n", "
\n", "
\n", " \n", " 24\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " 28479393\n", " \n", "
\n", "
\n", "
\n", " \n", " 20\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " 19748383\n", " \n", "
\n", "
\n", "
\n", " \n", " 27\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "\n", "
Viewing 5 of 5 rows / 4 columns
\n", "
1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 5 of 5 rows / 3 columns
\n", "
1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
cedula
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
edad
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
apellido
\n", "
3 (bool)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " 21857839\n", " \n", "
\n", "
\n", "
\n", " \n", " 26\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " 28479393\n", " \n", "
\n", "
\n", "
\n", " \n", " 20\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " 17484892\n", " \n", "
\n", "
\n", "
\n", " \n", " 28\n", " \n", "
\n", "
\n", "
\n", " \n", " True\n", " \n", "
\n", "
\n", "
\n", " \n", " 19748383\n", " \n", "
\n", "
\n", "
\n", " \n", " 27\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "
\n", " \n", " 23760628\n", " \n", "
\n", "
\n", "
\n", " \n", " 24\n", " \n", "
\n", "
\n", "
\n", " \n", " False\n", " \n", "
\n", "
\n", "\n", "
Viewing 5 of 5 rows / 3 columns
\n", "
1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "apellido, pattern, match" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'DataFrame' object has no attribute 'cols'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_window\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'df.cols.select(\"apellido\").cols.pattern().data[\"apellido\"]==\"lllllllll\"'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\rows.py\u001b[0m in \u001b[0;36mfind\u001b[1;34m(self, condition)\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 48\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_str\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcondition\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m \u001b[0mcondition\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcondition\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"__match__\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcondition\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\rows.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 5134\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5135\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5136\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5137\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5138\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'cols'" ] } ], "source": [ "df.ext.buffer_window(\"*\", 0, 5).rows.find('df.cols.select(\"apellido\").cols.pattern().data[\"apellido\"]==\"lllllllll\"')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 5 of 5 rows / 3 columns
\n", "
1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
cedula
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
edad
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
apellido
\n", "
3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " 17484892\n", " \n", "
\n", "
\n", "
\n", " \n", " 28\n", " \n", "
\n", "
\n", "
\n", " \n", " martinez\n", " \n", "
\n", "
\n", "
\n", " \n", " 21857839\n", " \n", "
\n", "
\n", "
\n", " \n", " 26\n", " \n", "
\n", "
\n", "
\n", " \n", " null\n", " \n", "
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\n", "
\n", " \n", " 28479393\n", " \n", "
\n", "
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\n", " \n", " 20\n", " \n", "
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\n", " \n", " 23760628\n", " \n", "
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\n", " \n", " 24\n", " \n", "
\n", "
\n", "
\n", " \n", " contreras\n", " \n", "
\n", "
\n", "
\n", " \n", " 19748383\n", " \n", "
\n", "
\n", "
\n", " \n", " 27\n", " \n", "
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\n", "
\n", " \n", " null\n", " \n", "
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Viewing 5 of 5 rows / 3 columns
\n", "
1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.select(\"*\").rows.limit(10)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 5 of 5 rows / 3 columns
\n", "
1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
cedula
\n", "
1 (int)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
edad
\n", "
2 (int)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
apellido
\n", "
3 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " 28479393\n", " \n", "
\n", "
\n", "
\n", " \n", " 20\n", " \n", "
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\n", " \n", " 17484892\n", " \n", "
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\n", "
\n", " \n", " 28\n", " \n", "
\n", "
\n", "
\n", " \n", " martinez\n", " \n", "
\n", "
\n", "
\n", " \n", " 23760628\n", " \n", "
\n", "
\n", "
\n", " \n", " 24\n", " \n", "
\n", "
\n", "
\n", " \n", " contreras\n", " \n", "
\n", "
\n", "
\n", " \n", " 21857839\n", " \n", "
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\n", " \n", " 26\n", " \n", "
\n", "
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\n", " \n", " null\n", " \n", "
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\n", "
\n", " \n", " 19748383\n", " \n", "
\n", "
\n", "
\n", " \n", " 27\n", " \n", "
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\n", " \n", " null\n", " \n", "
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\n", "\n", "
Viewing 5 of 5 rows / 3 columns
\n", "
1 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 5 of 5 rows / 3 columns
\n", "
1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
cedula
\n", "
1 (int)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
edad
\n", "
2 (int)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
apellido
\n", "
3 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " 28479393\n", " \n", "
\n", "
\n", "
\n", " \n", " 20\n", " \n", "
\n", "
\n", "
\n", " \n", " null\n", " \n", "
\n", "
\n", "
\n", " \n", " 17484892\n", " \n", "
\n", "
\n", "
\n", " \n", " 28\n", " \n", "
\n", "
\n", "
\n", " \n", " martinez\n", " \n", "
\n", "
\n", "
\n", " \n", " 23760628\n", " \n", "
\n", "
\n", "
\n", " \n", " 24\n", " \n", "
\n", "
\n", "
\n", " \n", " contreras\n", " \n", "
\n", "
\n", "
\n", " \n", " 21857839\n", " \n", "
\n", "
\n", "
\n", " \n", " 26\n", " \n", "
\n", "
\n", "
\n", " \n", " null\n", " \n", "
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\n", " \n", " 19748383\n", " \n", "
\n", "
\n", "
\n", " \n", " 27\n", " \n", "
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Viewing 5 of 5 rows / 3 columns
\n", "
1 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['null'], ['martinez'], ['null'], ['null'], ['contreras']]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.rows.to_list(\"apellido\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'sample': {'columns': [{'title': 'cedula'},\n", " {'title': 'edad'},\n", " {'title': 'apellido'}],\n", " 'value': [[28479393, 20, 'null'],\n", " [17484892, 28, 'martinez'],\n", " [19748383, 27, 'null'],\n", " [21857839, 26, 'null'],\n", " [23760628, 24, 'contreras']]}}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = op.load.file(\"data/edad.csv\").ext.cache()\n", "df = df.ext.repartition(8).ext.cache()\n", "\n", "assert(df.cols.names()==['cedula', 'edad', 'apellido'])\n", "\n", "res = df.ext.buffer_window(\"*\", 0, 5).ext.to_json(\"*\")\n", "\n", "res\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "ename": "Py4JJavaError", "evalue": "An error occurred while calling o211.csv.\n: java.io.IOException: No FileSystem for scheme: https\r\n\tat org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2660)\r\n\tat org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2667)\r\n\tat org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:94)\r\n\tat org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2703)\r\n\tat org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2685)\r\n\tat org.apache.hadoop.fs.FileSystem.get(FileSystem.java:373)\r\n\tat org.apache.hadoop.fs.Path.getFileSystem(Path.java:295)\r\n\tat org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary$1.apply(DataSource.scala:561)\r\n\tat org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary$1.apply(DataSource.scala:559)\r\n\tat scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)\r\n\tat scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)\r\n\tat scala.collection.immutable.List.foreach(List.scala:392)\r\n\tat scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)\r\n\tat scala.collection.immutable.List.flatMap(List.scala:355)\r\n\tat org.apache.spark.sql.execution.datasources.DataSource.org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary(DataSource.scala:559)\r\n\tat org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:373)\r\n\tat org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:242)\r\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:230)\r\n\tat org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:638)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\r\n\tat java.lang.reflect.Method.invoke(Method.java:498)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\n", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mPy4JJavaError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# df = op.load.file(\"https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv\").ext.cache()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrepartition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32massert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'cedula'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'edad'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'apellido'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\io\\load.py\u001b[0m in \u001b[0;36mfile\u001b[1;34m(self, path, *args, **kwargs)\u001b[0m\n\u001b[0;32m 62\u001b[0m **kwargs, engine=\"python\", na_values='nan')\n\u001b[0;32m 63\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 65\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\io\\load.py\u001b[0m in \u001b[0;36mfile\u001b[1;34m(self, path, *args, **kwargs)\u001b[0m\n\u001b[0;32m 60\u001b[0m \u001b[0mmime_info\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 61\u001b[0m df = self.csv(path, encoding=mime_info[\"encoding\"], dtype=str, **mime_info[\"properties\"],\n\u001b[1;32m---> 62\u001b[1;33m **kwargs, engine=\"python\", na_values='nan')\n\u001b[0m\u001b[0;32m 63\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\io\\load.py\u001b[0m in \u001b[0;36mcsv\u001b[1;34m(path, sep, header, infer_schema, encoding, null_value, n_rows, error_bad_lines, *args, **kwargs)\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[0mread\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"DROPMALFORMED\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 96\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 97\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mread\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcsv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 99\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mn_rows\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pyspark\\sql\\readwriter.py\u001b[0m in \u001b[0;36mcsv\u001b[1;34m(self, path, schema, sep, encoding, quote, escape, comment, header, inferSchema, ignoreLeadingWhiteSpace, ignoreTrailingWhiteSpace, nullValue, nanValue, positiveInf, negativeInf, dateFormat, timestampFormat, maxColumns, maxCharsPerColumn, maxMalformedLogPerPartition, mode, columnNameOfCorruptRecord, multiLine, charToEscapeQuoteEscaping, samplingRatio, enforceSchema, emptyValue)\u001b[0m\n\u001b[0;32m 474\u001b[0m \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 475\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 476\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jreader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcsv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_spark\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPythonUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoSeq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 477\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRDD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 478\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\py4j\\java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m 1255\u001b[0m \u001b[0manswer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1256\u001b[0m return_value = get_return_value(\n\u001b[1;32m-> 1257\u001b[1;33m answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[0;32m 1258\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1259\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pyspark\\sql\\utils.py\u001b[0m in \u001b[0;36mdeco\u001b[1;34m(*a, **kw)\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 64\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\py4j\\protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[1;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[0;32m 326\u001b[0m raise Py4JJavaError(\n\u001b[0;32m 327\u001b[0m \u001b[1;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 328\u001b[1;33m format(target_id, \".\", name), value)\n\u001b[0m\u001b[0;32m 329\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 330\u001b[0m raise Py4JError(\n", "\u001b[1;31mPy4JJavaError\u001b[0m: An error occurred while calling o211.csv.\n: java.io.IOException: No FileSystem for scheme: https\r\n\tat org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2660)\r\n\tat org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2667)\r\n\tat org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:94)\r\n\tat org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2703)\r\n\tat org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2685)\r\n\tat org.apache.hadoop.fs.FileSystem.get(FileSystem.java:373)\r\n\tat org.apache.hadoop.fs.Path.getFileSystem(Path.java:295)\r\n\tat org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary$1.apply(DataSource.scala:561)\r\n\tat org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary$1.apply(DataSource.scala:559)\r\n\tat scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)\r\n\tat scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)\r\n\tat scala.collection.immutable.List.foreach(List.scala:392)\r\n\tat scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)\r\n\tat scala.collection.immutable.List.flatMap(List.scala:355)\r\n\tat org.apache.spark.sql.execution.datasources.DataSource.org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary(DataSource.scala:559)\r\n\tat org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:373)\r\n\tat org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:242)\r\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:230)\r\n\tat org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:638)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\r\n\tat java.lang.reflect.Method.invoke(Method.java:498)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\n" ] } ], "source": [ "# df = op.load.file(\"https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv\").ext.cache()\n", "df = op.load.file(\"https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv\").ext.cache()\n", "df = df.ext.repartition(8).ext.cache()\n", "\n", "assert(df.cols.names()==['cedula', 'edad', 'apellido'])\n", "\n", "res = df.ext.buffer_window(\"*\", 0, 5).ext.to_json(\"*\")\n", "\n", "res\n", "\n", "# expected_res = res\n", "\n", "# diff = DeepDiff(expected_res, res)\n", "# pprint( diff, indent=2 )\n", "# assert(diff == {})\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "{'columns': {'cedula': {'stats': {'match': 5,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'int'},\n", " 'frequency': [{'value': '28479393', 'count': 1},\n", " {'value': '23760628', 'count': 1},\n", " {'value': '21857839', 'count': 1},\n", " {'value': '19748383', 'count': 1},\n", " {'value': '17484892', 'count': 1}],\n", " 'count_uniques': 5},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'int'}},\n", " 'edad': {'stats': {'match': 5,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'int'},\n", " 'frequency': [{'value': '28', 'count': 1},\n", " {'value': '27', 'count': 1},\n", " {'value': '26', 'count': 1},\n", " {'value': '24', 'count': 1},\n", " {'value': '20', 'count': 1}],\n", " 'count_uniques': 5},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'int'}},\n", " 'apellido': {'stats': {'match': 5,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'null', 'count': 3},\n", " {'value': 'martinez', 'count': 1},\n", " {'value': 'contreras', 'count': 1}],\n", " 'count_uniques': 3},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}}},\n", " 'name': 'edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv',\n", " 'file_name': 'https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv',\n", " 'summary': {'cols_count': 3,\n", " 'rows_count': 5,\n", " 'dtypes_list': ['object'],\n", " 'total_count_dtypes': 1,\n", " 'missing_count': 0,\n", " 'p_missing': 0.0}}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.profile(\"*\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['28479393', '20', 'null'],\n", " ['17484892', '28', 'martinez'],\n", " ['23760628', '24', 'contreras'],\n", " ['21857839', '26', 'null'],\n", " ['19748383', '27', 'null']]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.rows.to_list(\"*\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'NoneType' object has no attribute 'values'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_window\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\extension.py\u001b[0m in \u001b[0;36mto_json\u001b[1;34m(self, columns, format)\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0modf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 64\u001b[0m result = {\"sample\": {\"columns\": [{\"title\": col_name} for col_name in odf.cols.select(columns).cols.names()],\n\u001b[1;32m---> 65\u001b[1;33m \"value\": odf.rows.to_list(columns)}}\n\u001b[0m\u001b[0;32m 66\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 67\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0modf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_ascii\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mjson_converter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\rows.py\u001b[0m in \u001b[0;36mto_list\u001b[1;34m(self, input_cols)\u001b[0m\n\u001b[0;32m 86\u001b[0m \u001b[0modf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 87\u001b[0m \u001b[0minput_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0modf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_cols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 88\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0modf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minput_cols\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_pandas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 89\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 90\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'values'" ] } ], "source": [ "df.ext.buffer_window(\"*\", 0, 5).ext.to_json(\"*\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
name
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
id
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
nametype
\n", "
3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
recclass
\n", "
4 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
mass (g)
\n", "
5 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
fall
\n", "
6 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
year
\n", "
7 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclat
\n", "
8 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclong
\n", "
9 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
GeoLocation
\n", "
10 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " Graves⋅Nunataks⋅06127\n", " \n", "
\n", "
\n", "
\n", " \n", " 47380\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 254.1\n", " \n", "
\n", "
\n", "
\n", " \n", " Found\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/2006⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " Dhofar⋅1564\n", " \n", "
\n", "
\n", "
\n", " \n", " 51569\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 26\n", " \n", "
\n", "
\n", "
\n", " \n", " Found\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/2004⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 18.696170\n", " \n", "
\n", "
\n", "
\n", " \n", " 54.370000\n", " \n", "
\n", "
\n", "
\n", " \n", " (18.696170,⋅54.370000)\n", " \n", "
\n", "
\n", "
\n", " \n", " LaPaz⋅Icefield⋅04714\n", " \n", "
\n", "
\n", "
\n", " \n", " 46164\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " LL6\n", " \n", "
\n", "
\n", "
\n", " \n", " 61.7\n", " \n", "
\n", "
\n", "
\n", " \n", " Found\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/2004⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " Sayh⋅al⋅Uhaymir⋅176\n", " \n", "
\n", "
\n", "
\n", " \n", " 33999\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 19\n", " \n", "
\n", "
\n", "
\n", " \n", " Found\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/2001⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 21.048330\n", " \n", "
\n", "
\n", "
\n", " \n", " 57.258330\n", " \n", "
\n", "
\n", "
\n", " \n", " (21.048330,⋅57.258330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Elephant⋅Moraine⋅92103\n", " \n", "
\n", "
\n", "
\n", " \n", " 9505\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " CM2\n", " \n", "
\n", "
\n", "
\n", " \n", " 1.5\n", " \n", "
\n", "
\n", "
\n", " \n", " Found\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1992⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -76.037010\n", " \n", "
\n", "
\n", "
\n", " \n", " 156.129810\n", " \n", "
\n", "
\n", "
\n", " \n", " (-76.037010,⋅156.129810)\n", " \n", "
\n", "
\n", "
\n", " \n", " Landreth⋅Draw\n", " \n", "
\n", "
\n", "
\n", " \n", " 12459\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " Found\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1955⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 37.250000\n", " \n", "
\n", "
\n", "
\n", " \n", " -98.133330\n", " \n", "
\n", "
\n", "
\n", " \n", " (37.250000,⋅-98.133330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Happy⋅(a)\n", " \n", "
\n", "
\n", "
\n", " \n", " 11818\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H3\n", " \n", "
\n", "
\n", "
\n", " \n", " 2860\n", " \n", "
\n", "
\n", "
\n", " \n", " Found\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1971⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 35.680000\n", " \n", "
\n", "
\n", "
\n", " \n", " -101.993330\n", " \n", "
\n", "
\n", "
\n", " \n", " (35.680000,⋅-101.993330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Lewis⋅Cliff⋅88079\n", " \n", "
\n", "
\n", "
\n", " \n", " 13833\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 7\n", " \n", "
\n", "
\n", "
\n", " \n", " Found\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1988⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -84.244110\n", " \n", "
\n", "
\n", "
\n", " \n", " 161.421070\n", " \n", "
\n", "
\n", "
\n", " \n", " (-84.244110,⋅161.421070)\n", " \n", "
\n", "
\n", "
\n", " \n", " Meteorite⋅Hills⋅00718\n", " \n", "
\n", "
\n", "
\n", " \n", " 15952\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 28.4\n", " \n", "
\n", "
\n", "
\n", " \n", " Found\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/2000⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -79.683330\n", " \n", "
\n", "
\n", "
\n", " \n", " 159.750000\n", " \n", "
\n", "
\n", "
\n", " \n", " (-79.683330,⋅159.750000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Northwest⋅Africa⋅2278\n", " \n", "
\n", "
\n", "
\n", " \n", " 30937\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 62\n", " \n", "
\n", "
\n", "
\n", " \n", " Found\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/2003⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "
\n", " \n", " \n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['Aachen'],\n", " ['Aarhus'],\n", " ['Abee'],\n", " ['Acapulco'],\n", " ['Achiras'],\n", " ['Adhi Kot'],\n", " ['Adzhi-Bogdo (stone)'],\n", " ['Agen'],\n", " ['Aguada'],\n", " ['Aguila Blanca'],\n", " ['Aioun el Atrouss'],\n", " ['Aïr'],\n", " ['Aire-sur-la-Lys'],\n", " ['Akaba'],\n", " ['Akbarpur'],\n", " ['Akwanga'],\n", " ['Akyumak'],\n", " ['Al Rais'],\n", " ['Al Zarnkh'],\n", " ['Alais'],\n", " ['Albareto'],\n", " ['Alberta'],\n", " ['Alby sur Chéran'],\n", " ['Aldsworth'],\n", " ['Aleppo'],\n", " ['Alessandria'],\n", " ['Alexandrovsky'],\n", " ['Alfianello'],\n", " ['Allegan'],\n", " ['Allende'],\n", " ['Almahata Sitta'],\n", " [\"Alta'ameem\"],\n", " ['Ambapur Nagla'],\n", " ['Andhara'],\n", " ['Andover'],\n", " ['Andreevka'],\n", " ['Andura'],\n", " ['Northwest Africa 5815'],\n", " ['Angers'],\n", " ['Angra dos Reis (stone)'],\n", " ['Ankober'],\n", " ['Anlong'],\n", " ['Aomori'],\n", " ['Appley Bridge'],\n", " ['Apt'],\n", " ['Arbol Solo'],\n", " ['Archie'],\n", " ['Arroyo Aguiar'],\n", " ['Asco'],\n", " ['Ash Creek'],\n", " ['Ashdon'],\n", " ['Assisi'],\n", " ['Atarra'],\n", " ['Atemajac'],\n", " ['Athens'],\n", " ['Atoka'],\n", " ['Aubres'],\n", " ['Aumale'],\n", " ['Aumieres'],\n", " ['Ausson'],\n", " ['Avanhandava'],\n", " ['Avce'],\n", " ['Avilez'],\n", " ['Awere'],\n", " ['Aztec'],\n", " ['Bachmut'],\n", " ['Bahjoi'],\n", " ['Bald Mountain'],\n", " ['Baldwyn'],\n", " ['Bali'],\n", " ['Ban Rong Du'],\n", " ['Bandong'],\n", " ['Bansur'],\n", " ['Banswal'],\n", " ['Banten'],\n", " ['Barbotan'],\n", " ['Barcelona (stone)'],\n", " ['Barea'],\n", " ['Barnaul'],\n", " ['Barntrup'],\n", " ['Baroti'],\n", " ['Barwell'],\n", " ['Bassikounou'],\n", " ['Baszkówka'],\n", " ['Bath'],\n", " ['Bath Furnace'],\n", " ['Battle Mountain'],\n", " ['Bawku'],\n", " ['Baxter'],\n", " ['Beardsley'],\n", " ['Beaver Creek'],\n", " ['Beddgelert'],\n", " ['Bells'],\n", " ['Belville'],\n", " ['Benares (a)'],\n", " ['Benguerir'],\n", " [\"Beni M'hira\"],\n", " ['Benld'],\n", " ['Benoni'],\n", " ['Bensour'],\n", " ['Benton'],\n", " ['Berduc'],\n", " ['Béréba'],\n", " ['Berlanguillas'],\n", " ['Berthoud'],\n", " ['Bethlehem'],\n", " ['Beuste'],\n", " ['Beyrout'],\n", " ['Bhagur'],\n", " ['Bhawad'],\n", " ['Bherai'],\n", " ['Bhola'],\n", " ['Bholghati'],\n", " ['Bialystok'],\n", " ['Bielokrynitschie'],\n", " ['Bilanga'],\n", " ['Binningup'],\n", " [\"Birni N'konni\"],\n", " ['Bishopville'],\n", " ['Bishunpur'],\n", " ['Bjelaja Zerkov'],\n", " ['Bjurböle'],\n", " ['Black Moshannan Park'],\n", " ['Blackwell'],\n", " ['Blanket'],\n", " ['Blansko'],\n", " ['Bloomington'],\n", " ['Bo Xian'],\n", " ['Bocas'],\n", " ['Bogou'],\n", " ['Boguslavka'],\n", " ['Borgo San Donino'],\n", " ['Bori'],\n", " ['Boriskino'],\n", " ['Borkut'],\n", " ['Borodino'],\n", " ['Botschetschki'],\n", " ['Boumdeid (2003)'],\n", " ['Boumdeid (2011)'],\n", " ['Bovedy'],\n", " ['Bradford Woods'],\n", " ['Braunau'],\n", " ['Breitscheid'],\n", " ['Bremervörde'],\n", " ['Brient'],\n", " ['Bruderheim'],\n", " ['Bukhara'],\n", " ['Bulls Run'],\n", " ['Bunburra Rockhole'],\n", " ['Bununu'],\n", " ['Bur-Gheluai'],\n", " ['Burnwell'],\n", " ['Bursa'],\n", " ['Buschhof'],\n", " ['Bustee'],\n", " ['Butsura'],\n", " ['Buzzard Coulee'],\n", " ['Cabezo de Mayo'],\n", " ['Cabin Creek'],\n", " ['Cacak'],\n", " ['Cali'],\n", " ['Calivo'],\n", " ['Campos Sales'],\n", " ['Çanakkale'],\n", " ['Cañellas'],\n", " ['Cangas de Onis'],\n", " ['Canon City'],\n", " ['Cape Girardeau'],\n", " ['Capilla del Monte'],\n", " ['Carancas'],\n", " ['Caratash'],\n", " ['Castalia'],\n", " ['Castel Berardenga'],\n", " ['Castine'],\n", " ['Castrovillari'],\n", " ['Caswell County'],\n", " ['Ceniceros'],\n", " ['Centerville'],\n", " ['Cereseto'],\n", " ['Chadong'],\n", " ['Chail'],\n", " ['Chainpur'],\n", " ['Chajari'],\n", " ['Chandakapur'],\n", " ['Chandpur'],\n", " ['Changde'],\n", " ['Chantonnay'],\n", " ['Charlotte'],\n", " ['Charsonville'],\n", " ['Charwallas'],\n", " ['Chassigny'],\n", " ['Château-Renard'],\n", " ['Chaves'],\n", " ['Chela'],\n", " ['Chelyabinsk'],\n", " ['Chergach '],\n", " ['Chernyi Bor'],\n", " ['Cherokee Springs'],\n", " ['Chervettaz'],\n", " ['Chervony Kut'],\n", " ['Chetrinahatti'],\n", " ['Chiang Khan'],\n", " ['Chicora'],\n", " ['Chisenga'],\n", " ['Chitado'],\n", " ['Chitenay'],\n", " ['Cilimus'],\n", " ['Claxton'],\n", " ['Clohars'],\n", " ['Colby (Wisconsin)'],\n", " ['Cold Bokkeveld'],\n", " ['Coleman'],\n", " ['Collescipoli'],\n", " ['Conquista'],\n", " ['Cosina'],\n", " ['Cranganore'],\n", " ['Crescent'],\n", " ['Cronstad'],\n", " ['Cross Roads'],\n", " ['Crumlin'],\n", " ['Cumberland Falls'],\n", " ['Cynthiana'],\n", " ['Dahmani'],\n", " ['Dandapur'],\n", " [\"Daniel's Kuil\"],\n", " ['Danville'],\n", " ['Darmstadt'],\n", " ['Dashoguz'],\n", " ['Daule'],\n", " ['De Cewsville'],\n", " ['Deal'],\n", " ['Delhi'],\n", " ['Demina'],\n", " ['Denver'],\n", " ['Dergaon'],\n", " ['Desuri'],\n", " ['Devgaon'],\n", " ['Devri-Khera'],\n", " ['Dhajala'],\n", " ['Dharwar'],\n", " ['Dhurmsala'],\n", " ['Didim'],\n", " ['Diep River'],\n", " ['Distrito Quebracho'],\n", " ['Djati-Pengilon'],\n", " ['Djermaia'],\n", " ['Djoumine'],\n", " ['Dokachi'],\n", " ['Dolgovoli'],\n", " ['Domanitch'],\n", " ['Dong Ujimqin Qi'],\n", " ['Donga Kohrod'],\n", " ['Dongtai'],\n", " ['Doroninsk'],\n", " ['Dosso'],\n", " ['Douar Mghila'],\n", " ['Dowa'],\n", " ['Drake Creek'],\n", " ['Dresden (Ontario)'],\n", " ['Dubrovnik'],\n", " ['Dunbogan'],\n", " ['Dundrum'],\n", " ['Dunhua'],\n", " ['Durala'],\n", " ['Duruma'],\n", " ['Duwun'],\n", " ['Dwaleni'],\n", " ['Dyalpur'],\n", " ['Dyarrl Island'],\n", " ['Eagle'],\n", " ['Ehole'],\n", " ['Eichstädt'],\n", " ['Ekeby'],\n", " ['Ekh Khera'],\n", " ['El Idrissia'],\n", " ['El Paso de Aguila'],\n", " ['El Tigre'],\n", " ['Elbert'],\n", " ['Elbogen'],\n", " ['Elenovka'],\n", " ['Ellemeet'],\n", " ['Emmaville'],\n", " ['Enshi'],\n", " ['Ensisheim'],\n", " ['Épinal'],\n", " ['Erakot'],\n", " ['Erevan'],\n", " ['Ergheo'],\n", " ['Erxleben'],\n", " ['Esnandes'],\n", " ['Essebi'],\n", " ['Estherville'],\n", " ['Farmington'],\n", " ['Farmville'],\n", " ['Favars'],\n", " ['Fayetteville'],\n", " ['Feid Chair'],\n", " ['Felix'],\n", " ['Fenghsien-Ku'],\n", " ['Ferguson'],\n", " ['Fermo'],\n", " ['Fisher'],\n", " ['Florence'],\n", " ['Forest City'],\n", " ['Forest Vale'],\n", " ['Forksville'],\n", " ['Forsbach'],\n", " ['Forsyth'],\n", " ['Fort Flatters'],\n", " ['Frankfort (stone)'],\n", " ['Fuhe'],\n", " ['Fukutomi'],\n", " ['Fünen'],\n", " ['Futtehpur'],\n", " ['Fuyang'],\n", " ['Galapian'],\n", " ['Galim (a)'],\n", " ['Galim (b)'],\n", " ['Galkiv'],\n", " ['Gambat'],\n", " ['Gao-Guenie'],\n", " ['Garhi Yasin'],\n", " ['Garland'],\n", " ['Gashua'],\n", " ['Gasseltepaoua'],\n", " ['Geidam'],\n", " ['Gifu'],\n", " ['Girgenti'],\n", " ['Git-Git'],\n", " ['Glanerbrug'],\n", " ['Glanggang'],\n", " ['Glasatovo'],\n", " ['Glatton'],\n", " ['Gnadenfrei'],\n", " ['Gopalpur'],\n", " ['Gorlovka'],\n", " ['Granes'],\n", " ['Grefsheim'],\n", " ['Grimsby'],\n", " ['Grosnaja'],\n", " ['Gross-Divina'],\n", " ['Grossliebenthal'],\n", " ['Grüneberg'],\n", " ['Grzempach'],\n", " ['Gualeguaychú'],\n", " ['Guangmingshan'],\n", " ['Guangnan'],\n", " ['Guangrao'],\n", " ['Guareña'],\n", " ['Guêa'],\n", " ['Guibga'],\n", " ['Guidder'],\n", " ['Gujargaon'],\n", " ['Gujba'],\n", " ['Gumoschnik'],\n", " ['Gurram Konda'],\n", " ['Gursum'],\n", " ['Gütersloh'],\n", " ['Gyokukei'],\n", " ['Hachi-oji'],\n", " ['Hainaut'],\n", " ['Hallingeberg'],\n", " ['Hamlet'],\n", " ['Haraiya'],\n", " ['Haripura'],\n", " ['Harleton'],\n", " ['Harrison County'],\n", " ['Hashima'],\n", " ['Hassi-Jekna'],\n", " ['Hatford'],\n", " ['Haverö'],\n", " ['Hedeskoga'],\n", " ['Hedjaz'],\n", " ['Heredia'],\n", " ['Hessle'],\n", " ['Higashi-koen'],\n", " ['High Possil'],\n", " ['Hiroshima'],\n", " ['Hoima'],\n", " ['Hökmark'],\n", " ['Holbrook'],\n", " ['Holetta'],\n", " ['Homestead'],\n", " ['Honolulu'],\n", " ['Hotse'],\n", " ['Hoxie'],\n", " ['Hraschina'],\n", " ['Huaxi'],\n", " ['Hungen'],\n", " ['Hvittis'],\n", " ['Ibbenbüren'],\n", " ['Ibitira'],\n", " ['Ibrisim'],\n", " ['Ichkala'],\n", " ['Idutywa'],\n", " ['Iguaracu'],\n", " ['Ijopega'],\n", " ['Indarch'],\n", " ['Independence'],\n", " ['Inner Mongolia'],\n", " ['Innisfree'],\n", " ['Ipiranga'],\n", " ['Ishinga'],\n", " ['Isthilart'],\n", " ['Itapicuru-Mirim'],\n", " ['Itqiy'],\n", " ['Ivuna'],\n", " ['Jackalsfontein'],\n", " ['Jajh deh Kot Lalu'],\n", " ['Jalanash'],\n", " ['Jalandhar'],\n", " ['Jamkheir'],\n", " ['Jartai'],\n", " ['Jelica'],\n", " ['Jemlapur'],\n", " ['Jesenice'],\n", " ['Jhung'],\n", " ['Jiange'],\n", " ['Jianshi'],\n", " ['Jilin'],\n", " ['Jodiya'],\n", " ['Jodzie'],\n", " ['Johnstown'],\n", " ['Jolomba'],\n", " ['Jonzac'],\n", " ['Juancheng'],\n", " ['Judesegeri'],\n", " ['Jumapalo'],\n", " ['Junan'],\n", " ['Juromenha'],\n", " ['Juvinas'],\n", " ['Kaba'],\n", " ['Kabo'],\n", " ['Kadonah'],\n", " ['Kaee'],\n", " ['Kagarlyk'],\n", " ['Kaidun'],\n", " ['Kainsaz'],\n", " ['Kakangari'],\n", " ['Kakowa'],\n", " ['Kalaba'],\n", " ['Kalumbi'],\n", " ['Kamalpur'],\n", " ['Kamiomi'],\n", " ['Kamsagar'],\n", " ['Kandahar (Afghanistan)'],\n", " ['Kangean'],\n", " ['Kangra Valley'],\n", " ['Kapoeta'],\n", " ['Kaprada'],\n", " ['Kaptal-Aryk'],\n", " ['Karakol'],\n", " ['Karatu'],\n", " ['Karewar'],\n", " ['Karkh'],\n", " ['Karloowala'],\n", " ['Karoonda'],\n", " ['Kasamatsu'],\n", " ['Kasauli'],\n", " ['Katagum'],\n", " ['Kavarpura'],\n", " ['Kayakent'],\n", " ['Kediri'],\n", " ['Kemer'],\n", " ['Kendleton'],\n", " ['Kendrapara'],\n", " ['Kerilis'],\n", " ['Kernouve'],\n", " ['Kesen'],\n", " ['Khairpur'],\n", " ['Khanpur'],\n", " ['Kharkov'],\n", " ['Kheragur'],\n", " ['Khetri'],\n", " ['Khmelevka'],\n", " ['Khohar'],\n", " ['Khor Temiki'],\n", " ['Kidairat'],\n", " ['Kiel'],\n", " ['Kiffa'],\n", " ['Kijima (1906)'],\n", " ['Kikino'],\n", " ['Kilabo'],\n", " ['Kilbourn'],\n", " ['Killeter'],\n", " ['Kingai'],\n", " ['Kirbyville'],\n", " ['Kisvarsány'],\n", " ['Kitchener'],\n", " ['Klein-Wenden'],\n", " ['Knyahinya'],\n", " ['Kobe'],\n", " ['Kokubunji'],\n", " ['Komagome'],\n", " ['Konovo'],\n", " ['Košice'],\n", " ['Krähenberg'],\n", " ['Krasnoi-Ugol'],\n", " ['Krasnyi Klyuch'],\n", " ['Krutikha'],\n", " ['Krymka'],\n", " ['Kukschin'],\n", " ['Kulak'],\n", " ['Kuleschovka'],\n", " ['Kulp'],\n", " ['Kunashak'],\n", " ['Kunya-Urgench'],\n", " ['Kushiike'],\n", " ['Kusiali'],\n", " ['Kutais'],\n", " ['Kuttippuram'],\n", " ['Kuznetzovo'],\n", " ['Kyushu'],\n", " ['La Bécasse'],\n", " ['La Charca'],\n", " ['La Colina'],\n", " ['La Criolla'],\n", " ['Laborel'],\n", " ['Lahrauli'],\n", " [\"L'Aigle\"],\n", " ['Lakangaon'],\n", " ['Lalitpur'],\n", " ['Lancé'],\n", " ['Lancon'],\n", " ['Långhalsen'],\n", " ['Lanxi'],\n", " ['Lanzenkirchen'],\n", " ['Laochenzhen'],\n", " ['Launton'],\n", " ['Lavrentievka'],\n", " ['Le Pressoir'],\n", " ['Le Teilleul'],\n", " ['Leedey'],\n", " ['Leeuwfontein'],\n", " ['Leighlinbridge'],\n", " ['Leighton'],\n", " ['Leonovka'],\n", " ['Les Ormes'],\n", " ['Lesves'],\n", " ['Lichtenberg'],\n", " ['Lillaverke'],\n", " ['Limerick'],\n", " ['Linum'],\n", " ['Lishui'],\n", " ['Lissa'],\n", " ['Little Piney'],\n", " ['Lixna'],\n", " ['Lodran'],\n", " ['Lohawat'],\n", " ['Lorton'],\n", " ['Los Martinez'],\n", " ['Lost City'],\n", " ['Louisville'],\n", " ['Łowicz'],\n", " ['Lua'],\n", " ['Lucé'],\n", " ['Lumpkin'],\n", " ['Lunan'],\n", " ['Lundsgård'],\n", " ['Luotolax'],\n", " ['Luponnas'],\n", " ['Lusaka'],\n", " ['Mabwe-Khoywa'],\n", " ['Macau'],\n", " ['Machinga'],\n", " ['Macibini'],\n", " ['Madhipura'],\n", " ['Madiun'],\n", " ['Madrid'],\n", " ['Mafra'],\n", " ['Magnesia'],\n", " ['Magombedze'],\n", " ['Mahadevpur'],\n", " ['Maigatari-Danduma'],\n", " ['Malaga'],\n", " ['Malakal'],\n", " ['Malampaka'],\n", " ['Malotas'],\n", " ['Malvern'],\n", " ['Mamra Springs'],\n", " ['Manbhoom'],\n", " ['Manegaon'],\n", " ['Mangwendi'],\n", " ['Manych'],\n", " ['Mardan'],\n", " ['Maria Linden'],\n", " ['Mariaville'],\n", " ['Maribo'],\n", " ['Maridi'],\n", " ['Marilia'],\n", " ['Marion (Iowa)'],\n", " ['Marjalahti'],\n", " ['Marmande'],\n", " ['Maromandia'],\n", " ['Maryville'],\n", " ['Mascombes'],\n", " ['Mason Gully'],\n", " ['Mässing'],\n", " ['Mauerkirchen'],\n", " ['Mauritius'],\n", " ['Mayo Belwa'],\n", " ['Mazapil'],\n", " ['Maziba'],\n", " ['Mbale'],\n", " ['Medanitos'],\n", " ['Meerut'],\n", " ['Meester-Cornelis'],\n", " ['Menow'],\n", " ['Menziswyl'],\n", " ['Mern'],\n", " ['Meru'],\n", " ['Merua'],\n", " ['Messina'],\n", " ['Meuselbach'],\n", " ['Mezel'],\n", " ['Mezö-Madaras'],\n", " ['Mhow'],\n", " ['Mianchi'],\n", " ['Middlesbrough'],\n", " ['Mifflin'],\n", " ['Mighei'],\n", " ['Mihonoseki'],\n", " ['Mike'],\n", " ['Milena'],\n", " ['Millbillillie'],\n", " ['Miller (Arkansas)'],\n", " ['Minamino'],\n", " ['Mineo'],\n", " ['Min-Fan-Zhun'],\n", " ['Minnichhof'],\n", " ['Mirzapur'],\n", " ['Misshof'],\n", " ['Mjelleim'],\n", " ['Mocs'],\n", " ['Modoc (1905)'],\n", " ['Mokoia'],\n", " ['Molina'],\n", " ['Molteno'],\n", " ['Monahans (1998)'],\n", " ['Monroe'],\n", " ['Monte das Fortes'],\n", " ['Monte Milone'],\n", " ['Montferré'],\n", " ['Montlivault'],\n", " ['Monze'],\n", " ['Moore County'],\n", " ['Mooresfort'],\n", " ['Moorleah'],\n", " ['Moradabad'],\n", " ['Morávka'],\n", " ['Mornans'],\n", " ['Moss'],\n", " ['Moti-ka-nagla'],\n", " ['Motta di Conti'],\n", " ['Mount Browne'],\n", " ['Mount Tazerzait'],\n", " ['Mount Vaisi'],\n", " ['Mtola'],\n", " ['Muddoor'],\n", " ['Mulletiwu'],\n", " ['Muraid'],\n", " ['Murchison'],\n", " ['Murray'],\n", " ['Muzaffarpur'],\n", " ['Myhee Caunta'],\n", " ['Nadiabondi'],\n", " ['Nagai'],\n", " ['Nagaria'],\n", " ['Nagy-Borové'],\n", " ['Nakhla'],\n", " ['Nakhon Pathom'],\n", " ['Nammianthal'],\n", " ['Nan Yang Pao'],\n", " ['Nanjemoy'],\n", " ['Nantong'],\n", " ['Naoki'],\n", " ['Naragh'],\n", " ['Narellan'],\n", " ['Narni'],\n", " ['Nassirah'],\n", " ['Natal'],\n", " ['Nawapali'],\n", " ['Neagari'],\n", " ['Nedagolla'],\n", " ['Nejo'],\n", " ['Nerft'],\n", " ['Neuschwanstein'],\n", " ['New Concord'],\n", " ['New Halfa'],\n", " ['New Orleans'],\n", " ['Ngawi'],\n", " [\"N'Goureyma\"],\n", " ['Nicorps'],\n", " ['Niger (L6)'],\n", " ['Niger (LL6)'],\n", " ['Nikolaevka'],\n", " ['Nikolskoe'],\n", " ['Ningbo'],\n", " ['Ningqiang'],\n", " ['Nio'],\n", " [\"N'Kandhla\"],\n", " ['Nobleborough'],\n", " ['Noblesville'],\n", " ['Nogata'],\n", " ['Nogoya'],\n", " ['Norfork'],\n", " ['Norton County'],\n", " ['Noventa Vicentina'],\n", " ['Novo-Urei'],\n", " ['Novy-Ergi'],\n", " ['Novy-Projekt'],\n", " ['Noyan-Bogdo'],\n", " ['Nuevo Mercurio'],\n", " ['Nulles'],\n", " ['Numakai'],\n", " ['Nyaung'],\n", " ['Nyirábrany'],\n", " ['Ochansk'],\n", " ['Oesede'],\n", " ['Oesel'],\n", " ['Ofehértó'],\n", " ['Ogi'],\n", " ['Ohaba'],\n", " ['Ohuma'],\n", " ['Ojuelos Altos'],\n", " ['Okabe'],\n", " ['Okano'],\n", " ['Okniny'],\n", " ['Oldenburg (1930)'],\n", " ['Oliva-Gandia'],\n", " ['Olivenza'],\n", " ['Olmedilla de Alarcón'],\n", " ['Omolon'],\n", " ['Orgueil'],\n", " ['Orlando'],\n", " ['Ornans'],\n", " ['Ortenau'],\n", " ['Orvinio'],\n", " ['Oterøy'],\n", " ['Otomi'],\n", " ['Ottawa'],\n", " ['Ouadangou'],\n", " ['Oued el Hadjar'],\n", " ['Oum Dreyga'],\n", " ['Ourique'],\n", " ['Ovambo'],\n", " ['Oviedo'],\n", " ['Owrucz'],\n", " ['Pacula'],\n", " ['Padvarninkai'],\n", " ['Paitan'],\n", " ['Palahatchie'],\n", " ['Palca de Aparzo'],\n", " ['Palinshih'],\n", " ['Palmyra'],\n", " ['Palolo Valley'],\n", " ['Pampanga'],\n", " ['Pantar'],\n", " ['Paragould'],\n", " ['Parambu'],\n", " ['Paranaiba'],\n", " ['Park Forest'],\n", " ['Parnallee'],\n", " ['Parsa'],\n", " ['Pasamonte'],\n", " ['Patora'],\n", " ['Patrimonio'],\n", " ['Patti'],\n", " ['Patwar'],\n", " ['Pavel'],\n", " ['Pavlodar (stone)'],\n", " ['Pavlograd'],\n", " ['Pavlovka'],\n", " ['Pê'],\n", " ['Peace River'],\n", " ['Peckelsheim'],\n", " ['Peekskill'],\n", " ['Peña Blanca Spring'],\n", " ['Peramiho'],\n", " ['Perpeti'],\n", " ['Perth'],\n", " ['Pervomaisky'],\n", " ['Pesyanoe'],\n", " ['Pétèlkolé'],\n", " ['Petersburg'],\n", " ['Pettiswood'],\n", " ['Phillips County (stone)'],\n", " ['Phu Hong'],\n", " ['Phum Sambo'],\n", " ['Phuoc-Binh'],\n", " ['Piancaldoli'],\n", " ['Picote'],\n", " ['Pillistfer'],\n", " ['Piplia Kalan'],\n", " ['Piquetberg'],\n", " ['Pirgunje'],\n", " ['Pirthalla'],\n", " ['Pitts'],\n", " ['Plantersville'],\n", " ['Pleşcoi'],\n", " ['Ploschkovitz'],\n", " ['Pnompehn'],\n", " ['Pohlitz'],\n", " ['Pokhra'],\n", " ['Pollen'],\n", " ['Pontlyfni'],\n", " ['Portales Valley'],\n", " ['Portugal'],\n", " ['Po-wang Chen'],\n", " ['Prambachkirchen'],\n", " ['Pribram'],\n", " ['Pricetown'],\n", " ['Puerto Lápice'],\n", " ['Pulsora'],\n", " ['Pultusk'],\n", " ['Punganaru'],\n", " ['Putinga'],\n", " ['Qidong'],\n", " ['Qingzhen'],\n", " [\"Queen's Mercy\"],\n", " ['Quenggouk'],\n", " ['Quesa'],\n", " ['Quija'],\n", " ['Quincay'],\n", " ['Raco'],\n", " ['Raghunathpura'],\n", " ['Rahimyar Khan'],\n", " ['Rakovka'],\n", " ['Ramnagar'],\n", " ['Rampurhat'],\n", " ['Ramsdorf'],\n", " ['Ranchapur'],\n", " ['Rancho de la Presa'],\n", " ['Rangala'],\n", " ['Raoyang'],\n", " ['Ras Tanura'],\n", " ['Rasgrad'],\n", " ['Ratyn'],\n", " ['Red Canyon Lake'],\n", " ['Reliegos'],\n", " ['Rembang'],\n", " ['Renazzo'],\n", " ['Renca'],\n", " ['Renqiu'],\n", " ['Repeev Khutor'],\n", " ['Revelstoke'],\n", " ['Rewari'],\n", " ['Rich Mountain'],\n", " ['Richardton'],\n", " ['Richland Springs'],\n", " ['Richmond'],\n", " ['Rio Negro'],\n", " ['Rivolta de Bassi'],\n", " ['Rochester'],\n", " ['Rockhampton'],\n", " ['Roda'],\n", " ['Rodach'],\n", " ['Rose City'],\n", " ['Rowton'],\n", " ['Ruhobobo'],\n", " ['Rumuruti'],\n", " ['Rupota'],\n", " ['Ryechki'],\n", " ['Sabetmahet'],\n", " ['Sabrum'],\n", " ['Sagan'],\n", " ['Saint-Sauveur'],\n", " ['Saint-Séverin'],\n", " ['Sakauchi'],\n", " ['Salem'],\n", " ['Salles'],\n", " ['Salzwedel'],\n", " ['Samelia'],\n", " ['San Juan Capistrano'],\n", " ['San Michele'],\n", " ['San Pedro de Quiles'],\n", " ['San Pedro Jacuaro'],\n", " ['Santa Barbara'],\n", " ['Santa Cruz'],\n", " ['Santa Isabel'],\n", " ['Santa Lucia (2008)'],\n", " ['São Jose do Rio Preto'],\n", " ['Saratov'],\n", " ['Sasagase'],\n", " ['Sauguis'],\n", " ['Savtschenskoje'],\n", " ['Sayama'],\n", " ['Sazovice'],\n", " ['Schellin'],\n", " ['Schenectady'],\n", " ['Schönenberg'],\n", " ['Searsmont'],\n", " ['Sediköy'],\n", " ['Segowlie'],\n", " ['Selakopi'],\n", " ['Seldebourak'],\n", " ['Semarkona'],\n", " ['Sena'],\n", " ['Senboku'],\n", " ['Seoni'],\n", " ['Seres'],\n", " ['Serra de Magé'],\n", " ['Sete Lagoas'],\n", " ['Sevilla'],\n", " ['Sevrukovo'],\n", " ['Sfax'],\n", " ['Shalka'],\n", " ['Sharps'],\n", " ['Shelburne'],\n", " ['Shergotty'],\n", " ['Sheyang'],\n", " ['Shikarpur'],\n", " ['Shuangyang'],\n", " ['Shupiyan'],\n", " ['Shytal'],\n", " ['Siena'],\n", " ['Sikhote-Alin'],\n", " ['Silao'],\n", " ['Silistra'],\n", " ['Simmern'],\n", " ['Sinai'],\n", " ['Sindhri'],\n", " ['Sinnai'],\n", " ['Sioux County'],\n", " ['Sitathali'],\n", " ['Sivas'],\n", " ['Sixiangkou'],\n", " ['Ski'],\n", " ['Slavetic'],\n", " ['Slobodka'],\n", " ['Soheria'],\n", " ['Soko-Banja'],\n", " ['Sologne'],\n", " ['Sołtmany'],\n", " ['Sone'],\n", " ['Songyuan'],\n", " ['Sopot'],\n", " ['Soroti'],\n", " ['St. Caprais-de-Quinsac'],\n", " ['St. Christophe-la-Chartreuse'],\n", " ['St. Denis Westrem'],\n", " ['St. Germain-du-Pinel'],\n", " ['St. Louis'],\n", " [\"St. Mark's\"],\n", " [\"St. Mary's County\"],\n", " ['St. Mesmin'],\n", " ['St. Michel'],\n", " ['St.-Chinian'],\n", " ['Ställdalen'],\n", " ['Stannern'],\n", " ['Stavropol'],\n", " ['Ste. Marguerite'],\n", " ['Sterlitamak'],\n", " ['Stolzenau'],\n", " ['Stratford'],\n", " ['Strathmore'],\n", " ['Stretchleigh'],\n", " ['St-Robert'],\n", " ['Success'],\n", " ['Suchy Dul'],\n", " ['Suizhou'],\n", " ['Sulagiri'],\n", " ['Sultanpur'],\n", " ['Sungach'],\n", " ['Supuhee'],\n", " [\"Sutter's Mill\"],\n", " ['Sylacauga'],\n", " ['Tabor'],\n", " ['Tadjera'],\n", " ['Tagish Lake'],\n", " ['Tahara'],\n", " ['Takenouchi'],\n", " ['Talampaya'],\n", " ['Tambakwatu'],\n", " ['Tamdakht'],\n", " ['Tané'],\n", " ['Taonan'],\n", " ['Tatahouine'],\n", " ['Tathlith'],\n", " ['Tauk'],\n", " ['Tauti'],\n", " ['Tenham'],\n", " ['Tennasilm'],\n", " ['Thal'],\n", " ['Thika'],\n", " ['Thuathe'],\n", " ['Tianzhang'],\n", " ['Tieschitz'],\n", " ['Tilden'],\n", " ['Tillaberi'],\n", " ['Timochin'],\n", " ['Tirupati'],\n", " ['Tissint'],\n", " ['Tjabe'],\n", " ['Tjerebon'],\n", " ['Tomakovka'],\n", " ['Tomatlan'],\n", " ['Tomita'],\n", " ['Tomiya'],\n", " ['Tonk'],\n", " ...]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"name\"].ext.to_pandas().values.tolist()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['name',\n", " 'id',\n", " 'nametype',\n", " 'recclass',\n", " 'mass (g)',\n", " 'fall',\n", " 'year',\n", " 'reclat',\n", " 'reclong',\n", " 'GeoLocation']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.names()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['Aachen'],\n", " ['Aarhus'],\n", " ['Abee'],\n", " ['Acapulco'],\n", " ['Achiras'],\n", " ['Adhi Kot'],\n", " ['Adzhi-Bogdo (stone)'],\n", " ['Agen'],\n", " ['Aguada'],\n", " ['Aguila Blanca'],\n", " ['Aioun el Atrouss'],\n", " ['Aïr'],\n", " ['Aire-sur-la-Lys'],\n", " ['Akaba'],\n", " ['Akbarpur'],\n", " ['Akwanga'],\n", " ['Akyumak'],\n", " ['Al Rais'],\n", " ['Al Zarnkh'],\n", " ['Alais'],\n", " ['Albareto'],\n", " ['Alberta'],\n", " ['Alby sur Chéran'],\n", " ['Aldsworth'],\n", " ['Aleppo'],\n", " ['Alessandria'],\n", " ['Alexandrovsky'],\n", " ['Alfianello'],\n", " ['Allegan'],\n", " ['Allende'],\n", " ['Almahata Sitta'],\n", " [\"Alta'ameem\"],\n", " ['Ambapur Nagla'],\n", " ['Andhara'],\n", " ['Andover'],\n", " ['Andreevka'],\n", " ['Andura'],\n", " ['Northwest Africa 5815'],\n", " ['Angers'],\n", " ['Angra dos Reis (stone)'],\n", " ['Ankober'],\n", " ['Anlong'],\n", " ['Aomori'],\n", " ['Appley Bridge'],\n", " ['Apt'],\n", " ['Arbol Solo'],\n", " ['Archie'],\n", " ['Arroyo Aguiar'],\n", " ['Asco'],\n", " ['Ash Creek'],\n", " ['Ashdon'],\n", " ['Assisi'],\n", " ['Atarra'],\n", " ['Atemajac'],\n", " ['Athens'],\n", " ['Atoka'],\n", " ['Aubres'],\n", " ['Aumale'],\n", " ['Aumieres'],\n", " ['Ausson'],\n", " ['Avanhandava'],\n", " ['Avce'],\n", " ['Avilez'],\n", " ['Awere'],\n", " ['Aztec'],\n", " ['Bachmut'],\n", " ['Bahjoi'],\n", " ['Bald Mountain'],\n", " ['Baldwyn'],\n", " ['Bali'],\n", " ['Ban Rong Du'],\n", " ['Bandong'],\n", " ['Bansur'],\n", " ['Banswal'],\n", " ['Banten'],\n", " ['Barbotan'],\n", " ['Barcelona (stone)'],\n", " ['Barea'],\n", " ['Barnaul'],\n", " ['Barntrup'],\n", " ['Baroti'],\n", " ['Barwell'],\n", " ['Bassikounou'],\n", " ['Baszkówka'],\n", " ['Bath'],\n", " ['Bath Furnace'],\n", " ['Battle Mountain'],\n", " ['Bawku'],\n", " ['Baxter'],\n", " ['Beardsley'],\n", " ['Beaver Creek'],\n", " ['Beddgelert'],\n", " ['Bells'],\n", " ['Belville'],\n", " ['Benares (a)'],\n", " ['Benguerir'],\n", " [\"Beni M'hira\"],\n", " ['Benld'],\n", " ['Benoni'],\n", " ['Bensour'],\n", " ['Benton'],\n", " ['Berduc'],\n", " ['Béréba'],\n", " ['Berlanguillas'],\n", " ['Berthoud'],\n", " ['Bethlehem'],\n", " ['Beuste'],\n", " ['Beyrout'],\n", " ['Bhagur'],\n", " ['Bhawad'],\n", " ['Bherai'],\n", " ['Bhola'],\n", " ['Bholghati'],\n", " ['Bialystok'],\n", " ['Bielokrynitschie'],\n", " ['Bilanga'],\n", " ['Binningup'],\n", " [\"Birni N'konni\"],\n", " ['Bishopville'],\n", " ['Bishunpur'],\n", " ['Bjelaja Zerkov'],\n", " ['Bjurböle'],\n", " ['Black Moshannan Park'],\n", " ['Blackwell'],\n", " ['Blanket'],\n", " ['Blansko'],\n", " ['Bloomington'],\n", " ['Bo Xian'],\n", " ['Bocas'],\n", " ['Bogou'],\n", " ['Boguslavka'],\n", " ['Borgo San Donino'],\n", " ['Bori'],\n", " ['Boriskino'],\n", " ['Borkut'],\n", " ['Borodino'],\n", " ['Botschetschki'],\n", " ['Boumdeid (2003)'],\n", " ['Boumdeid (2011)'],\n", " ['Bovedy'],\n", " ['Bradford Woods'],\n", " ['Braunau'],\n", " ['Breitscheid'],\n", " ['Bremervörde'],\n", " ['Brient'],\n", " ['Bruderheim'],\n", " ['Bukhara'],\n", " ['Bulls Run'],\n", " ['Bunburra Rockhole'],\n", " ['Bununu'],\n", " ['Bur-Gheluai'],\n", " ['Burnwell'],\n", " ['Bursa'],\n", " ['Buschhof'],\n", " ['Bustee'],\n", " ['Butsura'],\n", " ['Buzzard Coulee'],\n", " ['Cabezo de Mayo'],\n", " ['Cabin Creek'],\n", " ['Cacak'],\n", " ['Cali'],\n", " ['Calivo'],\n", " ['Campos Sales'],\n", " ['Çanakkale'],\n", " ['Cañellas'],\n", " ['Cangas de Onis'],\n", " ['Canon City'],\n", " ['Cape Girardeau'],\n", " ['Capilla del Monte'],\n", " ['Carancas'],\n", " ['Caratash'],\n", " ['Castalia'],\n", " ['Castel Berardenga'],\n", " ['Castine'],\n", " ['Castrovillari'],\n", " ['Caswell County'],\n", " ['Ceniceros'],\n", " ['Centerville'],\n", " ['Cereseto'],\n", " ['Chadong'],\n", " ['Chail'],\n", " ['Chainpur'],\n", " ['Chajari'],\n", " ['Chandakapur'],\n", " ['Chandpur'],\n", " ['Changde'],\n", " ['Chantonnay'],\n", " ['Charlotte'],\n", " ['Charsonville'],\n", " ['Charwallas'],\n", " ['Chassigny'],\n", " ['Château-Renard'],\n", " ['Chaves'],\n", " ['Chela'],\n", " ['Chelyabinsk'],\n", " ['Chergach '],\n", " ['Chernyi Bor'],\n", " ['Cherokee Springs'],\n", " ['Chervettaz'],\n", " ['Chervony Kut'],\n", " ['Chetrinahatti'],\n", " ['Chiang Khan'],\n", " ['Chicora'],\n", " ['Chisenga'],\n", " ['Chitado'],\n", " ['Chitenay'],\n", " ['Cilimus'],\n", " ['Claxton'],\n", " ['Clohars'],\n", " ['Colby (Wisconsin)'],\n", " ['Cold Bokkeveld'],\n", " ['Coleman'],\n", " ['Collescipoli'],\n", " ['Conquista'],\n", " ['Cosina'],\n", " ['Cranganore'],\n", " ['Crescent'],\n", " ['Cronstad'],\n", " ['Cross Roads'],\n", " ['Crumlin'],\n", " ['Cumberland Falls'],\n", " ['Cynthiana'],\n", " ['Dahmani'],\n", " ['Dandapur'],\n", " [\"Daniel's Kuil\"],\n", " ['Danville'],\n", " ['Darmstadt'],\n", " ['Dashoguz'],\n", " ['Daule'],\n", " ['De Cewsville'],\n", " ['Deal'],\n", " ['Delhi'],\n", " ['Demina'],\n", " ['Denver'],\n", " ['Dergaon'],\n", " ['Desuri'],\n", " ['Devgaon'],\n", " ['Devri-Khera'],\n", " ['Dhajala'],\n", " ['Dharwar'],\n", " ['Dhurmsala'],\n", " ['Didim'],\n", " ['Diep River'],\n", " ['Distrito Quebracho'],\n", " ['Djati-Pengilon'],\n", " ['Djermaia'],\n", " ['Djoumine'],\n", " ['Dokachi'],\n", " ['Dolgovoli'],\n", " ['Domanitch'],\n", " ['Dong Ujimqin Qi'],\n", " ['Donga Kohrod'],\n", " ['Dongtai'],\n", " ['Doroninsk'],\n", " ['Dosso'],\n", " ['Douar Mghila'],\n", " ['Dowa'],\n", " ['Drake Creek'],\n", " ['Dresden (Ontario)'],\n", " ['Dubrovnik'],\n", " ['Dunbogan'],\n", " ['Dundrum'],\n", " ['Dunhua'],\n", " ['Durala'],\n", " ['Duruma'],\n", " ['Duwun'],\n", " ['Dwaleni'],\n", " ['Dyalpur'],\n", " ['Dyarrl Island'],\n", " ['Eagle'],\n", " ['Ehole'],\n", " ['Eichstädt'],\n", " ['Ekeby'],\n", " ['Ekh Khera'],\n", " ['El Idrissia'],\n", " ['El Paso de Aguila'],\n", " ['El Tigre'],\n", " ['Elbert'],\n", " ['Elbogen'],\n", " ['Elenovka'],\n", " ['Ellemeet'],\n", " ['Emmaville'],\n", " ['Enshi'],\n", " ['Ensisheim'],\n", " ['Épinal'],\n", " ['Erakot'],\n", " ['Erevan'],\n", " ['Ergheo'],\n", " ['Erxleben'],\n", " ['Esnandes'],\n", " ['Essebi'],\n", " ['Estherville'],\n", " ['Farmington'],\n", " ['Farmville'],\n", " ['Favars'],\n", " ['Fayetteville'],\n", " ['Feid Chair'],\n", " ['Felix'],\n", " ['Fenghsien-Ku'],\n", " ['Ferguson'],\n", " ['Fermo'],\n", " ['Fisher'],\n", " ['Florence'],\n", " ['Forest City'],\n", " ['Forest Vale'],\n", " ['Forksville'],\n", " ['Forsbach'],\n", " ['Forsyth'],\n", " ['Fort Flatters'],\n", " ['Frankfort (stone)'],\n", " ['Fuhe'],\n", " ['Fukutomi'],\n", " ['Fünen'],\n", " ['Futtehpur'],\n", " ['Fuyang'],\n", " ['Galapian'],\n", " ['Galim (a)'],\n", " ['Galim (b)'],\n", " ['Galkiv'],\n", " ['Gambat'],\n", " ['Gao-Guenie'],\n", " ['Garhi Yasin'],\n", " ['Garland'],\n", " ['Gashua'],\n", " ['Gasseltepaoua'],\n", " ['Geidam'],\n", " ['Gifu'],\n", " ['Girgenti'],\n", " ['Git-Git'],\n", " ['Glanerbrug'],\n", " ['Glanggang'],\n", " ['Glasatovo'],\n", " ['Glatton'],\n", " ['Gnadenfrei'],\n", " ['Gopalpur'],\n", " ['Gorlovka'],\n", " ['Granes'],\n", " ['Grefsheim'],\n", " ['Grimsby'],\n", " ['Grosnaja'],\n", " ['Gross-Divina'],\n", " ['Grossliebenthal'],\n", " ['Grüneberg'],\n", " ['Grzempach'],\n", " ['Gualeguaychú'],\n", " ['Guangmingshan'],\n", " ['Guangnan'],\n", " ['Guangrao'],\n", " ['Guareña'],\n", " ['Guêa'],\n", " ['Guibga'],\n", " ['Guidder'],\n", " ['Gujargaon'],\n", " ['Gujba'],\n", " ['Gumoschnik'],\n", " ['Gurram Konda'],\n", " ['Gursum'],\n", " ['Gütersloh'],\n", " ['Gyokukei'],\n", " ['Hachi-oji'],\n", " ['Hainaut'],\n", " ['Hallingeberg'],\n", " ['Hamlet'],\n", " ['Haraiya'],\n", " ['Haripura'],\n", " ['Harleton'],\n", " ['Harrison County'],\n", " ['Hashima'],\n", " ['Hassi-Jekna'],\n", " ['Hatford'],\n", " ['Haverö'],\n", " ['Hedeskoga'],\n", " ['Hedjaz'],\n", " ['Heredia'],\n", " ['Hessle'],\n", " ['Higashi-koen'],\n", " ['High Possil'],\n", " ['Hiroshima'],\n", " ['Hoima'],\n", " ['Hökmark'],\n", " ['Holbrook'],\n", " ['Holetta'],\n", " ['Homestead'],\n", " ['Honolulu'],\n", " ['Hotse'],\n", " ['Hoxie'],\n", " ['Hraschina'],\n", " ['Huaxi'],\n", " ['Hungen'],\n", " ['Hvittis'],\n", " ['Ibbenbüren'],\n", " ['Ibitira'],\n", " ['Ibrisim'],\n", " ['Ichkala'],\n", " ['Idutywa'],\n", " ['Iguaracu'],\n", " ['Ijopega'],\n", " ['Indarch'],\n", " ['Independence'],\n", " ['Inner Mongolia'],\n", " ['Innisfree'],\n", " ['Ipiranga'],\n", " ['Ishinga'],\n", " ['Isthilart'],\n", " ['Itapicuru-Mirim'],\n", " ['Itqiy'],\n", " ['Ivuna'],\n", " ['Jackalsfontein'],\n", " ['Jajh deh Kot Lalu'],\n", " ['Jalanash'],\n", " ['Jalandhar'],\n", " ['Jamkheir'],\n", " ['Jartai'],\n", " ['Jelica'],\n", " ['Jemlapur'],\n", " ['Jesenice'],\n", " ['Jhung'],\n", " ['Jiange'],\n", " ['Jianshi'],\n", " ['Jilin'],\n", " ['Jodiya'],\n", " ['Jodzie'],\n", " ['Johnstown'],\n", " ['Jolomba'],\n", " ['Jonzac'],\n", " ['Juancheng'],\n", " ['Judesegeri'],\n", " ['Jumapalo'],\n", " ['Junan'],\n", " ['Juromenha'],\n", " ['Juvinas'],\n", " ['Kaba'],\n", " ['Kabo'],\n", " ['Kadonah'],\n", " ['Kaee'],\n", " ['Kagarlyk'],\n", " ['Kaidun'],\n", " ['Kainsaz'],\n", " ['Kakangari'],\n", " ['Kakowa'],\n", " ['Kalaba'],\n", " ['Kalumbi'],\n", " ['Kamalpur'],\n", " ['Kamiomi'],\n", " ['Kamsagar'],\n", " ['Kandahar (Afghanistan)'],\n", " ['Kangean'],\n", " ['Kangra Valley'],\n", " ['Kapoeta'],\n", " ['Kaprada'],\n", " ['Kaptal-Aryk'],\n", " ['Karakol'],\n", " ['Karatu'],\n", " ['Karewar'],\n", " ['Karkh'],\n", " ['Karloowala'],\n", " ['Karoonda'],\n", " ['Kasamatsu'],\n", " ['Kasauli'],\n", " ['Katagum'],\n", " ['Kavarpura'],\n", " ['Kayakent'],\n", " ['Kediri'],\n", " ['Kemer'],\n", " ['Kendleton'],\n", " ['Kendrapara'],\n", " ['Kerilis'],\n", " ['Kernouve'],\n", " ['Kesen'],\n", " ['Khairpur'],\n", " ['Khanpur'],\n", " ['Kharkov'],\n", " ['Kheragur'],\n", " ['Khetri'],\n", " ['Khmelevka'],\n", " ['Khohar'],\n", " ['Khor Temiki'],\n", " ['Kidairat'],\n", " ['Kiel'],\n", " ['Kiffa'],\n", " ['Kijima (1906)'],\n", " ['Kikino'],\n", " ['Kilabo'],\n", " ['Kilbourn'],\n", " ['Killeter'],\n", " ['Kingai'],\n", " ['Kirbyville'],\n", " ['Kisvarsány'],\n", " ['Kitchener'],\n", " ['Klein-Wenden'],\n", " ['Knyahinya'],\n", " ['Kobe'],\n", " ['Kokubunji'],\n", " ['Komagome'],\n", " ['Konovo'],\n", " ['Košice'],\n", " ['Krähenberg'],\n", " ['Krasnoi-Ugol'],\n", " ['Krasnyi Klyuch'],\n", " ['Krutikha'],\n", " ['Krymka'],\n", " ['Kukschin'],\n", " ['Kulak'],\n", " ['Kuleschovka'],\n", " ['Kulp'],\n", " ['Kunashak'],\n", " ['Kunya-Urgench'],\n", " ['Kushiike'],\n", " ['Kusiali'],\n", " ['Kutais'],\n", " ['Kuttippuram'],\n", " ['Kuznetzovo'],\n", " ['Kyushu'],\n", " ['La Bécasse'],\n", " ['La Charca'],\n", " ['La Colina'],\n", " ['La Criolla'],\n", " ['Laborel'],\n", " ['Lahrauli'],\n", " [\"L'Aigle\"],\n", " ['Lakangaon'],\n", " ['Lalitpur'],\n", " ['Lancé'],\n", " ['Lancon'],\n", " ['Långhalsen'],\n", " ['Lanxi'],\n", " ['Lanzenkirchen'],\n", " ['Laochenzhen'],\n", " ['Launton'],\n", " ['Lavrentievka'],\n", " ['Le Pressoir'],\n", " ['Le Teilleul'],\n", " ['Leedey'],\n", " ['Leeuwfontein'],\n", " ['Leighlinbridge'],\n", " ['Leighton'],\n", " ['Leonovka'],\n", " ['Les Ormes'],\n", " ['Lesves'],\n", " ['Lichtenberg'],\n", " ['Lillaverke'],\n", " ['Limerick'],\n", " ['Linum'],\n", " ['Lishui'],\n", " ['Lissa'],\n", " ['Little Piney'],\n", " ['Lixna'],\n", " ['Lodran'],\n", " ['Lohawat'],\n", " ['Lorton'],\n", " ['Los Martinez'],\n", " ['Lost City'],\n", " ['Louisville'],\n", " ['Łowicz'],\n", " ['Lua'],\n", " ['Lucé'],\n", " ['Lumpkin'],\n", " ['Lunan'],\n", " ['Lundsgård'],\n", " ['Luotolax'],\n", " ['Luponnas'],\n", " ['Lusaka'],\n", " ['Mabwe-Khoywa'],\n", " ['Macau'],\n", " ['Machinga'],\n", " ['Macibini'],\n", " ['Madhipura'],\n", " ['Madiun'],\n", " ['Madrid'],\n", " ['Mafra'],\n", " ['Magnesia'],\n", " ['Magombedze'],\n", " ['Mahadevpur'],\n", " ['Maigatari-Danduma'],\n", " ['Malaga'],\n", " ['Malakal'],\n", " ['Malampaka'],\n", " ['Malotas'],\n", " ['Malvern'],\n", " ['Mamra Springs'],\n", " ['Manbhoom'],\n", " ['Manegaon'],\n", " ['Mangwendi'],\n", " ['Manych'],\n", " ['Mardan'],\n", " ['Maria Linden'],\n", " ['Mariaville'],\n", " ['Maribo'],\n", " ['Maridi'],\n", " ['Marilia'],\n", " ['Marion (Iowa)'],\n", " ['Marjalahti'],\n", " ['Marmande'],\n", " ['Maromandia'],\n", " ['Maryville'],\n", " ['Mascombes'],\n", " ['Mason Gully'],\n", " ['Mässing'],\n", " ['Mauerkirchen'],\n", " ['Mauritius'],\n", " ['Mayo Belwa'],\n", " ['Mazapil'],\n", " ['Maziba'],\n", " ['Mbale'],\n", " ['Medanitos'],\n", " ['Meerut'],\n", " ['Meester-Cornelis'],\n", " ['Menow'],\n", " ['Menziswyl'],\n", " ['Mern'],\n", " ['Meru'],\n", " ['Merua'],\n", " ['Messina'],\n", " ['Meuselbach'],\n", " ['Mezel'],\n", " ['Mezö-Madaras'],\n", " ['Mhow'],\n", " ['Mianchi'],\n", " ['Middlesbrough'],\n", " ['Mifflin'],\n", " ['Mighei'],\n", " ['Mihonoseki'],\n", " ['Mike'],\n", " ['Milena'],\n", " ['Millbillillie'],\n", " ['Miller (Arkansas)'],\n", " ['Minamino'],\n", " ['Mineo'],\n", " ['Min-Fan-Zhun'],\n", " ['Minnichhof'],\n", " ['Mirzapur'],\n", " ['Misshof'],\n", " ['Mjelleim'],\n", " ['Mocs'],\n", " ['Modoc (1905)'],\n", " ['Mokoia'],\n", " ['Molina'],\n", " ['Molteno'],\n", " ['Monahans (1998)'],\n", " ['Monroe'],\n", " ['Monte das Fortes'],\n", " ['Monte Milone'],\n", " ['Montferré'],\n", " ['Montlivault'],\n", " ['Monze'],\n", " ['Moore County'],\n", " ['Mooresfort'],\n", " ['Moorleah'],\n", " ['Moradabad'],\n", " ['Morávka'],\n", " ['Mornans'],\n", " ['Moss'],\n", " ['Moti-ka-nagla'],\n", " ['Motta di Conti'],\n", " ['Mount Browne'],\n", " ['Mount Tazerzait'],\n", " ['Mount Vaisi'],\n", " ['Mtola'],\n", " ['Muddoor'],\n", " ['Mulletiwu'],\n", " ['Muraid'],\n", " ['Murchison'],\n", " ['Murray'],\n", " ['Muzaffarpur'],\n", " ['Myhee Caunta'],\n", " ['Nadiabondi'],\n", " ['Nagai'],\n", " ['Nagaria'],\n", " ['Nagy-Borové'],\n", " ['Nakhla'],\n", " ['Nakhon Pathom'],\n", " ['Nammianthal'],\n", " ['Nan Yang Pao'],\n", " ['Nanjemoy'],\n", " ['Nantong'],\n", " ['Naoki'],\n", " ['Naragh'],\n", " ['Narellan'],\n", " ['Narni'],\n", " ['Nassirah'],\n", " ['Natal'],\n", " ['Nawapali'],\n", " ['Neagari'],\n", " ['Nedagolla'],\n", " ['Nejo'],\n", " ['Nerft'],\n", " ['Neuschwanstein'],\n", " ['New Concord'],\n", " ['New Halfa'],\n", " ['New Orleans'],\n", " ['Ngawi'],\n", " [\"N'Goureyma\"],\n", " ['Nicorps'],\n", " ['Niger (L6)'],\n", " ['Niger (LL6)'],\n", " ['Nikolaevka'],\n", " ['Nikolskoe'],\n", " ['Ningbo'],\n", " ['Ningqiang'],\n", " ['Nio'],\n", " [\"N'Kandhla\"],\n", " ['Nobleborough'],\n", " ['Noblesville'],\n", " ['Nogata'],\n", " ['Nogoya'],\n", " ['Norfork'],\n", " ['Norton County'],\n", " ['Noventa Vicentina'],\n", " ['Novo-Urei'],\n", " ['Novy-Ergi'],\n", " ['Novy-Projekt'],\n", " ['Noyan-Bogdo'],\n", " ['Nuevo Mercurio'],\n", " ['Nulles'],\n", " ['Numakai'],\n", " ['Nyaung'],\n", " ['Nyirábrany'],\n", " ['Ochansk'],\n", " ['Oesede'],\n", " ['Oesel'],\n", " ['Ofehértó'],\n", " ['Ogi'],\n", " ['Ohaba'],\n", " ['Ohuma'],\n", " ['Ojuelos Altos'],\n", " ['Okabe'],\n", " ['Okano'],\n", " ['Okniny'],\n", " ['Oldenburg (1930)'],\n", " ['Oliva-Gandia'],\n", " ['Olivenza'],\n", " ['Olmedilla de Alarcón'],\n", " ['Omolon'],\n", " ['Orgueil'],\n", " ['Orlando'],\n", " ['Ornans'],\n", " ['Ortenau'],\n", " ['Orvinio'],\n", " ['Oterøy'],\n", " ['Otomi'],\n", " ['Ottawa'],\n", " ['Ouadangou'],\n", " ['Oued el Hadjar'],\n", " ['Oum Dreyga'],\n", " ['Ourique'],\n", " ['Ovambo'],\n", " ['Oviedo'],\n", " ['Owrucz'],\n", " ['Pacula'],\n", " ['Padvarninkai'],\n", " ['Paitan'],\n", " ['Palahatchie'],\n", " ['Palca de Aparzo'],\n", " ['Palinshih'],\n", " ['Palmyra'],\n", " ['Palolo Valley'],\n", " ['Pampanga'],\n", " ['Pantar'],\n", " ['Paragould'],\n", " ['Parambu'],\n", " ['Paranaiba'],\n", " ['Park Forest'],\n", " ['Parnallee'],\n", " ['Parsa'],\n", " ['Pasamonte'],\n", " ['Patora'],\n", " ['Patrimonio'],\n", " ['Patti'],\n", " ['Patwar'],\n", " ['Pavel'],\n", " ['Pavlodar (stone)'],\n", " ['Pavlograd'],\n", " ['Pavlovka'],\n", " ['Pê'],\n", " ['Peace River'],\n", " ['Peckelsheim'],\n", " ['Peekskill'],\n", " ['Peña Blanca Spring'],\n", " ['Peramiho'],\n", " ['Perpeti'],\n", " ['Perth'],\n", " ['Pervomaisky'],\n", " ['Pesyanoe'],\n", " ['Pétèlkolé'],\n", " ['Petersburg'],\n", " ['Pettiswood'],\n", " ['Phillips County (stone)'],\n", " ['Phu Hong'],\n", " ['Phum Sambo'],\n", " ['Phuoc-Binh'],\n", " ['Piancaldoli'],\n", " ['Picote'],\n", " ['Pillistfer'],\n", " ['Piplia Kalan'],\n", " ['Piquetberg'],\n", " ['Pirgunje'],\n", " ['Pirthalla'],\n", " ['Pitts'],\n", " ['Plantersville'],\n", " ['Pleşcoi'],\n", " ['Ploschkovitz'],\n", " ['Pnompehn'],\n", " ['Pohlitz'],\n", " ['Pokhra'],\n", " ['Pollen'],\n", " ['Pontlyfni'],\n", " ['Portales Valley'],\n", " ['Portugal'],\n", " ['Po-wang Chen'],\n", " ['Prambachkirchen'],\n", " ['Pribram'],\n", " ['Pricetown'],\n", " ['Puerto Lápice'],\n", " ['Pulsora'],\n", " ['Pultusk'],\n", " ['Punganaru'],\n", " ['Putinga'],\n", " ['Qidong'],\n", " ['Qingzhen'],\n", " [\"Queen's Mercy\"],\n", " ['Quenggouk'],\n", " ['Quesa'],\n", " ['Quija'],\n", " ['Quincay'],\n", " ['Raco'],\n", " ['Raghunathpura'],\n", " ['Rahimyar Khan'],\n", " ['Rakovka'],\n", " ['Ramnagar'],\n", " ['Rampurhat'],\n", " ['Ramsdorf'],\n", " ['Ranchapur'],\n", " ['Rancho de la Presa'],\n", " ['Rangala'],\n", " ['Raoyang'],\n", " ['Ras Tanura'],\n", " ['Rasgrad'],\n", " ['Ratyn'],\n", " ['Red Canyon Lake'],\n", " ['Reliegos'],\n", " ['Rembang'],\n", " ['Renazzo'],\n", " ['Renca'],\n", " ['Renqiu'],\n", " ['Repeev Khutor'],\n", " ['Revelstoke'],\n", " ['Rewari'],\n", " ['Rich Mountain'],\n", " ['Richardton'],\n", " ['Richland Springs'],\n", " ['Richmond'],\n", " ['Rio Negro'],\n", " ['Rivolta de Bassi'],\n", " ['Rochester'],\n", " ['Rockhampton'],\n", " ['Roda'],\n", " ['Rodach'],\n", " ['Rose City'],\n", " ['Rowton'],\n", " ['Ruhobobo'],\n", " ['Rumuruti'],\n", " ['Rupota'],\n", " ['Ryechki'],\n", " ['Sabetmahet'],\n", " ['Sabrum'],\n", " ['Sagan'],\n", " ['Saint-Sauveur'],\n", " ['Saint-Séverin'],\n", " ['Sakauchi'],\n", " ['Salem'],\n", " ['Salles'],\n", " ['Salzwedel'],\n", " ['Samelia'],\n", " ['San Juan Capistrano'],\n", " ['San Michele'],\n", " ['San Pedro de Quiles'],\n", " ['San Pedro Jacuaro'],\n", " ['Santa Barbara'],\n", " ['Santa Cruz'],\n", " ['Santa Isabel'],\n", " ['Santa Lucia (2008)'],\n", " ['São Jose do Rio Preto'],\n", " ['Saratov'],\n", " ['Sasagase'],\n", " ['Sauguis'],\n", " ['Savtschenskoje'],\n", " ['Sayama'],\n", " ['Sazovice'],\n", " ['Schellin'],\n", " ['Schenectady'],\n", " ['Schönenberg'],\n", " ['Searsmont'],\n", " ['Sediköy'],\n", " ['Segowlie'],\n", " ['Selakopi'],\n", " ['Seldebourak'],\n", " ['Semarkona'],\n", " ['Sena'],\n", " ['Senboku'],\n", " ['Seoni'],\n", " ['Seres'],\n", " ['Serra de Magé'],\n", " ['Sete Lagoas'],\n", " ['Sevilla'],\n", " ['Sevrukovo'],\n", " ['Sfax'],\n", " ['Shalka'],\n", " ['Sharps'],\n", " ['Shelburne'],\n", " ['Shergotty'],\n", " ['Sheyang'],\n", " ['Shikarpur'],\n", " ['Shuangyang'],\n", " ['Shupiyan'],\n", " ['Shytal'],\n", " ['Siena'],\n", " ['Sikhote-Alin'],\n", " ['Silao'],\n", " ['Silistra'],\n", " ['Simmern'],\n", " ['Sinai'],\n", " ['Sindhri'],\n", " ['Sinnai'],\n", " ['Sioux County'],\n", " ['Sitathali'],\n", " ['Sivas'],\n", " ['Sixiangkou'],\n", " ['Ski'],\n", " ['Slavetic'],\n", " ['Slobodka'],\n", " ['Soheria'],\n", " ['Soko-Banja'],\n", " ['Sologne'],\n", " ['Sołtmany'],\n", " ['Sone'],\n", " ['Songyuan'],\n", " ['Sopot'],\n", " ['Soroti'],\n", " ['St. Caprais-de-Quinsac'],\n", " ['St. Christophe-la-Chartreuse'],\n", " ['St. Denis Westrem'],\n", " ['St. Germain-du-Pinel'],\n", " ['St. Louis'],\n", " [\"St. Mark's\"],\n", " [\"St. Mary's County\"],\n", " ['St. Mesmin'],\n", " ['St. Michel'],\n", " ['St.-Chinian'],\n", " ['Ställdalen'],\n", " ['Stannern'],\n", " ['Stavropol'],\n", " ['Ste. Marguerite'],\n", " ['Sterlitamak'],\n", " ['Stolzenau'],\n", " ['Stratford'],\n", " ['Strathmore'],\n", " ['Stretchleigh'],\n", " ['St-Robert'],\n", " ['Success'],\n", " ['Suchy Dul'],\n", " ['Suizhou'],\n", " ['Sulagiri'],\n", " ['Sultanpur'],\n", " ['Sungach'],\n", " ['Supuhee'],\n", " [\"Sutter's Mill\"],\n", " ['Sylacauga'],\n", " ['Tabor'],\n", " ['Tadjera'],\n", " ['Tagish Lake'],\n", " ['Tahara'],\n", " ['Takenouchi'],\n", " ['Talampaya'],\n", " ['Tambakwatu'],\n", " ['Tamdakht'],\n", " ['Tané'],\n", " ['Taonan'],\n", " ['Tatahouine'],\n", " ['Tathlith'],\n", " ['Tauk'],\n", " ['Tauti'],\n", " ['Tenham'],\n", " ['Tennasilm'],\n", " ['Thal'],\n", " ['Thika'],\n", " ['Thuathe'],\n", " ['Tianzhang'],\n", " ['Tieschitz'],\n", " ['Tilden'],\n", " ['Tillaberi'],\n", " ['Timochin'],\n", " ['Tirupati'],\n", " ['Tissint'],\n", " ['Tjabe'],\n", " ['Tjerebon'],\n", " ['Tomakovka'],\n", " ['Tomatlan'],\n", " ['Tomita'],\n", " ['Tomiya'],\n", " ['Tonk'],\n", " ...]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.rows.to_list(\"name\")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'frequency': {'name': {'values': [{'value': 'Święcany', 'count': 1},\n", " {'value': 'Hammadah al Hamra 012', 'count': 1},\n", " {'value': 'Hammadah al Hamra 045', 'count': 1},\n", " {'value': 'Hammadah al Hamra 046', 'count': 1},\n", " {'value': 'Hammadah al Hamra 047', 'count': 1},\n", " {'value': 'Hammadah al Hamra 048', 'count': 1},\n", " {'value': 'Hammadah al Hamra 049', 'count': 1},\n", " {'value': 'Hammadah al Hamra 050', 'count': 1},\n", " {'value': 'Hammadah al Hamra 051', 'count': 1},\n", " {'value': 'Hammadah al Hamra 043', 'count': 1},\n", " {'value': 'Hammadah al Hamra 032', 'count': 1},\n", " {'value': 'Hammadah al Hamra 031', 'count': 1},\n", " {'value': 'Hammadah al Hamra 030', 'count': 1},\n", " {'value': 'Hammadah al Hamra 009', 'count': 1},\n", " {'value': 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"output_type": "execute_result" } ], "source": [ "# df.cols.hist()\n", "df.cols.frequency()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameidnametyperecclassmass (g)fallyearreclatreclongGeoLocation
0Aachen1ValidL521Fell01/01/1880 12:00:00 AM50.7750006.083330(50.775000, 6.083330)
1Aarhus2ValidH6720Fell01/01/1951 12:00:00 AM56.18333010.233330(56.183330, 10.233330)
2Abee6ValidEH4107000Fell01/01/1952 12:00:00 AM54.216670-113.000000(54.216670, -113.000000)
3Acapulco10ValidAcapulcoite1914Fell01/01/1976 12:00:00 AM16.883330-99.900000(16.883330, -99.900000)
4Achiras370ValidL6780Fell01/01/1902 12:00:00 AM-33.166670-64.950000(-33.166670, -64.950000)
.................................
45711Zillah 00231356ValidEucrite172Found01/01/1990 12:00:00 AM29.03700017.018500(29.037000, 17.018500)
45712Zinder30409ValidPallasite, ungrouped46Found01/01/1999 12:00:00 AM13.7833308.966670(13.783330, 8.966670)
45713Zlin30410ValidH43.3Found01/01/1939 12:00:00 AM49.25000017.666670(49.250000, 17.666670)
45714Zubkovsky31357ValidL62167Found01/01/2003 12:00:00 AM49.78917041.504600(49.789170, 41.504600)
45715Zulu Queen30414ValidL3.7200Found01/01/1976 12:00:00 AM33.983330-115.683330(33.983330, -115.683330)
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45716 rows × 10 columns

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" ], "text/plain": [ " name id nametype recclass mass (g) fall \\\n", "0 Aachen 1 Valid L5 21 Fell \n", "1 Aarhus 2 Valid H6 720 Fell \n", "2 Abee 6 Valid EH4 107000 Fell \n", "3 Acapulco 10 Valid Acapulcoite 1914 Fell \n", "4 Achiras 370 Valid L6 780 Fell \n", "... ... ... ... ... ... ... \n", "45711 Zillah 002 31356 Valid Eucrite 172 Found \n", "45712 Zinder 30409 Valid Pallasite, ungrouped 46 Found \n", "45713 Zlin 30410 Valid H4 3.3 Found \n", "45714 Zubkovsky 31357 Valid L6 2167 Found \n", "45715 Zulu Queen 30414 Valid L3.7 200 Found \n", "\n", " year reclat reclong \\\n", "0 01/01/1880 12:00:00 AM 50.775000 6.083330 \n", "1 01/01/1951 12:00:00 AM 56.183330 10.233330 \n", "2 01/01/1952 12:00:00 AM 54.216670 -113.000000 \n", "3 01/01/1976 12:00:00 AM 16.883330 -99.900000 \n", "4 01/01/1902 12:00:00 AM -33.166670 -64.950000 \n", "... ... ... ... \n", "45711 01/01/1990 12:00:00 AM 29.037000 17.018500 \n", "45712 01/01/1999 12:00:00 AM 13.783330 8.966670 \n", "45713 01/01/1939 12:00:00 AM 49.250000 17.666670 \n", "45714 01/01/2003 12:00:00 AM 49.789170 41.504600 \n", "45715 01/01/1976 12:00:00 AM 33.983330 -115.683330 \n", "\n", " GeoLocation \n", "0 (50.775000, 6.083330) \n", "1 (56.183330, 10.233330) \n", "2 (54.216670, -113.000000) \n", "3 (16.883330, -99.900000) \n", "4 (-33.166670, -64.950000) \n", "... ... \n", "45711 (29.037000, 17.018500) \n", "45712 (13.783330, 8.966670) \n", "45713 (49.250000, 17.666670) \n", "45714 (49.789170, 41.504600) \n", "45715 (33.983330, -115.683330) \n", "\n", "[45716 rows x 10 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.to_pandas()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "df.ext.set_buffer()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameidnametyperecclassmass (g)fallyearreclatreclongGeoLocation
0Aachen1ValidL521.0Fell01/01/1880 12:00:00 AM50.775006.08333(50.775000, 6.083330)
1Aarhus2ValidH6720.0Fell01/01/1951 12:00:00 AM56.1833310.23333(56.183330, 10.233330)
2Abee6ValidEH4107000.0Fell01/01/1952 12:00:00 AM54.21667-113.00000(54.216670, -113.000000)
3Acapulco10ValidAcapulcoite1914.0Fell01/01/1976 12:00:00 AM16.88333-99.90000(16.883330, -99.900000)
4Achiras370ValidL6780.0Fell01/01/1902 12:00:00 AM-33.16667-64.95000(-33.166670, -64.950000)
5Adhi Kot379ValidEH44239.0Fell01/01/1919 12:00:00 AM32.1000071.80000(32.100000, 71.800000)
6Adzhi-Bogdo (stone)390ValidLL3-6910.0Fell01/01/1949 12:00:00 AM44.8333395.16667(44.833330, 95.166670)
7Agen392ValidH530000.0Fell01/01/1814 12:00:00 AM44.216670.61667(44.216670, 0.616670)
8Aguada398ValidL61620.0Fell01/01/1930 12:00:00 AM-31.60000-65.23333(-31.600000, -65.233330)
9Aguila Blanca417ValidL1440.0Fell01/01/1920 12:00:00 AM-30.86667-64.55000(-30.866670, -64.550000)
\n", "
" ], "text/plain": [ " name id nametype recclass mass (g) fall \\\n", "0 Aachen 1 Valid L5 21.0 Fell \n", "1 Aarhus 2 Valid H6 720.0 Fell \n", "2 Abee 6 Valid EH4 107000.0 Fell \n", "3 Acapulco 10 Valid Acapulcoite 1914.0 Fell \n", "4 Achiras 370 Valid L6 780.0 Fell \n", "5 Adhi Kot 379 Valid EH4 4239.0 Fell \n", "6 Adzhi-Bogdo (stone) 390 Valid LL3-6 910.0 Fell \n", "7 Agen 392 Valid H5 30000.0 Fell \n", "8 Aguada 398 Valid L6 1620.0 Fell \n", "9 Aguila Blanca 417 Valid L 1440.0 Fell \n", "\n", " year reclat reclong GeoLocation \n", "0 01/01/1880 12:00:00 AM 50.77500 6.08333 (50.775000, 6.083330) \n", "1 01/01/1951 12:00:00 AM 56.18333 10.23333 (56.183330, 10.233330) \n", "2 01/01/1952 12:00:00 AM 54.21667 -113.00000 (54.216670, -113.000000) \n", "3 01/01/1976 12:00:00 AM 16.88333 -99.90000 (16.883330, -99.900000) \n", "4 01/01/1902 12:00:00 AM -33.16667 -64.95000 (-33.166670, -64.950000) \n", "5 01/01/1919 12:00:00 AM 32.10000 71.80000 (32.100000, 71.800000) \n", "6 01/01/1949 12:00:00 AM 44.83333 95.16667 (44.833330, 95.166670) \n", "7 01/01/1814 12:00:00 AM 44.21667 0.61667 (44.216670, 0.616670) \n", "8 01/01/1930 12:00:00 AM -31.60000 -65.23333 (-31.600000, -65.233330) \n", "9 01/01/1920 12:00:00 AM -30.86667 -64.55000 (-30.866670, -64.550000) " ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.buffer_window(\"*\",0,10)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'id' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'id' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'year' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'year' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'reclat' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'reclat' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'reclong' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'reclong' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'name' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'nametype' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'recclass' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'fall' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'GeoLocation' with function _cast_to\n" ] }, { "data": { "text/plain": [ "{'columns': {'name': {'stats': {'match': 45716,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'Achiras', 'count': 1},\n", " {'value': 'Adhi Kot', 'count': 1},\n", " {'value': 'Aguada', 'count': 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Viewing 10 of 45716 rows / 10 columns
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2 partition(s)
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GeoLocation
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\n", "
\n", "
\n", " \n", " Achiras\n", " \n", "
\n", "
\n", "
\n", " \n", " 370\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 780.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1902⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -33.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.95\n", " \n", "
\n", "
\n", "
\n", " \n", " (-33.166670,⋅-64.950000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adhi⋅Kot\n", " \n", "
\n", "
\n", "
\n", " \n", " 379\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 4239.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1919⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 32.1\n", " \n", "
\n", "
\n", "
\n", " \n", " 71.8\n", " \n", "
\n", "
\n", "
\n", " \n", " (32.100000,⋅71.800000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
\n", "
\n", "
\n", " \n", " 390\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " LL3-6\n", " \n", "
\n", "
\n", "
\n", " \n", " 910.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1949⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.83333\n", " \n", "
\n", "
\n", "
\n", " \n", " 95.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.833330,⋅95.166670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Agen\n", " \n", "
\n", "
\n", "
\n", " \n", " 392\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 30000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1814⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " 0.61667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.216670,⋅0.616670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguada\n", " \n", "
\n", "
\n", "
\n", " \n", " 398\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 1620.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1930⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -31.6\n", " \n", "
\n", "
\n", "
\n", " \n", " -65.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (-31.600000,⋅-65.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
\n", "
\n", " \n", " 417\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L\n", " \n", "
\n", "
\n", "
\n", " \n", " 1440.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1920⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -30.86667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.55\n", " \n", "
\n", "
\n", "
\n", " \n", " (-30.866670,⋅-64.550000)\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "func_return_type1 string\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'id' with function to_string\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 1 columns
\n", "
2 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
id
\n", "
1 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " 370\n", " \n", "
\n", "
\n", "
\n", " \n", " 379\n", " \n", "
\n", "
\n", "
\n", " \n", " 390\n", " \n", "
\n", "
\n", "
\n", " \n", " 392\n", " \n", "
\n", "
\n", "
\n", " \n", " 398\n", " \n", "
\n", "
\n", "
\n", " \n", " 417\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 45716 rows / 1 columns
\n", "
2 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.cols.select(\"id\").cols.to_string().ext.display()\n", "# df.ext.profile(\"id\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "df.cols.remove_accents(\"name\").data.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
name
\n", "
1 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
id
\n", "
2 (int)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
nametype
\n", "
3 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
recclass
\n", "
4 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
mass (g)
\n", "
5 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
fall
\n", "
6 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
year
\n", "
7 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclat
\n", "
8 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclong
\n", "
9 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
GeoLocation
\n", "
10 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " Aachen\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L5\n", " \n", "
\n", "
\n", "
\n", " \n", " 21.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1880⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 50.775\n", " \n", "
\n", "
\n", "
\n", " \n", " 6.08333\n", " \n", "
\n", "
\n", "
\n", " \n", " (50.775000,⋅6.083330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aarhus\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 720.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1951⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 56.18333\n", " \n", "
\n", "
\n", "
\n", " \n", " 10.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (56.183330,⋅10.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Abee\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 107000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1952⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 54.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " -113.0\n", " \n", "
\n", "
\n", "
\n", " \n", " (54.216670,⋅-113.000000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulco\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulcoite\n", " \n", "
\n", "
\n", "
\n", " \n", " 1914.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1976⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 16.88333\n", " \n", "
\n", "
\n", "
\n", " \n", " -99.9\n", " \n", "
\n", "
\n", "
\n", " \n", " (16.883330,⋅-99.900000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Achiras\n", " \n", "
\n", "
\n", "
\n", " \n", " 370\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 780.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1902⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -33.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.95\n", " \n", "
\n", "
\n", "
\n", " \n", " (-33.166670,⋅-64.950000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adhi⋅Kot\n", " \n", "
\n", "
\n", "
\n", " \n", " 379\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 4239.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1919⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 32.1\n", " \n", "
\n", "
\n", "
\n", " \n", " 71.8\n", " \n", "
\n", "
\n", "
\n", " \n", " (32.100000,⋅71.800000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
\n", "
\n", "
\n", " \n", " 390\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " LL3-6\n", " \n", "
\n", "
\n", "
\n", " \n", " 910.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1949⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.83333\n", " \n", "
\n", "
\n", "
\n", " \n", " 95.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.833330,⋅95.166670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Agen\n", " \n", "
\n", "
\n", "
\n", " \n", " 392\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 30000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1814⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " 0.61667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.216670,⋅0.616670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguada\n", " \n", "
\n", "
\n", "
\n", " \n", " 398\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 1620.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1930⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -31.6\n", " \n", "
\n", "
\n", "
\n", " \n", " -65.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (-31.600000,⋅-65.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
\n", "
\n", " \n", " 417\n", " \n", "
\n", "
\n", "
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Viewing 10 of 45716 rows / 10 columns
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2 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
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(g)=1914.0, fall='Fell', year='01/01/1976 12:00:00 AM', reclat=16.88333, reclong=-99.9, GeoLocation='(16.883330, -99.900000)'),\n", " Row(name='Achiras', id=370, nametype='Valid', recclass='L6', mass (g)=780.0, fall='Fell', year='01/01/1902 12:00:00 AM', reclat=-33.16667, reclong=-64.95, GeoLocation='(-33.166670, -64.950000)'),\n", " Row(name='Adhi Kot', id=379, nametype='Valid', recclass='EH4', mass (g)=4239.0, fall='Fell', year='01/01/1919 12:00:00 AM', reclat=32.1, reclong=71.8, GeoLocation='(32.100000, 71.800000)'),\n", " Row(name='Adzhi-Bogdo (stone)', id=390, nametype='Valid', recclass='LL3-6', mass (g)=910.0, fall='Fell', year='01/01/1949 12:00:00 AM', reclat=44.83333, reclong=95.16667, GeoLocation='(44.833330, 95.166670)'),\n", " Row(name='Agen', id=392, nametype='Valid', recclass='H5', mass (g)=30000.0, fall='Fell', year='01/01/1814 12:00:00 AM', reclat=44.21667, reclong=0.61667, GeoLocation='(44.216670, 0.616670)'),\n", " Row(name='Aguada', id=398, nametype='Valid', recclass='L6', mass (g)=1620.0, fall='Fell', year='01/01/1930 12:00:00 AM', reclat=-31.6, reclong=-65.23333, GeoLocation='(-31.600000, -65.233330)'),\n", " Row(name='Aguila Blanca', id=417, nametype='Valid', recclass='L', mass (g)=1440.0, fall='Fell', year='01/01/1920 12:00:00 AM', reclat=-30.86667, reclong=-64.55, GeoLocation='(-30.866670, -64.550000)')]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.head()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'name': ['Aachen',\n", " 'Aarhus',\n", " 'Abee',\n", " 'Acapulco',\n", " 'Achiras',\n", " 'Adhi Kot',\n", " 'Adzhi-Bogdo (stone)',\n", " 'Agen',\n", " 'Aguada',\n", " 'Aguila Blanca',\n", " 'Aioun el Atrouss',\n", " 'Aïr',\n", " 'Aire-sur-la-Lys',\n", " 'Akaba',\n", " 'Akbarpur',\n", " 'Akwanga',\n", " 'Akyumak',\n", " 'Al Rais',\n", " 'Al Zarnkh',\n", " 'Alais',\n", " 'Albareto',\n", " 'Alberta',\n", " 'Alby sur Chéran',\n", " 'Aldsworth',\n", " 'Aleppo',\n", " 'Alessandria',\n", " 'Alexandrovsky',\n", " 'Alfianello',\n", " 'Allegan',\n", " 'Allende',\n", " 'Almahata Sitta',\n", " \"Alta'ameem\",\n", " 'Ambapur Nagla',\n", " 'Andhara',\n", " 'Andover',\n", " 'Andreevka',\n", " 'Andura',\n", " 'Northwest Africa 5815',\n", " 'Angers',\n", " 'Angra dos Reis (stone)',\n", " 'Ankober',\n", " 'Anlong',\n", " 'Aomori',\n", " 'Appley Bridge',\n", " 'Apt',\n", " 'Arbol Solo',\n", " 'Archie',\n", " 'Arroyo Aguiar',\n", " 'Asco',\n", " 'Ash Creek',\n", " 'Ashdon',\n", " 'Assisi',\n", " 'Atarra',\n", " 'Atemajac',\n", " 'Athens',\n", " 'Atoka',\n", " 'Aubres',\n", " 'Aumale',\n", " 'Aumieres',\n", " 'Ausson',\n", " 'Avanhandava',\n", " 'Avce',\n", " 'Avilez',\n", " 'Awere',\n", " 'Aztec',\n", " 'Bachmut',\n", " 'Bahjoi',\n", " 'Bald Mountain',\n", " 'Baldwyn',\n", " 'Bali',\n", " 'Ban Rong Du',\n", " 'Bandong',\n", " 'Bansur',\n", " 'Banswal',\n", " 'Banten',\n", " 'Barbotan',\n", " 'Barcelona (stone)',\n", " 'Barea',\n", " 'Barnaul',\n", " 'Barntrup',\n", " 'Baroti',\n", " 'Barwell',\n", " 'Bassikounou',\n", " 'Baszkówka',\n", " 'Bath',\n", " 'Bath Furnace',\n", " 'Battle Mountain',\n", " 'Bawku',\n", " 'Baxter',\n", " 'Beardsley',\n", " 'Beaver Creek',\n", " 'Beddgelert',\n", " 'Bells',\n", " 'Belville',\n", " 'Benares (a)',\n", " 'Benguerir',\n", " \"Beni M'hira\",\n", " 'Benld',\n", " 'Benoni',\n", " 'Bensour',\n", " 'Benton',\n", " 'Berduc',\n", " 'Béréba',\n", " 'Berlanguillas',\n", " 'Berthoud',\n", " 'Bethlehem',\n", " 'Beuste',\n", " 'Beyrout',\n", " 'Bhagur',\n", " 'Bhawad',\n", " 'Bherai',\n", " 'Bhola',\n", " 'Bholghati',\n", " 'Bialystok',\n", " 'Bielokrynitschie',\n", " 'Bilanga',\n", " 'Binningup',\n", " \"Birni N'konni\",\n", " 'Bishopville',\n", " 'Bishunpur',\n", " 'Bjelaja Zerkov',\n", " 'Bjurböle',\n", " 'Black Moshannan Park',\n", " 'Blackwell',\n", " 'Blanket',\n", " 'Blansko',\n", " 'Bloomington',\n", " 'Bo Xian',\n", " 'Bocas',\n", " 'Bogou',\n", " 'Boguslavka',\n", " 'Borgo San Donino',\n", " 'Bori',\n", " 'Boriskino',\n", " 'Borkut',\n", " 'Borodino',\n", " 'Botschetschki',\n", " 'Boumdeid (2003)',\n", " 'Boumdeid (2011)',\n", " 'Bovedy',\n", " 'Bradford Woods',\n", " 'Braunau',\n", " 'Breitscheid',\n", " 'Bremervörde',\n", " 'Brient',\n", " 'Bruderheim',\n", " 'Bukhara',\n", " 'Bulls Run',\n", " 'Bunburra Rockhole',\n", " 'Bununu',\n", " 'Bur-Gheluai',\n", " 'Burnwell',\n", " 'Bursa',\n", " 'Buschhof',\n", " 'Bustee',\n", " 'Butsura',\n", " 'Buzzard Coulee',\n", " 'Cabezo de Mayo',\n", " 'Cabin Creek',\n", " 'Cacak',\n", " 'Cali',\n", " 'Calivo',\n", " 'Campos Sales',\n", " 'Çanakkale',\n", " 'Cañellas',\n", " 'Cangas de Onis',\n", " 'Canon City',\n", " 'Cape Girardeau',\n", " 'Capilla del Monte',\n", " 'Carancas',\n", " 'Caratash',\n", " 'Castalia',\n", " 'Castel Berardenga',\n", " 'Castine',\n", " 'Castrovillari',\n", " 'Caswell County',\n", " 'Ceniceros',\n", " 'Centerville',\n", " 'Cereseto',\n", " 'Chadong',\n", " 'Chail',\n", " 'Chainpur',\n", " 'Chajari',\n", " 'Chandakapur',\n", " 'Chandpur',\n", " 'Changde',\n", " 'Chantonnay',\n", " 'Charlotte',\n", " 'Charsonville',\n", " 'Charwallas',\n", " 'Chassigny',\n", " 'Château-Renard',\n", " 'Chaves',\n", " 'Chela',\n", " 'Chelyabinsk',\n", " 'Chergach ',\n", " 'Chernyi Bor',\n", " 'Cherokee Springs',\n", " 'Chervettaz',\n", " 'Chervony Kut',\n", " 'Chetrinahatti',\n", " 'Chiang Khan',\n", " 'Chicora',\n", " 'Chisenga',\n", " 'Chitado',\n", " 'Chitenay',\n", " 'Cilimus',\n", " 'Claxton',\n", " 'Clohars',\n", " 'Colby (Wisconsin)',\n", " 'Cold Bokkeveld',\n", " 'Coleman',\n", " 'Collescipoli',\n", " 'Conquista',\n", " 'Cosina',\n", " 'Cranganore',\n", " 'Crescent',\n", " 'Cronstad',\n", " 'Cross Roads',\n", " 'Crumlin',\n", " 'Cumberland Falls',\n", " 'Cynthiana',\n", " 'Dahmani',\n", " 'Dandapur',\n", " \"Daniel's Kuil\",\n", " 'Danville',\n", " 'Darmstadt',\n", " 'Dashoguz',\n", " 'Daule',\n", " 'De Cewsville',\n", " 'Deal',\n", " 'Delhi',\n", " 'Demina',\n", " 'Denver',\n", " 'Dergaon',\n", " 'Desuri',\n", " 'Devgaon',\n", " 'Devri-Khera',\n", " 'Dhajala',\n", " 'Dharwar',\n", " 'Dhurmsala',\n", " 'Didim',\n", " 'Diep River',\n", " 'Distrito Quebracho',\n", " 'Djati-Pengilon',\n", " 'Djermaia',\n", " 'Djoumine',\n", " 'Dokachi',\n", " 'Dolgovoli',\n", " 'Domanitch',\n", " 'Dong Ujimqin Qi',\n", " 'Donga Kohrod',\n", " 'Dongtai',\n", " 'Doroninsk',\n", " 'Dosso',\n", " 'Douar Mghila',\n", " 'Dowa',\n", " 'Drake Creek',\n", " 'Dresden (Ontario)',\n", " 'Dubrovnik',\n", " 'Dunbogan',\n", " 'Dundrum',\n", " 'Dunhua',\n", " 'Durala',\n", " 'Duruma',\n", " 'Duwun',\n", " 'Dwaleni',\n", " 'Dyalpur',\n", " 'Dyarrl Island',\n", " 'Eagle',\n", " 'Ehole',\n", " 'Eichstädt',\n", " 'Ekeby',\n", " 'Ekh Khera',\n", " 'El Idrissia',\n", " 'El Paso de Aguila',\n", " 'El Tigre',\n", " 'Elbert',\n", " 'Elbogen',\n", " 'Elenovka',\n", " 'Ellemeet',\n", " 'Emmaville',\n", " 'Enshi',\n", " 'Ensisheim',\n", " 'Épinal',\n", " 'Erakot',\n", " 'Erevan',\n", " 'Ergheo',\n", " 'Erxleben',\n", " 'Esnandes',\n", " 'Essebi',\n", " 'Estherville',\n", " 'Farmington',\n", " 'Farmville',\n", " 'Favars',\n", " 'Fayetteville',\n", " 'Feid Chair',\n", " 'Felix',\n", " 'Fenghsien-Ku',\n", " 'Ferguson',\n", " 'Fermo',\n", " 'Fisher',\n", " 'Florence',\n", " 'Forest City',\n", " 'Forest Vale',\n", " 'Forksville',\n", " 'Forsbach',\n", " 'Forsyth',\n", " 'Fort Flatters',\n", " 'Frankfort (stone)',\n", " 'Fuhe',\n", " 'Fukutomi',\n", " 'Fünen',\n", " 'Futtehpur',\n", " 'Fuyang',\n", " 'Galapian',\n", " 'Galim (a)',\n", " 'Galim (b)',\n", " 'Galkiv',\n", " 'Gambat',\n", " 'Gao-Guenie',\n", " 'Garhi Yasin',\n", " 'Garland',\n", " 'Gashua',\n", " 'Gasseltepaoua',\n", " 'Geidam',\n", " 'Gifu',\n", " 'Girgenti',\n", " 'Git-Git',\n", " 'Glanerbrug',\n", " 'Glanggang',\n", " 'Glasatovo',\n", " 'Glatton',\n", " 'Gnadenfrei',\n", " 'Gopalpur',\n", " 'Gorlovka',\n", " 'Granes',\n", " 'Grefsheim',\n", " 'Grimsby',\n", " 'Grosnaja',\n", " 'Gross-Divina',\n", " 'Grossliebenthal',\n", " 'Grüneberg',\n", " 'Grzempach',\n", " 'Gualeguaychú',\n", " 'Guangmingshan',\n", " 'Guangnan',\n", " 'Guangrao',\n", " 'Guareña',\n", " 'Guêa',\n", " 'Guibga',\n", " 'Guidder',\n", " 'Gujargaon',\n", " 'Gujba',\n", " 'Gumoschnik',\n", " 'Gurram Konda',\n", " 'Gursum',\n", " 'Gütersloh',\n", " 'Gyokukei',\n", " 'Hachi-oji',\n", " 'Hainaut',\n", " 'Hallingeberg',\n", " 'Hamlet',\n", " 'Haraiya',\n", " 'Haripura',\n", " 'Harleton',\n", " 'Harrison County',\n", " 'Hashima',\n", " 'Hassi-Jekna',\n", " 'Hatford',\n", " 'Haverö',\n", " 'Hedeskoga',\n", " 'Hedjaz',\n", " 'Heredia',\n", " 'Hessle',\n", " 'Higashi-koen',\n", " 'High Possil',\n", " 'Hiroshima',\n", " 'Hoima',\n", " 'Hökmark',\n", " 'Holbrook',\n", " 'Holetta',\n", " 'Homestead',\n", " 'Honolulu',\n", " 'Hotse',\n", " 'Hoxie',\n", " 'Hraschina',\n", " 'Huaxi',\n", " 'Hungen',\n", " 'Hvittis',\n", " 'Ibbenbüren',\n", " 'Ibitira',\n", " 'Ibrisim',\n", " 'Ichkala',\n", " 'Idutywa',\n", " 'Iguaracu',\n", " 'Ijopega',\n", " 'Indarch',\n", " 'Independence',\n", " 'Inner Mongolia',\n", " 'Innisfree',\n", " 'Ipiranga',\n", " 'Ishinga',\n", " 'Isthilart',\n", " 'Itapicuru-Mirim',\n", " 'Itqiy',\n", " 'Ivuna',\n", " 'Jackalsfontein',\n", " 'Jajh deh Kot Lalu',\n", " 'Jalanash',\n", " 'Jalandhar',\n", " 'Jamkheir',\n", " 'Jartai',\n", " 'Jelica',\n", " 'Jemlapur',\n", " 'Jesenice',\n", " 'Jhung',\n", " 'Jiange',\n", " 'Jianshi',\n", " 'Jilin',\n", " 'Jodiya',\n", " 'Jodzie',\n", " 'Johnstown',\n", " 'Jolomba',\n", " 'Jonzac',\n", " 'Juancheng',\n", " 'Judesegeri',\n", " 'Jumapalo',\n", " 'Junan',\n", " 'Juromenha',\n", " 'Juvinas',\n", " 'Kaba',\n", " 'Kabo',\n", " 'Kadonah',\n", " 'Kaee',\n", " 'Kagarlyk',\n", " 'Kaidun',\n", " 'Kainsaz',\n", " 'Kakangari',\n", " 'Kakowa',\n", " 'Kalaba',\n", " 'Kalumbi',\n", " 'Kamalpur',\n", " 'Kamiomi',\n", " 'Kamsagar',\n", " 'Kandahar (Afghanistan)',\n", " 'Kangean',\n", " 'Kangra Valley',\n", " 'Kapoeta',\n", " 'Kaprada',\n", " 'Kaptal-Aryk',\n", " 'Karakol',\n", " 'Karatu',\n", " 'Karewar',\n", " 'Karkh',\n", " 'Karloowala',\n", " 'Karoonda',\n", " 'Kasamatsu',\n", " 'Kasauli',\n", " 'Katagum',\n", " 'Kavarpura',\n", " 'Kayakent',\n", " 'Kediri',\n", " 'Kemer',\n", " 'Kendleton',\n", " 'Kendrapara',\n", " 'Kerilis',\n", " 'Kernouve',\n", " 'Kesen',\n", " 'Khairpur',\n", " 'Khanpur',\n", " 'Kharkov',\n", " 'Kheragur',\n", " 'Khetri',\n", " 'Khmelevka',\n", " 'Khohar',\n", " 'Khor Temiki',\n", " 'Kidairat',\n", " 'Kiel',\n", " 'Kiffa',\n", " 'Kijima (1906)',\n", " 'Kikino',\n", " 'Kilabo',\n", " 'Kilbourn',\n", " 'Killeter',\n", " 'Kingai',\n", " 'Kirbyville',\n", " 'Kisvarsány',\n", " 'Kitchener',\n", " 'Klein-Wenden',\n", " 'Knyahinya',\n", " 'Kobe',\n", " 'Kokubunji',\n", " 'Komagome',\n", " 'Konovo',\n", " 'Košice',\n", " 'Krähenberg',\n", " 'Krasnoi-Ugol',\n", " 'Krasnyi Klyuch',\n", " 'Krutikha',\n", " 'Krymka',\n", " 'Kukschin',\n", " 'Kulak',\n", " 'Kuleschovka',\n", " 'Kulp',\n", " 'Kunashak',\n", " 'Kunya-Urgench',\n", " 'Kushiike',\n", " 'Kusiali',\n", " 'Kutais',\n", " 'Kuttippuram',\n", " 'Kuznetzovo',\n", " 'Kyushu',\n", " 'La Bécasse',\n", " 'La Charca',\n", " 'La Colina',\n", " 'La Criolla',\n", " 'Laborel',\n", " 'Lahrauli',\n", " \"L'Aigle\",\n", " 'Lakangaon',\n", " 'Lalitpur',\n", " 'Lancé',\n", " 'Lancon',\n", " 'Långhalsen',\n", " 'Lanxi',\n", " 'Lanzenkirchen',\n", " 'Laochenzhen',\n", " 'Launton',\n", " 'Lavrentievka',\n", " 'Le Pressoir',\n", " 'Le Teilleul',\n", " 'Leedey',\n", " 'Leeuwfontein',\n", " 'Leighlinbridge',\n", " 'Leighton',\n", " 'Leonovka',\n", " 'Les Ormes',\n", " 'Lesves',\n", " 'Lichtenberg',\n", " 'Lillaverke',\n", " 'Limerick',\n", " 'Linum',\n", " 'Lishui',\n", " 'Lissa',\n", " 'Little Piney',\n", " 'Lixna',\n", " 'Lodran',\n", " 'Lohawat',\n", " 'Lorton',\n", " 'Los Martinez',\n", " 'Lost City',\n", " 'Louisville',\n", " 'Łowicz',\n", " 'Lua',\n", " 'Lucé',\n", " 'Lumpkin',\n", " 'Lunan',\n", " 'Lundsgård',\n", " 'Luotolax',\n", " 'Luponnas',\n", " 'Lusaka',\n", " 'Mabwe-Khoywa',\n", " 'Macau',\n", " 'Machinga',\n", " 'Macibini',\n", " 'Madhipura',\n", " 'Madiun',\n", " 'Madrid',\n", " 'Mafra',\n", " 'Magnesia',\n", " 'Magombedze',\n", " 'Mahadevpur',\n", " 'Maigatari-Danduma',\n", " 'Malaga',\n", " 'Malakal',\n", " 'Malampaka',\n", " 'Malotas',\n", " 'Malvern',\n", " 'Mamra Springs',\n", " 'Manbhoom',\n", " 'Manegaon',\n", " 'Mangwendi',\n", " 'Manych',\n", " 'Mardan',\n", " 'Maria Linden',\n", " 'Mariaville',\n", " 'Maribo',\n", " 'Maridi',\n", " 'Marilia',\n", " 'Marion (Iowa)',\n", " 'Marjalahti',\n", " 'Marmande',\n", " 'Maromandia',\n", " 'Maryville',\n", " 'Mascombes',\n", " 'Mason Gully',\n", " 'Mässing',\n", " 'Mauerkirchen',\n", " 'Mauritius',\n", " 'Mayo Belwa',\n", " 'Mazapil',\n", " 'Maziba',\n", " 'Mbale',\n", " 'Medanitos',\n", " 'Meerut',\n", " 'Meester-Cornelis',\n", " 'Menow',\n", " 'Menziswyl',\n", " 'Mern',\n", " 'Meru',\n", " 'Merua',\n", " 'Messina',\n", " 'Meuselbach',\n", " 'Mezel',\n", " 'Mezö-Madaras',\n", " 'Mhow',\n", " 'Mianchi',\n", " 'Middlesbrough',\n", " 'Mifflin',\n", " 'Mighei',\n", " 'Mihonoseki',\n", " 'Mike',\n", " 'Milena',\n", " 'Millbillillie',\n", " 'Miller (Arkansas)',\n", " 'Minamino',\n", " 'Mineo',\n", " 'Min-Fan-Zhun',\n", " 'Minnichhof',\n", " 'Mirzapur',\n", " 'Misshof',\n", " 'Mjelleim',\n", " 'Mocs',\n", " 'Modoc (1905)',\n", " 'Mokoia',\n", " 'Molina',\n", " 'Molteno',\n", " 'Monahans (1998)',\n", " 'Monroe',\n", " 'Monte das Fortes',\n", " 'Monte Milone',\n", " 'Montferré',\n", " 'Montlivault',\n", " 'Monze',\n", " 'Moore County',\n", " 'Mooresfort',\n", " 'Moorleah',\n", " 'Moradabad',\n", " 'Morávka',\n", " 'Mornans',\n", " 'Moss',\n", " 'Moti-ka-nagla',\n", " 'Motta di Conti',\n", " 'Mount Browne',\n", " 'Mount Tazerzait',\n", " 'Mount Vaisi',\n", " 'Mtola',\n", " 'Muddoor',\n", " 'Mulletiwu',\n", " 'Muraid',\n", " 'Murchison',\n", " 'Murray',\n", " 'Muzaffarpur',\n", " 'Myhee Caunta',\n", " 'Nadiabondi',\n", " 'Nagai',\n", " 'Nagaria',\n", " 'Nagy-Borové',\n", " 'Nakhla',\n", " 'Nakhon Pathom',\n", " 'Nammianthal',\n", " 'Nan Yang Pao',\n", " 'Nanjemoy',\n", " 'Nantong',\n", " 'Naoki',\n", " 'Naragh',\n", " 'Narellan',\n", " 'Narni',\n", " 'Nassirah',\n", " 'Natal',\n", " 'Nawapali',\n", " 'Neagari',\n", " 'Nedagolla',\n", " 'Nejo',\n", " 'Nerft',\n", " 'Neuschwanstein',\n", " 'New Concord',\n", " 'New Halfa',\n", " 'New Orleans',\n", " 'Ngawi',\n", " \"N'Goureyma\",\n", " 'Nicorps',\n", " 'Niger (L6)',\n", " 'Niger (LL6)',\n", " 'Nikolaevka',\n", " 'Nikolskoe',\n", " 'Ningbo',\n", " 'Ningqiang',\n", " 'Nio',\n", " \"N'Kandhla\",\n", " 'Nobleborough',\n", " 'Noblesville',\n", " 'Nogata',\n", " 'Nogoya',\n", " 'Norfork',\n", " 'Norton County',\n", " 'Noventa Vicentina',\n", " 'Novo-Urei',\n", " 'Novy-Ergi',\n", " 'Novy-Projekt',\n", " 'Noyan-Bogdo',\n", " 'Nuevo Mercurio',\n", " 'Nulles',\n", " 'Numakai',\n", " 'Nyaung',\n", " 'Nyirábrany',\n", " 'Ochansk',\n", " 'Oesede',\n", " 'Oesel',\n", " 'Ofehértó',\n", " 'Ogi',\n", " 'Ohaba',\n", " 'Ohuma',\n", " 'Ojuelos Altos',\n", " 'Okabe',\n", " 'Okano',\n", " 'Okniny',\n", " 'Oldenburg (1930)',\n", " 'Oliva-Gandia',\n", " 'Olivenza',\n", " 'Olmedilla de Alarcón',\n", " 'Omolon',\n", " 'Orgueil',\n", " 'Orlando',\n", " 'Ornans',\n", " 'Ortenau',\n", " 'Orvinio',\n", " 'Oterøy',\n", " 'Otomi',\n", " 'Ottawa',\n", " 'Ouadangou',\n", " 'Oued el Hadjar',\n", " 'Oum Dreyga',\n", " 'Ourique',\n", " 'Ovambo',\n", " 'Oviedo',\n", " 'Owrucz',\n", " 'Pacula',\n", " 'Padvarninkai',\n", " 'Paitan',\n", " 'Palahatchie',\n", " 'Palca de Aparzo',\n", " 'Palinshih',\n", " 'Palmyra',\n", " 'Palolo Valley',\n", " 'Pampanga',\n", " 'Pantar',\n", " 'Paragould',\n", " 'Parambu',\n", " 'Paranaiba',\n", " 'Park Forest',\n", " 'Parnallee',\n", " 'Parsa',\n", " 'Pasamonte',\n", " 'Patora',\n", " 'Patrimonio',\n", " 'Patti',\n", " 'Patwar',\n", " 'Pavel',\n", " 'Pavlodar (stone)',\n", " 'Pavlograd',\n", " 'Pavlovka',\n", " 'Pê',\n", " 'Peace River',\n", " 'Peckelsheim',\n", " 'Peekskill',\n", " 'Peña Blanca Spring',\n", " 'Peramiho',\n", " 'Perpeti',\n", " 'Perth',\n", " 'Pervomaisky',\n", " 'Pesyanoe',\n", " 'Pétèlkolé',\n", " 'Petersburg',\n", " 'Pettiswood',\n", " 'Phillips County (stone)',\n", " 'Phu Hong',\n", " 'Phum Sambo',\n", " 'Phuoc-Binh',\n", " 'Piancaldoli',\n", " 'Picote',\n", " 'Pillistfer',\n", " 'Piplia Kalan',\n", " 'Piquetberg',\n", " 'Pirgunje',\n", " 'Pirthalla',\n", " 'Pitts',\n", " 'Plantersville',\n", " 'Pleşcoi',\n", " 'Ploschkovitz',\n", " 'Pnompehn',\n", " 'Pohlitz',\n", " 'Pokhra',\n", " 'Pollen',\n", " 'Pontlyfni',\n", " 'Portales Valley',\n", " 'Portugal',\n", " 'Po-wang Chen',\n", " 'Prambachkirchen',\n", " 'Pribram',\n", " 'Pricetown',\n", " 'Puerto Lápice',\n", " 'Pulsora',\n", " 'Pultusk',\n", " 'Punganaru',\n", " 'Putinga',\n", " 'Qidong',\n", " 'Qingzhen',\n", " \"Queen's Mercy\",\n", " 'Quenggouk',\n", " 'Quesa',\n", " 'Quija',\n", " 'Quincay',\n", " 'Raco',\n", " 'Raghunathpura',\n", " 'Rahimyar Khan',\n", " 'Rakovka',\n", " 'Ramnagar',\n", " 'Rampurhat',\n", " 'Ramsdorf',\n", " 'Ranchapur',\n", " 'Rancho de la Presa',\n", " 'Rangala',\n", " 'Raoyang',\n", " 'Ras Tanura',\n", " 'Rasgrad',\n", " 'Ratyn',\n", " 'Red Canyon Lake',\n", " 'Reliegos',\n", " 'Rembang',\n", " 'Renazzo',\n", " 'Renca',\n", " 'Renqiu',\n", " 'Repeev Khutor',\n", " 'Revelstoke',\n", " 'Rewari',\n", " 'Rich Mountain',\n", " 'Richardton',\n", " 'Richland Springs',\n", " 'Richmond',\n", " 'Rio Negro',\n", " 'Rivolta de Bassi',\n", " 'Rochester',\n", " 'Rockhampton',\n", " 'Roda',\n", " 'Rodach',\n", " 'Rose City',\n", " 'Rowton',\n", " 'Ruhobobo',\n", " 'Rumuruti',\n", " 'Rupota',\n", " 'Ryechki',\n", " 'Sabetmahet',\n", " 'Sabrum',\n", " 'Sagan',\n", " 'Saint-Sauveur',\n", " 'Saint-Séverin',\n", " 'Sakauchi',\n", " 'Salem',\n", " 'Salles',\n", " 'Salzwedel',\n", " 'Samelia',\n", " 'San Juan Capistrano',\n", " 'San Michele',\n", " 'San Pedro de Quiles',\n", " 'San Pedro Jacuaro',\n", " 'Santa Barbara',\n", " 'Santa Cruz',\n", " 'Santa Isabel',\n", " 'Santa Lucia (2008)',\n", " 'São Jose do Rio Preto',\n", " 'Saratov',\n", " 'Sasagase',\n", " 'Sauguis',\n", " 'Savtschenskoje',\n", " 'Sayama',\n", " 'Sazovice',\n", " 'Schellin',\n", " 'Schenectady',\n", " 'Schönenberg',\n", " 'Searsmont',\n", " 'Sediköy',\n", " 'Segowlie',\n", " 'Selakopi',\n", " 'Seldebourak',\n", " 'Semarkona',\n", " 'Sena',\n", " 'Senboku',\n", " 'Seoni',\n", " 'Seres',\n", " 'Serra de Magé',\n", " 'Sete Lagoas',\n", " 'Sevilla',\n", " 'Sevrukovo',\n", " 'Sfax',\n", " 'Shalka',\n", " 'Sharps',\n", " 'Shelburne',\n", " 'Shergotty',\n", " 'Sheyang',\n", " 'Shikarpur',\n", " 'Shuangyang',\n", " 'Shupiyan',\n", " 'Shytal',\n", " 'Siena',\n", " 'Sikhote-Alin',\n", " 'Silao',\n", " 'Silistra',\n", " 'Simmern',\n", " 'Sinai',\n", " 'Sindhri',\n", " 'Sinnai',\n", " 'Sioux County',\n", " 'Sitathali',\n", " 'Sivas',\n", " 'Sixiangkou',\n", " 'Ski',\n", " 'Slavetic',\n", " 'Slobodka',\n", " 'Soheria',\n", " 'Soko-Banja',\n", " 'Sologne',\n", " 'Sołtmany',\n", " 'Sone',\n", " 'Songyuan',\n", " 'Sopot',\n", " 'Soroti',\n", " 'St. Caprais-de-Quinsac',\n", " 'St. Christophe-la-Chartreuse',\n", " 'St. Denis Westrem',\n", " 'St. Germain-du-Pinel',\n", " 'St. Louis',\n", " \"St. Mark's\",\n", " \"St. Mary's County\",\n", " 'St. Mesmin',\n", " 'St. Michel',\n", " 'St.-Chinian',\n", " 'Ställdalen',\n", " 'Stannern',\n", " 'Stavropol',\n", " 'Ste. 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140.785560)',\n", " '(53.583330, -2.716670)',\n", " '(43.866670, 5.383330)',\n", " '(-33.000000, -66.000000)',\n", " '(38.500000, -94.300000)',\n", " '(-31.416670, -60.666670)',\n", " '(42.450000, 9.033330)',\n", " '(31.805000, -97.010000)',\n", " '(52.050000, 0.300000)',\n", " '(43.033330, 12.550000)',\n", " '(25.254170, 80.625000)',\n", " '(20.066670, -103.666670)',\n", " '(34.750000, -87.000000)',\n", " '(34.316670, -96.150000)',\n", " '(44.383330, 5.166670)',\n", " '(36.166670, 3.666670)',\n", " '(44.333330, 3.233330)',\n", " '(43.083330, 0.583330)',\n", " '(-21.460280, -49.950830)',\n", " '(46.000000, 13.500000)',\n", " '(25.000000, -103.500000)',\n", " '(2.716670, 32.833330)',\n", " '(36.800000, -108.000000)',\n", " '(48.600000, 38.000000)',\n", " '(28.483330, 78.500000)',\n", " '(35.966670, -82.483330)',\n", " '(34.500000, -88.666670)',\n", " '(5.383330, 16.383330)',\n", " '(16.666670, 101.183330)',\n", " '(-6.916670, 107.600000)',\n", " '(27.700000, 76.333330)',\n", " '(30.400000, 78.200000)',\n", " '(-6.333330, 106.000000)',\n", " '(43.950000, -0.050000)',\n", " '(41.366670, 2.166670)',\n", " '(42.383330, -2.500000)',\n", " '(52.733330, 84.083330)',\n", " '(52.000000, 9.100000)',\n", " '(31.616670, 76.800000)',\n", " '(52.565280, -1.339720)',\n", " '(15.783330, -5.900000)',\n", " '(52.033330, 20.935830)',\n", " '(45.416670, -98.316670)',\n", " '(38.250000, -83.750000)',\n", " '(40.668130, -117.189130)',\n", " '(11.083330, -0.183330)',\n", " '(36.750000, -93.500000)',\n", " '(39.800000, -101.200000)',\n", " '(51.166670, -117.333330)',\n", " '(53.016670, -4.100000)',\n", " '(33.600000, -96.466670)',\n", " '(-32.333330, -64.866670)',\n", " '(25.366670, 82.916670)',\n", " '(32.250000, -8.150000)',\n", " '(32.866670, 10.800000)',\n", " '(39.083330, -89.150000)',\n", " '(-26.166670, 28.416670)',\n", " '(30.000000, -7.000000)',\n", " '(45.950000, -67.550000)',\n", " '(-31.910000, -58.328330)',\n", " '(11.650000, -3.650000)',\n", " '(41.683330, -3.800000)',\n", " '(40.305830, -105.023250)',\n", " '(42.533330, -73.833330)',\n", " '(43.216670, -0.233330)',\n", " '(33.883330, 35.500000)',\n", " '(20.883330, 74.833330)',\n", " '(26.508330, 73.115280)',\n", " '(20.833330, 71.466670)',\n", " '(22.683330, 90.650000)',\n", " '(22.083330, 86.900000)',\n", " '(53.100000, 23.200000)',\n", " '(50.133330, 27.166670)',\n", " '(12.450000, -0.083330)',\n", " '(-33.156390, 115.676390)',\n", " '(13.766670, 5.300000)',\n", " '(34.166670, -80.283330)',\n", " '(25.383330, 82.600000)',\n", " '(49.783330, 30.166670)',\n", " '(60.400000, 25.800000)',\n", " '(40.916670, -78.083330)',\n", " '(36.833330, -97.333330)',\n", " '(31.833330, -98.833330)',\n", " '(49.366670, 16.633330)',\n", " '(40.480000, -89.004170)',\n", " '(33.833330, 115.833330)',\n", " '(23.000000, -102.000000)',\n", " '(12.500000, 0.700000)',\n", " '(44.550000, 131.633330)',\n", " '(44.866670, 10.050000)',\n", " '(21.950000, 78.033330)',\n", " '(54.233330, 52.483330)',\n", " '(48.150000, 24.283330)',\n", " '(55.466670, 35.866670)',\n", " '(51.333330, 33.883330)',\n", " '(17.710670, -11.371500)',\n", " '(17.174930, -11.341330)',\n", " '(54.566670, -6.333330)',\n", " '(40.500000, -80.083330)',\n", " '(50.600000, 16.300000)',\n", " '(50.666940, 8.183610)',\n", " '(53.400000, 9.100000)',\n", " '(52.133330, 59.316670)',\n", " '(53.900000, -112.883330)',\n", " '(39.779780, 64.600350)',\n", " None,\n", " '(-31.350000, 129.190000)',\n", " '(10.016670, 9.583330)',\n", " '(5.000000, 48.000000)',\n", " '(37.621940, -82.237220)',\n", " '(40.200000, 29.233330)',\n", " '(46.450000, 25.783330)',\n", " '(26.783330, 82.833330)',\n", " '(27.083330, 84.083330)',\n", " '(52.996000, -109.848170)',\n", " '(37.983330, -1.166670)',\n", " '(35.500000, -93.500000)',\n", " '(43.838890, 20.333330)',\n", " '(3.405000, -76.510000)',\n", " '(11.750000, 122.333330)',\n", " '(-7.033330, -40.166670)',\n", " '(39.800000, 26.600000)',\n", " '(41.250000, 1.666670)',\n", " '(43.383330, -5.150000)',\n", " '(38.470280, -105.241390)',\n", " '(37.266670, -89.583330)',\n", " '(-30.883330, -64.550000)',\n", " '(-16.664440, -69.043890)',\n", " '(38.500000, 27.000000)',\n", " '(36.083330, -78.066670)',\n", " '(43.350000, 11.500000)',\n", " '(44.383330, -68.750000)',\n", " '(39.800000, 16.200000)',\n", " '(36.500000, -79.250000)',\n", " '(26.466670, -105.233330)',\n", " '(43.200000, -96.916670)',\n", " '(45.083330, 8.300000)',\n", " '(28.533330, 109.316670)',\n", " '(25.366670, 81.666670)',\n", " '(25.850000, 83.483330)',\n", " '(-30.783330, -58.050000)',\n", " '(20.266670, 76.016670)',\n", " '(27.283330, 79.050000)',\n", " '(29.083330, 111.750000)',\n", " '(46.683330, 1.050000)',\n", " '(36.166670, -87.333330)',\n", " '(47.933330, 1.566670)',\n", " '(29.483330, 75.500000)',\n", " '(47.716670, 5.366670)',\n", " '(47.933330, 2.916670)',\n", " '(41.933330, -7.466670)',\n", " '(-3.666670, 32.500000)',\n", " '(54.816670, 61.116670)',\n", " '(23.696390, -5.014720)',\n", " '(53.700000, 30.100000)',\n", " '(35.033330, -81.883330)',\n", " '(46.550000, 6.816670)',\n", " '(50.833330, 34.000000)',\n", " '(14.500000, 76.500000)',\n", " '(17.900000, 101.633330)',\n", " '(40.933330, -79.733330)',\n", " '(-10.059440, 33.395000)',\n", " '(-17.350000, 13.966670)',\n", " '(47.470830, 0.976670)',\n", " '(-6.950000, 108.100000)',\n", " '(32.102500, -81.872780)',\n", " None,\n", " '(44.900000, -90.283330)',\n", " '(-33.133330, 19.383330)',\n", " '(43.761110, -84.507780)',\n", " '(42.533330, 12.616670)',\n", " '(-19.850000, -47.550000)',\n", " '(21.166670, -100.866670)',\n", " '(10.200000, 76.266670)',\n", " '(35.950000, -97.583330)',\n", " '(-27.700000, 27.300000)',\n", " '(35.633330, -78.133330)',\n", " '(54.616670, -6.216670)',\n", " '(36.833330, -84.350000)',\n", " '(38.400000, -84.250000)',\n", " '(35.616670, 8.833330)',\n", " '(26.916670, 83.966670)',\n", " '(-28.200000, 24.566670)',\n", " '(34.400000, -87.066670)',\n", " '(49.866670, 8.650000)',\n", " '(41.984440, 59.685000)',\n", " '(-1.870890, -79.957560)',\n", " '(43.000000, -80.000000)',\n", " '(40.250000, -74.000000)',\n", " '(28.566670, 77.250000)',\n", " '(51.466670, 84.766670)',\n", " '(39.782500, -104.930560)',\n", " '(26.683330, 93.866670)',\n", " '(25.733330, 73.616670)',\n", " '(19.000000, 81.000000)',\n", " '(24.225000, 76.525000)',\n", " '(22.377780, 71.427220)',\n", " '(14.883330, 75.600000)',\n", " '(32.233330, 76.466670)',\n", " '(37.351720, 27.329970)',\n", " '(-33.750000, 18.566670)',\n", " '(-31.883330, -60.466670)',\n", " '(-7.500000, 111.500000)',\n", " '(12.733330, 15.050000)',\n", " '(36.950000, 9.550000)',\n", " '(23.500000, 90.333330)',\n", " '(50.750000, 25.300000)',\n", " '(40.000000, 29.000000)',\n", " '(45.500000, 119.033330)',\n", " '(21.866670, 82.450000)',\n", " '(32.916670, 120.783330)',\n", " '(51.200000, 112.300000)',\n", " '(13.050000, 3.166670)',\n", " '(32.333330, -6.300000)',\n", " '(-13.666670, 33.916670)',\n", " '(36.400000, -86.500000)',\n", " '(42.520000, -82.260000)',\n", " '(42.458330, 18.441670)',\n", " '(-31.666670, 152.833330)',\n", " '(52.550000, -8.033330)',\n", " '(43.333330, 128.250000)',\n", " '(30.300000, 76.633330)',\n", " '(-4.000000, 39.500000)',\n", " '(33.433330, 127.266670)',\n", " '(-27.200000, 31.316670)',\n", " '(26.250000, 82.000000)',\n", " '(-3.000000, 151.000000)',\n", " '(40.781670, -96.471670)',\n", " '(-17.300000, 15.833330)',\n", " '(48.900000, 11.216670)',\n", " '(56.033330, 13.000000)',\n", " '(28.266670, 78.783330)',\n", " '(34.416670, 3.250000)',\n", " '(25.370000, -97.370000)',\n", " '(19.967220, -103.051670)',\n", " '(39.246670, -104.588170)',\n", " '(50.183330, 12.733330)',\n", " '(47.833330, 37.666670)',\n", " '(51.750000, 4.000000)',\n", " '(-29.466670, 151.616670)',\n", " '(30.300000, 109.500000)',\n", " '(47.866670, 7.350000)',\n", " '(48.183330, 6.466670)',\n", " '(19.033330, 81.891670)',\n", " '(40.300000, 44.500000)',\n", " '(1.166670, 44.166670)',\n", " '(52.216670, 11.250000)',\n", " '(46.250000, -1.100000)',\n", " '(2.883330, 30.833330)',\n", " '(43.416670, -94.833330)',\n", " '(39.750000, -97.033330)',\n", " '(35.550000, -77.533330)',\n", " '(44.383330, 2.816670)',\n", " '(36.050000, -94.166670)',\n", " '(36.883330, 8.450000)',\n", " '(32.533330, -87.166670)',\n", " '(34.600000, 116.750000)',\n", " '(36.100000, -81.416670)',\n", " '(43.181110, 13.753330)',\n", " '(47.816670, -96.850000)',\n", " '(30.833330, -97.766670)',\n", " '(43.250000, -93.666670)',\n", " '(-33.350000, 146.858330)',\n", " '(36.783330, -78.083330)',\n", " '(50.950000, 7.316670)',\n", " '(33.016670, -83.966670)',\n", " '(28.250000, 7.000000)',\n", " '(34.483330, -87.833330)',\n", " '(31.475560, 113.566940)',\n", " '(33.183330, 130.200000)',\n", " '(55.333330, 10.333330)',\n", " '(25.950000, 80.816670)',\n", " '(32.900000, 115.900000)',\n", " '(44.300000, 0.400000)',\n", " '(7.050000, 12.433330)',\n", " '(7.050000, 12.433330)',\n", " '(51.683330, 30.783330)',\n", " '(27.350000, 68.533330)',\n", " '(11.650000, -2.183330)',\n", " '(27.883330, 68.533330)',\n", " '(41.683330, -112.133330)',\n", " '(12.850000, 11.033330)',\n", " '(14.150830, -2.041670)',\n", " '(12.916670, 11.916670)',\n", " '(35.533330, 136.883330)',\n", " '(37.316670, 13.566670)',\n", " '(9.600000, 9.916670)',\n", " '(52.200000, 6.866670)',\n", " '(-7.250000, 107.700000)',\n", " '(57.350000, 37.616670)',\n", " '(52.459720, -0.300000)',\n", " '(50.666670, 16.766670)',\n", " '(24.233330, 89.050000)',\n", " '(48.283330, 38.083330)',\n", " '(42.900000, 2.250000)',\n", " '(60.666670, 11.000000)',\n", " '(43.200000, -79.616670)',\n", " '(43.666670, 45.383330)',\n", " '(49.266670, 18.716670)',\n", " '(46.350000, 30.583330)',\n", " '(51.933330, 15.500000)',\n", " '(52.866670, 16.633330)',\n", " '(-33.000000, -58.616670)',\n", " '(39.804170, 122.763890)',\n", " '(24.100000, 105.000000)',\n", " '(37.100000, 118.400000)',\n", " '(38.733330, -6.016670)',\n", " '(43.766670, 20.233330)',\n", " '(13.500000, -0.683330)',\n", " '(9.916670, 13.983330)',\n", " '(22.983330, 76.050000)',\n", " '(11.491670, 11.658330)',\n", " '(42.900000, 24.700000)',\n", " '(13.783330, 78.566670)',\n", " '(9.366670, 42.416670)',\n", " '(51.916670, 8.383330)',\n", " '(35.000000, 127.500000)',\n", " '(35.650000, 139.333330)',\n", " '(50.316670, 3.733330)',\n", " '(57.816670, 16.233330)',\n", " '(41.383330, -86.600000)',\n", " '(26.800000, 82.533330)',\n", " '(28.383330, 75.783330)',\n", " '(32.675000, -94.511670)',\n", " '(38.250000, -86.166670)',\n", " '(35.294500, 136.700330)',\n", " '(28.950000, 0.816670)',\n", " '(51.650000, -1.516670)',\n", " '(60.245560, 22.061940)',\n", " '(55.466670, 13.783330)',\n", " '(27.333330, 35.666670)',\n", " '(10.000000, -84.100000)',\n", " '(59.850000, 17.666670)',\n", " '(33.600000, 130.433330)',\n", " '(55.900000, -4.233330)',\n", " '(34.450000, 132.383330)',\n", " '(1.345000, 31.472780)',\n", " '(64.433330, 21.200000)',\n", " '(34.900000, -110.183330)',\n", " '(9.066670, 38.416670)',\n", " '(41.800000, -91.866670)',\n", " '(21.300000, -157.866670)',\n", " '(35.666670, 115.500000)',\n", " '(39.350000, -100.450000)',\n", " '(46.100000, 16.333330)',\n", " '(26.464690, 106.632410)',\n", " '(50.300000, 8.916670)',\n", " '(61.183330, 22.683330)',\n", " '(52.283330, 7.700000)',\n", " '(-20.000000, -45.000000)',\n", " '(38.000000, 35.000000)',\n", " '(58.200000, 82.933330)',\n", " '(-32.100000, 28.333330)',\n", " '(-23.200000, -51.833330)',\n", " '(-6.033330, 145.366670)',\n", " '(39.750000, 46.666670)',\n", " '(39.083330, -94.400000)',\n", " '(41.000000, 112.000000)',\n", " '(53.415000, -111.337500)',\n", " '(-25.500000, -54.500000)',\n", " '(-8.933330, 33.800000)',\n", " '(-31.183330, -57.950000)',\n", " '(-3.400000, -44.333330)',\n", " '(26.590830, -12.952170)',\n", " '(-8.416670, 32.433330)',\n", " '(-32.500000, 21.900000)',\n", " '(26.750000, 68.416670)',\n", " None,\n", " '(31.000000, 75.000000)',\n", " '(18.750000, 75.333330)',\n", " '(39.700000, 105.800000)',\n", " '(43.833330, 20.441670)',\n", " None,\n", " '(46.421370, 14.052170)',\n", " '(31.300000, 72.383330)',\n", " '(31.916670, 104.916670)',\n", " '(30.808330, 109.500000)',\n", " '(44.050000, 126.166670)',\n", " '(22.680000, 70.313330)',\n", " '(55.700000, 24.400000)',\n", " '(40.350000, -104.900000)',\n", " '(-11.850000, 15.833330)',\n", " '(45.433330, -0.450000)',\n", " '(35.500000, 115.416670)',\n", " '(12.850000, 76.800000)',\n", " '(-7.716670, 111.200000)',\n", " '(35.200000, 118.800000)',\n", " '(38.740280, -7.270000)',\n", " '(44.716670, 4.300000)',\n", " '(47.350000, 21.300000)',\n", " '(11.850000, 8.216670)',\n", " '(27.083330, 78.333330)',\n", " '(27.250000, 79.966670)',\n", " '(49.866670, 30.833330)',\n", " '(15.000000, 48.300000)',\n", " '(55.433330, 53.250000)',\n", " '(12.383330, 78.516670)',\n", " '(45.133330, 21.666670)',\n", " '(-6.833330, 29.500000)',\n", " '(17.833330, 73.983330)',\n", " '(26.033330, 81.466670)',\n", " '(36.041670, 139.956670)',\n", " '(14.183330, 75.800000)',\n", " '(31.600000, 65.783330)',\n", " '(-7.000000, 115.500000)',\n", " '(32.083330, 76.300000)',\n", " '(4.700000, 33.633330)',\n", " '(20.339160, 73.223290)',\n", " '(42.450000, 73.366670)',\n", " '(47.216670, 81.016670)',\n", " '(-3.500000, 35.583330)',\n", " '(12.900000, 7.150000)',\n", " '(27.800000, 67.166670)',\n", " '(31.583330, 71.600000)',\n", " '(-35.083330, 139.916670)',\n", " '(35.366670, 136.766670)',\n", " '(29.583330, 77.583330)',\n", " '(11.333330, 10.083330)',\n", " '(25.143330, 75.813330)',\n", " '(39.263330, 31.780000)',\n", " '(-7.750000, 112.016670)',\n", " '(36.541940, 29.418220)',\n", " '(29.450000, -96.000000)',\n", " '(20.462500, 86.702780)',\n", " '(48.400000, -3.300000)',\n", " '(48.116670, -3.083330)',\n", " '(38.983330, 141.616670)',\n", " '(29.533330, 72.300000)',\n", " '(25.550000, 83.116670)',\n", " '(50.625000, 35.075000)',\n", " '(26.950000, 77.883330)',\n", " '(28.016670, 75.816670)',\n", " '(56.750000, 75.333330)',\n", " '(25.100000, 81.533330)',\n", " '(16.000000, 36.000000)',\n", " '(14.000000, 28.000000)',\n", " '(54.400000, 10.150000)',\n", " '(16.583330, -11.333330)',\n", " '(36.850000, 138.383330)',\n", " '(55.000000, 34.000000)',\n", " '(12.766670, 9.800000)',\n", " '(43.583330, -89.600000)',\n", " '(54.666670, -7.666670)',\n", " '(11.633330, 24.683330)',\n", " '(30.750000, -95.950000)',\n", " '(48.166670, 22.308330)',\n", " '(43.383330, -80.383330)',\n", " '(51.600000, 10.800000)',\n", " '(48.900000, 22.400000)',\n", " '(34.733330, 135.166670)',\n", " '(34.300000, 133.950000)',\n", " '(35.733330, 139.750000)',\n", " '(42.516670, 26.166670)',\n", " '(48.763670, 21.176330)',\n", " '(49.326940, 7.464720)',\n", " '(54.033330, 40.900000)',\n", " '(54.333330, 56.083330)',\n", " '(56.800000, 77.000000)',\n", " '(47.833330, 30.766670)',\n", " '(51.150000, 31.700000)',\n", " '(30.731110, 66.802220)',\n", " '(50.750000, 33.500000)',\n", " '(41.116670, 45.000000)',\n", " '(55.783330, 61.366670)',\n", " '(42.250000, 59.200000)',\n", " '(37.050000, 138.383330)',\n", " '(29.683330, 78.383330)',\n", " '(44.516670, 39.300000)',\n", " '(10.833330, 76.033330)',\n", " '(55.200000, 75.333330)',\n", " '(32.033330, 130.633330)',\n", " '(47.083330, 1.750000)',\n", " '(20.666670, -101.283330)',\n", " '(-37.333330, -61.533330)',\n", " '(-31.233330, -58.166670)',\n", " '(44.283330, 5.583330)',\n", " '(26.783330, 82.716670)',\n", " '(48.766670, 0.633330)',\n", " '(21.866670, 76.033330)',\n", " '(24.450000, 78.566670)',\n", " '(47.700000, 1.066670)',\n", " '(43.750000, 5.116670)',\n", " '(58.850000, 16.733330)',\n", " '(46.241670, 126.196110)',\n", " '(47.750000, 16.233330)',\n", " '(33.133330, 115.166670)',\n", " '(51.900000, -1.116670)',\n", " '(52.450000, 51.566670)',\n", " '(47.166670, 0.433330)',\n", " '(48.533330, -0.866670)',\n", " '(35.883330, -99.333330)',\n", " '(-25.666670, 28.366670)',\n", " '(52.666670, -6.966670)',\n", " '(34.583330, -87.500000)',\n", " '(52.266670, 32.850000)',\n", " '(48.350000, 3.250000)',\n", " '(50.366670, 4.733330)',\n", " '(-26.150000, 26.183330)',\n", " '(56.650000, 15.866670)',\n", " '(52.566670, -8.783330)',\n", " '(52.750000, 12.900000)',\n", " '(31.633330, 118.983330)',\n", " '(50.200000, 14.850000)',\n", " '(37.916670, -92.083330)',\n", " '(56.000000, 26.433330)',\n", " '(29.533330, 71.800000)',\n", " '(26.965560, 72.626670)',\n", " '(38.700660, -77.211630)',\n", " '(38.000000, -0.833330)',\n", " '(36.008330, -95.150000)',\n", " '(38.250000, -85.750000)',\n", " '(52.000000, 19.916670)',\n", " '(24.950000, 75.150000)',\n", " '(47.850000, 0.483330)',\n", " '(32.033330, -84.766670)',\n", " '(24.800000, 103.300000)',\n", " '(56.216670, 13.033330)',\n", " '(61.200000, 27.700000)',\n", " '(46.216670, 5.000000)',\n", " '(-7.216670, 29.433330)',\n", " '(19.000000, 97.000000)',\n", " '(-5.200000, -36.666670)',\n", " '(-15.212220, 35.242220)',\n", " '(-28.833330, 31.950000)',\n", " '(25.916670, 86.366670)',\n", " '(-7.750000, 111.533330)',\n", " '(40.416670, -3.716670)',\n", " '(-26.166670, -49.933330)',\n", " '(37.866670, 27.516670)',\n", " '(-19.483330, 31.650000)',\n", " '(27.666670, 95.783330)',\n", " '(12.833330, 9.383330)',\n", " '(32.216670, -104.000000)',\n", " '(9.500000, 31.750000)',\n", " '(-3.133330, 33.516670)',\n", " '(-28.933330, -63.233330)',\n", " '(-29.450000, 26.766670)',\n", " '(45.216670, 62.083330)',\n", " '(23.050000, 86.700000)',\n", " '(20.966670, 76.100000)',\n", " '(-17.650000, 31.600000)',\n", " '(45.816670, 44.633330)',\n", " '(34.233330, 72.083330)',\n", " None,\n", " '(42.716670, -99.383330)',\n", " '(54.761830, 11.467450)',\n", " '(4.666670, 29.250000)',\n", " '(-22.250000, -49.933330)',\n", " '(41.900000, -91.600000)',\n", " '(61.500000, 30.500000)',\n", " '(44.500000, 0.150000)',\n", " '(-14.200000, 48.100000)',\n", " '(35.800000, -84.100000)',\n", " '(45.366670, 1.866670)',\n", " '(0.000000, 0.000000)',\n", " '(48.133330, 12.616670)',\n", " '(48.183330, 13.133330)',\n", " '(-20.000000, 57.000000)',\n", " '(8.966670, 12.083330)',\n", " '(24.683330, -101.683330)',\n", " '(-1.216670, 30.000000)',\n", " '(1.066670, 34.166670)',\n", " '(-27.250000, -67.500000)',\n", " '(29.016670, 77.800000)',\n", " '(-6.233330, 106.883330)',\n", " '(53.183330, 13.150000)',\n", " '(46.818670, 7.218170)',\n", " '(55.050000, 12.066670)',\n", " '(0.000000, 37.666670)',\n", " '(25.483330, 81.983330)',\n", " '(38.183330, 15.566670)',\n", " '(50.583330, 11.100000)',\n", " '(45.766670, 3.250000)',\n", " '(46.500000, 25.733330)',\n", " '(25.900000, 83.616670)',\n", " '(34.800000, 111.700000)',\n", " '(54.566670, -1.166670)',\n", " '(42.907500, -90.365560)',\n", " '(48.066670, 30.966670)',\n", " '(35.568330, 133.220000)',\n", " '(46.233330, 17.533330)',\n", " '(46.183330, 16.100000)',\n", " '(-26.450000, 120.366670)',\n", " '(35.400000, -92.050000)',\n", " '(35.078330, 136.933330)',\n", " '(37.283330, 14.700000)',\n", " '(32.333330, 120.666670)',\n", " '(47.700000, 16.600000)',\n", " '(25.683330, 83.250000)',\n", " '(56.666670, 23.000000)',\n", " '(61.733330, 5.933330)',\n", " '(46.800000, 24.033330)',\n", " '(38.500000, -101.100000)',\n", " '(-39.633330, 174.400000)',\n", " '(38.116670, -1.166670)',\n", " '(-31.250000, 26.466670)',\n", " '(31.608330, -102.858330)',\n", " '(35.250000, -80.500000)',\n", " '(38.016670, -8.250000)',\n", " '(43.266670, 13.350000)',\n", " '(43.390560, 1.962500)',\n", " '(47.633330, 1.583330)',\n", " '(-15.966670, 27.350000)',\n", " '(35.416670, -79.383330)',\n", " '(52.450000, -8.333330)',\n", " '(-40.975000, 145.600000)',\n", " '(28.783330, 78.833330)',\n", " '(49.600000, 18.533330)',\n", " '(44.600000, 5.133330)',\n", " '(59.433330, 10.700000)',\n", " '(26.833330, 77.333330)',\n", " '(45.200000, 8.500000)',\n", " '(-29.800000, 141.700000)',\n", " '(18.700000, 4.800000)',\n", " '(44.083330, 6.866670)',\n", " '(-11.500000, 33.500000)',\n", " '(12.633330, 77.016670)',\n", " '(9.333330, 80.833330)',\n", " '(24.500000, 90.216670)',\n", " '(-36.616670, 145.200000)',\n", " '(36.600000, -88.100000)',\n", " '(26.133330, 85.533330)',\n", " '(23.050000, 72.633330)',\n", " '(12.000000, 1.000000)',\n", " '(38.121670, 140.061670)',\n", " '(26.983330, 78.216670)',\n", " '(49.166670, 19.500000)',\n", " '(31.316670, 30.350000)',\n", " '(13.733330, 100.083330)',\n", " '(12.283330, 79.200000)',\n", " '(35.666670, 103.500000)',\n", " '(38.416670, -77.166670)',\n", " '(32.116670, 121.800000)',\n", " '(19.250000, 77.000000)',\n", " '(33.750000, 51.500000)',\n", " '(-34.050000, 150.688890)',\n", " '(42.516670, 12.516670)',\n", " '(-21.733330, 165.900000)',\n", " None,\n", " '(21.250000, 83.666670)',\n", " '(36.449170, 136.465280)',\n", " '(18.683330, 83.483330)',\n", " '(9.500000, 35.333330)',\n", " '(56.500000, 21.500000)',\n", " '(47.525000, 10.808330)',\n", " '(40.000000, -81.766670)',\n", " '(15.366670, 35.683330)',\n", " '(29.947180, -90.109760)',\n", " '(-7.450000, 111.416670)',\n", " '(13.850000, -4.383330)',\n", " '(49.033330, -1.433330)',\n", " None,\n", " None,\n", " '(52.450000, 78.633330)',\n", " '(56.116670, 37.333330)',\n", " '(29.866670, 121.483330)',\n", " '(32.925000, 105.906670)',\n", " '(34.200000, 131.566670)',\n", " '(-28.566670, 30.700000)',\n", " '(44.083330, -69.483330)',\n", " '(40.085280, -86.055000)',\n", " '(33.725000, 130.750000)',\n", " '(-32.366670, -59.833330)',\n", " '(36.216670, -92.266670)',\n", " '(39.683330, -99.866670)',\n", " '(45.291670, 11.527220)',\n", " '(54.816670, 46.000000)',\n", " '(58.550000, 31.333330)',\n", " '(56.000000, 22.000000)',\n", " '(42.916670, 102.466670)',\n", " '(24.300000, -102.133330)',\n", " '(41.633330, 0.750000)',\n", " '(43.333330, 141.866670)',\n", " '(21.208330, 94.916670)',\n", " '(47.550000, 22.025000)',\n", " '(57.783330, 55.266670)',\n", " '(52.283330, 8.050000)',\n", " '(58.500000, 23.000000)',\n", " '(47.883330, 22.033330)',\n", " '(33.283330, 130.200000)',\n", " '(46.066670, 23.583330)',\n", " '(6.750000, 8.500000)',\n", " '(38.183330, -5.400000)',\n", " '(36.183330, 139.216670)',\n", " '(35.083330, 135.200000)',\n", " '(50.833330, 25.500000)',\n", " '(52.950000, 8.166670)',\n", " '(39.000000, -0.033330)',\n", " '(38.716670, -7.066670)',\n", " '(39.566670, -2.100000)',\n", " '(64.020000, 161.808330)',\n", " '(43.883330, 1.383330)',\n", " '(28.547500, -81.362220)',\n", " '(47.116670, 6.150000)',\n", " '(48.500000, 8.000000)',\n", " '(42.133330, 12.933330)',\n", " '(58.883330, 9.400000)',\n", " '(38.400000, 140.350000)',\n", " '(38.600000, -95.216670)',\n", " '(12.900000, 0.080000)',\n", " '(30.180000, -6.577170)',\n", " '(24.300000, -13.100000)',\n", " '(37.608330, -8.280000)',\n", " '(-18.000000, 16.000000)',\n", " '(43.400000, -5.866670)',\n", " '(51.333330, 28.833330)',\n", " '(21.050000, -99.300000)',\n", " '(55.666670, 25.000000)',\n", " '(17.743330, 120.455830)',\n", " '(32.316670, -89.716670)',\n", " '(-23.116670, -65.100000)',\n", " '(43.483330, 118.616670)',\n", " '(39.800000, -91.500000)',\n", " '(21.300000, -157.783330)',\n", " '(15.083330, 120.700000)',\n", " '(8.066670, 124.283330)',\n", " '(36.066670, -90.500000)',\n", " '(-6.233330, -40.700000)',\n", " '(-19.133330, -51.666670)',\n", " '(41.484720, -87.679170)',\n", " '(9.233330, 78.350000)',\n", " '(26.200000, 85.400000)',\n", " '(36.216670, -103.400000)',\n", " '(20.936940, 82.050000)',\n", " '(-19.533330, -48.566670)',\n", " '(38.133330, 14.966670)',\n", " '(23.150000, 91.183330)',\n", " '(43.466670, 25.516670)',\n", " '(52.300000, 77.033330)',\n", " '(48.533330, 35.983330)',\n", " '(52.033330, 43.000000)',\n", " '(11.333670, -3.542170)',\n", " '(56.133330, -117.933330)',\n", " '(51.666670, 9.250000)',\n", " '(41.283330, -73.916670)',\n", " '(30.125000, -103.116670)',\n", " '(-10.666670, 35.500000)',\n", " '(23.325000, 91.000000)',\n", " '(56.400000, -3.433330)',\n", " '(56.633330, 39.433330)',\n", " '(55.500000, 66.083330)',\n", " '(14.052000, 0.420000)',\n", " '(35.300000, -86.633330)',\n", " '(53.533330, -7.333330)',\n", " '(40.000000, -99.250000)',\n", " '(11.250000, 108.583330)',\n", " '(12.000000, 105.483330)',\n", " '(15.716670, 108.100000)',\n", " '(44.244170, 11.502220)',\n", " '(41.366670, -6.233330)',\n", " '(58.666670, 25.733330)',\n", " '(26.034720, 73.941670)',\n", " '(-32.866670, 18.716670)',\n", " '(25.800000, 88.450000)',\n", " '(29.583330, 76.000000)',\n", " '(31.950000, -83.516670)',\n", " '(30.700000, -96.116670)',\n", " '(45.275000, 26.709720)',\n", " '(50.533330, 14.116670)',\n", " '(11.583330, 104.916670)',\n", " '(50.933330, 12.133330)',\n", " '(26.716670, 82.666670)',\n", " '(66.348330, 14.015000)',\n", " '(53.036390, -4.319440)',\n", " '(34.175000, -103.295000)',\n", " '(38.500000, -8.000000)',\n", " '(31.416670, 118.500000)',\n", " '(48.302500, 13.940830)',\n", " '(49.666670, 14.033330)',\n", " '(39.116670, -83.850000)',\n", " '(39.350000, -3.516670)',\n", " '(23.366670, 75.183330)',\n", " '(52.766670, 21.266670)',\n", " '(13.333330, 78.950000)',\n", " '(-29.033330, -53.050000)',\n", " '(32.083330, 121.500000)',\n", " '(26.533330, 106.466670)',\n", " '(-30.116670, 28.700000)',\n", " '(17.766670, 95.183330)',\n", " '(39.000000, -0.666670)',\n", " '(44.616670, 126.133330)',\n", " '(46.600000, 0.250000)',\n", " '(-26.666670, -65.450000)',\n", " '(27.725280, 76.465000)',\n", " '(28.225000, 70.200000)',\n", " '(52.983330, 37.033330)',\n", " '(26.450000, 82.900000)',\n", " '(24.166670, 87.766670)',\n", " '(51.883330, 6.933330)',\n", " '(23.983330, 87.083330)',\n", " '(19.866670, -100.816670)',\n", " '(25.383330, 72.016670)',\n", " '(38.200000, 115.700000)',\n", " '(26.666670, 50.150000)',\n", " '(43.500000, 26.533330)',\n", " '(52.200000, 17.983330)',\n", " '(38.137420, -119.758120)',\n", " '(42.475000, -5.333330)',\n", " '(-6.733330, 111.366670)',\n", " '(44.766670, 11.283330)',\n", " '(-32.750000, -65.283330)',\n", " '(38.666670, 116.133330)',\n", " '(48.600000, 45.666670)',\n", " '(51.333330, -118.950000)',\n", " '(28.200000, 76.666670)',\n", " '(35.033330, -83.033330)',\n", " '(46.883330, -102.316670)',\n", " '(31.250000, -99.033330)',\n", " '(37.466670, -77.500000)',\n", " '(-26.100000, -49.800000)',\n", " '(45.483330, 9.516670)',\n", " '(41.083330, -86.283330)',\n", " '(-23.383330, 150.516670)',\n", " '(42.300000, 0.550000)',\n", " '(50.350000, 10.800000)',\n", " '(44.516670, -83.950000)',\n", " '(52.766670, -2.516670)',\n", " '(-1.450000, 29.833330)',\n", " '(0.266670, 36.533330)',\n", " '(-10.266670, 38.766670)',\n", " '(51.133330, 34.500000)',\n", " '(27.433330, 82.083330)',\n", " '(23.083330, 91.666670)',\n", " '(51.533330, 14.883330)',\n", " '(43.733330, 1.383330)',\n", " '(45.300000, 0.233330)',\n", " '(35.666670, 136.300000)',\n", " '(44.979170, -122.969440)',\n", " '(46.050000, 4.633330)',\n", " '(52.750000, 11.050000)',\n", " '(25.666670, 74.866670)',\n", " '(33.484720, -117.662500)',\n", " '(43.666670, 13.000000)',\n", " '(-31.016670, -71.400000)',\n", " '(19.766670, -100.650000)',\n", " '(-29.200000, -51.866670)',\n", " '(24.166670, -99.333330)',\n", " '(-33.900000, -61.700000)',\n", " '(-31.535556, -68.489444)',\n", " '(-20.810000, -49.380560)',\n", " '(52.550000, 46.550000)',\n", " '(34.716670, 137.783330)',\n", " '(43.150000, -0.850000)',\n", " '(47.216670, 29.866670)',\n", " '(35.866670, 139.400000)',\n", " '(49.233330, 17.566670)',\n", " '(53.350000, 15.050000)',\n", " '(42.860830, -73.950280)',\n", " '(48.116670, 10.466670)',\n", " '(44.366670, -69.200000)',\n", " '(38.300000, 27.133330)',\n", " '(26.750000, 84.783330)',\n", " '(-7.233330, 107.333330)',\n", " '(22.833330, 4.983330)',\n", " '(22.250000, 79.000000)',\n", " '(41.716670, -0.050000)',\n", " '(39.438330, 140.511670)',\n", " '(21.683890, 79.500830)',\n", " '(41.050000, 23.566670)',\n", " '(-8.383330, -36.766670)',\n", " '(-19.466670, -44.216670)',\n", " '(37.416670, -6.000000)',\n", " '(50.616670, 36.600000)',\n", " '(34.750000, 10.716670)',\n", " '(23.100000, 87.300000)',\n", " '(37.833330, -76.700000)',\n", " '(44.050000, -80.166670)',\n", " '(24.550000, 84.833330)',\n", " '(33.650000, 120.066670)',\n", " '(25.850000, 87.577500)',\n", " '(43.500000, 125.666670)',\n", " '(33.716670, 74.833330)',\n", " '(24.333330, 90.166670)',\n", " '(43.116670, 11.600000)',\n", " '(46.160000, 134.653330)',\n", " '(20.933330, -101.383330)',\n", " '(44.116670, 27.266670)',\n", " '(49.983330, 7.533330)',\n", " '(30.900000, 32.483330)',\n", " '(26.216670, 69.550000)',\n", " '(39.300000, 9.200000)',\n", " '(42.583330, -103.666670)',\n", " '(20.916670, 82.583330)',\n", " '(39.824670, 36.135830)',\n", " '(32.433330, 119.866670)',\n", " '(59.733330, 10.866670)',\n", " '(45.683330, 15.600000)',\n", " '(55.000000, 35.000000)',\n", " '(27.133330, 84.066670)',\n", " '(43.666670, 21.866670)',\n", " '(47.366670, 1.733330)',\n", " '(54.008830, 22.005000)',\n", " '(35.166670, 135.333330)',\n", " '(45.250000, 125.000000)',\n", " '(44.416670, 23.500000)',\n", " '(1.700000, 33.633330)',\n", " '(44.750000, 0.050000)',\n", " '(46.950000, -1.500000)',\n", " '(51.050000, 3.750000)',\n", " '(48.016670, -1.150000)',\n", " '(38.700000, -90.233330)',\n", " '(-32.016670, 27.416670)',\n", " '(38.166670, -76.383330)',\n", " '(48.450000, 3.933330)',\n", " '(61.650000, 27.200000)',\n", " '(43.433330, 2.950000)',\n", " '(59.933330, 14.950000)',\n", " '(49.283330, 15.566670)',\n", " '(45.050000, 41.983330)',\n", " '(50.766670, 3.000000)',\n", " '(53.666670, 55.983330)',\n", " '(52.533330, 9.050000)',\n", " '(41.200000, -73.133330)',\n", " '(56.583330, -3.250000)',\n", " '(50.383330, -3.950000)',\n", " '(45.968610, -72.978060)',\n", " '(36.483330, -90.666670)',\n", " '(50.538060, 16.263330)',\n", " '(31.616670, 113.466670)',\n", " '(12.666670, 78.033330)',\n", " '(25.933330, 84.283330)',\n", " '(44.866670, 133.166670)',\n", " '(26.716670, 84.216670)',\n", " '(38.803890, -120.908060)',\n", " '(33.188360, -86.294500)',\n", " '(49.400000, 14.650000)',\n", " '(36.183330, 5.416670)',\n", " '(59.704440, -134.201390)',\n", " '(34.720000, 137.305000)',\n", " '(35.383330, 134.900000)',\n", " None,\n", " '(-7.750000, 112.766670)',\n", " '(31.163330, -7.015000)',\n", " '(35.433330, 136.233330)',\n", " '(45.400000, 122.900000)',\n", " '(32.950000, 10.416670)',\n", " '(19.383330, 43.733330)',\n", " '(35.133330, 44.450000)',\n", " '(46.716670, 23.500000)',\n", " '(-25.733330, 142.950000)',\n", " '(58.033330, 26.950000)',\n", " '(33.400000, 70.600000)',\n", " '(-1.002780, 37.150280)',\n", " '(-29.333330, 27.583330)',\n", " '(32.946670, 118.990000)',\n", " '(49.600000, 17.116670)',\n", " '(38.200000, -89.683330)',\n", " '(14.250000, 1.533330)',\n", " '(54.500000, 35.200000)',\n", " '(13.633330, 79.416670)',\n", " '(29.481950, -7.611230)',\n", " '(-7.083330, 111.533330)',\n", " '(-6.666670, 106.583330)',\n", " '(47.850000, 34.766670)',\n", " '(20.166670, -105.216670)',\n", " '(34.500000, 133.750000)',\n", " '(38.366670, 140.865560)',\n", " '(24.650000, 76.866670)',\n", " ...]}" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.set_buffer()\n", "df.ext.get_buffer()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "45716\n" ] }, { "data": { "text/plain": [ "{'frequency': {'id': {'values': [{'value': '2', 'count': 1},\n", " {'value': '6', 'count': 1},\n", " {'value': '379', 'count': 1},\n", " {'value': '392', 'count': 1},\n", " {'value': '398', 'count': 1},\n", " {'value': '423', 'count': 1},\n", " {'value': '424', 'count': 1},\n", " {'value': '425', 'count': 1},\n", " {'value': '427', 'count': 1},\n", " {'value': '432', 'count': 1}],\n", " 'count_uniques': 'N/A'}}}" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.frequency(\"id\", n=10, count_uniques=True, compute=True)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('year', ),\n", " ('reclat', ),\n", " ('reclong', ),\n", " ('GeoLocation', ),\n", " ('recclass', ),\n", " ('name', ),\n", " ('fall', ),\n", " ('id', ),\n", " ('mass (g)', ),\n", " ('nametype', )]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(df.data.select(\"*\").rdd\n", " .flatMap(lambda x: x.asDict().items())\n", " .map(lambda x: (x, 1))\n", " .reduceByKey(lambda a, b: a + b)\n", " .groupBy(lambda x: x[0][0])\n", " ).collect()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.count()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "func_return_type0 str\n", "func_return_type1 str\n", "func_return_type str\n", "pandas_udf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'pandas_udf' to process column 'name' with function _remove_accents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func_return_type str\n", "pandas_udf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'pandas_udf' to process column 'id' with function _remove_accents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func_return_type str\n", "pandas_udf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'pandas_udf' to process column 'nametype' with function _remove_accents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func_return_type str\n", "pandas_udf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'pandas_udf' to process column 'recclass' with function _remove_accents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func_return_type str\n", "pandas_udf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'pandas_udf' to process column 'mass (g)' with function _remove_accents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func_return_type str\n", "pandas_udf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'pandas_udf' to process column 'fall' with function _remove_accents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func_return_type str\n", "pandas_udf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'pandas_udf' to process column 'year' with function _remove_accents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func_return_type str\n", "pandas_udf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'pandas_udf' to process column 'reclat' with function _remove_accents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func_return_type str\n", "pandas_udf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'pandas_udf' to process column 'reclong' with function _remove_accents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func_return_type str\n", "pandas_udf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'pandas_udf' to process column 'GeoLocation' with function _remove_accents\n" ] }, { "ename": "Py4JJavaError", "evalue": "An error occurred while calling o5009.collectToPython.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 192.0 failed 1 times, most recent failure: Lost task 0.0 in stage 192.0 (TID 294, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 377, in main\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 372, in process\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\serializers.py\", line 290, in dump_stream\n for series in iterator:\n File \"\", line 1, in \n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 101, in \n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 92, in verify_result_length\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\util.py\", line 99, in wrapper\n return f(*args, **kwargs)\n File \"..\\optimus\\audf.py\", line 78, in to_serie\n return apply_to_series(value, args)\n File \"..\\optimus\\audf.py\", line 75, in apply_to_series\n return value.apply(func, args=args)\n File \"C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\", line 4200, in apply\n mapped = lib.map_infer(values, f, convert=convert_dtype)\n File \"pandas\\_libs\\lib.pyx\", line 2401, in pandas._libs.lib.map_infer\n File \"C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\", line 4185, in f\n return func(x, *args, **kwds)\nTypeError: _remove_accents() takes 1 positional argument but 2 were given\n\r\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:172)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)\r\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)\r\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.(ArrowEvalPythonExec.scala:102)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:100)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:123)\r\n\tat org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\n\nDriver stacktrace:\r\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1925)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1913)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1912)\r\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\r\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\r\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1912)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948)\r\n\tat scala.Option.foreach(Option.scala:257)\r\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:948)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2146)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2095)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2084)\r\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:759)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)\r\n\tat org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)\r\n\tat org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3263)\r\n\tat org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3260)\r\n\tat org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)\r\n\tat org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)\r\n\tat org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withAction(Dataset.scala:3369)\r\n\tat org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3260)\r\n\tat sun.reflect.GeneratedMethodAccessor114.invoke(Unknown Source)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\r\n\tat java.lang.reflect.Method.invoke(Method.java:498)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\nCaused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 377, in main\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 372, in process\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\serializers.py\", line 290, in dump_stream\n for series in iterator:\n File \"\", line 1, in \n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 101, in \n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 92, in verify_result_length\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\util.py\", line 99, in wrapper\n return f(*args, **kwargs)\n File \"..\\optimus\\audf.py\", line 78, in to_serie\n return apply_to_series(value, args)\n File \"..\\optimus\\audf.py\", line 75, in apply_to_series\n return value.apply(func, args=args)\n File \"C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\", line 4200, in apply\n mapped = lib.map_infer(values, f, convert=convert_dtype)\n File \"pandas\\_libs\\lib.pyx\", line 2401, in pandas._libs.lib.map_infer\n File \"C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\", line 4185, in f\n return func(x, *args, **kwds)\nTypeError: _remove_accents() takes 1 positional argument but 2 were given\n\r\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:172)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)\r\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)\r\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.(ArrowEvalPythonExec.scala:102)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:100)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:123)\r\n\tat org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\r\n\t... 1 more\r\n", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mPy4JJavaError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 700\u001b[0m \u001b[0mtype_pprinters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype_printers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 701\u001b[0m deferred_pprinters=self.deferred_printers)\n\u001b[1;32m--> 702\u001b[1;33m \u001b[0mprinter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpretty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 703\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 704\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mstream\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\IPython\\lib\\pretty.py\u001b[0m in \u001b[0;36mpretty\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 400\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 401\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'__repr__'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 402\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_repr_pprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrepr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 698\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutput_line\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 699\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\new_optimus.py\u001b[0m in \u001b[0;36m__repr__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[1;32mdef\u001b[0m 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\u001b[0m__IPYTHON__\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 511\u001b[0m \u001b[1;31m# TODO: move the html param to the ::: if __IPYTHON__ and engine.output is \"html\":\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 512\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtable_html\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtruncate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtruncate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 513\u001b[0m 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in \u001b[0;36mtoPandas\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2154\u001b[0m \u001b[1;31m# Below is toPandas without Arrow optimization.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2155\u001b[1;33m \u001b[0mpdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_records\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcollect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2156\u001b[0m \u001b[0mcolumn_counter\u001b[0m \u001b[1;33m=\u001b[0m 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aborted due to stage failure: Task 0 in stage 192.0 failed 1 times, most recent failure: Lost task 0.0 in stage 192.0 (TID 294, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 377, in main\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 372, in process\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\serializers.py\", line 290, in dump_stream\n for series in iterator:\n File \"\", line 1, in \n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 101, in \n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 92, in verify_result_length\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\util.py\", line 99, in wrapper\n return f(*args, **kwargs)\n File \"..\\optimus\\audf.py\", line 78, in to_serie\n return apply_to_series(value, args)\n File \"..\\optimus\\audf.py\", line 75, in apply_to_series\n return value.apply(func, args=args)\n File \"C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\", line 4200, in apply\n mapped = lib.map_infer(values, f, convert=convert_dtype)\n File \"pandas\\_libs\\lib.pyx\", line 2401, in pandas._libs.lib.map_infer\n File \"C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\", line 4185, in f\n return func(x, *args, **kwds)\nTypeError: _remove_accents() takes 1 positional argument but 2 were given\n\r\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:172)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)\r\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)\r\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.(ArrowEvalPythonExec.scala:102)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:100)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:123)\r\n\tat org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\n\nDriver stacktrace:\r\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1925)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1913)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1912)\r\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\r\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\r\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1912)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948)\r\n\tat scala.Option.foreach(Option.scala:257)\r\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:948)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2146)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2095)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2084)\r\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:759)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)\r\n\tat org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)\r\n\tat org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3263)\r\n\tat org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3260)\r\n\tat org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)\r\n\tat org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)\r\n\tat org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withAction(Dataset.scala:3369)\r\n\tat org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3260)\r\n\tat sun.reflect.GeneratedMethodAccessor114.invoke(Unknown Source)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\r\n\tat java.lang.reflect.Method.invoke(Method.java:498)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\nCaused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 377, in main\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 372, in process\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\serializers.py\", line 290, in dump_stream\n for series in iterator:\n File \"\", line 1, in \n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 101, in \n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\worker.py\", line 92, in verify_result_length\n File \"C:\\opt\\spark\\spark-2.4.7-bin-hadoop2.7\\python\\lib\\pyspark.zip\\pyspark\\util.py\", line 99, in wrapper\n return f(*args, **kwargs)\n File \"..\\optimus\\audf.py\", line 78, in to_serie\n return apply_to_series(value, args)\n File \"..\\optimus\\audf.py\", line 75, in apply_to_series\n return value.apply(func, args=args)\n File \"C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\", line 4200, in apply\n mapped = lib.map_infer(values, f, convert=convert_dtype)\n File \"pandas\\_libs\\lib.pyx\", line 2401, in pandas._libs.lib.map_infer\n File \"C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\", line 4185, in f\n return func(x, *args, **kwds)\nTypeError: _remove_accents() takes 1 positional argument but 2 were given\n\r\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:172)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)\r\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)\r\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.(ArrowEvalPythonExec.scala:102)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:100)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:123)\r\n\tat org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\r\n\t... 1 more\r\n" ] } ], "source": [ "df.cols.remove_accents(\"*\")" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "45716\n" ] }, { "data": { "text/plain": [ "{'frequency': {'id': {'count_uniques': 'N/A',\n", " 'values': [{'value': '2', 'count': 1},\n", " {'value': '6', 'count': 1},\n", " {'value': '379', 'count': 1},\n", " {'value': '392', 'count': 1},\n", " {'value': '398', 'count': 1},\n", " {'value': '423', 'count': 1},\n", " {'value': '424', 'count': 1},\n", " {'value': '425', 'count': 1},\n", " {'value': '427', 'count': 1},\n", " {'value': '432', 'count': 1}]}}}" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.frequency(\"id\", n=10, count_uniques=True, compute=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pyspark.sql.dataframe.DataFrame" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df.cols.select(\"*\").rows.limit(10).data)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Row(name='Aachen', id=1, nametype='Valid', recclass='L5', mass (g)=21.0, fall='Fell', year='01/01/1880 12:00:00 AM', reclat=50.775, reclong=6.08333, GeoLocation='(50.775000, 6.083330)')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.data.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'name': 'Aachen',\n", " 'id': 1,\n", " 'nametype': 'Valid',\n", " 'recclass': 'L5',\n", " 'mass (g)': 21.0,\n", " 'fall': 'Fell',\n", " 'year': '01/01/1880 12:00:00 AM',\n", " 'reclat': 50.775,\n", " 'reclong': 6.08333,\n", " 'GeoLocation': '(50.775000, 6.083330)'},\n", " {'name': 'Aarhus',\n", " 'id': 2,\n", " 'nametype': 'Valid',\n", " 'recclass': 'H6',\n", " 'mass (g)': 720.0,\n", " 'fall': 'Fell',\n", " 'year': '01/01/1951 12:00:00 AM',\n", " 'reclat': 56.18333,\n", " 'reclong': 10.23333,\n", " 'GeoLocation': '(56.183330, 10.233330)'},\n", " {'name': 'Abee',\n", " 'id': 6,\n", " 'nametype': 'Valid',\n", " 'recclass': 'EH4',\n", " 'mass (g)': 107000.0,\n", " 'fall': 'Fell',\n", " 'year': '01/01/1952 12:00:00 AM',\n", " 'reclat': 54.21667,\n", " 'reclong': -113.0,\n", " 'GeoLocation': '(54.216670, -113.000000)'},\n", " {'name': 'Acapulco',\n", " 'id': 10,\n", " 'nametype': 'Valid',\n", " 'recclass': 'Acapulcoite',\n", " 'mass (g)': 1914.0,\n", " 'fall': 'Fell',\n", " 'year': '01/01/1976 12:00:00 AM',\n", " 'reclat': 16.88333,\n", " 'reclong': -99.9,\n", " 'GeoLocation': '(16.883330, -99.900000)'},\n", " {'name': 'Achiras',\n", " 'id': 370,\n", " 'nametype': 'Valid',\n", " 'recclass': 'L6',\n", " 'mass (g)': 780.0,\n", " 'fall': 'Fell',\n", " 'year': '01/01/1902 12:00:00 AM',\n", " 'reclat': -33.16667,\n", " 'reclong': -64.95,\n", " 'GeoLocation': '(-33.166670, -64.950000)'},\n", " {'name': 'Adhi Kot',\n", " 'id': 379,\n", " 'nametype': 'Valid',\n", " 'recclass': 'EH4',\n", " 'mass (g)': 4239.0,\n", " 'fall': 'Fell',\n", " 'year': '01/01/1919 12:00:00 AM',\n", " 'reclat': 32.1,\n", " 'reclong': 71.8,\n", " 'GeoLocation': '(32.100000, 71.800000)'},\n", " {'name': 'Adzhi-Bogdo (stone)',\n", " 'id': 390,\n", " 'nametype': 'Valid',\n", " 'recclass': 'LL3-6',\n", " 'mass (g)': 910.0,\n", " 'fall': 'Fell',\n", " 'year': '01/01/1949 12:00:00 AM',\n", " 'reclat': 44.83333,\n", " 'reclong': 95.16667,\n", " 'GeoLocation': '(44.833330, 95.166670)'},\n", " {'name': 'Agen',\n", " 'id': 392,\n", " 'nametype': 'Valid',\n", " 'recclass': 'H5',\n", " 'mass (g)': 30000.0,\n", " 'fall': 'Fell',\n", " 'year': '01/01/1814 12:00:00 AM',\n", " 'reclat': 44.21667,\n", " 'reclong': 0.61667,\n", " 'GeoLocation': '(44.216670, 0.616670)'},\n", " {'name': 'Aguada',\n", " 'id': 398,\n", " 'nametype': 'Valid',\n", " 'recclass': 'L6',\n", " 'mass (g)': 1620.0,\n", " 'fall': 'Fell',\n", " 'year': '01/01/1930 12:00:00 AM',\n", " 'reclat': -31.6,\n", " 'reclong': -65.23333,\n", " 'GeoLocation': '(-31.600000, -65.233330)'},\n", " {'name': 'Aguila Blanca',\n", " 'id': 417,\n", " 'nametype': 'Valid',\n", " 'recclass': 'L',\n", " 'mass (g)': 1440.0,\n", " 'fall': 'Fell',\n", " 'year': '01/01/1920 12:00:00 AM',\n", " 'reclat': -30.86667,\n", " 'reclong': -64.55,\n", " 'GeoLocation': '(-30.866670, -64.550000)'}]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# df.ext.display()\n", "# df.ext.display()\n", "df.cols.select(\"*\").rows.limit(10).ext.to_dict()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'DataFrame' object has no attribute 'get_partition'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_partition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfrac\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pyspark\\sql\\dataframe.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 1303\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1304\u001b[0m raise AttributeError(\n\u001b[1;32m-> 1305\u001b[1;33m \"'%s' object has no attribute '%s'\" % (self.__class__.__name__, name))\n\u001b[0m\u001b[0;32m 1306\u001b[0m \u001b[0mjc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1307\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mColumn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'get_partition'" ] } ], "source": [ "len(df.data.get_partition(0).sample(frac=0.1))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'name' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'nametype' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'recclass' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'fall' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'GeoLocation' with function _cast_to\n" ] }, { "data": { "text/plain": [ "{'year': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323},\n", " {'value': '01/01/1979 12:00:00 AM', 'count': 3046},\n", " {'value': '01/01/1998 12:00:00 AM', 'count': 2697},\n", " {'value': '01/01/2006 12:00:00 AM', 'count': 2456},\n", " {'value': '01/01/1988 12:00:00 AM', 'count': 2296},\n", " {'value': '01/01/2002 12:00:00 AM', 'count': 2078},\n", " {'value': '01/01/2004 12:00:00 AM', 'count': 1940},\n", " {'value': '01/01/2000 12:00:00 AM', 'count': 1792},\n", " {'value': '01/01/1997 12:00:00 AM', 'count': 1696},\n", " {'value': '01/01/1999 12:00:00 AM', 'count': 1691}],\n", " 'reclat': [{'value': None, 'count': 7315},\n", " {'value': 0.0, 'count': 6438},\n", " {'value': -71.5, 'count': 4761},\n", " {'value': -84.0, 'count': 3040},\n", " {'value': -72.0, 'count': 1506},\n", " {'value': -79.68333, 'count': 1130},\n", " {'value': -76.71667, 'count': 680},\n", " {'value': -76.18333, 'count': 539},\n", " {'value': -84.21667, 'count': 263},\n", " {'value': -86.36667, 'count': 226}],\n", " 'reclong': [{'value': None, 'count': 7315},\n", " {'value': 0.0, 'count': 6214},\n", " {'value': 35.66667, 'count': 4985},\n", " {'value': 168.0, 'count': 3040},\n", " {'value': 26.0, 'count': 1506},\n", " {'value': 159.75, 'count': 657},\n", " {'value': 159.66667, 'count': 637},\n", " {'value': 157.16667, 'count': 542},\n", " {'value': 155.75, 'count': 473},\n", " {'value': 160.5, 'count': 263}],\n", " 'GeoLocation': [{'value': None, 'count': 7315},\n", " {'value': '(0.000000, 0.000000)', 'count': 6214},\n", " {'value': '(-71.500000, 35.666670)', 'count': 4761},\n", " {'value': '(-84.000000, 168.000000)', 'count': 3040},\n", " {'value': '(-72.000000, 26.000000)', 'count': 1505},\n", " {'value': '(-79.683330, 159.750000)', 'count': 657},\n", " {'value': '(-76.716670, 159.666670)', 'count': 637},\n", " {'value': '(-76.183330, 157.166670)', 'count': 539},\n", " {'value': '(-79.683330, 155.750000)', 'count': 473},\n", " {'value': '(-84.216670, 160.500000)', 'count': 263}],\n", " 'recclass': [{'value': 'L6', 'count': 8285},\n", " {'value': 'H5', 'count': 7142},\n", " {'value': 'L5', 'count': 4796},\n", " {'value': 'H6', 'count': 4528},\n", " {'value': 'H4', 'count': 4211},\n", " {'value': 'LL5', 'count': 2766},\n", " {'value': 'LL6', 'count': 2043},\n", " {'value': 'L4', 'count': 1253},\n", " {'value': 'H4/5', 'count': 428},\n", " {'value': 'CM2', 'count': 416}],\n", " 'name': [{'value': 'Achiras', 'count': 1},\n", " {'value': 'Adhi Kot', 'count': 1},\n", " {'value': 'Aguada', 'count': 1},\n", " {'value': 'Aguila Blanca', 'count': 1},\n", " {'value': 'Aïr', 'count': 1},\n", " {'value': 'Akaba', 'count': 1},\n", " {'value': 'Akwanga', 'count': 1},\n", " {'value': 'Alais', 'count': 1},\n", " {'value': 'Alberta', 'count': 1},\n", " {'value': 'Alby sur Chéran', 'count': 1}],\n", " 'fall': [{'value': 'Found', 'count': 44609},\n", " {'value': 'Fell', 'count': 1107}],\n", " 'id': [{'value': 1, 'count': 1},\n", " {'value': 379, 'count': 1},\n", " {'value': 417, 'count': 1},\n", " {'value': 423, 'count': 1},\n", " {'value': 425, 'count': 1},\n", " {'value': 427, 'count': 1},\n", " {'value': 433, 'count': 1},\n", " {'value': 447, 'count': 1},\n", " {'value': 453, 'count': 1},\n", " {'value': 461, 'count': 1}],\n", " 'mass (g)': [{'value': 1.3, 'count': 171},\n", " {'value': 1.2, 'count': 140},\n", " {'value': 1.4, 'count': 138},\n", " {'value': None, 'count': 131},\n", " {'value': 2.1, 'count': 130},\n", " {'value': 2.4, 'count': 126},\n", " {'value': 1.6, 'count': 120},\n", " {'value': 0.5, 'count': 119},\n", " {'value': 1.1, 'count': 116},\n", " {'value': 3.8, 'count': 114}],\n", " 'nametype': [{'value': 'Valid', 'count': 45641},\n", " {'value': 'Relict', 'count': 75}]}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.frequency()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s)
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\n", "
name
\n", "
1 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
id
\n", "
2 (int)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
nametype
\n", "
3 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
recclass
\n", "
4 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
mass (g)
\n", "
5 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
fall
\n", "
6 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
year
\n", "
7 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclat
\n", "
8 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclong
\n", "
9 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
GeoLocation
\n", "
10 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " Aachen\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L5\n", " \n", "
\n", "
\n", "
\n", " \n", " 21.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1880⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 50.775\n", " \n", "
\n", "
\n", "
\n", " \n", " 6.08333\n", " \n", "
\n", "
\n", "
\n", " \n", " (50.775000,⋅6.083330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aarhus\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 720.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1951⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 56.18333\n", " \n", "
\n", "
\n", "
\n", " \n", " 10.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (56.183330,⋅10.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Abee\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 107000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1952⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 54.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " -113.0\n", " \n", "
\n", "
\n", "
\n", " \n", " (54.216670,⋅-113.000000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulco\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulcoite\n", " \n", "
\n", "
\n", "
\n", " \n", " 1914.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1976⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 16.88333\n", " \n", "
\n", "
\n", "
\n", " \n", " -99.9\n", " \n", "
\n", "
\n", "
\n", " \n", " (16.883330,⋅-99.900000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Achiras\n", " \n", "
\n", "
\n", "
\n", " \n", " 370\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 780.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1902⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -33.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.95\n", " \n", "
\n", "
\n", "
\n", " \n", " (-33.166670,⋅-64.950000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adhi⋅Kot\n", " \n", "
\n", "
\n", "
\n", " \n", " 379\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 4239.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1919⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 32.1\n", " \n", "
\n", "
\n", "
\n", " \n", " 71.8\n", " \n", "
\n", "
\n", "
\n", " \n", " (32.100000,⋅71.800000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
\n", "
\n", "
\n", " \n", " 390\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " LL3-6\n", " \n", "
\n", "
\n", "
\n", " \n", " 910.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1949⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.83333\n", " \n", "
\n", "
\n", "
\n", " \n", " 95.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.833330,⋅95.166670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Agen\n", " \n", "
\n", "
\n", "
\n", " \n", " 392\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 30000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1814⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " 0.61667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.216670,⋅0.616670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguada\n", " \n", "
\n", "
\n", "
\n", " \n", " 398\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 1620.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1930⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -31.6\n", " \n", "
\n", "
\n", "
\n", " \n", " -65.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (-31.600000,⋅-65.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
\n", "
\n", " \n", " 417\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L\n", " \n", "
\n", "
\n", "
\n", " \n", " 1440.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1920⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -30.86667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.55\n", " \n", "
\n", "
\n", "
\n", " \n", " (-30.866670,⋅-64.550000)\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "func[dtype] ^\\d+$ int\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'id' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'id' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func[dtype] ^\\d+\\.\\d$ decimal\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func[dtype] . date\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'year' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'year' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func[dtype] ^\\d+\\.\\d$ decimal\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'reclat' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'reclat' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func[dtype] ^\\d+\\.\\d$ decimal\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'reclong' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'reclong' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'name' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'nametype' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'recclass' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'fall' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'GeoLocation' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "45716\n" ] }, { "data": { "text/plain": [ "{'columns': {'name': {'stats': {'match': 45716,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'Achiras', 'count': 1},\n", " {'value': 'Adhi Kot', 'count': 1},\n", " {'value': 'Aguada', 'count': 1},\n", " {'value': 'Aguila Blanca', 'count': 1},\n", " {'value': 'Aïr', 'count': 1},\n", " {'value': 'Akaba', 'count': 1},\n", " {'value': 'Akwanga', 'count': 1},\n", " {'value': 'Alais', 'count': 1},\n", " {'value': 'Alberta', 'count': 1},\n", " {'value': 'Alby sur Chéran', 'count': 1},\n", " {'value': 'Aleppo', 'count': 1},\n", " {'value': 'Alessandria', 'count': 1},\n", " {'value': 'Allegan', 'count': 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1189},\n", " {'value': '01/01/2010 12:00:00 AM', 'count': 1005},\n", " {'value': '01/01/1993 12:00:00 AM', 'count': 979},\n", " {'value': '01/01/2008 12:00:00 AM', 'count': 957},\n", " {'value': '01/01/1987 12:00:00 AM', 'count': 916},\n", " {'value': '01/01/1991 12:00:00 AM', 'count': 877},\n", " {'value': '01/01/2005 12:00:00 AM', 'count': 875},\n", " {'value': '01/01/1994 12:00:00 AM', 'count': 719},\n", " {'value': '01/01/2011 12:00:00 AM', 'count': 713},\n", " {'value': '01/01/1974 12:00:00 AM', 'count': 691},\n", " {'value': '01/01/1996 12:00:00 AM', 'count': 583},\n", " {'value': '01/01/1995 12:00:00 AM', 'count': 487},\n", " {'value': '01/01/1981 12:00:00 AM', 'count': 463},\n", " {'value': '01/01/1977 12:00:00 AM', 'count': 421},\n", " {'value': '01/01/1984 12:00:00 AM', 'count': 402},\n", " {'value': '01/01/1985 12:00:00 AM', 'count': 378},\n", " {'value': '01/01/1992 12:00:00 AM', 'count': 372},\n", " {'value': '01/01/1983 12:00:00 AM', 'count': 360},\n", " {'value': 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'count': 67},\n", " {'value': 58.58333, 'count': 64},\n", " {'value': -72.775, 'count': 57},\n", " {'value': -72.77833, 'count': 52},\n", " {'value': -72.99889, 'count': 41},\n", " {'value': 34.08333, 'count': 40},\n", " {'value': -72.77917, 'count': 40},\n", " {'value': -72.7825, 'count': 39},\n", " {'value': -72.983889, 'count': 37},\n", " {'value': -72.77861, 'count': 35},\n", " {'value': 29.91667, 'count': 35},\n", " {'value': -83.25, 'count': 35},\n", " {'value': -82.5, 'count': 32},\n", " {'value': -25.23333, 'count': 32},\n", " {'value': -72.98972, 'count': 31},\n", " {'value': -72.77361, 'count': 31},\n", " {'value': -72.77472, 'count': 31}],\n", " 'count_uniques': 'N/A'},\n", " 'dtype': 'double',\n", " 'profiler_dtype': {'dtype': 'decimal'}},\n", " 'reclong': {'stats': {'match': 0,\n", " 'missing': 7315,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'decimal'},\n", " 'frequency': [{'value': None, 'count': 7315},\n", " {'value': 0.0, 'count': 6214},\n", " {'value': 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'count': 30},\n", " {'value': '(-84.283330, 161.083330)', 'count': 29},\n", " {'value': '(-73.083330, 75.200000)', 'count': 28},\n", " {'value': '(-80.066670, 156.383330)', 'count': 27},\n", " {'value': '(-72.983889, 75.246389)', 'count': 27},\n", " {'value': '(34.083330, -103.500000)', 'count': 27},\n", " {'value': '(-24.683330, -69.766670)', 'count': 24}],\n", " 'count_uniques': 'N/A'},\n", " 'dtype': 'string',\n", " 'profiler_dtype': {'dtype': 'string'}}},\n", " 'file_name': 'data/Meteorite_Landings.csv',\n", " 'summary': {'cols_count': 10,\n", " 'rows_count': 45716,\n", " 'dtypes_list': ['double', 'string', 'int'],\n", " 'total_count_dtypes': 3,\n", " 'missing_count': 0}}" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.profile()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "func[dtype] ^\\d+$ int\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'id' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'id' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func[dtype] ^\\d+\\.\\d$ decimal\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func[dtype] . date\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'year' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'year' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func[dtype] ^\\d+\\.\\d$ decimal\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'reclat' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'reclat' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "func[dtype] ^\\d+\\.\\d$ decimal\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'reclong' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'reclong' with function _match\n", "INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'name' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'nametype' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'recclass' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'fall' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'GeoLocation' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "45716\n" ] }, { "data": { "text/plain": [ "{'columns': {'name': {'stats': {'match': 45716,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 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2 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
name
\n", "
1 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
id
\n", "
2 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
nametype
\n", "
3 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
recclass
\n", "
4 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
mass (g)
\n", "
5 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
fall
\n", "
6 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
year
\n", "
7 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclat
\n", "
8 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclong
\n", "
9 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
GeoLocation
\n", "
10 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " Aachen\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L5\n", " \n", "
\n", "
\n", "
\n", " \n", " 21.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1880⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 50.775\n", " \n", "
\n", "
\n", "
\n", " \n", " 6.08333\n", " \n", "
\n", "
\n", "
\n", " \n", " (50.775000,⋅6.083330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aarhus\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 720.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1951⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 56.18333\n", " \n", "
\n", "
\n", "
\n", " \n", " 10.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (56.183330,⋅10.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Abee\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 107000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1952⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 54.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " -113.0\n", " \n", "
\n", "
\n", "
\n", " \n", " (54.216670,⋅-113.000000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulco\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulcoite\n", " \n", "
\n", "
\n", "
\n", " \n", " 1914.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1976⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 16.88333\n", " \n", "
\n", "
\n", "
\n", " \n", " -99.9\n", " \n", "
\n", "
\n", "
\n", " \n", " (16.883330,⋅-99.900000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Achiras\n", " \n", "
\n", "
\n", "
\n", " \n", " 370\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 780.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1902⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -33.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.95\n", " \n", "
\n", "
\n", "
\n", " \n", " (-33.166670,⋅-64.950000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adhi⋅Kot\n", " \n", "
\n", "
\n", "
\n", " \n", " 379\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 4239.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1919⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 32.1\n", " \n", "
\n", "
\n", "
\n", " \n", " 71.8\n", " \n", "
\n", "
\n", "
\n", " \n", " (32.100000,⋅71.800000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
\n", "
\n", "
\n", " \n", " 390\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " LL3-6\n", " \n", "
\n", "
\n", "
\n", " \n", " 910.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1949⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.83333\n", " \n", "
\n", "
\n", "
\n", " \n", " 95.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.833330,⋅95.166670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Agen\n", " \n", "
\n", "
\n", "
\n", " \n", " 392\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 30000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1814⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " 0.61667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.216670,⋅0.616670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguada\n", " \n", "
\n", "
\n", "
\n", " \n", " 398\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 1620.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1930⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -31.6\n", " \n", "
\n", "
\n", "
\n", " \n", " -65.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (-31.600000,⋅-65.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
\n", "
\n", " \n", " 417\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L\n", " \n", "
\n", "
\n", "
\n", " \n", " 1440.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1920⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -30.86667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.55\n", " \n", "
\n", "
\n", "
\n", " \n", " (-30.866670,⋅-64.550000)\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.to_string(\"id\")" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Column', 'DataFrame', 'DataType', 'PandasUDFType', 'PythonEvalType', 'SparkContext', 'StringType', 'UserDefinedFunction', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_binary_mathfunctions', '_collect_list_doc', '_collect_set_doc', '_create_binary_mathfunction', '_create_column_from_literal', '_create_function', '_create_udf', '_create_window_function', '_functions', '_functions_1_4', '_functions_1_6', '_functions_2_1', '_functions_2_4', '_functions_deprecated', '_lit_doc', '_message', '_string_functions', '_test', '_to_java_column', '_to_seq', '_window_functions', '_wrap_deprecated_function', 'abs', 'acos', 'add_months', 'approxCountDistinct', 'approx_count_distinct', 'array', 'array_contains', 'array_distinct', 'array_except', 'array_intersect', 'array_join', 'array_max', 'array_min', 'array_position', 'array_remove', 'array_repeat', 'array_sort', 'array_union', 'arrays_overlap', 'arrays_zip', 'asc', 'asc_nulls_first', 'asc_nulls_last', 'ascii', 'asin', 'atan', 'atan2', 'avg', 'base64', 'basestring', 'bin', 'bitwiseNOT', 'blacklist', 'broadcast', 'bround', 'cbrt', 'ceil', 'coalesce', 'col', 'collect_list', 'collect_set', 'column', 'concat', 'concat_ws', 'conv', 'corr', 'cos', 'cosh', 'count', 'countDistinct', 'covar_pop', 'covar_samp', 'crc32', 'create_map', 'cume_dist', 'current_date', 'current_timestamp', 'date_add', 'date_format', 'date_sub', 'date_trunc', 'datediff', 'dayofmonth', 'dayofweek', 'dayofyear', 'decode', 'degrees', 'dense_rank', 'desc', 'desc_nulls_first', 'desc_nulls_last', 'element_at', 'encode', 'exp', 'explode', 'explode_outer', 'expm1', 'expr', 'factorial', 'first', 'flatten', 'floor', 'format_number', 'format_string', 'from_json', 'from_unixtime', 'from_utc_timestamp', 'functools', 'get_json_object', 'greatest', 'grouping', 'grouping_id', 'hash', 'hex', 'hour', 'hypot', 'ignore_unicode_prefix', 'initcap', 'input_file_name', 'instr', 'isnan', 'isnull', 'json_tuple', 'kurtosis', 'lag', 'last', 'last_day', 'lead', 'least', 'length', 'levenshtein', 'lit', 'locate', 'log', 'log10', 'log1p', 'log2', 'lower', 'lpad', 'ltrim', 'map_concat', 'map_from_arrays', 'map_from_entries', 'map_keys', 'map_values', 'max', 'md5', 'mean', 'min', 'minute', 'monotonically_increasing_id', 'month', 'months_between', 'nanvl', 'next_day', 'ntile', 'pandas_udf', 'percent_rank', 'posexplode', 'posexplode_outer', 'pow', 'quarter', 'radians', 'rand', 'randn', 'rank', 'regexp_extract', 'regexp_replace', 'repeat', 'reverse', 'rint', 'round', 'row_number', 'rpad', 'rtrim', 'schema_of_json', 'second', 'sequence', 'sha1', 'sha2', 'shiftLeft', 'shiftRight', 'shiftRightUnsigned', 'shuffle', 'signum', 'sin', 'since', 'sinh', 'size', 'skewness', 'slice', 'sort_array', 'soundex', 'spark_partition_id', 'split', 'sqrt', 'stddev', 'stddev_pop', 'stddev_samp', 'struct', 'substring', 'substring_index', 'sum', 'sumDistinct', 'sys', 'tan', 'tanh', 'toDegrees', 'toRadians', 'to_date', 'to_json', 'to_timestamp', 'to_utc_timestamp', 'translate', 'trim', 'trunc', 'udf', 'unbase64', 'unhex', 'unix_timestamp', 'upper', 'var_pop', 'var_samp', 'variance', 'warnings', 'weekofyear', 'when', 'window', 'year']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'id' with function func_regex\n" ] }, { "ename": "AttributeError", "evalue": "module 'pyspark.sql.functions' has no attribute 'match'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"id\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36mmatch\u001b[1;34m(self, input_cols, regex, value, output_cols)\u001b[0m\n\u001b[0;32m 966\u001b[0m \u001b[1;31m# df = self.parent\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 967\u001b[0m return self.apply(input_cols, func=func_regex, args=[regex, value], output_cols=output_cols,\n\u001b[1;32m--> 968\u001b[1;33m meta_action=Actions.REPLACE_REGEX.value)\n\u001b[0m\u001b[0;32m 969\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 970\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_cols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msearch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreplace_by\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msearch_by\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"chars\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_cols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, input_cols, func, func_return_type, args, func_type, when, filter_col_by_dtypes, output_cols, skip_output_cols_processing, meta_action, default, mode)\u001b[0m\n\u001b[0;32m 220\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0modf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 221\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0minput_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_col\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwithColumn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwhen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 223\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmeta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreserve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeta_action\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_col\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 224\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnew\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36mexpr\u001b[1;34m(_when)\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 212\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mexpr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_when\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 213\u001b[1;33m \u001b[0mmain_query\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maudf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc_return_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 214\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mwhen\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 215\u001b[0m \u001b[1;31m# Use the data type to filter the query\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\audf.py\u001b[0m in \u001b[0;36mabstract_udf\u001b[1;34m(col, func, func_return_type, args, func_type)\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;31m# print(\"-----------------df_func(_func, args)(col)\", df_func(_func, args)(col))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mdf_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_func\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 52\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\audf.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(col_name)\u001b[0m\n\u001b[0;32m 87\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 88\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 89\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 90\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 91\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36mfunc_regex\u001b[1;34m(_input_cols, attr)\u001b[0m\n\u001b[0;32m 961\u001b[0m \u001b[0m_replace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 962\u001b[0m \u001b[0mdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 963\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_input_cols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_search\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_replace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 964\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 965\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: module 'pyspark.sql.functions' has no attribute 'match'" ] } ], "source": [ "df.cols.match(\"id\")" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'name' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'nametype' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'recclass' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'fall' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to\n", "INFO:optimus:Using 'column_exp' to process column 'GeoLocation' with function _cast_to\n" ] }, { "data": { "text/plain": [ "{'year': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323},\n", " {'value': '01/01/1979 12:00:00 AM', 'count': 3046},\n", " {'value': '01/01/1998 12:00:00 AM', 'count': 2697},\n", " {'value': '01/01/2006 12:00:00 AM', 'count': 2456},\n", " {'value': '01/01/1988 12:00:00 AM', 'count': 2296},\n", " {'value': '01/01/2002 12:00:00 AM', 'count': 2078},\n", " {'value': '01/01/2004 12:00:00 AM', 'count': 1940},\n", " {'value': '01/01/2000 12:00:00 AM', 'count': 1792},\n", " {'value': '01/01/1997 12:00:00 AM', 'count': 1696},\n", " {'value': '01/01/1999 12:00:00 AM', 'count': 1691}],\n", " 'reclat': [{'value': None, 'count': 7315},\n", " {'value': 0.0, 'count': 6438},\n", " {'value': -71.5, 'count': 4761},\n", " {'value': -84.0, 'count': 3040},\n", " {'value': -72.0, 'count': 1506},\n", " {'value': -79.68333, 'count': 1130},\n", " {'value': -76.71667, 'count': 680},\n", " {'value': -76.18333, 'count': 539},\n", " {'value': -84.21667, 'count': 263},\n", " {'value': -86.36667, 'count': 226}],\n", " 'reclong': [{'value': None, 'count': 7315},\n", " {'value': 0.0, 'count': 6214},\n", " {'value': 35.66667, 'count': 4985},\n", " {'value': 168.0, 'count': 3040},\n", " {'value': 26.0, 'count': 1506},\n", " {'value': 159.75, 'count': 657},\n", " {'value': 159.66667, 'count': 637},\n", " {'value': 157.16667, 'count': 542},\n", " {'value': 155.75, 'count': 473},\n", " {'value': 160.5, 'count': 263}],\n", " 'GeoLocation': [{'value': None, 'count': 7315},\n", " {'value': '(0.000000, 0.000000)', 'count': 6214},\n", " {'value': '(-71.500000, 35.666670)', 'count': 4761},\n", " {'value': '(-84.000000, 168.000000)', 'count': 3040},\n", " {'value': '(-72.000000, 26.000000)', 'count': 1505},\n", " {'value': '(-79.683330, 159.750000)', 'count': 657},\n", " {'value': '(-76.716670, 159.666670)', 'count': 637},\n", " {'value': '(-76.183330, 157.166670)', 'count': 539},\n", " {'value': '(-79.683330, 155.750000)', 'count': 473},\n", " {'value': '(-84.216670, 160.500000)', 'count': 263}],\n", " 'recclass': [{'value': 'L6', 'count': 8285},\n", " {'value': 'H5', 'count': 7142},\n", " {'value': 'L5', 'count': 4796},\n", " {'value': 'H6', 'count': 4528},\n", " {'value': 'H4', 'count': 4211},\n", " {'value': 'LL5', 'count': 2766},\n", " {'value': 'LL6', 'count': 2043},\n", " {'value': 'L4', 'count': 1253},\n", " {'value': 'H4/5', 'count': 428},\n", " {'value': 'CM2', 'count': 416}],\n", " 'name': [{'value': 'Achiras', 'count': 1},\n", " {'value': 'Adhi Kot', 'count': 1},\n", " {'value': 'Aguada', 'count': 1},\n", " {'value': 'Aguila Blanca', 'count': 1},\n", " {'value': 'Aïr', 'count': 1},\n", " {'value': 'Akaba', 'count': 1},\n", " {'value': 'Akwanga', 'count': 1},\n", " {'value': 'Alais', 'count': 1},\n", " {'value': 'Alberta', 'count': 1},\n", " {'value': 'Alby sur Chéran', 'count': 1}],\n", " 'fall': [{'value': 'Found', 'count': 44609},\n", " {'value': 'Fell', 'count': 1107}],\n", " 'id': [{'value': 1, 'count': 1},\n", " {'value': 379, 'count': 1},\n", " {'value': 417, 'count': 1},\n", " {'value': 423, 'count': 1},\n", " {'value': 425, 'count': 1},\n", " {'value': 427, 'count': 1},\n", " {'value': 433, 'count': 1},\n", " {'value': 447, 'count': 1},\n", " {'value': 453, 'count': 1},\n", " {'value': 461, 'count': 1}],\n", " 'mass (g)': [{'value': 1.3, 'count': 171},\n", " {'value': 1.2, 'count': 140},\n", " {'value': 1.4, 'count': 138},\n", " {'value': None, 'count': 131},\n", " {'value': 2.1, 'count': 130},\n", " {'value': 2.4, 'count': 126},\n", " {'value': 1.6, 'count': 120},\n", " {'value': 0.5, 'count': 119},\n", " {'value': 1.1, 'count': 116},\n", " {'value': 3.8, 'count': 114}],\n", " 'nametype': [{'value': 'Valid', 'count': 45641},\n", " {'value': 'Relict', 'count': 75}]}" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.frequency(\"*\")" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'id' with function to_string\n", "INFO:optimus:Using 'column_exp' to process column 'id' with function _match\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "data_type str\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "data_type str\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to\n" ] }, { "ename": "KeyError", "evalue": "'frequency'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# df.cols.count_na()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# df.ext.to_dict()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"id\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;31m# df.cols.schema_dtype(\"*\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\extension.py\u001b[0m in \u001b[0;36mprofile\u001b[1;34m(self, columns, bins, output, flush, size)\u001b[0m\n\u001b[0;32m 659\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcompute\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 660\u001b[0m \u001b[0mhist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmismatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfreq_uniques\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmismatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfreq_uniques\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 661\u001b[1;33m \u001b[0mupdated_columns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols_to_profile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmismatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfreq_uniques\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 662\u001b[0m \u001b[0mprofiler_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mupdate_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprofiler_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupdated_columns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 663\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\extension.py\u001b[0m in \u001b[0;36mmerge\u001b[1;34m(_columns, _hist, _freq, _mismatch, _dtypes, _freq_uniques)\u001b[0m\n\u001b[0;32m 646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 647\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m_freq\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 648\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0m_col_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_freq\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"frequency\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 649\u001b[0m \u001b[0m_f\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_col_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"stats\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"frequency\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"values\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 650\u001b[0m \u001b[0m_f\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_col_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"stats\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"count_uniques\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"count_uniques\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'frequency'" ] } ], "source": [ "# df.ext.head()\n", "# df.rows.limit(10)\n", "# df.ext.profile()\n", "# df.cols.count_na()\n", "# df.ext.to_dict()\n", "df.ext.profile(\"id\")\n", "# df.cols.schema_dtype(\"*\")" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name 45716\n", "id 45716\n", "nametype 45716\n", "recclass 45716\n", "mass (g) 45585\n", "fall 45716\n", "year 45428\n", "reclat 38401\n", "reclong 38401\n", "GeoLocation 38401\n", "dtype: int64" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.data.tocount()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# import fastnumbers\n", "# # fastnumbers.fast_float(\"1\")\n", "# import numpy as np\n", "# fastnumbers.fast_float(\"1.2\", default=np.nan)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'id' with function to_float\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
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2 partition(s)
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name
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1 (string)
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id
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2 (float)
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nametype
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3 (string)
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recclass
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4 (string)
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mass (g)
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5 (double)
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fall
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6 (string)
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year
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7 (string)
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reclat
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8 (double)
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reclong
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9 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
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GeoLocation
\n", "
10 (string)
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\n", "
\n", "
\n", " \n", " 1.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
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\n", " \n", " 21.0\n", " \n", "
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\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1880⋅12:00:00⋅AM\n", " \n", "
\n", "
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\n", "
\n", "
\n", " \n", " 6.08333\n", " \n", "
\n", "
\n", "
\n", " \n", " (50.775000,⋅6.083330)\n", " \n", "
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\n", "
\n", "
\n", " \n", " 2.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 720.0\n", " \n", "
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\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1951⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
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\n", "
\n", "
\n", " \n", " 10.23333\n", " \n", "
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\n", "
\n", " \n", " (56.183330,⋅10.233330)\n", " \n", "
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\n", "
\n", "
\n", " \n", " 6.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 107000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
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\n", "
\n", " \n", " 01/01/1952⋅12:00:00⋅AM\n", " \n", "
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\n", "
\n", " \n", " 54.21667\n", " \n", "
\n", "
\n", "
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\n", "
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\n", " \n", " (54.216670,⋅-113.000000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulco\n", " \n", "
\n", "
\n", "
\n", " \n", " 10.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulcoite\n", " \n", "
\n", "
\n", "
\n", " \n", " 1914.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1976⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 16.88333\n", " \n", "
\n", "
\n", "
\n", " \n", " -99.9\n", " \n", "
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\n", "
\n", " \n", " (16.883330,⋅-99.900000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Achiras\n", " \n", "
\n", "
\n", "
\n", " \n", " 370.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 780.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1902⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -33.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.95\n", " \n", "
\n", "
\n", "
\n", " \n", " (-33.166670,⋅-64.950000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adhi⋅Kot\n", " \n", "
\n", "
\n", "
\n", " \n", " 379.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 4239.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1919⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 32.1\n", " \n", "
\n", "
\n", "
\n", " \n", " 71.8\n", " \n", "
\n", "
\n", "
\n", " \n", " (32.100000,⋅71.800000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
\n", "
\n", "
\n", " \n", " 390.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " LL3-6\n", " \n", "
\n", "
\n", "
\n", " \n", " 910.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1949⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.83333\n", " \n", "
\n", "
\n", "
\n", " \n", " 95.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.833330,⋅95.166670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Agen\n", " \n", "
\n", "
\n", "
\n", " \n", " 392.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 30000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1814⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " 0.61667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.216670,⋅0.616670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguada\n", " \n", "
\n", "
\n", "
\n", " \n", " 398.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
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\n", " \n", " 1620.0\n", " \n", "
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\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1930⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -31.6\n", " \n", "
\n", "
\n", "
\n", " \n", " -65.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (-31.600000,⋅-65.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
\n", "
\n", " \n", " 417.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L\n", " \n", "
\n", "
\n", "
\n", " \n", " 1440.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1920⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -30.86667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.55\n", " \n", "
\n", "
\n", "
\n", " \n", " (-30.866670,⋅-64.550000)\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# df.cols.to_float(\"id\")\n", "# df.cols.to_integer(\"id\")\n", "df.cols.to_float(\"id\")\n", "# df.cols.to_string(\"id\")\n", "# df.cols.to_integer(\"id\")\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'name': 'Święcany',\n", " 'id': 57458,\n", " 'nametype': 'Valid',\n", " 'recclass': 'Winonaite',\n", " 'mass (g)': 60000000.0,\n", " 'fall': 'Found',\n", " 'year': '12/28/0860 12:00:00 AM',\n", " 'reclat': 81.16667,\n", " 'reclong': 354.47333,\n", " 'GeoLocation': '(9.916670, 13.983330)'}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.max(\"*\")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameidnametyperecclassmass (g)fallyearreclatreclongGeoLocation
0Aachen1ValidL521.0Fell01/01/1880 12:00:00 AM50.775006.08333(50.775000, 6.083330)
1Aarhus2ValidH6720.0Fell01/01/1951 12:00:00 AM56.1833310.23333(56.183330, 10.233330)
2Abee6ValidEH4107000.0Fell01/01/1952 12:00:00 AM54.21667-113.00000(54.216670, -113.000000)
3Acapulco10ValidAcapulcoite1914.0Fell01/01/1976 12:00:00 AM16.88333-99.90000(16.883330, -99.900000)
4Achiras370ValidL6780.0Fell01/01/1902 12:00:00 AM-33.16667-64.95000(-33.166670, -64.950000)
5Adhi Kot379ValidEH44239.0Fell01/01/1919 12:00:00 AM32.1000071.80000(32.100000, 71.800000)
6Adzhi-Bogdo (stone)390ValidLL3-6910.0Fell01/01/1949 12:00:00 AM44.8333395.16667(44.833330, 95.166670)
7Agen392ValidH530000.0Fell01/01/1814 12:00:00 AM44.216670.61667(44.216670, 0.616670)
8Aguada398ValidL61620.0Fell01/01/1930 12:00:00 AM-31.60000-65.23333(-31.600000, -65.233330)
9Aguila Blanca417ValidL1440.0Fell01/01/1920 12:00:00 AM-30.86667-64.55000(-30.866670, -64.550000)
\n", "
" ], "text/plain": [ " name id nametype recclass mass (g) fall \\\n", "0 Aachen 1 Valid L5 21.0 Fell \n", "1 Aarhus 2 Valid H6 720.0 Fell \n", "2 Abee 6 Valid EH4 107000.0 Fell \n", "3 Acapulco 10 Valid Acapulcoite 1914.0 Fell \n", "4 Achiras 370 Valid L6 780.0 Fell \n", "5 Adhi Kot 379 Valid EH4 4239.0 Fell \n", "6 Adzhi-Bogdo (stone) 390 Valid LL3-6 910.0 Fell \n", "7 Agen 392 Valid H5 30000.0 Fell \n", "8 Aguada 398 Valid L6 1620.0 Fell \n", "9 Aguila Blanca 417 Valid L 1440.0 Fell \n", "\n", " year reclat reclong GeoLocation \n", "0 01/01/1880 12:00:00 AM 50.77500 6.08333 (50.775000, 6.083330) \n", "1 01/01/1951 12:00:00 AM 56.18333 10.23333 (56.183330, 10.233330) \n", "2 01/01/1952 12:00:00 AM 54.21667 -113.00000 (54.216670, -113.000000) \n", "3 01/01/1976 12:00:00 AM 16.88333 -99.90000 (16.883330, -99.900000) \n", "4 01/01/1902 12:00:00 AM -33.16667 -64.95000 (-33.166670, -64.950000) \n", "5 01/01/1919 12:00:00 AM 32.10000 71.80000 (32.100000, 71.800000) \n", "6 01/01/1949 12:00:00 AM 44.83333 95.16667 (44.833330, 95.166670) \n", "7 01/01/1814 12:00:00 AM 44.21667 0.61667 (44.216670, 0.616670) \n", "8 01/01/1930 12:00:00 AM -31.60000 -65.23333 (-31.600000, -65.233330) \n", "9 01/01/1920 12:00:00 AM -30.86667 -64.55000 (-30.866670, -64.550000) " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.data.limit(10).toPandas()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------------------+---+--------+-----------+--------+----+--------------------+---------+---------+--------------------+\n", "| name| id|nametype| recclass|mass (g)|fall| year| reclat| reclong| GeoLocation|\n", "+-------------------+---+--------+-----------+--------+----+--------------------+---------+---------+--------------------+\n", "| Aachen| 1| Valid| L5| 21.0|Fell|01/01/1880 12:00:...| 50.775| 6.08333|(50.775000, 6.083...|\n", "| Aarhus| 2| Valid| H6| 720.0|Fell|01/01/1951 12:00:...| 56.18333| 10.23333|(56.183330, 10.23...|\n", "| Abee| 6| Valid| EH4|107000.0|Fell|01/01/1952 12:00:...| 54.21667| -113.0|(54.216670, -113....|\n", "| Acapulco| 10| Valid|Acapulcoite| 1914.0|Fell|01/01/1976 12:00:...| 16.88333| -99.9|(16.883330, -99.9...|\n", "| Achiras|370| Valid| L6| 780.0|Fell|01/01/1902 12:00:...|-33.16667| -64.95|(-33.166670, -64....|\n", "| Adhi Kot|379| Valid| EH4| 4239.0|Fell|01/01/1919 12:00:...| 32.1| 71.8|(32.100000, 71.80...|\n", "|Adzhi-Bogdo (stone)|390| Valid| LL3-6| 910.0|Fell|01/01/1949 12:00:...| 44.83333| 95.16667|(44.833330, 95.16...|\n", "| Agen|392| Valid| H5| 30000.0|Fell|01/01/1814 12:00:...| 44.21667| 0.61667|(44.216670, 0.616...|\n", "| Aguada|398| Valid| L6| 1620.0|Fell|01/01/1930 12:00:...| -31.6|-65.23333|(-31.600000, -65....|\n", "| Aguila Blanca|417| Valid| L| 1440.0|Fell|01/01/1920 12:00:...|-30.86667| -64.55|(-30.866670, -64....|\n", "+-------------------+---+--------+-----------+--------+----+--------------------+---------+---------+--------------------+\n", "only showing top 10 rows\n", "\n" ] }, { "data": { "text/plain": [ "NoneType" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.data.show(10)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DataFrame[name: string, id: int, nametype: string, recclass: string, mass (g): double, fall: string, year: string, reclat: double, reclong: double, GeoLocation: string]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.rows.limit(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "df.ext.profile()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# df.ext.profile()\n", "type(df.rows.limit(10).ext.to_pandas())" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Row(name='Aachen', id=1, nametype='Valid', recclass='L5', mass (g)=21.0, fall='Fell', year='01/01/1880 12:00:00 AM', reclat=50.775, reclong=6.08333, GeoLocation='(50.775000, 6.083330)'),\n", " Row(name='Aarhus', id=2, nametype='Valid', recclass='H6', mass (g)=720.0, fall='Fell', year='01/01/1951 12:00:00 AM', reclat=56.18333, reclong=10.23333, GeoLocation='(56.183330, 10.233330)'),\n", " Row(name='Abee', id=6, nametype='Valid', recclass='EH4', mass (g)=107000.0, fall='Fell', year='01/01/1952 12:00:00 AM', reclat=54.21667, reclong=-113.0, GeoLocation='(54.216670, -113.000000)'),\n", " Row(name='Acapulco', id=10, nametype='Valid', recclass='Acapulcoite', mass (g)=1914.0, fall='Fell', year='01/01/1976 12:00:00 AM', reclat=16.88333, reclong=-99.9, GeoLocation='(16.883330, -99.900000)'),\n", " Row(name='Achiras', id=370, nametype='Valid', recclass='L6', mass (g)=780.0, fall='Fell', year='01/01/1902 12:00:00 AM', reclat=-33.16667, reclong=-64.95, GeoLocation='(-33.166670, -64.950000)'),\n", " Row(name='Adhi Kot', id=379, nametype='Valid', recclass='EH4', mass (g)=4239.0, fall='Fell', year='01/01/1919 12:00:00 AM', reclat=32.1, reclong=71.8, GeoLocation='(32.100000, 71.800000)'),\n", " Row(name='Adzhi-Bogdo (stone)', id=390, nametype='Valid', recclass='LL3-6', mass (g)=910.0, fall='Fell', year='01/01/1949 12:00:00 AM', reclat=44.83333, reclong=95.16667, GeoLocation='(44.833330, 95.166670)'),\n", " Row(name='Agen', id=392, nametype='Valid', recclass='H5', mass (g)=30000.0, fall='Fell', year='01/01/1814 12:00:00 AM', reclat=44.21667, reclong=0.61667, GeoLocation='(44.216670, 0.616670)'),\n", " Row(name='Aguada', id=398, nametype='Valid', recclass='L6', mass (g)=1620.0, fall='Fell', year='01/01/1930 12:00:00 AM', reclat=-31.6, reclong=-65.23333, GeoLocation='(-31.600000, -65.233330)'),\n", " Row(name='Aguila Blanca', id=417, nametype='Valid', recclass='L', mass (g)=1440.0, fall='Fell', year='01/01/1920 12:00:00 AM', reclat=-30.86667, reclong=-64.55, GeoLocation='(-30.866670, -64.550000)')]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.head()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "45716" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.rows.count()\n", "# len(df.data)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dask import dataframe as dd\n", "dd.compute(df[[\"id\"]].data.min())[0][0]\n", "# df[\"id\"].data.min().head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
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1 partition(s)
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\n", "
name
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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id
\n", "
2 (int64)
\n", "
\n", " \n", " not nullable\n", " \n", "
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nametype
\n", "
3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
recclass
\n", "
4 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
mass (g)
\n", "
5 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
fall
\n", "
6 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
year
\n", "
7 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclat
\n", "
8 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclong
\n", "
9 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
GeoLocation
\n", "
10 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " Aachen\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L5\n", " \n", "
\n", "
\n", "
\n", " \n", " 21\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1880⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 50.775000\n", " \n", "
\n", "
\n", "
\n", " \n", " 6.083330\n", " \n", "
\n", "
\n", "
\n", " \n", " (50.775000,⋅6.083330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aarhus\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 720\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1951⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 56.183330\n", " \n", "
\n", "
\n", "
\n", " \n", " 10.233330\n", " \n", "
\n", "
\n", "
\n", " \n", " (56.183330,⋅10.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Abee\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 107000\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1952⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 54.216670\n", " \n", "
\n", "
\n", "
\n", " \n", " -113.000000\n", " \n", "
\n", "
\n", "
\n", " \n", " (54.216670,⋅-113.000000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulco\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " valid\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulcoite\n", " \n", "
\n", "
\n", "
\n", " \n", " 1914\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1976⋅12:00:00⋅AM\n", " \n", "
\n", "
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Viewing 10 of 45716 rows / 10 columns
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{'value': '(-71.500000, 35.666670)', 'count': 4761},\n", " {'value': '(-84.000000, 168.000000)', 'count': 3040},\n", " {'value': '(-72.000000, 26.000000)', 'count': 1505},\n", " {'value': '(-79.683330, 159.750000)', 'count': 657},\n", " {'value': '(-76.716670, 159.666670)', 'count': 637},\n", " {'value': '(-76.183330, 157.166670)', 'count': 539},\n", " {'value': '(-79.683330, 155.750000)', 'count': 473},\n", " {'value': '(-84.216670, 160.500000)', 'count': 263},\n", " {'value': '(-86.366670, -70.000000)', 'count': 226},\n", " {'value': '(0.000000, 35.666670)', 'count': 223},\n", " {'value': '(-86.716670, -141.500000)', 'count': 217},\n", " {'value': '(-85.666670, 175.000000)', 'count': 185},\n", " {'value': '(-24.850000, -70.533330)', 'count': 178},\n", " {'value': '(-85.633330, -68.700000)', 'count': 105},\n", " {'value': '(-72.954880, 160.473280)', 'count': 74},\n", " {'value': '(58.583330, 13.433330)', 'count': 64},\n", " {'value': '(-76.716670, 159.333330)', 'count': 42},\n", " {'value': '(-72.778890, 75.313610)', 'count': 39},\n", " {'value': '(-72.983890, 75.246390)', 'count': 38},\n", " {'value': '(-83.250000, 157.000000)', 'count': 34},\n", " {'value': '(29.916670, -5.583330)', 'count': 33},\n", " {'value': '(-82.500000, 155.500000)', 'count': 32},\n", " {'value': '(-72.998890, 75.187220)', 'count': 32},\n", " {'value': '(-25.233330, -69.716670)', 'count': 32},\n", " {'value': '(-84.266670, 161.500000)', 'count': 30},\n", " {'value': '(-84.283330, 161.083330)', 'count': 29},\n", " {'value': '(-73.083330, 75.200000)', 'count': 28},\n", " {'value': '(-72.983889, 75.246389)', 'count': 27},\n", " {'value': '(-80.066670, 156.383330)', 'count': 27},\n", " {'value': '(34.083330, -103.500000)', 'count': 27},\n", " {'value': '(27.166670, -9.500000)', 'count': 24}],\n", " 'count_uniques': 17101},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}}},\n", " 'file_name': 'data/Meteorite_Landings.csv',\n", " 'summary': {'cols_count': 10,\n", " 'rows_count': 45716,\n", " 'dtypes_list': ['object', 'int64'],\n", " 'total_count_dtypes': 2,\n", " 'missing_count': 0}}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "df.ext.profile(\"*\")\n", "# df.cols.max(\"id\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 1 columns
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1 (int64)
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Viewing 10 of 45716 rows / 1 columns
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1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "1\n" ] }, { "data": { "text/plain": [ "nan" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from optimus.engines.dask.functions import DaskFunctions\n", "DaskFunctions().min(df[\"id\"])" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'Series' object has no attribute 'ext'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36mhist\u001b[1;34m(self, columns, buckets, compute)\u001b[0m\n\u001b[0;32m 1591\u001b[0m _columns}\n\u001b[0;32m 1592\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1593\u001b[1;33m \u001b[0m_min\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtidy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1594\u001b[0m \u001b[0m_max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtidy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1595\u001b[0m \u001b[0m_bins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_bins_col\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_min\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36mmin\u001b[1;34m(self, columns, tidy, compute)\u001b[0m\n\u001b[0;32m 761\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtidy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 762\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 763\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg_exprs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtidy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtidy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 764\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 765\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtidy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36magg_exprs\u001b[1;34m(self, columns, funcs, compute, tidy, *args)\u001b[0m\n\u001b[0;32m 740\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 741\u001b[0m \u001b[0mfuncs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mval_to_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfuncs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 742\u001b[1;33m \u001b[0mfuncs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m}\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcol_name\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 743\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexec_agg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfuncs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 744\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 740\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 741\u001b[0m \u001b[0mfuncs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mval_to_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfuncs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 742\u001b[1;33m \u001b[0mfuncs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m}\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcol_name\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 743\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexec_agg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfuncs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 744\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\functions.py\u001b[0m in \u001b[0;36mmin\u001b[1;34m(series)\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_float\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'ext'" ] } ], "source": [ "df.cols.hist()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# df.functions\n", "# from pyspark.sql import functions as F\n", "# dir(F)\n", "df.data.replace(\"name\",\"A\",\"a\").show()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s)
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\n", "
name
\n", "
1 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
id
\n", "
2 (int)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
nametype
\n", "
3 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
recclass
\n", "
4 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
mass (g)
\n", "
5 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
fall
\n", "
6 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
year
\n", "
7 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclat
\n", "
8 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclong
\n", "
9 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
GeoLocation
\n", "
10 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " Aachen\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L5\n", " \n", "
\n", "
\n", "
\n", " \n", " 21.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1880⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 50.775\n", " \n", "
\n", "
\n", "
\n", " \n", " 6.08333\n", " \n", "
\n", "
\n", "
\n", " \n", " (50.775000,⋅6.083330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aarhus\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 720.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1951⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 56.18333\n", " \n", "
\n", "
\n", "
\n", " \n", " 10.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (56.183330,⋅10.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Abee\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 107000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1952⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 54.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " -113.0\n", " \n", "
\n", "
\n", "
\n", " \n", " (54.216670,⋅-113.000000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulco\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulcoite\n", " \n", "
\n", "
\n", "
\n", " \n", " 1914.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1976⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 16.88333\n", " \n", "
\n", "
\n", "
\n", " \n", " -99.9\n", " \n", "
\n", "
\n", "
\n", " \n", " (16.883330,⋅-99.900000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Achiras\n", " \n", "
\n", "
\n", "
\n", " \n", " 370\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 780.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1902⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -33.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.95\n", " \n", "
\n", "
\n", "
\n", " \n", " (-33.166670,⋅-64.950000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adhi⋅Kot\n", " \n", "
\n", "
\n", "
\n", " \n", " 379\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 4239.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1919⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 32.1\n", " \n", "
\n", "
\n", "
\n", " \n", " 71.8\n", " \n", "
\n", "
\n", "
\n", " \n", " (32.100000,⋅71.800000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
\n", "
\n", "
\n", " \n", " 390\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " LL3-6\n", " \n", "
\n", "
\n", "
\n", " \n", " 910.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1949⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.83333\n", " \n", "
\n", "
\n", "
\n", " \n", " 95.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.833330,⋅95.166670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Agen\n", " \n", "
\n", "
\n", "
\n", " \n", " 392\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 30000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1814⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " 0.61667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.216670,⋅0.616670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguada\n", " \n", "
\n", "
\n", "
\n", " \n", " 398\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 1620.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1930⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -31.6\n", " \n", "
\n", "
\n", "
\n", " \n", " -65.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (-31.600000,⋅-65.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
\n", "
\n", " \n", " 417\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L\n", " \n", "
\n", "
\n", "
\n", " \n", " 1440.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1920⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -30.86667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.55\n", " \n", "
\n", "
\n", "
\n", " \n", " (-30.866670,⋅-64.550000)\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Row(name='Aachen', id=1, nametype='Valid', recclass='L5', mass (g)=21.0, fall='Fell', year='01/01/1880 12:00:00 AM', reclat=50.775, reclong=6.08333, GeoLocation='(50.775000, 6.083330)'),\n", " Row(name='Aarhus', id=2, nametype='Valid', recclass='H6', mass (g)=720.0, fall='Fell', year='01/01/1951 12:00:00 AM', reclat=56.18333, reclong=10.23333, GeoLocation='(56.183330, 10.233330)'),\n", " Row(name='Abee', id=6, nametype='Valid', recclass='EH4', mass (g)=107000.0, fall='Fell', year='01/01/1952 12:00:00 AM', reclat=54.21667, reclong=-113.0, GeoLocation='(54.216670, -113.000000)'),\n", " Row(name='Acapulco', id=10, nametype='Valid', recclass='Acapulcoite', mass (g)=1914.0, fall='Fell', year='01/01/1976 12:00:00 AM', reclat=16.88333, reclong=-99.9, GeoLocation='(16.883330, -99.900000)'),\n", " Row(name='Achiras', id=370, nametype='Valid', recclass='L6', mass (g)=780.0, fall='Fell', year='01/01/1902 12:00:00 AM', reclat=-33.16667, reclong=-64.95, GeoLocation='(-33.166670, -64.950000)'),\n", " Row(name='Adhi Kot', id=379, nametype='Valid', recclass='EH4', mass (g)=4239.0, fall='Fell', year='01/01/1919 12:00:00 AM', reclat=32.1, reclong=71.8, GeoLocation='(32.100000, 71.800000)'),\n", " Row(name='Adzhi-Bogdo (stone)', id=390, nametype='Valid', recclass='LL3-6', mass (g)=910.0, fall='Fell', year='01/01/1949 12:00:00 AM', reclat=44.83333, reclong=95.16667, GeoLocation='(44.833330, 95.166670)'),\n", " Row(name='Agen', id=392, nametype='Valid', recclass='H5', mass (g)=30000.0, fall='Fell', year='01/01/1814 12:00:00 AM', reclat=44.21667, reclong=0.61667, GeoLocation='(44.216670, 0.616670)'),\n", " Row(name='Aguada', id=398, nametype='Valid', recclass='L6', mass (g)=1620.0, fall='Fell', year='01/01/1930 12:00:00 AM', reclat=-31.6, reclong=-65.23333, GeoLocation='(-31.600000, -65.233330)'),\n", " Row(name='Aguila Blanca', id=417, nametype='Valid', recclass='L', mass (g)=1440.0, fall='Fell', year='01/01/1920 12:00:00 AM', reclat=-30.86667, reclong=-64.55, GeoLocation='(-30.866670, -64.550000)')]" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.head()" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'list' object has no attribute 'ext'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\extension.py\u001b[0m in \u001b[0;36mprofile\u001b[1;34m(self, columns, bins, output, flush, size)\u001b[0m\n\u001b[0;32m 591\u001b[0m \u001b[0mnumeric_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 592\u001b[0m \u001b[0mstring_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 593\u001b[1;33m \u001b[0mcols_and_inferred_dtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0modf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_profiler_dtypes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols_to_profile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 594\u001b[0m \u001b[0mcompute\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 595\u001b[0m \u001b[0mmismatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0modf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount_mismatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols_and_inferred_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36minfer_profiler_dtypes\u001b[1;34m(self, columns)\u001b[0m\n\u001b[0;32m 1710\u001b[0m \u001b[1;31m# Infer the data type from every element in a Series.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1711\u001b[0m \u001b[1;31m# FIX: could this be vectorized\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1712\u001b[1;33m \u001b[0msample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtotal_preview_rows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_pandas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1713\u001b[0m \u001b[0mpdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msample\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapplymap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mInfer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse_pandas\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1714\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'ext'" ] } ], "source": [ "%%time\n", "df.ext.profile()\n", "# df.cols.frequency()" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'Ext' object has no attribute 'cast'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"nametype\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"int\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: 'Ext' object has no attribute 'cast'" ] } ], "source": [ "df.cols.cast(\"nametype\",\"int\")" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'self' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrequency\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36mfrequency\u001b[1;34m(self, columns, n, percentage, total_rows)\u001b[0m\n\u001b[0;32m 1632\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1633\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnon_compatible_columns\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1634\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnon_compatible_columns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"str\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1635\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1636\u001b[0m freq = (df.select(columns).rdd\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36mcast\u001b[1;34m(input_cols, dtype, output_cols, columns)\u001b[0m\n\u001b[0;32m 353\u001b[0m \u001b[1;31m# Parse params\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 354\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 355\u001b[1;33m \u001b[0minput_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_cols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 356\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_cols\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_one_element\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_cols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 357\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'self' is not defined" ] } ], "source": [ "df.cols.frequency()" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_search_and_replace_by {'3': '10', '2': '10'}\n", "multiple_replace .func_chars_words..multiple_replace at 0x0000027178962A68>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'name' with function multiple_replace\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "expression_func .expression_func at 0x0000027178330A68>\n", "inner .expression_func..inner at 0x00000271791243A8>\n", "AAAAAAAAAAAA ({'3': '10', '2': '10'},)\n", "func .func_chars_words..multiple_replace at 0x0000027178962A68>\n", "__search_and_replace_by11111111111 {'3': '10', '2': '10'}\n", "result11111111----- name\n", "-----------------df_func(_func, args)(col) name\n", "inner .expression_func..inner at 0x0000027178962288>\n", "AAAAAAAAAAAA ({'3': '10', '2': '10'},)\n", "func .func_chars_words..multiple_replace at 0x0000027178962A68>\n", "__search_and_replace_by11111111111 {'3': '10', '2': '10'}\n", "result11111111----- name\n", "main_query name\n" ] }, { "ename": "AssertionError", "evalue": "col should be Column", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# from pyspark.sql import functions as F\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m# df.data.withColumn(\"name\", F.col(\"name\"))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"name\"\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msearch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreplace_by\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1034\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1035\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmeta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreserve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mActions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mREPLACE\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_col\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36mfunc_chars_words\u001b[1;34m(_df, _input_col, _output_col, _search, _replace_by)\u001b[0m\n\u001b[0;32m 993\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"_search_and_replace_by\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0m_search_and_replace_by\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 994\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"multiple_replace\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmultiple_replace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 995\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_input_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultiple_replace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"str\"\u001b[0m\u001b[1;33m,\u001b[0m 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skip_output_cols_processing, meta_action, default, mode)\u001b[0m\n\u001b[0;32m 225\u001b[0m \u001b[1;31m# print(\"expr(when)\",type(expr(when)),expr(when))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 227\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwithColumn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwhen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 228\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmeta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreserve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeta_action\u001b[0m\u001b[1;33m,\u001b[0m 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1998\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwithColumn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolName\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msql_ctx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1999\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAssertionError\u001b[0m: col should be Column" ] } ], "source": [ "# df.cols.lower(\"name\")\n", "# from pyspark.sql import functions as F\n", "# df.data.withColumn(\"name\", F.col(\"name\"))\n", "df.cols.replace(\"name\", [\"3\", 2], 10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "ename": "AssertionError", "evalue": "col should be Column", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwithColumn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"name\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"name\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pyspark\\sql\\dataframe.py\u001b[0m in \u001b[0;36mwithColumn\u001b[1;34m(self, colName, col)\u001b[0m\n\u001b[0;32m 1995\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1996\u001b[0m \"\"\"\n\u001b[1;32m-> 1997\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mColumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"col should be Column\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1998\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwithColumn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolName\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msql_ctx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1999\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAssertionError\u001b[0m: col should be Column" ] } ], "source": [ "df.data.withColumn(\"name\",\"name\")" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_search_and_replace_by {'A': 'a'}\n", "input_col, output_col name name .expr at 0x00000271789A1AF8>\n" ] }, { "ename": "AssertionError", "evalue": "col should be Column", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"name\"\u001b[0m\u001b[1;33m,\u001b[0m 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"\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36mfunc_chars_words\u001b[1;34m(_df, _input_col, _output_col, _search, _replace_by)\u001b[0m\n\u001b[0;32m 987\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 988\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"_search_and_replace_by\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0m_search_and_replace_by\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 989\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_input_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultiple_replace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"str\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0m_search_and_replace_by\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_cols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_output_col\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 990\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 991\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfunc_full\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_input_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_output_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_search\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_replace_by\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, input_cols, func, func_return_type, args, func_type, when, filter_col_by_dtypes, output_cols, skip_output_cols_processing, meta_action, default, mode)\u001b[0m\n\u001b[0;32m 221\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0minput_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_col\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 222\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"input_col, output_col\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 223\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwithColumn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_col\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 224\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmeta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreserve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeta_action\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_col\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 225\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnew\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pyspark\\sql\\dataframe.py\u001b[0m in \u001b[0;36mwithColumn\u001b[1;34m(self, colName, col)\u001b[0m\n\u001b[0;32m 1995\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1996\u001b[0m \"\"\"\n\u001b[1;32m-> 1997\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mColumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"col should be Column\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1998\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwithColumn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolName\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msql_ctx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1999\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAssertionError\u001b[0m: col should be Column" ] } ], "source": [ "df.cols.replace(\"name\", \"A\",\"a\")\n", "# df.cols.lower(\"name\")" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "input_col, output_col name name .expr at 0x000002717888EEE8>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'name' with function to_integer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "input_col, output_col id id .expr at 0x000002717888EEE8>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'id' with function to_integer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "input_col, output_col nametype nametype .expr at 0x000002717888EEE8>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'nametype' with function to_integer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "input_col, output_col recclass recclass .expr at 0x000002717888EEE8>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'recclass' with function to_integer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "input_col, output_col mass (g) mass (g) .expr at 0x000002717888EEE8>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function to_integer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "input_col, output_col fall fall .expr at 0x000002717888EEE8>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'fall' with function to_integer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "input_col, output_col year year .expr at 0x000002717888EEE8>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'year' with function to_integer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "input_col, output_col reclat reclat .expr at 0x000002717888EEE8>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'reclat' with function to_integer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "input_col, output_col reclong reclong .expr at 0x000002717888EEE8>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'reclong' with function to_integer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "input_col, output_col GeoLocation GeoLocation .expr at 0x000002717888EEE8>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'column_exp' to process column 'GeoLocation' with function to_integer\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s)
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name
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1 (int)
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id
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2 (int)
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nametype
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3 (int)
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recclass
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4 (int)
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mass (g)
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5 (int)
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fall
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6 (int)
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\n", " \n", " not nullable\n", " \n", "
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year
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7 (int)
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reclat
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8 (int)
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\n", " \n", " not nullable\n", " \n", "
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reclong
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9 (int)
\n", "
\n", " \n", " not nullable\n", " \n", "
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GeoLocation
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10 (int)
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Viewing 10 of 45716 rows / 10 columns
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2 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.to_integer()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'cudf'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"name\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"a\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"b\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36mreplace\u001b[1;34m(self, input_cols, search, replace_by, search_by, output_cols)\u001b[0m\n\u001b[0;32m 1020\u001b[0m \u001b[0mfilter_dtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0modf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconstants\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNUMERIC_TYPES\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1021\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1022\u001b[1;33m \u001b[0minput_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0modf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_cols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilter_by_column_dtypes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilter_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1023\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1024\u001b[0m \u001b[0mcheck_column_numbers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_cols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"*\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\columns.py\u001b[0m in \u001b[0;36mparse_columns\u001b[1;34m(df, cols_args, get_args, is_regex, filter_by_column_dtypes, accepts_missing_cols, invert)\u001b[0m\n\u001b[0;32m 167\u001b[0m \u001b[1;31m# if columns value is * get all dataframes columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 168\u001b[0m \u001b[0mattrs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m \u001b[0mdf_columns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumns_names\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 171\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_regex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\columns.py\u001b[0m in \u001b[0;36mcolumns_names\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m 137\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mis_pandas_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_dask_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 138\u001b[0m \u001b[0mcolumns_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m \u001b[1;32melif\u001b[0m \u001b[0mis_cudf_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 141\u001b[0m \u001b[0mcolumns_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\check.py\u001b[0m in \u001b[0;36mis_cudf_dataframe\u001b[1;34m(value)\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 74\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mis_cudf_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 75\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mcudf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mCUDFDataFrame\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 76\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCUDFDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'cudf'" ] } ], "source": [ "df.cols.replace(\"name\",\"a\",\"b\")" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "scrolled": false }, "outputs": [ { "ename": "TypeError", "evalue": "percentile() missing 1 required positional argument: 'relative_error'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# df.cols.lower(\"name\").cols.upper(\"nametype\").ext.display()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpercentile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"id\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36mpercentile\u001b[1;34m(self, columns, values, relative_error, tidy, compute)\u001b[0m\n\u001b[0;32m 779\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 780\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0.25\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.75\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 781\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg_exprs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpercentile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrelative_error\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtidy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtidy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 782\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 783\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrelative_error\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mRELATIVE_ERROR\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtidy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36magg_exprs\u001b[1;34m(self, columns, funcs, compute, tidy, *args)\u001b[0m\n\u001b[0;32m 562\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;32mreturn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 563\u001b[0m \"\"\"\n\u001b[1;32m--> 564\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexec_agg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate_exprs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 565\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 566\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mexec_agg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexprs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36mcreate_exprs\u001b[1;34m(self, columns, funcs, *args)\u001b[0m\n\u001b[0;32m 548\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_exprs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 549\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 550\u001b[1;33m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_agg_exprs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexprs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 551\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 552\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\spark\\columns.py\u001b[0m in \u001b[0;36m_agg_exprs\u001b[1;34m(_funcs)\u001b[0m\n\u001b[0;32m 541\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 542\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0m_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_col_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_func\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 543\u001b[1;33m \u001b[0magg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0m_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 544\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0magg\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 545\u001b[0m \u001b[0mfunc_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_beautify_col_names\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_func\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: percentile() missing 1 required positional argument: 'relative_error'" ] } ], "source": [ "# df.cols.lower(\"name\").cols.upper(\"nametype\").ext.display()\n", "df.cols.percentile(\"id\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s)
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\n", "
name
\n", "
1 (string)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
id
\n", "
2 (int)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
nametype
\n", "
3 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
recclass
\n", "
4 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
mass (g)
\n", "
5 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
fall
\n", "
6 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
year
\n", "
7 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclat
\n", "
8 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclong
\n", "
9 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
GeoLocation
\n", "
10 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " Aachen\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L5\n", " \n", "
\n", "
\n", "
\n", " \n", " 21.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1880⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 50.775\n", " \n", "
\n", "
\n", "
\n", " \n", " 6.08333\n", " \n", "
\n", "
\n", "
\n", " \n", " (50.775000,⋅6.083330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aarhus\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 720.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1951⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 56.18333\n", " \n", "
\n", "
\n", "
\n", " \n", " 10.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (56.183330,⋅10.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Abee\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 107000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1952⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 54.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " -113.0\n", " \n", "
\n", "
\n", "
\n", " \n", " (54.216670,⋅-113.000000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulco\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulcoite\n", " \n", "
\n", "
\n", "
\n", " \n", " 1914.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1976⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 16.88333\n", " \n", "
\n", "
\n", "
\n", " \n", " -99.9\n", " \n", "
\n", "
\n", "
\n", " \n", " (16.883330,⋅-99.900000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Achiras\n", " \n", "
\n", "
\n", "
\n", " \n", " 370\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 780.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1902⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -33.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.95\n", " \n", "
\n", "
\n", "
\n", " \n", " (-33.166670,⋅-64.950000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adhi⋅Kot\n", " \n", "
\n", "
\n", "
\n", " \n", " 379\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 4239.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1919⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 32.1\n", " \n", "
\n", "
\n", "
\n", " \n", " 71.8\n", " \n", "
\n", "
\n", "
\n", " \n", " (32.100000,⋅71.800000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
\n", "
\n", "
\n", " \n", " 390\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " LL3-6\n", " \n", "
\n", "
\n", "
\n", " \n", " 910.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1949⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.83333\n", " \n", "
\n", "
\n", "
\n", " \n", " 95.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.833330,⋅95.166670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Agen\n", " \n", "
\n", "
\n", "
\n", " \n", " 392\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 30000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1814⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " 0.61667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.216670,⋅0.616670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguada\n", " \n", "
\n", "
\n", "
\n", " \n", " 398\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 1620.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1930⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -31.6\n", " \n", "
\n", "
\n", "
\n", " \n", " -65.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (-31.600000,⋅-65.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
\n", "
\n", " \n", " 417\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L\n", " \n", "
\n", "
\n", "
\n", " \n", " 1440.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1920⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -30.86667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.55\n", " \n", "
\n", "
\n", "
\n", " \n", " (-30.866670,⋅-64.550000)\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'file_name': 'data/Meteorite_Landings.csv'}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.meta.get()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 1 columns
\n", "
2 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
name
\n", "
1 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " Aachen\n", " \n", "
\n", "
\n", "
\n", " \n", " Aarhus\n", " \n", "
\n", "
\n", "
\n", " \n", " Abee\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulco\n", " \n", "
\n", "
\n", "
\n", " \n", " Achiras\n", " \n", "
\n", "
\n", "
\n", " \n", " Adhi⋅Kot\n", " \n", "
\n", "
\n", "
\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
\n", "
\n", "
\n", " \n", " Agen\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguada\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 45716 rows / 1 columns
\n", "
2 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.select(\"name\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s)
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\n", "
name
\n", "
1 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
id
\n", "
2 (int)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
nametype
\n", "
3 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
recclass
\n", "
4 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
mass (g)
\n", "
5 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
fall
\n", "
6 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
year
\n", "
7 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclat
\n", "
8 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclong
\n", "
9 (double)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
GeoLocation
\n", "
10 (string)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " Aachen\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L5\n", " \n", "
\n", "
\n", "
\n", " \n", " 21.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1880⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 50.775\n", " \n", "
\n", "
\n", "
\n", " \n", " 6.08333\n", " \n", "
\n", "
\n", "
\n", " \n", " (50.775000,⋅6.083330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aarhus\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 720.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1951⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 56.18333\n", " \n", "
\n", "
\n", "
\n", " \n", " 10.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (56.183330,⋅10.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Abee\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 107000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1952⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 54.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " -113.0\n", " \n", "
\n", "
\n", "
\n", " \n", " (54.216670,⋅-113.000000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulco\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulcoite\n", " \n", "
\n", "
\n", "
\n", " \n", " 1914.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1976⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 16.88333\n", " \n", "
\n", "
\n", "
\n", " \n", " -99.9\n", " \n", "
\n", "
\n", "
\n", " \n", " (16.883330,⋅-99.900000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Achiras\n", " \n", "
\n", "
\n", "
\n", " \n", " 370\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 780.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1902⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -33.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.95\n", " \n", "
\n", "
\n", "
\n", " \n", " (-33.166670,⋅-64.950000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adhi⋅Kot\n", " \n", "
\n", "
\n", "
\n", " \n", " 379\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 4239.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1919⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 32.1\n", " \n", "
\n", "
\n", "
\n", " \n", " 71.8\n", " \n", "
\n", "
\n", "
\n", " \n", " (32.100000,⋅71.800000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
\n", "
\n", "
\n", " \n", " 390\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " LL3-6\n", " \n", "
\n", "
\n", "
\n", " \n", " 910.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1949⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.83333\n", " \n", "
\n", "
\n", "
\n", " \n", " 95.16667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.833330,⋅95.166670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Agen\n", " \n", "
\n", "
\n", "
\n", " \n", " 392\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 30000.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1814⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.21667\n", " \n", "
\n", "
\n", "
\n", " \n", " 0.61667\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.216670,⋅0.616670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguada\n", " \n", "
\n", "
\n", "
\n", " \n", " 398\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 1620.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1930⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -31.6\n", " \n", "
\n", "
\n", "
\n", " \n", " -65.23333\n", " \n", "
\n", "
\n", "
\n", " \n", " (-31.600000,⋅-65.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
\n", "
\n", " \n", " 417\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L\n", " \n", "
\n", "
\n", "
\n", " \n", " 1440.0\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1920⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -30.86667\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.55\n", " \n", "
\n", "
\n", "
\n", " \n", " (-30.866670,⋅-64.550000)\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
2 partition(s) <class 'optimus.new_optimus.SparkDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['name',\n", " 'id',\n", " 'nametype',\n", " 'recclass',\n", " 'mass (g)',\n", " 'fall',\n", " 'year',\n", " 'reclat',\n", " 'reclong',\n", " 'GeoLocation']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.names()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:optimus:Using 'pandas_udf' to process column 'name' with function _remove_accents\n" ] }, { "ename": "Py4JJavaError", "evalue": "An error occurred while calling o301.showString.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 11.0 failed 1 times, most recent failure: Lost task 0.0 in stage 11.0 (TID 16, localhost, executor driver): java.lang.IllegalArgumentException\r\n\tat java.nio.ByteBuffer.allocate(ByteBuffer.java:334)\r\n\tat org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543)\r\n\tat org.apache.arrow.vector.ipc.message.MessageChannelReader.readNext(MessageChannelReader.java:58)\r\n\tat org.apache.arrow.vector.ipc.ArrowStreamReader.readSchema(ArrowStreamReader.java:132)\r\n\tat org.apache.arrow.vector.ipc.ArrowReader.initialize(ArrowReader.java:181)\r\n\tat org.apache.arrow.vector.ipc.ArrowReader.ensureInitialized(ArrowReader.java:172)\r\n\tat org.apache.arrow.vector.ipc.ArrowReader.getVectorSchemaRoot(ArrowReader.java:65)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:162)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)\r\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)\r\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.(ArrowEvalPythonExec.scala:102)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:100)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:123)\r\n\tat org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\n\nDriver stacktrace:\r\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1925)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1913)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1912)\r\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\r\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\r\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1912)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948)\r\n\tat scala.Option.foreach(Option.scala:257)\r\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:948)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2146)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2095)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2084)\r\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:759)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)\r\n\tat org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)\r\n\tat org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389)\r\n\tat org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)\r\n\tat org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)\r\n\tat org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)\r\n\tat org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)\r\n\tat org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withAction(Dataset.scala:3369)\r\n\tat org.apache.spark.sql.Dataset.head(Dataset.scala:2550)\r\n\tat org.apache.spark.sql.Dataset.take(Dataset.scala:2764)\r\n\tat org.apache.spark.sql.Dataset.getRows(Dataset.scala:254)\r\n\tat org.apache.spark.sql.Dataset.showString(Dataset.scala:291)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\r\n\tat java.lang.reflect.Method.invoke(Method.java:498)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\nCaused by: java.lang.IllegalArgumentException\r\n\tat java.nio.ByteBuffer.allocate(ByteBuffer.java:334)\r\n\tat org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543)\r\n\tat org.apache.arrow.vector.ipc.message.MessageChannelReader.readNext(MessageChannelReader.java:58)\r\n\tat org.apache.arrow.vector.ipc.ArrowStreamReader.readSchema(ArrowStreamReader.java:132)\r\n\tat org.apache.arrow.vector.ipc.ArrowReader.initialize(ArrowReader.java:181)\r\n\tat org.apache.arrow.vector.ipc.ArrowReader.ensureInitialized(ArrowReader.java:172)\r\n\tat org.apache.arrow.vector.ipc.ArrowReader.getVectorSchemaRoot(ArrowReader.java:65)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:162)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)\r\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)\r\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.(ArrowEvalPythonExec.scala:102)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:100)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:123)\r\n\tat org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\r\n\t... 1 more\r\n", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mPy4JJavaError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove_accents\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"name\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pyspark\\sql\\dataframe.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(self, n, truncate, vertical)\u001b[0m\n\u001b[0;32m 379\u001b[0m \"\"\"\n\u001b[0;32m 380\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtruncate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mtruncate\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 381\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshowString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvertical\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 382\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 383\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshowString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtruncate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvertical\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\py4j\\java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m 1255\u001b[0m \u001b[0manswer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1256\u001b[0m return_value = get_return_value(\n\u001b[1;32m-> 1257\u001b[1;33m answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[0;32m 1258\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1259\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pyspark\\sql\\utils.py\u001b[0m in \u001b[0;36mdeco\u001b[1;34m(*a, **kw)\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 64\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\py4j\\protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[1;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[0;32m 326\u001b[0m raise Py4JJavaError(\n\u001b[0;32m 327\u001b[0m \u001b[1;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 328\u001b[1;33m format(target_id, \".\", name), value)\n\u001b[0m\u001b[0;32m 329\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 330\u001b[0m raise Py4JError(\n", "\u001b[1;31mPy4JJavaError\u001b[0m: An error occurred while calling o301.showString.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 11.0 failed 1 times, most recent failure: Lost task 0.0 in stage 11.0 (TID 16, localhost, executor driver): java.lang.IllegalArgumentException\r\n\tat java.nio.ByteBuffer.allocate(ByteBuffer.java:334)\r\n\tat org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543)\r\n\tat org.apache.arrow.vector.ipc.message.MessageChannelReader.readNext(MessageChannelReader.java:58)\r\n\tat org.apache.arrow.vector.ipc.ArrowStreamReader.readSchema(ArrowStreamReader.java:132)\r\n\tat org.apache.arrow.vector.ipc.ArrowReader.initialize(ArrowReader.java:181)\r\n\tat org.apache.arrow.vector.ipc.ArrowReader.ensureInitialized(ArrowReader.java:172)\r\n\tat org.apache.arrow.vector.ipc.ArrowReader.getVectorSchemaRoot(ArrowReader.java:65)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:162)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)\r\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)\r\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.(ArrowEvalPythonExec.scala:102)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:100)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:123)\r\n\tat org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\n\nDriver stacktrace:\r\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1925)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1913)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1912)\r\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\r\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\r\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1912)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948)\r\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948)\r\n\tat scala.Option.foreach(Option.scala:257)\r\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:948)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2146)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2095)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2084)\r\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:759)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)\r\n\tat org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)\r\n\tat org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389)\r\n\tat org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)\r\n\tat org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)\r\n\tat org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)\r\n\tat org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)\r\n\tat org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withAction(Dataset.scala:3369)\r\n\tat org.apache.spark.sql.Dataset.head(Dataset.scala:2550)\r\n\tat org.apache.spark.sql.Dataset.take(Dataset.scala:2764)\r\n\tat org.apache.spark.sql.Dataset.getRows(Dataset.scala:254)\r\n\tat org.apache.spark.sql.Dataset.showString(Dataset.scala:291)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\r\n\tat java.lang.reflect.Method.invoke(Method.java:498)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\nCaused by: java.lang.IllegalArgumentException\r\n\tat java.nio.ByteBuffer.allocate(ByteBuffer.java:334)\r\n\tat org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543)\r\n\tat org.apache.arrow.vector.ipc.message.MessageChannelReader.readNext(MessageChannelReader.java:58)\r\n\tat org.apache.arrow.vector.ipc.ArrowStreamReader.readSchema(ArrowStreamReader.java:132)\r\n\tat org.apache.arrow.vector.ipc.ArrowReader.initialize(ArrowReader.java:181)\r\n\tat org.apache.arrow.vector.ipc.ArrowReader.ensureInitialized(ArrowReader.java:172)\r\n\tat org.apache.arrow.vector.ipc.ArrowReader.getVectorSchemaRoot(ArrowReader.java:65)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:162)\r\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)\r\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)\r\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.(ArrowEvalPythonExec.scala:102)\r\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:100)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)\r\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:310)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:123)\r\n\tat org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\r\n\t... 1 more\r\n" ] } ], "source": [ "df.cols.remove_accents(\"name\").data.show()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "# df.cols.to_datetime(\"%Y/%m/%d\")" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Row(name='Aachen', id=1, nametype='Valid', recclass='L5', mass (g)=21.0, fall='Fell', year='01/01/1880 12:00:00 AM', reclat=50.775, reclong=6.08333, GeoLocation='(50.775000, 6.083330)')" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.data.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# df.cols.to_datetime(\"birth\", \"%Y/%m/%d\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# df.cols.lower()\n", "# df.cols.to_datetime(\"birth\", format= \"%Y/%m/%d\").cols.year(\"birth\")\n", "# df.cols.to_datetime(\"birth\", format= \"%Y/%m/%d\")\n", "\n", "# df.cols.year(\"birth\", format= \"%Y/%m/%d\")\n", "# df.cols.lower(\"firstName\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# df.cols.select(\"firstName\").cols.to_string().cols.remove_accents().cols.replace(\n", "# search=\"L\", replace_by=\"aaa\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 1 columns
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1 partition(s)
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name
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1 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", " \n", " Aachen\n", " \n", "
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\n", " \n", " Abee\n", " \n", "
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\n", " \n", " Acapulco\n", " \n", "
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\n", " \n", " Achiras\n", " \n", "
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\n", " \n", " Adhi⋅Kot\n", " \n", "
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\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
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\n", " \n", " Agen\n", " \n", "
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\n", " \n", " Aguada\n", " \n", "
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\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
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Viewing 10 of 45716 rows / 1 columns
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1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"name\"]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 2.76 s\n" ] }, { "data": { "text/plain": [ "{'name': {'values': [{'value': 'Ulllll ######', 'count': 5105},\n", " {'value': 'Ullllllll Ulllll ####', 'count': 4064},\n", " {'value': 'Ullll Ullllllll Ullll #####', 'count': 3393},\n", " {'value': 'Ullll Ullll #####', 'count': 2682},\n", " {'value': 'Ullll Ullllllll ######', 'count': 2462},\n", " {'value': 'Ulllll #####', 'count': 2304},\n", " {'value': 'Ulllllll Ullllll #####', 'count': 2177},\n", " {'value': 'Ullll ######', 'count': 1263},\n", " {'value': 'UlUll Ulllllll #####', 'count': 1152},\n", " {'value': 'Ulllll ###', 'count': 1076}],\n", " 'more': True,\n", " 'updated': 1604903513.3142393}}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "df.cols.pattern_counts(\"name\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'file_name': 'data/Meteorite_Landings.csv',\n", " 'profile': {'columns': {'name': {'patterns': {'values': [{'value': 'Ulllll ######',\n", " 'count': 5105},\n", " {'value': 'Ullllllll Ulllll ####', 'count': 4064},\n", " {'value': 'Ullll Ullllllll Ullll #####', 'count': 3393},\n", " {'value': 'Ullll Ullll #####', 'count': 2682},\n", " {'value': 'Ullll Ullllllll ######', 'count': 2462},\n", " {'value': 'Ulllll #####', 'count': 2304},\n", " {'value': 'Ulllllll Ullllll #####', 'count': 2177},\n", " {'value': 'Ullll ######', 'count': 1263},\n", " {'value': 'UlUll Ulllllll #####', 'count': 1152},\n", " {'value': 'Ulllll ###', 'count': 1076}],\n", " 'more': True,\n", " 'updated': 1604903513.3142393}}}}}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.meta.get()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dask Series Structure:\n", "npartitions=1\n", " object\n", " ...\n", "Name: name, dtype: object\n", "Dask Name: getitem, 7 tasks" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "df.cols.select(\"name\").cols.to_string().data[\"name\"]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'frequency': {'name': {'values': [{'value': 'Święcany', 'count': 1},\n", " {'value': 'Hammadah al Hamra 012', 'count': 1},\n", " {'value': 'Hammadah al Hamra 045', 'count': 1},\n", " {'value': 'Hammadah al Hamra 046', 'count': 1},\n", " {'value': 'Hammadah al Hamra 047', 'count': 1},\n", " {'value': 'Hammadah al Hamra 048', 'count': 1},\n", " {'value': 'Hammadah al Hamra 049', 'count': 1},\n", " {'value': 'Hammadah al Hamra 050', 'count': 1},\n", " {'value': 'Hammadah al Hamra 051', 'count': 1},\n", " {'value': 'Hammadah al Hamra 043', 'count': 1},\n", " {'value': 'Hammadah al Hamra 032', 'count': 1},\n", " {'value': 'Hammadah al Hamra 031', 'count': 1},\n", " {'value': 'Hammadah al Hamra 030', 'count': 1},\n", " {'value': 'Hammadah al Hamra 009', 'count': 1},\n", " {'value': 'Hammadah al Hamra 010', 'count': 1},\n", " {'value': 'Hammadah al Hamra 011', 'count': 1},\n", " {'value': 'Hammadah al Hamra 013', 'count': 1},\n", " {'value': 'Hammadah al Hamra 007', 'count': 1},\n", " {'value': 'Hammadah al Hamra 014', 'count': 1},\n", " {'value': 'Hammadah al Hamra 017', 'count': 1},\n", " {'value': 'Hammadah al Hamra 018', 'count': 1},\n", " {'value': 'Hammadah al Hamra 019', 'count': 1},\n", " {'value': 'Hammadah al Hamra 020', 'count': 1},\n", " {'value': 'Hammadah al Hamra 021', 'count': 1},\n", " {'value': 'Hammadah al Hamra 022', 'count': 1},\n", " {'value': 'Hammadah al Hamra 023', 'count': 1},\n", " {'value': 'Hammadah al Hamra 024', 'count': 1},\n", " {'value': 'Hammadah al Hamra 025', 'count': 1},\n", " {'value': 'Hammadah al Hamra 026', 'count': 1},\n", " {'value': 'Hammadah al Hamra 027', 'count': 1},\n", " {'value': 'Hammadah al Hamra 028', 'count': 1},\n", " {'value': 'Hammadah al Hamra 029', 'count': 1},\n", " {'value': 'Hammadah al Hamra 044', 'count': 1}]},\n", " 'id': {'values': [{'value': '9999', 'count': 1},\n", " {'value': '23983', 'count': 1},\n", " {'value': '24014', 'count': 1},\n", " {'value': '24015', 'count': 1},\n", " {'value': '24016', 'count': 1},\n", " {'value': '24017', 'count': 1},\n", " {'value': '24018', 'count': 1},\n", " {'value': '24019', 'count': 1},\n", " {'value': '24020', 'count': 1},\n", " {'value': '24012', 'count': 1},\n", " {'value': '240', 'count': 1},\n", " {'value': '24', 'count': 1},\n", " {'value': '23999', 'count': 1},\n", " {'value': '23980', 'count': 1},\n", " {'value': '23981', 'count': 1},\n", " {'value': '23982', 'count': 1},\n", " {'value': '23984', 'count': 1},\n", " {'value': '23978', 'count': 1},\n", " {'value': '23985', 'count': 1},\n", " {'value': '23986', 'count': 1},\n", " {'value': '23987', 'count': 1},\n", " {'value': '23988', 'count': 1},\n", " {'value': '23989', 'count': 1},\n", " {'value': '23990', 'count': 1},\n", " {'value': '23991', 'count': 1},\n", " {'value': '23992', 'count': 1},\n", " {'value': '23993', 'count': 1},\n", " {'value': '23994', 'count': 1},\n", " {'value': '23995', 'count': 1},\n", " {'value': '23996', 'count': 1},\n", " {'value': '23997', 'count': 1},\n", " {'value': '23998', 'count': 1},\n", " {'value': '24013', 'count': 1}]},\n", " 'nametype': {'values': [{'value': 'Valid', 'count': 45641},\n", " {'value': 'Relict', 'count': 75}]},\n", " 'recclass': {'values': [{'value': 'L6', 'count': 8285},\n", " {'value': 'H5', 'count': 7142},\n", " {'value': 'L5', 'count': 4796},\n", " {'value': 'H6', 'count': 4528},\n", " {'value': 'H4', 'count': 4211},\n", " {'value': 'LL5', 'count': 2766},\n", " {'value': 'LL6', 'count': 2043},\n", " {'value': 'L4', 'count': 1253},\n", " {'value': 'H4/5', 'count': 428},\n", " {'value': 'CM2', 'count': 416},\n", " {'value': 'H3', 'count': 386},\n", " {'value': 'L3', 'count': 365},\n", " {'value': 'CO3', 'count': 335},\n", " {'value': 'Ureilite', 'count': 300},\n", " {'value': 'Iron, IIIAB', 'count': 285},\n", " {'value': 'LL4', 'count': 268},\n", " {'value': 'CV3', 'count': 256},\n", " {'value': 'Diogenite', 'count': 241},\n", " {'value': 'Howardite', 'count': 240},\n", " {'value': 'LL', 'count': 225},\n", " {'value': 'Eucrite', 'count': 221},\n", " {'value': 'Eucrite-pmict', 'count': 207},\n", " {'value': 'E3', 'count': 206},\n", " {'value': 'H5/6', 'count': 193},\n", " {'value': 'Mesosiderite', 'count': 137},\n", " {'value': 'CR2', 'count': 135},\n", " {'value': 'LL3', 'count': 128},\n", " {'value': 'EH3', 'count': 120},\n", " {'value': 'Iron, IIAB', 'count': 118},\n", " {'value': 'Iron, ungrouped', 'count': 113},\n", " {'value': 'H~5', 'count': 111},\n", " {'value': 'L5/6', 'count': 109},\n", " {'value': 'Eucrite-mmict', 'count': 108}]},\n", " 'mass (g)': {'values': [{'value': '1.3', 'count': 171},\n", " {'value': '1.2', 'count': 140},\n", " {'value': '1.4', 'count': 138},\n", " {'value': '', 'count': 131},\n", " {'value': '2.1', 'count': 130},\n", " {'value': '2.4', 'count': 126},\n", " {'value': '1.6', 'count': 120},\n", " {'value': '0.5', 'count': 119},\n", " {'value': '1.1', 'count': 116},\n", " {'value': '3.8', 'count': 114},\n", " {'value': '0.7', 'count': 111},\n", " {'value': '1.5', 'count': 111},\n", " {'value': '3.2', 'count': 109},\n", " {'value': '3.1', 'count': 109},\n", " {'value': '1.7', 'count': 109},\n", " {'value': '3', 'count': 108},\n", " {'value': '0.9', 'count': 108},\n", " {'value': '0.6', 'count': 108},\n", " {'value': '0.8', 'count': 107},\n", " {'value': '1.8', 'count': 104},\n", " {'value': '2.5', 'count': 103},\n", " {'value': '2.7', 'count': 102},\n", " {'value': '3.6', 'count': 96},\n", " {'value': '2', 'count': 96},\n", " {'value': '1', 'count': 96},\n", " {'value': '4.2', 'count': 95},\n", " {'value': '2.8', 'count': 93},\n", " {'value': '2.9', 'count': 93},\n", " {'value': '2.2', 'count': 92},\n", " {'value': '2.6', 'count': 91},\n", " {'value': '3.3', 'count': 88},\n", " {'value': '4.6', 'count': 86},\n", " {'value': '1.9', 'count': 86}]},\n", " 'fall': {'values': [{'value': 'Found', 'count': 44609},\n", " {'value': 'Fell', 'count': 1107}]},\n", " 'year': {'values': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323},\n", " {'value': '01/01/1979 12:00:00 AM', 'count': 3046},\n", " {'value': '01/01/1998 12:00:00 AM', 'count': 2697},\n", " {'value': '01/01/2006 12:00:00 AM', 'count': 2456},\n", " {'value': '01/01/1988 12:00:00 AM', 'count': 2296},\n", " {'value': '01/01/2002 12:00:00 AM', 'count': 2078},\n", " {'value': '01/01/2004 12:00:00 AM', 'count': 1940},\n", " {'value': '01/01/2000 12:00:00 AM', 'count': 1792},\n", " {'value': '01/01/1997 12:00:00 AM', 'count': 1696},\n", " {'value': '01/01/1999 12:00:00 AM', 'count': 1691},\n", " {'value': '01/01/2001 12:00:00 AM', 'count': 1650},\n", " {'value': '01/01/1990 12:00:00 AM', 'count': 1518},\n", " {'value': '01/01/2009 12:00:00 AM', 'count': 1497},\n", " {'value': '01/01/1986 12:00:00 AM', 'count': 1375},\n", " {'value': '01/01/2007 12:00:00 AM', 'count': 1189},\n", " {'value': '01/01/2010 12:00:00 AM', 'count': 1005},\n", " {'value': '01/01/1993 12:00:00 AM', 'count': 979},\n", " {'value': '01/01/2008 12:00:00 AM', 'count': 957},\n", " {'value': '01/01/1987 12:00:00 AM', 'count': 916},\n", " {'value': '01/01/1991 12:00:00 AM', 'count': 877},\n", " {'value': '01/01/2005 12:00:00 AM', 'count': 875},\n", " {'value': '01/01/1994 12:00:00 AM', 'count': 719},\n", " {'value': '01/01/2011 12:00:00 AM', 'count': 713},\n", " {'value': '01/01/1974 12:00:00 AM', 'count': 691},\n", " {'value': '01/01/1996 12:00:00 AM', 'count': 583},\n", " {'value': '01/01/1995 12:00:00 AM', 'count': 487},\n", " {'value': '01/01/1981 12:00:00 AM', 'count': 463},\n", " {'value': '01/01/1977 12:00:00 AM', 'count': 421},\n", " {'value': '01/01/1984 12:00:00 AM', 'count': 402},\n", " {'value': '01/01/1985 12:00:00 AM', 'count': 378},\n", " {'value': '01/01/1992 12:00:00 AM', 'count': 372},\n", " {'value': '01/01/1983 12:00:00 AM', 'count': 360},\n", " {'value': '01/01/1982 12:00:00 AM', 'count': 344}]},\n", " 'reclat': {'values': [{'value': '', 'count': 7315},\n", " {'value': '0.000000', 'count': 6438},\n", " {'value': '-71.500000', 'count': 4761},\n", " {'value': '-84.000000', 'count': 3040},\n", " {'value': '-72.000000', 'count': 1506},\n", " {'value': '-79.683330', 'count': 1130},\n", " {'value': '-76.716670', 'count': 680},\n", " {'value': '-76.183330', 'count': 539},\n", " {'value': '-84.216670', 'count': 263},\n", " {'value': '-86.366670', 'count': 226},\n", " {'value': '-86.716670', 'count': 217},\n", " {'value': '-85.666670', 'count': 185},\n", " {'value': '-24.850000', 'count': 178},\n", " {'value': '-85.633330', 'count': 108},\n", " {'value': '-72.954880', 'count': 74},\n", " {'value': '-72.778890', 'count': 69},\n", " {'value': '-72.983890', 'count': 67},\n", " {'value': '58.583330', 'count': 64},\n", " {'value': '-72.775000', 'count': 57},\n", " {'value': '-72.778330', 'count': 52},\n", " {'value': '-72.998890', 'count': 41},\n", " {'value': '-72.779170', 'count': 40},\n", " {'value': '34.083330', 'count': 40},\n", " {'value': '-72.782500', 'count': 39},\n", " {'value': '-72.983889', 'count': 37},\n", " {'value': '29.916670', 'count': 35},\n", " {'value': '-72.778610', 'count': 35},\n", " {'value': '-83.250000', 'count': 35},\n", " {'value': '-82.500000', 'count': 32},\n", " {'value': '-25.233330', 'count': 32},\n", " {'value': '-72.774720', 'count': 31},\n", " {'value': '-72.773610', 'count': 31},\n", " {'value': '-72.989720', 'count': 31}]},\n", " 'reclong': {'values': [{'value': '', 'count': 7315},\n", " 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'75.187220', 'count': 33},\n", " {'value': '155.500000', 'count': 32},\n", " {'value': '-69.716670', 'count': 32},\n", " {'value': '75.200000', 'count': 32},\n", " {'value': '75.340000', 'count': 31},\n", " {'value': '161.500000', 'count': 30},\n", " {'value': '75.246389', 'count': 30},\n", " {'value': '161.083330', 'count': 29},\n", " {'value': '156.383330', 'count': 27},\n", " {'value': '-69.766670', 'count': 24}]},\n", " 'GeoLocation': {'values': [{'value': '', 'count': 7315},\n", " {'value': '(0.000000, 0.000000)', 'count': 6214},\n", " {'value': '(-71.500000, 35.666670)', 'count': 4761},\n", " {'value': '(-84.000000, 168.000000)', 'count': 3040},\n", " {'value': '(-72.000000, 26.000000)', 'count': 1505},\n", " {'value': '(-79.683330, 159.750000)', 'count': 657},\n", " {'value': '(-76.716670, 159.666670)', 'count': 637},\n", " {'value': '(-76.183330, 157.166670)', 'count': 539},\n", " {'value': '(-79.683330, 155.750000)', 'count': 473},\n", " {'value': '(-84.216670, 160.500000)', 'count': 263},\n", " {'value': '(-86.366670, -70.000000)', 'count': 226},\n", " {'value': '(0.000000, 35.666670)', 'count': 223},\n", " {'value': '(-86.716670, -141.500000)', 'count': 217},\n", " {'value': '(-85.666670, 175.000000)', 'count': 185},\n", " {'value': '(-24.850000, -70.533330)', 'count': 178},\n", " {'value': '(-85.633330, -68.700000)', 'count': 105},\n", " {'value': '(-72.954880, 160.473280)', 'count': 74},\n", " {'value': '(58.583330, 13.433330)', 'count': 64},\n", " {'value': '(-76.716670, 159.333330)', 'count': 42},\n", " {'value': '(-72.778890, 75.313610)', 'count': 39},\n", " {'value': '(-72.983890, 75.246390)', 'count': 38},\n", " {'value': '(-83.250000, 157.000000)', 'count': 34},\n", " {'value': '(29.916670, -5.583330)', 'count': 33},\n", " {'value': '(-82.500000, 155.500000)', 'count': 32},\n", " {'value': '(-72.998890, 75.187220)', 'count': 32},\n", " {'value': '(-25.233330, -69.716670)', 'count': 32},\n", " {'value': '(-84.266670, 161.500000)', 'count': 30},\n", " {'value': '(-84.283330, 161.083330)', 'count': 29},\n", " {'value': '(-73.083330, 75.200000)', 'count': 28},\n", " {'value': '(-72.983889, 75.246389)', 'count': 27},\n", " {'value': '(-80.066670, 156.383330)', 'count': 27},\n", " {'value': '(34.083330, -103.500000)', 'count': 27},\n", " {'value': '(27.166670, -9.500000)', 'count': 24}]}}}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.frequency()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'file_name': 'data/Meteorite_Landings.csv'}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.meta.get()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'frequency': {'name': {'values': [{'value': 'False', 'count': 45716}]}}}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "col_name = \"name\"\n", "df.cols.select(col_name).cols.to_string().cols.match(col_name, r\"^\\d+\\.\\d$\").cols.frequency()\n", "\n", "# df.cols.select(col_name).cols.to_string().data[col_name].str.match(func[dtype]).value_counts()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'columns': {'name': {'patterns': {'values': [{'value': 'Ulllll ######',\n", " 'count': 5105},\n", " {'value': 'Ullllllll Ulllll ####', 'count': 4064},\n", " {'value': 'Ullll Ullllllll Ullll #####', 'count': 3393},\n", " {'value': 'Ullll Ullll #####', 'count': 2682},\n", " {'value': 'Ullll Ullllllll ######', 'count': 2462},\n", " {'value': 'Ulllll #####', 'count': 2304},\n", " {'value': 'Ulllllll Ullllll #####', 'count': 2177},\n", " {'value': 'Ullll ######', 'count': 1263},\n", " {'value': 'UlUll Ulllllll #####', 'count': 1152},\n", " {'value': 'Ulllll ###', 'count': 1076}],\n", " 'more': True,\n", " 'updated': 1604903513.3142393},\n", 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17101},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}}},\n", " 'file_name': 'data/Meteorite_Landings.csv',\n", " 'summary': {'cols_count': 10,\n", " 'rows_count': 45716,\n", " 'dtypes_list': ['object', 'int64'],\n", " 'total_count_dtypes': 2,\n", " 'missing_count': 0}},\n", " 'transformations': {'actions': []}}" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.meta.get()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Dask DataFrame Structure:
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nameidnametyperecclassmass (g)fallyearreclatreclongGeoLocation
npartitions=1
objectint64objectobjectobjectobjectobjectobjectobjectobject
..............................
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Dask Name: read-csv, 1 tasks
" ], "text/plain": [ "Dask DataFrame Structure:\n", " name id nametype recclass mass (g) fall year reclat reclong GeoLocation\n", "npartitions=1 \n", " object int64 object object object object object object object object\n", " ... ... ... ... ... ... ... ... ... ...\n", "Dask Name: read-csv, 1 tasks" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.data" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
\n", "
1 partition(s)
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\n", "
name
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1 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
id
\n", "
2 (int64)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
nametype
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3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
recclass
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4 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
mass (g)
\n", "
5 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
fall
\n", "
6 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
year
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7 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclat
\n", "
8 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
reclong
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9 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
GeoLocation
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10 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " Aachen\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L5\n", " \n", "
\n", "
\n", "
\n", " \n", " 21\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1880⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 50.775000\n", " \n", "
\n", "
\n", "
\n", " \n", " 6.083330\n", " \n", "
\n", "
\n", "
\n", " \n", " (50.775000,⋅6.083330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aarhus\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 720\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1951⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 56.183330\n", " \n", "
\n", "
\n", "
\n", " \n", " 10.233330\n", " \n", "
\n", "
\n", "
\n", " \n", " (56.183330,⋅10.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Abee\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 107000\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1952⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 54.216670\n", " \n", "
\n", "
\n", "
\n", " \n", " -113.000000\n", " \n", "
\n", "
\n", "
\n", " \n", " (54.216670,⋅-113.000000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulco\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulcoite\n", " \n", "
\n", "
\n", "
\n", " \n", " 1914\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1976⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 16.883330\n", " \n", "
\n", "
\n", "
\n", " \n", " -99.900000\n", " \n", "
\n", "
\n", "
\n", " \n", " (16.883330,⋅-99.900000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Achiras\n", " \n", "
\n", "
\n", "
\n", " \n", " 370\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 780\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1902⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -33.166670\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.950000\n", " \n", "
\n", "
\n", "
\n", " \n", " (-33.166670,⋅-64.950000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adhi⋅Kot\n", " \n", "
\n", "
\n", "
\n", " \n", " 379\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 4239\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1919⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 32.100000\n", " \n", "
\n", "
\n", "
\n", " \n", " 71.800000\n", " \n", "
\n", "
\n", "
\n", " \n", " (32.100000,⋅71.800000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
\n", "
\n", "
\n", " \n", " 390\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " LL3-6\n", " \n", "
\n", "
\n", "
\n", " \n", " 910\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1949⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.833330\n", " \n", "
\n", "
\n", "
\n", " \n", " 95.166670\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.833330,⋅95.166670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Agen\n", " \n", "
\n", "
\n", "
\n", " \n", " 392\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 30000\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1814⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.216670\n", " \n", "
\n", "
\n", "
\n", " \n", " 0.616670\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.216670,⋅0.616670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguada\n", " \n", "
\n", "
\n", "
\n", " \n", " 398\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 1620\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1930⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -31.600000\n", " \n", "
\n", "
\n", "
\n", " \n", " -65.233330\n", " \n", "
\n", "
\n", "
\n", " \n", " (-31.600000,⋅-65.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
\n", "
\n", " \n", " 417\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L\n", " \n", "
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\n", "
\n", " \n", " 1440\n", " \n", "
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\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1920⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -30.866670\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.550000\n", " \n", "
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\n", "
\n", " \n", " (-30.866670,⋅-64.550000)\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 45716 rows / 10 columns
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1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameidnametyperecclassmass (g)fallyearreclatreclongGeoLocation
0Aachen1ValidL521Fell01/01/1880 12:00:00 AM50.7750006.083330(50.775000, 6.083330)
1Aarhus2ValidH6720Fell01/01/1951 12:00:00 AM56.18333010.233330(56.183330, 10.233330)
2Abee6ValidEH4107000Fell01/01/1952 12:00:00 AM54.216670-113.000000(54.216670, -113.000000)
3Acapulco10ValidAcapulcoite1914Fell01/01/1976 12:00:00 AM16.883330-99.900000(16.883330, -99.900000)
4Achiras370ValidL6780Fell01/01/1902 12:00:00 AM-33.166670-64.950000(-33.166670, -64.950000)
5Adhi Kot379ValidEH44239Fell01/01/1919 12:00:00 AM32.10000071.800000(32.100000, 71.800000)
6Adzhi-Bogdo (stone)390ValidLL3-6910Fell01/01/1949 12:00:00 AM44.83333095.166670(44.833330, 95.166670)
7Agen392ValidH530000Fell01/01/1814 12:00:00 AM44.2166700.616670(44.216670, 0.616670)
8Aguada398ValidL61620Fell01/01/1930 12:00:00 AM-31.600000-65.233330(-31.600000, -65.233330)
9Aguila Blanca417ValidL1440Fell01/01/1920 12:00:00 AM-30.866670-64.550000(-30.866670, -64.550000)
\n", "
" ], "text/plain": [ " name id nametype recclass mass (g) fall \\\n", "0 Aachen 1 Valid L5 21 Fell \n", "1 Aarhus 2 Valid H6 720 Fell \n", "2 Abee 6 Valid EH4 107000 Fell \n", "3 Acapulco 10 Valid Acapulcoite 1914 Fell \n", "4 Achiras 370 Valid L6 780 Fell \n", "5 Adhi Kot 379 Valid EH4 4239 Fell \n", "6 Adzhi-Bogdo (stone) 390 Valid LL3-6 910 Fell \n", "7 Agen 392 Valid H5 30000 Fell \n", "8 Aguada 398 Valid L6 1620 Fell \n", "9 Aguila Blanca 417 Valid L 1440 Fell \n", "\n", " year reclat reclong GeoLocation \n", "0 01/01/1880 12:00:00 AM 50.775000 6.083330 (50.775000, 6.083330) \n", "1 01/01/1951 12:00:00 AM 56.183330 10.233330 (56.183330, 10.233330) \n", "2 01/01/1952 12:00:00 AM 54.216670 -113.000000 (54.216670, -113.000000) \n", "3 01/01/1976 12:00:00 AM 16.883330 -99.900000 (16.883330, -99.900000) \n", "4 01/01/1902 12:00:00 AM -33.166670 -64.950000 (-33.166670, -64.950000) \n", "5 01/01/1919 12:00:00 AM 32.100000 71.800000 (32.100000, 71.800000) \n", "6 01/01/1949 12:00:00 AM 44.833330 95.166670 (44.833330, 95.166670) \n", "7 01/01/1814 12:00:00 AM 44.216670 0.616670 (44.216670, 0.616670) \n", "8 01/01/1930 12:00:00 AM -31.600000 -65.233330 (-31.600000, -65.233330) \n", "9 01/01/1920 12:00:00 AM -30.866670 -64.550000 (-30.866670, -64.550000) " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.head(\"*\",10)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "df Dask Series Structure:\n", "npartitions=1\n", "GeoLocation int64\n", "year ...\n", "dtype: int64\n", "Dask Name: dataframe-sum-agg, 4 tasks\n" ] }, { "ename": "ModuleNotFoundError", "evalue": "No module named 'cudf'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\extension.py\u001b[0m in \u001b[0;36mprofile\u001b[1;34m(self, columns, bins, output, flush, size)\u001b[0m\n\u001b[0;32m 598\u001b[0m \u001b[0mcols_and_inferred_dtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0modf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_profiler_dtypes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols_to_profile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 599\u001b[0m \u001b[0mcompute\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 600\u001b[1;33m \u001b[0mmismatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0modf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount_mismatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols_and_inferred_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 601\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 602\u001b[0m \u001b[1;31m# Get with columns are numerical and does not have mismatch so we can calculate the histogram\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36mcount_mismatch\u001b[1;34m(self, columns_type, **kwargs)\u001b[0m\n\u001b[0;32m 1623\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1624\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1625\u001b[1;33m \u001b[0mnulls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnew\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1626\u001b[0m \u001b[0mtotal_rows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1627\u001b[0m \u001b[1;31m# TODO: Test this cudf.Series(cudf.core.column.string.cpp_is_integer(a[\"A\"]._column)) and fast_numbers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\extension.py\u001b[0m in \u001b[0;36mto_dict\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"df\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mcollect_as_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 80\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 81\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\functions.py\u001b[0m in \u001b[0;36mcollect_as_dict\u001b[1;34m(df, limit)\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[0mdict_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 57\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0many_dataframe_to_pandas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 59\u001b[0m \u001b[1;31m# if there is only an element in the dict just return the value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\converter.py\u001b[0m in \u001b[0;36many_dataframe_to_pandas\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m 112\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mis_dask_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 113\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdask_dataframe_to_pandas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 114\u001b[1;33m \u001b[1;32melif\u001b[0m \u001b[0mis_cudf_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_cudf_series\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 115\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcudf_to_pandas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 116\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mis_dask_cudf_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\check.py\u001b[0m in \u001b[0;36mis_cudf_dataframe\u001b[1;34m(value)\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mis_cudf_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mcudf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mCUDFDataFrame\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 75\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCUDFDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'cudf'" ] } ], "source": [ "df.ext.profile()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "distributed.worker - WARNING - Compute Failed\n", "Function: getitem\n", "args: ( name id nametype recclass mass (g) fall \\\n", "0 Aachen 1 Valid L5 21 Fell \n", "1 Aarhus 2 Valid H6 720 Fell \n", "2 Abee 6 Valid EH4 107000 Fell \n", "3 Acapulco 10 Valid Acapulcoite 1914 Fell \n", "4 Achiras 370 Valid L6 780 Fell \n", "... ... ... ... ... ... ... \n", "45711 Zillah 002 31356 Valid Eucrite 172 Found \n", "45712 Zinder 30409 Valid Pallasite, ungrouped 46 Found \n", "45713 Zlin 30410 Valid H4 3.3 Found \n", "45714 Zubkovsky 31357 Valid L6 2167 Found \n", "45715 Zulu Queen 30414 Valid L3.7 200 Found \n", "\n", " year reclat reclong \\\n", "0 01/01/1880 12:00:00 AM 50.77500\n", "kwargs: {}\n", "Exception: KeyError('birth')\n", "\n" ] }, { "ename": "KeyError", "evalue": "'birth'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2890\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2891\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2892\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'birth'", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"birth\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"%Y/%m/%d\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36myear\u001b[1;34m(self, input_cols, format, output_cols)\u001b[0m\n\u001b[0;32m 1220\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1221\u001b[0m return self.apply(input_cols, F().year, args=format, output_cols=output_cols, meta_action=Actions.YEAR.value,\n\u001b[1;32m-> 1222\u001b[1;33m mode=\"pandas\", set_index=True)\n\u001b[0m\u001b[0;32m 1223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1224\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmonth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_cols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m 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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2891\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2892\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2893\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2895\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'birth'" ] } ], "source": [ "df.cols.year(\"birth\", \"%Y/%m/%d\")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'firstName'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2890\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2891\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2892\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'firstName'", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# df.cols.lower(\"firstName\").data.head()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"firstName\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"lastName\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_cols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mright\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_cols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36mlower\u001b[1;34m(self, input_cols, output_cols)\u001b[0m\n\u001b[0;32m 1101\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_cols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_cols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1102\u001b[0m return self.apply(input_cols, F.lower, func_return_type=str,\n\u001b[1;32m-> 1103\u001b[1;33m output_cols=output_cols, meta_action=Actions.LOWER.value, mode=\"vectorized\")\n\u001b[0m\u001b[0;32m 1104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1105\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mupper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_cols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_cols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, input_cols, func, func_return_type, args, func_type, when, filter_col_by_dtypes, output_cols, skip_output_cols_processing, meta_action, mode, set_index, default)\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 212\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"vectorized\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"pandas\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 213\u001b[1;33m \u001b[0m_ddf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minput_col\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 215\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_dask_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_ddf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3529\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3530\u001b[0m \u001b[1;31m# error is raised from pandas\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3531\u001b[1;33m \u001b[0mmeta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_meta\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_extract_meta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3532\u001b[0m \u001b[0mdsk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpartitionwise_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moperator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3533\u001b[0m \u001b[0mgraph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mHighLevelGraph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_collections\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdsk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdependencies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 2900\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2901\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2903\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2904\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2891\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2892\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2893\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2895\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'firstName'" ] } ], "source": [ "# df.cols.lower(\"firstName\").data.head()\n", "df.cols.lower(\"firstName\").cols.upper(\"lastName\").cols.lower().cols.left(input_cols=\"*\", n=5).cols.right(input_cols=\"*\", n=3).ext.display()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 19 rows / 8 columns
\n", "
1 partition(s)
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\n", "
id
\n", "
1 (int64)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
firstName
\n", "
2 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
lastName
\n", "
3 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
billingId
\n", "
4 (int64)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
product
\n", "
5 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
price
\n", "
6 (int64)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
birth
\n", "
7 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
dummyCol
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8 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
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\n", "
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\n", "
\n", "
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\n", " \n", " 1950/07/08\n", " \n", "
\n", "
\n", "
\n", " \n", " gonna\n", " \n", "
\n", "
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\n", "
\n", "
\n", " \n", " NiELS\n", " \n", "
\n", "
\n", "
\n", " \n", " Böhr//((%%\n", " \n", "
\n", "
\n", "
\n", " \n", " 551\n", " \n", "
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\n", "
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\n", " \n", " 1990/07/09\n", " \n", "
\n", "
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\n", " \n", " 4\n", " \n", "
\n", "
\n", "
\n", " \n", " PAUL\n", " \n", "
\n", "
\n", "
\n", " \n", " dirac$\n", " \n", "
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\n", "
\n", " \n", " 521\n", " \n", "
\n", "
\n", "
\n", " \n", " pizza\n", " \n", "
\n", "
\n", "
\n", " \n", " 8\n", " \n", "
\n", "
\n", "
\n", " \n", " 1954/07/10\n", " \n", "
\n", "
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\n", " \n", " you\n", " \n", "
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\n", "
\n", " \n", " 5\n", " \n", "
\n", "
\n", "
\n", " \n", " Albert\n", " \n", "
\n", "
\n", "
\n", " \n", " Einstein\n", " \n", "
\n", "
\n", "
\n", " \n", " 634\n", " \n", "
\n", "
\n", "
\n", " \n", " pizza\n", " \n", "
\n", "
\n", "
\n", " \n", " 8\n", " \n", "
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\n", "
\n", " \n", " 1990/07/11\n", " \n", "
\n", "
\n", "
\n", " \n", " up\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Galileo\n", " \n", "
\n", "
\n", "
\n", " \n", " ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI\n", " \n", "
\n", "
\n", "
\n", " \n", " 672\n", " \n", "
\n", "
\n", "
\n", " \n", " arepa\n", " \n", "
\n", "
\n", "
\n", " \n", " 5\n", " \n", "
\n", "
\n", "
\n", " \n", " 1930/08/12\n", " \n", "
\n", "
\n", "
\n", " \n", " never\n", " \n", "
\n", "
\n", "
\n", " \n", " 7\n", " \n", "
\n", "
\n", "
\n", " \n", " CaRL\n", " \n", "
\n", "
\n", "
\n", " \n", " Ga%%%uss\n", " \n", "
\n", "
\n", "
\n", " \n", " 323\n", " \n", "
\n", "
\n", "
\n", " \n", " taco\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1970/07/13\n", " \n", "
\n", "
\n", "
\n", " \n", " gonna\n", " \n", "
\n", "
\n", "
\n", " \n", " 8\n", " \n", "
\n", "
\n", "
\n", " \n", " David\n", " \n", "
\n", "
\n", "
\n", " \n", " H$$$ilbert\n", " \n", "
\n", "
\n", "
\n", " \n", " 624\n", " \n", "
\n", "
\n", "
\n", " \n", " taaaccoo\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1950/07/14\n", " \n", "
\n", "
\n", "
\n", " \n", " let\n", " \n", "
\n", "
\n", "
\n", " \n", " 9\n", " \n", "
\n", "
\n", "
\n", " \n", " Johannes\n", " \n", "
\n", "
\n", "
\n", " \n", " KEPLER\n", " \n", "
\n", "
\n", "
\n", " \n", " 735\n", " \n", "
\n", "
\n", "
\n", " \n", " taco\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1920/04/22\n", " \n", "
\n", "
\n", "
\n", " \n", " you\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " JaMES\n", " \n", "
\n", "
\n", "
\n", " \n", " M$$ax%%well\n", " \n", "
\n", "
\n", "
\n", " \n", " 875\n", " \n", "
\n", "
\n", "
\n", " \n", " taco\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1923/03/12\n", " \n", "
\n", "
\n", "
\n", " \n", " down\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 19 rows / 8 columns
\n", "
1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 19 rows / 8 columns
\n", "
1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
id
\n", "
1 (int64)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
firstName
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
lastName
\n", "
3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
billingId
\n", "
4 (int64)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
product
\n", "
5 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
price
\n", "
6 (int64)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
birth
\n", "
7 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
dummyCol
\n", "
8 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Luis\n", " \n", "
\n", "
\n", "
\n", " \n", " Alvarez$$%!\n", " \n", "
\n", "
\n", "
\n", " \n", " 123\n", " \n", "
\n", "
\n", "
\n", " \n", " Cake\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " 1980/07/07\n", " \n", "
\n", "
\n", "
\n", " \n", " never\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " André\n", " \n", "
\n", "
\n", "
\n", " \n", " Ampère\n", " \n", "
\n", "
\n", "
\n", " \n", " 423\n", " \n", "
\n", "
\n", "
\n", " \n", " piza\n", " \n", "
\n", "
\n", "
\n", " \n", " 8\n", " \n", "
\n", "
\n", "
\n", " \n", " 1950/07/08\n", " \n", "
\n", "
\n", "
\n", " \n", " gonna\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " NiELS\n", " \n", "
\n", "
\n", "
\n", " \n", " Böhr//((%%\n", " \n", "
\n", "
\n", "
\n", " \n", " 551\n", " \n", "
\n", "
\n", "
\n", " \n", " pizza\n", " \n", "
\n", "
\n", "
\n", " \n", " 8\n", " \n", "
\n", "
\n", "
\n", " \n", " 1990/07/09\n", " \n", "
\n", "
\n", "
\n", " \n", " give\n", " \n", "
\n", "
\n", "
\n", " \n", " 4\n", " \n", "
\n", "
\n", "
\n", " \n", " PAUL\n", " \n", "
\n", "
\n", "
\n", " \n", " dirac$\n", " \n", "
\n", "
\n", "
\n", " \n", " 521\n", " \n", "
\n", "
\n", "
\n", " \n", " pizza\n", " \n", "
\n", "
\n", "
\n", " \n", " 8\n", " \n", "
\n", "
\n", "
\n", " \n", " 1954/07/10\n", " \n", "
\n", "
\n", "
\n", " \n", " you\n", " \n", "
\n", "
\n", "
\n", " \n", " 5\n", " \n", "
\n", "
\n", "
\n", " \n", " Albert\n", " \n", "
\n", "
\n", "
\n", " \n", " Einstein\n", " \n", "
\n", "
\n", "
\n", " \n", " 634\n", " \n", "
\n", "
\n", "
\n", " \n", " pizza\n", " \n", "
\n", "
\n", "
\n", " \n", " 8\n", " \n", "
\n", "
\n", "
\n", " \n", " 1990/07/11\n", " \n", "
\n", "
\n", "
\n", " \n", " up\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Galileo\n", " \n", "
\n", "
\n", "
\n", " \n", " ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI\n", " \n", "
\n", "
\n", "
\n", " \n", " 672\n", " \n", "
\n", "
\n", "
\n", " \n", " arepa\n", " \n", "
\n", "
\n", "
\n", " \n", " 5\n", " \n", "
\n", "
\n", "
\n", " \n", " 1930/08/12\n", " \n", "
\n", "
\n", "
\n", " \n", " never\n", " \n", "
\n", "
\n", "
\n", " \n", " 7\n", " \n", "
\n", "
\n", "
\n", " \n", " CaRL\n", " \n", "
\n", "
\n", "
\n", " \n", " Ga%%%uss\n", " \n", "
\n", "
\n", "
\n", " \n", " 323\n", " \n", "
\n", "
\n", "
\n", " \n", " taco\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1970/07/13\n", " \n", "
\n", "
\n", "
\n", " \n", " gonna\n", " \n", "
\n", "
\n", "
\n", " \n", " 8\n", " \n", "
\n", "
\n", "
\n", " \n", " David\n", " \n", "
\n", "
\n", "
\n", " \n", " H$$$ilbert\n", " \n", "
\n", "
\n", "
\n", " \n", " 624\n", " \n", "
\n", "
\n", "
\n", " \n", " taaaccoo\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1950/07/14\n", " \n", "
\n", "
\n", "
\n", " \n", " let\n", " \n", "
\n", "
\n", "
\n", " \n", " 9\n", " \n", "
\n", "
\n", "
\n", " \n", " Johannes\n", " \n", "
\n", "
\n", "
\n", " \n", " KEPLER\n", " \n", "
\n", "
\n", "
\n", " \n", " 735\n", " \n", "
\n", "
\n", "
\n", " \n", " taco\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1920/04/22\n", " \n", "
\n", "
\n", "
\n", " \n", " you\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " JaMES\n", " \n", "
\n", "
\n", "
\n", " \n", " M$$ax%%well\n", " \n", "
\n", "
\n", "
\n", " \n", " 875\n", " \n", "
\n", "
\n", "
\n", " \n", " taco\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1923/03/12\n", " \n", "
\n", "
\n", "
\n", " \n", " down\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 19 rows / 8 columns
\n", "
1 partition(s) <class 'optimus.new_optimus.DaskDataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from optimus import OptimusDaskDataframe\n", "odd = OptimusDaskDataframe(df)\n", "# odd.cols.names()\n", "print(type(odd))\n", "odd.cols.lower(\"lastName\").cols.upper(\"lastName\").ext.display()\n", "# .cols.upper(\"lastName\").df.head()\n", "# odd.cols.proper(\"product\").compute()\n", "# odd.cols.replace(\"product\",\"C\",\"c\").compute()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "df .Ext'> ['__abstractmethods__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__slots__', '__str__', '__subclasshook__', '__weakref__', '_abc_impl', '_name', 'buffer_json', 'buffer_window', 'cache', 'calculate_cols_to_profile', 'cast_and_profile', 'compute', 'create_id', 'debug', 'delayed', 'df', 'display', 'export', 'get_buffer', 'get_name', 'head', 'is_cached', 'melt', 'optimize', 'partitioner', 'partitions', 'pivot', 'profile', 'query', 'repartition', 'reset', 'run', 'sample', 'send', 'set_buffer', 'set_name', 'show', 'size', 'stratified_sample', 'table', 'table_html', 'table_image', 'to_delayed', 'to_dict', 'to_json', 'to_pandas']\n" ] }, { "ename": "AttributeError", "evalue": "'DataFrame' object has no attribute 'ext'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0modd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\extension.py\u001b[0m in \u001b[0;36mprofile\u001b[1;34m(self, columns, bins, output, flush, size)\u001b[0m\n\u001b[0;32m 593\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"df\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 594\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mflush\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 595\u001b[1;33m \u001b[0mcols_to_profile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalculate_cols_to_profile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 596\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 597\u001b[0m \u001b[0mcols_to_profile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3606\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3607\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3608\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"'DataFrame' object has no attribute %r\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3610\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'ext'" ] } ], "source": [ "odd.ext.profile()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "odd.cols.select().rows.limit(10)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "self .Rows object at 0x000001D44B5EC6C8>\n" ] }, { "data": { "text/html": [ "
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idfirstNamelastNamebillingIdproductpricebirthdummyCol
01LuisAlvarez$$%!123Cake101980/07/07never
12AndréAmpère423piza81950/07/08gonna
23NiELSBöhr//((%%551pizza81990/07/09give
34PAULdirac$521pizza81954/07/10you
45AlbertEinstein634pizza81990/07/11up
56GalileoGALiLEI672arepa51930/08/12never
67CaRLGa%%%uss323taco31970/07/13gonna
78DavidH$$$ilbert624taaaccoo31950/07/14let
89JohannesKEPLER735taco31920/04/22you
910JaMESM$$ax%%well875taco31923/03/12down
\n", "
" ], "text/plain": [ " id firstName lastName billingId product price birth \\\n", "0 1 Luis Alvarez$$%! 123 Cake 10 1980/07/07 \n", "1 2 André Ampère 423 piza 8 1950/07/08 \n", "2 3 NiELS Böhr//((%% 551 pizza 8 1990/07/09 \n", "3 4 PAUL dirac$ 521 pizza 8 1954/07/10 \n", "4 5 Albert Einstein 634 pizza 8 1990/07/11 \n", "5 6 Galileo GALiLEI 672 arepa 5 1930/08/12 \n", "6 7 CaRL Ga%%%uss 323 taco 3 1970/07/13 \n", "7 8 David H$$$ilbert 624 taaaccoo 3 1950/07/14 \n", "8 9 Johannes KEPLER 735 taco 3 1920/04/22 \n", "9 10 JaMES M$$ax%%well 875 taco 3 1923/03/12 \n", "\n", " dummyCol \n", "0 never \n", "1 gonna \n", "2 give \n", "3 you \n", "4 up \n", "5 never \n", "6 gonna \n", "7 let \n", "8 you \n", "9 down " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "odd.cols.select().rows.limit(10).df" ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "df = pd.read_csv(\"data/foo.csv\")" ] }, { "cell_type": "code", "execution_count": 159, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cols init\n", "Cols init\n", "[1, 2, 3]\n", " id firstName lastName billingId product \\\n", "0 1 LUIS Alvarez$$%! 123 Cake \n", "1 2 ANDRÉ Ampère 423 piza \n", "2 3 NIELS Böhr//((%% 551 pizza \n", "3 4 PAUL dirac$ 521 pizza \n", "4 5 ALBERT Einstein 634 pizza \n", "5 6 GALILEO GALiLEI 672 arepa \n", "6 7 CARL Ga%%%uss 323 taco \n", "7 8 DAVID H$$$ilbert 624 taaaccoo \n", "8 9 JOHANNES KEPLER 735 taco \n", "9 10 JAMES M$$ax%%well 875 taco \n", "10 11 ISAAC Newton 992 pasta \n", "11 12 EMMY%% Nöether$ 234 pasta \n", "12 13 MAX!!! Planck!!! 111 hamburguer \n", "13 14 FRED Hoy&&&le 553 pizzza \n", "14 15 ((( HEINRICH ))))) Hertz 116 pizza \n", "15 16 WILLIAM Gilbert### 886 BEER \n", "16 17 MARIE CURIE 912 Rice \n", "17 18 ARTHUR COM%%%pton 812 110790 \n", "18 19 JAMES Chadwick 467 NaN \n", "\n", " price birth dummyCol \n", "0 10 1980/07/07 never \n", "1 8 1950/07/08 gonna \n", "2 8 1990/07/09 give \n", "3 8 1954/07/10 you \n", "4 8 1990/07/11 up \n", "5 5 1930/08/12 never \n", "6 3 1970/07/13 gonna \n", "7 3 1950/07/14 let \n", "8 3 1920/04/22 you \n", "9 3 1923/03/12 down \n", "10 9 1999/02/15 never \n", "11 9 1993/12/08 gonna \n", "12 4 1994/01/04 run \n", "13 8 1997/06/27 around \n", "14 8 1956/11/30 and \n", "15 2 1958/03/26 desert \n", "16 1 2000/03/22 you \n", "17 5 1899/01/01 # \n", "18 10 1921/05/03 # \n" ] } ], "source": [ "\n", "# df = \"dataframe\"\n", "a = A(df)\n", "# a.cols.replace().cols.replace()\n", "# a.cols.get_meta()\n", "# a.meta.get()\n", "# a.cols.split().cols.replace()\n", "# a.cols.split(\"firstName\",\"i\").df\n", "a.cols.lower(\"firstName\").cols.upper(\"firstName\")\n", "# a.cols.mix(\"firstName\")\n", "a.meta.set_value(1)\n", "a.meta.set_value(2)\n", "a.meta.set_value(3)\n", "b = a\n", "print(b.meta.get_value())\n", "print(b.data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "op = Optimus(\"dask\", n_workers=1, threads_per_worker=8, silence_logs=30, memory_limit=\"1G\", comm=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# df1 = op.load.file(\"data/github_stargazers.json\").ext.cache()\n", "df = op.load.file(\"data/Meteorite_Landings.csv\").ext.cache()\n", "\n", "df = df.ext.repartition(8).ext.cache()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# import pandas as pd\n", "# import numpy as np\n", "# df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f', 'h'], columns=['one', 'two', 'three'])\n", "# df.loc[1,2] =\"\"\n", "# df.astype(str)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'frequency': {'mass (g)': {'values': [{'value': '1.3', 'count': 171},\n", " {'value': '1.2', 'count': 140},\n", " {'value': '1.4', 'count': 138},\n", " {'value': '', 'count': 131},\n", " {'value': '2.1', 'count': 130},\n", " {'value': '2.4', 'count': 126},\n", " {'value': '1.6', 'count': 120},\n", " {'value': '0.5', 'count': 119},\n", " {'value': '1.1', 'count': 116},\n", " {'value': '3.8', 'count': 114},\n", " {'value': '0.7', 'count': 111},\n", " {'value': '1.5', 'count': 111},\n", " {'value': '3.2', 'count': 109},\n", " {'value': '3.1', 'count': 109},\n", " {'value': '1.7', 'count': 109},\n", " {'value': '3', 'count': 108},\n", " {'value': '0.9', 'count': 108},\n", " {'value': '0.6', 'count': 108},\n", " {'value': '0.8', 'count': 107},\n", " {'value': '1.8', 'count': 104},\n", " {'value': '2.5', 'count': 103},\n", " {'value': '2.7', 'count': 102},\n", " {'value': '3.6', 'count': 96},\n", " {'value': '2', 'count': 96},\n", " {'value': '1', 'count': 96},\n", " {'value': '4.2', 'count': 95},\n", " {'value': '2.8', 'count': 93},\n", " {'value': '2.9', 'count': 93},\n", " {'value': '2.2', 'count': 92},\n", " {'value': '2.6', 'count': 91},\n", " {'value': '3.3', 'count': 88},\n", " {'value': '4.6', 'count': 86},\n", " {'value': '1.9', 'count': 86}]}}}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.frequency(\"mass (g)\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'result' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# requests.get(f\"https://api.github.com/users/argenisleon\").json()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'result' is not defined" ] } ], "source": [ "# requests.get(f\"https://api.github.com/users/argenisleon\").json()\n", "print(result)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "count=0" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10\n", "11\n", "12\n", "13\n", "14\n", "15\n", 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"[('SSL routines', 'ssl3_read_bytes', 'sslv3 alert bad record mac')]", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m 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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\github\\MainClass.py\u001b[0m in \u001b[0;36mget_user\u001b[1;34m(self, login)\u001b[0m\n\u001b[0;32m 270\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 271\u001b[0m headers, data = self.__requester.requestJsonAndCheck(\n\u001b[1;32m--> 272\u001b[1;33m \u001b[1;34m\"GET\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"/users/\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mlogin\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 273\u001b[0m )\n\u001b[0;32m 274\u001b[0m return github.NamedUser.NamedUser(\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\github\\Requester.py\u001b[0m in \u001b[0;36mrequestJsonAndCheck\u001b[1;34m(self, verb, url, parameters, headers, input)\u001b[0m\n\u001b[0;32m 317\u001b[0m return self.__check(\n\u001b[0;32m 318\u001b[0m *self.requestJson(\n\u001b[1;32m--> 319\u001b[1;33m 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chunked=chunked)\n\u001b[0m\u001b[0;32m 601\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 602\u001b[0m \u001b[1;31m# If we're going to release the connection in ``finally:``, then\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m 382\u001b[0m \u001b[1;31m# Remove the TypeError from the exception chain in Python 3;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 383\u001b[0m \u001b[1;31m# otherwise it looks like a programming error was the cause.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 384\u001b[1;33m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m 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in \u001b[0;36mraise_from\u001b[1;34m(value, from_value)\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m 378\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# Python 3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 379\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 380\u001b[1;33m \u001b[0mhttplib_response\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 381\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m 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b)\u001b[0m\n\u001b[0;32m 587\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 588\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 589\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 590\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 591\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\urllib3\\contrib\\pyopenssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 310\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'The read operation timed out'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 311\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 312\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 314\u001b[0m \u001b[1;32mdef\u001b[0m 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EOF'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\OpenSSL\\SSL.py\u001b[0m in \u001b[0;36mrecv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m 1838\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1839\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_lib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSSL_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1840\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raise_ssl_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1841\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1842\u001b[0m \u001b[1;31m# This strange line is all to avoid a memory copy. The buffer protocol\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\OpenSSL\\SSL.py\u001b[0m in \u001b[0;36m_raise_ssl_error\u001b[1;34m(self, ssl, result)\u001b[0m\n\u001b[0;32m 1669\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1670\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1671\u001b[1;33m \u001b[0m_raise_current_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1672\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1673\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\OpenSSL\\_util.py\u001b[0m in \u001b[0;36mexception_from_error_queue\u001b[1;34m(exception_type)\u001b[0m\n\u001b[0;32m 52\u001b[0m text(lib.ERR_reason_error_string(error))))\n\u001b[0;32m 53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 54\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexception_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 55\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mError\u001b[0m: [('SSL routines', 'ssl3_read_bytes', 'sslv3 alert bad record mac')]" ] } ], "source": [ "#!pip install PyGithub\n", "\n", "import time\n", "import requests\n", "\n", "\n", "from github import Github\n", "g = Github(\"argenisleon\", \"Uz7J4O9wmCM4\")\n", "result =[]\n", "\n", "def _request(cell):\n", " time.sleep(0.1)\n", " result.append({\"login\":g.get_user(cell).login,\"bio\":g.get_user(cell).bio})\n", " print(len(result))\n", " \n", " \n", "df1.cols.select(\"login\").compute()[\"login\"].apply(_request)\n", "\n" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [], "source": [ "import json\n", "import pandas as pd\n", "from io import StringIO\n", "\n", "\n", "j =json.dumps(result)\n", "pd.read_json(StringIO(j)).to_csv(\"github_profile.csv\")\n", "pdf = pd.read_json(StringIO(j))" ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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loginbio
1hugounavezElectrical Engineer. MSc in Biomedical Enginee...
2juliomonagasUX/UI Designer, Frontend Developer, Graphic De...
3MarcoPortilloI like to code, that's all. Venezuelan.
4BonsantoI studied Electrical and Computer Engineering....
6vintaI failed the Turing Test.
.........
1009ikedaosushiI love 🍣, ♨️and ✈️.
1010shenxiangzhuangLife isn’t about waiting for the storm to pass...
1011devendraapAvid programmer
1013rsohlotBig data + ML
1015kitokii want to start my adventure, new kind challen...
\n", "

456 rows × 2 columns

\n", "
" ], "text/plain": [ " login bio\n", "1 hugounavez Electrical Engineer. MSc in Biomedical Enginee...\n", "2 juliomonagas UX/UI Designer, Frontend Developer, Graphic De...\n", "3 MarcoPortillo I like to code, that's all. Venezuelan.\n", "4 Bonsanto I studied Electrical and Computer Engineering....\n", "6 vinta I failed the Turing Test.\n", "... ... ...\n", "1009 ikedaosushi I love 🍣, ♨️and ✈️.\n", "1010 shenxiangzhuang Life isn’t about waiting for the storm to pass...\n", "1011 devendraap Avid programmer\n", "1013 rsohlot Big data + ML \n", "1015 kitoki i want to start my adventure, new kind challen...\n", "\n", "[456 rows x 2 columns]" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdf.dropna()" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " login \\\n", "1 hugounavez \n", "2 juliomonagas \n", "3 MarcoPortillo \n", "4 Bonsanto \n", "6 vinta \n", "7 pythonjokeun \n", "9 belgacea \n", "11 emptyr1 \n", "12 bigdatavik \n", "13 FavioVazquez \n", "14 tspannhw \n", "16 papagunit \n", "18 dustyny \n", "19 Diego999 \n", "21 mutabazi \n", "27 MiguelPeralvo \n", "31 ChrisCompton \n", "33 leoluyi \n", "36 razeone \n", "37 iht \n", "38 anooptp \n", "39 zaratsian \n", "42 silverstone1903 \n", "43 MrPowers \n", "45 manuzhang \n", "46 donelianc \n", "49 talegari \n", "51 daniel-sali \n", "53 mdagost \n", "54 esafak \n", "56 travisbrady \n", "57 gjreda \n", "58 adriantorrie \n", "60 fly51fly \n", "68 uptonking \n", "69 luismayta \n", "73 denisarnaud \n", "74 bveliqi \n", "76 dazajuandaniel \n", "78 datorresb \n", "80 ramanan-r \n", "82 vidalalcala \n", "84 DuyguA \n", "85 NumPoet \n", "87 EKami \n", "88 sbarman-mi9 \n", "90 Jaimeardp \n", "91 danielgrijalva \n", "94 evmisu \n", "95 aayushshah15 \n", "96 floresfdev \n", "97 diansheng92 \n", "98 ibrahimsharaf \n", "99 hahmed988 \n", "101 hugounavez \n", "102 juliomonagas \n", "103 MarcoPortillo \n", "104 Bonsanto \n", "106 vinta \n", "107 pythonjokeun \n", "109 belgacea \n", "111 emptyr1 \n", "112 bigdatavik \n", "113 FavioVazquez \n", "114 tspannhw \n", "116 papagunit \n", "118 dustyny \n", "119 Diego999 \n", "121 mutabazi \n", "127 MiguelPeralvo \n", "131 ChrisCompton \n", "133 leoluyi \n", "136 razeone \n", "137 iht \n", "138 anooptp \n", "139 zaratsian \n", "142 silverstone1903 \n", "143 MrPowers \n", "145 manuzhang \n", "146 donelianc \n", "149 talegari \n", "151 daniel-sali \n", "153 mdagost \n", "154 esafak \n", "156 travisbrady \n", "157 gjreda \n", "158 adriantorrie \n", "160 fly51fly \n", "168 uptonking \n", "169 luismayta \n", "173 denisarnaud \n", "174 bveliqi \n", "176 dazajuandaniel \n", "178 datorresb \n", "180 ramanan-r \n", "182 vidalalcala \n", "184 DuyguA \n", "185 NumPoet \n", "187 EKami \n", "188 sbarman-mi9 \n", "190 Jaimeardp \n", "191 danielgrijalva \n", "194 evmisu \n", "195 aayushshah15 \n", "196 floresfdev \n", "197 diansheng92 \n", "198 ibrahimsharaf \n", "199 hahmed988 \n", "201 anktplwl91 \n", "202 seanreed1111 \n", "205 ohuarcaya \n", "206 elchulito88 \n", "207 astrojuanlu \n", "208 avannaldas \n", "209 shalabhsingh \n", "211 nightscape \n", "212 kcnickerson \n", "213 linekin \n", "214 acrozes \n", "215 shubhampachori12110095 \n", "216 ventouris \n", "218 shukwong \n", "219 supern92 \n", "220 JamieShelley \n", "221 mmortazavi \n", "222 miike \n", "225 qiulin \n", "226 yuseferi \n", "228 paulochf \n", "229 WI-KIWI \n", "232 franzantejm \n", "234 thisisashukla \n", "235 t3hSeb \n", "238 GCPBigData \n", "240 Guillermogsjc \n", "247 satyaborg \n", "248 hzitoun \n", "257 jeremyjordan \n", "258 pratos \n", "259 treymerkley \n", "268 wguillema \n", "270 gmartinezramirez-old \n", "272 alexrigler \n", "273 akasranjan005 \n", "275 nick5435 \n", "278 dgopsq \n", "280 thdaraujo \n", "282 vutsalsinghal \n", "283 fenandosr \n", "285 bilalaslamseattle \n", "287 rcitterio \n", "288 suvayu \n", "289 jeanmidevacc \n", "290 spaghettifunk \n", "293 Vjais \n", "296 jamesacampbell \n", "298 plliao \n", "303 yutingyw \n", "304 smrjan \n", "306 Neuw84 \n", "308 ozcanyarimdunya \n", "310 ronaldfalcao \n", "311 liangdabiao \n", "314 oliviernguyenquoc \n", "315 o9812 \n", "317 david-gero-cp \n", "321 kevinsegal \n", "323 rbramwell \n", "324 clarksun \n", "325 praveensastry \n", "326 marius92mc \n", "328 Gaarv \n", "337 chaoyue729 \n", "339 masumsoft \n", "342 ShreyasFadnavis \n", "343 bitcoinfaf \n", "344 ishantbansal \n", "346 mpolatcan \n", "347 hussainasghar \n", "351 nfsrules \n", "353 raank \n", "355 5hirish \n", "358 xuexuemiao \n", "360 asmith26 \n", "361 naushadS \n", "362 bnerDY \n", "364 mentecuantica \n", "365 mithunmanohar \n", "366 VictorDataEngineer \n", "368 iflores12 \n", "369 zmughal \n", "370 hughcameron \n", "372 ptmaroct \n", "373 githubcyc \n", "376 beautifulNow1992 \n", "380 gaving \n", "384 ozlerhakan \n", "386 javogranda \n", "389 PyarakaSrikanth \n", "392 etng \n", "394 Florents-Tselai \n", "396 fjsj \n", "399 thnery \n", "405 gperakis \n", "409 jake-aft \n", "410 rosscdh \n", "412 BigDataRPG \n", "415 joaorafaelm \n", "417 pbamotra \n", "422 timkofu \n", "424 eyadsibai \n", "426 levytskyi \n", "427 wenzhihong2003 \n", "428 pingjiang \n", "429 emptymalei \n", "436 NP-compete \n", "437 noleto \n", "438 drkostas \n", "442 gmiretti \n", "443 ereynrs \n", "444 bgarcial \n", "447 jkgjoye \n", "448 rogues-gallery \n", "450 radu941208 \n", "451 aoelvp94 \n", "452 shafaypro \n", "454 sinemhasircioglu \n", "455 upendrak \n", "456 valware \n", "457 marioBD \n", "458 shravik \n", "460 eryk \n", "465 agladstein \n", "466 carterrees \n", "467 andreaschandra \n", "469 TsukiZombina \n", "470 manugarri \n", "472 carlosevi94 \n", "475 antoniomdk \n", "477 junqueira \n", "481 gregwchase \n", "482 Sehaba95 \n", "485 devendrasr \n", "487 luo1994 \n", "492 vivek2319 \n", "493 munael \n", "495 alimasri \n", "496 hskang9 \n", "498 Aymericr \n", "500 renatocf \n", "502 JoseCage \n", "503 abenpy \n", "505 absognety \n", "507 rajagurunath \n", "508 Vitiell0 \n", "510 michaelmior \n", "512 Aylr \n", "513 qytz \n", "514 joelkim \n", "520 hmelberg \n", "522 josefigueredo \n", "524 pavank \n", "526 johndpope \n", "527 LiutongZhou \n", "528 leslyarun \n", "529 timetobye \n", "530 rdempsey \n", "533 markjbaker \n", "534 MCaviezel \n", "535 huyhoang17 \n", "537 cjen07 \n", "545 shadowkun \n", "550 otrenav \n", "551 HackyRoot \n", "552 quillan86 \n", "553 souo \n", "554 zhenyulin \n", "556 danielsobrado \n", "557 harryprince \n", "559 gavinzhu1127 \n", "563 HaithamMaya \n", "564 lw334 \n", "567 dbalabka \n", "568 nicolasfguillaume \n", "572 brandhaug \n", "573 franchb \n", "577 vlukiyanov \n", "578 Y-oHr-N \n", "590 hichMEN \n", "591 bramvds \n", "592 wuya2357 \n", "595 sylviaxxy \n", "597 mzntaka0 \n", "598 foxan \n", "600 capncodewash \n", "602 JBalloonist \n", "603 mohitatgithub \n", "604 mgc26 \n", "607 aljabr0 \n", "609 bahattincinic \n", "612 foowaa \n", "613 tomstesco \n", "615 luiscruz \n", "617 bavuongco10 \n", "618 ArchTaqi \n", "620 lujin77 \n", "624 fyrk \n", "626 zkn365 \n", "628 prashant-kikani \n", "629 niakki \n", "630 greird \n", "631 spokerman12 \n", "632 TankMasterRL \n", "636 pratikbarjatya \n", "643 DataEngg \n", "646 zhiruiwang \n", "647 kuoteng \n", "648 apsamuel \n", "650 s0kil \n", "651 Siva227 \n", "652 senthilnayagam \n", "658 bsc-ampiant \n", "659 OsirisXTLS \n", "667 settinghead \n", "674 siansiansu \n", "677 carsondahlberg \n", "679 zyuanlim \n", "685 giacomolanciano \n", "689 jm-gutierrezh \n", "693 jhermann \n", "694 baifengbai \n", "695 djfan \n", "696 garciacaleb904 \n", "699 viktorchukhantsev \n", "701 sidhu177 \n", "706 Skyblueballykid \n", "709 nyartsgnaw \n", "713 burness \n", "714 STHSF \n", "717 RafaelZarate \n", "720 MariusMez \n", "721 JLuisRojas \n", "725 RodolfoFerro \n", "728 da-edra \n", "729 jdayllon \n", "731 krzjoa \n", "736 shaboi \n", "737 arpit1997 \n", "738 crew102 \n", "740 TomMonkeyMan \n", "742 asdf247 \n", "750 pauldevos \n", "751 elwarren \n", "753 brunowego \n", "757 JamesLoong \n", "762 pbadeer \n", "763 RodrigoVillatoro \n", "764 jhuangtw-dev \n", "765 umer7 \n", "767 sainide \n", "772 analyticalmonk \n", "778 javiergodoy \n", "789 cvd \n", "790 muhammetenes \n", "792 wasi0013 \n", "793 wieshka \n", "797 PraneethKarnena \n", "799 eyaltrabelsi \n", "803 josephwinston \n", "806 Frans06 \n", "811 senthilmk \n", "815 sarikayamerts \n", "816 ariosramirez \n", "817 joseangel-sc \n", "818 huaji1992 \n", "819 jospaqui \n", "823 hmatalonga \n", "826 shauryashaurya \n", "827 p-gourseaud \n", "829 bryanyang0528 \n", "831 diegotony \n", "834 eschizoid \n", "835 adamnietopfizer \n", "843 foxgem \n", "844 argenisleon \n", "850 dblue0406 \n", "851 georgeblu1 \n", "852 volpatto \n", "854 neeksor \n", "856 liuzhenqi77 \n", "859 souradeepta \n", "861 rachaeltay \n", "863 nachoalvarez \n", "864 salotz \n", "867 Shifu-Engineer \n", "869 r4um \n", "870 ettorerizza \n", "875 mcolebrook \n", "876 aleksihakli \n", "880 ecogit \n", "883 alfonso777 \n", "885 wtznc \n", "886 franccesco \n", "888 vivekmishra369 \n", "892 rohithsrinivaas \n", "897 dogrdon \n", "898 dentarg \n", "899 RainFung \n", "900 sarikayamehmet \n", "903 sogasg \n", "908 yennanliu \n", "910 bt3gl \n", "911 NishantBhavsar \n", "913 JustEdro \n", "914 williamsne2 \n", "915 hujinghaoabcd \n", "922 deepdreamer0 \n", "925 andrassy \n", "926 algunion \n", "929 zephoon \n", "931 rafpyprog \n", "932 VirTek \n", "934 shangzixie \n", "941 ae86208 \n", "944 asears \n", "948 DataBoyTX \n", "951 hangtwenty \n", "952 ParzivalWins \n", "953 aliisakroe \n", "960 ALEXKIRNAS \n", "961 truskovskiyk \n", "962 J0hnG4lt \n", "966 mjschock \n", "967 gvvynplaine \n", "971 pdeguzman96 \n", "974 rhymiz \n", "975 adamnieto \n", "981 ansegundo \n", "982 philMarius \n", "985 cnnrrss \n", "988 The-Gupta \n", "989 yangjue-han \n", "991 ankur-gupta \n", "993 atgmello \n", "999 rschlaefli \n", "1000 prabha6kar \n", "1003 dcgithub \n", "1004 stjordanis \n", "1005 romanofficial \n", "1006 SongshGeo \n", "1008 LuiGGi629 \n", "1009 ikedaosushi \n", "1010 shenxiangzhuang \n", "1011 devendraap \n", "1013 rsohlot \n", "1015 kitoki \n", "\n", " bio \n", "1 Electrical Engineer. MSc in Biomedical Enginee... \n", "2 UX/UI Designer, Frontend Developer, Graphic De... \n", "3 I like to code, that's all. Venezuelan. \n", "4 I studied Electrical and Computer Engineering.... \n", "6 I failed the Turing Test. \n", "7 Data Engineer \n", "9 TryHarder \n", "11 Data Science Engineer \n", "12 (graphs) - [:ARE] - > (everywhere) \n", "13 Physicist and computational engineer. I have a... \n", "14 DataFlow Engineer, Java developer, Cloud Anal... \n", "16 Solutions Architect at DemandGen.\\r\\nMinister ... \n", "18 I'm a polygot engineer who codes when he can. ... \n", "19 Ph.D. Student in NLP & ML. Interests in artifi... \n", "21 (big|small|open) #data #nerd ≡ #AI, #datascien... \n", "27 Solutions Architect at Databricks \n", "31 Health Informatics and Technology\\r\\n \n", "33 Data Scientist, Engineer \n", "36 I like the web, arts and traveling \n", "37 Strategic Cloud Engineer @GoogleCloudPlatform \n", "38 I am a Software Professional. I have a passion... \n", "39 AI/Machine Learning Cloud Engineer @ Google. S... \n", "42 Data Scientist & DevOps,\\r\\nB.Sc. & M.Sc. Stat... \n", "43 Data engineer at prognos.ai. Like Scala, Spar... \n", "45 Fault Tolerant; Eventually Consistent; Not Ava... \n", "46 Data hacker (level 3). Actuary with mathematic... \n", "49 Code and explorations in Statistical Machine L... \n", "51 Data Engineer @ Alphacruncher \n", "53 Director of Data Science @ShopRunner; \\r\\nForm... \n", "54 Data scientist, machine learning engineer \n", "56 Machine learning, whole page optimization, sea... \n", "57 Machine Learning Engineer / Data Scientist \n", "58 Likes AI, autonomous vehicles, and finance. Re... \n", "60 新浪微博 @爱可可-爱生活 \n", "68 HelloWord \n", "69 Hermit | Passionate Coder | Blockchain, IOTA, ... \n", "73 Scientist, dreamer, data lover \n", "74 Co-Founder & CTO @layer7ai \n", "76 All things Data - Master of Information Techno... \n", "78 I am a; Mathematical physicist, Data Scientist... \n", "80 Big Data, NLP and Machine Learning enthusiast \n", "82 Applied Mathematician working as a Data Scientist \n", "84 Senior NLP developer, working on Conversationa... \n", "85 An ordinary person that on a command line ever... \n", "87 A freelancer passionate about everything revol... \n", "88 Data Scientist | WPI Grad | \\r\\nFocused on Mac... \n", "90 Apasionado por los datos me gusta poder verlos... \n", "91 Freelancer @ Upwork \n", "94 a data passionate from financial industry \n", "95 software developer @cockroachdb. \n", "96 Data scientist and Consultant in data science ... \n", "97 Android Software Developer \n", "98 NLP Research Engineer \n", "99 A curious and detail oriented professional who... \n", "101 Electrical Engineer. MSc in Biomedical Enginee... \n", "102 UX/UI Designer, Frontend Developer, Graphic De... \n", "103 I like to code, that's all. Venezuelan. \n", "104 I studied Electrical and Computer Engineering.... \n", "106 I failed the Turing Test. \n", "107 Data Engineer \n", "109 TryHarder \n", "111 Data Science Engineer \n", "112 (graphs) - [:ARE] - > (everywhere) \n", "113 Physicist and computational engineer. I have a... \n", "114 DataFlow Engineer, Java developer, Cloud Anal... \n", "116 Solutions Architect at DemandGen.\\r\\nMinister ... \n", "118 I'm a polygot engineer who codes when he can. ... \n", "119 Ph.D. Student in NLP & ML. Interests in artifi... \n", "121 (big|small|open) #data #nerd ≡ #AI, #datascien... \n", "127 Solutions Architect at Databricks \n", "131 Health Informatics and Technology\\r\\n \n", "133 Data Scientist, Engineer \n", "136 I like the web, arts and traveling \n", "137 Strategic Cloud Engineer @GoogleCloudPlatform \n", "138 I am a Software Professional. I have a passion... \n", "139 AI/Machine Learning Cloud Engineer @ Google. S... \n", "142 Data Scientist & DevOps,\\r\\nB.Sc. & M.Sc. Stat... \n", "143 Data engineer at prognos.ai. Like Scala, Spar... \n", "145 Fault Tolerant; Eventually Consistent; Not Ava... \n", "146 Data hacker (level 3). Actuary with mathematic... \n", "149 Code and explorations in Statistical Machine L... \n", "151 Data Engineer @ Alphacruncher \n", "153 Director of Data Science @ShopRunner; \\r\\nForm... \n", "154 Data scientist, machine learning engineer \n", "156 Machine learning, whole page optimization, sea... \n", "157 Machine Learning Engineer / Data Scientist \n", "158 Likes AI, autonomous vehicles, and finance. Re... \n", "160 新浪微博 @爱可可-爱生活 \n", "168 HelloWord \n", "169 Hermit | Passionate Coder | Blockchain, IOTA, ... \n", "173 Scientist, dreamer, data lover \n", "174 Co-Founder & CTO @layer7ai \n", "176 All things Data - Master of Information Techno... \n", "178 I am a; Mathematical physicist, Data Scientist... \n", "180 Big Data, NLP and Machine Learning enthusiast \n", "182 Applied Mathematician working as a Data Scientist \n", "184 Senior NLP developer, working on Conversationa... \n", "185 An ordinary person that on a command line ever... \n", "187 A freelancer passionate about everything revol... \n", "188 Data Scientist | WPI Grad | \\r\\nFocused on Mac... \n", "190 Apasionado por los datos me gusta poder verlos... \n", "191 Freelancer @ Upwork \n", "194 a data passionate from financial industry \n", "195 software developer @cockroachdb. \n", "196 Data scientist and Consultant in data science ... \n", "197 Android Software Developer \n", "198 NLP Research Engineer \n", "199 A curious and detail oriented professional who... \n", "201 PostGrad in Machine Intelligence, love to expl... \n", "202 I write picture books for kids about science a... \n", "205 Bsc. Computer Science, UNI\\r\\n \n", "206 Python Hacker!!! \n", "207 Astrodynamics enthusiast, open source software... \n", "208 abhijitannaldas.com \n", "209 Previously Undergraduate at @IIT_Ropar. Into M... \n", "211 Big fan of Functional Programming, Scala, Idri... \n", "212 Maker, Coder, dTaz, Magniware, i6, OmersV, CDL... \n", "213 doing enterprise software \n", "214 CTO: Data Scientist / Big Data ; Mobile : iOS... \n", "215 Learning to extract signal from noise. \n", "216 Data Scientist @ Hattrick Ltd \n", "218 Genomic Data Scientist \n", "219 Playing with Random data. \n", "220 Software developer at Linx Printing \n", "221 A T-shaped data scientist passionate about dat... \n", "222 @poplindata \n", "225 A coder focus on big data \n", "226 Senior BackEnd(Symfony,Drupal,Django) Engineer... \n", "228 Data Science, Python, R, LaTeX \n", "229 Data Scientist, Python & open source evangelist \n", "232 Systems Engineer - BI & Big Data \n", "234 ML & Web Enthusiast | CSRE, IIT Bombay | KNIT ... \n", "235 Data Scientist (Milan, ITA) \n", "238 Data Scientist | (56) 990542812 | web2ajax@gma... \n", "240 A monkey hitting keys at random on a keyboard... \n", "247 Coding is an art and just another conduit for ... \n", "248 Machine Learning Engineer | Deep Learning & Te... \n", "257 Machine learning engineer. Broadly curious. \\r... \n", "258 Learning to build\\r\\n \n", "259 DevOps Engineer. He/Him \n", "268 I work with models ;) \n", "270 This is a old account. \n", "272 Founder/CEO of Raedan Technologies. Supporting... \n", "273 Full stack Python Developer | Machine Learning... \n", "275 Mathematics Graduate Student\\r\\nUniversity of ... \n", "278 Full stack engineer @efficiam \n", "280 Software Engineer, interested in Artificial In... \n", "282 The more I learn, the more I realize how much ... \n", "283 \\r\\n mx\\r\\n \n", "285 Databricks. Previously DocuSign, Appuri and Mi... \n", "287 Desarrollador web, Scrum y Devops. \n", "288 A former experimental particle physicist @ CER... \n", "289 Data scientist and R&D engineer who like to te... \n", "290 I love le coding so much \n", "293 I am grad student in CS dept at University of ... \n", "296 Artist working in Technology.\\r\\n \n", "298 Data Scientist, Data Engineer, Software Engineer \n", "303 implementations and experiments during my leis... \n", "304 AI/ML researcher \n", "306 Artificial Intelligence team leader at Ikerlan \n", "308 A python developer. \n", "310 Product Owner! Sempre no meio de clientes e de... \n", "311 programmer in shenzhen\\r\\nhttps://www.liangdab... \n", "314 Data Scientist @ L'Oréal \n", "315 Use the Source, Luke! \n", "317 also @davidpgero \n", "321 Interstellar Real Estate Developer \n", "323 Senior DevOps Engineer at Pythian :: Intereste... \n", "324 programmer, father. \n", "325 FullStack/Cloud/SRE, open source contributor a... \n", "326 Computer Science Student, Former Data Engineer... \n", "328 Developer of data applications / pipelines in ... \n", "337 DL&ML with awesome products \n", "339 Co-Founder, CTO at CodeMarshal \n", "342 \\r\\n PhD Student IU Bloomington\\r\\n \n", "343 bitcoinfaf = BitcoinFastasFuk \n", "344 Computer Vision Engineer \n", "346 Senior Data Engineer \n", "347 Big Data Enthusiastic \n", "351 Deep Learner | Machine Learner \n", "353 Full Stack Developer; PHP, Python and NodeJS \n", "355 Automating Automation \n", "358 Director of Big Data Engineering \n", "360 Love Python | Distributed Computing | Data Sci... \n", "361 Data Science | Machine Learning | Python \n", "362 @UQComputingSociety, Australia\\r\\n@UoMCS, Unit... \n", "364 From Russia with love to Reverse Engineering, ... \n", "365 Data Engineer | Python Dev \n", "366 DataEngineer/DataAnalyst \n", "368 Currently working in political tech but I'd ra... \n", "369 biomedical image analysis (@CBL-ORION), scient... \n", "370 Data @lexerdev \n", "372 React Native and React JS developer \n", "373 fintech \n", "376 代码搬运工,进入了一个新的层次,庖丁解牛,从各种开源框架中提取设计,提取核心代码算法,搬运参... \n", "380 comin' to you 1000bpm \n", "384 Search Engine Developer at n11.com. Duke's Cho... \n", "386 Data Manager, Customer Cluster \n", "389 I m Data science consultant, ML/DL practitione... \n", "392 PHP==>Python==>Go==>Anything Possible! \n", "394 Data Strategist & Engineer • Think, Code, Read... \n", "396 Partner at @vintasoftware \n", "399 I'm a software engineer from Recife who likes ... \n", "405 Business-minded data scientist with a demonstr... \n", "409 Stuff with Data, Products, Agile, and Block-Ch... \n", "410 2x Startup founder, Software Engineer, Dev-ops... \n", "412 I'm just ordinary Data Scientist who love in A... \n", "415 \\r\\n https://joaorafaelm.github.io\\r\\n \n", "417 pythonista, machine learning, numbers, basicml... \n", "422 Software Engineer \n", "424 Software engineer building reliable machine le... \n", "426 Just a regular guy. \n", "427 java&python&golang程序猿一枚 \n", "428 站在巨人的肩膀上开发卓越软件 \n", "429 I make data speak in Germany. Data Scientist, ... \n", "436 The full \"stuck\" Developer | The Dev-Oops Engi... \n", "437 I'm a data scientist and software engineer wit... \n", "438 I enjoy making Bots and using Machine Learning... \n", "442 Computer Scientist as Data Scientist / Enginee... \n", "443 Researcher. Professor. Data Architect. \n", "444 Guy in constant learning. \n", "447 Ciencia de Datos \n", "448 I'm a loner, Dottie. A rebel. \n", "450 ML/NLP//Energy Efficiency/ Market \n", "451 Data Engineer at Mutt Data \n", "452 Muhammad Shafay Amjad\\r\\nProgressing as Data ... \n", "454 Computer engineer \n", "455 I am a Data Scientist at Greenlight Bioscience... \n", "456 Computer Science - UNMSM\\r\\n \n", "457 Candidato a Maestro de Ciencias de Datos, Inge... \n", "458 Computer Science graduate student \n", "460 微信公众号: pyquant \n", "465 Postdoc in Genetics.\\r\\nMachine learning in po... \n", "466 Sr. Manager, Data Science \n", "467 I love NLP, ASR, and Deep Learning | Indonesia \n", "469 I'm a MSc in Computer Science and a Chemistry ... \n", "470 Send bitcoin plz https://manugarri.bit.ac/ \n", "472 Data Scientist.\\r\\n\\r\\nPython Lover & Developer. \n", "475 Strathclyde University Postgraduate Student.\\r... \n", "477 Software engineers: ... \n", "481 Machine learning engineer in pursuit of creati... \n", "482 Junior Machine Learning Researcher. I am inter... \n", "485 Building something awesome @47Billion , @gotuk... \n", "487 no bb,just do it \n", "492 Data Scientist 💡 Curious, 🔍 skeptic, humble, ... \n", "493 Eternally confuzzled. CS apprentice, currently... \n", "495 I am a Research and Development Engineer dedic... \n", "496 🤷 \n", "498 Co-founder, CTO at Ledger Investing \n", "500 Principal Machine Learning Engineer @elo7 | Ma... \n", "502 Showing to the world how Open Source can chang... \n", "503 Master Student at NYU Center for DataScience\\r... \n", "505 I am a Data enthusiast and a Coder. Interested... \n", "507 Pythonist ,Data Engineer, Deep learning \n", "508 Founder at Cooklist and Handground \n", "510 \\r\\n Assistant Professor in the Department ... \n", "512 Data Scientist. Engineer. Maker. Endlessly cur... \n", "513 云在青天水在瓶 \n", "514 Quant, Data Scientist \n", "520 Health economist, analyzing data from hospital... \n", "522 Developer since forever. Always learning and t... \n", "524 I work with Data \n", "526 swift / crypto / ai / ml / quant \n", "527 Data Scientist @ AWS Machine Learning Solution... \n", "528 For Christ - NLP/AI/Deep Learning/Data Science \n", "529 Jr.Data Analyst // 데이터 분석 및 개발 시작 - 2018년 06월 \n", "530 I build machine learning systems, teach data s... \n", "533 nice kid \n", "534 I love data! \n", "535 #HUST #SAMI #BTN #PYMI #SUN* #PHH \\r\\nhttps:... \n", "537 Christian \n", "545 https://shadowkun.github.io/ \n", "550 I help data-intensive companies be smarter and... \n", "551 A dynamic Data Scientist with expertise in Mac... \n", "552 Data Scientist \n", "553 Big data hacker, do stuff with python and scal... \n", "554 λ => { :ok, data |> model.predict } \n", "556 Worked in New York, London, Tokyo, Madrid and ... \n", "557 A Simmer/Keras/Stan/Spark player \n", "559 Data Scientist | CRM Expert | Digital Analytic... \n", "563 Senior Data Scientist @Kumanu \n", "564 Looking for a spacetime singularity \n", "567 M.Sc. in Computer Science, +10 years experienc... \n", "568 Independent Python Data Engineer \n", "572 Full-stack Software Engineer and Entrepreneur ... \n", "573 Go at work \n", "577 Data-focused software engineer. \n", "578 ML Researcher \n", "590 Innovation Addict \\r\\nIoT, Big Data, Cloud & F... \n", "591 Bioinformatician in Life Sciences \n", "592 i can't tell \n", "595 Data Engineer, former Data Scientist \n", "597 Machine Learning Engineer\\r\\nSpeech Processing... \n", "598 Data Engineer @smartnews // ex Software Engine... \n", "600 Data science, ML, NLP, and more. \n", "602 Data Scientist \n", "603 Data Science Enthusiast \n", "604 Otolaryngologist, Author & Machine Learning Re... \n", "607 Freelancer | Math | Machine Learning | C++ | P... \n", "609 Software Developer, Computer Engineer, Travel,... \n", "612 Frustra fit per plura quod potest fieri per pa... \n", "613 Data scientist \n", "615 Postdoc \n", "617 Your regular dev, talk much, code much, hide i... \n", "618 Tᴇᴄʜ ᴇɴᴛʜᴜsɪᴀsᴛɪᴄ ᴡʜᴏ ʟᴏᴠᴇs ᴛᴏ ʟᴇᴀʀɴ ɴᴇᴡ ᴛʜɪɴɢs! \n", "620 Data Mining Engineer \n", "624 Games, AI, and random widgetry. \n", "626 learn and practice \n", "628 Data scientist | Kaggle 1x Master, 2x Expert \n", "629 Data Science Engineer \n", "630 I'm a Product Manager from Paris 🥐, now living... \n", "631 Information is finite because attention span i... \n", "632 Electronic engineer, with an interest in embed... \n", "636 ML Practitioner | Data Science | Data Engineer... \n", "643 Researcher of Database and on Data \n", "646 Senior Data Scientist at BCG \n", "647 still not a software developer \n", "648 I'm an engineer \n", "650 Ψ ∞ Φ - μαθητής \n", "651 Data Scientist at Quadratyx, Hyderabad \n", "652 Blockchain and Crypto Currency Consultant and ... \n", "658 Boston Scientific account \n", "659 Long time SysAdmin, aspiring DevOps with a hin... \n", "667 FREEDOMIZE ALL TEH CODE \n", "674 Weeks of coding can save you hours of planning. \n", "677 Data Scientist as Wells Fargo \n", "679 Data Scientist \n", "685 Computer Engineer | Ph.D. Student in Data Science \n", "689 Machine Learning Engineer, Piano player, Yoga ... \n", "693 Pythonista. Writer of docs. DevOps practitione... \n", "694 python NLP R \n", "695 \"The purpose of computing is insight, not numb... \n", "696 Physics undergrad at the University of Oklahom... \n", "699 Ruby on Rails 5Y+\\r\\nPostgreSQL 4Y+\\r\\nReact 3... \n", "701 Student of Science | Exploring Knowledge Work \n", "706 Data Scientist @ Dynamo Software \n", "709 Go suck the data ;) \n", "713 code player \n", "714 NLP, Machine learning, DeepLearning, AI \n", "717 Software Engineer @aplijobs \n", "720 Co-founder startup studio Hola-Up\\r\\n\\r\\nGeek ... \n", "721 UASLP, Ing. en Sistemas Inteligentes \n", "725 Community lead @CodeandoMexico 👨🏻‍💻 · Co-found... \n", "728 main = putStrLn \"Hello, World!\" \n", "729 Statician, Marketeer and Developer 😃\\r\\nPython... \n", "731 Data Scientist & Software Developer \n", "736 Data Scientist \n", "737 Data Engineer \n", "738 Data Scientist @GEICO \n", "740 aHa, I found you! \n", "742 Python enthusiast \n", "750 Data Scientist, Data Engineer, AWS Data Archit... \n", "751 meh. \n", "753 I'm a passionate and highly opinionated softwa... \n", "757 AIops \n", "762 Data Science, Product Management, and building... \n", "763 Data Scientist @ Skyscanner \n", "764 lobster at @lob \n", "765 Certified Salesforce.com Consultant || Data En... \n", "767 I'm passionate about problem-solving, learning... \n", "772 Wanderer - previously @atlanhq @socialcopsdev ... \n", "778 Digital Analytics Development & Innovation. BB... \n", "789 Passionate about Python, Machine Learning, Jav... \n", "790 Python, Golang, Javascript \n", "792 Freelance Full-Stack Software Engineer \n", "793 Head of Video @ PlayTech.com \n", "797 Python & Django Developer. Proficient in MySQL... \n", "799 Enthusiastic Software Engineer 👷 with big pass... \n", "803 I clearly remember programming on computers th... \n", "806 Mechatronic trying to level up my knowledge fr... \n", "811 Go/Python/Java + Cloud = Jack of all trades \n", "815 Data Scientist \n", "816 Data Scientist and Machine Learning Developer \n", "817 Professional Tetris player \\r\\n \n", "818 study hard and make progress every day \n", "819 Computer and Systems Engineering Student \n", "823 🚀 Data scientist and Web developer. \n", "826 Over 17 years of solution architecture, consul... \n", "827 Data Scientist & Big Data Engineer \n", "829 https://www.linkedin.com/in/bryanyang0528/ \n", "831 :computer: :alien: :ghost: :question: \\r\\n:1... \n", "834 Happy Hacking! \n", "835 Data Scientist and Software Developer \n", "843 iiot + eth + ml \n", "844 Venezuelan in Mexico @argenisleon \n", "850 personal GitHub from Allison. \n", "851 Learning new stuff, keeping good stuffs privat... \n", "852 Numerical Developer @ESSS. MSc and DSc (ongoin... \n", "854 teamwork makes the dream work. \n", "856 neuroscience, machine learning, python and more \n", "859 coding = 💛 \n", "861 Tech + Data + Design \n", "863 eCommerce, Payments & Bots \n", "864 PhD Student in Biochemistry & Molecular Biolog... \n", "867 Just a Typical Engineer trying to automate the... \n", "869 systems guy • curious and intrusive • 99.999% ... \n", "870 Researcher & PhD student in Information Scienc... \n", "875 Associate Professor \n", "876 Software Engineer @Vincit \n", "880 Independent ecopreneur who is supporting proje... \n", "883 Computer Scientist | \\r\\nMsc at IME at Univers... \n", "885 Product Data Scientist.\\r\\nComputer science gr... \n", "886 Author of CodingDose a programming blog about ... \n", "888 Data Scientist with interests in Complex Syste... \n", "892 I am studying B.Tech in Metallurgical and Mate... \n", "897 absurdist data hound \n", "898 Software developer with interest in security, ... \n", "899 Game Algorithm Engineer in Timi Studio| \\r\\n\\r... \n", "900 Researcher of Artificial Intelligence at ITU, ... \n", "903 Blockchain and cryptocurrency enthusiast, dApp... \n", "908 Data dev | Python | Scala | Spark | Distribut... \n", "910 Ph.D. in astrophysics, senior software enginee... \n", "911 Data Science Enthusiast. \n", "913 Data Engineer @ Miro \n", "914 Recent PhD graduate in PChem with experience i... \n", "915 Life is short, you need python \n", "922 data analysis \n", "925 EM for Search at Cookpad - building the world'... \n", "926 \"What I cannot create, I do not understand.\" -... \n", "929 Python Dev \n", "931 Making life easier with Python. \n", "932 Business Intelligence Developer , Data Enginee... \n", "934 Master of Science in Columbia University \n", "941 CV & ML \n", "944 I star things... \n", "948 https://www.linkedin.com/in/thomasccook/ \n", "951 Independent software developer in Portland, OR... \n", "952 Living the Oasis \n", "953 Currently: Data Science Engineer \n", "960 Researcher at Computer Vision domain \n", "961 Machine Learning Engineer 🇺🇦🇨🇦 \n", "962 \\r\\n A computer scientist who is interested... \n", "966 Machine Learning Engineer \n", "967 NULL VOID NONE \n", "971 MS in Business Analytics at UCLA | Aspiring Da... \n", "974 Can be bribed with good food and coffee. 🇸🇷🇸🇽🇨🇼🇺🇸 \n", "975 Data Scientist interested in engineering softw... \n", "981 Master's student on Applied Computing at USP \n", "982 \\r\\n Data Scientist, spend my time in Edinb... \n", "985 (╯°□°)╯︵ ┻━┻ \n", "988 AI Software Engineer, Bosch R&D [ARiSE, RBEI] ... \n", "989 Macro-finance Economist \n", "991 Lead Data Scientist @ Salesforce AI Research. ... \n", "993 Passionate about all things FLOS. Special inte... \n", "999 \\r\\n Studying data science @uzh - \\r\\nWeb d... \n", "1000 a techno freak of Industry 4.0 \n", "1003 leaning.. \n", "1004 ML/Data Scientist \n", "1005 Front-End Developer \n", "1006 Beijing Normal University\\r\\n \n", "1008 Pythonista 🐍\\r\\nDataScientist 📊\\r\\nBayesian 🧙 \n", "1009 I love 🍣, ♨️and ✈️. \n", "1010 Life isn’t about waiting for the storm to pass... \n", "1011 Avid programmer \n", "1013 Big data + ML \n", "1015 i want to start my adventure, new kind challen... \n" ] } ], "source": [ "def print_full(x):\n", " pd.set_option('display.max_rows', len(x))\n", " print(x)\n", " pd.reset_option('display.max_rows')\n", " \n", "ppdf = pdf.dropna(subset=[\"bio\"])\n", "print_full(pdf)\n", "# print_full(pdf[\"bio\"].str.lower())" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data 194\n", "and 150\n", "engineer 94\n", "in 87\n", "scientist 76\n", "learning 73\n", "| 68\n", "a 63\n", "i 53\n", "of 53\n", "machine 52\n", "science 49\n", "developer 47\n", "at 46\n", "software 44\n", "python 40\n", "& 35\n", "to 31\n", "with 28\n", "the 26\n", "big 25\n", "computer 24\n", "on 23\n", "ai 21\n", "for 21\n", "- 19\n", "@ 19\n", "engineering 17\n", "ml 17\n", "student 16\n", "about 15\n", "systems 14\n", "i'm 14\n", "nlp 14\n", "passionate 14\n", "from 13\n", "intelligence 13\n", "development 13\n", "my 13\n", "is 13\n", "/ 12\n", "code 12\n", "more 12\n", "analytics 12\n", "phd 11\n", "am 11\n", "who 11\n", "interested 10\n", "web 10\n", "enthusiast 10\n", "working 10\n", "cloud 10\n", "artificial 10\n", "msc 10\n", "programming 10\n", "love 9\n", "not 9\n", "researcher 9\n", "deep 9\n", "technology 9\n", "devops 9\n", "all 9\n", "open 8\n", "consultant 8\n", "me 8\n", "master 8\n", "full 8\n", "senior 8\n", "professional 8\n", "like 8\n", "an 8\n", "business 7\n", "work 7\n", "source 7\n", "research 7\n", "co-founder 7\n", "time 7\n", "architect 7\n", "can 7\n", "dev 7\n", "stuff 7\n", "university 7\n", "distributed 7\n", "stack 7\n", "that 6\n", "lover 6\n", "go 6\n", "scala 6\n", "computing 6\n", "+ 6\n", "have 6\n", "curious 6\n", "as 6\n", "things 6\n", "ruby 5\n", "studying 5\n", "information 5\n", "coder 5\n", "former 5\n", "solutions 5\n", "search 5\n", "coding 5\n", "freelancer 5\n", "do 5\n", "vision 5\n", "de 5\n", "experience 5\n", "much 5\n", "en 5\n", "manager 5\n", "models 5\n", "specializing 5\n", "r 5\n", "physics 5\n", "learner 5\n", "aws 5\n", "physicist 5\n", "ex 5\n", "// 5\n", "passion 5\n", "natural 4\n", "looking 4\n", "new 4\n", "just 4\n", "language 4\n", "bsc 4\n", "hacker 4\n", "gusta 4\n", "datos 4\n", "interests 4\n", "person 4\n", "biomedical 4\n", "programmer 4\n", "technologies 4\n", "building 4\n", "make 4\n", "designer 4\n", "expert 4\n", "test 4\n", "guy 4\n", "knowledge 4\n", "mathematician 4\n", "blockchain 4\n", "• 4\n", "statistical 4\n", "processing 4\n", "product 4\n", "computational 4\n", "you 4\n", "electrical 4\n", "build 4\n", "statistics 4\n", "= 4\n", "enthusiastic 4\n", "life 4\n", "health 4\n", "previously 4\n", "por 4\n", "cto 4\n", "practitioner 4\n", "pythonista 4\n", "player 4\n", "functional 4\n", "products 4\n", "be 4\n", "graduate 4\n", "always 4\n", "focused 4\n", "problems 4\n", "likes 4\n", "scientific 4\n", "everything 4\n", "verlos 4\n", "own 3\n", "mining 3\n", "r&d 3\n", "since 3\n", "learn 3\n", "trying 3\n", "|| 3\n", "math 3\n", "iot 3\n", "solving 3\n", "professor 3\n", "using 3\n", "industry 3\n", "write 3\n", "architecture 3\n", "team 3\n", "databricks 3\n", "focus 3\n", "also 3\n", "great 3\n", "intern 3\n", "digital 3\n", "director 3\n", "react 3\n", "interest 3\n", "spark 3\n", "or 3\n", "related 3\n", "years 3\n", "independent 3\n", "automation 3\n", "venezuelan 3\n", "fan 3\n", "kubernetes 3\n", "financial 3\n", "text 3\n", "loves 3\n", "applied 3\n", "c++ 3\n", "cosmology 3\n", "ordinary 3\n", "mathematical 3\n", "random 3\n", "robotics 3\n", "musician 3\n", "grad 3\n", "-> 3\n", "· 3\n", "into 3\n", "complex 3\n", "full-stack 3\n", "available 3\n", "linux 3\n", "enchiladas 2\n", "fanboy 2\n", "insights 2\n", "#data 2\n", "startup 2\n", "every 2\n", "scale 2\n", "data! 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1\n", "kind 1\n", "intrusive 1\n", "basicmlcom 1\n", "pipeline 1\n", "windows 1\n", "interaction 1\n", "genomic 1\n", "(usp) 1\n", "singularity 1\n", "showing 1\n", "their 1\n", "being 1\n", "meditation 1\n", "(milan 1\n", "λ 1\n", "shakespeare 1\n", "use 1\n", "inclinations 1\n", "tetris 1\n", "motion 1\n", "dtaz 1\n", "another 1\n", "companies 1\n", "widgetry 1\n", "kingdom 1\n", "surely 1\n", "lobster 1\n", "world!\" 1\n", "ios 1\n", "principal 1\n", "시작 1\n", "journalist 1\n", "@surfline 1\n", "compreenderem 1\n", "way 1\n", "enough 1\n", "db0b 1\n", "\"the 1\n", "pharmacological 1\n", "experiments 1\n", "@aeropython 1\n", "dedicated 1\n", "speech 1\n", "crm 1\n", "uni 1\n", "opinionated 1\n", "snotra 1\n", "6710 1\n", "👷 1\n", "@elo7 1\n", "data-intensive 1\n", "co-host 1\n", "aha 1\n", "ing 1\n", "dfb8 1\n", "paulo 1\n", "day 1\n", "found 1\n", "luke! 1\n", "practice 1\n", "interplanetary 1\n", "|> 1\n", "old 1\n", "topics 1\n", "ikerlan 1\n", "auc 1\n", "best 1\n", "in-person 1\n", "planning 1\n", "sometimes 1\n", "changed 1\n", "reverse 1\n", "jrdata 1\n", "dance 1\n", "i6 1\n", "mechatronic 1\n", "shafay 1\n", "mountain 1\n", "hyderabad 1\n", "learning/data 1\n", "entire 1\n", "d8f4 1\n", "finding 1\n", "job 1\n", "biology 1\n", "frustrated 1\n", "btech 1\n", "header 1\n", "e-commerce 1\n", "📊🎨📉 1\n", "(ongoing) 1\n", "cool 1\n", "https://shadowkungithubio/ 1\n", "xrogers 1\n", "amount 1\n", "neuron 1\n", "gigs 1\n", "he/him 1\n", "nosql 1\n", "works 1\n", "recent 1\n", "@iit_ropar 1\n", "a910 1\n", "ecommerce 1\n", "dtype: int64\n" ] } ], "source": [ "words = pdf['bio'].str.replace(\".\",\"\").str.replace(\",\",\"\").str.lower().str.split()\n", "word_counts = pd.value_counts(words.apply(pd.Series).stack())\n", "print_full(word_counts)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[, , , , , , , ]\n" ] } ], "source": [ "print(result)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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01000Luis000Alvarez$$%!123Cake101980/07/07never
1200André000Ampère423piza81950/07/08gonna
2300NiELS000Böhr//((%%551pizza81990/07/09give
34000PAUL000dirac$521pizza81954/07/10you
4500Albert00Einstein634pizza81990/07/11up
560Galileo00GALiLEI672arepa51930/08/12never
67000CaRL000Ga%%%uss323taco31970/07/13gonna
7800David000H$$$ilbert624taaaccoo31950/07/14let
890Johannes0KEPLER735taco31920/04/22you
91000JaMES000M$$ax%%well875taco31923/03/12down
101100Isaac000Newton992pasta91999/02/15never
111200Emmy%%00Nöether$234pasta91993/12/08gonna
121300Max!!!00Planck!!!111hamburguer41994/01/04run
1314000Fred000Hoy&&&le553pizzza81997/06/27around
1415((( Heinrich )))))Hertz116pizza81956/11/30and
15160William00Gilbert###886BEER21958/03/26desert
161700Marie000CURIE912Rice12000/03/22you
171800Arthur00COM%%%pton81211079051899/01/01#
181900JAMES000Chadwick467NaN101921/05/03#
\n", "
" ], "text/plain": [ " id firstName lastName billingId product \\\n", "0 1 000Luis000 Alvarez$$%! 123 Cake \n", "1 2 00André000 Ampère 423 piza \n", "2 3 00NiELS000 Böhr//((%% 551 pizza \n", "3 4 000PAUL000 dirac$ 521 pizza \n", "4 5 00Albert00 Einstein 634 pizza \n", "5 6 0Galileo00 GALiLEI 672 arepa \n", "6 7 000CaRL000 Ga%%%uss 323 taco \n", "7 8 00David000 H$$$ilbert 624 taaaccoo \n", "8 9 0Johannes0 KEPLER 735 taco \n", "9 10 00JaMES000 M$$ax%%well 875 taco \n", "10 11 00Isaac000 Newton 992 pasta \n", "11 12 00Emmy%%00 Nöether$ 234 pasta \n", "12 13 00Max!!!00 Planck!!! 111 hamburguer \n", "13 14 000Fred000 Hoy&&&le 553 pizzza \n", "14 15 ((( Heinrich ))))) Hertz 116 pizza \n", "15 16 0William00 Gilbert### 886 BEER \n", "16 17 00Marie000 CURIE 912 Rice \n", "17 18 00Arthur00 COM%%%pton 812 110790 \n", "18 19 00JAMES000 Chadwick 467 NaN \n", "\n", " price birth dummyCol \n", "0 10 1980/07/07 never \n", "1 8 1950/07/08 gonna \n", "2 8 1990/07/09 give \n", "3 8 1954/07/10 you \n", "4 8 1990/07/11 up \n", "5 5 1930/08/12 never \n", "6 3 1970/07/13 gonna \n", "7 3 1950/07/14 let \n", "8 3 1920/04/22 you \n", "9 3 1923/03/12 down \n", "10 9 1999/02/15 never \n", "11 9 1993/12/08 gonna \n", "12 4 1994/01/04 run \n", "13 8 1997/06/27 around \n", "14 8 1956/11/30 and \n", "15 2 1958/03/26 desert \n", "16 1 2000/03/22 you \n", "17 5 1899/01/01 # \n", "18 10 1921/05/03 # " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# df1.cols.pattern_counts(\"firstName\", n=5, mode=0)\n", "df1.cols.pad(\"firstName\",10, \"both\",\"0\").compute()\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# import sys,os,os.path\n", "# os.environ" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# import string_grouper\n", "# string_grouper.__file__" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from optimus import Optimus\n", "op = Optimus(\"dask\", n_workers=1, threads_per_worker=8, processes=False, memory_limit=\"3G\", comm=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "df = op.load.file(\"data/crime.csv\").ext.cache()\n", "df = df.ext.repartition(8).ext.cache()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 319073 rows / 17 columns
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8 partition(s)
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\n", "
INCIDENT_NUMBER
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1 (object)
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OFFENSE_CODE
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2 (object)
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\n", " \n", " not nullable\n", " \n", "
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OFFENSE_CODE_GROUP
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3 (object)
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OFFENSE_DESCRIPTION
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4 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
DISTRICT
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5 (object)
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\n", " \n", " not nullable\n", " \n", "
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REPORTING_AREA
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6 (object)
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SHOOTING
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7 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
OCCURRED_ON_DATE
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8 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
YEAR
\n", "
9 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
MONTH
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10 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
DAY_OF_WEEK
\n", "
11 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
HOUR
\n", "
12 (object)
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\n", "
\n", "
UCR_PART
\n", "
13 (object)
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\n", "
\n", "
STREET
\n", "
14 (object)
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\n", "
\n", "
Lat
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15 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Long
\n", "
16 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Location
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17 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
\n", "
\n", " \n", " 9\n", " \n", "
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\n", " \n", " VANDALISM\n", " \n", "
\n", "
\n", "
\n", " \n", " C11\n", " \n", "
\n", "
\n", "
\n", " \n", " 347\n", " \n", "
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\n", "
\n", " \n", " nan\n", " \n", "
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\n", "
\n", " \n", " 8\n", " \n", "
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\n", "
\n", " \n", " HECLA⋅ST\n", " \n", "
\n", "
\n", "
\n", " \n", " 42.30682138\n", " \n", "
\n", "
\n", "
\n", " \n", " -71.06030035\n", " \n", "
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\n", " \n", " (42.30682138,⋅-71.06030035)\n", " \n", "
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\n", "
\n", " \n", " TOWED⋅MOTOR⋅VEHICLE\n", " \n", "
\n", "
\n", "
\n", " \n", " D4\n", " \n", "
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\n", "
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\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " 2018-09-03⋅19:27:00\n", " \n", "
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\n", "
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\n", "
\n", " \n", " 9\n", " \n", "
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\n", " \n", " Part⋅Three\n", " \n", "
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\n", " \n", " CAZENOVE⋅ST\n", " \n", "
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\n", " \n", " I182070940\n", " \n", "
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\n", "
\n", " \n", " Investigate⋅Property\n", " \n", "
\n", "
\n", "
\n", " \n", " INVESTIGATE⋅PROPERTY\n", " \n", "
\n", "
\n", "
\n", " \n", " D4\n", " \n", "
\n", "
\n", "
\n", " \n", " 272\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " 2018-09-03⋅21:16:00\n", " \n", "
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\n", "
\n", " \n", " 2018\n", " \n", "
\n", "
\n", "
\n", " \n", " 9\n", " \n", "
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\n", "
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\n", " \n", " Part⋅Three\n", " \n", "
\n", "
\n", "
\n", " \n", " NEWCOMB⋅ST\n", " \n", "
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\n", " \n", " (42.33418175,⋅-71.07866441)\n", " \n", "
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\n", " \n", " I182070938\n", " \n", "
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\n", "
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\n", "
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\n", "
\n", "
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\n", "
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\n", "
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\n", " \n", " 42.29019621\n", " \n", "
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\n", " \n", " B2\n", " \n", "
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\n", "
\n", " \n", " Part⋅One\n", " \n", "
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\n", " \n", " NORMANDY⋅ST\n", " \n", "
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\n", " \n", " 42.30607218\n", " \n", "
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\n", "
\n", " \n", " -71.08273260\n", " \n", "
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.........................................................
39880NaNI050310906-0003125Warrant ArrestsWARRANT ARRESTD4285NaN2016-06-05 17:25:0020166Sunday17Part ThreeCOVENTRY ST42.33695098-71.08574813(42.33695098, -71.08574813)
39881NaNI030217815-0800111HomicideMURDER, NON-NEGLIGIENT MANSLAUGHTERE18520NaN2015-07-09 13:38:0020157Thursday13Part OneRIVER ST42.25592648-71.12317207(42.25592648, -71.12317207)
39882NaNI030217815-0803125Warrant ArrestsWARRANT ARRESTE18520NaN2015-07-09 13:38:0020157Thursday13Part ThreeRIVER ST42.25592648-71.12317207(42.25592648, -71.12317207)
39883NaNI010370257-0003125Warrant ArrestsWARRANT ARRESTE13569NaN2016-05-31 19:35:0020165Tuesday19Part ThreeNEW WASHINGTON ST42.30233307-71.11156487(42.30233307, -71.11156487)
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319073 rows × 18 columns

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" ], "text/plain": [ " 0 INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP \\\n", "0 L I182070945 00619 Larceny \n", "1 NaN I182070943 01402 Vandalism \n", "2 NaN I182070941 03410 Towed \n", "3 NaN I182070940 03114 Investigate Property \n", "4 NaN I182070938 03114 Investigate Property \n", "... ... ... ... ... \n", "39880 NaN I050310906-00 03125 Warrant Arrests \n", "39881 NaN I030217815-08 00111 Homicide \n", "39882 NaN I030217815-08 03125 Warrant Arrests \n", "39883 NaN I010370257-00 03125 Warrant Arrests \n", "39884 NaN 142052550 03125 Warrant Arrests \n", "\n", " OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING \\\n", "0 LARCENY ALL OTHERS D14 808 NaN \n", "1 VANDALISM C11 347 NaN \n", "2 TOWED MOTOR VEHICLE D4 151 NaN \n", "3 INVESTIGATE PROPERTY D4 272 NaN \n", "4 INVESTIGATE PROPERTY B3 421 NaN \n", "... ... ... ... ... \n", "39880 WARRANT ARREST D4 285 NaN \n", "39881 MURDER, NON-NEGLIGIENT MANSLAUGHTER E18 520 NaN \n", "39882 WARRANT ARREST E18 520 NaN \n", "39883 WARRANT ARREST E13 569 NaN \n", "39884 WARRANT ARREST D4 903 NaN \n", "\n", " OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART \\\n", "0 2018-09-02 13:00:00 2018 9 Sunday 13 Part One \n", "1 2018-08-21 00:00:00 2018 8 Tuesday 0 Part Two \n", "2 2018-09-03 19:27:00 2018 9 Monday 19 Part Three \n", "3 2018-09-03 21:16:00 2018 9 Monday 21 Part Three \n", "4 2018-09-03 21:05:00 2018 9 Monday 21 Part Three \n", "... ... ... ... ... ... ... \n", "39880 2016-06-05 17:25:00 2016 6 Sunday 17 Part Three \n", "39881 2015-07-09 13:38:00 2015 7 Thursday 13 Part One \n", "39882 2015-07-09 13:38:00 2015 7 Thursday 13 Part Three \n", "39883 2016-05-31 19:35:00 2016 5 Tuesday 19 Part Three \n", "39884 2015-06-22 00:12:00 2015 6 Monday 0 Part Three \n", "\n", " STREET Lat Long \\\n", "0 LINCOLN ST 42.35779134 -71.13937053 \n", "1 HECLA ST 42.30682138 -71.06030035 \n", "2 CAZENOVE ST 42.34658879 -71.07242943 \n", "3 NEWCOMB ST 42.33418175 -71.07866441 \n", "4 DELHI ST 42.27536542 -71.09036101 \n", "... ... ... ... \n", "39880 COVENTRY ST 42.33695098 -71.08574813 \n", "39881 RIVER ST 42.25592648 -71.12317207 \n", "39882 RIVER ST 42.25592648 -71.12317207 \n", "39883 NEW WASHINGTON ST 42.30233307 -71.11156487 \n", "39884 WASHINGTON ST 42.33383935 -71.08029038 \n", "\n", " Location \n", "0 (42.35779134, -71.13937053) \n", "1 (42.30682138, -71.06030035) \n", "2 (42.34658879, -71.07242943) \n", "3 (42.33418175, -71.07866441) \n", "4 (42.27536542, -71.09036101) \n", "... ... \n", "39880 (42.33695098, -71.08574813) \n", "39881 (42.25592648, -71.12317207) \n", "39882 (42.25592648, -71.12317207) \n", "39883 (42.30233307, -71.11156487) \n", "39884 (42.33383935, -71.08029038) \n", "\n", "[319073 rows x 18 columns]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# df[\"OFFENSE_CODE_GROUP\"].str.extract(\"(L)\").compute()\n", "\n", "df.cols.extract(\"OFFENSE_CODE_GROUP\", \"(L)\").compute()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'r' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'r' is not defined" ] } ], "source": [ "df.cols.append(r).compute()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['id',\n", " 'firstName',\n", " 'lastName',\n", " 'billingId',\n", " 'product',\n", " 'price',\n", " 'birth',\n", " 'dummyCol']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.names()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 997 µs\n" ] }, { "data": { "text/plain": [ "{'firstName': {'values': [{'value': 'Ullll', 'count': 4},\n", " {'value': 'Ullllll', 'count': 2}],\n", " 'more': True,\n", " 'updated': 1603206510.6664248}}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "df.cols.pattern_counts(\"firstName\", 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "df= pd.DataFrame(data={'col1': [\"http://www.google.com:8080?param1=param_name\"],\"col2\":[\"argenisleon@gmail.com\"]})" ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " col1 col2\n", "0 http://www.google.com:8080?param1=param_name argenisleon\n", " col1 col2\n", "0 http://www.google.com:8080?param1=param_name gmail.com\n", " col1 col2\n", "0 http://www.google.com:8080?param1=param_name gmail\n" ] } ], "source": [ "# df.cols.port(\"col1\")\n", "# df.cols.domain_scheme(\"col1\")\n", "# df.cols.host(\"col1\")\n", "\n", "# df.cols.domain_params(\"col1\")\n", "# df.cols.host(\"col1\")\n", "\n", "#Email funcions\n", "print(df.cols.email_user(\"col2\"))\n", "print(df.cols.email_domain(\"col2\"))\n", "print(df.cols.email_extension(\"col2\"))\n", "\n" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (, line 1)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m df.cols.select(\"meta_key__wp_s2member_custom\").cols.\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "df.cols.select(\"meta_key__wp_s2member_custom\").cols." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 5.91 s\n" ] } ], "source": [ "%%time\n", "for col_name in df.cols.names():\n", " df[col_name].astype(str).compute()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ParseResult(scheme='http', netloc='sub.stackoverflow.com:8080', path='/questions/1234567/blah-blah-blah-blah', params='', query='123=123&234=234', fragment='')\n" ] } ], "source": [ "from urllib.parse import urlparse\n", "# from urlparse import urlparse # Python 2\n", "parsed_uri = urlparse('http://sub.stackoverflow.com:8080/questions/1234567/blah-blah-blah-blah?123=123&234=234' )\n", "result = '{uri.scheme}://{uri.netloc}/'.format(uri=parsed_uri)\n", "print(parsed_uri)\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 41.8 s\n" ] }, { "data": { "text/plain": [ "{'columns': {'ID': {'patterns': {'values': [{'value': '#####', 'count': 2738},\n", " {'value': '####', 'count': 1657},\n", " {'value': '######', 'count': 1647},\n", " {'value': '###', 'count': 159}],\n", " 'updated': 1603073291.0049632},\n", " 'stats': {'match': 6213,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'int'},\n", " 'frequency': [{'value': '9997', 'count': 1},\n", " {'value': '125928', 'count': 1},\n", " {'value': '125905', 'count': 1},\n", " {'value': '125906', 'count': 1},\n", " {'value': '125907', 'count': 1}],\n", " 'count_uniques': 6213},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'int'}},\n", " 'user_login': {'patterns': {'values': [{'value': 'lllllllll', 'count': 316},\n", " {'value': 'llllllll', 'count': 313},\n", " {'value': 'lllllll', 'count': 311},\n", " {'value': 'llllllllll', 'count': 280},\n", " {'value': 'lllllllllll', 'count': 261}],\n", " 'more': True,\n", " 'updated': 1603073294.3630424},\n", " 'stats': {'match': 6213,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'zuriavvega', 'count': 1},\n", " {'value': 'Vivi21', 'count': 1},\n", " {'value': 'Veronicasol', 'count': 1},\n", " {'value': 'VeronikaAmaya', 'count': 1},\n", " {'value': 'Verounica', 'count': 1}],\n", " 'count_uniques': 6213},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}},\n", " 'user_nicename': {'patterns': {'values': [{'value': 'lllllll', 'count': 508},\n", " {'value': 'llllllll', 'count': 506},\n", " {'value': 'lllllllll', 'count': 453},\n", " {'value': 'llllll', 'count': 396},\n", " {'value': 'llllllllll', 'count': 387}],\n", " 'more': True,\n", " 'updated': 1603073297.6715984},\n", " 'stats': {'match': 6213,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'zuriavvega', 'count': 1},\n", " {'value': 'flor-cecilia', 'count': 1},\n", " {'value': 'fersedano', 'count': 1},\n", " {'value': 'ferstefi', 'count': 1},\n", " {'value': 'fertorneria', 'count': 1}],\n", " 'count_uniques': 6213},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}},\n", " 'user_email': {'patterns': {'values': [{'value': 'lllllllllll@lllll.lll',\n", " 'count': 230},\n", " {'value': 'llllllllllll@lllll.lll', 'count': 215},\n", " {'value': 'llllllllll@lllll.lll', 'count': 196},\n", " {'value': 'lllllllllllll@lllll.lll', 'count': 190},\n", " {'value': 'lllllllll@lllll.lll', 'count': 182}],\n", " 'more': True,\n", " 'updated': 1603073302.4047518},\n", " 'stats': {'match': 6213,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'email'},\n", " 'frequency': [{'value': 'zuriavega11@yahoo.com', 'count': 1},\n", " {'value': 'estherncohen@yahoo.com', 'count': 1},\n", " {'value': 'espaciolisto.peru@gmail.com', 'count': 1},\n", " {'value': 'espiritualidadgloval@gmail.com', 'count': 1},\n", " {'value': 'estefania.becerraz@gmail.com', 'count': 1}],\n", " 'count_uniques': 6213},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'email'}},\n", " 'user_url': {'patterns': {'values': [], 'updated': 1603073305.8370538},\n", " 'stats': {'match': 6213,\n", " 'missing': 6213,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'object'},\n", " 'frequency': [{'value': 'nan', 'count': 6213}],\n", " 'count_uniques': 1},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'object'}},\n", " 'user_registered': {'patterns': {'values': [],\n", " 'updated': 1603073308.8844736},\n", " 'stats': {'match': 6213,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'date', 'format': '%Y-%m-%d %H:%M:%S'},\n", " 'frequency': [{'value': '2020-09-16 15:28:51', 'count': 1},\n", " {'value': '2018-03-13 14:12:29', 'count': 1},\n", " {'value': '2018-03-02 21:20:18', 'count': 1},\n", " {'value': '2018-03-02 21:22:49', 'count': 1},\n", " {'value': '2018-03-02 22:03:05', 'count': 1}],\n", " 'count_uniques': 6213},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'date', 'format': '%Y-%m-%d %H:%M:%S'}},\n", " 'user_activation_key': {'patterns': {'values': [{'value': 'lll',\n", " 'count': 5289},\n", " {'value': '##########:$U$UUUll##ll#UUUUUUUUlU#U.UllUlll#', 'count': 1},\n", " {'value': '##########:$U$UUUlUlllUl#U/l.UllU.llllllUUUU.', 'count': 1},\n", " {'value': '##########:$U$UUUlUllUUUU#UlUll#lUUUUll#UlUl/', 'count': 1},\n", " {'value': '##########:$U$UUUlUllU..UUlUUUllUlllUUlUU#l##', 'count': 1}],\n", " 'more': True,\n", " 'updated': 1603073312.3147514},\n", " 'stats': {'match': 924,\n", " 'missing': 5289,\n", " 'mismatch': 5289,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'nan', 'count': 5289},\n", " {'value': '1567028512:$P$B8fxVPdCiYNOec7NRYYKDRZNJo5USy0', 'count': 1},\n", " {'value': '1566873569:$P$BWzgfl5UzWiXtstQBo4OZo1L18gmiX.', 'count': 1},\n", " {'value': '1566864028:$P$BnrO9Hh3Jl3D8b30rks1FqxubHrpF51', 'count': 1},\n", " {'value': '1566833065:$P$B6WXCwt04ryx3QrvraUuuGyBL6Iv.90', 'count': 1}],\n", " 'count_uniques': 925},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}},\n", " 'user_status': {'patterns': {'values': [], 'updated': 1603073315.464049},\n", " 'stats': {'match': 6213,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'int'},\n", " 'frequency': [{'value': '0', 'count': 6213}],\n", " 'count_uniques': 1},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'int'}},\n", " 'display_name': {'patterns': {'values': [{'value': 'Ulllll Ulllll',\n", " 'count': 223},\n", " {'value': 'Ullllll Ullllll', 'count': 220},\n", " {'value': 'Ulllll Ullllll', 'count': 199},\n", " {'value': 'Ullllll Ulllll', 'count': 185},\n", " {'value': 'Ullll Ullllll', 'count': 170}],\n", " 'more': True,\n", " 'updated': 1603073318.638414},\n", " 'stats': {'match': 6213,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'Daniela Salazar', 'count': 3},\n", " {'value': 'Natalia', 'count': 3},\n", " {'value': 'Christine Berrios', 'count': 2},\n", " {'value': 'Fabiola Cáceres Quiroga', 'count': 2},\n", " {'value': 'Sandra Vasquez', 'count': 2}],\n", " 'count_uniques': 6115},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}},\n", " 'role': {'patterns': {'values': [{'value': 'l#llllll_lllll#', 'count': 5392},\n", " {'value': 'llllllllll', 'count': 820}],\n", " 'updated': 1603073321.6246245},\n", " 'stats': {'match': 6213,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 's2member_level4', 'count': 5392},\n", " {'value': 'subscriber', 'count': 820},\n", " {'value': 'administrator', 'count': 1}],\n", " 'count_uniques': 3},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}},\n", " 'ccaps': {'patterns': {'values': [], 'updated': 1603073324.6873155},\n", " 'stats': {'match': 6213,\n", " 'missing': 6213,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'object'},\n", " 'frequency': [{'value': 'nan', 'count': 6213}],\n", " 'count_uniques': 1},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'object'}},\n", " 'meta_key__first_name': {'patterns': {'values': [{'value': 'Ulllll',\n", " 'count': 1029},\n", " {'value': 'Ullllll', 'count': 991},\n", " {'value': 'Ullll', 'count': 950},\n", " {'value': 'Ulllllll', 'count': 580},\n", " {'value': 'Ulll', 'count': 312}],\n", " 'more': True,\n", " 'updated': 1603073328.3634589},\n", " 'stats': {'match': 6211,\n", " 'missing': 2,\n", " 'mismatch': 2,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'Ana', 'count': 83},\n", " {'value': 'Carolina', 'count': 80},\n", " {'value': 'Maria', 'count': 76},\n", " {'value': 'Alejandra', 'count': 63},\n", " {'value': 'Andrea', 'count': 62}],\n", " 'count_uniques': 2543},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}},\n", " 'meta_key__last_name': {'patterns': {'values': [{'value': 'Ulllll',\n", " 'count': 941},\n", " {'value': 'Ullllll', 'count': 904},\n", " {'value': 'Ullll', 'count': 694},\n", " {'value': 'Ulllllll', 'count': 609},\n", " {'value': 'Ullllllll', 'count': 371}],\n", " 'more': True,\n", " 'updated': 1603073331.663825},\n", " 'stats': {'match': 6027,\n", " 'missing': 186,\n", " 'mismatch': 186,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'nan', 'count': 186},\n", " {'value': 'Gonzalez', 'count': 57},\n", " {'value': 'Rodriguez', 'count': 41},\n", " {'value': 'Martinez', 'count': 39},\n", " {'value': 'Garcia', 'count': 33}],\n", " 'count_uniques': 3993},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}},\n", " 'meta_key__nickname': {'patterns': {'values': [{'value': 'lllllllll',\n", " 'count': 316},\n", " {'value': 'llllllll', 'count': 313},\n", " {'value': 'lllllll', 'count': 312},\n", " {'value': 'llllllllll', 'count': 280},\n", " {'value': 'lllllllllll', 'count': 261}],\n", " 'more': True,\n", " 'updated': 1603073335.505216},\n", " 'stats': {'match': 6212,\n", " 'missing': 1,\n", " 'mismatch': 1,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'zuriavvega', 'count': 1},\n", " {'value': 'Viviana Beatriz Sportelli', 'count': 1},\n", " {'value': 'Verounica', 'count': 1},\n", " {'value': 'Vesta1', 'count': 1},\n", " {'value': 'Vi', 'count': 1}],\n", " 'count_uniques': 6213},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}},\n", " 'meta_key__description': {'patterns': {'values': [{'value': 'lll',\n", " 'count': 6211},\n", " {'value': 'Ull lllllllll, llll ll Ulllll Ullll. Ull llllllllll ll Ullllllll, llllll llllllllll lllll ll lllll l lllllll Ulllllllll. Ull llllll l ll llllllllll.',\n", " 'count': 1}],\n", " 'updated': 1603073338.8230784},\n", " 'stats': {'match': 6213,\n", " 'missing': 6211,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'object'},\n", " 'frequency': [{'value': 'nan', 'count': 6211},\n", " {'value': \"♀ Happy World Citizen ♥ #AlphaFemale Arquitecta♥ we're all made of stars..*-* Amar,Viajar,Conocer,Aprender,Soñar & Evolucionar ♥ #Wanderlust∞\",\n", " 'count': 1},\n", " {'value': 'Soy argentina, vivo en Buenos Aires. Soy licenciada en Marketing, diseño accesorios tengo mi marca y estudio Astrología. Amo viajar y la fotografia.',\n", " 'count': 1}],\n", " 'count_uniques': 3},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'object'}},\n", " 'meta_key__rich_editing': {'patterns': {'values': [{'value': 'llll',\n", " 'count': 6212}],\n", " 'updated': 1603073342.1639295},\n", " 'stats': {'match': 6212,\n", " 'missing': 1,\n", " 'mismatch': 1,\n", " 'profiler_dtype': {'dtype': 'boolean'},\n", " 'frequency': [{'value': 'true', 'count': 6212},\n", " {'value': 'nan', 'count': 1}],\n", " 'count_uniques': 2},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'boolean'}},\n", " 'meta_key__comment_shortcuts': {'patterns': {'values': [{'value': 'lllll',\n", " 'count': 6212}],\n", " 'updated': 1603073347.3704548},\n", " 'stats': {'match': 6212,\n", " 'missing': 1,\n", " 'mismatch': 1,\n", " 'profiler_dtype': {'dtype': 'boolean'},\n", " 'frequency': [{'value': 'false', 'count': 6212},\n", " {'value': 'nan', 'count': 1}],\n", " 'count_uniques': 2},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'boolean'}},\n", " 'meta_key__admin_color': {'patterns': {'values': [{'value': 'lllll',\n", " 'count': 6202},\n", " {'value': 'lllllllll', 'count': 7},\n", " {'value': 'lllllll', 'count': 2},\n", " {'value': 'llllll', 'count': 1}],\n", " 'updated': 1603073352.8033557},\n", " 'stats': {'match': 6212,\n", " 'missing': 1,\n", " 'mismatch': 1,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'fresh', 'count': 6202},\n", " {'value': 'ectoplasm', 'count': 7},\n", " {'value': 'sunrise', 'count': 2},\n", " {'value': 'nan', 'count': 1},\n", " {'value': 'coffee', 'count': 1}],\n", " 'count_uniques': 5},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}},\n", " 'meta_key__use_ssl': {'patterns': {'values': [{'value': '#', 'count': 6212}],\n", " 'updated': 1603073358.2343516},\n", " 'stats': {'match': 6212,\n", " 'missing': 1,\n", " 'mismatch': 1,\n", " 'profiler_dtype': {'dtype': 'int'},\n", " 'frequency': [{'value': '0', 'count': 6212}, {'value': 'nan', 'count': 1}],\n", " 'count_uniques': 2},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'int'}},\n", " 'meta_key__show_admin_bar_front': {'patterns': {'values': [{'value': 'llll',\n", " 'count': 6212}],\n", " 'updated': 1603073364.4061916},\n", " 'stats': {'match': 6212,\n", " 'missing': 1,\n", " 'mismatch': 1,\n", " 'profiler_dtype': {'dtype': 'boolean'},\n", " 'frequency': [{'value': 'true', 'count': 6212},\n", " {'value': 'nan', 'count': 1}],\n", " 'count_uniques': 2},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'boolean'}},\n", " 'meta_key__wp_capabilities': {'patterns': {'values': [{'value': '{\"l#llllll_lllll#\":llll}',\n", " 'count': 5392},\n", " {'value': '{\"llllllllll\":llll}', 'count': 820}],\n", " 'updated': 1603073369.3421445},\n", " 'stats': {'match': 6213,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': '{\"s2member_level4\":true}', 'count': 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npartitions=8
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Dask Name: astype, 16 tasks
" ], "text/plain": [ "Dask DataFrame Structure:\n", " ID user_login user_nicename user_email user_url user_registered user_activation_key user_status display_name role ccaps meta_key__first_name meta_key__last_name meta_key__nickname meta_key__description meta_key__rich_editing meta_key__comment_shortcuts meta_key__admin_color meta_key__use_ssl meta_key__show_admin_bar_front meta_key__wp_capabilities meta_key__wp_user_level meta_key__dismissed_wp_pointers meta_key__wp_dashboard_quick_press_last_post_id meta_key__wp_user-settings meta_key__wp_user-settings-time meta_key__AtD_options meta_key__AtD_check_when meta_key__AtD_guess_lang meta_key__AtD_ignored_phrases meta_key__aim meta_key__yim meta_key__jabber meta_key__nav_menu_recently_edited meta_key__managenav-menuscolumnshidden meta_key__metaboxhidden_nav-menus meta_key__closedpostboxes_post meta_key__metaboxhidden_post meta_key__pmpro_logins meta_key__pmpro_visits meta_key__pmpro_views meta_key__wp_s2member_last_login_time meta_key__wp_s2member_registration_ip meta_key__wp_s2member_login_counter meta_key__wp_s2member_paid_registration_times meta_key__default_password_nag meta_key__wp_s2member_subscr_gateway meta_key__wp_s2member_subscr_id meta_key__wp_s2member_custom meta_key__wp_s2member_ipn_signup_vars meta_key__wp_s2member_first_payment_txn_id meta_key__wp_s2member_last_payment_time meta_key__meta-box-order_dashboard meta_key__screen_layout_dashboard meta_key__wp_s2member_notes meta_key__wp_s2member_auto_eot_time meta_key___yoast_wpseo_profile_updated meta_key__wpseo_title meta_key__wpseo_metadesc meta_key__wpseo_metakey meta_key__googleplus meta_key__twitter meta_key__facebook meta_key__meta-box-order_post meta_key__screen_layout_post meta_key__closedpostboxes_page meta_key__metaboxhidden_page meta_key__manageedit-postcolumnshidden meta_key__easycontactforms_stat_pointer meta_key__wp_s2member_capability_times meta_key__wp_s2member_subscr_baid meta_key__closedpostboxes_dashboard meta_key__metaboxhidden_dashboard meta_key__closedpostboxes_attachment meta_key__metaboxhidden_attachment meta_key__meta-box-order_attachment meta_key__screen_layout_attachment meta_key__session_tokens meta_key___edd_htaccess_missing_dismissed meta_key__wp_media_library_mode meta_key__closedpostboxes_download meta_key__metaboxhidden_download meta_key__billing_country meta_key__billing_first_name meta_key__billing_last_name meta_key__billing_company meta_key__billing_address_1 meta_key__billing_address_2 meta_key__billing_city meta_key__billing_state meta_key__billing_postcode meta_key__billing_email meta_key__billing_phone meta_key__shipping_country meta_key__shipping_first_name meta_key__shipping_last_name meta_key__shipping_company meta_key__shipping_address_1 meta_key__shipping_address_2 meta_key__shipping_city meta_key__shipping_state meta_key__shipping_postcode meta_key__user_title meta_key__linkedin meta_key__digg meta_key__flickr meta_key__stumbleupon meta_key__youtube meta_key__yelp meta_key__reddit meta_key__delicious meta_key__closedpostboxes_spucpt meta_key__metaboxhidden_spucpt meta_key__default_category_id_for_user meta_key___edd_nginx_redirect_dismissed meta_key__meta-box-order_download meta_key__screen_layout_download meta_key___edd_pending_verification meta_key__ecae_premium_ignore_count_notice meta_key__ecae_premium_ignore_notice meta_key__ecae_premium_ignore_count_notice_total meta_key__wpseo_ignore_tour meta_key__wpseo_noindex_author meta_key__wpseo_seen_about_version meta_key__meta-box-order_page meta_key__screen_layout_page meta_key__amt_twitter_author_username meta_key__amt_facebook_author_profile_url meta_key__amt_googleplus_author_profile_url meta_key__locale meta_key__wp_s2member_access_cap_times meta_key__wp_s2member_last_auto_eot_time meta_key__manageedit-shop_ordercolumnshidden meta_key__wp_s2member_subscr_cid meta_key___woocommerce_persistent_cart meta_key__last_update meta_key__wp_s2member_coupon_codes meta_key__wp_s2member_reminders_enable meta_key__as3cfpro-dismiss-licence-notice meta_key__edit_page_per_page meta_key__wpseo_content_analysis_disable meta_key__wpseo_keyword_analysis_disable meta_key__closedpostboxes_surl meta_key__metaboxhidden_surl meta_key__edit_download_per_page meta_key__edit_post_per_page meta_key__closedpostboxes_acf-field-group meta_key__metaboxhidden_acf-field-group meta_key__meta-box-order_surl meta_key__screen_layout_surl meta_key__closedpostboxes_popupbuilder meta_key__metaboxhidden_popupbuilder meta_key__meta-box-order_popupbuilder meta_key__screen_layout_popupbuilder meta_key__community-events-location meta_key__as3cf_dismissed_notices meta_key__syntax_highlighting meta_key__instagram meta_key__myspace meta_key__pinterest meta_key__soundcloud meta_key__tumblr meta_key__wikipedia meta_key___edd_user_address meta_key__closedpostboxes_wbcr-snippets meta_key__metaboxhidden_wbcr-snippets meta_key__q_eud_exports meta_key___wtlwp_user meta_key___wtlwp_created meta_key___wtlwp_expire meta_key___wtlwp_token meta_key__show_welcome_panel meta_key__tlwp_feedback_do_not_ask_again meta_key__tlwp_feedback_do_not_ask_again_time meta_key___wtlwp_last_login meta_key___wtlwp_login_count meta_key__wp_table_pixie_options meta_key__wp_yoast_notifications meta_key__tlwp_feedback_review_done meta_key__tlwp_feedback_review_done_time meta_key___aal_elementor_install_notice meta_key__jetpack_tracks_anon_id meta_key__jetpack_tracks_wpcom_id\n", "npartitions=8 \n", " object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object object\n", " ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", " ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", " ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", "Dask Name: astype, 16 tasks" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.astype(\"str\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Skipping line 852: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead\n", "Skipping line 996: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead\n", "Skipping line 2196: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead\n", "Skipping line 2711: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead\n", "Skipping line 4785: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead\n" ] }, { "data": { "text/plain": [ "'http://192.168.86.250:43766/status'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "op.client.dashboard_link" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Skipping line 46: unexpected end of data\n", "Skipping line 852: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead\n", "Skipping line 996: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead\n", "Skipping line 2196: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead\n", "Skipping line 2711: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead\n", "Skipping line 4785: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead\n" ] } ], "source": [ "preview_df = op.load.file(\"https://bumblebee.nyc3.digitaloceanspaces.com/argenisleon/users-data%20%281%29%20%281%29-535c306e-d5fe-4b09-bd6a-0693e6db26e8.csv\", n_rows=35).ext.cache() \n", "_output = {**preview_df.ext.to_json(\"*\"), \"meta\": preview_df.meta.get() if (preview_df.meta and preview_df.meta.get) else {} } \n", "_output = preview_df.ext.profile(columns=\"*\", output=\"json\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.27.0'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import dask\n", "dask.__version__" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\"columns\": {\"ID\": {\"stats\": {\"match\": 35, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"int\"}, \"frequency\": [{\"value\": \"98\", \"count\": 1}, {\"value\": \"261\", \"count\": 1}, {\"value\": \"276\", \"count\": 1}, {\"value\": \"288\", \"count\": 1}, {\"value\": \"31\", \"count\": 1}, {\"value\": \"32\", \"count\": 1}, {\"value\": \"271\", \"count\": 1}, {\"value\": \"33\", \"count\": 1}, {\"value\": \"44\", \"count\": 1}, {\"value\": \"45\", \"count\": 1}, {\"value\": \"48\", \"count\": 1}, {\"value\": \"61\", \"count\": 1}, {\"value\": \"66\", \"count\": 1}, {\"value\": \"71\", \"count\": 1}, {\"value\": \"42\", \"count\": 1}, {\"value\": \"75\", \"count\": 1}, {\"value\": \"251\", \"count\": 1}, {\"value\": \"208\", \"count\": 1}, {\"value\": \"131\", \"count\": 1}, {\"value\": \"132\", \"count\": 1}, {\"value\": \"139\", \"count\": 1}, {\"value\": \"152\", \"count\": 1}, {\"value\": \"155\", \"count\": 1}, {\"value\": \"166\", \"count\": 1}, {\"value\": \"254\", \"count\": 1}, {\"value\": \"187\", \"count\": 1}, {\"value\": \"211\", \"count\": 1}, {\"value\": \"233\", \"count\": 1}, {\"value\": \"235\", \"count\": 1}, {\"value\": \"236\", \"count\": 1}, {\"value\": \"243\", \"count\": 1}, {\"value\": \"249\", \"count\": 1}, {\"value\": \"273\", \"count\": 1}], \"count_uniques\": 35}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"int\"}}, \"user_login\": {\"stats\": {\"match\": 35, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"string\"}, \"frequency\": [{\"value\": \"zuriavvega\", \"count\": 1}, {\"value\": \"caralunapr\", \"count\": 1}, {\"value\": \"claudiacj\", \"count\": 1}, {\"value\": \"franciscaroni\", \"count\": 1}, {\"value\": \"gabette\", \"count\": 1}, {\"value\": \"georgi2608\", \"count\": 1}, {\"value\": \"cataguio\", \"count\": 1}, {\"value\": \"gloriag\", \"count\": 1}, {\"value\": \"mariana\", \"count\": 1}, {\"value\": \"silvyfalquez\", \"count\": 1}, {\"value\": \"soniasoglia\", \"count\": 1}, {\"value\": \"valentinac\", \"count\": 1}, {\"value\": \"vaneggiare\", \"count\": 1}, {\"value\": \"wenvillarruel\", \"count\": 1}, {\"value\": \"irenea\", \"count\": 1}, {\"value\": \"zeina gabriela\", \"count\": 1}, {\"value\": \"arimaui\", \"count\": 1}, {\"value\": \"Satoriv\", \"count\": 1}, {\"value\": \"CarlaECM\", \"count\": 1}, {\"value\": \"Clara\", \"count\": 1}, {\"value\": \"Itavaamonde\", \"count\": 1}, {\"value\": \"Luisa55\", \"count\": 1}, {\"value\": \"MINEMARQUEZ\", \"count\": 1}, {\"value\": \"Nina\", \"count\": 1}, {\"value\": \"brigittesaint\", \"count\": 1}, {\"value\": \"Rebe22222\", \"count\": 1}, {\"value\": \"SuperBela\", \"count\": 1}, {\"value\": \"Susanacfg\", \"count\": 1}, {\"value\": \"Suza\", \"count\": 1}, {\"value\": \"almarincon\", \"count\": 1}, {\"value\": \"altruisted\", \"count\": 1}, {\"value\": \"amaliaamoedo\", \"count\": 1}, {\"value\": \"cinthia7\", \"count\": 1}], \"count_uniques\": 35}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"string\"}}, \"user_nicename\": {\"stats\": {\"match\": 35, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"string\"}, \"frequency\": [{\"value\": \"zuriavvega\", \"count\": 1}, {\"value\": \"irenea\", \"count\": 1}, {\"value\": \"minemarquez\", \"count\": 1}, {\"value\": \"nina\", \"count\": 1}, {\"value\": \"rebe22222\", \"count\": 1}, {\"value\": \"satoriv\", \"count\": 1}, {\"value\": \"itavaamonde\", \"count\": 1}, {\"value\": \"silvyfalquez\", \"count\": 1}, {\"value\": \"superbela\", \"count\": 1}, {\"value\": \"susanacfg\", \"count\": 1}, {\"value\": \"suza\", \"count\": 1}, {\"value\": \"valentinac\", \"count\": 1}, {\"value\": \"vaneggiare\", \"count\": 1}, {\"value\": \"wenvillarruel\", \"count\": 1}, {\"value\": \"soniasoglia\", \"count\": 1}, {\"value\": \"zeina-gabriela\", \"count\": 1}, {\"value\": \"georgi2608\", \"count\": 1}, {\"value\": \"cataguio\", \"count\": 1}, {\"value\": \"almarincon\", \"count\": 1}, {\"value\": \"altruisted\", \"count\": 1}, {\"value\": \"amaliaamoedo\", \"count\": 1}, {\"value\": \"arimaui\", \"count\": 1}, {\"value\": \"brigittesaint\", \"count\": 1}, {\"value\": \"caralunapr\", \"count\": 1}, {\"value\": \"gloriag\", \"count\": 1}, {\"value\": \"carlaecm\", \"count\": 1}, {\"value\": \"cgarcia23\", \"count\": 1}, {\"value\": \"cinthia7\", \"count\": 1}, {\"value\": \"clara\", \"count\": 1}, {\"value\": \"claudiacj\", \"count\": 1}, {\"value\": \"franciscaroni\", \"count\": 1}, {\"value\": \"gabette\", \"count\": 1}, {\"value\": \"mariana\", \"count\": 1}], \"count_uniques\": 35}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"string\"}}, \"user_email\": {\"stats\": {\"match\": 35, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"email\"}, \"frequency\": [{\"value\": \"zuriavega11@yahoo.com\", \"count\": 1}, {\"value\": \"glogon71@gmail.com\", \"count\": 1}, {\"value\": \"m.vaamonde@gmail.com\", \"count\": 1}, {\"value\": \"marianavq@hotmail.com.ar\", \"count\": 1}, {\"value\": \"minnemarquez@gmail.com\", \"count\": 1}, {\"value\": \"rebeca.omana@yahoo.com.mx\", \"count\": 1}, {\"value\": \"ireneabreu@gmail.com\", \"count\": 1}, {\"value\": \"satori.chi@gmail.com\", \"count\": 1}, {\"value\": \"soniasoglia@yahoo.com.ar\", \"count\": 1}, {\"value\": \"susana.fernandes@gmail.com\", \"count\": 1}, {\"value\": \"susanavasquez17@gmail.com\", \"count\": 1}, {\"value\": \"unmailparanina@gmail.com\", \"count\": 1}, {\"value\": \"valentinaisabel@gmail.com\", \"count\": 1}, {\"value\": \"vanem03@hotmail.com\", \"count\": 1}, {\"value\": \"sfalquez@hotmail.com\", \"count\": 1}, {\"value\": \"zeinaname@gmail.com\", \"count\": 1}, {\"value\": \"ganabela12@hotmail.com\", \"count\": 1}, {\"value\": \"catalinaguio@gmail.com\", \"count\": 1}, {\"value\": \"almarinconruiz@hotmail.com\", \"count\": 1}, {\"value\": \"altruisted@gmail.com\", \"count\": 1}, {\"value\": \"ama.amoedo@gmail.com\", \"count\": 1}, {\"value\": \"ariadnarome@gmail.com\", \"count\": 1}, {\"value\": \"awhernandez75@gmail.com\", \"count\": 1}, {\"value\": \"brigittesaint@hotmail.com\", \"count\": 1}, {\"value\": \"georgi2608@gmail.com\", \"count\": 1}, {\"value\": \"carlacastillom@gmail.com\", \"count\": 1}, {\"value\": \"cinthialarreategui@hotmail.com\", \"count\": 1}, {\"value\": \"claraalfaro@me.com\", \"count\": 1}, {\"value\": \"claudiajimenez@hotmail.com\", \"count\": 1}, {\"value\": \"cynthiagar64@hotmail.com\", \"count\": 1}, {\"value\": \"franciscaroni@gmail.com\", \"count\": 1}, {\"value\": \"gabrielablon@yahoo.com.ar\", \"count\": 1}, {\"value\": \"luisita.g.i@gmail.com\", \"count\": 1}], \"count_uniques\": 35}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"email\"}}, \"user_url\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"user_registered\": {\"stats\": {\"match\": 35, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"date\", \"format\": \"%Y-%m-%d %H:%M:%S\"}, \"frequency\": [{\"value\": \"2013-11-06 18:46:21\", \"count\": 1}, {\"value\": \"2013-09-11 20:13:46\", \"count\": 1}, {\"value\": \"2013-10-06 16:54:35\", \"count\": 1}, {\"value\": \"2013-10-15 17:50:58\", \"count\": 1}, {\"value\": \"2013-10-15 22:57:50\", \"count\": 1}, {\"value\": \"2013-10-16 01:39:11\", \"count\": 1}, {\"value\": \"2013-09-16 16:54:45\", \"count\": 1}, {\"value\": \"2013-10-20 03:34:49\", \"count\": 1}, {\"value\": \"2013-10-23 04:07:46\", \"count\": 1}, {\"value\": \"2013-10-24 22:39:40\", \"count\": 1}, {\"value\": \"2013-10-29 02:40:17\", \"count\": 1}, {\"value\": \"2013-10-31 01:26:49\", \"count\": 1}, {\"value\": \"2013-10-31 21:57:25\", \"count\": 1}, {\"value\": \"2013-11-01 03:22:54\", \"count\": 1}, {\"value\": \"2013-10-22 19:28:11\", \"count\": 1}, {\"value\": \"2013-11-02 16:47:36\", \"count\": 1}, {\"value\": \"2013-09-02 18:39:50\", \"count\": 1}, {\"value\": \"2013-07-13 23:33:40\", \"count\": 1}, {\"value\": \"2013-07-07 14:28:03\", \"count\": 1}, {\"value\": \"2013-07-07 14:33:45\", \"count\": 1}, {\"value\": \"2013-07-08 14:12:40\", \"count\": 1}, {\"value\": \"2013-07-08 14:57:53\", \"count\": 1}, {\"value\": \"2013-07-08 16:46:56\", \"count\": 1}, {\"value\": \"2013-07-08 21:57:02\", \"count\": 1}, {\"value\": \"2013-09-11 20:09:00\", \"count\": 1}, {\"value\": \"2013-07-12 22:58:21\", \"count\": 1}, {\"value\": \"2013-07-15 19:06:34\", \"count\": 1}, {\"value\": \"2013-07-16 19:23:10\", \"count\": 1}, {\"value\": \"2013-07-30 15:05:31\", \"count\": 1}, {\"value\": \"2013-08-09 00:16:38\", \"count\": 1}, {\"value\": \"2013-08-28 06:43:41\", \"count\": 1}, {\"value\": \"2013-08-28 18:30:27\", \"count\": 1}, {\"value\": \"2013-10-04 09:46:13\", \"count\": 1}], \"count_uniques\": 35}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"date\", \"format\": \"%Y-%m-%d %H:%M:%S\"}}, \"user_activation_key\": {\"stats\": {\"match\": 6, \"missing\": 29, \"mismatch\": 29, \"profiler_dtype\": {\"dtype\": \"string\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 29}, {\"value\": \"iE9NCjqVrAJtHWHtMAUG\", \"count\": 1}, {\"value\": \"1551405832:$P$BZTT/wFqoQhPE8K9..9c/nRhewlGMp.\", \"count\": 1}, {\"value\": \"$P$BdT5dfJ/ya0i2YoprnjEf/JTkWfdsE0\", \"count\": 1}, {\"value\": \"$P$BKwfdu7aoH3uac8WSkMziuyef1.qvd.\", \"count\": 1}, {\"value\": \"$P$BKU7gDOauoNL1/ValTgGTIQ/T3UyFM.\", \"count\": 1}, {\"value\": \"$P$BIwpSjEYtbb0r2Yop4HvgbHSl6f0qZ/\", \"count\": 1}], \"count_uniques\": 7}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"string\"}}, \"user_status\": {\"stats\": {\"match\": 35, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"int\"}, \"frequency\": [{\"value\": \"0\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"int\"}}, \"display_name\": {\"stats\": {\"match\": 35, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": 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\"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key___wtlwp_token\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key__show_welcome_panel\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key__tlwp_feedback_do_not_ask_again\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key__tlwp_feedback_do_not_ask_again_time\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key___wtlwp_last_login\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key___wtlwp_login_count\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key__wp_table_pixie_options\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key__wp_yoast_notifications\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key__tlwp_feedback_review_done\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key__tlwp_feedback_review_done_time\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key___aal_elementor_install_notice\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key__jetpack_tracks_anon_id\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"meta_key__jetpack_tracks_wpcom_id\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}}, \"name\": null, \"file_name\": \"https://bumblebee.nyc3.digitaloceanspaces.com/argenisleon/users-data%20%281%29%20%281%29-535c306e-d5fe-4b09-bd6a-0693e6db26e8.csv\", \"summary\": {\"cols_count\": 183, \"rows_count\": 35, \"dtypes_list\": [\"object\"], \"total_count_dtypes\": 1, \"missing_count\": 0, \"p_missing\": 0.0}}\n" ] } ], "source": [ "print(_output)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting dask==2.27.0\n", " Downloading dask-2.27.0-py3-none-any.whl (843 kB)\n", "Requirement already satisfied: pyyaml in c:\\users\\argenisleon\\appdata\\roaming\\python\\python37\\site-packages (from dask==2.27.0) (5.3.1)\n", "Installing collected packages: dask\n", " Attempting uninstall: dask\n", " Found existing installation: dask 2.25.0\n", " Uninstalling dask-2.25.0:\n", " Successfully uninstalled dask-2.25.0\n", "Successfully installed dask-2.27.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "ERROR: datashader 0.8.0 requires datashape>=0.5.1, which is not installed.\n", "ERROR: datashader 0.8.0 requires scikit-image, which is not installed.\n" ] } ], "source": [ "!pip install dask==2.27.0" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.25.0'" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import dask\n", "dask.__version__" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\"columns\": {\"ID\": {\"stats\": {\"match\": 35, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"int\"}, \"frequency\": [{\"value\": \"98\", \"count\": 1}, {\"value\": \"261\", \"count\": 1}, {\"value\": \"276\", \"count\": 1}, {\"value\": \"288\", \"count\": 1}, {\"value\": \"31\", \"count\": 1}, {\"value\": \"32\", \"count\": 1}, {\"value\": \"271\", \"count\": 1}, {\"value\": \"33\", \"count\": 1}, {\"value\": \"44\", \"count\": 1}, {\"value\": \"45\", \"count\": 1}, {\"value\": \"48\", \"count\": 1}, {\"value\": \"61\", \"count\": 1}, {\"value\": \"66\", \"count\": 1}, {\"value\": \"71\", \"count\": 1}, {\"value\": \"42\", \"count\": 1}, {\"value\": \"75\", \"count\": 1}, {\"value\": \"251\", \"count\": 1}, {\"value\": \"208\", \"count\": 1}, {\"value\": \"131\", \"count\": 1}, {\"value\": \"132\", \"count\": 1}, {\"value\": \"139\", \"count\": 1}, {\"value\": \"152\", \"count\": 1}, {\"value\": \"155\", \"count\": 1}, {\"value\": \"166\", \"count\": 1}, {\"value\": \"254\", \"count\": 1}, {\"value\": \"187\", \"count\": 1}, {\"value\": \"211\", \"count\": 1}, {\"value\": \"233\", \"count\": 1}, {\"value\": \"235\", \"count\": 1}, {\"value\": \"236\", \"count\": 1}, {\"value\": \"243\", \"count\": 1}, {\"value\": \"249\", \"count\": 1}, {\"value\": \"273\", \"count\": 1}], \"count_uniques\": 35}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"int\"}}, \"user_login\": {\"stats\": {\"match\": 35, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"string\"}, \"frequency\": [{\"value\": \"zuriavvega\", \"count\": 1}, {\"value\": \"caralunapr\", \"count\": 1}, {\"value\": \"claudiacj\", \"count\": 1}, {\"value\": \"franciscaroni\", \"count\": 1}, {\"value\": \"gabette\", \"count\": 1}, {\"value\": \"georgi2608\", \"count\": 1}, {\"value\": \"cataguio\", \"count\": 1}, {\"value\": \"gloriag\", \"count\": 1}, {\"value\": \"mariana\", \"count\": 1}, {\"value\": \"silvyfalquez\", \"count\": 1}, {\"value\": \"soniasoglia\", \"count\": 1}, {\"value\": \"valentinac\", \"count\": 1}, {\"value\": \"vaneggiare\", \"count\": 1}, {\"value\": \"wenvillarruel\", \"count\": 1}, {\"value\": \"irenea\", \"count\": 1}, {\"value\": \"zeina gabriela\", \"count\": 1}, {\"value\": \"arimaui\", \"count\": 1}, {\"value\": \"Satoriv\", \"count\": 1}, {\"value\": \"CarlaECM\", \"count\": 1}, {\"value\": \"Clara\", \"count\": 1}, {\"value\": \"Itavaamonde\", \"count\": 1}, {\"value\": \"Luisa55\", \"count\": 1}, {\"value\": \"MINEMARQUEZ\", \"count\": 1}, {\"value\": \"Nina\", \"count\": 1}, {\"value\": \"brigittesaint\", \"count\": 1}, {\"value\": \"Rebe22222\", \"count\": 1}, {\"value\": \"SuperBela\", \"count\": 1}, {\"value\": \"Susanacfg\", \"count\": 1}, {\"value\": \"Suza\", \"count\": 1}, {\"value\": \"almarincon\", \"count\": 1}, {\"value\": \"altruisted\", \"count\": 1}, {\"value\": \"amaliaamoedo\", \"count\": 1}, {\"value\": \"cinthia7\", \"count\": 1}], \"count_uniques\": 35}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"string\"}}, \"user_nicename\": {\"stats\": {\"match\": 35, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"string\"}, \"frequency\": [{\"value\": \"zuriavvega\", \"count\": 1}, {\"value\": \"irenea\", \"count\": 1}, {\"value\": \"minemarquez\", \"count\": 1}, {\"value\": \"nina\", \"count\": 1}, {\"value\": \"rebe22222\", \"count\": 1}, {\"value\": \"satoriv\", \"count\": 1}, {\"value\": \"itavaamonde\", \"count\": 1}, {\"value\": \"silvyfalquez\", \"count\": 1}, {\"value\": \"superbela\", \"count\": 1}, {\"value\": \"susanacfg\", \"count\": 1}, {\"value\": \"suza\", \"count\": 1}, {\"value\": \"valentinac\", \"count\": 1}, {\"value\": \"vaneggiare\", \"count\": 1}, {\"value\": \"wenvillarruel\", \"count\": 1}, {\"value\": \"soniasoglia\", \"count\": 1}, {\"value\": \"zeina-gabriela\", \"count\": 1}, {\"value\": \"georgi2608\", \"count\": 1}, {\"value\": \"cataguio\", \"count\": 1}, {\"value\": \"almarincon\", \"count\": 1}, {\"value\": \"altruisted\", \"count\": 1}, {\"value\": \"amaliaamoedo\", \"count\": 1}, {\"value\": \"arimaui\", \"count\": 1}, {\"value\": \"brigittesaint\", \"count\": 1}, {\"value\": \"caralunapr\", \"count\": 1}, {\"value\": \"gloriag\", \"count\": 1}, {\"value\": \"carlaecm\", \"count\": 1}, {\"value\": \"cgarcia23\", \"count\": 1}, {\"value\": \"cinthia7\", \"count\": 1}, {\"value\": \"clara\", \"count\": 1}, {\"value\": \"claudiacj\", \"count\": 1}, {\"value\": \"franciscaroni\", \"count\": 1}, {\"value\": \"gabette\", \"count\": 1}, {\"value\": \"mariana\", \"count\": 1}], \"count_uniques\": 35}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"string\"}}, \"user_email\": {\"stats\": {\"match\": 35, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"email\"}, \"frequency\": [{\"value\": \"zuriavega11@yahoo.com\", \"count\": 1}, {\"value\": \"glogon71@gmail.com\", \"count\": 1}, {\"value\": \"m.vaamonde@gmail.com\", \"count\": 1}, {\"value\": \"marianavq@hotmail.com.ar\", \"count\": 1}, {\"value\": \"minnemarquez@gmail.com\", \"count\": 1}, {\"value\": \"rebeca.omana@yahoo.com.mx\", \"count\": 1}, {\"value\": \"ireneabreu@gmail.com\", \"count\": 1}, {\"value\": \"satori.chi@gmail.com\", \"count\": 1}, {\"value\": \"soniasoglia@yahoo.com.ar\", \"count\": 1}, {\"value\": \"susana.fernandes@gmail.com\", \"count\": 1}, {\"value\": \"susanavasquez17@gmail.com\", \"count\": 1}, {\"value\": \"unmailparanina@gmail.com\", \"count\": 1}, {\"value\": \"valentinaisabel@gmail.com\", \"count\": 1}, {\"value\": \"vanem03@hotmail.com\", \"count\": 1}, {\"value\": \"sfalquez@hotmail.com\", \"count\": 1}, {\"value\": \"zeinaname@gmail.com\", \"count\": 1}, {\"value\": \"ganabela12@hotmail.com\", \"count\": 1}, {\"value\": \"catalinaguio@gmail.com\", \"count\": 1}, {\"value\": \"almarinconruiz@hotmail.com\", \"count\": 1}, {\"value\": \"altruisted@gmail.com\", \"count\": 1}, {\"value\": \"ama.amoedo@gmail.com\", \"count\": 1}, {\"value\": \"ariadnarome@gmail.com\", \"count\": 1}, {\"value\": \"awhernandez75@gmail.com\", \"count\": 1}, {\"value\": \"brigittesaint@hotmail.com\", \"count\": 1}, {\"value\": \"georgi2608@gmail.com\", \"count\": 1}, {\"value\": \"carlacastillom@gmail.com\", \"count\": 1}, {\"value\": \"cinthialarreategui@hotmail.com\", \"count\": 1}, {\"value\": \"claraalfaro@me.com\", \"count\": 1}, {\"value\": \"claudiajimenez@hotmail.com\", \"count\": 1}, {\"value\": \"cynthiagar64@hotmail.com\", \"count\": 1}, {\"value\": \"franciscaroni@gmail.com\", \"count\": 1}, {\"value\": \"gabrielablon@yahoo.com.ar\", \"count\": 1}, {\"value\": \"luisita.g.i@gmail.com\", \"count\": 1}], \"count_uniques\": 35}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"email\"}}, \"user_url\": {\"stats\": {\"match\": 35, \"missing\": 35, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"object\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 35}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}, \"user_registered\": {\"stats\": {\"match\": 35, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"date\", \"format\": \"%Y-%m-%d %H:%M:%S\"}, \"frequency\": [{\"value\": \"2013-11-06 18:46:21\", \"count\": 1}, {\"value\": \"2013-09-11 20:13:46\", \"count\": 1}, {\"value\": \"2013-10-06 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\"count\": 1}, {\"value\": \"2013-07-08 16:46:56\", \"count\": 1}, {\"value\": \"2013-07-08 21:57:02\", \"count\": 1}, {\"value\": \"2013-09-11 20:09:00\", \"count\": 1}, {\"value\": \"2013-07-12 22:58:21\", \"count\": 1}, {\"value\": \"2013-07-15 19:06:34\", \"count\": 1}, {\"value\": \"2013-07-16 19:23:10\", \"count\": 1}, {\"value\": \"2013-07-30 15:05:31\", \"count\": 1}, {\"value\": \"2013-08-09 00:16:38\", \"count\": 1}, {\"value\": \"2013-08-28 06:43:41\", \"count\": 1}, {\"value\": \"2013-08-28 18:30:27\", \"count\": 1}, {\"value\": \"2013-10-04 09:46:13\", \"count\": 1}], \"count_uniques\": 35}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"date\", \"format\": \"%Y-%m-%d %H:%M:%S\"}}, \"user_activation_key\": {\"stats\": {\"match\": 6, \"missing\": 29, \"mismatch\": 29, \"profiler_dtype\": {\"dtype\": \"string\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 29}, {\"value\": \"iE9NCjqVrAJtHWHtMAUG\", \"count\": 1}, {\"value\": 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1}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"object\"}}}, \"name\": null, \"file_name\": \"https://bumblebee.nyc3.digitaloceanspaces.com/argenisleon/users-data%20%281%29%20%281%29-535c306e-d5fe-4b09-bd6a-0693e6db26e8.csv\", \"summary\": {\"cols_count\": 183, \"rows_count\": 35, \"dtypes_list\": [\"object\"], \"total_count_dtypes\": 1, \"missing_count\": 0, \"p_missing\": 0.0}}\n" ] } ], "source": [ "print(_output)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'sample': {'columns': [{'title': 'ID'}, {'title': 'user_login'}, {'title': 'user_nicename'}, {'title': 'user_email'}, {'title': 'user_url'}, {'title': 'user_registered'}, {'title': 'user_activation_key'}, {'title': 'user_status'}, {'title': 'display_name'}, {'title': 'role'}, {'title': 'ccaps'}, {'title': 'meta_key__first_name'}, {'title': 'meta_key__last_name'}, {'title': 'meta_key__nickname'}, {'title': 'meta_key__description'}, {'title': 'meta_key__rich_editing'}, {'title': 'meta_key__comment_shortcuts'}, {'title': 'meta_key__admin_color'}, {'title': 'meta_key__use_ssl'}, {'title': 'meta_key__show_admin_bar_front'}, {'title': 'meta_key__wp_capabilities'}, {'title': 'meta_key__wp_user_level'}, {'title': 'meta_key__dismissed_wp_pointers'}, {'title': 'meta_key__wp_dashboard_quick_press_last_post_id'}, {'title': 'meta_key__wp_user-settings'}, {'title': 'meta_key__wp_user-settings-time'}, {'title': 'meta_key__AtD_options'}, {'title': 'meta_key__AtD_check_when'}, {'title': 'meta_key__AtD_guess_lang'}, {'title': 'meta_key__AtD_ignored_phrases'}, {'title': 'meta_key__aim'}, {'title': 'meta_key__yim'}, {'title': 'meta_key__jabber'}, {'title': 'meta_key__nav_menu_recently_edited'}, {'title': 'meta_key__managenav-menuscolumnshidden'}, {'title': 'meta_key__metaboxhidden_nav-menus'}, {'title': 'meta_key__closedpostboxes_post'}, {'title': 'meta_key__metaboxhidden_post'}, {'title': 'meta_key__pmpro_logins'}, {'title': 'meta_key__pmpro_visits'}, {'title': 'meta_key__pmpro_views'}, {'title': 'meta_key__wp_s2member_last_login_time'}, {'title': 'meta_key__wp_s2member_registration_ip'}, {'title': 'meta_key__wp_s2member_login_counter'}, {'title': 'meta_key__wp_s2member_paid_registration_times'}, {'title': 'meta_key__default_password_nag'}, {'title': 'meta_key__wp_s2member_subscr_gateway'}, {'title': 'meta_key__wp_s2member_subscr_id'}, {'title': 'meta_key__wp_s2member_custom'}, {'title': 'meta_key__wp_s2member_ipn_signup_vars'}, {'title': 'meta_key__wp_s2member_first_payment_txn_id'}, {'title': 'meta_key__wp_s2member_last_payment_time'}, {'title': 'meta_key__meta-box-order_dashboard'}, {'title': 'meta_key__screen_layout_dashboard'}, {'title': 'meta_key__wp_s2member_notes'}, {'title': 'meta_key__wp_s2member_auto_eot_time'}, {'title': 'meta_key___yoast_wpseo_profile_updated'}, {'title': 'meta_key__wpseo_title'}, {'title': 'meta_key__wpseo_metadesc'}, {'title': 'meta_key__wpseo_metakey'}, {'title': 'meta_key__googleplus'}, {'title': 'meta_key__twitter'}, {'title': 'meta_key__facebook'}, {'title': 'meta_key__meta-box-order_post'}, {'title': 'meta_key__screen_layout_post'}, {'title': 'meta_key__closedpostboxes_page'}, {'title': 'meta_key__metaboxhidden_page'}, {'title': 'meta_key__manageedit-postcolumnshidden'}, {'title': 'meta_key__easycontactforms_stat_pointer'}, {'title': 'meta_key__wp_s2member_capability_times'}, {'title': 'meta_key__wp_s2member_subscr_baid'}, {'title': 'meta_key__closedpostboxes_dashboard'}, {'title': 'meta_key__metaboxhidden_dashboard'}, {'title': 'meta_key__closedpostboxes_attachment'}, {'title': 'meta_key__metaboxhidden_attachment'}, {'title': 'meta_key__meta-box-order_attachment'}, {'title': 'meta_key__screen_layout_attachment'}, {'title': 'meta_key__session_tokens'}, {'title': 'meta_key___edd_htaccess_missing_dismissed'}, {'title': 'meta_key__wp_media_library_mode'}, {'title': 'meta_key__closedpostboxes_download'}, {'title': 'meta_key__metaboxhidden_download'}, {'title': 'meta_key__billing_country'}, {'title': 'meta_key__billing_first_name'}, {'title': 'meta_key__billing_last_name'}, {'title': 'meta_key__billing_company'}, {'title': 'meta_key__billing_address_1'}, {'title': 'meta_key__billing_address_2'}, {'title': 'meta_key__billing_city'}, {'title': 'meta_key__billing_state'}, {'title': 'meta_key__billing_postcode'}, {'title': 'meta_key__billing_email'}, {'title': 'meta_key__billing_phone'}, {'title': 'meta_key__shipping_country'}, {'title': 'meta_key__shipping_first_name'}, {'title': 'meta_key__shipping_last_name'}, {'title': 'meta_key__shipping_company'}, {'title': 'meta_key__shipping_address_1'}, {'title': 'meta_key__shipping_address_2'}, {'title': 'meta_key__shipping_city'}, {'title': 'meta_key__shipping_state'}, {'title': 'meta_key__shipping_postcode'}, {'title': 'meta_key__user_title'}, {'title': 'meta_key__linkedin'}, {'title': 'meta_key__digg'}, {'title': 'meta_key__flickr'}, {'title': 'meta_key__stumbleupon'}, {'title': 'meta_key__youtube'}, {'title': 'meta_key__yelp'}, {'title': 'meta_key__reddit'}, {'title': 'meta_key__delicious'}, {'title': 'meta_key__closedpostboxes_spucpt'}, {'title': 'meta_key__metaboxhidden_spucpt'}, {'title': 'meta_key__default_category_id_for_user'}, {'title': 'meta_key___edd_nginx_redirect_dismissed'}, {'title': 'meta_key__meta-box-order_download'}, {'title': 'meta_key__screen_layout_download'}, {'title': 'meta_key___edd_pending_verification'}, {'title': 'meta_key__ecae_premium_ignore_count_notice'}, {'title': 'meta_key__ecae_premium_ignore_notice'}, {'title': 'meta_key__ecae_premium_ignore_count_notice_total'}, {'title': 'meta_key__wpseo_ignore_tour'}, {'title': 'meta_key__wpseo_noindex_author'}, {'title': 'meta_key__wpseo_seen_about_version'}, {'title': 'meta_key__meta-box-order_page'}, {'title': 'meta_key__screen_layout_page'}, {'title': 'meta_key__amt_twitter_author_username'}, {'title': 'meta_key__amt_facebook_author_profile_url'}, {'title': 'meta_key__amt_googleplus_author_profile_url'}, {'title': 'meta_key__locale'}, {'title': 'meta_key__wp_s2member_access_cap_times'}, {'title': 'meta_key__wp_s2member_last_auto_eot_time'}, {'title': 'meta_key__manageedit-shop_ordercolumnshidden'}, {'title': 'meta_key__wp_s2member_subscr_cid'}, {'title': 'meta_key___woocommerce_persistent_cart'}, {'title': 'meta_key__last_update'}, {'title': 'meta_key__wp_s2member_coupon_codes'}, {'title': 'meta_key__wp_s2member_reminders_enable'}, {'title': 'meta_key__as3cfpro-dismiss-licence-notice'}, {'title': 'meta_key__edit_page_per_page'}, {'title': 'meta_key__wpseo_content_analysis_disable'}, {'title': 'meta_key__wpseo_keyword_analysis_disable'}, {'title': 'meta_key__closedpostboxes_surl'}, {'title': 'meta_key__metaboxhidden_surl'}, {'title': 'meta_key__edit_download_per_page'}, {'title': 'meta_key__edit_post_per_page'}, {'title': 'meta_key__closedpostboxes_acf-field-group'}, {'title': 'meta_key__metaboxhidden_acf-field-group'}, {'title': 'meta_key__meta-box-order_surl'}, {'title': 'meta_key__screen_layout_surl'}, {'title': 'meta_key__closedpostboxes_popupbuilder'}, {'title': 'meta_key__metaboxhidden_popupbuilder'}, {'title': 'meta_key__meta-box-order_popupbuilder'}, {'title': 'meta_key__screen_layout_popupbuilder'}, {'title': 'meta_key__community-events-location'}, {'title': 'meta_key__as3cf_dismissed_notices'}, {'title': 'meta_key__syntax_highlighting'}, {'title': 'meta_key__instagram'}, {'title': 'meta_key__myspace'}, {'title': 'meta_key__pinterest'}, {'title': 'meta_key__soundcloud'}, {'title': 'meta_key__tumblr'}, {'title': 'meta_key__wikipedia'}, {'title': 'meta_key___edd_user_address'}, {'title': 'meta_key__closedpostboxes_wbcr-snippets'}, {'title': 'meta_key__metaboxhidden_wbcr-snippets'}, {'title': 'meta_key__q_eud_exports'}, {'title': 'meta_key___wtlwp_user'}, {'title': 'meta_key___wtlwp_created'}, {'title': 'meta_key___wtlwp_expire'}, {'title': 'meta_key___wtlwp_token'}, {'title': 'meta_key__show_welcome_panel'}, {'title': 'meta_key__tlwp_feedback_do_not_ask_again'}, {'title': 'meta_key__tlwp_feedback_do_not_ask_again_time'}, {'title': 'meta_key___wtlwp_last_login'}, {'title': 'meta_key___wtlwp_login_count'}, {'title': 'meta_key__wp_table_pixie_options'}, {'title': 'meta_key__wp_yoast_notifications'}, {'title': 'meta_key__tlwp_feedback_review_done'}, {'title': 'meta_key__tlwp_feedback_review_done_time'}, {'title': 'meta_key___aal_elementor_install_notice'}, {'title': 'meta_key__jetpack_tracks_anon_id'}, {'title': 'meta_key__jetpack_tracks_wpcom_id'}], 'value': [['44', 'Rebe22222', 'rebe22222', 'rebeca.omana@yahoo.com.mx', nan, '2013-07-08 14:57:53', nan, '0', 'Rebeca Omana Bruguera', 's2member_level4', nan, 'Rebeca', 'Omana Bruguera', 'Rebe22222', nan, 'true', 'false', 'fresh', '0', 'true', '{\"s2member_level4\":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Sun Jul 05 2020 11:50:35 GMT-0400', '69.174.87.140', '996', '{\"level\":1373295473,\"level4\":1373295473}', '1', 'paypal', 'I-GDDHAAPKNT4R', nan, nan, nan, 'Wed Dec 31 1969 20:00:00 GMT-0400', nan, nan, nan, nan, '1379337854', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:1:{s:64:\"1ab370b34ee335ca3db5979209ac1a1e1ff3c4458d04c9077f30a21f9f5270e9\";a:4:{s:10:\"expiration\";i:1595173835;s:2:\"ip\";s:13:\"187.237.25.96\";s:2:\"ua\";s:139:\"Mozilla/5.0 (iPhone; CPU iPhone OS 13_5_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.1 Mobile/15E148 Safari/604.1\";s:5:\"login\";i:1593964235;}}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 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ID @ time of demotion: paypal -› I-W9DBUEX7NHS9\\nDemoted by s2Member: Wed Jan 27, 2016 8:34 pm UTC\\nPaid Subscr. ID @ time of demotion: paypal -› I-M6UGL1XS2BCU\\nDemoted by s2Member: Mon Apr 4, 2016 12:04 pm UTC\\nPaid Subscr. ID @ time of demotion: paypal -› I-MUM7AHAY3D59\\nDemoted by s2Member: Thu Apr 19, 2018 11:24 am UTC\\nPaid Subscr. 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Anual. 12/08/16 // Cuenta venció el 12/08/17\\n\\nArg. Anual reactivada el 25 de agosto 2017 / Cuenta venció el 25 de agosto de 2018\\n\\nArg. Anual. Cuenta reactivada el 31 de agosto de 2018/ Cuenta venció el 31 de Agosto de 2019\\n\\nArg. Anual. Cuenta reactivada el 08 de abril de 2020', nan, '1589222571', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:4:{i:1437395984;s:16:\"-s2member_level4\";i:1438029027;s:15:\"s2member_level4\";i:1470838373;s:16:\"-s2member_level4\";i:1471031664;s:15:\"s2member_level4\";}', nan, nan, nan, nan, nan, nan, nan, 'a:2:{s:64:\"18f501ddc6919dcb0043f0839530a6230d31ab49d3727b0aea8dcd3ebbef876d\";a:4:{s:10:\"expiration\";i:1627288581;s:2:\"ip\";s:14:\"90.170.101.179\";s:2:\"ua\";s:139:\"Mozilla/5.0 (iPhone; CPU iPhone OS 13_5_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.1 Mobile/15E148 Safari/604.1\";s:5:\"login\";i:1595752581;}s:64:\"1b0b1356a237e82552f530bdf6f6cb74c2482e55296927e808bf28068476ed8e\";a:4:{s:10:\"expiration\";i:1631036237;s:2:\"ip\";s:14:\"90.170.101.179\";s:2:\"ua\";s:139:\"Mozilla/5.0 (iPhone; CPU iPhone OS 13_6_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.2 Mobile/15E148 Safari/604.1\";s:5:\"login\";i:1599500237;}}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, '{\"1503603078.0001\":\"-level1\",\"1503603078.0002\":\"-level2\",\"1503603078.0003\":\"-level3\",\"1503603078.0004\":\"-level4\",\"1503689282.0001\":\"level1\",\"1503689282.0002\":\"level2\",\"1503689282.0003\":\"level3\",\"1503689282.0004\":\"level4\",\"1535385039.0001\":\"-level1\",\"1535385039.0002\":\"-level2\",\"1535385039.0003\":\"-level3\",\"1535385039.0004\":\"-level4\",\"1535733296.0001\":\"level1\",\"1535733296.0002\":\"level2\",\"1535733296.0003\":\"level3\",\"1535733296.0004\":\"level4\",\"1567442527.0001\":\"-level1\",\"1567442527.0002\":\"-level2\",\"1567442527.0003\":\"-level3\",\"1567442527.0004\":\"-level4\",\"1586383310.0001\":\"level1\",\"1586383310.0002\":\"level2\",\"1586383310.0003\":\"level3\",\"1586383310.0004\":\"level4\",\"1588978737.0001\":\"-level1\",\"1588978737.0002\":\"-level2\",\"1588978737.0003\":\"-level3\",\"1588978737.0004\":\"-level4\",\"1589222571.0001\":\"level1\",\"1589222571.0002\":\"level2\",\"1589222571.0003\":\"level3\",\"1589222571.0004\":\"level4\"}', nan, nan, nan, nan, nan, 'a:0:{}', '1', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'true', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None], ['139', 'cgarcia23', 'cgarcia23', 'cynthiagar64@hotmail.com', nan, '2013-09-02 18:39:50', nan, '0', 'Cinthia Garcia', 's2member_level4', nan, 'Cinthia', 'Garcia', 'cgarcia23', nan, 'true', 'false', 'fresh', '0', 'true', '{\"s2member_level4\":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Mon Sep 14 2020 07:24:54 GMT-0400', '186.7.16.203', '816', '{\"level\":1378147190,\"level2\":1378147190,\"level4\":1522343971}', nan, 'paypal', 'I-VG1LUTKFP5DX', 'miastral.com', '{\"txn_type\":\"subscr_signup\",\"subscr_id\":\"I-VG1LUTKFP5DX\",\"last_name\":\"Garcia\",\"option_selection1\":\"139\",\"option_selection2\":\"168.196.126.99\",\"residence_country\":\"DO\",\"mc_currency\":\"USD\",\"item_name\":\"Aware Supervixen (Mensual)\",\"business\":\"miastral.servicios@gmail.com\",\"amount3\":\"30.00\",\"recurring\":\"30.00\",\"verify_sign\":\"A00ZnsNH5LtCdimPdJCzMdGJgE5JAwMUDcmJM7W9DXsB3u8RU9LAU6jj\",\"payer_status\":\"verified\",\"payer_email\":\"csfashionproject@live.com\",\"first_name\":\"Cinthia\",\"receiver_email\":\"miastral.servicios@gmail.com\",\"option_name1\":\"Referencing Customer ID\",\"payer_id\":\"AFSRT8N8VT4AN\",\"invoice\":\"5f39a5e1a3e70~168.196.126.99\",\"option_name2\":\"Customer IP Address\",\"reattempt\":\"1\",\"item_number\":\"4\",\"payer_business_name\":\"Xcentriss By Cinthia Garcia\",\"subscr_date\":\"14:33:35 Aug 16, 2020 PDT\",\"custom\":\"miastral.com\",\"charset\":\"UTF-8\",\"notify_version\":\"3.9\",\"period3\":\"1 M\",\"mc_amount3\":\"30.00\",\"ipn_track_id\":\"8d60b49de9500\",\"option_selection\":\"139\",\"amount\":\"30.00\",\"option_name\":\"Referencing Customer ID\",\"period\":\"1 M\",\"mc_amount\":\"30.00\",\"subscr_gateway\":\"paypal\",\"subscr_baid\":\"I-VG1LUTKFP5DX\",\"subscr_cid\":\"I-VG1LUTKFP5DX\",\"level\":\"4\",\"ccaps\":null,\"eotper\":null,\"ip\":\"168.196.126.99\",\"period1\":\"0 D\",\"mc_amount1\":\"0.00\",\"initial_term\":\"0 D\",\"initial\":\"30.00\",\"regular\":\"30.00\",\"regular_term\":\"1 M\",\"currency\":\"USD\",\"currency_symbol\":\"$\"}', '6E234920B5860774C', 'Wed Sep 16 2020 07:15:40 GMT-0400', nan, nan, 'Demoted by s2Member: Thu Oct 3, 2013 6:49 pm UTC\\nPaid Subscr. ID @ time of demotion: paypal -› I-3CJBPAY5PARY\\nDemoted by s2Member: Mon Jul 9, 2018 11:28 am UTC\\nPaid Subscr. ID @ time of demotion: paypal → I-E0S5BY671BJ2\\nDemoted by s2Member: Mon Jun 1, 2020 12:51 pm UTC\\nPaid Subscr. ID @ time of demotion: paypal → I-KYCJMVLNSV43\\nDemoted by s2Member: Fri Aug 7, 2020 12:06 pm UTC\\nPaid Subscr. 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ID @ time of demotion: paypal -› I-HY91M6YNPF4W\\nDemoted by s2Member: Fri Mar 24, 2017 1:48 pm UTC\\nPaid Subscr. 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ID @ time of demotion: paypal → I-LDELF0D981L7', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:2:{i:1452342174;s:16:\"-s2member_level4\";i:1465313779;s:15:\"s2member_level4\";}', nan, nan, nan, nan, nan, nan, nan, 'a:1:{s:64:\"4f1a90eadb66ba5514ba1b2ea3c16a8a75b64f134e48dfa81c0533e517cc1681\";a:4:{s:10:\"expiration\";i:1578095744;s:2:\"ip\";s:14:\"170.51.140.188\";s:2:\"ua\";s:139:\"Mozilla/5.0 (iPhone; CPU iPhone OS 12_4_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.1.2 Mobile/15E148 Safari/604.1\";s:5:\"login\";i:1577922944;}}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, '{\"1497704664.0001\":\"-level1\",\"1497704664.0002\":\"-level2\",\"1497704664.0003\":\"-level3\",\"1497704664.0004\":\"-level4\",\"1536774862.0001\":\"level1\",\"1536774862.0002\":\"level2\",\"1536774862.0003\":\"level3\",\"1536774862.0004\":\"level4\",\"1601055002.0013\":\"-level1\",\"1601055002.0014\":\"-level2\",\"1601055002.0015\":\"-level3\",\"1601055002.0016\":\"-level4\"}', '1599658670', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None], ['272', 'soniasoglia', 'soniasoglia', 'soniasoglia@yahoo.com.ar', nan, '2013-10-31 21:57:25', nan, '0', 'sonia soglia', 's2member_level4', nan, 'sonia', 'soglia', 'soniasoglia', nan, 'true', 'false', 'fresh', '0', 'true', '{\"s2member_level4\":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Mon Jul 21 2014 10:31:49 GMT-0430', '67.176.62.150', '95', '{\"level\":1383256645,\"level2\":1383256645,\"level4\":1388756838}', nan, nan, nan, nan, nan, nan, 'Wed Dec 31 1969 20:00:00 GMT-0400', nan, nan, nan, nan, '1388893190', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:1:{i:1405696490;s:15:\"s2member_level4\";}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None], ['273', 'brigittesaint', 'brigittesaint', 'brigittesaint@hotmail.com', nan, '2013-11-01 03:22:54', nan, '0', 'Brigitte Saint-Hilaire', 's2member_level4', nan, 'Brigitte', 'Saint-Hilaire', 'brigittesaint', nan, 'true', 'false', 'fresh', '0', 'true', '{\"s2member_level4\":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Wed Feb 08 2017 17:53:16 GMT-0400', '190.166.194.59', '941', '{\"level\":1383276174,\"level4\":1383276174}', nan, 'paypal', 'I-NJPP0XAG56RR', nan, nan, '0PD28027EU836990P', 'Tue Sep 15 2020 07:25:15 GMT-0400', nan, nan, 'Demoted by s2Member: Sat May 16, 2015 11:02 am UTC\\nPaid Subscr. ID @ time of demotion: paypal -› I-FD73U8AUR6TA', nan, '1561491092', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:3:{i:1430669094;s:16:\"-s2member_level4\";i:1430923956;s:15:\"s2member_level4\";i:1431774161;s:16:\"-s2member_level4\";}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, '{\"1550071595.0001\":\"level1\",\"1550071595.0002\":\"level2\",\"1550071595.0003\":\"level3\",\"1550071595.0004\":\"level4\",\"1555344325.0001\":\"-level1\",\"1555344325.0002\":\"-level2\",\"1555344325.0003\":\"-level3\",\"1555344325.0004\":\"-level4\",\"1561491092.0001\":\"level1\",\"1561491092.0002\":\"level2\",\"1561491092.0003\":\"level3\",\"1561491092.0004\":\"level4\"}', nan, nan, nan, nan, nan, 'a:0:{}', '1', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'true', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None], ['276', 'cinthia7', 'cinthia7', 'cinthialarreategui@hotmail.com', nan, '2013-11-02 16:47:36', nan, '0', 'cinthia larreategui', 's2member_level4', nan, 'cinthia', 'larreategui', 'cinthia7', nan, 'true', 'false', 'fresh', '0', 'true', '{\"s2member_level4\":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Tue Nov 20 2018 21:57:05 GMT-0400', '198.228.200.18', '17', '{\"level\":1383410856,\"level2\":1383410856,\"level4\":1409012003}', nan, 'paypal', 'I-17N8H4D81V7F', nan, nan, '3YA91413A3269413D', 'Mon Nov 26 2018 09:19:49 GMT-0400', nan, nan, 'Demoted by s2Member: Wed Nov 12, 2014 12:45 pm UTC\\nPaid Subscr. ID @ time of demotion: paypal -› I-CWE6A31VPVYW', nan, '1542810233', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:2:{i:1409012003;s:16:\"-s2member_level2\";i:1415796358;s:16:\"-s2member_level4\";}', nan, nan, nan, nan, nan, nan, nan, 'a:1:{s:64:\"9406ec4b9027d978a2b3e8d409d22fbe171ad7ae64b254f957001a788373cb32\";a:4:{s:10:\"expiration\";i:1543975025;s:2:\"ip\";s:12:\"68.199.48.72\";s:2:\"ua\";s:143:\"Mozilla/5.0 (iPhone; CPU iPhone OS 12_0_1 like Mac OS X) AppleWebKit/604.1.34 (KHTML, like Gecko) GSA/62.1.220348572 Mobile/16A404 Safari/604.1\";s:5:\"login\";i:1542765425;}}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, '{\"1542810233.0001\":\"level1\",\"1542810233.0002\":\"level2\",\"1542810233.0003\":\"level3\",\"1542810233.0004\":\"level4\"}', nan, nan, nan, nan, nan, 'a:0:{}', '1', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None]]}}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py:6167: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 6213 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] } ], "source": [ "_output = df.ext.buffer_window(\"*\", 4, 34).ext.to_json(\"*\")\n", "print(_output)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'columns': {'ID': {'stats': {'match': 6213,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'int'},\n", " 'frequency': [{'value': '9997', 'count': 1},\n", " {'value': '125921', 'count': 1},\n", " {'value': '125885', 'count': 1},\n", " {'value': '125884', 'count': 1},\n", " {'value': '125883', 'count': 1},\n", " {'value': '125882', 'count': 1},\n", " {'value': '125881', 'count': 1},\n", " {'value': '125890', 'count': 1},\n", " {'value': '125902', 'count': 1},\n", " {'value': '125903', 'count': 1},\n", " {'value': '125904', 'count': 1},\n", " {'value': '125926', 'count': 1},\n", " {'value': '125925', 'count': 1},\n", " {'value': '125924', 'count': 1},\n", " 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ID @ time of demotion: paypal → I-BUMB5D6RPPAW',\n", " 'count': 1},\n", " {'value': 'Demoted by s2Member: Mon Aug 10, 2020 10:24 am UTC\\nPaid Subscr. ID @ time of demotion: paypal → I-DBX9UK1US9Y1',\n", " 'count': 1},\n", " {'value': 'Demoted by s2Member: Mon Aug 1, 2016 12:54 pm UTC\\nPaid Subscr. ID @ time of demotion: paypal -› I-E8M999N9LH5H\\nDemoted by s2Member: Tue Mar 14, 2017 12:47 pm UTC\\nPaid Subscr. ID @ time of demotion: paypal → I-ACN5C0RVCAJF\\nDemoted by s2Member: Tue Nov 13, 2018 7:34 pm UTC\\nPaid Subscr. ID @ time of demotion: paypal → I-BDU5LFTWPV8F\\nDemoted by s2Member: Sun May 12, 2019 1:32 pm UTC\\nPaid Subscr. 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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
ID
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
user_login
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
user_nicename
\n", "
3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
user_email
\n", "
4 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
user_url
\n", "
5 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
user_registered
\n", "
6 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
user_activation_key
\n", "
7 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
user_status
\n", "
8 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
display_name
\n", "
9 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
role
\n", "
10 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
ccaps
\n", "
11 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__first_name
\n", "
12 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__last_name
\n", "
13 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__nickname
\n", "
14 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__description
\n", "
15 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__rich_editing
\n", "
16 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__comment_shortcuts
\n", "
17 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__admin_color
\n", "
18 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__use_ssl
\n", "
19 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__show_admin_bar_front
\n", "
20 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_capabilities
\n", "
21 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_user_level
\n", "
22 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__dismissed_wp_pointers
\n", "
23 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_dashboard_quick_press_last_post_id
\n", "
24 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_user-settings
\n", "
25 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_user-settings-time
\n", "
26 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__AtD_options
\n", "
27 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__AtD_check_when
\n", "
28 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__AtD_guess_lang
\n", "
29 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__AtD_ignored_phrases
\n", "
30 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__aim
\n", "
31 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__yim
\n", "
32 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__jabber
\n", "
33 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__nav_menu_recently_edited
\n", "
34 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__managenav-menuscolumnshidden
\n", "
35 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__metaboxhidden_nav-menus
\n", "
36 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__closedpostboxes_post
\n", "
37 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__metaboxhidden_post
\n", "
38 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__pmpro_logins
\n", "
39 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__pmpro_visits
\n", "
40 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__pmpro_views
\n", "
41 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_last_login_time
\n", "
42 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_registration_ip
\n", "
43 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_login_counter
\n", "
44 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_paid_registration_times
\n", "
45 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__default_password_nag
\n", "
46 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_subscr_gateway
\n", "
47 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_subscr_id
\n", "
48 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_custom
\n", "
49 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_ipn_signup_vars
\n", "
50 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_first_payment_txn_id
\n", "
51 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_last_payment_time
\n", "
52 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__meta-box-order_dashboard
\n", "
53 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__screen_layout_dashboard
\n", "
54 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_notes
\n", "
55 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_auto_eot_time
\n", "
56 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key___yoast_wpseo_profile_updated
\n", "
57 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wpseo_title
\n", "
58 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wpseo_metadesc
\n", "
59 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wpseo_metakey
\n", "
60 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__googleplus
\n", "
61 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__twitter
\n", "
62 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__facebook
\n", "
63 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__meta-box-order_post
\n", "
64 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__screen_layout_post
\n", "
65 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__closedpostboxes_page
\n", "
66 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__metaboxhidden_page
\n", "
67 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__manageedit-postcolumnshidden
\n", "
68 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__easycontactforms_stat_pointer
\n", "
69 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_capability_times
\n", "
70 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_subscr_baid
\n", "
71 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__closedpostboxes_dashboard
\n", "
72 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__metaboxhidden_dashboard
\n", "
73 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__closedpostboxes_attachment
\n", "
74 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__metaboxhidden_attachment
\n", "
75 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__meta-box-order_attachment
\n", "
76 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__screen_layout_attachment
\n", "
77 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__session_tokens
\n", "
78 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key___edd_htaccess_missing_dismissed
\n", "
79 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_media_library_mode
\n", "
80 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__closedpostboxes_download
\n", "
81 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__metaboxhidden_download
\n", "
82 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__billing_country
\n", "
83 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__billing_first_name
\n", "
84 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__billing_last_name
\n", "
85 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__billing_company
\n", "
86 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__billing_address_1
\n", "
87 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__billing_address_2
\n", "
88 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__billing_city
\n", "
89 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__billing_state
\n", "
90 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__billing_postcode
\n", "
91 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__billing_email
\n", "
92 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__billing_phone
\n", "
93 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__shipping_country
\n", "
94 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__shipping_first_name
\n", "
95 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__shipping_last_name
\n", "
96 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__shipping_company
\n", "
97 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__shipping_address_1
\n", "
98 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__shipping_address_2
\n", "
99 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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meta_key__shipping_city
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100 (object)
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meta_key__shipping_state
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101 (object)
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meta_key__shipping_postcode
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102 (object)
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meta_key__user_title
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103 (object)
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meta_key__linkedin
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104 (object)
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meta_key__digg
\n", "
105 (object)
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meta_key__flickr
\n", "
106 (object)
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meta_key__stumbleupon
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107 (object)
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meta_key__youtube
\n", "
108 (object)
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meta_key__yelp
\n", "
109 (object)
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meta_key__reddit
\n", "
110 (object)
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meta_key__delicious
\n", "
111 (object)
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meta_key__closedpostboxes_spucpt
\n", "
112 (object)
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meta_key__metaboxhidden_spucpt
\n", "
113 (object)
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meta_key__default_category_id_for_user
\n", "
114 (object)
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meta_key___edd_nginx_redirect_dismissed
\n", "
115 (object)
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meta_key__meta-box-order_download
\n", "
116 (object)
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meta_key__screen_layout_download
\n", "
117 (object)
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meta_key___edd_pending_verification
\n", "
118 (object)
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meta_key__ecae_premium_ignore_count_notice
\n", "
119 (object)
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meta_key__ecae_premium_ignore_notice
\n", "
120 (object)
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meta_key__ecae_premium_ignore_count_notice_total
\n", "
121 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__wpseo_ignore_tour
\n", "
122 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__wpseo_noindex_author
\n", "
123 (object)
\n", "
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meta_key__wpseo_seen_about_version
\n", "
124 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__meta-box-order_page
\n", "
125 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__screen_layout_page
\n", "
126 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__amt_twitter_author_username
\n", "
127 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__amt_facebook_author_profile_url
\n", "
128 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__amt_googleplus_author_profile_url
\n", "
129 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__locale
\n", "
130 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__wp_s2member_access_cap_times
\n", "
131 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__wp_s2member_last_auto_eot_time
\n", "
132 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__manageedit-shop_ordercolumnshidden
\n", "
133 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_subscr_cid
\n", "
134 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key___woocommerce_persistent_cart
\n", "
135 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__last_update
\n", "
136 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_coupon_codes
\n", "
137 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__wp_s2member_reminders_enable
\n", "
138 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__as3cfpro-dismiss-licence-notice
\n", "
139 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__edit_page_per_page
\n", "
140 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__wpseo_content_analysis_disable
\n", "
141 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__wpseo_keyword_analysis_disable
\n", "
142 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__closedpostboxes_surl
\n", "
143 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__metaboxhidden_surl
\n", "
144 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__edit_download_per_page
\n", "
145 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__edit_post_per_page
\n", "
146 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__closedpostboxes_acf-field-group
\n", "
147 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__metaboxhidden_acf-field-group
\n", "
148 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__meta-box-order_surl
\n", "
149 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__screen_layout_surl
\n", "
150 (object)
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\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__closedpostboxes_popupbuilder
\n", "
151 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__metaboxhidden_popupbuilder
\n", "
152 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__meta-box-order_popupbuilder
\n", "
153 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__screen_layout_popupbuilder
\n", "
154 (object)
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\n", " \n", " not nullable\n", " \n", "
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meta_key__community-events-location
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155 (object)
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\n", " \n", " not nullable\n", " \n", "
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meta_key__as3cf_dismissed_notices
\n", "
156 (object)
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\n", "
meta_key__syntax_highlighting
\n", "
157 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__instagram
\n", "
158 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__myspace
\n", "
159 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__pinterest
\n", "
160 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__soundcloud
\n", "
161 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__tumblr
\n", "
162 (object)
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\n", " \n", " not nullable\n", " \n", "
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meta_key__wikipedia
\n", "
163 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key___edd_user_address
\n", "
164 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__closedpostboxes_wbcr-snippets
\n", "
165 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__metaboxhidden_wbcr-snippets
\n", "
166 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__q_eud_exports
\n", "
167 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key___wtlwp_user
\n", "
168 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key___wtlwp_created
\n", "
169 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key___wtlwp_expire
\n", "
170 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key___wtlwp_token
\n", "
171 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__show_welcome_panel
\n", "
172 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__tlwp_feedback_do_not_ask_again
\n", "
173 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__tlwp_feedback_do_not_ask_again_time
\n", "
174 (object)
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\n", "
meta_key___wtlwp_last_login
\n", "
175 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key___wtlwp_login_count
\n", "
176 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__wp_table_pixie_options
\n", "
177 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__wp_yoast_notifications
\n", "
178 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__tlwp_feedback_review_done
\n", "
179 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__tlwp_feedback_review_done_time
\n", "
180 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key___aal_elementor_install_notice
\n", "
181 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", "
meta_key__jetpack_tracks_anon_id
\n", "
182 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
meta_key__jetpack_tracks_wpcom_id
\n", "
183 (object)
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Viewing 10 of 6213 rows / 183 columns
\n", "
8 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "_output = df.ext.profile(columns=\"*\", output=\"json\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py:6167: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 45716 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] } ], "source": [ "_output = df.ext.set_buffer(\"*\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py:6167: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 45716 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "data": { "text/plain": [ "{'columns': {'new year': {'stats': {'match': 45716,\n", " 'missing': 312,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'date', 'format': '%d-%m-%Y'},\n", " 'frequency': [{'value': '01-01-2003', 'count': 3323},\n", " {'value': '01-01-1979', 'count': 3046},\n", " {'value': '01-01-1998', 'count': 2697},\n", " {'value': '01-01-2006', 'count': 2456},\n", " {'value': '01-01-1988', 'count': 2296},\n", " {'value': '01-01-2002', 'count': 2078},\n", " {'value': '01-01-2004', 'count': 1940},\n", " {'value': '01-01-2000', 'count': 1792},\n", " {'value': '01-01-1997', 'count': 1696},\n", " {'value': '01-01-1999', 'count': 1691},\n", " {'value': '01-01-2001', 'count': 1650},\n", " {'value': '01-01-1990', 'count': 1518},\n", " {'value': '01-01-2009', 'count': 1497},\n", " {'value': '01-01-1986', 'count': 1375},\n", " {'value': '01-01-2007', 'count': 1189},\n", " {'value': '01-01-2010', 'count': 1005},\n", " {'value': '01-01-1993', 'count': 979},\n", " {'value': '01-01-2008', 'count': 957},\n", " {'value': '01-01-1987', 'count': 916},\n", " {'value': '01-01-1991', 'count': 877},\n", " {'value': '01-01-2005', 'count': 875},\n", " {'value': '01-01-1994', 'count': 719},\n", " {'value': '01-01-2011', 'count': 713},\n", " {'value': '01-01-1974', 'count': 691},\n", " {'value': '01-01-1996', 'count': 583},\n", " {'value': '01-01-1995', 'count': 487},\n", " {'value': '01-01-1981', 'count': 463},\n", " {'value': '01-01-1977', 'count': 421},\n", " {'value': '01-01-1984', 'count': 402},\n", " {'value': '01-01-1985', 'count': 378},\n", " {'value': '01-01-1992', 'count': 372},\n", " {'value': '01-01-1983', 'count': 360},\n", " {'value': '01-01-1982', 'count': 344}],\n", " 'count_uniques': 245},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'date', 'format': '%d-%m-%Y'}}},\n", " 'name': None,\n", " 'file_name': None,\n", " 'summary': {'cols_count': 11,\n", " 'rows_count': 45716,\n", " 'dtypes_list': ['int64', 'object', 'float64'],\n", " 'total_count_dtypes': 3,\n", " 'missing_count': 0,\n", " 'p_missing': 0.0}}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.buffer_window(\"*\").cols.date_format(\"year\", \"%d/%m/%Y %I:%M:%S %p\", \"%d-%m-%Y\", output_cols=\"new year\").ext.profile([\"new year\"])\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'http://192.168.86.250:23223/status'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "op.client.dashboard_link" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data/foo.csv\n" ] }, { "data": { "text/plain": [ "{'file_name': 'data/foo.csv',\n", " 'mime_info': [{'mime': 'text/plain',\n", " 'encoding': 'utf-8',\n", " 'file_ext': 'csv',\n", " 'file_type': 'csv',\n", " 'properties': {'delimiter': ',',\n", " 'doublequote': False,\n", " 'escapechar': None,\n", " 'line_terminator': '\\r\\n',\n", " 'quotechar': '\"',\n", " 'quoting': 0,\n", " 'skipinitialspace': False}}]}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = op.load.file(\"data/foo.csv\").ext.cache()\n", "df.meta.get()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "df= df.cols.sqrt(\"id\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'file_name': 'data/foo.csv',\n", " 'mime_info': [{'mime': 'text/plain',\n", " 'encoding': 'utf-8',\n", " 'file_ext': 'csv',\n", " 'file_type': 'csv',\n", " 'properties': {'delimiter': ',',\n", " 'doublequote': False,\n", " 'escapechar': None,\n", " 'line_terminator': '\\r\\n',\n", " 'quotechar': '\"',\n", " 'quoting': 0,\n", " 'skipinitialspace': False}}],\n", " 'last_action_time': 1602444573,\n", " 'transformations': {'actions': [{'math': 'id'}]}}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.meta.get()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py:6167: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 19 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "data": { "text/html": [ "
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idfirstNamelastNamebillingIdproductpricebirthdummyCol
01.000000LuisAlvarez$$%!123Cake101980/07/07never
11.414214AndréAmpère423piza81950/07/08gonna
21.732051NiELSBöhr//((%%551pizza81990/07/09give
32.000000PAULdirac$521pizza81954/07/10you
42.236068AlbertEinstein634pizza81990/07/11up
52.449490GalileoGALiLEI672arepa51930/08/12never
62.645751CaRLGa%%%uss323taco31970/07/13gonna
72.828427DavidH$$$ilbert624taaaccoo31950/07/14let
83.000000JohannesKEPLER735taco31920/04/22you
93.162278JaMESM$$ax%%well875taco31923/03/12down
103.316625IsaacNewton992pasta91999/02/15never
113.464102Emmy%%Nöether$234pasta91993/12/08gonna
123.605551Max!!!Planck!!!111hamburguer41994/01/04run
133.741657FredHoy&&&le553pizzza81997/06/27around
143.872983((( Heinrich )))))Hertz116pizza81956/11/30and
154.000000WilliamGilbert###886BEER21958/03/26desert
164.123106MarieCURIE912Rice12000/03/22you
174.242641ArthurCOM%%%pton81211079051899/01/01#
184.358899JAMESChadwick467NaN101921/05/03#
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" ], "text/plain": [ " id firstName lastName billingId \\\n", "0 1.000000 Luis Alvarez$$%! 123 \n", "1 1.414214 André Ampère 423 \n", "2 1.732051 NiELS Böhr//((%% 551 \n", "3 2.000000 PAUL dirac$ 521 \n", "4 2.236068 Albert Einstein 634 \n", "5 2.449490 Galileo GALiLEI 672 \n", "6 2.645751 CaRL Ga%%%uss 323 \n", "7 2.828427 David H$$$ilbert 624 \n", "8 3.000000 Johannes KEPLER 735 \n", "9 3.162278 JaMES M$$ax%%well 875 \n", "10 3.316625 Isaac Newton 992 \n", "11 3.464102 Emmy%% Nöether$ 234 \n", "12 3.605551 Max!!! Planck!!! 111 \n", "13 3.741657 Fred Hoy&&&le 553 \n", "14 3.872983 ((( Heinrich ))))) Hertz 116 \n", "15 4.000000 William Gilbert### 886 \n", "16 4.123106 Marie CURIE 912 \n", "17 4.242641 Arthur COM%%%pton 812 \n", "18 4.358899 JAMES Chadwick 467 \n", "\n", " product price birth dummyCol \n", "0 Cake 10 1980/07/07 never \n", "1 piza 8 1950/07/08 gonna \n", "2 pizza 8 1990/07/09 give \n", "3 pizza 8 1954/07/10 you \n", "4 pizza 8 1990/07/11 up \n", "5 arepa 5 1930/08/12 never \n", "6 taco 3 1970/07/13 gonna \n", "7 taaaccoo 3 1950/07/14 let \n", "8 taco 3 1920/04/22 you \n", "9 taco 3 1923/03/12 down \n", "10 pasta 9 1999/02/15 never \n", "11 pasta 9 1993/12/08 gonna \n", "12 hamburguer 4 1994/01/04 run \n", "13 pizzza 8 1997/06/27 around \n", "14 pizza 8 1956/11/30 and \n", "15 BEER 2 1958/03/26 desert \n", "16 Rice 1 2000/03/22 you \n", "17 110790 5 1899/01/01 # \n", "18 NaN 10 1921/05/03 # " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.buffer_window()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py:6167: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 19 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] } ], "source": [ "df.ext.set_buffer()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'file_name': 'data/foo.csv',\n", " 'mime_info': [{'mime': 'text/plain',\n", " 'encoding': 'utf-8',\n", " 'file_ext': 'csv',\n", " 'file_type': 'csv',\n", " 'properties': {'delimiter': ',',\n", " 'doublequote': False,\n", " 'escapechar': None,\n", " 'line_terminator': '\\r\\n',\n", " 'quotechar': '\"',\n", " 'quoting': 0,\n", " 'skipinitialspace': False}}],\n", " 'last_action_time': 1602444021,\n", " 'transformations': {'actions': [{'math': 'id'}]},\n", " 'buffer_time': 1602444194}" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.meta.get()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.meta.get(\"buffer_time\") > df.meta.get(\"last_action_time\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['id',\n", " 'firstName',\n", " 'lastName',\n", " 'billingId',\n", " 'product',\n", " 'price',\n", " 'birth',\n", " 'dummyCol']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.names()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dask DataFrame Structure:\n", " id firstName lastName billingId product price birth dummyCol\n", "npartitions=1 \n", " object object object object object object object object\n", " ... ... ... ... ... ... ... ...\n", "Dask Name: read-csv, 1 tasks *\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py:6167: UserWarning: Insufficient elements for `head`. 30 elements requested, only 19 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "data": { "text/plain": [ "{'columns': {'id': {'stats': {'match': 19,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'int'},\n", " 'frequency': [{'value': '9', 'count': 1},\n", " {'value': '17', 'count': 1},\n", " {'value': '10', 'count': 1},\n", " {'value': '11', 'count': 1},\n", " {'value': '12', 'count': 1},\n", " {'value': '13', 'count': 1},\n", " {'value': '14', 'count': 1},\n", " {'value': '15', 'count': 1},\n", " {'value': '16', 'count': 1},\n", " {'value': '18', 'count': 1},\n", " {'value': '8', 'count': 1},\n", " {'value': '19', 'count': 1},\n", " {'value': '2', 'count': 1},\n", " {'value': '3', 'count': 1},\n", " {'value': '4', 'count': 1},\n", " {'value': '5', 'count': 1},\n", " {'value': '6', 'count': 1},\n", " {'value': '7', 'count': 1},\n", " {'value': '1', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'int'}},\n", " 'firstName': {'stats': {'match': 19,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'William', 'count': 1},\n", " {'value': 'Galileo', 'count': 1},\n", " {'value': 'Albert', 'count': 1},\n", " {'value': 'André', 'count': 1},\n", " {'value': 'Arthur', 'count': 1},\n", " {'value': 'CaRL', 'count': 1},\n", " {'value': 'David', 'count': 1},\n", " {'value': 'Emmy%%', 'count': 1},\n", " {'value': 'Fred', 'count': 1},\n", " {'value': 'Isaac', 'count': 1},\n", " {'value': 'PAUL', 'count': 1},\n", " {'value': 'JAMES', 'count': 1},\n", " {'value': 'JaMES', 'count': 1},\n", " {'value': 'Johannes', 'count': 1},\n", " {'value': 'Luis', 'count': 1},\n", " {'value': 'Marie', 'count': 1},\n", " {'value': 'Max!!!', 'count': 1},\n", " {'value': 'NiELS', 'count': 1},\n", " {'value': '((( Heinrich )))))', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}},\n", " 'lastName': {'stats': {'match': 19,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'dirac$', 'count': 1},\n", " {'value': 'Ga%%%uss', 'count': 1},\n", " {'value': 'Alvarez$$%!', 'count': 1},\n", " {'value': 'Ampère', 'count': 1},\n", " {'value': 'Böhr//((%%', 'count': 1},\n", " {'value': 'COM%%%pton', 'count': 1},\n", " {'value': 'CURIE', 'count': 1},\n", " {'value': 'Chadwick', 'count': 1},\n", " {'value': 'Einstein', 'count': 1},\n", " {'value': 'Gilbert###', 'count': 1},\n", " {'value': 'Planck!!!', 'count': 1},\n", " {'value': 'H$$$ilbert', 'count': 1},\n", " {'value': 'Hertz', 'count': 1},\n", " {'value': 'Hoy&&&le', 'count': 1},\n", " {'value': 'KEPLER', 'count': 1},\n", " {'value': 'M$$ax%%well', 'count': 1},\n", " {'value': 'Newton', 'count': 1},\n", " {'value': 'Nöether$', 'count': 1},\n", " {'value': ' GALiLEI', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}},\n", " 'billingId': {'stats': {'match': 19,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'int'},\n", " 'frequency': [{'value': '992', 'count': 1},\n", " {'value': '551', 'count': 1},\n", " {'value': '116', 'count': 1},\n", " {'value': '123', 'count': 1},\n", " {'value': '234', 'count': 1},\n", " {'value': '323', 'count': 1},\n", " {'value': '423', 'count': 1},\n", " {'value': '467', 'count': 1},\n", " {'value': '521', 'count': 1},\n", " {'value': '553', 'count': 1},\n", " {'value': '912', 'count': 1},\n", " {'value': '624', 'count': 1},\n", " {'value': '634', 'count': 1},\n", " {'value': '672', 'count': 1},\n", " {'value': '735', 'count': 1},\n", " {'value': '812', 'count': 1},\n", " {'value': '875', 'count': 1},\n", " {'value': '886', 'count': 1},\n", " {'value': '111', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'int'}},\n", " 'product': {'stats': {'match': 18,\n", " 'missing': 1,\n", " 'mismatch': 1,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'pizza', 'count': 4},\n", " {'value': 'taco', 'count': 3},\n", " {'value': 'pasta', 'count': 2},\n", " {'value': 'taaaccoo', 'count': 1},\n", " {'value': 'pizzza', 'count': 1},\n", " {'value': 'piza', 'count': 1},\n", " {'value': 'nan', 'count': 1},\n", " {'value': 'hamburguer', 'count': 1},\n", " {'value': 'arepa', 'count': 1},\n", " {'value': 'Rice', 'count': 1},\n", " {'value': 'Cake', 'count': 1},\n", " {'value': 'BEER', 'count': 1},\n", " {'value': '110790', 'count': 1}],\n", " 'count_uniques': 13},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}},\n", " 'price': {'stats': {'match': 19,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'int'},\n", " 'frequency': [{'value': '8', 'count': 6},\n", " {'value': '3', 'count': 4},\n", " {'value': '9', 'count': 2},\n", " {'value': '5', 'count': 2},\n", " {'value': '10', 'count': 2},\n", " {'value': '4', 'count': 1},\n", " {'value': '2', 'count': 1},\n", " {'value': '1', 'count': 1}],\n", " 'count_uniques': 8},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'int'}},\n", " 'birth': {'stats': {'match': 19,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'date', 'format': '%Y/%m/%d'},\n", " 'frequency': [{'value': '2000/03/22', 'count': 1},\n", " {'value': '1956/11/30', 'count': 1},\n", " {'value': '1920/04/22', 'count': 1},\n", " {'value': '1921/05/03', 'count': 1},\n", " {'value': '1923/03/12', 'count': 1},\n", " {'value': '1930/08/12', 'count': 1},\n", " {'value': '1950/07/08', 'count': 1},\n", " {'value': '1950/07/14', 'count': 1},\n", " {'value': '1954/07/10', 'count': 1},\n", " {'value': '1958/03/26', 'count': 1},\n", " {'value': '1999/02/15', 'count': 1},\n", " {'value': '1970/07/13', 'count': 1},\n", " {'value': '1980/07/07', 'count': 1},\n", " {'value': '1990/07/09', 'count': 1},\n", " {'value': '1990/07/11', 'count': 1},\n", " {'value': '1993/12/08', 'count': 1},\n", " {'value': '1994/01/04', 'count': 1},\n", " {'value': '1997/06/27', 'count': 1},\n", " {'value': '1899/01/01', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'date', 'format': '%Y/%m/%d'}},\n", " 'dummyCol': {'stats': {'match': 19,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'profiler_dtype': {'dtype': 'string'},\n", " 'frequency': [{'value': 'you', 'count': 3},\n", " {'value': 'gonna', 'count': 3},\n", " {'value': 'never', 'count': 2},\n", " {'value': '#', 'count': 2},\n", " {'value': 'up', 'count': 1},\n", " {'value': 'run ', 'count': 1},\n", " {'value': 'never ', 'count': 1},\n", " {'value': 'let', 'count': 1},\n", " {'value': 'give', 'count': 1},\n", " {'value': 'down', 'count': 1},\n", " {'value': 'desert', 'count': 1},\n", " {'value': 'around', 'count': 1},\n", " {'value': 'and', 'count': 1}],\n", " 'count_uniques': 13},\n", " 'dtype': 'object',\n", " 'profiler_dtype': {'dtype': 'string'}}},\n", " 'name': None,\n", " 'file_name': 'data/foo.csv',\n", " 'summary': {'cols_count': 8,\n", " 'rows_count': 19,\n", " 'dtypes_list': ['object'],\n", " 'total_count_dtypes': 1,\n", " 'missing_count': 0,\n", " 'p_missing': 0.0}}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.profile(columns=\"*\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data/crime.csv\n" ] }, { "data": { "text/plain": [ "{'file_name': 'data/crime.csv',\n", " 'mime_info': [{'mime': 'text/plain',\n", " 'encoding': 'iso-8859-1',\n", " 'file_ext': 'csv',\n", " 'file_type': 'csv',\n", " 'properties': {'delimiter': ',',\n", " 'doublequote': False,\n", " 'escapechar': None,\n", " 'line_terminator': '\\r\\n',\n", " 'quotechar': '\"',\n", " 'quoting': 0,\n", " 'skipinitialspace': False}}]}" ] }, "execution_count": 7, "metadata": {}, 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idfirstNamelastNamebillingIdproductpricebirthdummyCol
01LuisAlvarez$$%!123Cake101980/07/07never
12AndréAmpère423piza81950/07/08gonna
23NiELSBöhr//((%%551pizza81990/07/09give
34PAULdirac$521pizza81954/07/10you
45AlbertEinstein634pizza81990/07/11up
56GalileoGALiLEI672arepa51930/08/12never
67CaRLGa%%%uss323taco31970/07/13gonna
78DavidH$$$ilbert624taaaccoo31950/07/14let
89JohannesKEPLER735taco31920/04/22you
910JaMESM$$ax%%well875taco31923/03/12down
1011IsaacNewton992pasta91999/02/15never
1112Emmy%%Nöether$234pasta91993/12/08gonna
1213Max!!!Planck!!!111hamburguer41994/01/04run
1314FredHoy&&&le553pizzza81997/06/27around
1415((( Heinrich )))))Hertz116pizza81956/11/30and
1516WilliamGilbert###886BEER21958/03/26desert
1617MarieCURIE912Rice12000/03/22you
1718ArthurCOM%%%pton81211079051899/01/01#
1819JAMESChadwick467NaN101921/05/03#
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" ], "text/plain": [ " id firstName lastName billingId product \\\n", "0 1 Luis Alvarez$$%! 123 Cake \n", "1 2 André Ampère 423 piza \n", "2 3 NiELS Böhr//((%% 551 pizza \n", "3 4 PAUL dirac$ 521 pizza \n", "4 5 Albert Einstein 634 pizza \n", "5 6 Galileo GALiLEI 672 arepa \n", "6 7 CaRL Ga%%%uss 323 taco \n", "7 8 David H$$$ilbert 624 taaaccoo \n", "8 9 Johannes KEPLER 735 taco \n", "9 10 JaMES M$$ax%%well 875 taco \n", "10 11 Isaac Newton 992 pasta \n", "11 12 Emmy%% Nöether$ 234 pasta \n", "12 13 Max!!! Planck!!! 111 hamburguer \n", "13 14 Fred Hoy&&&le 553 pizzza \n", "14 15 ((( Heinrich ))))) Hertz 116 pizza \n", "15 16 William Gilbert### 886 BEER \n", "16 17 Marie CURIE 912 Rice \n", "17 18 Arthur COM%%%pton 812 110790 \n", "18 19 JAMES Chadwick 467 NaN \n", "\n", " price birth dummyCol \n", "0 10 1980/07/07 never \n", "1 8 1950/07/08 gonna \n", "2 8 1990/07/09 give \n", "3 8 1954/07/10 you \n", "4 8 1990/07/11 up \n", "5 5 1930/08/12 never \n", "6 3 1970/07/13 gonna \n", "7 3 1950/07/14 let \n", "8 3 1920/04/22 you \n", "9 3 1923/03/12 down \n", "10 9 1999/02/15 never \n", "11 9 1993/12/08 gonna \n", "12 4 1994/01/04 run \n", "13 8 1997/06/27 around \n", "14 8 1956/11/30 and \n", "15 2 1958/03/26 desert \n", "16 1 2000/03/22 you \n", "17 5 1899/01/01 # \n", "18 10 1921/05/03 # " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.compute()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idfirstNamelastNamebillingIdproductpricebirthdummyCol
01.000000LuisAlvarez$$%!123Cake101980/07/07never
11.414214AndréAmpère423piza81950/07/08gonna
21.732051NiELSBöhr//((%%551pizza81990/07/09give
32.000000PAULdirac$521pizza81954/07/10you
42.236068AlbertEinstein634pizza81990/07/11up
52.449490GalileoGALiLEI672arepa51930/08/12never
62.645751CaRLGa%%%uss323taco31970/07/13gonna
72.828427DavidH$$$ilbert624taaaccoo31950/07/14let
83.000000JohannesKEPLER735taco31920/04/22you
93.162278JaMESM$$ax%%well875taco31923/03/12down
103.316625IsaacNewton992pasta91999/02/15never
113.464102Emmy%%Nöether$234pasta91993/12/08gonna
123.605551Max!!!Planck!!!111hamburguer41994/01/04run
133.741657FredHoy&&&le553pizzza81997/06/27around
143.872983((( Heinrich )))))Hertz116pizza81956/11/30and
154.000000WilliamGilbert###886BEER21958/03/26desert
164.123106MarieCURIE912Rice12000/03/22you
174.242641ArthurCOM%%%pton81211079051899/01/01#
184.358899JAMESChadwick467NaN101921/05/03#
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" ], "text/plain": [ " id firstName lastName billingId \\\n", "0 1.000000 Luis Alvarez$$%! 123 \n", "1 1.414214 André Ampère 423 \n", "2 1.732051 NiELS Böhr//((%% 551 \n", "3 2.000000 PAUL dirac$ 521 \n", "4 2.236068 Albert Einstein 634 \n", "5 2.449490 Galileo GALiLEI 672 \n", "6 2.645751 CaRL Ga%%%uss 323 \n", "7 2.828427 David H$$$ilbert 624 \n", "8 3.000000 Johannes KEPLER 735 \n", "9 3.162278 JaMES M$$ax%%well 875 \n", "10 3.316625 Isaac Newton 992 \n", "11 3.464102 Emmy%% Nöether$ 234 \n", "12 3.605551 Max!!! Planck!!! 111 \n", "13 3.741657 Fred Hoy&&&le 553 \n", "14 3.872983 ((( Heinrich ))))) Hertz 116 \n", "15 4.000000 William Gilbert### 886 \n", "16 4.123106 Marie CURIE 912 \n", "17 4.242641 Arthur COM%%%pton 812 \n", "18 4.358899 JAMES Chadwick 467 \n", "\n", " product price birth dummyCol \n", "0 Cake 10 1980/07/07 never \n", "1 piza 8 1950/07/08 gonna \n", "2 pizza 8 1990/07/09 give \n", "3 pizza 8 1954/07/10 you \n", "4 pizza 8 1990/07/11 up \n", "5 arepa 5 1930/08/12 never \n", "6 taco 3 1970/07/13 gonna \n", "7 taaaccoo 3 1950/07/14 let \n", "8 taco 3 1920/04/22 you \n", "9 taco 3 1923/03/12 down \n", "10 pasta 9 1999/02/15 never \n", "11 pasta 9 1993/12/08 gonna \n", "12 hamburguer 4 1994/01/04 run \n", "13 pizzza 8 1997/06/27 around \n", "14 pizza 8 1956/11/30 and \n", "15 BEER 2 1958/03/26 desert \n", "16 Rice 1 2000/03/22 you \n", "17 110790 5 1899/01/01 # \n", "18 NaN 10 1921/05/03 # " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.sqrt('id').compute()" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "module 'math' has no attribute 'kurtosis'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkurtosis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'1'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: module 'math' has no attribute 'kurtosis'" ] } ], "source": [ "import math\n", "math.kurtosis('1')" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "..\\optimus\\engines\\functions.py:187: RuntimeWarning: invalid value encountered in arctanh\n", " \n" ] }, { "data": { "text/html": [ "
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idfirstNamelastNamebillingIdproductpricebirthdummyColida
01LuisAlvarez$$%!123Cake101980/07/07neverNaN
12AndréAmpère423piza81950/07/08gonnaNaN
23NiELSBöhr//((%%551pizza81990/07/09giveNaN
34PAULdirac$521pizza81954/07/10youNaN
45AlbertEinstein634pizza81990/07/11upNaN
56GalileoGALiLEI672arepa51930/08/12neverNaN
67CaRLGa%%%uss323taco31970/07/13gonnaNaN
78DavidH$$$ilbert624taaaccoo31950/07/14letNaN
89JohannesKEPLER735taco31920/04/22youNaN
910JaMESM$$ax%%well875taco31923/03/12downNaN
1011IsaacNewton992pasta91999/02/15neverNaN
1112Emmy%%Nöether$234pasta91993/12/08gonnaNaN
1213Max!!!Planck!!!111hamburguer41994/01/04runNaN
1314FredHoy&&&le553pizzza81997/06/27aroundNaN
1415((( Heinrich )))))Hertz116pizza81956/11/30andNaN
1516WilliamGilbert###886BEER21958/03/26desertNaN
1617MarieCURIE912Rice12000/03/22youNaN
1718ArthurCOM%%%pton81211079051899/01/01#NaN
1819JAMESChadwick467NaN101921/05/03#NaN
\n", "
" ], "text/plain": [ " id firstName lastName billingId product \\\n", "0 1 Luis Alvarez$$%! 123 Cake \n", "1 2 André Ampère 423 piza \n", "2 3 NiELS Böhr//((%% 551 pizza \n", "3 4 PAUL dirac$ 521 pizza \n", "4 5 Albert Einstein 634 pizza \n", "5 6 Galileo GALiLEI 672 arepa \n", "6 7 CaRL Ga%%%uss 323 taco \n", "7 8 David H$$$ilbert 624 taaaccoo \n", "8 9 Johannes KEPLER 735 taco \n", "9 10 JaMES M$$ax%%well 875 taco \n", "10 11 Isaac Newton 992 pasta \n", "11 12 Emmy%% Nöether$ 234 pasta \n", "12 13 Max!!! Planck!!! 111 hamburguer \n", "13 14 Fred Hoy&&&le 553 pizzza \n", "14 15 ((( Heinrich ))))) Hertz 116 pizza \n", "15 16 William Gilbert### 886 BEER \n", "16 17 Marie CURIE 912 Rice \n", "17 18 Arthur COM%%%pton 812 110790 \n", "18 19 JAMES Chadwick 467 NaN \n", "\n", " price birth dummyCol ida \n", "0 10 1980/07/07 never NaN \n", "1 8 1950/07/08 gonna NaN \n", "2 8 1990/07/09 give NaN \n", "3 8 1954/07/10 you NaN \n", "4 8 1990/07/11 up NaN \n", "5 5 1930/08/12 never NaN \n", "6 3 1970/07/13 gonna NaN \n", "7 3 1950/07/14 let NaN \n", "8 3 1920/04/22 you NaN \n", "9 3 1923/03/12 down NaN \n", "10 9 1999/02/15 never NaN \n", "11 9 1993/12/08 gonna NaN \n", "12 4 1994/01/04 run NaN \n", "13 8 1997/06/27 around NaN \n", "14 8 1956/11/30 and NaN \n", "15 2 1958/03/26 desert NaN \n", "16 1 2000/03/22 you NaN \n", "17 5 1899/01/01 # NaN \n", "18 10 1921/05/03 # NaN " ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from optimus.expressions import reserved_words, Parser\n", "p = Parser()\n", "\n", "df.cols.set(value=p.parse('ATANH(1.1)'), output_cols=\"ida\").compute()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'DataFrame' object has no attribute '_buffer'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_window\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\extension.py\u001b[0m in \u001b[0;36mbuffer_window\u001b[1;34m(self, columns, lower_bound, upper_bound)\u001b[0m\n\u001b[0;32m 131\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbuffer_window\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlower_bound\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupper_bound\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 133\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 134\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_buffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 135\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3597\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3598\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3599\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"'DataFrame' object has no attribute %r\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3600\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3601\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute '_buffer'" ] } ], "source": [ "df.ext.buffer_window(\"*\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".Ext object at 0x0000025054888A48>\n", ".Ext object at 0x0000025054888388>\n", ".Ext object at 0x000002505488AE48>\n", ".Ext object at 0x00000250548888C8>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\core.py:6167: UserWarning: Insufficient elements for `head`. 30 elements requested, only 19 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".Ext object at 0x0000025054B42B48>\n", ".Ext object at 0x0000025054A8F2C8>\n", ".Ext object at 0x0000025054A5C788>\n" ] } ], "source": [ "_output = df.ext.profile(columns=\"*\", output=\"json\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'28479393': 1, '23760628': 1, '21857839': 1, '19748383': 1, '17484892': 1}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[col_name].astype(str).str.match(func[dtype]).value_counts().ext.to_dict()\n", "df[\"cedula\"].astype(str).value_counts().ext.to_dict()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['cedula', 'edad', 'apellido']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.names()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\"columns\": {\"cedula\": {\"stats\": {\"match\": 5, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"int\"}, \"frequency\": [{\"value\": \"28479393\", \"count\": 1}, {\"value\": \"23760628\", \"count\": 1}, {\"value\": \"21857839\", \"count\": 1}, {\"value\": \"19748383\", \"count\": 1}, {\"value\": \"17484892\", \"count\": 1}], \"count_uniques\": 5}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"int\"}}, \"edad\": {\"stats\": {\"match\": 5, \"missing\": 0, \"mismatch\": 0, \"profiler_dtype\": {\"dtype\": \"int\"}, \"frequency\": [{\"value\": \"28\", \"count\": 1}, {\"value\": \"27\", \"count\": 1}, {\"value\": \"26\", \"count\": 1}, {\"value\": \"24\", \"count\": 1}, {\"value\": \"20\", \"count\": 1}], \"count_uniques\": 5}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"int\"}}, \"apellido\": {\"stats\": {\"match\": 2, \"missing\": 3, \"mismatch\": 3, \"profiler_dtype\": {\"dtype\": \"string\"}, \"frequency\": [{\"value\": \"nan\", \"count\": 3}, {\"value\": \"martinez\", \"count\": 1}, {\"value\": \"contreras\", \"count\": 1}], \"count_uniques\": 3}, \"dtype\": \"object\", \"profiler_dtype\": {\"dtype\": \"string\"}}}, \"name\": null, \"file_name\": \"https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-a3b720f7-931e-49f7-bc82-c31e97c98768.csv\", \"summary\": {\"cols_count\": 3, \"rows_count\": 5, \"dtypes_list\": [\"object\"], \"total_count_dtypes\": 1, \"missing_count\": 0, \"p_missing\": 0.0}}\n" ] } ], "source": [ "print(_output)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 13 rows / 2 columns
\n", "
1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
A
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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B
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2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", " \n", " 1\n", " \n", "
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\n", " \n", " 2\n", " \n", "
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\n", " \n", " 1.1\n", " \n", "
\n", "
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\n", " \n", " 2.2\n", " \n", "
\n", "
\n", "
\n", " \n", " Optimus\n", " \n", "
\n", "
\n", "
\n", " \n", " Megatron\n", " \n", "
\n", "
\n", "
\n", " \n", " true\n", " \n", "
\n", "
\n", "
\n", " \n", " false\n", " \n", "
\n", "
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\n", " \n", " 10/4/1980\n", " \n", "
\n", "
\n", "
\n", " \n", " 29/11/2007\n", " \n", "
\n", "
\n", "
\n", " \n", " [1,2,3,4,5]\n", " \n", "
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\n", " \n", " [10,20,30,40,50]\n", " \n", "
\n", "
\n", "
\n", " \n", " {\"a\":1,\"b\":2,\"c\":3}\n", " \n", "
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\n", "
\n", " \n", " {\"d\":1,\"e\":2,\"f\":3}\n", " \n", "
\n", "
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\n", " \n", " male\n", " \n", "
\n", "
\n", "
\n", " \n", " female\n", " \n", "
\n", "
\n", "
\n", " \n", " 111.111.111.111\n", " \n", "
\n", "
\n", "
\n", " \n", " 192.0.0.1\n", " \n", "
\n", "
\n", "
\n", " \n", " https://hi-bumblebee.com\n", " \n", "
\n", "
\n", "
\n", " \n", " https://google.com\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 13 rows / 2 columns
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1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'frequency': {'A': {'values': [{'value': '{\"a\":1,\"b\":2,\"c\":3}', 'count': 1},\n", " {'value': 'true', 'count': 1},\n", " {'value': 'male', 'count': 1},\n", " {'value': 'https://hi-bumblebee.com', 'count': 1},\n", " {'value': 'argenis@hi-bumblebee.com', 'count': 1},\n", " {'value': '[1,2,3,4,5]', 'count': 1},\n", " {'value': 'Optimus', 'count': 1},\n", " {'value': '5555555555554444', 'count': 1},\n", " {'value': '30345', 'count': 1},\n", " {'value': '111.111.111.111', 'count': 1},\n", " {'value': '10/4/1980', 'count': 1},\n", " {'value': '1.1', 'count': 1},\n", " {'value': '1', 'count': 1}]},\n", " 'B': {'values': [{'value': '{\"d\":1,\"e\":2,\"f\":3}', 'count': 1},\n", " {'value': 'luis@hi-bumblebee.com', 'count': 1},\n", " {'value': 'https://google.com', 'count': 1},\n", " {'value': 'female', 'count': 1},\n", " {'value': 'false', 'count': 1},\n", " {'value': '[10,20,30,40,50]', 'count': 1},\n", " {'value': 'Megatron', 'count': 1},\n", " {'value': '4111111111111111', 'count': 1},\n", " {'value': '29/11/2007', 'count': 1},\n", " {'value': '2.2', 'count': 1},\n", " {'value': '2', 'count': 1},\n", " {'value': '192.0.0.1', 'count': 1},\n", " {'value': '11529', 'count': 1}]}}}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.frequency(\"*\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "---\n", "False\n", "True\n" ] } ], "source": [ "import fastnumbers\n", "value = \"1.1\"\n", "print(fastnumbers.isfloat(value) is True and fastnumbers.isint(value) is False)\n", "print(\"---\")\n", "print(fastnumbers.isint(value))\n", "print(fastnumbers.isfloat(value))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "._int at 0x000001EAE9428438> 1\n", "._int at 0x000001EAE9428438> 5555555555554444\n", "._int at 0x000001EAE9428438> 30345\n", "{'A': {'mismatch': 10, 'missing': 0, 'match': 3, 'profiler_dtype': 'int'}}\n", "._float at 0x000001EAE88CC828> 1.1\n", "{'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'decimal'}}\n", " TRUE\n", "{'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'boolean'}}\n", " 29/11/2007\n", "{'B': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'date'}}\n", " 1\n", " 1.1\n", " Optimus\n", " TRUE\n", " 10/4/1980\n", " [1,2,3,4,5]\n", " {\"a\":1,\"b\":2,\"c\":3}\n", " Male\n", " 111.111.111.111\n", " https://hi-bumblebee.com\n", " argenis@hi-bumblebee.com\n", " 5555555555554444\n", " 30345\n", "{'A': {'mismatch': 0, 'missing': 0, 'match': 13, 'profiler_dtype': 'array'}}\n", " {\"a\":1,\"b\":2,\"c\":3}\n", "{'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'object'}}\n", " Male\n", "{'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'gender'}}\n", " 111.111.111.111\n", "{'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'ip'}}\n", " https://hi-bumblebee.com\n", "{'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'url'}}\n", " argenis@hi-bumblebee.com\n", "{'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'email'}}\n", "{'A': {'mismatch': 13, 'missing': 0, 'match': 0, 'profiler_dtype': 'credit_card_number'}}\n", " 30345\n", "{'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'zip_code'}}\n" ] } ], "source": [ "print(df.cols.count_mismatch({\"A\":\"int\"}))\n", "print(df.cols.count_mismatch({\"A\":\"decimal\"}))\n", "print(df.cols.count_mismatch({\"A\":\"boolean\"}))\n", "print(df.cols.count_mismatch({\"B\":\"date\"}))\n", "print(df.cols.count_mismatch({\"A\":\"array\"}))\n", "print(df.cols.count_mismatch({\"A\":\"object\"}))\n", "print(df.cols.count_mismatch({\"A\":\"gender\"}))\n", "print(df.cols.count_mismatch({\"A\":\"ip\"}))\n", "print(df.cols.count_mismatch({\"A\":\"url\"}))\n", "print(df.cols.count_mismatch({\"A\":\"email\"}))\n", "print(df.cols.count_mismatch({\"A\":\"credit_card_number\"}))\n", "print(df.cols.count_mismatch({\"A\":\"zip_code\"}))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1\n", " 1.1\n", " Optimus\n", " TRUE\n", " 10/4/1980\n", " [1,2,3,4,5]\n", " {\"a\":1,\"b\":2,\"c\":3}\n", " Male\n", " 111.111.111.111\n", " https://hi-bumblebee.com\n", " argenis@hi-bumblebee.com\n", " 5555555555554444\n", " 30345\n" ] } ], "source": [ "for i in df[\"A\"]:\n", " print(type(i),i)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Package Version \n", "----------------------------- -------------------\n", "-aex-core 0.7.2 \n", "-illow 7.0.0 \n", "-umba 0.48.0 \n", "-umpy 1.15.4 \n", "-ymongo 3.8.0 \n", "absl-py 0.7.0 \n", "airtable-python-wrapper 0.11.3.post1 \n", "alabaster 0.7.12 \n", "alembic 1.4.2 \n", "altair 3.0.1 \n", "amqp 2.4.1 \n", "anaconda-client 1.7.2 \n", "anaconda-navigator 1.9.12 \n", "ansiwrap 0.8.4 \n", "apache-airflow 1.10.9 \n", "apiclient 1.0.4 \n", "apispec 1.3.3 \n", "aplus 0.11.0 \n", "argcomplete 1.11.1 \n", "argh 0.26.2 \n", "arrow 0.15.6 \n", "asn1crypto 1.3.0 \n", "astor 0.7.1 \n", "astroid 2.3.3 \n", "astropy 4.0.1.post1 \n", "atomicwrites 1.3.0 \n", "attrs 19.3.0 \n", "autopep8 1.4.4 \n", "autovizwidget 0.12.9 \n", "Babel 2.8.0 \n", "backcall 0.1.0 \n", "backoff 1.8.0 \n", "backports.functools-lru-cache 1.6.1 \n", "backports.tempfile 1.0 \n", "backports.weakref 1.0.post1 \n", "bcrypt 3.1.7 \n", "beautifulsoup4 4.8.2 \n", "bleach 3.1.4 \n", "bokeh 2.0.1 \n", "boltons 19.1.0 \n", "boto3 1.9.139 \n", "botocore 1.12.139 \n", "bqplot 0.12.3 \n", "branca 0.3.1 \n", "cached-property 1.5.1 \n", "cachetools 3.1.1 \n", "cattrs 0.9.0 \n", "certifi 2020.4.5.1 \n", "cffi 1.14.0 \n", "chardet 3.0.4 \n", "ciso8601 2.1.3 \n", "click 7.1.1 \n", "cloudpickle 1.3.0 \n", "clyent 1.2.2 \n", "colorama 0.4.3 \n", "colorcet 2.0.1 \n", "colorlog 4.0.2 \n", "conda 4.8.3 \n", "conda-build 3.17.8 \n", "conda-package-handling 1.6.0 \n", "conda-verify 3.4.2 \n", "configparser 3.5.3 \n", "croniter 0.3.31 \n", "cryptography 2.8 \n", "cycler 0.10.0 \n", "Cython 0.29.19 \n", "cytoolz 0.10.1 \n", "dask 2.14.0 \n", "dask-glm 0.2.0 \n", "dask-ml 1.2.0 \n", "dask-tensorflow 0.0.2 \n", "dask-xgboost 0.1.9 \n", "databricks-cli 0.8.6 \n", "datashader 0.8.0 \n", "decorator 4.4.2 \n", "deepdiff 4.0.6 \n", "defusedxml 0.6.0 \n", "Deprecated 1.2.5 \n", "diff-match-patch 20181111 \n", "dill 0.3.0 \n", "distributed 2.14.0 \n", "dlib 19.17.0 \n", "docker 3.7.2 \n", "docker-pycreds 0.4.0 \n", "docutils 0.15.2 \n", "dufte 0.2.3 \n", "entrypoints 0.3 \n", "face 19.1.2 \n", "face-recognition 1.2.3 \n", "face-recognition-models 0.3.0 \n", "fast-histogram 0.8 \n", "fastavro 0.22.7 \n", "fastnumbers 3.0.0 \n", "fastparquet 0.3.3 \n", "filelock 3.0.12 \n", "findspark 1.3.0 \n", "flake8 3.7.9 \n", "Flask-Admin 1.5.4 \n", "Flask-AppBuilder 2.3.0 \n", "Flask-Babel 1.0.0 \n", "Flask-Caching 1.3.3 \n", "Flask-JWT-Extended 3.24.1 \n", "Flask-Login 0.4.1 \n", "Flask-OpenID 1.2.5 \n", "Flask-SQLAlchemy 2.4.1 \n", "flask-swagger 0.2.13 \n", "Flask-WTF 0.14.3 \n", "fsspec 0.7.1 \n", "funcsigs 1.0.2 \n", "future 0.16.0 \n", "gast 0.2.2 \n", "genson 1.1.0 \n", "gitdb2 2.0.5 \n", "GitPython 2.1.11 \n", "glob2 0.7 \n", "glom 19.2.0 \n", "google-api-python-client 1.7.9 \n", "google-auth 1.6.3 \n", "google-auth-httplib2 0.0.3 \n", "google-auth-oauthlib 0.2.0 \n", "graphviz 0.13 \n", "grpcio 1.19.0 \n", "gspread 3.1.0 \n", "gunicorn 19.9.0 \n", "h2o-pysparkling-2.3 2.3.24 \n", "h2o-pysparkling-2.4 2.4.10 \n", "h5py 2.10.0 \n", "handyspark 0.2.1a1 \n", "hdijupyterutils 0.12.9 \n", "HeapDict 1.0.1 \n", "holoviews 1.12.6 \n", "httplib2 0.12.1 \n", "humanize 0.5.1 \n", "hyperloglog 0.0.12 \n", "hypothesis 5.8.3 \n", "idna 2.9 \n", "imagesize 1.2.0 \n", "imgkit 1.0.1 \n", "importlib-metadata 1.5.0 \n", "intervaltree 3.0.2 \n", "ipydatawidgets 4.0.1 \n", "ipykernel 5.1.4 \n", "ipyleaflet 0.12.2 \n", "ipympl 0.4.1 \n", "ipython 7.5.0 \n", "ipython-genutils 0.2.0 \n", "ipyvolume 0.5.2 \n", "ipyvue 1.1.0 \n", "ipyvuetify 1.1.1 \n", "ipywebrtc 0.5.0 \n", "ipywidgets 7.5.1 \n", "iso8601 0.1.12 \n", "isort 4.3.21 \n", "jedi 0.14.1 \n", "Jinja2 2.11.1 \n", "jmespath 0.9.4 \n", "joblib 0.14.1 \n", "json-merge-patch 0.2 \n", "json5 0.9.4 \n", "jsonpickle 1.1 \n", "jsonschema 3.2.0 \n", "jupyter-client 6.1.2 \n", "jupyter-core 4.6.3 \n", "jupyterlab 1.2.5 \n", "jupyterlab-server 1.1.0 \n", "jupytext 1.1.1 \n", "Keras 2.2.4 \n", "Keras-Applications 1.0.7 \n", "Keras-Preprocessing 1.0.9 \n", "keyring 21.1.1 \n", "kiwisolver 1.1.0 \n", "kombu 4.5.0 \n", "lazy-object-proxy 1.4.3 \n", "libarchive-c 2.9 \n", "livy 0.6.0 \n", "llvmlite 0.31.0 \n", "locket 0.2.0 \n", "lockfile 0.12.2 \n", "Mako 1.1.2 \n", "Markdown 2.6.11 \n", "MarkupSafe 1.1.1 \n", "marshmallow 3.5.1 \n", "marshmallow-enum 1.5.1 \n", "marshmallow-oneofschema 2.0.1 \n", "marshmallow-sqlalchemy 0.22.3 \n", "matplotlib 3.0.3 \n", "mccabe 0.6.1 \n", "menuinst 1.4.16 \n", "mistune 0.8.4 \n", "mkl-fft 1.0.15 \n", "mkl-random 1.1.0 \n", "mkl-service 2.3.0 \n", "mleap 0.8.1 \n", "mlflow 0.9.1 \n", "mock 4.0.1 \n", "modin 0.7.0 \n", "more-itertools 8.2.0 \n", "msgpack 1.0.0 \n", "multipledispatch 0.6.0 \n", "mypy-extensions 0.4.3 \n", "mysqlclient 1.4.6 \n", "navigator-updater 0.2.1 \n", "nbconvert 5.6.1 \n", "nbformat 5.0.4 \n", "nltk 3.4.5 \n", "nose-exclude 0.5.0 \n", "notebook 6.0.2 \n", "numba 0.49.0 \n", "numpy 1.18.2 \n", "numpydoc 0.9.2 \n", "oauthlib 3.0.1 \n", "olefile 0.46 \n", "ordered-set 3.1.1 \n", "ordered-set-stubs 0.1.3 \n", "orjson 2.6.1 \n", "packaging 20.3 \n", "paho-mqtt 1.4.0 \n", "pandas 1.0.3 \n", "pandocfilters 1.4.2 \n", "panel 0.6.4 \n", "papermill 1.0.1 \n", "param 1.9.0 \n", "paramiko 2.7.1 \n", "parso 0.5.2 \n", "partd 1.1.0 \n", "pathtools 0.1.2 \n", "patsy 0.5.1 \n", "pendulum 2.1.0 \n", "perfplot 0.8.1 \n", "pexpect 4.8.0 \n", "pickleshare 0.7.5 \n", "pika 0.12.0 \n", "Pillow 7.1.1 \n", "pip 20.0.2 \n", "pkginfo 1.5.0.1 \n", "plotly 4.1.0 \n", "pluggy 0.13.1 \n", "prefect 0.9.8 \n", "prison 0.1.2 \n", "progressbar2 3.37.1 \n", "prometheus-client 0.7.1 \n", "prompt-toolkit 3.0.4 \n", "protobuf 3.7.0 \n", "psutil 5.7.0 \n", "py 1.8.1 \n", "py4j 0.10.7 \n", "pyarrow 0.16.0 \n", "pyasn1 0.4.5 \n", "pyasn1-modules 0.2.4 \n", "pybind11 2.5.0 \n", "pycodestyle 2.5.0 \n", "pycosat 0.6.3 \n", "pycparser 2.20 \n", "pycryptodome 3.8.2 \n", "pycryptodomex 3.8.2 \n", "pyct 0.4.6 \n", "pydocstyle 4.0.1 \n", "pyflakes 2.1.1 \n", "Pygments 2.6.1 \n", "PyJWT 1.7.1 \n", "pylint 2.4.4 \n", "pylivy 0.0.3 \n", "pymongo 3.8.0 \n", "PyMySQL 0.9.3 \n", "PyNaCl 1.3.0 \n", "pyOpenSSL 19.1.0 \n", "pyparsing 2.4.6 \n", "PyPika 0.32.0 \n", "pypiwin32 223 \n", "pyreadline 2.1 \n", "pyrsistent 0.16.0 \n", "PySocks 1.7.1 \n", "pyspark 2.3.1 \n", "pytest 5.4.1 \n", "pytest-arraydiff 0.3 \n", "pytest-astropy 0.8.0 \n", "pytest-astropy-header 0.1.2 \n", "pytest-doctestplus 0.5.0 \n", "pytest-openfiles 0.4.0 \n", "pytest-remotedata 0.3.2 \n", "pytest-runner 5.2 \n", "python-box 4.2.2 \n", "python-daemon 2.1.2 \n", "python-dateutil 2.8.1 \n", "python-editor 1.0.4 \n", "python-jsonrpc-server 0.3.4 \n", "python-language-server 0.31.9 \n", "python-magic 0.4.15 \n", "python-magic-bin 0.4.14 \n", "python-slugify 4.0.0 \n", "python-utils 2.3.0 \n", "python3-openid 3.1.0 \n", "pythreejs 2.1.1 \n", "pytz 2019.3 \n", "pytzdata 2019.3 \n", "pyviz-comms 0.7.2 \n", "pywin32 227 \n", "pywin32-ctypes 0.2.0 \n", "pywinpty 0.5.7 \n", "PyYAML 5.3.1 \n", "pyzmq 18.1.1 \n", "QDarkStyle 2.8 \n", "QtAwesome 0.7.0 \n", "qtconsole 4.7.2 \n", "QtPy 1.9.0 \n", "querystring-parser 1.2.3 \n", "ratelimit 2.2.1 \n", "readme-renderer 24.0 \n", "requests 2.23.0 \n", "requests-kerberos 0.12.0 \n", "requests-oauthlib 1.2.0 \n", "requests-toolbelt 0.9.1 \n", "retrying 1.3.3 \n", "rope 0.16.0 \n", "rsa 4.0 \n", "Rtree 0.9.3 \n", "ruamel-yaml 0.15.87 \n", "ruamel.yaml 0.16.10 \n", "ruamel.yaml.clib 0.2.0 \n", "s3fs 0.2.2 \n", "s3transfer 0.2.0 \n", "scikit-learn 0.22.1 \n", "scipy 1.4.1 \n", "seaborn 0.10.0 \n", "Send2Trash 1.5.0 \n", "setproctitle 1.1.10 \n", "setuptools 46.1.3.post20200330\n", "sightengine 1.4.0 \n", "simplejson 3.16.0 \n", "singleton-decorator 1.0.0 \n", "six 1.14.0 \n", "sklearn 0.0 \n", "smmap2 2.0.5 \n", "snowballstemmer 2.0.0 \n", "sortedcontainers 2.1.0 \n", "soupsieve 2.0 \n", "sparkmagic 0.12.9 \n", "sparse-dot-topn 0.2.9 \n", "Sphinx 2.4.4 \n", "sphinxcontrib-applehelp 1.0.2 \n", "sphinxcontrib-devhelp 1.0.2 \n", "sphinxcontrib-htmlhelp 1.0.3 \n", "sphinxcontrib-jsmath 1.0.1 \n", "sphinxcontrib-qthelp 1.0.3 \n", "sphinxcontrib-serializinghtml 1.1.4 \n", "spyder 4.0.1 \n", "spyder-kernels 1.8.1 \n", "SQLAlchemy 1.3.16 \n", "SQLAlchemy-JSONField 0.9.0 \n", "SQLAlchemy-Utils 0.36.3 \n", "sqlparse 0.3.0 \n", "statsmodels 0.11.0 \n", "streamz 0.5.3 \n", "string-grouper 0.1.0 \n", "supervisor 4.1.0 \n", "tabulate 0.8.7 \n", "tblib 1.6.0 \n", "tenacity 4.12.0 \n", "tensorboard 1.13.0 \n", "tensorflow 1.13.1 \n", "tensorflow-estimator 1.13.0 \n", "termcolor 1.1.0 \n", "terminado 0.8.3 \n", "termtables 0.1.2 \n", "testfixtures 6.10.3 \n", "testpath 0.4.4 \n", "text-unidecode 1.3 \n", "textwrap3 0.9.2 \n", "thrift 0.13.0 \n", "toml 0.10.0 \n", "toolz 0.10.0 \n", "tornado 6.0.4 \n", "tqdm 4.44.1 \n", "traitlets 4.3.3 \n", "traittypes 0.2.1 \n", "turbodbc 4.0.0 \n", "twine 2.0.0 \n", "typing 3.7.4.1 \n", "typing-extensions 3.7.4.1 \n", "tzlocal 1.5.1 \n", "ujson 1.35 \n", "uritemplate 3.0.0 \n", "urllib3 1.25.9 \n", "vaex 2.6.1 \n", "vaex-arrow 0.4.2 \n", "vaex-astro 0.6.1 \n", "vaex-core 1.5.0 \n", "vaex-hdf5 0.5.6 \n", "vaex-jupyter 0.4.1 \n", "vaex-ml 0.8.0 \n", "vaex-server 0.2.1 \n", "vaex-viz 0.3.8 \n", "vega 2.1.0 \n", "vega-datasets 0.7.0 \n", "vine 1.2.0 \n", "virtualenv 16.6.2 \n", "watchdog 0.10.2 \n", "wcwidth 0.1.9 \n", "webencodings 0.5.1 \n", "websocket-client 0.56.0 \n", "wheel 0.34.2 \n", "widgetsnbextension 3.5.1 \n", "win-inet-pton 1.1.0 \n", "wincertstore 0.2 \n", "winkerberos 0.7.0 \n", "wrapt 1.12.1 \n", "WTForms 2.2.1 \n", "xarray 0.12.1 \n", "xgboost 0.90 \n", "xlrd 1.2.0 \n", "xmltodict 0.12.0 \n", "xxhash 1.4.3 \n", "yapf 0.28.0 \n", "zict 2.0.0 \n", "zipp 2.2.0 \n", "zope.deprecation 4.4.0 \n" ] } ], "source": [ "!pip list" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DateTime(2007, 11, 29, 0, 0, 0, tzinfo=Timezone('UTC'))" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pendulum\n", "pendulum.from_format(\"29/11/2007\", 'DD/MM/YYYY')\n", "# import ciso8601\n", "# ciso8601.parse_datetime(\"11 11 1980\", strict= False)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "df = df.ext.optimize().persist()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "df = df.ext.repartition(8).ext.cache()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 42 ms\n" ] } ], "source": [ "%%time\n", "cols_to_profile = {'INCIDENT_NUMBER': 'string', 'OFFENSE_CODE': 'int', 'OFFENSE_CODE_GROUP': 'string', 'OFFENSE_DESCRIPTION': 'string', 'DISTRICT': 'string', 'REPORTING_AREA': 'int', 'SHOOTING': 'object', 'OCCURRED_ON_DATE': 'date', 'YEAR': 'int', 'MONTH': 'int', 'DAY_OF_WEEK': 'date', 'HOUR': 'int', 'UCR_PART': 'string', 'STREET': 'string', 'Lat': 'decimal', 'Long': 'decimal', 'Location': 'array'}\n", "cols_and_inferred_dtype = df.cols.infer_profiler_dtypes(cols_to_profile)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INCIDENT_NUMBER object\n", "OFFENSE_CODE int\n", "OFFENSE_CODE_GROUP object\n", "OFFENSE_DESCRIPTION object\n", "DISTRICT object\n", "REPORTING_AREA int\n", "SHOOTING object\n", "OCCURRED_ON_DATE date\n", "YEAR int\n", "MONTH int\n", "DAY_OF_WEEK object\n", "HOUR int\n", "UCR_PART object\n", "STREET object\n", "Lat float\n", "Long float\n", "Location object\n" ] } ], "source": [ "df = df.cols.cast_to_profiler_dtypes(columns=cols_and_inferred_dtype).persist()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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INCIDENT_NUMBEROFFENSE_CODEOFFENSE_CODE_GROUPOFFENSE_DESCRIPTIONDISTRICTREPORTING_AREASHOOTINGOCCURRED_ON_DATEYEARMONTHDAY_OF_WEEKHOURUCR_PARTSTREETLatLongLocation
0I182070945619LarcenyLARCENY ALL OTHERSD14808NaN2018-09-02 13:00:0020189Sunday13Part OneLINCOLN ST42.3578-71.1394(42.35779134, -71.13937053)
1I1820709431402VandalismVANDALISMC11347NaN2018-08-21 00:00:0020188Tuesday0Part TwoHECLA ST42.3068-71.0603(42.30682138, -71.06030035)
2I1820709413410TowedTOWED MOTOR VEHICLED4151NaN2018-09-03 19:27:0020189Monday19Part ThreeCAZENOVE ST42.3466-71.0724(42.34658879, -71.07242943)
3I1820709403114Investigate PropertyINVESTIGATE PROPERTYD4272NaN2018-09-03 21:16:0020189Monday21Part ThreeNEWCOMB ST42.3342-71.0787(42.33418175, -71.07866441)
4I1820709383114Investigate PropertyINVESTIGATE PROPERTYB3421NaN2018-09-03 21:05:0020189Monday21Part ThreeDELHI ST42.2754-71.0904(42.27536542, -71.09036101)
......................................................
319068I050310906-003125Warrant ArrestsWARRANT ARRESTD4285NaN2016-06-05 17:25:0020166Sunday17Part ThreeCOVENTRY ST42.337-71.0857(42.33695098, -71.08574813)
319069I030217815-08111HomicideMURDER, NON-NEGLIGIENT MANSLAUGHTERE18520NaN2015-07-09 13:38:0020157Thursday13Part OneRIVER ST42.2559-71.1232(42.25592648, -71.12317207)
319070I030217815-083125Warrant ArrestsWARRANT ARRESTE18520NaN2015-07-09 13:38:0020157Thursday13Part ThreeRIVER ST42.2559-71.1232(42.25592648, -71.12317207)
319071I010370257-003125Warrant ArrestsWARRANT ARRESTE13569NaN2016-05-31 19:35:0020165Tuesday19Part ThreeNEW WASHINGTON ST42.3023-71.1116(42.30233307, -71.11156487)
3190721420525503125Warrant ArrestsWARRANT ARRESTD4903NaN2015-06-22 00:12:0020156Monday0Part ThreeWASHINGTON ST42.3338-71.0803(42.33383935, -71.08029038)
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319073 rows × 17 columns

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" ], "text/plain": [ " INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP \\\n", "0 I182070945 619 Larceny \n", "1 I182070943 1402 Vandalism \n", "2 I182070941 3410 Towed \n", "3 I182070940 3114 Investigate Property \n", "4 I182070938 3114 Investigate Property \n", "... ... ... ... \n", "319068 I050310906-00 3125 Warrant Arrests \n", "319069 I030217815-08 111 Homicide \n", "319070 I030217815-08 3125 Warrant Arrests \n", "319071 I010370257-00 3125 Warrant Arrests \n", "319072 142052550 3125 Warrant Arrests \n", "\n", " OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING \\\n", "0 LARCENY ALL OTHERS D14 808 NaN \n", "1 VANDALISM C11 347 NaN \n", "2 TOWED MOTOR VEHICLE D4 151 NaN \n", "3 INVESTIGATE PROPERTY D4 272 NaN \n", "4 INVESTIGATE PROPERTY B3 421 NaN \n", "... ... ... ... ... \n", "319068 WARRANT ARREST D4 285 NaN \n", "319069 MURDER, NON-NEGLIGIENT MANSLAUGHTER E18 520 NaN \n", "319070 WARRANT ARREST E18 520 NaN \n", "319071 WARRANT ARREST E13 569 NaN \n", "319072 WARRANT ARREST D4 903 NaN \n", "\n", " OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART \\\n", "0 2018-09-02 13:00:00 2018 9 Sunday 13 Part One \n", "1 2018-08-21 00:00:00 2018 8 Tuesday 0 Part Two \n", "2 2018-09-03 19:27:00 2018 9 Monday 19 Part Three \n", "3 2018-09-03 21:16:00 2018 9 Monday 21 Part Three \n", "4 2018-09-03 21:05:00 2018 9 Monday 21 Part Three \n", "... ... ... ... ... ... ... \n", "319068 2016-06-05 17:25:00 2016 6 Sunday 17 Part Three \n", "319069 2015-07-09 13:38:00 2015 7 Thursday 13 Part One \n", "319070 2015-07-09 13:38:00 2015 7 Thursday 13 Part Three \n", "319071 2016-05-31 19:35:00 2016 5 Tuesday 19 Part Three \n", "319072 2015-06-22 00:12:00 2015 6 Monday 0 Part Three \n", "\n", " STREET Lat Long Location \n", "0 LINCOLN ST 42.3578 -71.1394 (42.35779134, -71.13937053) \n", "1 HECLA ST 42.3068 -71.0603 (42.30682138, -71.06030035) \n", "2 CAZENOVE ST 42.3466 -71.0724 (42.34658879, -71.07242943) \n", "3 NEWCOMB ST 42.3342 -71.0787 (42.33418175, -71.07866441) \n", "4 DELHI ST 42.2754 -71.0904 (42.27536542, -71.09036101) \n", "... ... ... ... ... \n", "319068 COVENTRY ST 42.337 -71.0857 (42.33695098, -71.08574813) \n", "319069 RIVER ST 42.2559 -71.1232 (42.25592648, -71.12317207) \n", "319070 RIVER ST 42.2559 -71.1232 (42.25592648, -71.12317207) \n", "319071 NEW WASHINGTON ST 42.3023 -71.1116 (42.30233307, -71.11156487) \n", "319072 WASHINGTON ST 42.3338 -71.0803 (42.33383935, -71.08029038) \n", "\n", "[319073 rows x 17 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.compute()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 6.58 s\n" ] }, { "data": { "text/plain": [ "{'INCIDENT_NUMBER': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 39885,\n", " 'profiler_dtype': 'string'},\n", " 'OFFENSE_CODE': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 39885,\n", " 'profiler_dtype': 'int'},\n", " 'OFFENSE_CODE_GROUP': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 39885,\n", " 'profiler_dtype': 'string'},\n", " 'OFFENSE_DESCRIPTION': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 39885,\n", " 'profiler_dtype': 'string'},\n", " 'DISTRICT': {'mismatch': 0,\n", " 'missing': 112,\n", " 'match': 39773,\n", " 'profiler_dtype': 'string'},\n", " 'REPORTING_AREA': {'mismatch': 0,\n", " 'missing': 1217,\n", " 'match': 38668,\n", " 'profiler_dtype': 'int'},\n", " 'SHOOTING': {'mismatch': 133,\n", " 'missing': 39752,\n", " 'match': 0,\n", " 'profiler_dtype': 'object'},\n", " 'OCCURRED_ON_DATE': {'mismatch': 39885,\n", " 'missing': 0,\n", " 'match': 0,\n", " 'profiler_dtype': 'date'},\n", " 'YEAR': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 39885,\n", " 'profiler_dtype': 'int'},\n", " 'MONTH': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 39885,\n", " 'profiler_dtype': 'int'},\n", " 'DAY_OF_WEEK': {'mismatch': 39885,\n", " 'missing': 0,\n", " 'match': 0,\n", " 'profiler_dtype': 'date'},\n", " 'HOUR': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 39885,\n", " 'profiler_dtype': 'int'},\n", " 'UCR_PART': {'mismatch': 0,\n", " 'missing': 3,\n", " 'match': 39882,\n", " 'profiler_dtype': 'string'},\n", " 'STREET': {'mismatch': 0,\n", " 'missing': 925,\n", " 'match': 38960,\n", " 'profiler_dtype': 'string'},\n", " 'Lat': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 39885,\n", " 'profiler_dtype': 'decimal'},\n", " 'Long': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 39885,\n", " 'profiler_dtype': 'decimal'},\n", " 'Location': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 39885,\n", " 'profiler_dtype': 'array'}}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time df.cols.count_mismatch(cols_to_profile, infer= True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\"string\"}, \"Lat\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 159537, \"frequency\": [{\"value\": \"42.34862382\", \"count\": 1243}, {\"value\": \"42.36183857\", \"count\": 1208}, {\"value\": \"42.28482577\", \"count\": 1121}, {\"value\": \"42.32866284\", \"count\": 1042}, {\"value\": \"42.25621592\", \"count\": 898}, {\"value\": \"42.29755533\", \"count\": 783}, {\"value\": \"42.34128751\", \"count\": 773}, {\"value\": \"-1.00000000\", \"count\": 745}, {\"value\": \"42.33152148\", \"count\": 735}, {\"value\": \"42.35231190\", \"count\": 688}, {\"value\": \"42.33954199\", \"count\": 655}, {\"value\": \"42.32696647\", \"count\": 652}, {\"value\": \"42.35512339\", \"count\": 584}, {\"value\": \"42.30971857\", \"count\": 573}, {\"value\": \"42.29848866\", \"count\": 562}, {\"value\": \"42.33401829\", \"count\": 561}, {\"value\": \"42.33367922\", \"count\": 550}, {\"value\": \"42.35095909\", \"count\": 523}, {\"value\": \"42.31043400\", \"count\": 523}, {\"value\": 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{\"value\": \"-71.12401947\", \"count\": 898}, {\"value\": \"-71.05970910\", \"count\": 783}, {\"value\": \"-71.05467933\", \"count\": 773}, {\"value\": \"-1.00000000\", \"count\": 745}, {\"value\": \"-71.07085307\", \"count\": 735}, {\"value\": \"-71.06370510\", \"count\": 688}, {\"value\": \"-71.06940877\", \"count\": 655}, {\"value\": \"-71.06198607\", \"count\": 652}, {\"value\": \"-71.06087980\", \"count\": 584}, {\"value\": \"-71.10429432\", \"count\": 573}, {\"value\": \"-71.06313294\", \"count\": 562}, {\"value\": \"-71.07638124\", \"count\": 561}, {\"value\": \"-71.09187755\", \"count\": 550}, {\"value\": \"-71.07412780\", \"count\": 523}, {\"value\": \"-71.06134010\", \"count\": 523}, {\"value\": \"-71.06525499\", \"count\": 515}, {\"value\": \"-71.03929078\", \"count\": 507}, {\"value\": \"-71.07239518\", \"count\": 504}, {\"value\": \"-71.08051941\", \"count\": 472}, {\"value\": \"-71.07840978\", \"count\": 472}, {\"value\": \"-71.07491710\", \"count\": 455}, {\"value\": \"-71.07626098\", \"count\": 445}, {\"value\": \"-71.09880598\", \"count\": 444}, {\"value\": \"-71.06135413\", \"count\": 440}, {\"value\": \"-71.14822128\", \"count\": 438}, {\"value\": \"-71.06177615\", \"count\": 436}, {\"value\": \"-71.08688339\", \"count\": 432}, {\"value\": \"-71.15049850\", \"count\": 427}, {\"value\": \"-71.07436364\", \"count\": 421}], \"count_uniques\": 18178}, \"dtype\": \"object\", \"profiler_dtype\": \"decimal\"}, \"Location\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 159537, \"frequency\": [{\"value\": \"(0.00000000, 0.00000000)\", \"count\": 19999}, {\"value\": \"(42.34862382, -71.08277637)\", \"count\": 1243}, {\"value\": \"(42.36183857, -71.05976489)\", \"count\": 1208}, {\"value\": \"(42.28482577, -71.09137369)\", \"count\": 1121}, {\"value\": \"(42.32866284, -71.08563401)\", \"count\": 1042}, {\"value\": \"(42.25621592, -71.12401947)\", \"count\": 898}, {\"value\": \"(42.29755533, -71.05970910)\", \"count\": 783}, {\"value\": \"(42.34128751, -71.05467933)\", \"count\": 773}, {\"value\": \"(-1.00000000, -1.00000000)\", \"count\": 745}, {\"value\": \"(42.33152148, -71.07085307)\", \"count\": 735}, {\"value\": \"(42.35231190, -71.06370510)\", \"count\": 688}, {\"value\": \"(42.33954199, -71.06940877)\", \"count\": 655}, {\"value\": \"(42.32696647, -71.06198607)\", \"count\": 652}, {\"value\": \"(42.35512339, -71.06087980)\", \"count\": 584}, {\"value\": \"(42.30971857, -71.10429432)\", \"count\": 573}, {\"value\": \"(42.29848866, -71.06313294)\", \"count\": 562}, {\"value\": \"(42.33401829, -71.07638124)\", \"count\": 561}, {\"value\": \"(42.33367922, -71.09187755)\", \"count\": 550}, {\"value\": \"(42.31043400, -71.06134010)\", \"count\": 523}, {\"value\": \"(42.35095909, -71.07412780)\", \"count\": 523}, {\"value\": \"(42.35241815, -71.06525499)\", \"count\": 515}, {\"value\": \"(42.37081805, -71.03929078)\", \"count\": 507}, {\"value\": \"(42.33428841, -71.07239518)\", \"count\": 504}, {\"value\": \"(42.34980175, -71.07840978)\", \"count\": 472}, {\"value\": \"(42.32696802, -71.08051941)\", \"count\": 472}, {\"value\": \"(42.33511904, -71.07491710)\", \"count\": 455}, {\"value\": \"(42.35037870, -71.07626098)\", \"count\": 445}, {\"value\": \"(42.34653820, -71.09880598)\", \"count\": 444}, {\"value\": \"(42.36643546, -71.06135413)\", \"count\": 440}, {\"value\": \"(42.28709355, -71.14822128)\", \"count\": 438}, {\"value\": \"(42.35602373, -71.06177615)\", \"count\": 436}, {\"value\": \"(42.34840576, -71.08688339)\", \"count\": 432}, {\"value\": \"(42.34905600, -71.15049850)\", \"count\": 427}], \"count_uniques\": 18194}, \"dtype\": \"object\", \"profiler_dtype\": \"string\"}}, \"name\": null, \"file_name\": \"crime.csv\", \"summary\": {\"cols_count\": 17, \"rows_count\": 319073, \"dtypes_list\": [\"uint8\", \"uint16\", \"object\"], \"total_count_dtypes\": 3, \"missing_count\": 0, \"p_missing\": 0.0}}'" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "# for col_name in df.cols.names():\n", "df.ext.profile(columns=df.cols.names(), infer=True, output=\"json\", flush=True)" ] }, { "cell_type": "code", "execution_count": 333, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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" ], "text/plain": [ "" ] }, "execution_count": 333, "metadata": {}, "output_type": "execute_result" } ], "source": [ "op.client" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "columns = df.cols.names()\n", "cols_to_profile = df.ext.calculate_cols_to_profile(df, columns)\n", "cols_and_inferred_dtype = df.cols.infer_profiler_dtypes(cols_to_profile) \n", "df = df.cols.cast_to_profiler_dtypes(columns=cols_and_inferred_dtype).persist()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'STREET': 'string', 'YEAR': 'int', 'Location': 'array', 'REPORTING_AREA': 'int', 'DISTRICT': 'string', 'DAY_OF_WEEK': 'int', 'OFFENSE_CODE': 'int', 'Lat': 'decimal', 'INCIDENT_NUMBER': 'string', 'Long': 'decimal', 'OFFENSE_CODE_GROUP': 'string', 'UCR_PART': 'string', 'OCCURRED_ON_DATE': 'int', 'HOUR': 'int', 'SHOOTING': 'object', 'OFFENSE_DESCRIPTION': 'string', 'MONTH': 'int'}\n" ] } ], "source": [ "print(cols_and_inferred_dtype)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.visualize()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "partitions() takes 0 positional arguments but 1 was given", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartitions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: partitions() takes 0 positional arguments but 1 was given" ] } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "df.ext.profile(columns=\"*\", infer=True, output=\"json\", flush=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'DataFrame' object has no attribute 'visualize'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvisualize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 5272\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5273\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5274\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5276\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'visualize'" ] } ], "source": [ "df.visualize()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "bins = 33\n", "compute = False\n", "if numeric_cols is not None:\n", " hist = df.cols.hist(numeric_cols, buckets=bins, compute=compute)\n", " freq_uniques = df.cols.count_uniques(numeric_cols, estimate=False, compute=compute)\n", "freq = None\n", "if string_cols is not None:\n", " freq = df.cols.frequency(string_cols, n=bins, count_uniques=True, compute=compute)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hist.visualize()\n", "freq.visualize()\n", "\n", "df.visualize()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 319073 rows / 17 columns
\n", "
1 partition(s)
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INCIDENT_NUMBER
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1 (object)
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OFFENSE_CODE
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2 (object)
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OFFENSE_CODE_GROUP
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3 (object)
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OFFENSE_DESCRIPTION
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4 (object)
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DISTRICT
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5 (object)
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REPORTING_AREA
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6 (object)
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SHOOTING
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7 (object)
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OCCURRED_ON_DATE
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8 (object)
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YEAR
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9 (object)
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MONTH
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10 (object)
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DAY_OF_WEEK
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11 (object)
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HOUR
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12 (object)
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UCR_PART
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13 (object)
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STREET
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14 (object)
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Lat
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15 (object)
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\n", " \n", " not nullable\n", " \n", "
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Long
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16 (object)
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\n", " \n", " not nullable\n", " \n", "
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Location
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17 (object)
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\n", " \n", " not nullable\n", " \n", "
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\n", " \n", " 421\n", " \n", "
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\n", " \n", " 3820\n", " \n", "
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\n", "
\n", " \n", " Motor⋅Vehicle⋅Accident⋅Response\n", " \n", "
\n", "
\n", "
\n", " \n", " M/V⋅ACCIDENT⋅INVOLVING⋅PEDESTRIAN⋅-⋅INJURY\n", " \n", "
\n", "
\n", "
\n", " \n", " C11\n", " \n", "
\n", "
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\n", " \n", " 398\n", " \n", "
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\n", "
\n", "
\n", " \n", " 724\n", " \n", "
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\n", " \n", " Auto⋅Theft\n", " \n", "
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\n", "
\n", " \n", " AUTO⋅THEFT\n", " \n", "
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\n", " \n", " B2\n", " \n", "
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\n", " \n", " 330\n", " \n", "
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\n", " \n", " NORMANDY⋅ST\n", " \n", "
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\n", " \n", " 42.30607218\n", " \n", "
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\n", "
\n", " \n", " -71.0827326\n", " \n", "
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\n", "
\n", " \n", " (42.30607218,⋅-71.08273260)\n", " \n", "
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\n", "
\n", " \n", " I182070932\n", " \n", "
\n", "
\n", "
\n", " \n", " 3301\n", " \n", "
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\n", "
\n", " \n", " Verbal⋅Disputes\n", " \n", "
\n", "
\n", "
\n", " \n", " VERBAL⋅DISPUTE\n", " \n", "
\n", "
\n", "
\n", " \n", " B2\n", " \n", "
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\n", " \n", " 584\n", " \n", "
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\n", " \n", " nan\n", " \n", "
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\n", "
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\n", "
\n", " \n", " LAWN⋅ST\n", " \n", "
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\n", " \n", " 42.32701648\n", " \n", "
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\n", "
\n", " \n", " -71.10555088\n", " \n", "
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\n", "
\n", " \n", " (42.32701648,⋅-71.10555088)\n", " \n", "
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\n", " \n", " I182070931\n", " \n", "
\n", "
\n", "
\n", " \n", " 301\n", " \n", "
\n", "
\n", "
\n", " \n", " Robbery\n", " \n", "
\n", "
\n", "
\n", " \n", " ROBBERY⋅-⋅STREET\n", " \n", "
\n", "
\n", "
\n", " \n", " C6\n", " \n", "
\n", "
\n", "
\n", " \n", " 177\n", " \n", "
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\n", " \n", " nan\n", " \n", "
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\n", "
\n", "
\n", " \n", " Part⋅One\n", " \n", "
\n", "
\n", "
\n", " \n", " MASSACHUSETTS⋅AVE\n", " \n", "
\n", "
\n", "
\n", " \n", " 42.33152148\n", " \n", "
\n", "
\n", "
\n", " \n", " -71.07085307\n", " \n", "
\n", "
\n", "
\n", " \n", " (42.33152148,⋅-71.07085307)\n", " \n", "
\n", "
\n", "
\n", " \n", " I182070929\n", " \n", "
\n", "
\n", "
\n", " \n", " 3301\n", " \n", "
\n", "
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\n", " \n", " Verbal⋅Disputes\n", " \n", "
\n", "
\n", "
\n", " \n", " VERBAL⋅DISPUTE\n", " \n", "
\n", "
\n", "
\n", " \n", " C11\n", " \n", "
\n", "
\n", "
\n", " \n", " 364\n", " \n", "
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\n", " \n", " 20\n", " \n", "
\n", "
\n", "
\n", " \n", " Part⋅Three\n", " \n", "
\n", "
\n", "
\n", " \n", " LESLIE⋅ST\n", " \n", "
\n", "
\n", "
\n", " \n", " 42.29514664\n", " \n", "
\n", "
\n", "
\n", " \n", " -71.05860832\n", " \n", "
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\n", " \n", " (42.29514664,⋅-71.05860832)\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 319073 rows / 17 columns
\n", "
1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datetime.datetime(2020, 5, 24, 0, 0)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dateutil.parser import parse as dparse\n", "dparse(\"Sunday\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.cols.cast(\"\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unhashable type: 'list'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"OFFENSE_CODE_GROUP\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"OFFENSE_DESCRIPTION\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseparator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\", \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_col\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"OFFENSE_CODE_GROUP_OFFENSE_DESCRIPTION\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0m_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"json\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\dask\\extension.py\u001b[0m in \u001b[0;36mprofile\u001b[1;34m(columns, bins, output, infer, flush, size)\u001b[0m\n\u001b[0;32m 104\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 105\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mflush\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 106\u001b[1;33m \u001b[0mcols_to_profile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalculate_cols_to_profile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 107\u001b[0m \u001b[1;31m# print(\"cols to profile\", cols_to_profile)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 108\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\extension.py\u001b[0m in \u001b[0;36mcalculate_cols_to_profile\u001b[1;34m(self, df, columns)\u001b[0m\n\u001b[0;32m 362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 363\u001b[0m \u001b[1;31m# Remove duplicated.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 364\u001b[1;33m \u001b[0mcalculate_columns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcalculate_columns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 365\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 366\u001b[0m \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mare_actions\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'" ] } ], "source": [ "df = df.cols.nest([\"OFFENSE_CODE_GROUP\", \"OFFENSE_DESCRIPTION\"], separator=\", \", output_col=\"OFFENSE_CODE_GROUP_OFFENSE_DESCRIPTION\").ext.cache()\n", "_output = df.ext.profile(columns=\"*\", infer=True, output=\"json\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# for i in df.columns:\n", "# df.cols.count_mismatch({i:\"int\"})" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "columns {'INCIDENT_NUMBER': 'string', 'OFFENSE_CODE': 'int', 'OFFENSE_CODE_GROUP': 'string', 'OFFENSE_DESCRIPTION': 'string', 'DISTRICT': 'string', 'REPORTING_AREA': 'int', 'SHOOTING': 'object', 'OCCURRED_ON_DATE': 'date', 'YEAR': 'int', 'MONTH': 'int', 'DAY_OF_WEEK': 'date', 'HOUR': 'int', 'UCR_PART': 'string', 'STREET': 'string', 'Lat': 'decimal', 'Long': 'decimal', 'Location': 'array'}\n", "dtype--- object\n", "dtype--- int\n", "dtype--- object\n", "dtype--- object\n", "dtype--- object\n", "dtype--- int\n", "dtype--- object\n", "dtype--- date\n", "dtype--- int\n", "dtype--- int\n", "dtype--- date\n", "dtype--- int\n", "dtype--- object\n", "dtype--- object\n", "dtype--- float\n", "dtype--- float\n", "dtype--- object\n" ] }, { "ename": "KeyError", "evalue": "'birth'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflush\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"columns\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"birth\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mKeyError\u001b[0m: 'birth'" ] } ], "source": [ "df.ext.profile(columns=\"*\", infer=True, flush=True)[\"columns\"][\"birth\"]" ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "'missing_columns' must be 'id', 'firstName', 'lastName', 'billingId', 'product', 'price', 'birth', 'dummyCol', received '['OFFENSE_CODE_GROUP', 'OFFENSE_DESCRIPTION']'. ", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"OFFENSE_CODE_GROUP\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"OFFENSE_DESCRIPTION\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseparator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\", \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_col\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"OFFENSE_CODE_GROUP_OFFENSE_DESCRIPTION\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0m_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"json\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\dask\\columns.py\u001b[0m in \u001b[0;36mnest\u001b[1;34m(self, input_cols, shape, separator, output_col)\u001b[0m\n\u001b[0;32m 1178\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1179\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1180\u001b[1;33m \u001b[0minput_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_cols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1181\u001b[0m \u001b[1;31m# output_col = val_to_list(output_col)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1182\u001b[0m \u001b[0mcheck_column_numbers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_cols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\columns.py\u001b[0m in \u001b[0;36mparse_columns\u001b[1;34m(df, cols_args, get_args, is_regex, filter_by_column_dtypes, accepts_missing_cols, invert)\u001b[0m\n\u001b[0;32m 196\u001b[0m \u001b[1;31m# Check for missing columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 197\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0maccepts_missing_cols\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 198\u001b[1;33m \u001b[0mcheck_for_missing_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 199\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 200\u001b[0m \u001b[1;31m# Filter by column data type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\columns.py\u001b[0m in \u001b[0;36mcheck_for_missing_columns\u001b[1;34m(df, col_names)\u001b[0m\n\u001b[0;32m 346\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 347\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmissing_columns\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 348\u001b[1;33m \u001b[0mRaiseIt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmissing_columns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_col_names\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 349\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 350\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\raiseit.py\u001b[0m in \u001b[0;36mvalue_error\u001b[1;34m(var, data_values, extra_text)\u001b[0m\n\u001b[0;32m 74\u001b[0m type=divisor.join(map(\n\u001b[0;32m 75\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m\"'\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"'\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m data_values)), var_type=one_list_to_val(var), extra_text=extra_text))\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: 'missing_columns' must be 'id', 'firstName', 'lastName', 'billingId', 'product', 'price', 'birth', 'dummyCol', received '['OFFENSE_CODE_GROUP', 'OFFENSE_DESCRIPTION']'. " ] } ], "source": [ "\n", "df = df.cols.nest([\"OFFENSE_CODE_GROUP\", \"OFFENSE_DESCRIPTION\"], separator=\", \", output_col=\"OFFENSE_CODE_GROUP_OFFENSE_DESCRIPTION\").ext.cache()\n", "_output = df.ext.profile(columns=\"*\", infer=True, output=\"json\")\n" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idfirstNamelastNamebillingIdproductpricebirthdummyCol
01LuisAlvarez$$%!123Cake101980-07-07never
12AndréAmpère423piza81950-07-08gonna
23NiELSBöhr//((%%551pizza81990-07-09give
34PAULdirac$521pizza81954-07-10you
45AlbertEinstein634pizza81990-07-11up
56GalileoGALiLEI672arepa51930-08-12never
67CaRLGa%%%uss323taco31970-07-13gonna
78DavidH$$$ilbert624taaaccoo31950-07-14let
89JohannesKEPLER735taco31920-04-22you
910JaMESM$$ax%%well875taco31923-03-12down
1011IsaacNewton992pasta91999-02-15never
1112Emmy%%Nöether$234pasta91993-12-08gonna
1213Max!!!Planck!!!111hamburguer41994-01-04run
1314FredHoy&&&le553pizzza81997-06-27around
1415((( Heinrich )))))Hertz116pizza81956-11-30and
1516WilliamGilbert###886BEER21958-03-26desert
1617MarieCURIE912Rice12000-03-22you
1718ArthurCOM%%%pton81211079051899-01-01#
1819JAMESChadwick467NaN101921-05-03#
\n", "
" ], "text/plain": [ " id firstName lastName billingId product \\\n", "0 1 Luis Alvarez$$%! 123 Cake \n", "1 2 André Ampère 423 piza \n", "2 3 NiELS Böhr//((%% 551 pizza \n", "3 4 PAUL dirac$ 521 pizza \n", "4 5 Albert Einstein 634 pizza \n", "5 6 Galileo GALiLEI 672 arepa \n", "6 7 CaRL Ga%%%uss 323 taco \n", "7 8 David H$$$ilbert 624 taaaccoo \n", "8 9 Johannes KEPLER 735 taco \n", "9 10 JaMES M$$ax%%well 875 taco \n", "10 11 Isaac Newton 992 pasta \n", "11 12 Emmy%% Nöether$ 234 pasta \n", "12 13 Max!!! Planck!!! 111 hamburguer \n", "13 14 Fred Hoy&&&le 553 pizzza \n", "14 15 ((( Heinrich ))))) Hertz 116 pizza \n", "15 16 William Gilbert### 886 BEER \n", "16 17 Marie CURIE 912 Rice \n", "17 18 Arthur COM%%%pton 812 110790 \n", "18 19 JAMES Chadwick 467 NaN \n", "\n", " price birth dummyCol \n", "0 10 1980-07-07 never \n", "1 8 1950-07-08 gonna \n", "2 8 1990-07-09 give \n", "3 8 1954-07-10 you \n", "4 8 1990-07-11 up \n", "5 5 1930-08-12 never \n", "6 3 1970-07-13 gonna \n", "7 3 1950-07-14 let \n", "8 3 1920-04-22 you \n", "9 3 1923-03-12 down \n", "10 9 1999-02-15 never \n", "11 9 1993-12-08 gonna \n", "12 4 1994-01-04 run \n", "13 8 1997-06-27 around \n", "14 8 1956-11-30 and \n", "15 2 1958-03-26 desert \n", "16 1 2000-03-22 you \n", "17 5 1899-01-01 # \n", "18 10 1921-05-03 # " ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.compute()" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1980 7 7 0 0 0 0 28\n", "1950 7 8 0 0 0 5 27\n", "1990 7 9 0 0 0 0 28\n", "1954 7 10 0 0 0 5 27\n", "1990 7 11 0 0 0 2 28\n", "1930 8 12 0 0 0 1 33\n", "1970 7 13 0 0 0 0 29\n", "1950 7 14 0 0 0 4 28\n", "1920 4 22 0 0 0 3 17\n", "1923 3 12 0 0 0 0 11\n", "1999 2 15 0 0 0 0 7\n", "1993 12 8 0 0 0 2 49\n", "1994 1 4 0 0 0 1 1\n", "1997 6 27 0 0 0 4 26\n", "1956 11 30 0 0 0 4 48\n", "1958 3 26 0 0 0 2 13\n", "2000 3 22 0 0 0 2 12\n", "1899 1 1 0 0 0 6 52\n", "1921 5 3 0 0 0 1 18\n" ] }, { "data": { "text/plain": [ "['__add__',\n", " '__array_priority__',\n", " '__class__',\n", " '__delattr__',\n", " '__dict__',\n", " '__dir__',\n", " '__doc__',\n", " '__eq__',\n", " '__format__',\n", " '__ge__',\n", " '__getattribute__',\n", " '__gt__',\n", " '__hash__',\n", " '__init__',\n", " '__init_subclass__',\n", " '__le__',\n", " '__lt__',\n", " '__module__',\n", " '__ne__',\n", " '__new__',\n", " '__pyx_vtable__',\n", " '__radd__',\n", " '__reduce__',\n", " '__reduce_ex__',\n", " '__repr__',\n", " '__rsub__',\n", " '__setattr__',\n", " '__setstate__',\n", " '__sizeof__',\n", " '__str__',\n", " '__sub__',\n", " '__subclasshook__',\n", " '__weakref__',\n", " '_date_attributes',\n", " '_date_repr',\n", " '_get_date_name_field',\n", " '_get_start_end_field',\n", " '_has_time_component',\n", " '_repr_base',\n", " '_round',\n", " '_short_repr',\n", " '_time_repr',\n", " 'asm8',\n", " 'astimezone',\n", " 'ceil',\n", " 'combine',\n", " 'ctime',\n", " 'date',\n", " 'day',\n", " 'day_name',\n", " 'dayofweek',\n", " 'dayofyear',\n", " 'days_in_month',\n", " 'daysinmonth',\n", " 'dst',\n", " 'floor',\n", " 'fold',\n", " 'freq',\n", " 'freqstr',\n", " 'fromisoformat',\n", " 'fromordinal',\n", " 'fromtimestamp',\n", " 'hour',\n", " 'is_leap_year',\n", " 'is_month_end',\n", " 'is_month_start',\n", " 'is_quarter_end',\n", " 'is_quarter_start',\n", " 'is_year_end',\n", " 'is_year_start',\n", " 'isocalendar',\n", " 'isoformat',\n", " 'isoweekday',\n", " 'max',\n", " 'microsecond',\n", " 'min',\n", " 'minute',\n", " 'month',\n", " 'month_name',\n", " 'nanosecond',\n", " 'normalize',\n", " 'now',\n", " 'quarter',\n", " 'replace',\n", " 'resolution',\n", " 'round',\n", " 'second',\n", " 'strftime',\n", " 'strptime',\n", " 'time',\n", " 'timestamp',\n", " 'timetuple',\n", " 'timetz',\n", " 'to_datetime64',\n", " 'to_julian_date',\n", " 'to_numpy',\n", " 'to_period',\n", " 'to_pydatetime',\n", " 'today',\n", " 'toordinal',\n", " 'tz',\n", " 'tz_convert',\n", " 'tz_localize',\n", " 'tzinfo',\n", " 'tzname',\n", " 'utcfromtimestamp',\n", " 'utcnow',\n", " 'utcoffset',\n", " 'utctimetuple',\n", " 'value',\n", " 'week',\n", " 'weekday',\n", " 'weekofyear',\n", " 'year']" ] }, "execution_count": 135, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "for i in df[\"birth\"]: \n", " print(i.year, i.month, i.day, i.hour, i.minute, i.second, i.weekday(), i.weekofyear)\n", "dir(i)" ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idfirstNamelastNamebillingIdproductpricebirthdummyColnew_birth
01LuisAlvarez$$%!123Cake101980-07-07never1980
12AndréAmpère423piza81950-07-08gonna1950
23NiELSBöhr//((%%551pizza81990-07-09give1990
34PAULdirac$521pizza81954-07-10you1954
45AlbertEinstein634pizza81990-07-11up1990
56GalileoGALiLEI672arepa51930-08-12never1930
67CaRLGa%%%uss323taco31970-07-13gonna1970
78DavidH$$$ilbert624taaaccoo31950-07-14let1950
89JohannesKEPLER735taco31920-04-22you1920
910JaMESM$$ax%%well875taco31923-03-12down1923
1011IsaacNewton992pasta91999-02-15never1999
1112Emmy%%Nöether$234pasta91993-12-08gonna1993
1213Max!!!Planck!!!111hamburguer41994-01-04run1994
1314FredHoy&&&le553pizzza81997-06-27around1997
1415((( Heinrich )))))Hertz116pizza81956-11-30and1956
1516WilliamGilbert###886BEER21958-03-26desert1958
1617MarieCURIE912Rice12000-03-22you2000
1718ArthurCOM%%%pton81211079051899-01-01#1899
1819JAMESChadwick467NaN101921-05-03#1921
\n", "
" ], "text/plain": [ " id firstName lastName billingId product \\\n", "0 1 Luis Alvarez$$%! 123 Cake \n", "1 2 André Ampère 423 piza \n", "2 3 NiELS Böhr//((%% 551 pizza \n", "3 4 PAUL dirac$ 521 pizza \n", "4 5 Albert Einstein 634 pizza \n", "5 6 Galileo GALiLEI 672 arepa \n", "6 7 CaRL Ga%%%uss 323 taco \n", "7 8 David H$$$ilbert 624 taaaccoo \n", "8 9 Johannes KEPLER 735 taco \n", "9 10 JaMES M$$ax%%well 875 taco \n", "10 11 Isaac Newton 992 pasta \n", "11 12 Emmy%% Nöether$ 234 pasta \n", "12 13 Max!!! Planck!!! 111 hamburguer \n", "13 14 Fred Hoy&&&le 553 pizzza \n", "14 15 ((( Heinrich ))))) Hertz 116 pizza \n", "15 16 William Gilbert### 886 BEER \n", "16 17 Marie CURIE 912 Rice \n", "17 18 Arthur COM%%%pton 812 110790 \n", "18 19 JAMES Chadwick 467 NaN \n", "\n", " price birth dummyCol new_birth \n", "0 10 1980-07-07 never 1980 \n", "1 8 1950-07-08 gonna 1950 \n", "2 8 1990-07-09 give 1990 \n", "3 8 1954-07-10 you 1954 \n", "4 8 1990-07-11 up 1990 \n", "5 5 1930-08-12 never 1930 \n", "6 3 1970-07-13 gonna 1970 \n", "7 3 1950-07-14 let 1950 \n", "8 3 1920-04-22 you 1920 \n", "9 3 1923-03-12 down 1923 \n", "10 9 1999-02-15 never 1999 \n", "11 9 1993-12-08 gonna 1993 \n", "12 4 1994-01-04 run 1994 \n", "13 8 1997-06-27 around 1997 \n", "14 8 1956-11-30 and 1956 \n", "15 2 1958-03-26 desert 1958 \n", "16 1 2000-03-22 you 2000 \n", "17 5 1899-01-01 # 1899 \n", "18 10 1921-05-03 # 1921 " ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.year(\"birth\", output_cols=\"new_birth\").compute()" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "Can only use .dt accessor with datetimelike values", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"birth\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mpandas\\_libs\\properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36mdt\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2616\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2617\u001b[0m \u001b[1;34m\"\"\" Namespace of datetime methods \"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2618\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mDatetimeAccessor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2619\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2620\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\accessor.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, series)\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries_meta\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"to_series\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# is index-like\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[0mseries_meta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseries_meta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_series\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m \u001b[0mmeta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries_meta\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessor_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 38\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_meta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeta\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 5268\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5269\u001b[0m ):\n\u001b[1;32m-> 5270\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5271\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5272\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\accessor.py\u001b[0m in \u001b[0;36m__get__\u001b[1;34m(self, obj, cls)\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[1;31m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 186\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 187\u001b[1;33m \u001b[0maccessor_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 188\u001b[0m \u001b[1;31m# Replace the property with the accessor object. Inspired by:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[1;31m# http://www.pydanny.com/cached-property.html\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\indexes\\accessors.py\u001b[0m in \u001b[0;36m__new__\u001b[1;34m(cls, data)\u001b[0m\n\u001b[0;32m 336\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mDatetimeProperties\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 337\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 338\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Can only use .dt accessor with datetimelike values\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: Can only use .dt accessor with datetimelike values" ] } ], "source": [ "df[\"birth\"].dt" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datetime.datetime" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dateutil.parser import parse as dparse\n", "type(dparse(\"1\"))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': '2000/03/22', 'count': 1},\n", " {'value': '1999/02/15', 'count': 1},\n", " {'value': '1997/06/27', 'count': 1},\n", " {'value': '1994/01/04', 'count': 1},\n", " {'value': '1993/12/08', 'count': 1},\n", " {'value': '1990/07/11', 'count': 1},\n", " {'value': '1990/07/09', 'count': 1},\n", " {'value': '1980/07/07', 'count': 1},\n", " {'value': '1970/07/13', 'count': 1},\n", " {'value': '1958/03/26', 'count': 1},\n", " {'value': '1956/11/30', 'count': 1},\n", " {'value': '1954/07/10', 'count': 1},\n", " {'value': '1950/07/14', 'count': 1},\n", " {'value': '1950/07/08', 'count': 1},\n", " {'value': '1930/08/12', 'count': 1},\n", " {'value': '1923/03/12', 'count': 1},\n", " {'value': '1921/05/03', 'count': 1},\n", " {'value': '1920/04/22', 'count': 1},\n", " {'value': '1899/01/01', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'date'}" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for col_name in df.cols.names():\n", "# print(col_name)\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idfirstNamelastNamebillingIdproductpricebirthdummyCol
01LuisAlvarez$$%!123Cake101980-07-07never
12AndréAmpère423piza81950-07-08gonna
23NiELSBöhr//((%%551pizza81990-07-09give
34PAULdirac$521pizza81954-07-10you
45AlbertEinstein634pizza81990-07-11up
56GalileoGALiLEI672arepa51930-08-12never
67CaRLGa%%%uss323taco31970-07-13gonna
78DavidH$$$ilbert624taaaccoo31950-07-14let
89JohannesKEPLER735taco31920-04-22you
910JaMESM$$ax%%well875taco31923-03-12down
1011IsaacNewton992pasta91999-02-15never
1112Emmy%%Nöether$234pasta91993-12-08gonna
1213Max!!!Planck!!!111hamburguer41994-01-04run
1314FredHoy&&&le553pizzza81997-06-27around
1415((( Heinrich )))))Hertz116pizza81956-11-30and
1516WilliamGilbert###886BEER21958-03-26desert
1617MarieCURIE912Rice12000-03-22you
1718ArthurCOM%%%pton81211079051899-01-01#
1819JAMESChadwick467NaN101921-05-03#
\n", "
" ], "text/plain": [ " id firstName lastName billingId product \\\n", "0 1 Luis Alvarez$$%! 123 Cake \n", "1 2 André Ampère 423 piza \n", "2 3 NiELS Böhr//((%% 551 pizza \n", "3 4 PAUL dirac$ 521 pizza \n", "4 5 Albert Einstein 634 pizza \n", "5 6 Galileo GALiLEI 672 arepa \n", "6 7 CaRL Ga%%%uss 323 taco \n", "7 8 David H$$$ilbert 624 taaaccoo \n", "8 9 Johannes KEPLER 735 taco \n", "9 10 JaMES M$$ax%%well 875 taco \n", "10 11 Isaac Newton 992 pasta \n", "11 12 Emmy%% Nöether$ 234 pasta \n", "12 13 Max!!! Planck!!! 111 hamburguer \n", "13 14 Fred Hoy&&&le 553 pizzza \n", "14 15 ((( Heinrich ))))) Hertz 116 pizza \n", "15 16 William Gilbert### 886 BEER \n", "16 17 Marie CURIE 912 Rice \n", "17 18 Arthur COM%%%pton 812 110790 \n", "18 19 JAMES Chadwick 467 NaN \n", "\n", " price birth dummyCol \n", "0 10 1980-07-07 never \n", "1 8 1950-07-08 gonna \n", "2 8 1990-07-09 give \n", "3 8 1954-07-10 you \n", "4 8 1990-07-11 up \n", "5 5 1930-08-12 never \n", "6 3 1970-07-13 gonna \n", "7 3 1950-07-14 let \n", "8 3 1920-04-22 you \n", "9 3 1923-03-12 down \n", "10 9 1999-02-15 never \n", "11 9 1993-12-08 gonna \n", "12 4 1994-01-04 run \n", "13 8 1997-06-27 around \n", "14 8 1956-11-30 and \n", "15 2 1958-03-26 desert \n", "16 1 2000-03-22 you \n", "17 5 1899-01-01 # \n", "18 10 1921-05-03 # " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.compute()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import arrow\n", "date = 1\n", "arrow.get(date)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datetime.datetime(2020, 5, 10, 0, 0)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dateutil.parser import parse as dparse\n", "dparse(\"10\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
INCIDENT_NUMBEROFFENSE_CODEOFFENSE_CODE_GROUPOFFENSE_DESCRIPTIONDISTRICTREPORTING_AREASHOOTINGOCCURRED_ON_DATEYEARMONTHDAY_OF_WEEKHOURUCR_PARTSTREETLatLongLocation
0I182070945619LarcenyLARCENY ALL OTHERSD14808NaN2018-09-02 13:00:0020189Sunday13Part OneLINCOLN ST42.35779134-71.13937053(42.35779134, -71.13937053)
1I1820709431402VandalismVANDALISMC11347NaN2018-08-21 00:00:0020188Tuesday0Part TwoHECLA ST42.30682138-71.06030035(42.30682138, -71.06030035)
2I1820709413410TowedTOWED MOTOR VEHICLED4151NaN2018-09-03 19:27:0020189Monday19Part ThreeCAZENOVE ST42.34658879-71.07242943(42.34658879, -71.07242943)
3I1820709403114Investigate PropertyINVESTIGATE PROPERTYD4272NaN2018-09-03 21:16:0020189Monday21Part ThreeNEWCOMB ST42.33418175-71.07866441(42.33418175, -71.07866441)
4I1820709383114Investigate PropertyINVESTIGATE PROPERTYB3421NaN2018-09-03 21:05:0020189Monday21Part ThreeDELHI ST42.27536542-71.09036101(42.27536542, -71.09036101)
......................................................
319068I050310906-003125Warrant ArrestsWARRANT ARRESTD4285NaN2016-06-05 17:25:0020166Sunday17Part ThreeCOVENTRY ST42.33695098-71.08574813(42.33695098, -71.08574813)
319069I030217815-08111HomicideMURDER, NON-NEGLIGIENT MANSLAUGHTERE18520NaN2015-07-09 13:38:0020157Thursday13Part OneRIVER ST42.25592648-71.12317207(42.25592648, -71.12317207)
319070I030217815-083125Warrant ArrestsWARRANT ARRESTE18520NaN2015-07-09 13:38:0020157Thursday13Part ThreeRIVER ST42.25592648-71.12317207(42.25592648, -71.12317207)
319071I010370257-003125Warrant ArrestsWARRANT ARRESTE13569NaN2016-05-31 19:35:0020165Tuesday19Part ThreeNEW WASHINGTON ST42.30233307-71.11156487(42.30233307, -71.11156487)
3190721420525503125Warrant ArrestsWARRANT ARRESTD4903NaN2015-06-22 00:12:0020156Monday0Part ThreeWASHINGTON ST42.33383935-71.08029038(42.33383935, -71.08029038)
\n", "

319073 rows × 17 columns

\n", "
" ], "text/plain": [ " INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP \\\n", "0 I182070945 619 Larceny \n", "1 I182070943 1402 Vandalism \n", "2 I182070941 3410 Towed \n", "3 I182070940 3114 Investigate Property \n", "4 I182070938 3114 Investigate Property \n", "... ... ... ... \n", "319068 I050310906-00 3125 Warrant Arrests \n", "319069 I030217815-08 111 Homicide \n", "319070 I030217815-08 3125 Warrant Arrests \n", "319071 I010370257-00 3125 Warrant Arrests \n", "319072 142052550 3125 Warrant Arrests \n", "\n", " OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING \\\n", "0 LARCENY ALL OTHERS D14 808 NaN \n", "1 VANDALISM C11 347 NaN \n", "2 TOWED MOTOR VEHICLE D4 151 NaN \n", "3 INVESTIGATE PROPERTY D4 272 NaN \n", "4 INVESTIGATE PROPERTY B3 421 NaN \n", "... ... ... ... ... \n", "319068 WARRANT ARREST D4 285 NaN \n", "319069 MURDER, NON-NEGLIGIENT MANSLAUGHTER E18 520 NaN \n", "319070 WARRANT ARREST E18 520 NaN \n", "319071 WARRANT ARREST E13 569 NaN \n", "319072 WARRANT ARREST D4 903 NaN \n", "\n", " OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART \\\n", "0 2018-09-02 13:00:00 2018 9 Sunday 13 Part One \n", "1 2018-08-21 00:00:00 2018 8 Tuesday 0 Part Two \n", "2 2018-09-03 19:27:00 2018 9 Monday 19 Part Three \n", "3 2018-09-03 21:16:00 2018 9 Monday 21 Part Three \n", "4 2018-09-03 21:05:00 2018 9 Monday 21 Part Three \n", "... ... ... ... ... ... ... \n", "319068 2016-06-05 17:25:00 2016 6 Sunday 17 Part Three \n", "319069 2015-07-09 13:38:00 2015 7 Thursday 13 Part One \n", "319070 2015-07-09 13:38:00 2015 7 Thursday 13 Part Three \n", "319071 2016-05-31 19:35:00 2016 5 Tuesday 19 Part Three \n", "319072 2015-06-22 00:12:00 2015 6 Monday 0 Part Three \n", "\n", " STREET Lat Long \\\n", "0 LINCOLN ST 42.35779134 -71.13937053 \n", "1 HECLA ST 42.30682138 -71.06030035 \n", "2 CAZENOVE ST 42.34658879 -71.07242943 \n", "3 NEWCOMB ST 42.33418175 -71.07866441 \n", "4 DELHI ST 42.27536542 -71.09036101 \n", "... ... ... ... \n", "319068 COVENTRY ST 42.33695098 -71.08574813 \n", "319069 RIVER ST 42.25592648 -71.12317207 \n", "319070 RIVER ST 42.25592648 -71.12317207 \n", "319071 NEW WASHINGTON ST 42.30233307 -71.11156487 \n", "319072 WASHINGTON ST 42.33383935 -71.08029038 \n", "\n", " Location \n", "0 (42.35779134, -71.13937053) \n", "1 (42.30682138, -71.06030035) \n", "2 (42.34658879, -71.07242943) \n", "3 (42.33418175, -71.07866441) \n", "4 (42.27536542, -71.09036101) \n", "... ... \n", "319068 (42.33695098, -71.08574813) \n", "319069 (42.25592648, -71.12317207) \n", "319070 (42.25592648, -71.12317207) \n", "319071 (42.30233307, -71.11156487) \n", "319072 (42.33383935, -71.08029038) \n", "\n", "[319073 rows x 17 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.compute()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "columns {'SHOOTING': 'object'}\n", "_dtype object\n", "cols_and_inferred_dtype {'SHOOTING': 'object'}\n" ] }, { "data": { "text/plain": [ "'{\"columns\": {\"SHOOTING\": {\"stats\": {\"mismatch\": 1019, \"missing\": 318054, \"match\": 0, \"frequency\": [{\"value\": \"Y\", \"count\": 1019}], \"count_uniques\": 1}, \"dtype\": \"object\", \"profiler_dtype\": \"object\"}}, \"name\": null, \"file_name\": \"crime.csv\", \"summary\": {\"cols_count\": 17, \"rows_count\": 319073, \"size\": \"35.1 MB\", \"dtypes_list\": [\"uint16\", \"object\", \"uint8\"], \"total_count_dtypes\": 3, \"missing_count\": 318054, \"p_missing\": 99.68}}'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'preview_df' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0m_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreview_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"json\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'preview_df' is not defined" ] } ], "source": [ "_output = preview_df.ext.profile(columns=\"*\", infer=True, output=\"json\")\n" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "# df = op.load.csv(\"http://159.65.217.17:5003/uploads/datasetFile-1589815911139.csv\", sep=\",\", error_bad_lines=False, header=True, null_value=\"null\", infer_schema=\"true\", encoding=\"UTF-8\", quoting=0, lineterminator=None, cache=True).ext.cache()\n", "# df = op.load.csv(\"http://159.65.217.17:5003/uploads/datasetFile-1590006020188.csv\", sep=\",\", error_bad_lines=False, header=True, null_value=\"null\", infer_schema=\"true\", encoding=\"UTF-8\", quoting=0, lineterminator=None, cache=True).ext.cache()\n", "# df = op.load.file(\"data/dataset - transactions.csv\").ext.cache()\n", "# df = op.load.file(\"data/crime.csv\", n_rows=10).ext.cache()\n", "df = op.load.file(\"data/foo.csv\", n_rows=10).ext.cache()\n", "\n", "\n", "df = df.ext.optimize()\n" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idfirstNamelastNamebillingIdproductpricebirthdummyCol
01LuisAlvarez$$%!123Cake101980/07/07never
12AndréAmpère423piza81950/07/08gonna
23NiELSBöhr//((%%551pizza81990/07/09give
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45AlbertEinstein634pizza81990/07/11up
56GalileoGALiLEI672arepa51930/08/12never
67CaRLGa%%%uss323taco31970/07/13gonna
78DavidH$$$ilbert624taaaccoo31950/07/14let
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910JaMESM$$ax%%well875taco31923/03/12down
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" ], "text/plain": [ " id firstName lastName billingId product price birth \\\n", "0 1 Luis Alvarez$$%! 123 Cake 10 1980/07/07 \n", "1 2 André Ampère 423 piza 8 1950/07/08 \n", "2 3 NiELS Böhr//((%% 551 pizza 8 1990/07/09 \n", "3 4 PAUL dirac$ 521 pizza 8 1954/07/10 \n", "4 5 Albert Einstein 634 pizza 8 1990/07/11 \n", "5 6 Galileo GALiLEI 672 arepa 5 1930/08/12 \n", "6 7 CaRL Ga%%%uss 323 taco 3 1970/07/13 \n", "7 8 David H$$$ilbert 624 taaaccoo 3 1950/07/14 \n", "8 9 Johannes KEPLER 735 taco 3 1920/04/22 \n", "9 10 JaMES M$$ax%%well 875 taco 3 1923/03/12 \n", "\n", " dummyCol \n", "0 never \n", "1 gonna \n", "2 give \n", "3 you \n", "4 up \n", "5 never \n", "6 gonna \n", "7 let \n", "8 you \n", "9 down " ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.compute()" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 19 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "columns {'id': 'int', 'firstName': 'string', 'lastName': 'string', 'billingId': 'int', 'product': 'string', 'price': 'int', 'birth': 'date', 'dummyCol': 'string'}\n", "dtype--- int\n", "dtype--- object\n", "dtype--- object\n", "dtype--- int\n", "dtype--- object\n", "dtype--- int\n", "dtype--- date\n", "dtype--- object\n" ] }, { "data": { "text/plain": [ "{'columns': {'id': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'hist': [{'lower': 1.0, 'upper': 1.5625, 'count': 1},\n", " {'lower': 1.5625, 'upper': 2.125, 'count': 1},\n", " {'lower': 2.125, 'upper': 2.6875, 'count': 0},\n", " {'lower': 2.6875, 'upper': 3.25, 'count': 1},\n", " {'lower': 3.25, 'upper': 3.8125, 'count': 0},\n", " {'lower': 3.8125, 'upper': 4.375, 'count': 1},\n", " {'lower': 4.375, 'upper': 4.9375, 'count': 0},\n", " {'lower': 4.9375, 'upper': 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{'value': 'David', 'count': 1},\n", " {'value': 'CaRL', 'count': 1},\n", " {'value': 'Arthur', 'count': 1},\n", " {'value': 'André', 'count': 1},\n", " {'value': 'Albert', 'count': 1},\n", " {'value': '((( Heinrich )))))', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'lastName': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': 'dirac$', 'count': 1},\n", " {'value': 'Planck!!!', 'count': 1},\n", " {'value': 'Nöether$', 'count': 1},\n", " {'value': 'Newton', 'count': 1},\n", " {'value': 'M$$ax%%well', 'count': 1},\n", " {'value': 'KEPLER', 'count': 1},\n", " {'value': 'Hoy&&&le', 'count': 1},\n", " {'value': 'Hertz', 'count': 1},\n", " {'value': 'H$$$ilbert', 'count': 1},\n", " {'value': 'Gilbert###', 'count': 1},\n", " {'value': 'Ga%%%uss', 'count': 1},\n", " {'value': 'Einstein', 'count': 1},\n", " {'value': 'Chadwick', 'count': 1},\n", " {'value': 'CURIE', 'count': 1},\n", " {'value': 'COM%%%pton', 'count': 1},\n", " {'value': 'Böhr//((%%', 'count': 1},\n", " {'value': 'Ampère', 'count': 1},\n", " {'value': 'Alvarez$$%!', 'count': 1},\n", " {'value': ' GALiLEI', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'billingId': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'hist': [{'lower': 111.0, 'upper': 138.53125, 'count': 3},\n", " {'lower': 138.53125, 'upper': 166.0625, 'count': 0},\n", " {'lower': 166.0625, 'upper': 193.59375, 'count': 0},\n", " {'lower': 193.59375, 'upper': 221.125, 'count': 0},\n", " {'lower': 221.125, 'upper': 248.65625, 'count': 1},\n", " {'lower': 248.65625, 'upper': 276.1875, 'count': 0},\n", " {'lower': 276.1875, 'upper': 303.71875, 'count': 0},\n", " {'lower': 303.71875, 'upper': 331.25, 'count': 1},\n", " {'lower': 331.25, 'upper': 358.78125, 'count': 0},\n", " {'lower': 358.78125, 'upper': 386.3125, 'count': 0},\n", " {'lower': 386.3125, 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881.875, 'count': 1},\n", " {'lower': 881.875, 'upper': 909.40625, 'count': 1},\n", " {'lower': 909.40625, 'upper': 936.9375, 'count': 1},\n", " {'lower': 936.9375, 'upper': 964.46875, 'count': 0},\n", " {'lower': 964.46875, 'upper': 992.0, 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'int'},\n", " 'product': {'stats': {'mismatch': 0,\n", " 'missing': 1,\n", " 'match': 18,\n", " 'frequency': [{'value': 'pizza', 'count': 4},\n", " {'value': 'taco', 'count': 3},\n", " {'value': 'pasta', 'count': 2},\n", " {'value': 'taaaccoo', 'count': 1},\n", " {'value': 'pizzza', 'count': 1},\n", " {'value': 'piza', 'count': 1},\n", " {'value': 'hamburguer', 'count': 1},\n", " {'value': 'arepa', 'count': 1},\n", " {'value': 'Rice', 'count': 1},\n", " {'value': 'Cake', 'count': 1},\n", " {'value': 'BEER', 'count': 1},\n", " {'value': '110790', 'count': 1}],\n", " 'count_uniques': 12},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'price': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'hist': [{'lower': 1.0, 'upper': 1.28125, 'count': 1},\n", " {'lower': 1.28125, 'upper': 1.5625, 'count': 0},\n", " {'lower': 1.5625, 'upper': 1.84375, 'count': 0},\n", " {'lower': 1.84375, 'upper': 2.125, 'count': 1},\n", " {'lower': 2.125, 'upper': 2.40625, 'count': 0},\n", " {'lower': 2.40625, 'upper': 2.6875, 'count': 0},\n", " {'lower': 2.6875, 'upper': 2.96875, 'count': 0},\n", " {'lower': 2.96875, 'upper': 3.25, 'count': 4},\n", " {'lower': 3.25, 'upper': 3.53125, 'count': 0},\n", " {'lower': 3.53125, 'upper': 3.8125, 'count': 0},\n", " {'lower': 3.8125, 'upper': 4.09375, 'count': 1},\n", " {'lower': 4.09375, 'upper': 4.375, 'count': 0},\n", " {'lower': 4.375, 'upper': 4.65625, 'count': 0},\n", " {'lower': 4.65625, 'upper': 4.9375, 'count': 0},\n", " {'lower': 4.9375, 'upper': 5.21875, 'count': 2},\n", " {'lower': 5.21875, 'upper': 5.5, 'count': 0},\n", " {'lower': 5.5, 'upper': 5.78125, 'count': 0},\n", " {'lower': 5.78125, 'upper': 6.0625, 'count': 0},\n", " {'lower': 6.0625, 'upper': 6.34375, 'count': 0},\n", " {'lower': 6.34375, 'upper': 6.625, 'count': 0},\n", " {'lower': 6.625, 'upper': 6.90625, 'count': 0},\n", " {'lower': 6.90625, 'upper': 7.1875, 'count': 0},\n", " {'lower': 7.1875, 'upper': 7.46875, 'count': 0},\n", " {'lower': 7.46875, 'upper': 7.75, 'count': 0},\n", " {'lower': 7.75, 'upper': 8.03125, 'count': 6},\n", " {'lower': 8.03125, 'upper': 8.3125, 'count': 0},\n", " {'lower': 8.3125, 'upper': 8.59375, 'count': 0},\n", " {'lower': 8.59375, 'upper': 8.875, 'count': 0},\n", " {'lower': 8.875, 'upper': 9.15625, 'count': 2},\n", " {'lower': 9.15625, 'upper': 9.4375, 'count': 0},\n", " {'lower': 9.4375, 'upper': 9.71875, 'count': 0},\n", " {'lower': 9.71875, 'upper': 10.0, 'count': 2}],\n", " 'count_uniques': 8},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'int'},\n", " 'birth': {'stats': {'mismatch': 19,\n", " 'missing': 0,\n", " 'match': 0,\n", " 'frequency': [{'value': '2000/03/22', 'count': 1},\n", " {'value': '1999/02/15', 'count': 1},\n", " {'value': '1997/06/27', 'count': 1},\n", " {'value': '1994/01/04', 'count': 1},\n", " {'value': '1993/12/08', 'count': 1},\n", " {'value': '1990/07/11', 'count': 1},\n", " {'value': '1990/07/09', 'count': 1},\n", " {'value': '1980/07/07', 'count': 1},\n", " {'value': '1970/07/13', 'count': 1},\n", " {'value': '1958/03/26', 'count': 1},\n", " {'value': '1956/11/30', 'count': 1},\n", " {'value': '1954/07/10', 'count': 1},\n", " {'value': '1950/07/14', 'count': 1},\n", " {'value': '1950/07/08', 'count': 1},\n", " {'value': '1930/08/12', 'count': 1},\n", " {'value': '1923/03/12', 'count': 1},\n", " {'value': '1921/05/03', 'count': 1},\n", " {'value': '1920/04/22', 'count': 1},\n", " {'value': '1899/01/01', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'date'},\n", " 'dummyCol': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': 'you', 'count': 3},\n", " {'value': 'gonna', 'count': 3},\n", " {'value': 'never', 'count': 2},\n", " {'value': '#', 'count': 2},\n", " {'value': 'up', 'count': 1},\n", " {'value': 'run ', 'count': 1},\n", " {'value': 'never ', 'count': 1},\n", " {'value': 'let', 'count': 1},\n", " {'value': 'give', 'count': 1},\n", " {'value': 'down', 'count': 1},\n", " {'value': 'desert', 'count': 1},\n", " {'value': 'around', 'count': 1},\n", " {'value': 'and', 'count': 1}],\n", " 'count_uniques': 13},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'}},\n", " 'name': None,\n", " 'file_name': 'foo.csv',\n", " 'summary': {'cols_count': 8,\n", " 'rows_count': 19,\n", " 'size': '1.3 kB',\n", " 'dtypes_list': ['object'],\n", " 'total_count_dtypes': 1,\n", " 'missing_count': 1,\n", " 'p_missing': 5.26}}" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.profile(columns=\"*\", infer=True,)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 10 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] } ], "source": [ "df = df.cols.nest([\"firstName\", \"lastName\"], separator=\", \", output_col=\"firstName_lastName\").ext.cache()\n", "_output = df.ext.profile(columns=\"*\", infer=True, output=\"json\")" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\"columns\": {\"id\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 10, \"hist\": [{\"lower\": 1.0, \"upper\": 1.28125, \"count\": 1}, {\"lower\": 1.28125, \"upper\": 1.5625, \"count\": 0}, {\"lower\": 1.5625, \"upper\": 1.84375, \"count\": 0}, {\"lower\": 1.84375, \"upper\": 2.125, \"count\": 1}, {\"lower\": 2.125, \"upper\": 2.40625, \"count\": 0}, {\"lower\": 2.40625, \"upper\": 2.6875, \"count\": 0}, {\"lower\": 2.6875, \"upper\": 2.96875, \"count\": 0}, {\"lower\": 2.96875, \"upper\": 3.25, \"count\": 1}, {\"lower\": 3.25, \"upper\": 3.53125, \"count\": 0}, {\"lower\": 3.53125, \"upper\": 3.8125, \"count\": 0}, {\"lower\": 3.8125, \"upper\": 4.09375, \"count\": 1}, {\"lower\": 4.09375, \"upper\": 4.375, \"count\": 0}, {\"lower\": 4.375, \"upper\": 4.65625, \"count\": 0}, {\"lower\": 4.65625, \"upper\": 4.9375, \"count\": 0}, {\"lower\": 4.9375, \"upper\": 5.21875, \"count\": 1}, {\"lower\": 5.21875, \"upper\": 5.5, \"count\": 0}, {\"lower\": 5.5, \"upper\": 5.78125, \"count\": 0}, {\"lower\": 5.78125, \"upper\": 6.0625, \"count\": 1}, {\"lower\": 6.0625, \"upper\": 6.34375, \"count\": 0}, {\"lower\": 6.34375, \"upper\": 6.625, \"count\": 0}, {\"lower\": 6.625, \"upper\": 6.90625, \"count\": 0}, {\"lower\": 6.90625, \"upper\": 7.1875, \"count\": 1}, {\"lower\": 7.1875, \"upper\": 7.46875, \"count\": 0}, {\"lower\": 7.46875, \"upper\": 7.75, \"count\": 0}, {\"lower\": 7.75, \"upper\": 8.03125, \"count\": 1}, {\"lower\": 8.03125, \"upper\": 8.3125, \"count\": 0}, {\"lower\": 8.3125, \"upper\": 8.59375, \"count\": 0}, {\"lower\": 8.59375, \"upper\": 8.875, \"count\": 0}, {\"lower\": 8.875, \"upper\": 9.15625, \"count\": 1}, {\"lower\": 9.15625, \"upper\": 9.4375, \"count\": 0}, {\"lower\": 9.4375, \"upper\": 9.71875, \"count\": 0}, {\"lower\": 9.71875, \"upper\": 10.0, \"count\": 1}], \"count_uniques\": 10}, \"dtype\": \"int32\", \"profiler_dtype\": \"int\"}, \"firstName\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 10, \"frequency\": [{\"value\": \"PAUL\", \"count\": 1}, {\"value\": \"NiELS\", \"count\": 1}, {\"value\": \"Luis\", \"count\": 1}, {\"value\": \"Johannes\", \"count\": 1}, {\"value\": \"JaMES\", \"count\": 1}, {\"value\": \"Galileo\", \"count\": 1}, {\"value\": \"David\", \"count\": 1}, {\"value\": \"CaRL\", \"count\": 1}, {\"value\": \"Andr\\u00e9\", \"count\": 1}, {\"value\": \"Albert\", \"count\": 1}], \"count_uniques\": 10}, \"dtype\": \"object\", \"profiler_dtype\": \"string\"}, \"lastName\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 10, \"frequency\": [{\"value\": \"dirac$\", \"count\": 1}, {\"value\": \"M$$ax%%well\", \"count\": 1}, {\"value\": \"KEPLER\", \"count\": 1}, {\"value\": \"H$$$ilbert\", \"count\": 1}, {\"value\": \"Ga%%%uss\", \"count\": 1}, {\"value\": \"Einstein\", \"count\": 1}, {\"value\": \"B\\u00f6hr//((%%\", \"count\": 1}, {\"value\": \"Amp\\u00e8re\", \"count\": 1}, {\"value\": \"Alvarez$$%!\", \"count\": 1}, {\"value\": \" GALiLEI\", \"count\": 1}], \"count_uniques\": 10}, \"dtype\": \"object\", \"profiler_dtype\": \"string\"}, \"firstName_lastName\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 10, \"frequency\": [{\"value\": \"PAUL, dirac$\", \"count\": 1}, {\"value\": \"NiELS, B\\u00f6hr//((%%\", \"count\": 1}, {\"value\": \"Luis, Alvarez$$%!\", \"count\": 1}, {\"value\": \"Johannes, KEPLER\", \"count\": 1}, {\"value\": \"JaMES, M$$ax%%well\", \"count\": 1}, {\"value\": \"Galileo, GALiLEI\", \"count\": 1}, {\"value\": \"David, H$$$ilbert\", \"count\": 1}, {\"value\": \"CaRL, Ga%%%uss\", \"count\": 1}, {\"value\": \"Andr\\u00e9, Amp\\u00e8re\", \"count\": 1}, {\"value\": \"Albert, Einstein\", \"count\": 1}], \"count_uniques\": 10}, \"dtype\": \"object\", \"profiler_dtype\": \"string\"}, \"billingId\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 10, \"hist\": [{\"lower\": 123.0, \"upper\": 146.5, \"count\": 1}, {\"lower\": 146.5, \"upper\": 170.0, \"count\": 0}, {\"lower\": 170.0, \"upper\": 193.5, \"count\": 0}, {\"lower\": 193.5, \"upper\": 217.0, \"count\": 0}, {\"lower\": 217.0, \"upper\": 240.5, \"count\": 0}, {\"lower\": 240.5, \"upper\": 264.0, \"count\": 0}, {\"lower\": 264.0, \"upper\": 287.5, \"count\": 0}, {\"lower\": 287.5, \"upper\": 311.0, \"count\": 0}, {\"lower\": 311.0, \"upper\": 334.5, \"count\": 1}, {\"lower\": 334.5, \"upper\": 358.0, \"count\": 0}, {\"lower\": 358.0, \"upper\": 381.5, \"count\": 0}, {\"lower\": 381.5, \"upper\": 405.0, \"count\": 0}, {\"lower\": 405.0, \"upper\": 428.5, \"count\": 1}, {\"lower\": 428.5, \"upper\": 452.0, \"count\": 0}, {\"lower\": 452.0, \"upper\": 475.5, \"count\": 0}, {\"lower\": 475.5, \"upper\": 499.0, \"count\": 0}, {\"lower\": 499.0, \"upper\": 522.5, \"count\": 1}, {\"lower\": 522.5, \"upper\": 546.0, \"count\": 0}, {\"lower\": 546.0, \"upper\": 569.5, \"count\": 1}, {\"lower\": 569.5, \"upper\": 593.0, \"count\": 0}, {\"lower\": 593.0, \"upper\": 616.5, \"count\": 0}, {\"lower\": 616.5, \"upper\": 640.0, \"count\": 2}, {\"lower\": 640.0, \"upper\": 663.5, \"count\": 0}, {\"lower\": 663.5, \"upper\": 687.0, \"count\": 1}, {\"lower\": 687.0, \"upper\": 710.5, \"count\": 0}, {\"lower\": 710.5, \"upper\": 734.0, \"count\": 0}, {\"lower\": 734.0, \"upper\": 757.5, \"count\": 1}, {\"lower\": 757.5, \"upper\": 781.0, \"count\": 0}, {\"lower\": 781.0, \"upper\": 804.5, \"count\": 0}, {\"lower\": 804.5, \"upper\": 828.0, \"count\": 0}, {\"lower\": 828.0, \"upper\": 851.5, \"count\": 0}, {\"lower\": 851.5, \"upper\": 875.0, \"count\": 1}], \"count_uniques\": 10}, \"dtype\": \"int32\", \"profiler_dtype\": \"int\"}, \"product\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 10, \"frequency\": [{\"value\": \"taco\", \"count\": 3}, {\"value\": \"pizza\", \"count\": 3}, {\"value\": \"taaaccoo\", \"count\": 1}, {\"value\": \"piza\", \"count\": 1}, {\"value\": \"arepa\", \"count\": 1}, {\"value\": \"Cake\", \"count\": 1}], \"count_uniques\": 6}, \"dtype\": \"object\", \"profiler_dtype\": \"string\"}, \"price\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 10, \"hist\": [{\"lower\": 3.0, \"upper\": 3.21875, \"count\": 4}, {\"lower\": 3.21875, \"upper\": 3.4375, \"count\": 0}, {\"lower\": 3.4375, \"upper\": 3.65625, \"count\": 0}, {\"lower\": 3.65625, \"upper\": 3.875, \"count\": 0}, {\"lower\": 3.875, \"upper\": 4.09375, \"count\": 0}, {\"lower\": 4.09375, \"upper\": 4.3125, \"count\": 0}, {\"lower\": 4.3125, \"upper\": 4.53125, \"count\": 0}, {\"lower\": 4.53125, \"upper\": 4.75, \"count\": 0}, {\"lower\": 4.75, \"upper\": 4.96875, \"count\": 0}, {\"lower\": 4.96875, \"upper\": 5.1875, \"count\": 1}, {\"lower\": 5.1875, \"upper\": 5.40625, \"count\": 0}, {\"lower\": 5.40625, \"upper\": 5.625, \"count\": 0}, {\"lower\": 5.625, \"upper\": 5.84375, \"count\": 0}, {\"lower\": 5.84375, \"upper\": 6.0625, \"count\": 0}, {\"lower\": 6.0625, \"upper\": 6.28125, \"count\": 0}, {\"lower\": 6.28125, \"upper\": 6.5, \"count\": 0}, {\"lower\": 6.5, \"upper\": 6.71875, \"count\": 0}, {\"lower\": 6.71875, \"upper\": 6.9375, \"count\": 0}, {\"lower\": 6.9375, \"upper\": 7.15625, \"count\": 0}, {\"lower\": 7.15625, \"upper\": 7.375, \"count\": 0}, {\"lower\": 7.375, \"upper\": 7.59375, \"count\": 0}, {\"lower\": 7.59375, \"upper\": 7.8125, \"count\": 0}, {\"lower\": 7.8125, \"upper\": 8.03125, \"count\": 4}, {\"lower\": 8.03125, \"upper\": 8.25, \"count\": 0}, {\"lower\": 8.25, \"upper\": 8.46875, \"count\": 0}, {\"lower\": 8.46875, \"upper\": 8.6875, \"count\": 0}, {\"lower\": 8.6875, \"upper\": 8.90625, \"count\": 0}, {\"lower\": 8.90625, \"upper\": 9.125, \"count\": 0}, {\"lower\": 9.125, \"upper\": 9.34375, \"count\": 0}, {\"lower\": 9.34375, \"upper\": 9.5625, \"count\": 0}, {\"lower\": 9.5625, \"upper\": 9.78125, \"count\": 0}, {\"lower\": 9.78125, \"upper\": 10.0, \"count\": 1}], \"count_uniques\": 4}, \"dtype\": \"int32\", \"profiler_dtype\": \"int\"}, \"birth\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 10, \"frequency\": [{\"value\": \"1990/07/11\", \"count\": 1}, {\"value\": \"1990/07/09\", \"count\": 1}, {\"value\": \"1980/07/07\", \"count\": 1}, {\"value\": \"1970/07/13\", \"count\": 1}, {\"value\": \"1954/07/10\", \"count\": 1}, {\"value\": \"1950/07/14\", \"count\": 1}, {\"value\": \"1950/07/08\", \"count\": 1}, {\"value\": \"1930/08/12\", \"count\": 1}, {\"value\": \"1923/03/12\", \"count\": 1}, {\"value\": \"1920/04/22\", \"count\": 1}], \"count_uniques\": 10}, \"dtype\": \"object\", \"profiler_dtype\": \"date\"}, \"dummyCol\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 10, \"frequency\": [{\"value\": \"you\", \"count\": 2}, {\"value\": \"never\", \"count\": 2}, {\"value\": \"gonna\", \"count\": 2}, {\"value\": \"up\", \"count\": 1}, {\"value\": \"let\", \"count\": 1}, {\"value\": \"give\", \"count\": 1}, {\"value\": \"down\", \"count\": 1}], \"count_uniques\": 7}, \"dtype\": \"object\", \"profiler_dtype\": \"string\"}}, \"name\": null, \"file_name\": \"foo.csv\", \"summary\": {\"cols_count\": 9, \"rows_count\": 10, \"size\": \"728 Bytes\", \"dtypes_list\": [\"int32\", \"object\"], \"total_count_dtypes\": 2, \"missing_count\": 0, \"p_missing\": 0.0}}\n" ] } ], "source": [ "print(_output)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 10 rows / 9 columns
\n", "
1 partition(s)
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id
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1 (int32)
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firstName
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2 (object)
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lastName
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3 (object)
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firstName_lastName
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4 (object)
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billingId
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5 (int32)
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product
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6 (object)
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price
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7 (int32)
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birth
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8 (object)
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dummyCol
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9 (object)
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\n", " \n", " ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI\n", " \n", "
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\n", " \n", " 8\n", " \n", "
\n", "
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\n", " \n", " David\n", " \n", "
\n", "
\n", "
\n", " \n", " H$$$ilbert\n", " \n", "
\n", "
\n", "
\n", " \n", " David,⋅H$$$ilbert\n", " \n", "
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\n", "
\n", " \n", " 624\n", " \n", "
\n", "
\n", "
\n", " \n", " taaaccoo\n", " \n", "
\n", "
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\n", " \n", " 3\n", " \n", "
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\n", "
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\n", " \n", " let\n", " \n", "
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\n", "
\n", " \n", " Johannes\n", " \n", "
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\n", "
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\n", " \n", " Johannes,⋅KEPLER\n", " \n", "
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\n", " \n", " 3\n", " \n", "
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\n", " \n", " JaMES\n", " \n", "
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\n", " \n", " M$$ax%%well\n", " \n", "
\n", "
\n", "
\n", " \n", " JaMES,⋅M$$ax%%well\n", " \n", "
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0}, {\"lower\": 6.28125, \"upper\": 6.5, \"count\": 0}, {\"lower\": 6.5, \"upper\": 6.71875, \"count\": 0}, {\"lower\": 6.71875, \"upper\": 6.9375, \"count\": 0}, {\"lower\": 6.9375, \"upper\": 7.15625, \"count\": 0}, {\"lower\": 7.15625, \"upper\": 7.375, \"count\": 0}, {\"lower\": 7.375, \"upper\": 7.59375, \"count\": 0}, {\"lower\": 7.59375, \"upper\": 7.8125, \"count\": 0}, {\"lower\": 7.8125, \"upper\": 8.03125, \"count\": 4}, {\"lower\": 8.03125, \"upper\": 8.25, \"count\": 0}, {\"lower\": 8.25, \"upper\": 8.46875, \"count\": 0}, {\"lower\": 8.46875, \"upper\": 8.6875, \"count\": 0}, {\"lower\": 8.6875, \"upper\": 8.90625, \"count\": 0}, {\"lower\": 8.90625, \"upper\": 9.125, \"count\": 0}, {\"lower\": 9.125, \"upper\": 9.34375, \"count\": 0}, {\"lower\": 9.34375, \"upper\": 9.5625, \"count\": 0}, {\"lower\": 9.5625, \"upper\": 9.78125, \"count\": 0}, {\"lower\": 9.78125, \"upper\": 10.0, \"count\": 1}], \"count_uniques\": 4}, \"dtype\": \"int32\", \"profiler_dtype\": \"int\"}, \"birth\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 10, \"frequency\": [{\"value\": \"1990/07/11\", \"count\": 1}, {\"value\": \"1990/07/09\", \"count\": 1}, {\"value\": \"1980/07/07\", \"count\": 1}, {\"value\": \"1970/07/13\", \"count\": 1}, {\"value\": \"1954/07/10\", \"count\": 1}, {\"value\": \"1950/07/14\", \"count\": 1}, {\"value\": \"1950/07/08\", \"count\": 1}, {\"value\": \"1930/08/12\", \"count\": 1}, {\"value\": \"1923/03/12\", \"count\": 1}, {\"value\": \"1920/04/22\", \"count\": 1}], \"count_uniques\": 10}, \"dtype\": \"object\", \"profiler_dtype\": \"date\"}, \"dummyCol\": {\"stats\": {\"mismatch\": 0, \"missing\": 0, \"match\": 10, \"frequency\": [{\"value\": \"you\", \"count\": 2}, {\"value\": \"never\", \"count\": 2}, {\"value\": \"gonna\", \"count\": 2}, {\"value\": \"up\", \"count\": 1}, {\"value\": \"let\", \"count\": 1}, {\"value\": \"give\", \"count\": 1}, {\"value\": \"down\", \"count\": 1}], \"count_uniques\": 7}, \"dtype\": \"object\", \"profiler_dtype\": \"string\"}}, \"name\": null, \"file_name\": \"foo.csv\", \"summary\": {\"cols_count\": 9, \"rows_count\": 10, \"size\": \"728 Bytes\", \"dtypes_list\": [\"int32\", \"object\"], \"total_count_dtypes\": 2, \"missing_count\": 0, \"p_missing\": 0.0}}'" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.profile(columns=\"*\", infer=True, output=\"json\", flush=True)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 10 rows / 9 columns
\n", "
1 partition(s)
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\n", "
id
\n", "
1 (int32)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
firstName
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
lastName
\n", "
3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
lastName1
\n", "
4 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
billingId
\n", "
5 (int32)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
product
\n", "
6 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
price
\n", "
7 (int32)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
birth
\n", "
8 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
dummyCol
\n", "
9 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Luis\n", " \n", "
\n", "
\n", "
\n", " \n", " Alvarez$$%!\n", " \n", "
\n", "
\n", "
\n", " \n", " LuisAlvarez$$%!\n", " \n", "
\n", "
\n", "
\n", " \n", " 123\n", " \n", "
\n", "
\n", "
\n", " \n", " Cake\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " 1980/07/07\n", " \n", "
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\n", "
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\n", "
\n", "
\n", " \n", " André\n", " \n", "
\n", "
\n", "
\n", " \n", " Ampère\n", " \n", "
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\n", "
\n", " \n", " 423\n", " \n", "
\n", "
\n", "
\n", " \n", " piza\n", " \n", "
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\n", " \n", " 8\n", " \n", "
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\n", "
\n", " \n", " 1950/07/08\n", " \n", "
\n", "
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\n", " \n", " gonna\n", " \n", "
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\n", "
\n", " \n", " 3\n", " \n", "
\n", "
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\n", "
\n", " \n", " 8\n", " \n", "
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\n", "
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\n", " \n", " Albert\n", " \n", "
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\n", "
\n", "
\n", " \n", " AlbertEinstein\n", " \n", "
\n", "
\n", "
\n", " \n", " 634\n", " \n", "
\n", "
\n", "
\n", " \n", " pizza\n", " \n", "
\n", "
\n", "
\n", " \n", " 8\n", " \n", "
\n", "
\n", "
\n", " \n", " 1990/07/11\n", " \n", "
\n", "
\n", "
\n", " \n", " up\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Galileo\n", " \n", "
\n", "
\n", "
\n", " \n", " ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI\n", " \n", "
\n", "
\n", "
\n", " \n", " Galileo⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI\n", " \n", "
\n", "
\n", "
\n", " \n", " 672\n", " \n", "
\n", "
\n", "
\n", " \n", " arepa\n", " \n", "
\n", "
\n", "
\n", " \n", " 5\n", " \n", "
\n", "
\n", "
\n", " \n", " 1930/08/12\n", " \n", "
\n", "
\n", "
\n", " \n", " never\n", " \n", "
\n", "
\n", "
\n", " \n", " 7\n", " \n", "
\n", "
\n", "
\n", " \n", " CaRL\n", " \n", "
\n", "
\n", "
\n", " \n", " Ga%%%uss\n", " \n", "
\n", "
\n", "
\n", " \n", " CaRLGa%%%uss\n", " \n", "
\n", "
\n", "
\n", " \n", " 323\n", " \n", "
\n", "
\n", "
\n", " \n", " taco\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1970/07/13\n", " \n", "
\n", "
\n", "
\n", " \n", " gonna\n", " \n", "
\n", "
\n", "
\n", " \n", " 8\n", " \n", "
\n", "
\n", "
\n", " \n", " David\n", " \n", "
\n", "
\n", "
\n", " \n", " H$$$ilbert\n", " \n", "
\n", "
\n", "
\n", " \n", " DavidH$$$ilbert\n", " \n", "
\n", "
\n", "
\n", " \n", " 624\n", " \n", "
\n", "
\n", "
\n", " \n", " taaaccoo\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1950/07/14\n", " \n", "
\n", "
\n", "
\n", " \n", " let\n", " \n", "
\n", "
\n", "
\n", " \n", " 9\n", " \n", "
\n", "
\n", "
\n", " \n", " Johannes\n", " \n", "
\n", "
\n", "
\n", " \n", " KEPLER\n", " \n", "
\n", "
\n", "
\n", " \n", " JohannesKEPLER\n", " \n", "
\n", "
\n", "
\n", " \n", " 735\n", " \n", "
\n", "
\n", "
\n", " \n", " taco\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1920/04/22\n", " \n", "
\n", "
\n", "
\n", " \n", " you\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " JaMES\n", " \n", "
\n", "
\n", "
\n", " \n", " M$$ax%%well\n", " \n", "
\n", "
\n", "
\n", " \n", " JaMESM$$ax%%well\n", " \n", "
\n", "
\n", "
\n", " \n", " 875\n", " \n", "
\n", "
\n", "
\n", " \n", " taco\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1923/03/12\n", " \n", "
\n", "
\n", "
\n", " \n", " down\n", " \n", "
\n", "
\n", "\n", "
Viewing 10 of 10 rows / 9 columns
\n", "
1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 533, "metadata": {}, "outputs": [], "source": [ "# df[[\"customer_id\", \"product\"]]" ] }, { "cell_type": "code", "execution_count": 534, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " customer_id_x transactoin_date\n", "0 0f345kjh345oiuy345 hola\n" ] } ], "source": [ "from dask import dataframe as dd\n", "import pandas as pd\n", "data = {\"customer_id_x\":[\"0f345kjh345oiuy345\"], \"transactoin_date\":[\"hola\"]}\n", "pdf = pd.DataFrame(data)\n", "print(pdf)\n", "df_right = dd.from_pandas(pdf, npartitions=1)" ] }, { "cell_type": "code", "execution_count": 535, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 17 rows / 6 columns
\n", "
1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
customer_id
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
transactoin_date
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
ticket_price
\n", "
3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
discount
\n", "
4 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
product
\n", "
5 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
info
\n", "
6 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " 0f345kjh345oiuy345\n", " \n", "
\n", "
\n", "
\n", " \n", " 2010/08/19\n", " \n", "
\n", "
\n", "
\n", " \n", " 29.99\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " platinum\n", " \n", "
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\n", "
\n", " \n", " 1\n", " \n", "
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\n", " \n", " 0fju234978rfjkhsdf\n", " \n", "
\n", "
\n", "
\n", " \n", " 2012/01/05\n", " \n", "
\n", "
\n", "
\n", " \n", " 29.99\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
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\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " 0f34ruiy23e78y2r\n", " \n", "
\n", "
\n", "
\n", " \n", " 2009/08/11\n", " \n", "
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Viewing 10 of 17 rows / 6 columns
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1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 536, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 100 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 17 of 17 rows / 7 columns
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1 partition(s)
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transactoin_date_left
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1 (object)
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ticket_price
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2 (object)
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discount
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3 (object)
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product
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4 (object)
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customer_id
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5 (object)
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info
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6 (uint8)
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transactoin_date_right
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7 (object)
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Viewing 17 of 17 rows / 7 columns
\n", "
1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.cols.join(df_right,how=\"left\", left_on=\"customer_id\", right_on=\"customer_id_x\").ext.display(100)" ] }, { "cell_type": "code", "execution_count": 537, "metadata": {}, "outputs": [], "source": [ "data = {\"customer_id\":[\"0f345kjh345oiuy345\"], \"transactoin_date\":[\"hola\"]}\n", "pdf = pd.DataFrame(data)\n", "# print(pdf)\n", "df_right = dd.from_pandas(pdf, npartitions=1)" ] }, { "cell_type": "code", "execution_count": 538, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Dask DataFrame Structure:
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transactoin_date_leftticket_pricediscountproductcustomer_idinfotransactoin_date_right
npartitions=1
objectobjectobjectobjectobjectuint8object
.....................
\n", "
\n", "
Dask Name: getitem, 14 tasks
" ], "text/plain": [ "Dask DataFrame Structure:\n", " transactoin_date_left ticket_price discount product customer_id info transactoin_date_right\n", "npartitions=1 \n", " object object object object object uint8 object\n", " ... ... ... ... ... ... ...\n", "Dask Name: getitem, 14 tasks" ] }, "execution_count": 538, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.join(df_right,how=\"inner\", left_on=\"customer_id\", right_on=\"customer_id\")" ] }, { "cell_type": "code", "execution_count": 530, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
transactoin_date_leftticket_pricediscountproductcustomer_idinfotransactoin_date_right
02010/08/1929.99NaNplatinum0f345kjh345oiuy3451hola
\n", "
" ], "text/plain": [ " transactoin_date_left ticket_price discount product customer_id \\\n", "0 2010/08/19 29.99 NaN platinum 0f345kjh345oiuy345 \n", "\n", " info transactoin_date_right \n", "0 1 hola " ] }, "execution_count": 530, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.compute()" ] }, { "cell_type": "code", "execution_count": 490, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n" ] }, { "data": { "text/plain": [ "['transactoin_date_left',\n", " 'ticket_price',\n", " 'discount',\n", " 'product',\n", " 'info',\n", " 'customer_id',\n", " 'customer_id',\n", " 'info',\n", " 'transactoin_date_right']" ] }, "execution_count": 490, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# z = test_df.cols.count()\n", "# lz = test_df.cols.names()\n", "lz = test_df.cols.count()\n", "\n", "li = df.cols.count()\n", "lj = df_right.cols.count()\n", "print(lz-lj)\n", "test_df.cols.names()[:li] + [\"customer_id\"]+test_df.cols.names()[lz-lj:lz]\n" ] }, { "cell_type": "code", "execution_count": 388, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['transactoin_date_left',\n", " 'ticket_price',\n", " 'discount',\n", " 'product',\n", " 'info',\n", " 'info',\n", " 'transactoin_date_right']" ] }, "execution_count": 388, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "'customer_id', 'transactoin_date', 'ticket_price', 'discount', 'product', 'info', 'A', 'B'" ] }, { "cell_type": "code", "execution_count": 252, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 17 rows / 17 columns
\n", "
1 partition(s)
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customer_id
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1 (object)
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transactoin_date
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ticket_price
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3 (object)
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discount
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4 (object)
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product
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5 (object)
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info
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6 (object)
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Viewing 10 of 17 rows / 17 columns
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1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 17 rows / 17 columns
\n", "
1 partition(s)
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\n", "
customer_id
\n", "
1 (object)
\n", "
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transactoin_date
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
ticket_price
\n", "
3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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discount
\n", "
4 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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product
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5 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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info
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6 (uint8)
\n", "
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Viewing 10 of 17 rows / 17 columns
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1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = df.cols.replace(\"discount\", search=[\"%\"], replace_by=\"\", search_by=\"chars\", ignore_case=True, output_cols=\"discount\").ext.cache()\n", "df.ext.display()\n", "# pdf = df.compute()\n", "# print(\"AAAAA\",df.ext.profile(columns=\"*\", infer=True)[\"columns\"][\"discount\"])" ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nan \n", "nan \n", "nan \n", "nan \n", "nan \n", "5 \n", "nan \n", "nan \n", "nan \n", "5 \n", "5 \n", "nan \n", "nan \n", "5 \n", "nan \n", "5 \n", "nan \n" ] } ], "source": [ "for i in pdf[\"discount\"]:\n", " print(i, type(i))" ] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'customer_id': None,\n", " 'transactoin_date': None,\n", " 'ticket_price': None,\n", " 'discount': None,\n", " 'product': None,\n", " 'info': None}" ] }, "execution_count": 191, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdf.cols.profiler_dtypes(\"*\")" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 17 rows / 17 columns
\n", "
1 partition(s)
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\n", "
customer_id
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
transactoin_date
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
ticket_price
\n", "
3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
discount
\n", "
4 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
product
\n", "
5 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
info
\n", "
6 (uint8)
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Viewing 10 of 17 rows / 17 columns
\n", "
1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BBBB {'discount': 'int'}\n", "result {'discount': {'mismatch': 0, 'missing': 12, 'match': 5, 'profiler_dtype': 'int'}}\n" ] }, { "data": { "text/plain": [ "{'discount': {'mismatch': 0,\n", " 'missing': 12,\n", " 'match': 5,\n", " 'profiler_dtype': 'int'}}" ] }, "execution_count": 193, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.count_mismatch({\"discount\":\"int\"})" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "'missing_columns' must be 'customer_id', 'transactoin_date', 'ticket_price', 'discount', 'product', 'info', received 'first_name'. ", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrequency\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"first_name\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcount_uniques\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\dask\\columns.py\u001b[0m in \u001b[0;36mfrequency\u001b[1;34m(self, columns, n, percentage, total_rows, count_uniques, compute)\u001b[0m\n\u001b[0;32m 142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 143\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 144\u001b[1;33m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 146\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\columns.py\u001b[0m in \u001b[0;36mparse_columns\u001b[1;34m(df, cols_args, get_args, is_regex, filter_by_column_dtypes, accepts_missing_cols, invert)\u001b[0m\n\u001b[0;32m 196\u001b[0m \u001b[1;31m# Check for missing columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 197\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0maccepts_missing_cols\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 198\u001b[1;33m \u001b[0mcheck_for_missing_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 199\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 200\u001b[0m \u001b[1;31m# Filter by column data type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\columns.py\u001b[0m in \u001b[0;36mcheck_for_missing_columns\u001b[1;34m(df, col_names)\u001b[0m\n\u001b[0;32m 346\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 347\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmissing_columns\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 348\u001b[1;33m \u001b[0mRaiseIt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmissing_columns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_col_names\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 349\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 350\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\raiseit.py\u001b[0m in \u001b[0;36mvalue_error\u001b[1;34m(var, data_values, extra_text)\u001b[0m\n\u001b[0;32m 74\u001b[0m type=divisor.join(map(\n\u001b[0;32m 75\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m\"'\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"'\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m data_values)), var_type=one_list_to_val(var), extra_text=extra_text))\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: 'missing_columns' must be 'customer_id', 'transactoin_date', 'ticket_price', 'discount', 'product', 'info', received 'first_name'. " ] } ], "source": [ "df.cols.frequency(\"first_name\", count_uniques = True)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.57 ms ± 328 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%timeit df.ext.profile(\"*\")" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'customer_id': {'count_uniques': 1}}" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.count_uniques(\"customer_id\")" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'customer_id': None,\n", " 'first_name': None,\n", " 'last_name': None,\n", " 'current_credit_card': None,\n", " 'address_zip': None,\n", " 'email': None,\n", " 'address_street_number': None,\n", " 'address_city': None,\n", " 'address_state': None,\n", " 'adress_zip': None,\n", " 'phone_number': None,\n", " 'start_date': None,\n", " 'end_date': None,\n", " 'transaction_date': None,\n", " 'ticket_price': None,\n", " 'discount': None,\n", " 'discounted_ticket_price': None}" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.profiler_dtypes(\"*\")" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\numpy\\lib\\histograms.py:433: RuntimeWarning: invalid value encountered in greater\n", " if np.any(bin_edges[:-1] > bin_edges[1:]):\n", "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 9 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "result {'customer_id': {'mismatch': 0, 'missing': 9, 'match': 0, 'profiler_dtype': 'object'}, 'first_name': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'last_name': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'current_credit_card': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'address_zip': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'email': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'email'}, 'address_street_number': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'address_city': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'address_state': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'adress_zip': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'phone_number': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'start_date': {'mismatch': 9, 'missing': 0, 'match': 0, 'profiler_dtype': 'date'}, 'end_date': {'mismatch': 3, 'missing': 6, 'match': 0, 'profiler_dtype': 'date'}, 'transaction_date': {'mismatch': 9, 'missing': 0, 'match': 0, 'profiler_dtype': 'date'}, 'ticket_price': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'decimal'}, 'discount': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'decimal'}, 'discounted_ticket_price': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'decimal'}}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\numpy\\lib\\histograms.py:433: RuntimeWarning: invalid value encountered in greater\n", " if np.any(bin_edges[:-1] > bin_edges[1:]):\n" ] }, { "data": { "text/plain": [ "{'columns': {'customer_id': {'stats': {'mismatch': 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{'lower': 9.745, 'upper': 10.150625, 'count': 2},\n", " {'lower': 10.150625, 'upper': 10.55625, 'count': 0},\n", " {'lower': 10.55625, 'upper': 10.961875000000001, 'count': 0},\n", " {'lower': 10.961875000000001, 'upper': 11.3675, 'count': 0},\n", " {'lower': 11.3675, 'upper': 11.773125, 'count': 1},\n", " {'lower': 11.773125, 'upper': 12.17875, 'count': 0},\n", " {'lower': 12.17875, 'upper': 12.584375, 'count': 0},\n", " {'lower': 12.584375, 'upper': 12.99, 'count': 3}],\n", " 'count_uniques': 4},\n", " 'dtype': 'float64',\n", " 'profiler_dtype': 'decimal'}},\n", " 'name': None,\n", " 'file_name': 'datasetFile-1590006020188.csv',\n", " 'summary': {'cols_count': 17,\n", " 'rows_count': 9,\n", " 'size': '1.4 kB',\n", " 'dtypes_list': ['datetime64[ns]', 'object', 'float64'],\n", " 'total_count_dtypes': 3,\n", " 'missing_count': 15,\n", " 'p_missing': 166.67}}" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for col_name in df.cols.names():\n", "# print(col_name)\n", "df.ext.profile(\"*\")" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'customer_id': {'mismatch': 0, 'missing': 9, 'match': 0, 'profiler_dtype': 'object'}, 'first_name': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'last_name': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'current_credit_card': {'mismatch': 9, 'missing': 0, 'match': 0, 'profiler_dtype': 'credit_card_number'}}\n" ] } ], "source": [ "a = ({'customer_id': 'object', 'first_name': 'string', 'last_name': 'string', 'current_credit_card': 'credit_card_number', \n", " \n", " })\n", "mismatch = df.cols.count_mismatch(a, infer=True)\n", "print(mismatch)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.cols.cast(\"email\", object).compute()" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'customer_id': None,\n", " 'transactoin_date': None,\n", " 'ticket_price': None,\n", " 'discount': None,\n", " 'product': None}" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.profiler_dtypes(\"*\")" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "data": { "text/plain": [ "{'columns': {'customer_id': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 17,\n", " 'frequency': [{'value': '0fju234978rfjkhsdf', 'count': 4},\n", " {'value': '0f34ruiy23e78y2r', 'count': 4},\n", " {'value': '0fue298y2r23r23r5', 'count': 1},\n", " {'value': '0fue298y2r23r23r4', 'count': 1},\n", " {'value': '0fue298y2r23r23r3', 'count': 1},\n", " {'value': '0fue298y2r23r23r2', 'count': 1},\n", " {'value': '0f345kjh345oiuy349', 'count': 1},\n", " {'value': '0f345kjh345oiuy348', 'count': 1},\n", " {'value': '0f345kjh345oiuy347', 'count': 1},\n", " {'value': '0f345kjh345oiuy346', 'count': 1},\n", " {'value': '0f345kjh345oiuy345', 'count': 1}],\n", " 'count_uniques': 11},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'transactoin_date': {'stats': {'mismatch': 0,\n", " 'missing': 6,\n", " 'match': 11,\n", " 'frequency': [{'value': '2010/08/19', 'count': 7},\n", " {'value': '2015/08/09', 'count': 1},\n", " {'value': '2012/01/05', 'count': 1},\n", " {'value': '2011/08/11', 'count': 1},\n", " {'value': '2009/08/11', 'count': 1}],\n", " 'count_uniques': 5},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'date'},\n", " 'ticket_price': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 17,\n", " 'frequency': [{'value': 9.99, 'count': 7},\n", " {'value': 29.99, 'count': 5},\n", " {'value': 14.99, 'count': 4},\n", " {'value': 9.91, 'count': 1}],\n", " 'count_uniques': 4},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'decimal'},\n", " 'discount': {'stats': {'mismatch': 0,\n", " 'missing': 12,\n", " 'match': 5,\n", " 'frequency': [{'value': '5%', 'count': 5}],\n", " 'count_uniques': 1},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'product': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 17,\n", " 'frequency': [{'value': 'basic', 'count': 8},\n", " {'value': 'platinum', 'count': 5},\n", " {'value': 'deluxe', 'count': 4}],\n", " 'count_uniques': 3},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'}},\n", " 'name': None,\n", " 'file_name': 'datasetFile-1589815911139.csv',\n", " 'summary': {'cols_count': 5,\n", " 'rows_count': 17,\n", " 'size': '808 Bytes',\n", " 'dtypes_list': ['object'],\n", " 'total_count_dtypes': 1,\n", " 'missing_count': 18,\n", " 'p_missing': 105.88}}" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.profile(columns=\"*\", infer=True)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'customer_id': 'string',\n", " 'transactoin_date': 'date',\n", " 'ticket_price': 'decimal',\n", " 'discount': 'string',\n", " 'product': 'string'}" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.profiler_dtypes(\"*\")" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n", "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 17 rows / 17 columns
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1 (object)
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Viewing 10 of 17 rows / 17 columns
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1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_output = df.ext.profile(columns=\"*\", infer=True, output=\"json\")\n", "df.ext.set_buffer(\"*\")\n", "df = df.cols.replace(\"discount\", search=[\"%\"], replace_by=\"\", search_by=\"chars\", ignore_case=True, output_cols=\"discount\").ext.cache()\n", "\n", "_output = df.ext.profile(columns=\"*\", output=\"json\")\n", "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'customer_id': 'string',\n", " 'transactoin_date': 'date',\n", " 'ticket_price': 'decimal',\n", " 'discount': 'int',\n", " 'product': 'string'}" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.profiler_dtypes(\"*\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "pdf= df.compute()\n", "for i in pdf[\"discount\"]:\n", " print(type(i))" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ASDASDF\n", "AAA {'discount': 'int'}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "data": { "text/plain": [ "{'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 17,\n", " 'frequency': [{'value': 0, 'count': 12}, {'value': 5, 'count': 5}],\n", " 'count_uniques': 2},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'int'}" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "# df.ext.set_buffer(\"*\")\n", "\n", "# df = df.cols.set( value=\"0\", where='df[\"discount\"].isnull()', output_cols=[\"discount\"] ).ext.cache()\n", "# _output = df.ext.profile(columns=\"*\", infer=True, output=\"json\")\n", "\n", "# df.ext.set_buffer(\"*\")\n", "\n", "df = df.cols.set(value='0', where='df[\"discount\"].isnull()', output_cols=\"discount\").ext.cache()\n", "df.ext.profile(columns=\"*\", infer=True)[\"columns\"][\"discount\"]\n", "# df.ext.set_buffer(\"*\") # error \n", "# print(df.ext.buffer_window(\"*\", 0, 17).ext.to_json(\"*\")) # error" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] } ], "source": [ "df.ext.set_buffer(\"*\")" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "profile_preview = df.ext.buffer_window(\"*\", 0, 17).cols.set(value='mask[\"discount\"]*mask[\"ticket_price\"]', where='df[\"discount\"]!=None', output_cols=\"new column\")" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customer_idtransactoin_dateticket_pricediscountproductnew column
00f345kjh345oiuy3452010/08/1929.990platinum
10fju234978rfjkhsdf2012/01/0529.990platinum
20f34ruiy23e78y2r2009/08/1129.990platinum
30fue298y2r23r23r22010/08/1929.990platinum
40f345kjh345oiuy3462010/08/1929.990platinum
50fju234978rfjkhsdf2010/08/199.995basic9.999.999.999.999.99
60f34ruiy23e78y2r2010/08/199.990basic
70fue298y2r23r23r32010/08/199.990basic
80f345kjh345oiuy3472010/08/199.990basic
90fju234978rfjkhsdf2011/08/119.995basic9.999.999.999.999.99
100f34ruiy23e78y2r2015/08/099.995basic9.999.999.999.999.99
110fue298y2r23r23r4None9.910basic
120f345kjh345oiuy348None9.990basic
130fju234978rfjkhsdfNone14.995deluxe14.9914.9914.9914.9914.99
140f34ruiy23e78y2rNone14.990deluxe
150fue298y2r23r23r5None14.995deluxe14.9914.9914.9914.9914.99
160f345kjh345oiuy349None14.990deluxe
\n", "
" ], "text/plain": [ " customer_id transactoin_date ticket_price discount product \\\n", "0 0f345kjh345oiuy345 2010/08/19 29.99 0 platinum \n", "1 0fju234978rfjkhsdf 2012/01/05 29.99 0 platinum \n", "2 0f34ruiy23e78y2r 2009/08/11 29.99 0 platinum \n", "3 0fue298y2r23r23r2 2010/08/19 29.99 0 platinum \n", "4 0f345kjh345oiuy346 2010/08/19 29.99 0 platinum \n", "5 0fju234978rfjkhsdf 2010/08/19 9.99 5 basic \n", "6 0f34ruiy23e78y2r 2010/08/19 9.99 0 basic \n", "7 0fue298y2r23r23r3 2010/08/19 9.99 0 basic \n", "8 0f345kjh345oiuy347 2010/08/19 9.99 0 basic \n", "9 0fju234978rfjkhsdf 2011/08/11 9.99 5 basic \n", "10 0f34ruiy23e78y2r 2015/08/09 9.99 5 basic \n", "11 0fue298y2r23r23r4 None 9.91 0 basic \n", "12 0f345kjh345oiuy348 None 9.99 0 basic \n", "13 0fju234978rfjkhsdf None 14.99 5 deluxe \n", "14 0f34ruiy23e78y2r None 14.99 0 deluxe \n", "15 0fue298y2r23r23r5 None 14.99 5 deluxe \n", "16 0f345kjh345oiuy349 None 14.99 0 deluxe \n", "\n", " new column \n", "0 \n", "1 \n", "2 \n", "3 \n", "4 \n", "5 9.999.999.999.999.99 \n", "6 \n", "7 \n", "8 \n", "9 9.999.999.999.999.99 \n", "10 9.999.999.999.999.99 \n", "11 \n", "12 \n", "13 14.9914.9914.9914.9914.99 \n", "14 \n", "15 14.9914.9914.9914.9914.99 \n", "16 " ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profile_preview" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'stats': {'match': 17,\n", " 'missing': 0,\n", " 'mismatch': 0,\n", " 'frequency': [{'value': 0, 'count': 12}, {'value': 5, 'count': 5}],\n", " 'count_uniques': 2},\n", " 'dtype': 'object'}" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profile_preview.ext.profile(\"*\")[\"columns\"][\"discount\"]" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "'missing_columns' must be 'customer_id', 'transactoin_date', 'ticket_price', 'discount', 'product', received 'new_discount'. ", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofiler_dtypes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"new_discount\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\dask\\columns.py\u001b[0m in \u001b[0;36mprofiler_dtypes\u001b[1;34m(self, columns)\u001b[0m\n\u001b[0;32m 1040\u001b[0m \"\"\"\n\u001b[0;32m 1041\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1042\u001b[1;33m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1043\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1044\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcol_name\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\columns.py\u001b[0m in \u001b[0;36mparse_columns\u001b[1;34m(df, cols_args, get_args, is_regex, filter_by_column_dtypes, accepts_missing_cols, invert)\u001b[0m\n\u001b[0;32m 196\u001b[0m \u001b[1;31m# Check for missing columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 197\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0maccepts_missing_cols\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 198\u001b[1;33m \u001b[0mcheck_for_missing_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 199\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 200\u001b[0m \u001b[1;31m# Filter by column data type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\columns.py\u001b[0m in \u001b[0;36mcheck_for_missing_columns\u001b[1;34m(df, col_names)\u001b[0m\n\u001b[0;32m 346\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 347\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmissing_columns\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 348\u001b[1;33m \u001b[0mRaiseIt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmissing_columns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_col_names\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 349\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 350\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\raiseit.py\u001b[0m in \u001b[0;36mvalue_error\u001b[1;34m(var, data_values, extra_text)\u001b[0m\n\u001b[0;32m 74\u001b[0m type=divisor.join(map(\n\u001b[0;32m 75\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m\"'\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"'\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m data_values)), var_type=one_list_to_val(var), extra_text=extra_text))\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: 'missing_columns' must be 'customer_id', 'transactoin_date', 'ticket_price', 'discount', 'product', received 'new_discount'. " ] } ], "source": [ "df.cols.profiler_dtypes(\"new_discount\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customer_idtransactoin_dateticket_pricediscountproduct
00f345kjh345oiuy3452010/08/1929.990platinum
10fju234978rfjkhsdf2012/01/0529.990platinum
20f34ruiy23e78y2r2009/08/1129.990platinum
30fue298y2r23r23r22010/08/1929.990platinum
40f345kjh345oiuy3462010/08/1929.990platinum
50fju234978rfjkhsdf2010/08/199.995basic
60f34ruiy23e78y2r2010/08/199.990basic
70fue298y2r23r23r32010/08/199.990basic
80f345kjh345oiuy3472010/08/199.990basic
90fju234978rfjkhsdf2011/08/119.995basic
100f34ruiy23e78y2r2015/08/099.995basic
110fue298y2r23r23r4None9.910basic
120f345kjh345oiuy348None9.990basic
130fju234978rfjkhsdfNone14.995deluxe
140f34ruiy23e78y2rNone14.990deluxe
150fue298y2r23r23r5None14.995deluxe
160f345kjh345oiuy349None14.990deluxe
\n", "
" ], "text/plain": [ " customer_id transactoin_date ticket_price discount product\n", "0 0f345kjh345oiuy345 2010/08/19 29.99 0 platinum\n", "1 0fju234978rfjkhsdf 2012/01/05 29.99 0 platinum\n", "2 0f34ruiy23e78y2r 2009/08/11 29.99 0 platinum\n", "3 0fue298y2r23r23r2 2010/08/19 29.99 0 platinum\n", "4 0f345kjh345oiuy346 2010/08/19 29.99 0 platinum\n", "5 0fju234978rfjkhsdf 2010/08/19 9.99 5 basic\n", "6 0f34ruiy23e78y2r 2010/08/19 9.99 0 basic\n", "7 0fue298y2r23r23r3 2010/08/19 9.99 0 basic\n", "8 0f345kjh345oiuy347 2010/08/19 9.99 0 basic\n", "9 0fju234978rfjkhsdf 2011/08/11 9.99 5 basic\n", "10 0f34ruiy23e78y2r 2015/08/09 9.99 5 basic\n", "11 0fue298y2r23r23r4 None 9.91 0 basic\n", "12 0f345kjh345oiuy348 None 9.99 0 basic\n", "13 0fju234978rfjkhsdf None 14.99 5 deluxe\n", "14 0f34ruiy23e78y2r None 14.99 0 deluxe\n", "15 0fue298y2r23r23r5 None 14.99 5 deluxe\n", "16 0f345kjh345oiuy349 None 14.99 0 deluxe" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'customer_id': 'string',\n", " 'transactoin_date': 'date',\n", " 'ticket_price': 'decimal',\n", " 'discount': 'int',\n", " 'product': 'string'}" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.profiler_dtypes(\"*\")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "# df.cols.count_mismatch({\"discount\":\"int\"}, infer=True)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'stats': {'mismatch': 0,\n", " 'missing': 12,\n", " 'match': 5,\n", " 'frequency': [{'value': '5', 'count': 5}],\n", " 'count_uniques': 1},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'int'}" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.profile(columns=\"*\", infer=True)[\"columns\"][\"discount\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "ename": "TypeError", "evalue": "'NoneType' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofiler_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"ticket_price\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"decimal\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9\u001b[0m \u001b[0m_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_buffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\dask\\columns.py\u001b[0m in \u001b[0;36mprofiler_dtype\u001b[1;34m(self, input_col, dtype, columns)\u001b[0m\n\u001b[0;32m 1068\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1069\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1070\u001b[1;33m \u001b[0mcolumns\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minput_col\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1071\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1072\u001b[0m \u001b[1;31m# Map from profiler dtype to python dtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object does not support item assignment" ] } ], "source": [ "df = op.load.csv(\"http://159.65.217.17:5003/uploads/datasetFile-1589815911139.csv\", sep=\",\", error_bad_lines=False, header=True, null_value=\"null\", infer_schema=\"true\", encoding=\"UTF-8\", quoting=0, lineterminator=None, cache=True).ext.cache()\n", "df = df.ext.optimize()\n", "_output = df.ext.profile(columns=\"*\", infer=True, output=\"json\")\n", "\n", "\n", "\n", "\n", "df = df.cols.profiler_dtype(\"ticket_price\", \"decimal\").ext.cache()\n", "_output = df.ext.set_buffer(\"*\")\n", "\n", "df.ext.get_buffer()\n", "_output = df.ext.profile(columns=\"*\", infer=True, output=\"json\")\n", "_output = df.ext.set_buffer(\"*\")\n", "df.ext.table()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customer_idtransactoin_dateticket_pricediscountproduct
00f345kjh345oiuy3452010/08/1929.99NaNplatinum
10fju234978rfjkhsdf2012/01/0529.99NaNplatinum
20f34ruiy23e78y2r2009/08/1129.99NaNplatinum
30fue298y2r23r23r22010/08/1929.99NaNplatinum
40f345kjh345oiuy3462010/08/1929.99NaNplatinum
50fju234978rfjkhsdf2010/08/199.995%basic
60f34ruiy23e78y2r2010/08/199.99NaNbasic
70fue298y2r23r23r32010/08/199.99NaNbasic
80f345kjh345oiuy3472010/08/199.99NaNbasic
90fju234978rfjkhsdf2011/08/119.995%basic
100f34ruiy23e78y2r2015/08/099.995%basic
110fue298y2r23r23r4NaN9.91NaNbasic
120f345kjh345oiuy348NaN9.99NaNbasic
130fju234978rfjkhsdfNaN14.995%deluxe
140f34ruiy23e78y2rNaN14.99NaNdeluxe
150fue298y2r23r23r5NaN14.995%deluxe
160f345kjh345oiuy349NaN14.99NaNdeluxe
\n", "
" ], "text/plain": [ " customer_id transactoin_date ticket_price discount product\n", "0 0f345kjh345oiuy345 2010/08/19 29.99 NaN platinum\n", "1 0fju234978rfjkhsdf 2012/01/05 29.99 NaN platinum\n", "2 0f34ruiy23e78y2r 2009/08/11 29.99 NaN platinum\n", "3 0fue298y2r23r23r2 2010/08/19 29.99 NaN platinum\n", "4 0f345kjh345oiuy346 2010/08/19 29.99 NaN platinum\n", "5 0fju234978rfjkhsdf 2010/08/19 9.99 5% basic\n", "6 0f34ruiy23e78y2r 2010/08/19 9.99 NaN basic\n", "7 0fue298y2r23r23r3 2010/08/19 9.99 NaN basic\n", "8 0f345kjh345oiuy347 2010/08/19 9.99 NaN basic\n", "9 0fju234978rfjkhsdf 2011/08/11 9.99 5% basic\n", "10 0f34ruiy23e78y2r 2015/08/09 9.99 5% basic\n", "11 0fue298y2r23r23r4 NaN 9.91 NaN basic\n", "12 0f345kjh345oiuy348 NaN 9.99 NaN basic\n", "13 0fju234978rfjkhsdf NaN 14.99 5% deluxe\n", "14 0f34ruiy23e78y2r NaN 14.99 NaN deluxe\n", "15 0fue298y2r23r23r5 NaN 14.99 5% deluxe\n", "16 0f345kjh345oiuy349 NaN 14.99 NaN deluxe" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.get_buffer()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'customer_id': 'category',\n", " 'transactoin_date': 'category',\n", " 'ticket_price': 'object',\n", " 'discount': 'category',\n", " 'product': 'category'}" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.dtypes()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "pdf = df.compute()\n", "for i in pdf[\"ticket_price\"]:\n", " print(type(i))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# df.ext.profile(\"*\", infer=True)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'customer_id': 'string',\n", " 'transactoin_date': 'date',\n", " 'ticket_price': 'decimal',\n", " 'discount': 'string',\n", " 'product': 'string'}" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.profiler_dtypes(\"*\")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "df = df.cols.profiler_dtype(\"ticket_price\",\"int\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 17 rows / 17 columns
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1 partition(s)
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1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "_output = df.ext.buffer_window(\"*\", 0, 17).cols.set(value='mask[\"ticket_price\"]+mask[\"ticket_price\"]', where='df[\"ticket_price\"]!=None', output_cols=\"new ticket_price\").ext.to_json(\"*\")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customer_idtransactoin_dateticket_pricediscountproduct
00f345kjh345oiuy3452010/08/193364NaNplatinum
10fju234978rfjkhsdf2012/01/053364NaNplatinum
20f34ruiy23e78y2r2009/08/113364NaNplatinum
30fue298y2r23r23r22010/08/193364NaNplatinum
40f345kjh345oiuy3462010/08/193364NaNplatinum
50fju234978rfjkhsdf2010/08/193245%basic
60f34ruiy23e78y2r2010/08/19324NaNbasic
70fue298y2r23r23r32010/08/19324NaNbasic
80f345kjh345oiuy3472010/08/19324NaNbasic
90fju234978rfjkhsdf2011/08/113245%basic
100f34ruiy23e78y2r2015/08/093245%basic
110fue298y2r23r23r4NaN324NaNbasic
120f345kjh345oiuy348NaN324NaNbasic
130fju234978rfjkhsdfNaN7845%deluxe
140f34ruiy23e78y2rNaN784NaNdeluxe
150fue298y2r23r23r5NaN7845%deluxe
160f345kjh345oiuy349NaN784NaNdeluxe
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" ], "text/plain": [ " customer_id transactoin_date ticket_price discount product\n", "0 0f345kjh345oiuy345 2010/08/19 3364 NaN platinum\n", "1 0fju234978rfjkhsdf 2012/01/05 3364 NaN platinum\n", "2 0f34ruiy23e78y2r 2009/08/11 3364 NaN platinum\n", "3 0fue298y2r23r23r2 2010/08/19 3364 NaN platinum\n", "4 0f345kjh345oiuy346 2010/08/19 3364 NaN platinum\n", "5 0fju234978rfjkhsdf 2010/08/19 324 5% basic\n", "6 0f34ruiy23e78y2r 2010/08/19 324 NaN basic\n", "7 0fue298y2r23r23r3 2010/08/19 324 NaN basic\n", "8 0f345kjh345oiuy347 2010/08/19 324 NaN basic\n", "9 0fju234978rfjkhsdf 2011/08/11 324 5% basic\n", "10 0f34ruiy23e78y2r 2015/08/09 324 5% basic\n", "11 0fue298y2r23r23r4 NaN 324 NaN basic\n", "12 0f345kjh345oiuy348 NaN 324 NaN basic\n", "13 0fju234978rfjkhsdf NaN 784 5% deluxe\n", "14 0f34ruiy23e78y2r NaN 784 NaN deluxe\n", "15 0fue298y2r23r23r5 NaN 784 5% deluxe\n", "16 0f345kjh345oiuy349 NaN 784 NaN deluxe" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.cols.set(value='mask[\"ticket_price\"]*mask[\"ticket_price\"]', where='df[\"ticket_price\"]!=None', output_cols=\"ticket_price\").ext.cache()\n", "df.compute()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 45716 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] } ], "source": [ "df = op.load.file(\"data/Meteorite_Landings.csv\").ext.cache()\n", "# df1 = op.load.file(\"http://159.65.217.17:5003/uploads/datasetFile-1589317752438.csv\").ext.cache()\n", "# df.ext.profile(\"*\", output=\"json\")\n", "\n", "# df = df.ext.optimize()\n", "df1 = df.ext.optimize()\n", "df1.ext.profile(\"*\", output=\"json\", infer=True)\n", "# df.ext.set_buffer(\"*\")\n", "df1.ext.set_buffer(\"*\")\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import fastnumbers\n", "fastnumbers.isfloat(1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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Client

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Cluster

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  • Workers: 4
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  • Cores: 8
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  • Memory: 12.00 GB
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" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "op.client" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 45716 columns
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1 partition(s)
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name
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1 (object)
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id
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2 (object)
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nametype
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3 (object)
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recclass
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4 (object)
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mass (g)
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5 (object)
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fall
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6 (object)
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\n", " \n", " not nullable\n", " \n", "
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year
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7 (object)
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reclat
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8 (object)
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reclong
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9 (object)
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\n", " \n", " not nullable\n", " \n", "
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GeoLocation
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10 (object)
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\n", " \n", " 1\n", " \n", "
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\n", " \n", " Valid\n", " \n", "
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\n", " \n", " L5\n", " \n", "
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\n", " \n", " 21\n", " \n", "
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\n", " \n", " Fell\n", " \n", "
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\n", " \n", " 01/01/1880⋅12:00:00⋅AM\n", " \n", "
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\n", " \n", " 6.083330\n", " \n", "
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\n", "
\n", " \n", " (50.775000,⋅6.083330)\n", " \n", "
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\n", " \n", " Aarhus\n", " \n", "
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\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
\n", "
\n", "
\n", " \n", " 720\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1951⋅12:00:00⋅AM\n", " \n", "
\n", "
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\n", " \n", " 56.183330\n", " \n", "
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\n", "
\n", " \n", " 10.233330\n", " \n", "
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\n", " \n", " (56.183330,⋅10.233330)\n", " \n", "
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\n", "
\n", " \n", " Abee\n", " \n", "
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\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 107000\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1952⋅12:00:00⋅AM\n", " \n", "
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\n", " \n", " -113.000000\n", " \n", "
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\n", " \n", " (54.216670,⋅-113.000000)\n", " \n", "
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\n", "
\n", " \n", " Acapulco\n", " \n", "
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\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
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\n", "
\n", " \n", " Acapulcoite\n", " \n", "
\n", "
\n", "
\n", " \n", " 1914\n", " \n", "
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\n", "
\n", " \n", " Fell\n", " \n", "
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\n", " \n", " -99.900000\n", " \n", "
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\n", " \n", " (16.883330,⋅-99.900000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Achiras\n", " \n", "
\n", "
\n", "
\n", " \n", " 370\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
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\n", "
\n", " \n", " L6\n", " \n", "
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\n", "
\n", " \n", " 780\n", " \n", "
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\n", "
\n", " \n", " Fell\n", " \n", "
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\n", "
\n", "
\n", " \n", " -64.950000\n", " \n", "
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\n", "
\n", " \n", " (-33.166670,⋅-64.950000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adhi⋅Kot\n", " \n", "
\n", "
\n", "
\n", " \n", " 379\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 4239\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1919⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 32.100000\n", " \n", "
\n", "
\n", "
\n", " \n", " 71.800000\n", " \n", "
\n", "
\n", "
\n", " \n", " (32.100000,⋅71.800000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
\n", "
\n", "
\n", " \n", " 390\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " LL3-6\n", " \n", "
\n", "
\n", "
\n", " \n", " 910\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1949⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.833330\n", " \n", "
\n", "
\n", "
\n", " \n", " 95.166670\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.833330,⋅95.166670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Agen\n", " \n", "
\n", "
\n", "
\n", " \n", " 392\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 30000\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1814⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.216670\n", " \n", "
\n", "
\n", "
\n", " \n", " 0.616670\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.216670,⋅0.616670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguada\n", " \n", "
\n", "
\n", "
\n", " \n", " 398\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 1620\n", " \n", "
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\n", "
\n", " \n", " Fell\n", " \n", "
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\n", "
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\n", "
\n", "
\n", " \n", " -31.600000\n", " \n", "
\n", "
\n", "
\n", " \n", " -65.233330\n", " \n", "
\n", "
\n", "
\n", " \n", " (-31.600000,⋅-65.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
\n", "
\n", " \n", " 417\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
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\n", "
\n", " \n", " L\n", " \n", "
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\n", " \n", " 1440\n", " \n", "
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\n", " \n", " Fell\n", " \n", "
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\n", " \n", " 01/01/1920⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -30.866670\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.550000\n", " \n", "
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\n", "
\n", "\n", "
Viewing 10 of 45716 rows / 45716 columns
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1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", " ... \n", "45711 False\n", "45712 False\n", "45713 False\n", "45714 False\n", "45715 False\n", "Name: nametype, Length: 45716, dtype: bool" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Try https://stackoverflow.com/questions/42742810/speed-up-millions-of-regex-replacements-in-python-3/42789508#42789508\n", "df[\"nametype\"].str.match(\"^(\\d{5})([- ])?(\\d{4})?$\").compute()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'name': 'string',\n", " 'id': 'int',\n", " 'nametype': 'string',\n", " 'recclass': 'string',\n", " 'mass (g)': 'int',\n", " 'fall': 'string',\n", " 'year': 'date',\n", " 'reclat': 'decimal',\n", " 'reclong': 'decimal',\n", " 'GeoLocation': 'array'}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.cols.profiler_dtypes(\"*\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name object\n", "id uint16\n", "nametype category\n", "recclass object\n", "mass (g) object\n", "fall category\n", "year object\n", "reclat object\n", "reclong object\n", "GeoLocation object\n", "dtype: object" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.dtypes" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dask.dataframe.core.DataFrame" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'name': {'mismatch': 45716,\n", " 'missing': 0,\n", " 'match': 0,\n", " 'profiler_dtype': 'int'}}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.count_mismatch({\"name\":\"int\"})" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df1 = df1.cols.profiler_dtype(\"name\",\"string\")\n", "# df1.cols.replace(\"name\",\"1\",\"2\")\n", "# df1 =df1.cols.unnest(\"GeoLocation\",\",\")\n", "\n", "# df1.ext.profile(\"*\",infer=True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ACTION NAME profiler_dtype\n", "modified_columns ['name']\n", "new_columns []\n" ] }, { "data": { "text/plain": [ "{'columns': {'name': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 45716,\n", " 'frequency': [{'value': 'Święcany', 'count': 1},\n", " {'value': 'Hammadah al Hamra 007', 'count': 1},\n", " {'value': 'Hammadah al Hamra 029', 'count': 1},\n", " {'value': 'Hammadah al Hamra 028', 'count': 1},\n", " {'value': 'Hammadah al Hamra 027', 'count': 1},\n", " {'value': 'Hammadah al Hamra 026', 'count': 1},\n", " {'value': 'Hammadah al Hamra 025', 'count': 1},\n", " {'value': 'Hammadah al Hamra 024', 'count': 1},\n", " {'value': 'Hammadah al Hamra 023', 'count': 1},\n", " {'value': 'Hammadah al Hamra 022', 'count': 1},\n", " {'value': 'Hammadah al Hamra 021', 'count': 1},\n", " {'value': 'Hammadah al Hamra 020', 'count': 1},\n", " {'value': 'Hammadah al Hamra 019', 'count': 1},\n", " {'value': 'Hammadah al Hamra 018', 'count': 1},\n", " {'value': 'Hammadah al Hamra 017', 'count': 1},\n", " {'value': 'Hammadah al Hamra 014', 'count': 1},\n", " {'value': 'Hammadah al Hamra 013', 'count': 1},\n", " {'value': 'Hammadah al Hamra 012', 'count': 1},\n", " {'value': 'Hammadah al Hamra 011', 'count': 1},\n", " {'value': 'Hammadah al Hamra 010', 'count': 1},\n", " {'value': 'Hammadah al Hamra 009', 'count': 1},\n", " {'value': 'Hammadah al Hamra 030', 'count': 1},\n", " {'value': 'Hammadah al Hamra 031', 'count': 1},\n", " {'value': 'Hammadah al Hamra 032', 'count': 1},\n", " {'value': 'Hammadah al Hamra 043', 'count': 1},\n", " {'value': 'Hammadah al Hamra 051', 'count': 1},\n", " {'value': 'Hammadah al Hamra 050', 'count': 1},\n", " {'value': 'Hammadah al Hamra 049', 'count': 1},\n", " {'value': 'Hammadah al Hamra 048', 'count': 1},\n", " {'value': 'Hammadah al Hamra 047', 'count': 1},\n", " {'value': 'Hammadah al Hamra 046', 'count': 1},\n", " {'value': 'Hammadah al Hamra 045', 'count': 1},\n", " {'value': 'Hammadah al Hamra 044', 'count': 1}],\n", " 'count_uniques': 45716},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'id': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 45716,\n", " 'hist': [{'lower': 1.0, 'upper': 1796.53125, 'count': 1712},\n", " {'lower': 1796.53125, 'upper': 3592.0625, 'count': 1030},\n", " {'lower': 3592.0625, 'upper': 5387.59375, 'count': 1498},\n", " {'lower': 5387.59375, 'upper': 7183.125, 'count': 1765},\n", " {'lower': 7183.125, 'upper': 8978.65625, 'count': 1787},\n", " {'lower': 8978.65625, 'upper': 10774.1875, 'count': 1770},\n", " {'lower': 10774.1875, 'upper': 12569.71875, 'count': 1748},\n", " {'lower': 12569.71875, 'upper': 14365.25, 'count': 1791},\n", " {'lower': 14365.25, 'upper': 16160.78125, 'count': 1774},\n", " {'lower': 16160.78125, 'upper': 17956.3125, 'count': 1771},\n", " {'lower': 17956.3125, 'upper': 19751.84375, 'count': 1768},\n", " {'lower': 19751.84375, 'upper': 21547.375, 'count': 1796},\n", " {'lower': 21547.375, 'upper': 23342.90625, 'count': 1784},\n", " {'lower': 23342.90625, 'upper': 25138.4375, 'count': 1733},\n", " {'lower': 25138.4375, 'upper': 26933.96875, 'count': 1597},\n", " {'lower': 26933.96875, 'upper': 28729.5, 'count': 1250},\n", " {'lower': 28729.5, 'upper': 30525.03125, 'count': 1654},\n", " {'lower': 30525.03125, 'upper': 32320.5625, 'count': 872},\n", " {'lower': 32320.5625, 'upper': 34116.09375, 'count': 1016},\n", " {'lower': 34116.09375, 'upper': 35911.625, 'count': 1401},\n", " {'lower': 35911.625, 'upper': 37707.15625, 'count': 1166},\n", " {'lower': 37707.15625, 'upper': 39502.6875, 'count': 819},\n", " {'lower': 39502.6875, 'upper': 41298.21875, 'count': 848},\n", " {'lower': 41298.21875, 'upper': 43093.75, 'count': 0},\n", " {'lower': 43093.75, 'upper': 44889.28125, 'count': 544},\n", " {'lower': 44889.28125, 'upper': 46684.8125, 'count': 1660},\n", " {'lower': 46684.8125, 'upper': 48480.34375, 'count': 1618},\n", " {'lower': 48480.34375, 'upper': 50275.875, 'count': 1487},\n", " {'lower': 50275.875, 'upper': 52071.40625, 'count': 1555},\n", " {'lower': 52071.40625, 'upper': 53866.9375, 'count': 1293},\n", " {'lower': 53866.9375, 'upper': 55662.46875, 'count': 1618},\n", " {'lower': 55662.46875, 'upper': 57458.0, 'count': 1591}],\n", " 'count_uniques': 45716},\n", " 'dtype': 'uint16',\n", " 'profiler_dtype': 'int'},\n", " 'nametype': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 45716,\n", " 'frequency': [{'value': 'Valid', 'count': 45641},\n", " {'value': 'Relict', 'count': 75}],\n", " 'count_uniques': 2},\n", " 'dtype': 'category',\n", " 'profiler_dtype': 'string'},\n", " 'recclass': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 45716,\n", " 'frequency': [{'value': 'L6', 'count': 8285},\n", " {'value': 'H5', 'count': 7142},\n", " {'value': 'L5', 'count': 4796},\n", " {'value': 'H6', 'count': 4528},\n", " {'value': 'H4', 'count': 4211},\n", " {'value': 'LL5', 'count': 2766},\n", " {'value': 'LL6', 'count': 2043},\n", " {'value': 'L4', 'count': 1253},\n", " {'value': 'H4/5', 'count': 428},\n", " {'value': 'CM2', 'count': 416},\n", " {'value': 'H3', 'count': 386},\n", " {'value': 'L3', 'count': 365},\n", " {'value': 'CO3', 'count': 335},\n", " {'value': 'Ureilite', 'count': 300},\n", " {'value': 'Iron, IIIAB', 'count': 285},\n", " {'value': 'LL4', 'count': 268},\n", " {'value': 'CV3', 'count': 256},\n", " {'value': 'Diogenite', 'count': 241},\n", " {'value': 'Howardite', 'count': 240},\n", " {'value': 'LL', 'count': 225},\n", " {'value': 'Eucrite', 'count': 221},\n", " {'value': 'Eucrite-pmict', 'count': 207},\n", " {'value': 'E3', 'count': 206},\n", " {'value': 'H5/6', 'count': 193},\n", " {'value': 'Mesosiderite', 'count': 137},\n", " {'value': 'CR2', 'count': 135},\n", " {'value': 'LL3', 'count': 128},\n", " {'value': 'EH3', 'count': 120},\n", " {'value': 'Iron, IIAB', 'count': 118},\n", " {'value': 'Iron, ungrouped', 'count': 113},\n", " {'value': 'H~5', 'count': 111},\n", " {'value': 'L5/6', 'count': 109},\n", " {'value': 'Eucrite-mmict', 'count': 108}],\n", " 'count_uniques': 466},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'mass (g)': {'stats': {'mismatch': 32526,\n", " 'missing': 131,\n", " 'match': 13059,\n", " 'frequency': [{'value': '1.3', 'count': 171},\n", " {'value': '1.2', 'count': 140},\n", " {'value': '1.4', 'count': 138},\n", " {'value': '2.1', 'count': 130},\n", " {'value': '2.4', 'count': 126},\n", " {'value': '1.6', 'count': 120},\n", " {'value': '0.5', 'count': 119},\n", " {'value': '1.1', 'count': 116},\n", " {'value': '3.8', 'count': 114},\n", " {'value': '0.7', 'count': 111},\n", " {'value': '1.5', 'count': 111},\n", " {'value': '3.1', 'count': 109},\n", " {'value': '3.2', 'count': 109},\n", " {'value': '1.7', 'count': 109},\n", " {'value': '3', 'count': 108},\n", " {'value': '0.6', 'count': 108},\n", " {'value': '0.9', 'count': 108},\n", " {'value': '0.8', 'count': 107},\n", " {'value': '1.8', 'count': 104},\n", " {'value': '2.5', 'count': 103},\n", " {'value': '2.7', 'count': 102},\n", " {'value': '3.6', 'count': 96},\n", " {'value': '1', 'count': 96},\n", " {'value': '2', 'count': 96},\n", " {'value': '4.2', 'count': 95},\n", " {'value': '2.9', 'count': 93},\n", " {'value': '2.8', 'count': 93},\n", " {'value': '2.2', 'count': 92},\n", " {'value': '2.6', 'count': 91},\n", " {'value': '3.3', 'count': 88},\n", " {'value': '4.1', 'count': 86},\n", " {'value': '1.9', 'count': 86},\n", " {'value': '4.6', 'count': 86}],\n", " 'count_uniques': 12576},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'int'},\n", " 'fall': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 45716,\n", " 'frequency': [{'value': 'Found', 'count': 44609},\n", " {'value': 'Fell', 'count': 1107}],\n", " 'count_uniques': 2},\n", " 'dtype': 'category',\n", " 'profiler_dtype': 'string'},\n", " 'year': {'stats': {'mismatch': 0,\n", " 'missing': 288,\n", " 'match': 45428,\n", " 'frequency': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323},\n", " {'value': '01/01/1979 12:00:00 AM', 'count': 3046},\n", " {'value': '01/01/1998 12:00:00 AM', 'count': 2697},\n", " {'value': '01/01/2006 12:00:00 AM', 'count': 2456},\n", " {'value': '01/01/1988 12:00:00 AM', 'count': 2296},\n", " {'value': '01/01/2002 12:00:00 AM', 'count': 2078},\n", " {'value': '01/01/2004 12:00:00 AM', 'count': 1940},\n", " {'value': '01/01/2000 12:00:00 AM', 'count': 1792},\n", " {'value': '01/01/1997 12:00:00 AM', 'count': 1696},\n", " {'value': '01/01/1999 12:00:00 AM', 'count': 1691},\n", " {'value': '01/01/2001 12:00:00 AM', 'count': 1650},\n", " {'value': '01/01/1990 12:00:00 AM', 'count': 1518},\n", " {'value': '01/01/2009 12:00:00 AM', 'count': 1497},\n", " {'value': '01/01/1986 12:00:00 AM', 'count': 1375},\n", " {'value': '01/01/2007 12:00:00 AM', 'count': 1189},\n", " {'value': '01/01/2010 12:00:00 AM', 'count': 1005},\n", " {'value': '01/01/1993 12:00:00 AM', 'count': 979},\n", " {'value': '01/01/2008 12:00:00 AM', 'count': 957},\n", " {'value': '01/01/1987 12:00:00 AM', 'count': 916},\n", " {'value': '01/01/1991 12:00:00 AM', 'count': 877},\n", " {'value': '01/01/2005 12:00:00 AM', 'count': 875},\n", " {'value': '01/01/1994 12:00:00 AM', 'count': 719},\n", " {'value': '01/01/2011 12:00:00 AM', 'count': 713},\n", " {'value': '01/01/1974 12:00:00 AM', 'count': 691},\n", " {'value': '01/01/1996 12:00:00 AM', 'count': 583},\n", " {'value': '01/01/1995 12:00:00 AM', 'count': 487},\n", " {'value': '01/01/1981 12:00:00 AM', 'count': 463},\n", " {'value': '01/01/1977 12:00:00 AM', 'count': 421},\n", " {'value': '01/01/1984 12:00:00 AM', 'count': 402},\n", " {'value': '01/01/1985 12:00:00 AM', 'count': 378},\n", " {'value': '01/01/1992 12:00:00 AM', 'count': 372},\n", " {'value': '01/01/1983 12:00:00 AM', 'count': 360},\n", " {'value': '01/01/1982 12:00:00 AM', 'count': 344}],\n", " 'count_uniques': 269},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'date'},\n", " 'reclat': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 45716,\n", " 'frequency': [{'value': '0.000000', 'count': 6438},\n", " {'value': '-71.500000', 'count': 4761},\n", " {'value': '-84.000000', 'count': 3040},\n", " {'value': '-72.000000', 'count': 1506},\n", " {'value': '-79.683330', 'count': 1130},\n", " {'value': '-76.716670', 'count': 680},\n", " {'value': '-76.183330', 'count': 539},\n", " {'value': '-84.216670', 'count': 263},\n", " {'value': '-86.366670', 'count': 226},\n", " {'value': '-86.716670', 'count': 217},\n", " {'value': '-85.666670', 'count': 185},\n", " {'value': '-24.850000', 'count': 178},\n", " {'value': '-85.633330', 'count': 108},\n", " {'value': '-72.954880', 'count': 74},\n", " {'value': '-72.778890', 'count': 69},\n", " {'value': '-72.983890', 'count': 67},\n", " {'value': '58.583330', 'count': 64},\n", " {'value': '-72.775000', 'count': 57},\n", " {'value': '-72.778330', 'count': 52},\n", " {'value': '-72.998890', 'count': 41},\n", " {'value': '-72.779170', 'count': 40},\n", " {'value': '34.083330', 'count': 40},\n", " {'value': '-72.782500', 'count': 39},\n", " {'value': '-72.983889', 'count': 37},\n", " {'value': '-72.778610', 'count': 35},\n", " {'value': '29.916670', 'count': 35},\n", " {'value': '-83.250000', 'count': 35},\n", " {'value': '-25.233330', 'count': 32},\n", " {'value': '-82.500000', 'count': 32},\n", " {'value': '-72.773610', 'count': 31},\n", " {'value': '-72.989720', 'count': 31},\n", " {'value': '-72.774720', 'count': 31},\n", " {'value': '-72.773330', 'count': 30}],\n", " 'count_uniques': 12738},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'decimal'},\n", " 'reclong': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 45716,\n", " 'frequency': [{'value': '0.000000', 'count': 6214},\n", " {'value': '35.666670', 'count': 4985},\n", " {'value': '168.000000', 'count': 3040},\n", " {'value': '26.000000', 'count': 1506},\n", " {'value': '159.750000', 'count': 657},\n", " {'value': '159.666670', 'count': 637},\n", " {'value': '157.166670', 'count': 542},\n", " {'value': '155.750000', 'count': 473},\n", " {'value': '160.500000', 'count': 263},\n", " {'value': '-70.000000', 'count': 228},\n", " {'value': '-141.500000', 'count': 217},\n", " {'value': '175.000000', 'count': 185},\n", " {'value': '-70.533330', 'count': 178},\n", " {'value': '-68.700000', 'count': 105},\n", " {'value': '160.473280', 'count': 74},\n", " {'value': '13.433330', 'count': 64},\n", " {'value': '159.333330', 'count': 44},\n", " {'value': '75.246390', 'count': 42},\n", " {'value': '75.313610', 'count': 42},\n", " {'value': '-103.500000', 'count': 35},\n", " {'value': '157.000000', 'count': 34},\n", " {'value': '75.187220', 'count': 33},\n", " {'value': '-5.583330', 'count': 33},\n", " {'value': '155.500000', 'count': 32},\n", " {'value': '75.200000', 'count': 32},\n", " {'value': '-69.716670', 'count': 32},\n", " {'value': '75.340000', 'count': 31},\n", " {'value': '75.246389', 'count': 30},\n", " {'value': '161.500000', 'count': 30},\n", " {'value': '161.083330', 'count': 29},\n", " {'value': '156.383330', 'count': 27},\n", " {'value': '-9.500000', 'count': 24},\n", " {'value': '-69.766670', 'count': 24}],\n", " 'count_uniques': 14640},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'decimal'},\n", " 'GeoLocation': {'stats': {'mismatch': 0,\n", " 'missing': 7315,\n", " 'match': 38401,\n", " 'frequency': [{'value': '(0.000000, 0.000000)', 'count': 6214},\n", " {'value': '(-71.500000, 35.666670)', 'count': 4761},\n", " {'value': '(-84.000000, 168.000000)', 'count': 3040},\n", " {'value': '(-72.000000, 26.000000)', 'count': 1505},\n", " {'value': '(-79.683330, 159.750000)', 'count': 657},\n", " {'value': '(-76.716670, 159.666670)', 'count': 637},\n", " {'value': '(-76.183330, 157.166670)', 'count': 539},\n", " {'value': '(-79.683330, 155.750000)', 'count': 473},\n", " {'value': '(-84.216670, 160.500000)', 'count': 263},\n", " {'value': '(-86.366670, -70.000000)', 'count': 226},\n", " {'value': '(0.000000, 35.666670)', 'count': 223},\n", " {'value': '(-86.716670, -141.500000)', 'count': 217},\n", " {'value': '(-85.666670, 175.000000)', 'count': 185},\n", " {'value': '(-24.850000, -70.533330)', 'count': 178},\n", " {'value': '(-85.633330, -68.700000)', 'count': 105},\n", " {'value': '(-72.954880, 160.473280)', 'count': 74},\n", " {'value': '(58.583330, 13.433330)', 'count': 64},\n", " {'value': '(-76.716670, 159.333330)', 'count': 42},\n", " {'value': '(-72.778890, 75.313610)', 'count': 39},\n", " {'value': '(-72.983890, 75.246390)', 'count': 38},\n", " {'value': '(-83.250000, 157.000000)', 'count': 34},\n", " {'value': '(29.916670, -5.583330)', 'count': 33},\n", " {'value': '(-82.500000, 155.500000)', 'count': 32},\n", " {'value': '(-72.998890, 75.187220)', 'count': 32},\n", " {'value': '(-25.233330, -69.716670)', 'count': 32},\n", " {'value': '(-84.266670, 161.500000)', 'count': 30},\n", " {'value': '(-84.283330, 161.083330)', 'count': 29},\n", " {'value': '(-73.083330, 75.200000)', 'count': 28},\n", " {'value': '(-72.983889, 75.246389)', 'count': 27},\n", " {'value': '(34.083330, -103.500000)', 'count': 27},\n", " {'value': '(-80.066670, 156.383330)', 'count': 27},\n", " {'value': '(-24.683330, -69.766670)', 'count': 24},\n", " {'value': '(27.166670, -9.500000)', 'count': 24}],\n", " 'count_uniques': 17100},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'array'}},\n", " 'name': None,\n", " 'file_name': 'Meteorite_Landings.csv',\n", " 'summary': {'cols_count': 10,\n", " 'rows_count': 45716,\n", " 'size': '2.7 MB',\n", " 'dtypes_list': ['object', 'category', 'uint16'],\n", " 'total_count_dtypes': 3,\n", " 'missing_count': 0,\n", " 'p_missing': 0.0}}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.ext.profile(\"*\")" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'int'" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.meta.get()[\"profile\"][\"columns\"][\"id\"][\"profiler_dtype\"]\n", "# print(df.meta.get()[\"transformations\"][\"actions\"])\n", "# print(df.meta.get()[\"transformations\"][\"columns\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df1.meta.get()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "The truth value of a Series is ambiguous. Use a.any() or a.all().", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"name\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m!=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"a\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\base\\dask\\columns.py\u001b[0m in \u001b[0;36mset\u001b[1;34m(self, where, value, output_cols)\u001b[0m\n\u001b[0;32m 647\u001b[0m \"\"\"\n\u001b[0;32m 648\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 649\u001b[1;33m \u001b[0moutput_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_cols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccepts_missing_cols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 650\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 651\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0moutput_col\u001b[0m \u001b[1;32min\u001b[0m \u001b[0moutput_cols\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\helpers\\columns.py\u001b[0m in \u001b[0;36mparse_columns\u001b[1;34m(df, cols_args, get_args, is_regex, filter_by_column_dtypes, accepts_missing_cols, invert)\u001b[0m\n\u001b[0;32m 173\u001b[0m \u001b[0mcols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf_columns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 174\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 175\u001b[1;33m \u001b[1;32melif\u001b[0m \u001b[0mcols_args\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"*\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mcols_args\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 176\u001b[0m \u001b[0mcols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 177\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36m__bool__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 535\u001b[0m raise ValueError(\n\u001b[0;32m 536\u001b[0m \u001b[1;34m\"The truth value of a {0} is ambiguous. \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 537\u001b[1;33m \u001b[1;34m\"Use a.any() or a.all().\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 538\u001b[0m )\n\u001b[0;32m 539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.any() or a.all()." ] } ], "source": [ "df.cols.set(\"name\", df.name!= None, df.name+\"a\").compute()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 45716 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] } ], "source": [ "df.ext.set_buffer(\"*\")" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[0, 1]]" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import re\n", "_value = \"50\"\n", "regex = re.compile('|'.join(\"5\"))\n", "[[match.start(), match.end()] for match in regex.finditer(_value)]\n", " \n", "# length = [[match.start(), match.end()] for match in\n", "# regex.finditer(re.escape(_value), re.IGNORECASE)]\n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameidnametyperecclassmass (g)fallyearreclatreclongGeoLocationGeoLocation_0GeoLocation_1
0Aachen1ValidL521Fell01/01/1880 12:00:00 AM50.7750006.083330(50.775000, 6.083330)(50.7750006.083330)
1Aarhus2ValidH6720Fell01/01/1951 12:00:00 AM56.18333010.233330(56.183330, 10.233330)(56.18333010.233330)
2Abee6ValidEH4107000Fell01/01/1952 12:00:00 AM54.216670-113.000000(54.216670, -113.000000)(54.216670-113.000000)
3Acapulco10ValidAcapulcoite1914Fell01/01/1976 12:00:00 AM16.883330-99.900000(16.883330, -99.900000)(16.883330-99.900000)
4Achiras370ValidL6780Fell01/01/1902 12:00:00 AM-33.166670-64.950000(-33.166670, -64.950000)(-33.166670-64.950000)
.......................................
45711Zillah 00231356ValidEucrite172Found01/01/1990 12:00:00 AM29.03700017.018500(29.037000, 17.018500)(29.03700017.018500)
45712Zinder30409ValidPallasite, ungrouped46Found01/01/1999 12:00:00 AM13.7833308.966670(13.783330, 8.966670)(13.7833308.966670)
45713Zlin30410ValidH43.3Found01/01/1939 12:00:00 AM49.25000017.666670(49.250000, 17.666670)(49.25000017.666670)
45714Zubkovsky31357ValidL62167Found01/01/2003 12:00:00 AM49.78917041.504600(49.789170, 41.504600)(49.78917041.504600)
45715Zulu Queen30414ValidL3.7200Found01/01/1976 12:00:00 AM33.983330-115.683330(33.983330, -115.683330)(33.983330-115.683330)
\n", "

45716 rows × 12 columns

\n", "
" ], "text/plain": [ " name id nametype recclass mass (g) fall \\\n", "0 Aachen 1 Valid L5 21 Fell \n", "1 Aarhus 2 Valid H6 720 Fell \n", "2 Abee 6 Valid EH4 107000 Fell \n", "3 Acapulco 10 Valid Acapulcoite 1914 Fell \n", "4 Achiras 370 Valid L6 780 Fell \n", "... ... ... ... ... ... ... \n", "45711 Zillah 002 31356 Valid Eucrite 172 Found \n", "45712 Zinder 30409 Valid Pallasite, ungrouped 46 Found \n", "45713 Zlin 30410 Valid H4 3.3 Found \n", "45714 Zubkovsky 31357 Valid L6 2167 Found \n", "45715 Zulu Queen 30414 Valid L3.7 200 Found \n", "\n", " year reclat reclong \\\n", "0 01/01/1880 12:00:00 AM 50.775000 6.083330 \n", "1 01/01/1951 12:00:00 AM 56.183330 10.233330 \n", "2 01/01/1952 12:00:00 AM 54.216670 -113.000000 \n", "3 01/01/1976 12:00:00 AM 16.883330 -99.900000 \n", "4 01/01/1902 12:00:00 AM -33.166670 -64.950000 \n", "... ... ... ... \n", "45711 01/01/1990 12:00:00 AM 29.037000 17.018500 \n", "45712 01/01/1999 12:00:00 AM 13.783330 8.966670 \n", "45713 01/01/1939 12:00:00 AM 49.250000 17.666670 \n", "45714 01/01/2003 12:00:00 AM 49.789170 41.504600 \n", "45715 01/01/1976 12:00:00 AM 33.983330 -115.683330 \n", "\n", " GeoLocation GeoLocation_0 GeoLocation_1 \n", "0 (50.775000, 6.083330) (50.775000 6.083330) \n", "1 (56.183330, 10.233330) (56.183330 10.233330) \n", "2 (54.216670, -113.000000) (54.216670 -113.000000) \n", "3 (16.883330, -99.900000) (16.883330 -99.900000) \n", "4 (-33.166670, -64.950000) (-33.166670 -64.950000) \n", "... ... ... ... \n", "45711 (29.037000, 17.018500) (29.037000 17.018500) \n", "45712 (13.783330, 8.966670) (13.783330 8.966670) \n", "45713 (49.250000, 17.666670) (49.250000 17.666670) \n", "45714 (49.789170, 41.504600) (49.789170 41.504600) \n", "45715 (33.983330, -115.683330) (33.983330 -115.683330) \n", "\n", "[45716 rows x 12 columns]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.cols.unnest(\"GeoLocation\",\",\").compute()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 45716 rows / 45716 columns
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1 partition(s)
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\n", "
name
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1 (object)
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id
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2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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nametype
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3 (object)
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recclass
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4 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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mass (g)
\n", "
5 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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fall
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6 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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year
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7 (object)
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\n", " \n", " not nullable\n", " \n", "
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reclat
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8 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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reclong
\n", "
9 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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GeoLocation
\n", "
10 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " Aachen\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
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\n", " \n", " L5\n", " \n", "
\n", "
\n", "
\n", " \n", " 21\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1880⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 50.775000\n", " \n", "
\n", "
\n", "
\n", " \n", " 6.083330\n", " \n", "
\n", "
\n", "
\n", " \n", " (50.775000,⋅6.083330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aarhus\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H6\n", " \n", "
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\n", "
\n", " \n", " 720\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1951⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 56.183330\n", " \n", "
\n", "
\n", "
\n", " \n", " 10.233330\n", " \n", "
\n", "
\n", "
\n", " \n", " (56.183330,⋅10.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Abee\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 107000\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1952⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 54.216670\n", " \n", "
\n", "
\n", "
\n", " \n", " -113.000000\n", " \n", "
\n", "
\n", "
\n", " \n", " (54.216670,⋅-113.000000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulco\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " Acapulcoite\n", " \n", "
\n", "
\n", "
\n", " \n", " 1914\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1976⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 16.883330\n", " \n", "
\n", "
\n", "
\n", " \n", " -99.900000\n", " \n", "
\n", "
\n", "
\n", " \n", " (16.883330,⋅-99.900000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Achiras\n", " \n", "
\n", "
\n", "
\n", " \n", " 370\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 780\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1902⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -33.166670\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.950000\n", " \n", "
\n", "
\n", "
\n", " \n", " (-33.166670,⋅-64.950000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adhi⋅Kot\n", " \n", "
\n", "
\n", "
\n", " \n", " 379\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " EH4\n", " \n", "
\n", "
\n", "
\n", " \n", " 4239\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1919⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 32.100000\n", " \n", "
\n", "
\n", "
\n", " \n", " 71.800000\n", " \n", "
\n", "
\n", "
\n", " \n", " (32.100000,⋅71.800000)\n", " \n", "
\n", "
\n", "
\n", " \n", " Adzhi-Bogdo⋅(stone)\n", " \n", "
\n", "
\n", "
\n", " \n", " 390\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " LL3-6\n", " \n", "
\n", "
\n", "
\n", " \n", " 910\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1949⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.833330\n", " \n", "
\n", "
\n", "
\n", " \n", " 95.166670\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.833330,⋅95.166670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Agen\n", " \n", "
\n", "
\n", "
\n", " \n", " 392\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " H5\n", " \n", "
\n", "
\n", "
\n", " \n", " 30000\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1814⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " 44.216670\n", " \n", "
\n", "
\n", "
\n", " \n", " 0.616670\n", " \n", "
\n", "
\n", "
\n", " \n", " (44.216670,⋅0.616670)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguada\n", " \n", "
\n", "
\n", "
\n", " \n", " 398\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L6\n", " \n", "
\n", "
\n", "
\n", " \n", " 1620\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1930⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -31.600000\n", " \n", "
\n", "
\n", "
\n", " \n", " -65.233330\n", " \n", "
\n", "
\n", "
\n", " \n", " (-31.600000,⋅-65.233330)\n", " \n", "
\n", "
\n", "
\n", " \n", " Aguila⋅Blanca\n", " \n", "
\n", "
\n", "
\n", " \n", " 417\n", " \n", "
\n", "
\n", "
\n", " \n", " Valid\n", " \n", "
\n", "
\n", "
\n", " \n", " L\n", " \n", "
\n", "
\n", "
\n", " \n", " 1440\n", " \n", "
\n", "
\n", "
\n", " \n", " Fell\n", " \n", "
\n", "
\n", "
\n", " \n", " 01/01/1920⋅12:00:00⋅AM\n", " \n", "
\n", "
\n", "
\n", " \n", " -30.866670\n", " \n", "
\n", "
\n", "
\n", " \n", " -64.550000\n", " \n", "
\n", "
\n", "
\n", " \n", " (-30.866670,⋅-64.550000)\n", " \n", "
\n", "
\n", "\n", "
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nameidnametyperecclassmass (g)fallyearreclatreclongGeoLocationnew GeoLocationGeoLocation__match_positions__
0Aachen1ValidL521Fell01/01/1880 12:00:00 AM50.7750006.083330(50.775000, 6.083330)(0.77000, 6.083330)[[1, 2], [6, 7]]
1Aarhus2ValidH6720Fell01/01/1951 12:00:00 AM56.18333010.233330(56.183330, 10.233330)(6.183330, 10.233330)[[1, 2]]
2Abee6ValidEH4107000Fell01/01/1952 12:00:00 AM54.216670-113.000000(54.216670, -113.000000)(4.216670, -113.000000)[[1, 2]]
3Acapulco10ValidAcapulcoite1914Fell01/01/1976 12:00:00 AM16.883330-99.900000(16.883330, -99.900000)(16.883330, -99.900000)None
4Achiras370ValidL6780Fell01/01/1902 12:00:00 AM-33.166670-64.950000(-33.166670, -64.950000)(-33.166670, -64.90000)[[18, 19]]
5Adhi Kot379ValidEH44239Fell01/01/1919 12:00:00 AM32.10000071.800000(32.100000, 71.800000)(32.100000, 71.800000)None
6Adzhi-Bogdo (stone)390ValidLL3-6910Fell01/01/1949 12:00:00 AM44.83333095.166670(44.833330, 95.166670)(44.833330, 9.166670)[[13, 14]]
7Agen392ValidH530000Fell01/01/1814 12:00:00 AM44.2166700.616670(44.216670, 0.616670)(44.216670, 0.616670)None
8Aguada398ValidL61620Fell01/01/1930 12:00:00 AM-31.600000-65.233330(-31.600000, -65.233330)(-31.600000, -6.233330)[[15, 16]]
9Aguila Blanca417ValidL1440Fell01/01/1920 12:00:00 AM-30.866670-64.550000(-30.866670, -64.550000)(-30.866670, -64.0000)[[17, 18], [18, 19]]
10Aioun el Atrouss423ValidDiogenite-pm1000Fell01/01/1974 12:00:00 AM16.398060-9.570280(16.398060, -9.570280)(16.398060, -9.70280)[[15, 16]]
11Aïr424ValidL624000Fell01/01/1925 12:00:00 AM19.0833308.383330(19.083330, 8.383330)(19.083330, 8.383330)None
12Aire-sur-la-Lys425ValidUnknownNaNFell01/01/1769 12:00:00 AM50.6666702.333330(50.666670, 2.333330)(0.666670, 2.333330)[[1, 2]]
13Akaba426ValidL6779Fell01/01/1949 12:00:00 AM29.51667035.050000(29.516670, 35.050000)(29.16670, 3.00000)[[4, 5], [13, 14], [16, 17]]
14Akbarpur427ValidH41800Fell01/01/1838 12:00:00 AM29.71667077.950000(29.716670, 77.950000)(29.716670, 77.90000)[[16, 17]]
15Akwanga432ValidH3000Fell01/01/1959 12:00:00 AM8.9166708.433330(8.916670, 8.433330)(8.916670, 8.433330)None
16Akyumak433ValidIron, IVA50000Fell01/01/1981 12:00:00 AM39.91667042.816670(39.916670, 42.816670)(39.916670, 42.816670)None
17Al Rais446ValidCR2-an160Fell01/01/1957 12:00:00 AM24.41667039.516670(24.416670, 39.516670)(24.416670, 39.16670)[[15, 16]]
18Al Zarnkh447ValidLL5700Fell01/01/2001 12:00:00 AM13.66033028.960000(13.660330, 28.960000)(13.660330, 28.960000)None
19Alais448ValidCI16000Fell01/01/1806 12:00:00 AM44.1166704.083330(44.116670, 4.083330)(44.116670, 4.083330)None
20Albareto453ValidL/LL42000Fell01/01/1766 12:00:00 AM44.65000011.016670(44.650000, 11.016670)(44.60000, 11.016670)[[5, 6]]
21Alberta454ValidL625Fell01/01/1949 12:00:00 AM2.00000022.666670(2.000000, 22.666670)(2.000000, 22.666670)None
22Alby sur Chéran458ValidEucrite-mmict252Fell01/01/2002 12:00:00 AM45.8213306.015330(45.821330, 6.015330)(4.821330, 6.01330)[[2, 3], [16, 17]]
23Aldsworth461ValidLL5700Fell01/01/1835 12:00:00 AM51.783330-1.783330(51.783330, -1.783330)(1.783330, -1.783330)[[1, 2]]
24Aleppo462ValidL63200Fell01/01/1873 12:00:00 AM36.23333037.133330(36.233330, 37.133330)(36.233330, 37.133330)None
25Alessandria463ValidH5908Fell01/01/1860 12:00:00 AM44.8833308.750000(44.883330, 8.750000)(44.883330, 8.70000)[[15, 16]]
26Alexandrovsky465ValidH49251Fell01/01/1900 12:00:00 AM50.95000031.816670(50.950000, 31.816670)(0.90000, 31.816670)[[1, 2], [5, 6]]
27Alfianello466ValidL6228000Fell01/01/1883 12:00:00 AM45.26667010.150000(45.266670, 10.150000)(4.266670, 10.10000)[[2, 3], [16, 17]]
28Allegan2276ValidH532000Fell01/01/1899 12:00:00 AM42.533330-85.883330(42.533330, -85.883330)(42.33330, -8.883330)[[4, 5], [14, 15]]
29Allende2278ValidCV32000000Fell01/01/1969 12:00:00 AM26.966670-105.316670(26.966670, -105.316670)(26.966670, -10.316670)[[15, 16]]
30Almahata Sitta48915ValidUreilite-an3950Fell01/01/2008 12:00:00 AM20.74575032.412750(20.745750, 32.412750)(20.7470, 32.41270)[[6, 7], [8, 9], [19, 20]]
31Alta'ameem2284ValidLL56000Fell01/01/1977 12:00:00 AM35.27333044.215560(35.273330, 44.215560)(3.273330, 44.2160)[[2, 3], [17, 18], [18, 19]]
32Ambapur Nagla2290ValidH56400Fell01/01/1895 12:00:00 AM27.66667078.250000(27.666670, 78.250000)(27.666670, 78.20000)[[16, 17]]
33Andhara2294ValidStone-uncl2700Fell01/01/1880 12:00:00 AM26.58333085.566670(26.583330, 85.566670)(26.83330, 8.66670)[[4, 5], [13, 14], [15, 16]]
\n", "
" ], "text/plain": [ " name id nametype recclass mass (g) fall \\\n", "0 Aachen 1 Valid L5 21 Fell \n", "1 Aarhus 2 Valid H6 720 Fell \n", "2 Abee 6 Valid EH4 107000 Fell \n", "3 Acapulco 10 Valid Acapulcoite 1914 Fell \n", "4 Achiras 370 Valid L6 780 Fell \n", "5 Adhi Kot 379 Valid EH4 4239 Fell \n", "6 Adzhi-Bogdo (stone) 390 Valid LL3-6 910 Fell \n", "7 Agen 392 Valid H5 30000 Fell \n", "8 Aguada 398 Valid L6 1620 Fell \n", "9 Aguila Blanca 417 Valid L 1440 Fell \n", "10 Aioun el Atrouss 423 Valid Diogenite-pm 1000 Fell \n", "11 Aïr 424 Valid L6 24000 Fell \n", "12 Aire-sur-la-Lys 425 Valid Unknown NaN Fell \n", "13 Akaba 426 Valid L6 779 Fell \n", "14 Akbarpur 427 Valid H4 1800 Fell \n", "15 Akwanga 432 Valid H 3000 Fell \n", "16 Akyumak 433 Valid Iron, IVA 50000 Fell \n", "17 Al Rais 446 Valid CR2-an 160 Fell \n", "18 Al Zarnkh 447 Valid LL5 700 Fell \n", "19 Alais 448 Valid CI1 6000 Fell \n", "20 Albareto 453 Valid L/LL4 2000 Fell \n", "21 Alberta 454 Valid L 625 Fell \n", "22 Alby sur Chéran 458 Valid Eucrite-mmict 252 Fell \n", "23 Aldsworth 461 Valid LL5 700 Fell \n", "24 Aleppo 462 Valid L6 3200 Fell \n", "25 Alessandria 463 Valid H5 908 Fell \n", "26 Alexandrovsky 465 Valid H4 9251 Fell \n", "27 Alfianello 466 Valid L6 228000 Fell \n", "28 Allegan 2276 Valid H5 32000 Fell \n", "29 Allende 2278 Valid CV3 2000000 Fell \n", "30 Almahata Sitta 48915 Valid Ureilite-an 3950 Fell \n", "31 Alta'ameem 2284 Valid LL5 6000 Fell \n", "32 Ambapur Nagla 2290 Valid H5 6400 Fell \n", "33 Andhara 2294 Valid Stone-uncl 2700 Fell \n", "\n", " year reclat reclong GeoLocation \\\n", "0 01/01/1880 12:00:00 AM 50.775000 6.083330 (50.775000, 6.083330) \n", "1 01/01/1951 12:00:00 AM 56.183330 10.233330 (56.183330, 10.233330) \n", "2 01/01/1952 12:00:00 AM 54.216670 -113.000000 (54.216670, -113.000000) \n", "3 01/01/1976 12:00:00 AM 16.883330 -99.900000 (16.883330, -99.900000) \n", "4 01/01/1902 12:00:00 AM -33.166670 -64.950000 (-33.166670, -64.950000) \n", "5 01/01/1919 12:00:00 AM 32.100000 71.800000 (32.100000, 71.800000) \n", "6 01/01/1949 12:00:00 AM 44.833330 95.166670 (44.833330, 95.166670) \n", "7 01/01/1814 12:00:00 AM 44.216670 0.616670 (44.216670, 0.616670) \n", "8 01/01/1930 12:00:00 AM -31.600000 -65.233330 (-31.600000, -65.233330) \n", "9 01/01/1920 12:00:00 AM -30.866670 -64.550000 (-30.866670, -64.550000) \n", "10 01/01/1974 12:00:00 AM 16.398060 -9.570280 (16.398060, -9.570280) \n", "11 01/01/1925 12:00:00 AM 19.083330 8.383330 (19.083330, 8.383330) \n", "12 01/01/1769 12:00:00 AM 50.666670 2.333330 (50.666670, 2.333330) \n", "13 01/01/1949 12:00:00 AM 29.516670 35.050000 (29.516670, 35.050000) \n", "14 01/01/1838 12:00:00 AM 29.716670 77.950000 (29.716670, 77.950000) \n", "15 01/01/1959 12:00:00 AM 8.916670 8.433330 (8.916670, 8.433330) \n", "16 01/01/1981 12:00:00 AM 39.916670 42.816670 (39.916670, 42.816670) \n", "17 01/01/1957 12:00:00 AM 24.416670 39.516670 (24.416670, 39.516670) \n", "18 01/01/2001 12:00:00 AM 13.660330 28.960000 (13.660330, 28.960000) \n", "19 01/01/1806 12:00:00 AM 44.116670 4.083330 (44.116670, 4.083330) \n", "20 01/01/1766 12:00:00 AM 44.650000 11.016670 (44.650000, 11.016670) \n", "21 01/01/1949 12:00:00 AM 2.000000 22.666670 (2.000000, 22.666670) \n", "22 01/01/2002 12:00:00 AM 45.821330 6.015330 (45.821330, 6.015330) \n", "23 01/01/1835 12:00:00 AM 51.783330 -1.783330 (51.783330, -1.783330) \n", "24 01/01/1873 12:00:00 AM 36.233330 37.133330 (36.233330, 37.133330) \n", "25 01/01/1860 12:00:00 AM 44.883330 8.750000 (44.883330, 8.750000) \n", "26 01/01/1900 12:00:00 AM 50.950000 31.816670 (50.950000, 31.816670) \n", "27 01/01/1883 12:00:00 AM 45.266670 10.150000 (45.266670, 10.150000) \n", "28 01/01/1899 12:00:00 AM 42.533330 -85.883330 (42.533330, -85.883330) \n", "29 01/01/1969 12:00:00 AM 26.966670 -105.316670 (26.966670, -105.316670) \n", "30 01/01/2008 12:00:00 AM 20.745750 32.412750 (20.745750, 32.412750) \n", "31 01/01/1977 12:00:00 AM 35.273330 44.215560 (35.273330, 44.215560) \n", "32 01/01/1895 12:00:00 AM 27.666670 78.250000 (27.666670, 78.250000) \n", "33 01/01/1880 12:00:00 AM 26.583330 85.566670 (26.583330, 85.566670) \n", "\n", " new GeoLocation GeoLocation__match_positions__ \n", "0 (0.77000, 6.083330) [[1, 2], [6, 7]] \n", "1 (6.183330, 10.233330) [[1, 2]] \n", "2 (4.216670, -113.000000) [[1, 2]] \n", "3 (16.883330, -99.900000) None \n", "4 (-33.166670, -64.90000) [[18, 19]] \n", "5 (32.100000, 71.800000) None \n", "6 (44.833330, 9.166670) [[13, 14]] \n", "7 (44.216670, 0.616670) None \n", "8 (-31.600000, -6.233330) [[15, 16]] \n", "9 (-30.866670, -64.0000) [[17, 18], [18, 19]] \n", "10 (16.398060, -9.70280) [[15, 16]] \n", "11 (19.083330, 8.383330) None \n", "12 (0.666670, 2.333330) [[1, 2]] \n", "13 (29.16670, 3.00000) [[4, 5], [13, 14], [16, 17]] \n", "14 (29.716670, 77.90000) [[16, 17]] \n", "15 (8.916670, 8.433330) None \n", "16 (39.916670, 42.816670) None \n", "17 (24.416670, 39.16670) [[15, 16]] \n", "18 (13.660330, 28.960000) None \n", "19 (44.116670, 4.083330) None \n", "20 (44.60000, 11.016670) [[5, 6]] \n", "21 (2.000000, 22.666670) None \n", "22 (4.821330, 6.01330) [[2, 3], [16, 17]] \n", "23 (1.783330, -1.783330) [[1, 2]] \n", "24 (36.233330, 37.133330) None \n", "25 (44.883330, 8.70000) [[15, 16]] \n", "26 (0.90000, 31.816670) [[1, 2], [5, 6]] \n", "27 (4.266670, 10.10000) [[2, 3], [16, 17]] \n", "28 (42.33330, -8.883330) [[4, 5], [14, 15]] \n", "29 (26.966670, -10.316670) [[15, 16]] \n", "30 (20.7470, 32.41270) [[6, 7], [8, 9], [19, 20]] \n", "31 (3.273330, 44.2160) [[2, 3], [17, 18], [18, 19]] \n", "32 (27.666670, 78.20000) [[16, 17]] \n", "33 (26.83330, 8.66670) [[4, 5], [13, 14], [15, 16]] " ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.buffer_window(\"*\", 0, 34).cols.replace(\"GeoLocation\", search=[\"5\"], replace_by=\"\", search_by=\"chars\", ignore_case=True, output_cols=\"new GeoLocation\").cols.find(\"GeoLocation\", sub=[\"5\"], ignore_case=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "df = df.cols.replace(\"id\",\"a\",\"b\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'file_name': 'tmpd9yzm44r.csv',\n", " 'mime_info': [{'mime': 'text/plain',\n", " 'encoding': 'utf-8',\n", " 'file_ext': 'csv',\n", " 'file_type': 'csv',\n", " 'properties': {'delimiter': ',',\n", " 'doublequote': False,\n", " 'escapechar': None,\n", " 'lineterminator': '\\r\\n',\n", " 'quotechar': '\"',\n", " 'quoting': 0,\n", " 'skipinitialspace': False}}],\n", " 'transformations': {'actions': {}},\n", " 'profile': {'columns': {'id': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': '9', 'count': 1},\n", " {'value': '8', 'count': 1},\n", " {'value': '7', 'count': 1},\n", " {'value': '6', 'count': 1},\n", " {'value': '5', 'count': 1},\n", " {'value': '4', 'count': 1},\n", " {'value': '3', 'count': 1},\n", " {'value': '2', 'count': 1},\n", " {'value': '19', 'count': 1},\n", " {'value': '18', 'count': 1},\n", " {'value': '17', 'count': 1},\n", " {'value': '16', 'count': 1},\n", " {'value': '15', 'count': 1},\n", " {'value': '14', 'count': 1},\n", " {'value': '13', 'count': 1},\n", " {'value': '12', 'count': 1},\n", " {'value': '11', 'count': 1},\n", " {'value': '10', 'count': 1},\n", " {'value': '1', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'firstName': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': 'William', 'count': 1},\n", " {'value': 'PAUL', 'count': 1},\n", " {'value': 'NiELS', 'count': 1},\n", " {'value': 'Max!!!', 'count': 1},\n", " {'value': 'Marie', 'count': 1},\n", " {'value': 'Luis', 'count': 1},\n", " {'value': 'Johannes', 'count': 1},\n", " {'value': 'JaMES', 'count': 1},\n", " {'value': 'JAMES', 'count': 1},\n", " {'value': 'Isaac', 'count': 1},\n", " {'value': 'Galileo', 'count': 1},\n", " {'value': 'Fred', 'count': 1},\n", " {'value': 'Emmy%%', 'count': 1},\n", " {'value': 'David', 'count': 1},\n", " {'value': 'CaRL', 'count': 1},\n", " {'value': 'Arthur', 'count': 1},\n", " {'value': 'André', 'count': 1},\n", " {'value': 'Albert', 'count': 1},\n", " {'value': '((( Heinrich )))))', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'lastName': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': 'dirac$', 'count': 1},\n", " {'value': 'Planck!!!', 'count': 1},\n", " {'value': 'Nöether$', 'count': 1},\n", " {'value': 'Newton', 'count': 1},\n", " {'value': 'M$$ax%%well', 'count': 1},\n", " {'value': 'KEPLER', 'count': 1},\n", " {'value': 'Hoy&&&le', 'count': 1},\n", " {'value': 'Hertz', 'count': 1},\n", " {'value': 'H$$$ilbert', 'count': 1},\n", " {'value': 'Gilbert###', 'count': 1},\n", " {'value': 'Ga%%%uss', 'count': 1},\n", " {'value': 'Einstein', 'count': 1},\n", " {'value': 'Chadwick', 'count': 1},\n", " {'value': 'CURIE', 'count': 1},\n", " {'value': 'COM%%%pton', 'count': 1},\n", " {'value': 'Böhr//((%%', 'count': 1},\n", " {'value': 'Ampère', 'count': 1},\n", " {'value': 'Alvarez$$%!', 'count': 1},\n", " {'value': ' GALiLEI', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'billingId': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': '992', 'count': 1},\n", " {'value': '912', 'count': 1},\n", " {'value': '886', 'count': 1},\n", " {'value': '875', 'count': 1},\n", " {'value': '812', 'count': 1},\n", " {'value': '735', 'count': 1},\n", " {'value': '672', 'count': 1},\n", " {'value': '634', 'count': 1},\n", " {'value': '624', 'count': 1},\n", " {'value': '553', 'count': 1},\n", " {'value': '551', 'count': 1},\n", " {'value': '521', 'count': 1},\n", " {'value': '467', 'count': 1},\n", " {'value': '423', 'count': 1},\n", " {'value': '323', 'count': 1},\n", " {'value': '234', 'count': 1},\n", " {'value': '123', 'count': 1},\n", " {'value': '116', 'count': 1},\n", " {'value': '111', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'int'},\n", " 'product': {'stats': {'mismatch': 0,\n", " 'missing': 1,\n", " 'match': 18,\n", " 'frequency': [{'value': 'pizza', 'count': 4},\n", " {'value': 'taco', 'count': 3},\n", " {'value': 'pasta', 'count': 2},\n", " {'value': 'taaaccoo', 'count': 1},\n", " {'value': 'pizzza', 'count': 1},\n", " {'value': 'piza', 'count': 1},\n", " {'value': 'hamburguer', 'count': 1},\n", " {'value': 'arepa', 'count': 1},\n", " {'value': 'Rice', 'count': 1},\n", " {'value': 'Cake', 'count': 1},\n", " {'value': 'BEER', 'count': 1},\n", " {'value': '110790', 'count': 1}],\n", " 'count_uniques': 12},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'price': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': '8', 'count': 6},\n", " {'value': '3', 'count': 4},\n", " {'value': '9', 'count': 2},\n", " {'value': '5', 'count': 2},\n", " {'value': '10', 'count': 2},\n", " {'value': '4', 'count': 1},\n", " {'value': '2', 'count': 1},\n", " {'value': '1', 'count': 1}],\n", " 'count_uniques': 8},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'int'},\n", " 'birth': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': '2000/03/22', 'count': 1},\n", " {'value': '1999/02/15', 'count': 1},\n", " {'value': '1997/06/27', 'count': 1},\n", " {'value': '1994/01/04', 'count': 1},\n", " {'value': '1993/12/08', 'count': 1},\n", " {'value': '1990/07/11', 'count': 1},\n", " {'value': '1990/07/09', 'count': 1},\n", " {'value': '1980/07/07', 'count': 1},\n", " {'value': '1970/07/13', 'count': 1},\n", " {'value': '1958/03/26', 'count': 1},\n", " {'value': '1956/11/30', 'count': 1},\n", " {'value': '1954/07/10', 'count': 1},\n", " {'value': '1950/07/14', 'count': 1},\n", " {'value': '1950/07/08', 'count': 1},\n", " {'value': '1930/08/12', 'count': 1},\n", " {'value': '1923/03/12', 'count': 1},\n", " {'value': '1921/05/03', 'count': 1},\n", " {'value': '1920/04/22', 'count': 1},\n", " {'value': '1899/01/01', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'date'},\n", " 'dummyCol': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': 'you', 'count': 3},\n", " {'value': 'gonna', 'count': 3},\n", " {'value': 'never', 'count': 2},\n", " {'value': '#', 'count': 2},\n", " {'value': 'up', 'count': 1},\n", " {'value': 'run ', 'count': 1},\n", " {'value': 'never ', 'count': 1},\n", " {'value': 'let', 'count': 1},\n", " {'value': 'give', 'count': 1},\n", " {'value': 'down', 'count': 1},\n", " {'value': 'desert', 'count': 1},\n", " {'value': 'around', 'count': 1},\n", " {'value': 'and', 'count': 1}],\n", " 'count_uniques': 13},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'}},\n", " 'name': None,\n", " 'file_name': 'tmpd9yzm44r.csv',\n", " 'summary': {'cols_count': 8,\n", " 'rows_count': 19,\n", " 'size': '1.3 kB',\n", " 'dtypes_list': ['object'],\n", " 'total_count_dtypes': 1,\n", " 'missing_count': 1,\n", " 'p_missing': 5.26},\n", " 'id': {'columns': {'profiler_dtype': 'string'}}}}" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.meta.get()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1\n", "1 2\n", "dtype: int32" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "ser = pd.Series([\"1\", \"2\"], dtype='object')\n", "ser.astype(int)\n", "# ser.dtypes\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'file_name': 'tmpd9yzm44r.csv',\n", " 'mime_info': [{'mime': 'text/plain',\n", " 'encoding': 'utf-8',\n", " 'file_ext': 'csv',\n", " 'file_type': 'csv',\n", " 'properties': {'delimiter': ',',\n", " 'doublequote': False,\n", " 'escapechar': None,\n", " 'lineterminator': '\\r\\n',\n", " 'quotechar': '\"',\n", " 'quoting': 0,\n", " 'skipinitialspace': False}}],\n", " 'transformations': {'actions': {}},\n", " 'profile': {'columns': {'id': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': '9', 'count': 1},\n", " {'value': '8', 'count': 1},\n", " {'value': '7', 'count': 1},\n", " {'value': '6', 'count': 1},\n", " {'value': '5', 'count': 1},\n", " {'value': '4', 'count': 1},\n", " {'value': '3', 'count': 1},\n", " {'value': '2', 'count': 1},\n", " {'value': '19', 'count': 1},\n", " {'value': '18', 'count': 1},\n", " {'value': '17', 'count': 1},\n", " {'value': '16', 'count': 1},\n", " {'value': '15', 'count': 1},\n", " {'value': '14', 'count': 1},\n", " {'value': '13', 'count': 1},\n", " {'value': '12', 'count': 1},\n", " {'value': '11', 'count': 1},\n", " {'value': '10', 'count': 1},\n", " {'value': '1', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'int'},\n", " 'firstName': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': 'William', 'count': 1},\n", " {'value': 'PAUL', 'count': 1},\n", " {'value': 'NiELS', 'count': 1},\n", " {'value': 'Max!!!', 'count': 1},\n", " {'value': 'Marie', 'count': 1},\n", " {'value': 'Luis', 'count': 1},\n", " {'value': 'Johannes', 'count': 1},\n", " {'value': 'JaMES', 'count': 1},\n", " {'value': 'JAMES', 'count': 1},\n", " {'value': 'Isaac', 'count': 1},\n", " {'value': 'Galileo', 'count': 1},\n", " {'value': 'Fred', 'count': 1},\n", " {'value': 'Emmy%%', 'count': 1},\n", " {'value': 'David', 'count': 1},\n", " {'value': 'CaRL', 'count': 1},\n", " {'value': 'Arthur', 'count': 1},\n", " {'value': 'André', 'count': 1},\n", " {'value': 'Albert', 'count': 1},\n", " {'value': '((( Heinrich )))))', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'lastName': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': 'dirac$', 'count': 1},\n", " {'value': 'Planck!!!', 'count': 1},\n", " {'value': 'Nöether$', 'count': 1},\n", " {'value': 'Newton', 'count': 1},\n", " {'value': 'M$$ax%%well', 'count': 1},\n", " {'value': 'KEPLER', 'count': 1},\n", " {'value': 'Hoy&&&le', 'count': 1},\n", " {'value': 'Hertz', 'count': 1},\n", " {'value': 'H$$$ilbert', 'count': 1},\n", " {'value': 'Gilbert###', 'count': 1},\n", " {'value': 'Ga%%%uss', 'count': 1},\n", " {'value': 'Einstein', 'count': 1},\n", " {'value': 'Chadwick', 'count': 1},\n", " {'value': 'CURIE', 'count': 1},\n", " {'value': 'COM%%%pton', 'count': 1},\n", " {'value': 'Böhr//((%%', 'count': 1},\n", " {'value': 'Ampère', 'count': 1},\n", " {'value': 'Alvarez$$%!', 'count': 1},\n", " {'value': ' GALiLEI', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'billingId': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': '992', 'count': 1},\n", " {'value': '912', 'count': 1},\n", " {'value': '886', 'count': 1},\n", " {'value': '875', 'count': 1},\n", " {'value': '812', 'count': 1},\n", " {'value': '735', 'count': 1},\n", " {'value': '672', 'count': 1},\n", " {'value': '634', 'count': 1},\n", " {'value': '624', 'count': 1},\n", " {'value': '553', 'count': 1},\n", " {'value': '551', 'count': 1},\n", " {'value': '521', 'count': 1},\n", " {'value': '467', 'count': 1},\n", " {'value': '423', 'count': 1},\n", " {'value': '323', 'count': 1},\n", " {'value': '234', 'count': 1},\n", " {'value': '123', 'count': 1},\n", " {'value': '116', 'count': 1},\n", " {'value': '111', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'int'},\n", " 'product': {'stats': {'mismatch': 0,\n", " 'missing': 1,\n", " 'match': 18,\n", " 'frequency': [{'value': 'pizza', 'count': 4},\n", " {'value': 'taco', 'count': 3},\n", " {'value': 'pasta', 'count': 2},\n", " {'value': 'taaaccoo', 'count': 1},\n", " {'value': 'pizzza', 'count': 1},\n", " {'value': 'piza', 'count': 1},\n", " {'value': 'hamburguer', 'count': 1},\n", " {'value': 'arepa', 'count': 1},\n", " {'value': 'Rice', 'count': 1},\n", " {'value': 'Cake', 'count': 1},\n", " {'value': 'BEER', 'count': 1},\n", " {'value': '110790', 'count': 1}],\n", " 'count_uniques': 12},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'},\n", " 'price': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': '8', 'count': 6},\n", " {'value': '3', 'count': 4},\n", " {'value': '9', 'count': 2},\n", " {'value': '5', 'count': 2},\n", " {'value': '10', 'count': 2},\n", " {'value': '4', 'count': 1},\n", " {'value': '2', 'count': 1},\n", " {'value': '1', 'count': 1}],\n", " 'count_uniques': 8},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'int'},\n", " 'birth': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': '2000/03/22', 'count': 1},\n", " {'value': '1999/02/15', 'count': 1},\n", " {'value': '1997/06/27', 'count': 1},\n", " {'value': '1994/01/04', 'count': 1},\n", " {'value': '1993/12/08', 'count': 1},\n", " {'value': '1990/07/11', 'count': 1},\n", " {'value': '1990/07/09', 'count': 1},\n", " {'value': '1980/07/07', 'count': 1},\n", " {'value': '1970/07/13', 'count': 1},\n", " {'value': '1958/03/26', 'count': 1},\n", " {'value': '1956/11/30', 'count': 1},\n", " {'value': '1954/07/10', 'count': 1},\n", " {'value': '1950/07/14', 'count': 1},\n", " {'value': '1950/07/08', 'count': 1},\n", " {'value': '1930/08/12', 'count': 1},\n", " {'value': '1923/03/12', 'count': 1},\n", " {'value': '1921/05/03', 'count': 1},\n", " {'value': '1920/04/22', 'count': 1},\n", " {'value': '1899/01/01', 'count': 1}],\n", " 'count_uniques': 19},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'date'},\n", " 'dummyCol': {'stats': {'mismatch': 0,\n", " 'missing': 0,\n", " 'match': 19,\n", " 'frequency': [{'value': 'you', 'count': 3},\n", " {'value': 'gonna', 'count': 3},\n", " {'value': 'never', 'count': 2},\n", " {'value': '#', 'count': 2},\n", " {'value': 'up', 'count': 1},\n", " {'value': 'run ', 'count': 1},\n", " {'value': 'never ', 'count': 1},\n", " {'value': 'let', 'count': 1},\n", " {'value': 'give', 'count': 1},\n", " {'value': 'down', 'count': 1},\n", " {'value': 'desert', 'count': 1},\n", " {'value': 'around', 'count': 1},\n", " {'value': 'and', 'count': 1}],\n", " 'count_uniques': 13},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'string'}},\n", " 'name': None,\n", " 'file_name': 'tmpd9yzm44r.csv',\n", " 'summary': {'cols_count': 8,\n", " 'rows_count': 19,\n", " 'size': '1.3 kB',\n", " 'dtypes_list': ['object'],\n", " 'total_count_dtypes': 1,\n", " 'missing_count': 1,\n", " 'p_missing': 5.26}}}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.meta.get()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "pdf = df1.ext.buffer_window(\"*\", 0, 19).cols.unnest(\"birth\", separator=\"/\", splits=2, output_cols=\"birth\")[:1]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'category'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdf.cols.dtypes(\"birth\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id uint8\n", "firstName category\n", "lastName category\n", "billingId uint16\n", "product category\n", "price uint8\n", "birth category\n", "dummyCol category\n", "birth_0 object\n", "birth_1 object\n", "dtype: object" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdf.dtypes" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'pdf' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"birth\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msub\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"/\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'pdf' is not defined" ] } ], "source": [ "pdf.cols.find(\"birth\", sub=[\"/\"]).ext.display()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "df = op.load.csv(\"https://raw.githubusercontent.com/ironmussa/Optimus/master/examples/data/foo.csv\", sep=\",\", error_bad_lines=False, header=True, null_value=\"null\", infer_schema=\"true\", encoding=\"UTF-8\", quoting=0, lineterminator=None, cache=True).ext.cache()\n", "df = df.rows.drop( df[\"lastName\"].isin([\"KEPLER\",\"Hoy&&&le\",\"Hertz\",\"H$$$ilbert\",\"Gilbert###\"]) ).ext.cache()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# from dask.distributed import Client, LocalCluster\n", "# cluster = LocalCluster()\n", "# client = Client(cluster)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale29.00000024160211.3375B5S2NaNSt Louis, MO
111Allison, Master. Hudson Trevormale0.916712113781151.5500C22 C26S11NaNMontreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale2.000012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale30.000012113781151.5500C22 C26SNaN135.0Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.000012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
\n", "
" ], "text/plain": [ " pclass survived name sex \\\n", "0 1 1 Allen, Miss. Elisabeth Walton female \n", "1 1 1 Allison, Master. Hudson Trevor male \n", "2 1 0 Allison, Miss. Helen Loraine female \n", "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", "\n", " age sibsp parch ticket fare cabin embarked boat body \\\n", "0 29.0000 0 0 24160 211.3375 B5 S 2 NaN \n", "1 0.9167 1 2 113781 151.5500 C22 C26 S 11 NaN \n", "2 2.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", "3 30.0000 1 2 113781 151.5500 C22 C26 S NaN 135.0 \n", "4 25.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", "\n", " home.dest \n", "0 St Louis, MO \n", "1 Montreal, PQ / Chesterville, ON \n", "2 Montreal, PQ / Chesterville, ON \n", "3 Montreal, PQ / Chesterville, ON \n", "4 Montreal, PQ / Chesterville, ON " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = op.load.file(\"data/nvidia.xlsx\", n_rows=10).ext.cache()\n", "df.head()\n", "# df= df.ext.optimize()\n", "# df = op.load.file(\"data/Meteorite_Landings.csv\", n_rows=100).ext.cache()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 100 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] } ], "source": [ "df.ext.set_buffer(\"*\")\n", "pdf = df.ext.get_buffer()" ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name object\n", "id uint8\n", "nametype category\n", "recclass category\n", "mass (g) object\n", "fall category\n", "year object\n", "reclat object\n", "reclong object\n", "GeoLocation object\n", "dtype: object" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 21\n", "1 720\n", "2 107000\n", "3 1914\n", "4 780\n", " ... \n", "95 25000\n", "96 19000\n", "97 1770.5\n", "98 3880\n", "99 45000\n", "Name: mass (g), Length: 100, dtype: object" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"mass (g)\"].compute()" ] }, { "cell_type": "code", "execution_count": 200, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 200, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fastnumbers.isreal(1, num_only=True)" ] }, { "cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [], "source": [ "import fastnumbers" ] }, { "cell_type": "code", "execution_count": 217, "metadata": {}, "outputs": [], "source": [ "pdf = df.compute()" ] }, { "cell_type": "code", "execution_count": 245, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'>' not supported between instances of 'str' and 'int'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbetween\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'mass (g)'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\pandas\\rows.py\u001b[0m in \u001b[0;36mbetween\u001b[1;34m(columns, lower_bound, upper_bound, invert, equal, bounds)\u001b[0m\n\u001b[0;32m 195\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 196\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcol_name\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 197\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_between\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 198\u001b[0m \u001b[1;31m# df = df.meta.preserve(self, Actions.DROP_ROW.value, df.cols.names())\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 199\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\optimus\\engines\\pandas\\rows.py\u001b[0m in \u001b[0;36m_between\u001b[1;34m(_col_name)\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mbound\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 190\u001b[0m \u001b[0m_lower_bound\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_upper_bound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbound\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 191\u001b[1;33m \u001b[0msub_query\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopb\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_col_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_lower_bound\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_col_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_upper_bound\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 192\u001b[0m \u001b[0mquery\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunctools\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moperator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__or__\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msub_query\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 193\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\ops\\common.py\u001b[0m in \u001b[0;36mnew_method\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[0mother\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\ops\\__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m 527\u001b[0m \u001b[0mrvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 528\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 529\u001b[1;33m \u001b[0mres_values\u001b[0m \u001b[1;33m=\u001b[0m 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245\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m \u001b[0mres_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcomp_method_OBJECT_ARRAY\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\ops\\array_ops.py\u001b[0m in \u001b[0;36mcomp_method_OBJECT_ARRAY\u001b[1;34m(op, x, y)\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlibops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvec_compare\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 56\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 57\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlibops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscalar_compare\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 58\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\ops.pyx\u001b[0m in \u001b[0;36mpandas._libs.ops.scalar_compare\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: '>' not supported between instances of 'str' and 'int'" ] } ], "source": [ "pdf.rows.between('mass (g)',10,200)" ] }, { "cell_type": "code", "execution_count": 236, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 True\n", "1 True\n", "2 True\n", "3 True\n", "4 True\n", " ... \n", "95 True\n", "96 True\n", "97 True\n", "98 True\n", "99 True\n", "Length: 100, dtype: bool" ] }, "execution_count": 236, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def func(value): \n", "# print(value, fastnumbers.isreal(value) and value==value)\n", " if fastnumbers.isreal(value) is True and value==value: \n", " return True\n", " else:\n", " return False\n", " \n", "df['mass (g)'].apply(func, meta=bool).compute()\n", "# pdf['mass (g)'].apply(func)" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "a = pd.to_numeric(df[\"mass (g)\"])" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Metadata inference failed in `query`.\n\nYou have supplied a custom function and Dask is unable to \ndetermine the type of output that that function returns. \n\nTo resolve this please provide a meta= keyword.\nThe docstring of the Dask function you ran should have more information.\n\nOriginal error is below:\n------------------------\nTypeError(\"'<=' not supported between instances of 'str' and 'int'\")\n\nTraceback:\n---------\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\utils.py\", line 172, in raise_on_meta_error\n yield\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py\", line 4941, in _emulate\n return func(*_extract_meta(args, True), **_extract_meta(kwargs, True))\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\utils.py\", line 880, in __call__\n return getattr(obj, self.method)(*args, **kwargs)\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\frame.py\", line 3231, in query\n res = self.eval(expr, **kwargs)\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\frame.py\", line 3346, in eval\n return _eval(expr, inplace=inplace, **kwargs)\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\computation\\eval.py\", line 337, in eval\n ret = eng_inst.evaluate()\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\computation\\engines.py\", line 127, in evaluate\n return self.expr()\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\computation\\expr.py\", line 771, in __call__\n return self.terms(self.env)\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\computation\\ops.py\", line 396, in __call__\n return self.func(left, right)\n", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\utils.py\u001b[0m in \u001b[0;36mraise_on_meta_error\u001b[1;34m(funcname, udf)\u001b[0m\n\u001b[0;32m 171\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 172\u001b[1;33m \u001b[1;32myield\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 173\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36m_emulate\u001b[1;34m(func, *args, **kwargs)\u001b[0m\n\u001b[0;32m 4940\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mraise_on_meta_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfuncname\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mudf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"udf\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4941\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0m_extract_meta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0m_extract_meta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4942\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\utils.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m 879\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4942\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4943\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\lib\\contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type, value, traceback)\u001b[0m\n\u001b[0;32m 128\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 130\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 131\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 132\u001b[0m \u001b[1;31m# Suppress StopIteration *unless* it's the same exception that\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\utils.py\u001b[0m in \u001b[0;36mraise_on_meta_error\u001b[1;34m(funcname, udf)\u001b[0m\n\u001b[0;32m 191\u001b[0m )\n\u001b[0;32m 192\u001b[0m \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\" in `{0}`\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfuncname\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfuncname\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m\"\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrepr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 195\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: Metadata inference failed in `query`.\n\nYou have supplied a custom function and Dask is unable to \ndetermine the type of output that that function returns. \n\nTo resolve this please provide a meta= keyword.\nThe docstring of the Dask function you ran should have more information.\n\nOriginal error is below:\n------------------------\nTypeError(\"'<=' not supported between instances of 'str' and 'int'\")\n\nTraceback:\n---------\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\utils.py\", line 172, in raise_on_meta_error\n yield\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py\", line 4941, in _emulate\n return func(*_extract_meta(args, True), **_extract_meta(kwargs, True))\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\utils.py\", line 880, in __call__\n return getattr(obj, self.method)(*args, **kwargs)\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\frame.py\", line 3231, in query\n res = self.eval(expr, **kwargs)\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\frame.py\", line 3346, in eval\n return _eval(expr, inplace=inplace, **kwargs)\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\computation\\eval.py\", line 337, in eval\n ret = eng_inst.evaluate()\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\computation\\engines.py\", line 127, in evaluate\n return self.expr()\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\computation\\expr.py\", line 771, in __call__\n return self.terms(self.env)\n File \"C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\computation\\ops.py\", line 396, in __call__\n return self.func(left, right)\n" ] } ], "source": [ "df.query(\"'mass (g)'<=10000\")" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'>=' not supported between instances of 'str' and 'int'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0m_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_window\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m75\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m113\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_buffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"mass (g)\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m>=\u001b[0m\u001b[1;36m9000\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_buffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"mass (g)\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m<=\u001b[0m\u001b[1;36m10000\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"*\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\ops\\common.py\u001b[0m in \u001b[0;36mnew_method\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[0mother\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\ops\\__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m 527\u001b[0m \u001b[0mrvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 528\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 529\u001b[1;33m \u001b[0mres_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcomparison_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 530\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 531\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_construct_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mres_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\ops\\array_ops.py\u001b[0m in \u001b[0;36mcomparison_op\u001b[1;34m(left, right, op)\u001b[0m\n\u001b[0;32m 245\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m \u001b[0mres_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcomp_method_OBJECT_ARRAY\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\ops\\array_ops.py\u001b[0m in \u001b[0;36mcomp_method_OBJECT_ARRAY\u001b[1;34m(op, x, y)\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlibops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvec_compare\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 56\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 57\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlibops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscalar_compare\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 58\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\ops.pyx\u001b[0m in \u001b[0;36mpandas._libs.ops.scalar_compare\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: '>=' not supported between instances of 'str' and 'int'" ] } ], "source": [ "_output = df.ext.buffer_window(\"*\", 75, 113).rows.find( (df.ext.get_buffer()[\"mass (g)\"]>=9000) & (df.ext.get_buffer()[\"mass (g)\"]<=10000) ).ext.to_json(\"*\")" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[3, 4], [9, 10], [10, 11], [11, 12], [18, 19], [23, 24]]\n", "[[11, 12], [16, 17], [24, 25]]\n", "[[11, 12], [22, 23], [23, 24], [24, 25], [25, 26], [26, 27], [27, 28]]\n", "[[11, 12], [22, 23], [23, 24], [24, 25], [25, 26], [26, 27]]\n", "[[13, 14], [25, 26], [26, 27], [27, 28], [28, 29]]\n", "[[7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [20, 21], [21, 22], [22, 23], [23, 24], [24, 25]]\n", "[[11, 12], [24, 25]]\n", "[[11, 12], [15, 16], [23, 24]]\n", "[[9, 10], [10, 11], [11, 12], [12, 13], [13, 14], [28, 29]]\n", "[[5, 6], [13, 14], [25, 26], [26, 27], [27, 28], [28, 29]]\n", "[[9, 10], [11, 12], [22, 23], [25, 26]]\n", "[[6, 7], [11, 12], [23, 24]]\n", "[[3, 4], [11, 12], [23, 24]]\n", "[[11, 12], [19, 20], [21, 22], [22, 23], [23, 24], [24, 25]]\n", "[[11, 12], [21, 22], [22, 23], [23, 24], [24, 25]]\n", "[[10, 11], [22, 23]]\n", "[[11, 12], [24, 25]]\n", "[[11, 12], [24, 25]]\n", "[[8, 9], [11, 12], [21, 22], [22, 23], [23, 24], [24, 25]]\n", "[[11, 12], [18, 19], [23, 24]]\n", "[[8, 9], [9, 10], [10, 11], [11, 12], [19, 20], [24, 25]]\n", "[[5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [23, 24]]\n", "[[11, 12], [18, 19], [23, 24]]\n", "[[11, 12], [25, 26]]\n", "[[11, 12], [24, 25]]\n", "[[11, 12], [20, 21], [21, 22], [22, 23], [23, 24]]\n", "[[3, 4], [8, 9], [9, 10], [10, 11], [11, 12], [24, 25]]\n", "[[11, 12], [16, 17], [21, 22], [22, 23], [23, 24], [24, 25]]\n", "[[11, 12], [26, 27]]\n", "[[11, 12], [18, 19], [27, 28]]\n", "[[3, 4], [11, 12], [24, 25]]\n", "[[11, 12], [24, 25]]\n", "[[11, 12], [21, 22], [22, 23], [23, 24], [24, 25]]\n", "[[11, 12], [24, 25]]\n" ] }, { "data": { "text/html": [ "
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nameidnametyperecclassmass (g)fallyearreclatreclongGeoLocationnew GeoLocationGeoLocation__match_positions__
0Aachen1ValidL521Fell01/01/1880 12:00:00 AM50.7750006.083330(50.775000, 6.083330)(50.775000, .083330)[[3, 4], [9, 10], [10, 11], [11, 12], [18, 19]...
1Aarhus2ValidH6720Fell01/01/1951 12:00:00 AM56.18333010.233330(56.183330, 10.233330)(5.183330, 10.233330)[[11, 12], [16, 17], [24, 25]]
2Abee6ValidEH4107000Fell01/01/1952 12:00:00 AM54.216670-113.000000(54.216670, -113.000000)(54.2170, -113.000000)[[11, 12], [22, 23], [23, 24], [24, 25], [25, ...
3Acapulco10ValidAcapulcoite1914Fell01/01/1976 12:00:00 AM16.883330-99.900000(16.883330, -99.900000)(1.883330, -99.900000)[[11, 12], [22, 23], [23, 24], [24, 25], [25, ...
4Achiras370ValidL6780Fell01/01/1902 12:00:00 AM-33.166670-64.950000(-33.166670, -64.950000)(-33.170, -4.950000)[[13, 14], [25, 26], [26, 27], [27, 28], [28, ...
5Adhi Kot379ValidEH44239Fell01/01/1919 12:00:00 AM32.10000071.800000(32.100000, 71.800000)(32.100000, 71.800000)[[7, 8], [8, 9], [9, 10], [10, 11], [11, 12], ...
6Adzhi-Bogdo (stone)390ValidLL3-6910Fell01/01/1949 12:00:00 AM44.83333095.166670(44.833330, 95.166670)(44.833330, 95.170)[[11, 12], [24, 25]]
7Agen392ValidH530000Fell01/01/1814 12:00:00 AM44.2166700.616670(44.216670, 0.616670)(44.2170, 0.170)[[11, 12], [15, 16], [23, 24]]
8Aguada398ValidL61620Fell01/01/1930 12:00:00 AM-31.600000-65.233330(-31.600000, -65.233330)(-31.00000, -5.233330)[[9, 10], [10, 11], [11, 12], [12, 13], [13, 1...
9Aguila Blanca417ValidL1440Fell01/01/1920 12:00:00 AM-30.866670-64.550000(-30.866670, -64.550000)(-30.870, -4.550000)[[5, 6], [13, 14], [25, 26], [26, 27], [27, 28...
10Aioun el Atrouss423ValidDiogenite-pm1000Fell01/01/1974 12:00:00 AM16.398060-9.570280(16.398060, -9.570280)(1.39800, -9.570280)[[9, 10], [11, 12], [22, 23], [25, 26]]
11Aïr424ValidL624000Fell01/01/1925 12:00:00 AM19.0833308.383330(19.083330, 8.383330)(19.083330, 8.383330)[[6, 7], [11, 12], [23, 24]]
12Aire-sur-la-Lys425ValidUnknownNaNFell01/01/1769 12:00:00 AM50.6666702.333330(50.666670, 2.333330)(50.70, 2.333330)[[3, 4], [11, 12], [23, 24]]
13Akaba426ValidL6779Fell01/01/1949 12:00:00 AM29.51667035.050000(29.516670, 35.050000)(29.5170, 35.050000)[[11, 12], [19, 20], [21, 22], [22, 23], [23, ...
14Akbarpur427ValidH41800Fell01/01/1838 12:00:00 AM29.71667077.950000(29.716670, 77.950000)(29.7170, 77.950000)[[11, 12], [21, 22], [22, 23], [23, 24], [24, ...
15Akwanga432ValidH3000Fell01/01/1959 12:00:00 AM8.9166708.433330(8.916670, 8.433330)(8.9170, 8.433330)[[10, 11], [22, 23]]
16Akyumak433ValidIron, IVA50000Fell01/01/1981 12:00:00 AM39.91667042.816670(39.916670, 42.816670)(39.9170, 42.8170)[[11, 12], [24, 25]]
17Al Rais446ValidCR2-an160Fell01/01/1957 12:00:00 AM24.41667039.516670(24.416670, 39.516670)(24.4170, 39.5170)[[11, 12], [24, 25]]
18Al Zarnkh447ValidLL5700Fell01/01/2001 12:00:00 AM13.66033028.960000(13.660330, 28.960000)(13.0330, 28.90000)[[8, 9], [11, 12], [21, 22], [22, 23], [23, 24...
19Alais448ValidCI16000Fell01/01/1806 12:00:00 AM44.1166704.083330(44.116670, 4.083330)(44.1170, 4.083330)[[11, 12], [18, 19], [23, 24]]
20Albareto453ValidL/LL42000Fell01/01/1766 12:00:00 AM44.65000011.016670(44.650000, 11.016670)(44.50000, 11.0170)[[8, 9], [9, 10], [10, 11], [11, 12], [19, 20]...
21Alberta454ValidL625Fell01/01/1949 12:00:00 AM2.00000022.666670(2.000000, 22.666670)(2.000000, 22.70)[[5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10,...
22Alby sur Chéran458ValidEucrite-mmict252Fell01/01/2002 12:00:00 AM45.8213306.015330(45.821330, 6.015330)(45.821330, .015330)[[11, 12], [18, 19], [23, 24]]
23Aldsworth461ValidLL5700Fell01/01/1835 12:00:00 AM51.783330-1.783330(51.783330, -1.783330)(51.783330, -1.783330)[[11, 12], [25, 26]]
24Aleppo462ValidL63200Fell01/01/1873 12:00:00 AM36.23333037.133330(36.233330, 37.133330)(3.233330, 37.133330)[[11, 12], [24, 25]]
25Alessandria463ValidH5908Fell01/01/1860 12:00:00 AM44.8833308.750000(44.883330, 8.750000)(44.883330, 8.750000)[[11, 12], [20, 21], [21, 22], [22, 23], [23, ...
26Alexandrovsky465ValidH49251Fell01/01/1900 12:00:00 AM50.95000031.816670(50.950000, 31.816670)(50.950000, 31.8170)[[3, 4], [8, 9], [9, 10], [10, 11], [11, 12], ...
27Alfianello466ValidL6228000Fell01/01/1883 12:00:00 AM45.26667010.150000(45.266670, 10.150000)(45.270, 10.150000)[[11, 12], [16, 17], [21, 22], [22, 23], [23, ...
28Allegan2276ValidH532000Fell01/01/1899 12:00:00 AM42.533330-85.883330(42.533330, -85.883330)(42.533330, -85.883330)[[11, 12], [26, 27]]
29Allende2278ValidCV32000000Fell01/01/1969 12:00:00 AM26.966670-105.316670(26.966670, -105.316670)(2.970, -105.3170)[[11, 12], [18, 19], [27, 28]]
30Almahata Sitta48915ValidUreilite-an3950Fell01/01/2008 12:00:00 AM20.74575032.412750(20.745750, 32.412750)(20.745750, 32.412750)[[3, 4], [11, 12], [24, 25]]
31Alta'ameem2284ValidLL56000Fell01/01/1977 12:00:00 AM35.27333044.215560(35.273330, 44.215560)(35.273330, 44.21550)[[11, 12], [24, 25]]
32Ambapur Nagla2290ValidH56400Fell01/01/1895 12:00:00 AM27.66667078.250000(27.666670, 78.250000)(27.70, 78.250000)[[11, 12], [21, 22], [22, 23], [23, 24], [24, ...
33Andhara2294ValidStone-uncl2700Fell01/01/1880 12:00:00 AM26.58333085.566670(26.583330, 85.566670)(2.583330, 85.570)[[11, 12], [24, 25]]
\n", "
" ], "text/plain": [ " name id nametype recclass mass (g) fall \\\n", "0 Aachen 1 Valid L5 21 Fell \n", "1 Aarhus 2 Valid H6 720 Fell \n", "2 Abee 6 Valid EH4 107000 Fell \n", "3 Acapulco 10 Valid Acapulcoite 1914 Fell \n", "4 Achiras 370 Valid L6 780 Fell \n", "5 Adhi Kot 379 Valid EH4 4239 Fell \n", "6 Adzhi-Bogdo (stone) 390 Valid LL3-6 910 Fell \n", "7 Agen 392 Valid H5 30000 Fell \n", "8 Aguada 398 Valid L6 1620 Fell \n", "9 Aguila Blanca 417 Valid L 1440 Fell \n", "10 Aioun el Atrouss 423 Valid Diogenite-pm 1000 Fell \n", "11 Aïr 424 Valid L6 24000 Fell \n", "12 Aire-sur-la-Lys 425 Valid Unknown NaN Fell \n", "13 Akaba 426 Valid L6 779 Fell \n", "14 Akbarpur 427 Valid H4 1800 Fell \n", "15 Akwanga 432 Valid H 3000 Fell \n", "16 Akyumak 433 Valid Iron, IVA 50000 Fell \n", "17 Al Rais 446 Valid CR2-an 160 Fell \n", "18 Al Zarnkh 447 Valid LL5 700 Fell \n", "19 Alais 448 Valid CI1 6000 Fell \n", "20 Albareto 453 Valid L/LL4 2000 Fell \n", "21 Alberta 454 Valid L 625 Fell \n", "22 Alby sur Chéran 458 Valid Eucrite-mmict 252 Fell \n", "23 Aldsworth 461 Valid LL5 700 Fell \n", "24 Aleppo 462 Valid L6 3200 Fell \n", "25 Alessandria 463 Valid H5 908 Fell \n", "26 Alexandrovsky 465 Valid H4 9251 Fell \n", "27 Alfianello 466 Valid L6 228000 Fell \n", "28 Allegan 2276 Valid H5 32000 Fell \n", "29 Allende 2278 Valid CV3 2000000 Fell \n", "30 Almahata Sitta 48915 Valid Ureilite-an 3950 Fell \n", "31 Alta'ameem 2284 Valid LL5 6000 Fell \n", "32 Ambapur Nagla 2290 Valid H5 6400 Fell \n", "33 Andhara 2294 Valid Stone-uncl 2700 Fell \n", "\n", " year reclat reclong GeoLocation \\\n", "0 01/01/1880 12:00:00 AM 50.775000 6.083330 (50.775000, 6.083330) \n", "1 01/01/1951 12:00:00 AM 56.183330 10.233330 (56.183330, 10.233330) \n", "2 01/01/1952 12:00:00 AM 54.216670 -113.000000 (54.216670, -113.000000) \n", "3 01/01/1976 12:00:00 AM 16.883330 -99.900000 (16.883330, -99.900000) \n", "4 01/01/1902 12:00:00 AM -33.166670 -64.950000 (-33.166670, -64.950000) \n", "5 01/01/1919 12:00:00 AM 32.100000 71.800000 (32.100000, 71.800000) \n", "6 01/01/1949 12:00:00 AM 44.833330 95.166670 (44.833330, 95.166670) \n", "7 01/01/1814 12:00:00 AM 44.216670 0.616670 (44.216670, 0.616670) \n", "8 01/01/1930 12:00:00 AM -31.600000 -65.233330 (-31.600000, -65.233330) \n", "9 01/01/1920 12:00:00 AM -30.866670 -64.550000 (-30.866670, -64.550000) \n", "10 01/01/1974 12:00:00 AM 16.398060 -9.570280 (16.398060, -9.570280) \n", "11 01/01/1925 12:00:00 AM 19.083330 8.383330 (19.083330, 8.383330) \n", "12 01/01/1769 12:00:00 AM 50.666670 2.333330 (50.666670, 2.333330) \n", "13 01/01/1949 12:00:00 AM 29.516670 35.050000 (29.516670, 35.050000) \n", "14 01/01/1838 12:00:00 AM 29.716670 77.950000 (29.716670, 77.950000) \n", "15 01/01/1959 12:00:00 AM 8.916670 8.433330 (8.916670, 8.433330) \n", "16 01/01/1981 12:00:00 AM 39.916670 42.816670 (39.916670, 42.816670) \n", "17 01/01/1957 12:00:00 AM 24.416670 39.516670 (24.416670, 39.516670) \n", "18 01/01/2001 12:00:00 AM 13.660330 28.960000 (13.660330, 28.960000) \n", "19 01/01/1806 12:00:00 AM 44.116670 4.083330 (44.116670, 4.083330) \n", "20 01/01/1766 12:00:00 AM 44.650000 11.016670 (44.650000, 11.016670) \n", "21 01/01/1949 12:00:00 AM 2.000000 22.666670 (2.000000, 22.666670) \n", "22 01/01/2002 12:00:00 AM 45.821330 6.015330 (45.821330, 6.015330) \n", "23 01/01/1835 12:00:00 AM 51.783330 -1.783330 (51.783330, -1.783330) \n", "24 01/01/1873 12:00:00 AM 36.233330 37.133330 (36.233330, 37.133330) \n", "25 01/01/1860 12:00:00 AM 44.883330 8.750000 (44.883330, 8.750000) \n", "26 01/01/1900 12:00:00 AM 50.950000 31.816670 (50.950000, 31.816670) \n", "27 01/01/1883 12:00:00 AM 45.266670 10.150000 (45.266670, 10.150000) \n", "28 01/01/1899 12:00:00 AM 42.533330 -85.883330 (42.533330, -85.883330) \n", "29 01/01/1969 12:00:00 AM 26.966670 -105.316670 (26.966670, -105.316670) \n", "30 01/01/2008 12:00:00 AM 20.745750 32.412750 (20.745750, 32.412750) \n", "31 01/01/1977 12:00:00 AM 35.273330 44.215560 (35.273330, 44.215560) \n", "32 01/01/1895 12:00:00 AM 27.666670 78.250000 (27.666670, 78.250000) \n", "33 01/01/1880 12:00:00 AM 26.583330 85.566670 (26.583330, 85.566670) \n", "\n", " new GeoLocation GeoLocation__match_positions__ \n", "0 (50.775000, .083330) [[3, 4], [9, 10], [10, 11], [11, 12], [18, 19]... \n", "1 (5.183330, 10.233330) [[11, 12], [16, 17], [24, 25]] \n", "2 (54.2170, -113.000000) [[11, 12], [22, 23], [23, 24], [24, 25], [25, ... \n", "3 (1.883330, -99.900000) [[11, 12], [22, 23], [23, 24], [24, 25], [25, ... \n", "4 (-33.170, -4.950000) [[13, 14], [25, 26], [26, 27], [27, 28], [28, ... \n", "5 (32.100000, 71.800000) [[7, 8], [8, 9], [9, 10], [10, 11], [11, 12], ... \n", "6 (44.833330, 95.170) [[11, 12], [24, 25]] \n", "7 (44.2170, 0.170) [[11, 12], [15, 16], [23, 24]] \n", "8 (-31.00000, -5.233330) [[9, 10], [10, 11], [11, 12], [12, 13], [13, 1... \n", "9 (-30.870, -4.550000) [[5, 6], [13, 14], [25, 26], [26, 27], [27, 28... \n", "10 (1.39800, -9.570280) [[9, 10], [11, 12], [22, 23], [25, 26]] \n", "11 (19.083330, 8.383330) [[6, 7], [11, 12], [23, 24]] \n", "12 (50.70, 2.333330) [[3, 4], [11, 12], [23, 24]] \n", "13 (29.5170, 35.050000) [[11, 12], [19, 20], [21, 22], [22, 23], [23, ... \n", "14 (29.7170, 77.950000) [[11, 12], [21, 22], [22, 23], [23, 24], [24, ... \n", "15 (8.9170, 8.433330) [[10, 11], [22, 23]] \n", "16 (39.9170, 42.8170) [[11, 12], [24, 25]] \n", "17 (24.4170, 39.5170) [[11, 12], [24, 25]] \n", "18 (13.0330, 28.90000) [[8, 9], [11, 12], [21, 22], [22, 23], [23, 24... \n", "19 (44.1170, 4.083330) [[11, 12], [18, 19], [23, 24]] \n", "20 (44.50000, 11.0170) [[8, 9], [9, 10], [10, 11], [11, 12], [19, 20]... \n", "21 (2.000000, 22.70) [[5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10,... \n", "22 (45.821330, .015330) [[11, 12], [18, 19], [23, 24]] \n", "23 (51.783330, -1.783330) [[11, 12], [25, 26]] \n", "24 (3.233330, 37.133330) [[11, 12], [24, 25]] \n", "25 (44.883330, 8.750000) [[11, 12], [20, 21], [21, 22], [22, 23], [23, ... \n", "26 (50.950000, 31.8170) [[3, 4], [8, 9], [9, 10], [10, 11], [11, 12], ... \n", "27 (45.270, 10.150000) [[11, 12], [16, 17], [21, 22], [22, 23], [23, ... \n", "28 (42.533330, -85.883330) [[11, 12], [26, 27]] \n", "29 (2.970, -105.3170) [[11, 12], [18, 19], [27, 28]] \n", "30 (20.745750, 32.412750) [[3, 4], [11, 12], [24, 25]] \n", "31 (35.273330, 44.21550) [[11, 12], [24, 25]] \n", "32 (27.70, 78.250000) [[11, 12], [21, 22], [22, 23], [23, 24], [24, ... \n", "33 (2.583330, 85.570) [[11, 12], [24, 25]] " ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.buffer_window(\"*\", 0, 34).cols.replace(\"GeoLocation\", search=[\"6\"], replace_by=\"\", search_by=\"chars\", ignore_case=True, output_cols=\"new GeoLocation\").cols.find(\"GeoLocation\", sub=[\"0\"], ignore_case=True)" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameidnametyperecclassmass (g)fallyearreclatreclongGeoLocationnew GeoLocation
0Aachen1ValidL521Fell01/01/1880 12:00:00 AM50.7750006.083330(50.775000, 6.083330)(50.775000, .083330)
1Aarhus2ValidH6720Fell01/01/1951 12:00:00 AM56.18333010.233330(56.183330, 10.233330)(5.183330, 10.233330)
2Abee6ValidEH4107000Fell01/01/1952 12:00:00 AM54.216670-113.000000(54.216670, -113.000000)(54.2170, -113.000000)
3Acapulco10ValidAcapulcoite1914Fell01/01/1976 12:00:00 AM16.883330-99.900000(16.883330, -99.900000)(1.883330, -99.900000)
4Achiras370ValidL6780Fell01/01/1902 12:00:00 AM-33.166670-64.950000(-33.166670, -64.950000)(-33.170, -4.950000)
....................................
95Benguerir30443ValidLL625000Fell01/01/2004 12:00:00 AM32.250000-8.150000(32.250000, -8.150000)(32.250000, -8.150000)
96Beni M'hira5018ValidL619000Fell01/01/2001 12:00:00 AM32.86667010.800000(32.866670, 10.800000)(32.870, 10.800000)
97Benld5021ValidH61770.5Fell01/01/1938 12:00:00 AM39.083330-89.150000(39.083330, -89.150000)(39.083330, -89.150000)
98Benoni5023ValidH63880Fell01/01/1943 12:00:00 AM-26.16667028.416670(-26.166670, 28.416670)(-2.170, 28.4170)
99Bensour5024ValidLL645000Fell01/01/2002 12:00:00 AM30.000000-7.000000(30.000000, -7.000000)(30.000000, -7.000000)
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100 rows × 11 columns

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" ], "text/plain": [ " name id nametype recclass mass (g) fall \\\n", "0 Aachen 1 Valid L5 21 Fell \n", "1 Aarhus 2 Valid H6 720 Fell \n", "2 Abee 6 Valid EH4 107000 Fell \n", "3 Acapulco 10 Valid Acapulcoite 1914 Fell \n", "4 Achiras 370 Valid L6 780 Fell \n", ".. ... ... ... ... ... ... \n", "95 Benguerir 30443 Valid LL6 25000 Fell \n", "96 Beni M'hira 5018 Valid L6 19000 Fell \n", "97 Benld 5021 Valid H6 1770.5 Fell \n", "98 Benoni 5023 Valid H6 3880 Fell \n", "99 Bensour 5024 Valid LL6 45000 Fell \n", "\n", " year reclat reclong GeoLocation \\\n", "0 01/01/1880 12:00:00 AM 50.775000 6.083330 (50.775000, 6.083330) \n", "1 01/01/1951 12:00:00 AM 56.183330 10.233330 (56.183330, 10.233330) \n", "2 01/01/1952 12:00:00 AM 54.216670 -113.000000 (54.216670, -113.000000) \n", "3 01/01/1976 12:00:00 AM 16.883330 -99.900000 (16.883330, -99.900000) \n", "4 01/01/1902 12:00:00 AM -33.166670 -64.950000 (-33.166670, -64.950000) \n", ".. ... ... ... ... \n", "95 01/01/2004 12:00:00 AM 32.250000 -8.150000 (32.250000, -8.150000) \n", "96 01/01/2001 12:00:00 AM 32.866670 10.800000 (32.866670, 10.800000) \n", "97 01/01/1938 12:00:00 AM 39.083330 -89.150000 (39.083330, -89.150000) \n", "98 01/01/1943 12:00:00 AM -26.166670 28.416670 (-26.166670, 28.416670) \n", "99 01/01/2002 12:00:00 AM 30.000000 -7.000000 (30.000000, -7.000000) \n", "\n", " new GeoLocation \n", "0 (50.775000, .083330) \n", "1 (5.183330, 10.233330) \n", "2 (54.2170, -113.000000) \n", "3 (1.883330, -99.900000) \n", "4 (-33.170, -4.950000) \n", ".. ... \n", "95 (32.250000, -8.150000) \n", "96 (32.870, 10.800000) \n", "97 (39.083330, -89.150000) \n", "98 (-2.170, 28.4170) \n", "99 (30.000000, -7.000000) \n", "\n", "[100 rows x 11 columns]" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.buffer_window(\"*\").cols.replace(\"GeoLocation\", search=[\"6\"], replace_by=\"\", search_by=\"chars\", ignore_case=True, output_cols=\"new GeoLocation\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ASD ['/']\n" ] }, { "data": { "text/plain": [ "{'sample': {'columns': [{'title': 'id'},\n", " {'title': 'firstName'},\n", " {'title': 'lastName'},\n", " {'title': 'billingId'},\n", " {'title': 'product'},\n", " {'title': 'price'},\n", " {'title': 'birth'},\n", " {'title': 'dummyCol'},\n", " {'title': 'birth_0'},\n", " {'title': 'birth_1'},\n", " {'title': 'birth__match_positions__'}],\n", " 'value': [['1',\n", " 'Luis',\n", " 'Alvarez$$%!',\n", " '123',\n", " 'Cake',\n", " '10',\n", " '1980/07/07',\n", " 'never',\n", " '1980',\n", " '07',\n", " None],\n", " ['2',\n", " 'André',\n", " 'Ampère',\n", " '423',\n", " 'piza',\n", " '8',\n", " '1950/07/08',\n", " 'gonna',\n", " '1950',\n", " '07',\n", " None],\n", " ['3',\n", " 'NiELS',\n", " 'Böhr//((%%',\n", " '551',\n", " 'pizza',\n", " '8',\n", " '1990/07/09',\n", " 'give',\n", " '1990',\n", " '07',\n", " None],\n", " ['4',\n", " 'PAUL',\n", " 'dirac$',\n", " '521',\n", " 'pizza',\n", " '8',\n", " '1954/07/10',\n", " 'you',\n", " '1954',\n", " '07',\n", " None],\n", " ['5',\n", " 'Albert',\n", " 'Einstein',\n", " '634',\n", " 'pizza',\n", " '8',\n", " '1990/07/11',\n", " 'up',\n", " '1990',\n", " '07',\n", " None],\n", " ['6',\n", " 'Galileo',\n", " ' GALiLEI',\n", " '672',\n", " 'arepa',\n", " '5',\n", " '1930/08/12',\n", " 'never',\n", " '1930',\n", " '08',\n", " None],\n", " ['7',\n", " 'CaRL',\n", " 'Ga%%%uss',\n", " '323',\n", " 'taco',\n", " '3',\n", " '1970/07/13',\n", " 'gonna',\n", " '1970',\n", " '07',\n", " None],\n", " ['8',\n", " 'David',\n", " 'H$$$ilbert',\n", " '624',\n", " 'taaaccoo',\n", " '3',\n", " '1950/07/14',\n", " 'let',\n", " '1950',\n", " '07',\n", " None],\n", " ['9',\n", " 'Johannes',\n", " 'KEPLER',\n", " '735',\n", " 'taco',\n", " '3',\n", " '1920/04/22',\n", " 'you',\n", " '1920',\n", " '04',\n", " None],\n", " ['10',\n", " 'JaMES',\n", " 'M$$ax%%well',\n", " '875',\n", " 'taco',\n", " '3',\n", " '1923/03/12',\n", " 'down',\n", " '1923',\n", " '03',\n", " None],\n", " ['11',\n", " 'Isaac',\n", " 'Newton',\n", " '992',\n", " 'pasta',\n", " '9',\n", " '1999/02/15',\n", " 'never ',\n", " '1999',\n", " '02',\n", " None],\n", " ['12',\n", " 'Emmy%%',\n", " 'Nöether$',\n", " '234',\n", " 'pasta',\n", " '9',\n", " '1993/12/08',\n", " 'gonna',\n", " '1993',\n", " '12',\n", " None],\n", " ['13',\n", " 'Max!!!',\n", " 'Planck!!!',\n", " '111',\n", " 'hamburguer',\n", " '4',\n", " '1994/01/04',\n", " 'run ',\n", " '1994',\n", " '01',\n", " None],\n", " ['14',\n", " 'Fred',\n", " 'Hoy&&&le',\n", " '553',\n", " 'pizzza',\n", " '8',\n", " '1997/06/27',\n", " 'around',\n", " '1997',\n", " '06',\n", " None],\n", " ['15',\n", " '((( Heinrich )))))',\n", " 'Hertz',\n", " '116',\n", " 'pizza',\n", " '8',\n", " '1956/11/30',\n", " 'and',\n", " '1956',\n", " '11',\n", " None],\n", " ['16',\n", " 'William',\n", " 'Gilbert###',\n", " '886',\n", " 'BEER',\n", " '2',\n", " '1958/03/26',\n", " 'desert',\n", " '1958',\n", " '03',\n", " None],\n", " ['17',\n", " 'Marie',\n", " 'CURIE',\n", " '912',\n", " 'Rice',\n", " '1',\n", " '2000/03/22',\n", " 'you',\n", " '2000',\n", " '03',\n", " None],\n", " ['18',\n", " 'Arthur',\n", " 'COM%%%pton',\n", " '812',\n", " '110790',\n", " '5',\n", " '1899/01/01',\n", " '#',\n", " '1899',\n", " '01',\n", " None],\n", " ['19',\n", " 'JAMES',\n", " 'Chadwick',\n", " '467',\n", " nan,\n", " '10',\n", " '1921/05/03',\n", " '#',\n", " '1921',\n", " '05',\n", " None]]}}" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.buffer_window(\"*\", 0, 19).cols.unnest(\"birth\", separator=\"/\", splits=2, output_cols=\"birth\").cols.find(\"birth\", sub=[\"/\"]).ext.to_json(\"*\")\n", "# df.ext.buffer_window(\"*\", 0, 19).cols.unnest(\"birth\", separator=\"/\", splits=2, output_cols=\"birth\").cols.find(\"birth\", sub=[\"/\"]).ext.to_json(\"*\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'sample': {'columns': [{'title': 'id'},\n", " {'title': 'firstName'},\n", " {'title': 'lastName'},\n", " {'title': 'billingId'},\n", " {'title': 'product'},\n", " {'title': 'price'},\n", " {'title': 'birth'},\n", " {'title': 'dummyCol'},\n", " {'title': 'birth_0'},\n", " {'title': 'birth_1'},\n", " {'title': 'birth__match_positions__'}],\n", " 'value': [['1',\n", " 'Luis',\n", " 'Alvarez$$%!',\n", " '123',\n", " 'Cake',\n", " '10',\n", " '1980/07/07',\n", " 'never',\n", " '1980',\n", " '07',\n", " [[4, 5], [7, 8]]],\n", " ['2',\n", " 'André',\n", " 'Ampère',\n", " '423',\n", " 'piza',\n", " '8',\n", " '1950/07/08',\n", " 'gonna',\n", " '1950',\n", " '07',\n", " [[4, 5], [7, 8]]],\n", " ['3',\n", " 'NiELS',\n", " 'Böhr//((%%',\n", " '551',\n", " 'pizza',\n", " '8',\n", " '1990/07/09',\n", " 'give',\n", " '1990',\n", " '07',\n", " [[4, 5], [7, 8]]],\n", " ['4',\n", " 'PAUL',\n", " 'dirac$',\n", " '521',\n", " 'pizza',\n", " '8',\n", " '1954/07/10',\n", " 'you',\n", " '1954',\n", " '07',\n", " [[4, 5], [7, 8]]],\n", " ['5',\n", " 'Albert',\n", " 'Einstein',\n", " '634',\n", " 'pizza',\n", " '8',\n", " '1990/07/11',\n", " 'up',\n", " '1990',\n", " '07',\n", " [[4, 5], [7, 8]]],\n", " ['6',\n", " 'Galileo',\n", " ' GALiLEI',\n", " '672',\n", " 'arepa',\n", " '5',\n", " '1930/08/12',\n", " 'never',\n", " '1930',\n", " '08',\n", " [[4, 5], [7, 8]]],\n", " ['7',\n", " 'CaRL',\n", " 'Ga%%%uss',\n", " '323',\n", " 'taco',\n", " '3',\n", " '1970/07/13',\n", " 'gonna',\n", " '1970',\n", " '07',\n", " [[4, 5], [7, 8]]],\n", " ['8',\n", " 'David',\n", " 'H$$$ilbert',\n", " '624',\n", " 'taaaccoo',\n", " '3',\n", " '1950/07/14',\n", " 'let',\n", " '1950',\n", " '07',\n", " [[4, 5], [7, 8]]],\n", " ['9',\n", " 'Johannes',\n", " 'KEPLER',\n", " '735',\n", " 'taco',\n", " '3',\n", " '1920/04/22',\n", " 'you',\n", " '1920',\n", " '04',\n", " [[4, 5], [7, 8]]],\n", " ['10',\n", " 'JaMES',\n", " 'M$$ax%%well',\n", " '875',\n", " 'taco',\n", " '3',\n", " '1923/03/12',\n", " 'down',\n", " '1923',\n", " '03',\n", " [[4, 5], [7, 8]]],\n", " ['11',\n", " 'Isaac',\n", " 'Newton',\n", " '992',\n", " 'pasta',\n", " '9',\n", " '1999/02/15',\n", " 'never ',\n", " '1999',\n", " '02',\n", " [[4, 5], [7, 8]]],\n", " ['12',\n", " 'Emmy%%',\n", " 'Nöether$',\n", " '234',\n", " 'pasta',\n", " '9',\n", " '1993/12/08',\n", " 'gonna',\n", " '1993',\n", " '12',\n", " [[4, 5], [7, 8]]],\n", " ['13',\n", " 'Max!!!',\n", " 'Planck!!!',\n", " '111',\n", " 'hamburguer',\n", " '4',\n", " '1994/01/04',\n", " 'run ',\n", " '1994',\n", " '01',\n", " [[4, 5], [7, 8]]],\n", " ['14',\n", " 'Fred',\n", " 'Hoy&&&le',\n", " '553',\n", " 'pizzza',\n", " '8',\n", " '1997/06/27',\n", " 'around',\n", " '1997',\n", " '06',\n", " [[4, 5], [7, 8]]],\n", " ['15',\n", " '((( Heinrich )))))',\n", " 'Hertz',\n", " '116',\n", " 'pizza',\n", " '8',\n", " '1956/11/30',\n", " 'and',\n", " '1956',\n", " '11',\n", " [[4, 5], [7, 8]]],\n", " ['16',\n", " 'William',\n", " 'Gilbert###',\n", " '886',\n", " 'BEER',\n", " '2',\n", " '1958/03/26',\n", " 'desert',\n", " '1958',\n", " '03',\n", " [[4, 5], [7, 8]]],\n", " ['17',\n", " 'Marie',\n", " 'CURIE',\n", " '912',\n", " 'Rice',\n", " '1',\n", " '2000/03/22',\n", " 'you',\n", " '2000',\n", " '03',\n", " [[4, 5], [7, 8]]],\n", " ['18',\n", " 'Arthur',\n", " 'COM%%%pton',\n", " '812',\n", " '110790',\n", " '5',\n", " '1899/01/01',\n", " '#',\n", " '1899',\n", " '01',\n", " [[4, 5], [7, 8]]],\n", " ['19',\n", " 'JAMES',\n", " 'Chadwick',\n", " '467',\n", " nan,\n", " '10',\n", " '1921/05/03',\n", " '#',\n", " '1921',\n", " '05',\n", " [[4, 5], [7, 8]]]]}}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_output" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SDFSF ['Id'] ['new id']\n" ] } ], "source": [ "_output = df.ext.buffer_window(\"*\", 0, 34).cols.lower(\"Id\", output_cols=\"new id\").ext.to_json(\"*\")" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [], "source": [ "df1 = df.ext.optimize()" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 100 of 100 rows / 100 columns
\n", "
1 partition(s)
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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", "
Id
\n", "
1 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
PID
\n", "
2 (uint32)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
MS SubClass
\n", "
3 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
MS Zoning
\n", "
4 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Lot Frontage
\n", "
5 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Lot Area
\n", "
6 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Street
\n", "
7 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Alley
\n", "
8 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Lot Shape
\n", "
9 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Land Contour
\n", "
10 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Utilities
\n", "
11 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Lot Config
\n", "
12 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Land Slope
\n", "
13 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Neighborhood
\n", "
14 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Condition 1
\n", "
15 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Condition 2
\n", "
16 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Bldg Type
\n", "
17 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
House Style
\n", "
18 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Overall Qual
\n", "
19 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Overall Cond
\n", "
20 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Year Built
\n", "
21 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Year Remod/Add
\n", "
22 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Roof Style
\n", "
23 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Roof Matl
\n", "
24 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Exterior 1st
\n", "
25 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Exterior 2nd
\n", "
26 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Mas Vnr Type
\n", "
27 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Mas Vnr Area
\n", "
28 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Exter Qual
\n", "
29 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Exter Cond
\n", "
30 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Foundation
\n", "
31 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Bsmt Qual
\n", "
32 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Bsmt Cond
\n", "
33 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Bsmt Exposure
\n", "
34 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
BsmtFin Type 1
\n", "
35 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
BsmtFin SF 1
\n", "
36 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
BsmtFin Type 2
\n", "
37 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
BsmtFin SF 2
\n", "
38 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Bsmt Unf SF
\n", "
39 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Total Bsmt SF
\n", "
40 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Heating
\n", "
41 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Heating QC
\n", "
42 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Central Air
\n", "
43 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Electrical
\n", "
44 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
1st Flr SF
\n", "
45 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
2nd Flr SF
\n", "
46 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Low Qual Fin SF
\n", "
47 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Gr Liv Area
\n", "
48 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Bsmt Full Bath
\n", "
49 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Bsmt Half Bath
\n", "
50 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Full Bath
\n", "
51 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Half Bath
\n", "
52 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Bedroom AbvGr
\n", "
53 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Kitchen AbvGr
\n", "
54 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Kitchen Qual
\n", "
55 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
TotRms AbvGrd
\n", "
56 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Functional
\n", "
57 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Fireplaces
\n", "
58 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Fireplace Qu
\n", "
59 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Garage Type
\n", "
60 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Garage Yr Blt
\n", "
61 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Garage Finish
\n", "
62 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Garage Cars
\n", "
63 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Garage Area
\n", "
64 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Garage Qual
\n", "
65 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Garage Cond
\n", "
66 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Paved Drive
\n", "
67 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Wood Deck SF
\n", "
68 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Open Porch SF
\n", "
69 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Enclosed Porch
\n", "
70 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
3Ssn Porch
\n", "
71 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Screen Porch
\n", "
72 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Pool Area
\n", "
73 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Pool QC
\n", "
74 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Fence
\n", "
75 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Misc Feature
\n", "
76 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Misc Val
\n", "
77 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Mo Sold
\n", "
78 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Yr Sold
\n", "
79 (uint16)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
Sale Type
\n", "
80 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
SalePrice
\n", "
81 (uint32)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " 109\n", " \n", "
\n", "
\n", "
\n", " \n", " 533352170\n", " \n", "
\n", "
\n", "
\n", " \n", " 60\n", " \n", "
\n", "
\n", "
\n", " \n", " RL\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " 13517\n", " \n", "
\n", "
\n", "
\n", " \n", " Pave\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " IR1\n", " \n", "
\n", "
\n", "
\n", " \n", " Lvl\n", " \n", "
\n", "
\n", "
\n", " \n", " AllPub\n", " \n", "
\n", "
\n", "
\n", " \n", " CulDSac\n", " \n", "
\n", "
\n", "
\n", " \n", " Gtl\n", " \n", "
\n", "
\n", "
\n", " \n", " Sawyer\n", " \n", "
\n", "
\n", "
\n", " \n", " RRAe\n", " \n", "
\n", "
\n", "
\n", " \n", " Norm\n", " \n", "
\n", "
\n", "
\n", " \n", " 1Fam\n", " \n", "
\n", "
\n", "
\n", " \n", " 2Story\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " 8\n", " \n", "
\n", "
\n", "
\n", " \n", " 1976\n", " \n", "
\n", "
\n", "
\n", " \n", " 2005\n", " \n", "
\n", "
\n", "
\n", " \n", " Gable\n", " \n", "
\n", "
\n", "
\n", " \n", " CompShg\n", " \n", "
\n", "
\n", "
\n", " \n", " HdBoard\n", " \n", "
\n", "
\n", "
\n", " \n", " Plywood\n", " \n", "
\n", "
\n", "
\n", " \n", " BrkFace\n", " \n", "
\n", "
\n", "
\n", " \n", " 289\n", " \n", "
\n", "
\n", "
\n", " \n", " Gd\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " CBlock\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " No\n", " \n", "
\n", "
\n", "
\n", " \n", " GLQ\n", " \n", "
\n", "
\n", "
\n", " \n", " 533\n", " \n", "
\n", "
\n", "
\n", " \n", " Unf\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 192\n", " \n", "
\n", "
\n", "
\n", " \n", " 725\n", " \n", "
\n", "
\n", "
\n", " \n", " GasA\n", " \n", "
\n", "
\n", "
\n", " \n", " Ex\n", " \n", "
\n", "
\n", "
\n", " \n", " Y\n", " \n", "
\n", "
\n", "
\n", " \n", " SBrkr\n", " \n", "
\n", "
\n", "
\n", " \n", " 725\n", " \n", "
\n", "
\n", "
\n", " \n", " 754\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 1479\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Gd\n", " \n", "
\n", "
\n", "
\n", " \n", " 6\n", " \n", "
\n", "
\n", "
\n", " \n", " Typ\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " Attchd\n", " \n", "
\n", "
\n", "
\n", " \n", " 1976\n", " \n", "
\n", "
\n", "
\n", " \n", " RFn\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " 475\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " Y\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 44\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 2010\n", " \n", "
\n", "
\n", "
\n", " \n", " WD⋅\n", " \n", "
\n", "
\n", "
\n", " \n", " 130500\n", " \n", "
\n", "
\n", "
\n", " \n", " 544\n", " \n", "
\n", "
\n", "
\n", " \n", " 531379050\n", " \n", "
\n", "
\n", "
\n", " \n", " 60\n", " \n", "
\n", "
\n", "
\n", " \n", " RL\n", " \n", "
\n", "
\n", "
\n", " \n", " 43\n", " \n", "
\n", "
\n", "
\n", " \n", " 11492\n", " \n", "
\n", "
\n", "
\n", " \n", " Pave\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " IR1\n", " \n", "
\n", "
\n", "
\n", " \n", " Lvl\n", " \n", "
\n", "
\n", "
\n", " \n", " AllPub\n", " \n", "
\n", "
\n", "
\n", " \n", " CulDSac\n", " \n", "
\n", "
\n", "
\n", " \n", " Gtl\n", " \n", "
\n", "
\n", "
\n", " \n", " SawyerW\n", " \n", "
\n", "
\n", "
\n", " \n", " Norm\n", " \n", "
\n", "
\n", "
\n", " \n", " Norm\n", " \n", "
\n", "
\n", "
\n", " \n", " 1Fam\n", " \n", "
\n", "
\n", "
\n", " \n", " 2Story\n", " \n", "
\n", "
\n", "
\n", " \n", " 7\n", " \n", "
\n", "
\n", "
\n", " \n", " 5\n", " \n", "
\n", "
\n", "
\n", " \n", " 1996\n", " \n", "
\n", "
\n", "
\n", " \n", " 1997\n", " \n", "
\n", "
\n", "
\n", " \n", " Gable\n", " \n", "
\n", "
\n", "
\n", " \n", " CompShg\n", " \n", "
\n", "
\n", "
\n", " \n", " VinylSd\n", " \n", "
\n", "
\n", "
\n", " \n", " VinylSd\n", " \n", "
\n", "
\n", "
\n", " \n", " BrkFace\n", " \n", "
\n", "
\n", "
\n", " \n", " 132\n", " \n", "
\n", "
\n", "
\n", " \n", " Gd\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " PConc\n", " \n", "
\n", "
\n", "
\n", " \n", " Gd\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " No\n", " \n", "
\n", "
\n", "
\n", " \n", " GLQ\n", " \n", "
\n", "
\n", "
\n", " \n", " 637\n", " \n", "
\n", "
\n", "
\n", " \n", " Unf\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 20\n", " \n", "
\n", "
\n", "
\n", " \n", " 913\n", " \n", "
\n", "
\n", "
\n", " \n", " GasA\n", " \n", "
\n", "
\n", "
\n", " \n", " Ex\n", " \n", "
\n", "
\n", "
\n", " \n", " Y\n", " \n", "
\n", "
\n", "
\n", " \n", " SBrkr\n", " \n", "
\n", "
\n", "
\n", " \n", " 913\n", " \n", "
\n", "
\n", "
\n", " \n", " 1209\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 2122\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " 4\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " Gd\n", " \n", "
\n", "
\n", "
\n", " \n", " 8\n", " \n", "
\n", "
\n", "
\n", " \n", " Typ\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " Attchd\n", " \n", "
\n", "
\n", "
\n", " \n", " 1997\n", " \n", "
\n", "
\n", "
\n", " \n", " RFn\n", " \n", "
\n", "
\n", "
\n", " \n", " 2\n", " \n", "
\n", "
\n", "
\n", " \n", " 559\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " Y\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 74\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 4\n", " \n", "
\n", "
\n", "
\n", " \n", " 2009\n", " \n", "
\n", "
\n", "
\n", " \n", " WD⋅\n", " \n", "
\n", "
\n", "
\n", " \n", " 220000\n", " \n", "
\n", "
\n", "
\n", " \n", " 153\n", " \n", "
\n", "
\n", "
\n", " \n", " 535304180\n", " \n", "
\n", "
\n", "
\n", " \n", " 20\n", " \n", "
\n", "
\n", "
\n", " \n", " RL\n", " \n", "
\n", "
\n", "
\n", " \n", " 68\n", " \n", "
\n", "
\n", "
\n", " \n", " 7922\n", " \n", "
\n", "
\n", "
\n", " \n", " Pave\n", " \n", "
\n", "
\n", "
\n", " \n", " nan\n", " \n", "
\n", "
\n", "
\n", " \n", " Reg\n", " \n", "
\n", "
\n", "
\n", " \n", " Lvl\n", " \n", "
\n", "
\n", "
\n", " \n", " AllPub\n", " \n", "
\n", "
\n", "
\n", " \n", " Inside\n", " \n", "
\n", "
\n", "
\n", " \n", " Gtl\n", " \n", "
\n", "
\n", "
\n", " \n", " NAmes\n", " \n", "
\n", "
\n", "
\n", " \n", " Norm\n", " \n", "
\n", "
\n", "
\n", " \n", " Norm\n", " \n", "
\n", "
\n", "
\n", " \n", " 1Fam\n", " \n", "
\n", "
\n", "
\n", " \n", " 1Story\n", " \n", "
\n", "
\n", "
\n", " \n", " 5\n", " \n", "
\n", "
\n", "
\n", " \n", " 7\n", " \n", "
\n", "
\n", "
\n", " \n", " 1953\n", " \n", "
\n", "
\n", "
\n", " \n", " 2007\n", " \n", "
\n", "
\n", "
\n", " \n", " Gable\n", " \n", "
\n", "
\n", "
\n", " \n", " CompShg\n", " \n", "
\n", "
\n", "
\n", " \n", " VinylSd\n", " \n", "
\n", "
\n", "
\n", " \n", " VinylSd\n", " \n", "
\n", "
\n", "
\n", " \n", " None\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " Gd\n", " \n", "
\n", "
\n", "
\n", " \n", " CBlock\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " TA\n", " \n", "
\n", "
\n", "
\n", " \n", " No\n", " \n", "
\n", "
\n", "
\n", " \n", " GLQ\n", " \n", "
\n", "
\n", "
\n", " \n", " 731\n", " \n", "
\n", "
\n", "
\n", " \n", " Unf\n", " \n", "
\n", "
\n", "
\n", " \n", " 0\n", " \n", "
\n", "
\n", "
\n", " \n", " 70\n", " \n", "
\n", "
\n", "
\n", " \n", " 1057\n", " \n", "
\n", "
\n", "
\n", " \n", " GasA\n", " \n", "
\n", "
\n", "
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nameidnametyperecclassmass (g)fallyearreclatreclongGeoLocation__match__
0Aachen1ValidL521Fell01/01/1880 12:00:00 AM50.7750006.083330(50.775000, 6.083330)False
1Aarhus2ValidH6720Fell01/01/1951 12:00:00 AM56.18333010.233330(56.183330, 10.233330)False
2Abee6ValidEH4107000Fell01/01/1952 12:00:00 AM54.216670-113.000000(54.216670, -113.000000)False
3Acapulco10ValidAcapulcoite1914Fell01/01/1976 12:00:00 AM16.883330-99.900000(16.883330, -99.900000)False
4Achiras370ValidL6780Fell01/01/1902 12:00:00 AM-33.166670-64.950000(-33.166670, -64.950000)False
5Adhi Kot379ValidEH44239Fell01/01/1919 12:00:00 AM32.10000071.800000(32.100000, 71.800000)False
6Adzhi-Bogdo (stone)390ValidLL3-6910Fell01/01/1949 12:00:00 AM44.83333095.166670(44.833330, 95.166670)False
7Agen392ValidH530000Fell01/01/1814 12:00:00 AM44.2166700.616670(44.216670, 0.616670)False
8Aguada398ValidL61620Fell01/01/1930 12:00:00 AM-31.600000-65.233330(-31.600000, -65.233330)False
9Aguila Blanca417ValidL1440Fell01/01/1920 12:00:00 AM-30.866670-64.550000(-30.866670, -64.550000)False
10Aioun el Atrouss423ValidDiogenite-pm1000Fell01/01/1974 12:00:00 AM16.398060-9.570280(16.398060, -9.570280)False
11Aïr424ValidL624000Fell01/01/1925 12:00:00 AM19.0833308.383330(19.083330, 8.383330)False
12Aire-sur-la-Lys425ValidUnknownNaNFell01/01/1769 12:00:00 AM50.6666702.333330(50.666670, 2.333330)False
13Akaba426ValidL6779Fell01/01/1949 12:00:00 AM29.51667035.050000(29.516670, 35.050000)False
14Akbarpur427ValidH41800Fell01/01/1838 12:00:00 AM29.71667077.950000(29.716670, 77.950000)False
15Akwanga432ValidH3000Fell01/01/1959 12:00:00 AM8.9166708.433330(8.916670, 8.433330)False
16Akyumak433ValidIron, IVA50000Fell01/01/1981 12:00:00 AM39.91667042.816670(39.916670, 42.816670)False
17Al Rais446ValidCR2-an160Fell01/01/1957 12:00:00 AM24.41667039.516670(24.416670, 39.516670)False
18Al Zarnkh447ValidLL5700Fell01/01/2001 12:00:00 AM13.66033028.960000(13.660330, 28.960000)False
19Alais448ValidCI16000Fell01/01/1806 12:00:00 AM44.1166704.083330(44.116670, 4.083330)False
20Albareto453ValidL/LL42000Fell01/01/1766 12:00:00 AM44.65000011.016670(44.650000, 11.016670)False
21Alberta454ValidL625Fell01/01/1949 12:00:00 AM2.00000022.666670(2.000000, 22.666670)False
22Alby sur Chéran458ValidEucrite-mmict252Fell01/01/2002 12:00:00 AM45.8213306.015330(45.821330, 6.015330)False
23Aldsworth461ValidLL5700Fell01/01/1835 12:00:00 AM51.783330-1.783330(51.783330, -1.783330)False
24Aleppo462ValidL63200Fell01/01/1873 12:00:00 AM36.23333037.133330(36.233330, 37.133330)False
25Alessandria463ValidH5908Fell01/01/1860 12:00:00 AM44.8833308.750000(44.883330, 8.750000)False
26Alexandrovsky465ValidH49251Fell01/01/1900 12:00:00 AM50.95000031.816670(50.950000, 31.816670)False
27Alfianello466ValidL6228000Fell01/01/1883 12:00:00 AM45.26667010.150000(45.266670, 10.150000)False
28Allegan2276ValidH532000Fell01/01/1899 12:00:00 AM42.533330-85.883330(42.533330, -85.883330)False
29Allende2278ValidCV32000000Fell01/01/1969 12:00:00 AM26.966670-105.316670(26.966670, -105.316670)False
30Almahata Sitta48915ValidUreilite-an3950Fell01/01/2008 12:00:00 AM20.74575032.412750(20.745750, 32.412750)False
31Alta'ameem2284ValidLL56000Fell01/01/1977 12:00:00 AM35.27333044.215560(35.273330, 44.215560)False
32Ambapur Nagla2290ValidH56400Fell01/01/1895 12:00:00 AM27.66667078.250000(27.666670, 78.250000)False
33Andhara2294ValidStone-uncl2700Fell01/01/1880 12:00:00 AM26.58333085.566670(26.583330, 85.566670)False
\n", "
" ], "text/plain": [ " name id nametype recclass mass (g) fall \\\n", "0 Aachen 1 Valid L5 21 Fell \n", "1 Aarhus 2 Valid H6 720 Fell \n", "2 Abee 6 Valid EH4 107000 Fell \n", "3 Acapulco 10 Valid Acapulcoite 1914 Fell \n", "4 Achiras 370 Valid L6 780 Fell \n", "5 Adhi Kot 379 Valid EH4 4239 Fell \n", "6 Adzhi-Bogdo (stone) 390 Valid LL3-6 910 Fell \n", "7 Agen 392 Valid H5 30000 Fell \n", "8 Aguada 398 Valid L6 1620 Fell \n", "9 Aguila Blanca 417 Valid L 1440 Fell \n", "10 Aioun el Atrouss 423 Valid Diogenite-pm 1000 Fell \n", "11 Aïr 424 Valid L6 24000 Fell \n", "12 Aire-sur-la-Lys 425 Valid Unknown NaN Fell \n", "13 Akaba 426 Valid L6 779 Fell \n", "14 Akbarpur 427 Valid H4 1800 Fell \n", "15 Akwanga 432 Valid H 3000 Fell \n", "16 Akyumak 433 Valid Iron, IVA 50000 Fell \n", "17 Al Rais 446 Valid CR2-an 160 Fell \n", "18 Al Zarnkh 447 Valid LL5 700 Fell \n", "19 Alais 448 Valid CI1 6000 Fell \n", "20 Albareto 453 Valid L/LL4 2000 Fell \n", "21 Alberta 454 Valid L 625 Fell \n", "22 Alby sur Chéran 458 Valid Eucrite-mmict 252 Fell \n", "23 Aldsworth 461 Valid LL5 700 Fell \n", "24 Aleppo 462 Valid L6 3200 Fell \n", "25 Alessandria 463 Valid H5 908 Fell \n", "26 Alexandrovsky 465 Valid H4 9251 Fell \n", "27 Alfianello 466 Valid L6 228000 Fell \n", "28 Allegan 2276 Valid H5 32000 Fell \n", "29 Allende 2278 Valid CV3 2000000 Fell \n", "30 Almahata Sitta 48915 Valid Ureilite-an 3950 Fell \n", "31 Alta'ameem 2284 Valid LL5 6000 Fell \n", "32 Ambapur Nagla 2290 Valid H5 6400 Fell \n", "33 Andhara 2294 Valid Stone-uncl 2700 Fell \n", "\n", " year reclat reclong GeoLocation \\\n", "0 01/01/1880 12:00:00 AM 50.775000 6.083330 (50.775000, 6.083330) \n", "1 01/01/1951 12:00:00 AM 56.183330 10.233330 (56.183330, 10.233330) \n", "2 01/01/1952 12:00:00 AM 54.216670 -113.000000 (54.216670, -113.000000) \n", "3 01/01/1976 12:00:00 AM 16.883330 -99.900000 (16.883330, -99.900000) \n", "4 01/01/1902 12:00:00 AM -33.166670 -64.950000 (-33.166670, -64.950000) \n", "5 01/01/1919 12:00:00 AM 32.100000 71.800000 (32.100000, 71.800000) \n", "6 01/01/1949 12:00:00 AM 44.833330 95.166670 (44.833330, 95.166670) \n", "7 01/01/1814 12:00:00 AM 44.216670 0.616670 (44.216670, 0.616670) \n", "8 01/01/1930 12:00:00 AM -31.600000 -65.233330 (-31.600000, -65.233330) \n", "9 01/01/1920 12:00:00 AM -30.866670 -64.550000 (-30.866670, -64.550000) \n", "10 01/01/1974 12:00:00 AM 16.398060 -9.570280 (16.398060, -9.570280) \n", "11 01/01/1925 12:00:00 AM 19.083330 8.383330 (19.083330, 8.383330) \n", "12 01/01/1769 12:00:00 AM 50.666670 2.333330 (50.666670, 2.333330) \n", "13 01/01/1949 12:00:00 AM 29.516670 35.050000 (29.516670, 35.050000) \n", "14 01/01/1838 12:00:00 AM 29.716670 77.950000 (29.716670, 77.950000) \n", "15 01/01/1959 12:00:00 AM 8.916670 8.433330 (8.916670, 8.433330) \n", "16 01/01/1981 12:00:00 AM 39.916670 42.816670 (39.916670, 42.816670) \n", "17 01/01/1957 12:00:00 AM 24.416670 39.516670 (24.416670, 39.516670) \n", "18 01/01/2001 12:00:00 AM 13.660330 28.960000 (13.660330, 28.960000) \n", "19 01/01/1806 12:00:00 AM 44.116670 4.083330 (44.116670, 4.083330) \n", "20 01/01/1766 12:00:00 AM 44.650000 11.016670 (44.650000, 11.016670) \n", "21 01/01/1949 12:00:00 AM 2.000000 22.666670 (2.000000, 22.666670) \n", "22 01/01/2002 12:00:00 AM 45.821330 6.015330 (45.821330, 6.015330) \n", "23 01/01/1835 12:00:00 AM 51.783330 -1.783330 (51.783330, -1.783330) \n", "24 01/01/1873 12:00:00 AM 36.233330 37.133330 (36.233330, 37.133330) \n", "25 01/01/1860 12:00:00 AM 44.883330 8.750000 (44.883330, 8.750000) \n", "26 01/01/1900 12:00:00 AM 50.950000 31.816670 (50.950000, 31.816670) \n", "27 01/01/1883 12:00:00 AM 45.266670 10.150000 (45.266670, 10.150000) \n", "28 01/01/1899 12:00:00 AM 42.533330 -85.883330 (42.533330, -85.883330) \n", "29 01/01/1969 12:00:00 AM 26.966670 -105.316670 (26.966670, -105.316670) \n", "30 01/01/2008 12:00:00 AM 20.745750 32.412750 (20.745750, 32.412750) \n", "31 01/01/1977 12:00:00 AM 35.273330 44.215560 (35.273330, 44.215560) \n", "32 01/01/1895 12:00:00 AM 27.666670 78.250000 (27.666670, 78.250000) \n", "33 01/01/1880 12:00:00 AM 26.583330 85.566670 (26.583330, 85.566670) \n", "\n", " __match__ \n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", "5 False \n", "6 False \n", "7 False \n", "8 False \n", "9 False \n", "10 False \n", "11 False \n", "12 False \n", "13 False \n", "14 False \n", "15 False \n", "16 False \n", "17 False \n", "18 False \n", "19 False \n", "20 False \n", "21 False \n", "22 False \n", "23 False \n", "24 False \n", "25 False \n", "26 False \n", "27 False \n", "28 False \n", "29 False \n", "30 False \n", "31 False \n", "32 False \n", "33 False " ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.set_buffer(\"*\")\n", "df.ext.buffer_window(\"*\", 0, 34).rows.find( ~df.ext.get_buffer().cols.is_match(\"mass (g)\", \"int\") )" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'columns': {'mass (g)': {'stats': {'mismatch': 5,\n", " 'missing': 4,\n", " 'match': 91,\n", " 'frequency': [{'value': '3200', 'count': 4},\n", " {'value': '50000', 'count': 3},\n", " {'value': '2000', 'count': 3},\n", " {'value': '6000', 'count': 2},\n", " {'value': '15000', 'count': 2},\n", " {'value': '6400', 'count': 2},\n", " {'value': '700', 'count': 2},\n", " {'value': '1000', 'count': 2},\n", " {'value': '3700', 'count': 2},\n", " {'value': '2000000', 'count': 1},\n", " {'value': '21', 'count': 1},\n", " {'value': '21000', 'count': 1},\n", " {'value': '1914', 'count': 1},\n", " {'value': '16700', 'count': 1},\n", " {'value': '19000', 'count': 1},\n", " {'value': '228000', 'count': 1},\n", " {'value': '18000', 'count': 1},\n", " {'value': '23.2', 'count': 1},\n", " {'value': '24000', 'count': 1},\n", " {'value': '1800', 'count': 1},\n", " {'value': '17900', 'count': 1},\n", " {'value': '2500', 'count': 1},\n", " {'value': '1770.5', 'count': 1},\n", " {'value': '17', 'count': 1},\n", " {'value': '16000', 'count': 1},\n", " {'value': '1620', 'count': 1},\n", " {'value': '1384.2', 'count': 1},\n", " {'value': '10322', 'count': 1},\n", " {'value': '107000', 'count': 1},\n", " {'value': '11500', 'count': 1},\n", " {'value': '1230', 'count': 1},\n", " {'value': '1280', 'count': 1},\n", " {'value': '1300', 'count': 1}],\n", " 'count_uniques': 83},\n", " 'dtype': 'object',\n", " 'profiler_dtype': 'int'}},\n", " 'name': None,\n", " 'file_name': 'Meteorite_Landings.csv',\n", " 'summary': {'cols_count': 10,\n", " 'rows_count': 100,\n", " 'size': '8.1 kB',\n", " 'dtypes_list': ['object'],\n", " 'total_count_dtypes': 1,\n", " 'missing_count': 4,\n", " 'p_missing': 4.0}}" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ext.profile(columns=\"mass (g)\", infer=True, flush=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 5 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] } ], "source": [ "# df = op.load.file(\"http://159.65.217.17:5003/uploads/datasetFile-1589317774772.csv\").ext.cache()\n", "df = op.load.file(\"http://159.65.217.17:5003/uploads/datasetFile-1589317774772.csv\").ext.cache()\n", "df1 = op.load.file(\"http://159.65.217.17:5003/uploads/datasetFile-1589317774772.csv\").ext.cache()\n", "# df.ext.profile(\"*\", output=\"json\")\n", "df1.ext.profile(\"*\", output=\"json\")\n", "# df = df.ext.optimize()\n", "df1 = df1.ext.optimize()\n", "# df.ext.set_buffer(\"*\")\n", "df1.ext.set_buffer(\"*\")\n" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 19 rows / 19 columns
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1 partition(s)
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id
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1 (object)
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\n", " \n", " not nullable\n", " \n", "
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firstName
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Viewing 10 of 19 rows / 19 columns
\n", "
1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 19 rows / 19 columns
\n", "
1 partition(s)
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1 (uint8)
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4 (uint8)
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6 (uint8)
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8 (category)
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\n", "
\n", " \n", " 223\n", " \n", "
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\n", "
\n", " \n", " 1920/04/22\n", " \n", "
\n", "
\n", "
\n", " \n", " you\n", " \n", "
\n", "
\n", "
\n", " \n", " 10\n", " \n", "
\n", "
\n", "
\n", " \n", " JaMES\n", " \n", "
\n", "
\n", "
\n", " \n", " M$$ax%%well\n", " \n", "
\n", "
\n", "
\n", " \n", " 107\n", " \n", "
\n", "
\n", "
\n", " \n", " taco\n", " \n", "
\n", "
\n", "
\n", " \n", " 3\n", " \n", "
\n", "
\n", "
\n", " \n", " 1923/03/12\n", " \n", "
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\n", "
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\n", "
\n", "\n", "
Viewing 10 of 19 rows / 19 columns
\n", "
1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()\n", "df1.ext.display()" ] }, { "cell_type": "code", "execution_count": 225, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'>' not supported between instances of 'NoneType' and 'NoneType'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"object\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m 2296\u001b[0m \u001b[0mmeta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclear_known_categories\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmeta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2297\u001b[0m return self.map_partitions(\n\u001b[1;32m-> 2298\u001b[1;33m \u001b[0mM\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeta\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmeta\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menforce_metadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2299\u001b[0m )\n\u001b[0;32m 2300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36mmap_partitions\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m 636\u001b[0m \u001b[1;33m>>\u001b[0m\u001b[1;33m>\u001b[0m \u001b[0mddf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_partitions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclear_divisions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# doctest: +SKIP\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 637\u001b[0m \"\"\"\n\u001b[1;32m--> 638\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmap_partitions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 639\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 640\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0minsert_meta_param_description\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py\u001b[0m in \u001b[0;36mmap_partitions\u001b[1;34m(func, meta, enforce_metadata, transform_divisions, *args, **kwargs)\u001b[0m\n\u001b[0;32m 5062\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5063\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5064\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mvalid_divisions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdivisions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5065\u001b[0m \u001b[0mdivisions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdfs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnpartitions\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5066\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\utils.py\u001b[0m in \u001b[0;36mvalid_divisions\u001b[1;34m(divisions)\u001b[0m\n\u001b[0;32m 952\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 953\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 954\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mdivisions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mdivisions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 955\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 956\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: '>' not supported between instances of 'NoneType' and 'NoneType'" ] } ], "source": [ "df.index.astype(\"object\")" ] }, { "cell_type": "code", "execution_count": 256, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idfirstNamelastNamebillingIdproductpricebirthdummyCol
01LuisAlvarez$$%!123Cake101980/07/07never
12AndréAmpère167piza81950/07/08gonna
23NiELSBöhr//((%%39pizza81990/07/09give
34PAULdirac$9pizza81954/07/10you
45AlbertEinstein122pizza81990/07/11up
56GalileoGALiLEI160arepa51930/08/12never
67CaRLGa%%%uss67taco31970/07/13gonna
78DavidH$$$ilbert112taaaccoo31950/07/14let
89JohannesKEPLER223taco31920/04/22you
910JaMESM$$ax%%well107taco31923/03/12down
1011IsaacNewton224pasta91999/02/15never
1112Emmy%%Nöether$234pasta91993/12/08gonna
1213Max!!!Planck!!!111hamburguer41994/01/04run
1314FredHoy&&&le41pizzza81997/06/27around
1415((( Heinrich )))))Hertz116pizza81956/11/30and
1516WilliamGilbert###118BEER21958/03/26desert
1617MarieCURIE144Rice12000/03/22you
1718ArthurCOM%%%pton4411079051899/01/01#
1819JAMESChadwick211NaN101921/05/03#
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" ], "text/plain": [ " id firstName lastName billingId product \\\n", "0 1 Luis Alvarez$$%! 123 Cake \n", "1 2 André Ampère 167 piza \n", "2 3 NiELS Böhr//((%% 39 pizza \n", "3 4 PAUL dirac$ 9 pizza \n", "4 5 Albert Einstein 122 pizza \n", "5 6 Galileo GALiLEI 160 arepa \n", "6 7 CaRL Ga%%%uss 67 taco \n", "7 8 David H$$$ilbert 112 taaaccoo \n", "8 9 Johannes KEPLER 223 taco \n", "9 10 JaMES M$$ax%%well 107 taco \n", "10 11 Isaac Newton 224 pasta \n", "11 12 Emmy%% Nöether$ 234 pasta \n", "12 13 Max!!! Planck!!! 111 hamburguer \n", "13 14 Fred Hoy&&&le 41 pizzza \n", "14 15 ((( Heinrich ))))) Hertz 116 pizza \n", "15 16 William Gilbert### 118 BEER \n", "16 17 Marie CURIE 144 Rice \n", "17 18 Arthur COM%%%pton 44 110790 \n", "18 19 JAMES Chadwick 211 NaN \n", "\n", " price birth dummyCol \n", "0 10 1980/07/07 never \n", "1 8 1950/07/08 gonna \n", "2 8 1990/07/09 give \n", "3 8 1954/07/10 you \n", "4 8 1990/07/11 up \n", "5 5 1930/08/12 never \n", "6 3 1970/07/13 gonna \n", "7 3 1950/07/14 let \n", "8 3 1920/04/22 you \n", "9 3 1923/03/12 down \n", "10 9 1999/02/15 never \n", "11 9 1993/12/08 gonna \n", "12 4 1994/01/04 run \n", "13 8 1997/06/27 around \n", "14 8 1956/11/30 and \n", "15 2 1958/03/26 desert \n", "16 1 2000/03/22 you \n", "17 5 1899/01/01 # \n", "18 10 1921/05/03 # " ] }, "execution_count": 256, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.compute()" ] }, { "cell_type": "code", "execution_count": 252, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id uint8\n", "firstName category\n", "lastName category\n", "billingId uint8\n", "product category\n", "price uint8\n", "birth category\n", "dummyCol category\n", "dtype: object" ] }, "execution_count": 252, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.dtypes" ] }, { "cell_type": "code", "execution_count": 270, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [firstName_x, lastName_x, billingId_x, product, price_x, birth_x, dummyCol_x, firstName_y, lastName_y, billingId_y, price_y, birth_y, dummyCol_y]\n", "Index: []" ] }, "execution_count": 270, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.cols.join(df1,left_on= \"id\",right_on=\"product\").compute()" ] }, { "cell_type": "code", "execution_count": 240, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1'" ] }, "execution_count": 240, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[\"id\"][0]" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idfirstNamelastNamebillingIdproductpricebirthdummyCol
01LuisAlvarez$$%!123Cake101980/07/07never
12AndréAmpère167piza81950/07/08gonna
23NiELSBöhr//((%%39pizza81990/07/09give
34PAULdirac$9pizza81954/07/10you
45AlbertEinstein122pizza81990/07/11up
56GalileoGALiLEI160arepa51930/08/12never
67CaRLGa%%%uss67taco31970/07/13gonna
78DavidH$$$ilbert112taaaccoo31950/07/14let
89JohannesKEPLER223taco31920/04/22you
910JaMESM$$ax%%well107taco31923/03/12down
1011IsaacNewton224pasta91999/02/15never
1112Emmy%%Nöether$234pasta91993/12/08gonna
1213Max!!!Planck!!!111hamburguer41994/01/04run
1314FredHoy&&&le41pizzza81997/06/27around
1415((( Heinrich )))))Hertz116pizza81956/11/30and
1516WilliamGilbert###118BEER21958/03/26desert
1617MarieCURIE144Rice12000/03/22you
1718ArthurCOM%%%pton4411079051899/01/01#
1819JAMESChadwick211NaN101921/05/03#
\n", "
" ], "text/plain": [ " id firstName lastName billingId product \\\n", "0 1 Luis Alvarez$$%! 123 Cake \n", "1 2 André Ampère 167 piza \n", "2 3 NiELS Böhr//((%% 39 pizza \n", "3 4 PAUL dirac$ 9 pizza \n", "4 5 Albert Einstein 122 pizza \n", "5 6 Galileo GALiLEI 160 arepa \n", "6 7 CaRL Ga%%%uss 67 taco \n", "7 8 David H$$$ilbert 112 taaaccoo \n", "8 9 Johannes KEPLER 223 taco \n", "9 10 JaMES M$$ax%%well 107 taco \n", "10 11 Isaac Newton 224 pasta \n", "11 12 Emmy%% Nöether$ 234 pasta \n", "12 13 Max!!! Planck!!! 111 hamburguer \n", "13 14 Fred Hoy&&&le 41 pizzza \n", "14 15 ((( Heinrich ))))) Hertz 116 pizza \n", "15 16 William Gilbert### 118 BEER \n", "16 17 Marie CURIE 144 Rice \n", "17 18 Arthur COM%%%pton 44 110790 \n", "18 19 JAMES Chadwick 211 NaN \n", "\n", " price birth dummyCol \n", "0 10 1980/07/07 never \n", "1 8 1950/07/08 gonna \n", "2 8 1990/07/09 give \n", "3 8 1954/07/10 you \n", "4 8 1990/07/11 up \n", "5 5 1930/08/12 never \n", "6 3 1970/07/13 gonna \n", "7 3 1950/07/14 let \n", "8 3 1920/04/22 you \n", "9 3 1923/03/12 down \n", "10 9 1999/02/15 never \n", "11 9 1993/12/08 gonna \n", "12 4 1994/01/04 run \n", "13 8 1997/06/27 around \n", "14 8 1956/11/30 and \n", "15 2 1958/03/26 desert \n", "16 1 2000/03/22 you \n", "17 5 1899/01/01 # \n", "18 10 1921/05/03 # " ] }, "execution_count": 199, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.cols.cast(\"id\",\"object\").compute()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df1.cols.set(\"nombre\",df1[\"nombre\"]==\"luis\",1,\"new name\").compute()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df1.dtypes" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 5 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "data": { "text/plain": [ "{'columns': {'cedula': {'stats': {'mismatch': 5,\n", " 'missing': 0,\n", " 'match': 0,\n", " 'frequency': [{'value': '24949394', 'count': 1},\n", " {'value': '23760628', 'count': 1},\n", " {'value': '21857839', 'count': 1},\n", " {'value': '19748383', 'count': 1},\n", " {'value': '17484892', 'count': 1}],\n", " 'count_uniques': 5,\n", " 'profiler_dtype': 'object',\n", " 'hist': [{'count': 1, 'lower': 1.0, 'upper': 0.0},\n", " {'count': 0, 'lower': 0.0, 'upper': 1.0},\n", " {'count': 1, 'lower': 1.0, 'upper': 0.0},\n", " {'count': 0, 'lower': 0.0, 'upper': 0.0},\n", " {'count': 0, 'lower': 0.0, 'upper': 1.0},\n", " {'count': 1, 'lower': 1.0, 'upper': 0.0},\n", " {'count': 0, 'lower': 0.0, 'upper': 1.0},\n", " {'count': 1, 'lower': 1.0, 'upper': 1.0}]},\n", " 'dtype': 'uint32'},\n", " 'nombre': {'stats': {'mismatch': 5,\n", " 'missing': 0,\n", " 'match': 0,\n", " 'frequency': [{'value': 'pedro', 'count': 1},\n", " {'value': 'luis', 'count': 1},\n", " {'value': 'juan', 'count': 1},\n", " {'value': 'jose', 'count': 1},\n", " {'value': 'eddy', 'count': 1}],\n", " 'count_uniques': 5,\n", " 'profiler_dtype': 'object'},\n", " 'dtype': 'category'},\n", " 'apellido': {'stats': {'mismatch': 5,\n", " 'missing': 0,\n", " 'match': 0,\n", " 'frequency': [{'value': 'aguirre', 'count': 3},\n", " {'value': 'martinez', 'count': 1},\n", " {'value': 'gonzalez', 'count': 1}],\n", " 'count_uniques': 3,\n", " 'profiler_dtype': 'object'},\n", " 'dtype': 'category'}},\n", " 'name': None,\n", " 'file_name': 'tmp60lylob3.csv',\n", " 'summary': {'cols_count': 3,\n", " 'rows_count': 5,\n", " 'size': '462 Bytes',\n", " 'dtypes_list': ['uint32', 'category'],\n", " 'total_count_dtypes': 2,\n", " 'missing_count': 0,\n", " 'p_missing': 0.0}}" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.ext.profile(\"*\",infer=True, flush=True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'sample': {'columns': [{'title': 'cedula'},\n", " {'title': 'nombre'},\n", " {'title': 'apellido'}],\n", " 'value': [[23760628, 'luis', 'aguirre'],\n", " [21857839, 'jose', 'aguirre'],\n", " [19748383, 'eddy', 'aguirre'],\n", " [24949394, 'juan', 'gonzalez'],\n", " [17484892, 'pedro', 'martinez']]}}" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.ext.buffer_window(\"*\", 0, 10).ext.to_json(\"*\")" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "cedula uint32\n", "nombre category\n", "apellido category\n", "dtype: object" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.dtypes" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "asdfasf nombre\n", "AAA ['nombre'] ['new nombre']\n" ] }, { "data": { "text/plain": [ "{'sample': {'columns': [{'title': 'cedula'},\n", " {'title': 'nombre'},\n", " {'title': 'apellido'},\n", " {'title': 'new nombre'}],\n", " 'value': [[23760628, 'luis', 'aguirre', 'LUIS'],\n", " [21857839, 'jose', 'aguirre', 'JOSE'],\n", " [19748383, 'eddy', 'aguirre', 'EDDY'],\n", " [24949394, 'juan', 'gonzalez', 'JUAN'],\n", " [17484892, 'pedro', 'martinez', 'PEDRO']]}}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.ext.buffer_window(\"*\", 0, 10).cols.upper(\"nombre\", output_cols=\"new nombre\").ext.to_json(\"*\")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 10 elements requested, only 5 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df1.ext.display()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "pdf = df1.compute()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "cedula uint32\n", "nombre category\n", "apellido category\n", "dtype: object" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdf.dtypes" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'CategoricalAccessor' object has no attribute 'get_categories'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"nombre\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_categories\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: 'CategoricalAccessor' object has no attribute 'get_categories'" ] } ], "source": [ "pdf[\"nombre\"].cat.get_categories()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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cedulanombreapellidonew name
023760628luisaguirre1
121857839joseaguirrejose
219748383eddyaguirreeddy
324949394juangonzalezjuan
417484892pedromartinezpedro
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" ], "text/plain": [ " cedula nombre apellido new name\n", "0 23760628 luis aguirre 1\n", "1 21857839 jose aguirre jose\n", "2 19748383 eddy aguirre eddy\n", "3 24949394 juan gonzalez juan\n", "4 17484892 pedro martinez pedro" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdf = df1.compute()\n", "# print(pdf)\n", "pdf.cols.set(\"nombre\",pdf[\"nombre\"]==\"luis\",1,\"new name\")\n", "# pdf[\"nombre\"].where(~(pdf[\"nombre\"]==\"luis\"),1)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "new categories must not include old categories: {1}", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_series\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_meta_nonempty\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessor_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 64\u001b[0m )\n\u001b[0;32m 65\u001b[0m \u001b[0mtoken\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"%s-%s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessor_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\accessor.py\u001b[0m in \u001b[0;36m_delegate_method\u001b[1;34m(obj, accessor, attr, args, kwargs)\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_delegate_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccessor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccessor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 49\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mmaybe_wrap_pandas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\accessor.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 97\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_create_delegator_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 98\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 99\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_delegate_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 101\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\arrays\\categorical.py\u001b[0m in \u001b[0;36m_delegate_method\u001b[1;34m(self, name, *args, **kwargs)\u001b[0m\n\u001b[0;32m 2558\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2559\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parent\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2560\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2561\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2562\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\arrays\\categorical.py\u001b[0m in \u001b[0;36madd_categories\u001b[1;34m(self, new_categories, inplace)\u001b[0m\n\u001b[0;32m 1026\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malready_included\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1027\u001b[0m raise ValueError(\n\u001b[1;32m-> 1028\u001b[1;33m \u001b[1;34mf\"new categories must not include old categories: {already_included}\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1029\u001b[0m )\n\u001b[0;32m 1030\u001b[0m \u001b[0mnew_categories\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcategories\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_categories\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: new categories must not include old categories: {1}" ] } ], "source": [ "df1.cols.set(\"nombre\",df1[\"nombre\"]==\"luis\",1,\"new name\").compute()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\argenisleon\\AppData\\Roaming\\Python\\Python37\\site-packages\\dask\\dataframe\\core.py:5979: UserWarning: Insufficient elements for `head`. 10 elements requested, only 5 elements available. Try passing larger `npartitions` to `head`.\n", " warnings.warn(msg.format(n, len(r)))\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 5 of 5 rows / 5 columns
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1 partition(s)
\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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cedula
\n", "
1 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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nombre
\n", "
2 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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apellido
\n", "
3 (object)
\n", "
\n", " \n", " not nullable\n", " \n", "
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\n", " \n", " 23760628\n", " \n", "
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\n", " \n", " luis\n", " \n", "
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Viewing 5 of 5 rows / 5 columns
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1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df1.ext.display()" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "distributed.worker - WARNING - Compute Failed\n", "Function: execute_task\n", "args: ((, (, , [.parser_f at 0x00000132CAC063A8>, (, , 0, 64000000, b'\\n'), b'Company,City,State,District,County,Metro.Area,Product,Petitioner,Layoff.Date,Est..No..Workers,Cause,Country\\r\\n', (, [['sep', ','], ['header', 0], ['encoding', 'latin1'], ['quoting', 0], ['error_bad_lines', False], ['keep_default_na', True], ['na_values', None], ['engine', 'c']]), (, [['Company', dtype('O')], ['City', dtype('O')], ['State', dtype('O')], ['District', dtype('int64')], ['County', dtype('O')], ['Metro.Area', dtype('O')], ['Product', dtype('O')], ['Petitioner', dtype('O')], ['Layoff.Date', dtype('O')], ['Est..No..Workers', dtype('float64')], ['Cause', dtype('O')], ['Country', dtype('O'\n", "kwargs: {}\n", "Exception: ValueError(\"Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\\n\\n+----------+---------+----------+\\n| Column | Found | Expected |\\n+----------+---------+----------+\\n| District | float64 | int64 |\\n+----------+---------+----------+\\n\\nUsually this is due to dask's dtype inference failing, and\\n*may* be fixed by specifying dtypes manually by adding:\\n\\ndtype={'District': 'float64'}\\n\\nto the call to `read_csv`/`read_table`.\\n\\nAlternatively, provide `assume_missing=True` to interpret\\nall unspecified integer columns as floats.\")\n", "\n" ] } ], "source": [ "df = op.load.csv(\"data/taa.csv\", sep=\",\", error_bad_lines=False, header=True, null_value=\"null\", infer_schema='true', encoding=\"latin1\").ext.cache()\n", "# Meteorite_Landings" ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [], "source": [ "df = op.load.file(\"data/crime.csv\").ext.cache()" ] }, { "cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [], "source": [ "df =df.ext.optimize()" ] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'file_name': 'foo.csv',\n", " 'mime_info': [{'mime': 'text/plain',\n", " 'encoding': 'utf-8',\n", " 'file_ext': 'csv',\n", " 'file_type': 'csv',\n", " 'properties': {'delimiter': ',',\n", " 'doublequote': False,\n", " 'escapechar': None,\n", " 'lineterminator': '\\r\\n',\n", " 'quotechar': '\"',\n", " 'quoting': 0,\n", " 'skipinitialspace': False}}]}" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.meta.get()" ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 19 rows / 19 columns
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1 partition(s)
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id
\n", "
1 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
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firstName
\n", "
2 (category)
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lastName
\n", "
3 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
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billingId
\n", "
4 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
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product
\n", "
5 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
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price
\n", "
6 (uint8)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
birth
\n", "
7 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
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dummyCol
\n", "
8 (category)
\n", "
\n", " \n", " not nullable\n", " \n", "
\n", "
\n", "
\n", " \n", " 1\n", " \n", "
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\n", "
\n", " \n", " Luis\n", " \n", "
\n", "
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\n", " \n", " Alvarez$$%!\n", " \n", "
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\n", " \n", " 123\n", " \n", "
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\n", "
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\n", " \n", " 10\n", " \n", "
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Viewing 10 of 19 rows / 19 columns
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1 partition(s) <class 'dask.dataframe.core.DataFrame'>
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.ext.display()" ] }, { "cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [], "source": [ "CHAR_DICT = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 .!?:,\\'%-\\(\\)/$|&;[]\"'\n", "def build_char_dictionary(char_dict=None, unknown_label='UNK'):\n", " \"\"\"\n", " Define possbile char set. Using \"UNK\" if character does not exist in this set\n", " \"\"\" \n", "\n", " if char_dict is None:\n", " char_dict = CHAR_DICT\n", "\n", " unknown_label = unknown_label\n", "\n", " chars = []\n", "\n", " for c in char_dict:\n", " chars.append(c)\n", "\n", " chars = list(set(chars))\n", "\n", " chars.insert(0, unknown_label)\n", "\n", " num_of_char = len(chars)\n", " char_indices = dict((c, i) for i, c in enumerate(chars))\n", " indices_char = dict((i, c) for i, c in enumerate(chars))\n", "\n", " print('Totoal number of chars:', num_of_char)\n", "\n", " print('First 3 char_indices sample:', {k: char_indices[k] for k in list(char_indices)[:3]})\n", " print('First 3 indices_char sample:', {k: indices_char[k] for k in list(indices_char)[:3]})\n", "\n", "\n", " return char_indices, indices_char, num_of_char\n", "\n", "def preporcess(labels, char_dict=None, unknown_label='UNK'):\n", "\n", " print('-----> Stage: preprocess')\n", "\n", " build_char_dictionary(char_dict, unknown_label)\n", " convert_labels(labels)\n", " \n", "def convert_labels( labels):\n", " \"\"\"\n", " Convert label to numeric\n", " \"\"\"\n", " label2indexes = dict((l, i) for i, l in enumerate(labels))\n", " index2labels = dict((i, l) for i, l in enumerate(labels))\n", "\n", " \n", " print('Label to Index: ', label2indexes)\n", " print('Index to Label: ', index2labels)\n", "\n", " num_of_label = len(label2indexes)\n", "\n", " return label2indexes, index2labels" ] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----> Stage: preprocess\n", "Totoal number of chars: 83\n", "First 3 char_indices sample: {'UNK': 0, '1': 1, ']': 2}\n", "First 3 indices_char sample: {0: 'UNK', 1: '1', 2: ']'}\n", "Label to Index: {'Luis': 0, 'André': 1, 'NiELS': 2, 'PAUL': 3, 'Albert': 4, 'Galileo': 5, 'CaRL': 6, 'David': 7, 'Johannes': 8, 'JaMES': 9, 'Isaac': 10, 'Emmy%%': 11, 'Max!!!': 12, 'Fred': 13, '((( Heinrich )))))': 14, 'William': 15, 'Marie': 16, 'Arthur': 17, 'JAMES': 18}\n", "Index to Label: {0: 'Luis', 1: 'André', 2: 'NiELS', 3: 'PAUL', 4: 'Albert', 5: 'Galileo', 6: 'CaRL', 7: 'David', 8: 'Johannes', 9: 'JaMES', 10: 'Isaac', 11: 'Emmy%%', 12: 'Max!!!', 13: 'Fred', 14: '((( Heinrich )))))', 15: 'William', 16: 'Marie', 17: 'Arthur', 18: 'JAMES'}\n" ] } ], "source": [ "preporcess(labels=df['firstName'].unique())" ] }, { "cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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INCIDENT_NUMBEROFFENSE_CODEOFFENSE_CODE_GROUPOFFENSE_DESCRIPTIONDISTRICTREPORTING_AREASHOOTINGOCCURRED_ON_DATEYEARMONTHDAY_OF_WEEKHOURUCR_PARTSTREETLatLongLocation
0I18207094500619LarcenyLARCENY ALL OTHERSD14808NaN2018-09-02 13:00:0020189Sunday13Part OneLINCOLN ST42.35779134-71.13937053(42.35779134, -71.13937053)
1I18207094301402VandalismVANDALISMC11347NaN2018-08-21 00:00:0020188Tuesday0Part TwoHECLA ST42.30682138-71.06030035(42.30682138, -71.06030035)
2I18207094103410TowedTOWED MOTOR VEHICLED4151NaN2018-09-03 19:27:0020189Monday19Part ThreeCAZENOVE ST42.34658879-71.07242943(42.34658879, -71.07242943)
3I18207094003114Investigate PropertyINVESTIGATE PROPERTYD4272NaN2018-09-03 21:16:0020189Monday21Part ThreeNEWCOMB ST42.33418175-71.07866441(42.33418175, -71.07866441)
4I18207093803114Investigate PropertyINVESTIGATE PROPERTYB3421NaN2018-09-03 21:05:0020189Monday21Part ThreeDELHI ST42.27536542-71.09036101(42.27536542, -71.09036101)
......................................................
319068I050310906-0003125Warrant ArrestsWARRANT ARRESTD4285NaN2016-06-05 17:25:0020166Sunday17Part ThreeCOVENTRY ST42.33695098-71.08574813(42.33695098, -71.08574813)
319069I030217815-0800111HomicideMURDER, NON-NEGLIGIENT MANSLAUGHTERE18520NaN2015-07-09 13:38:0020157Thursday13Part OneRIVER ST42.25592648-71.12317207(42.25592648, -71.12317207)
319070I030217815-0803125Warrant ArrestsWARRANT ARRESTE18520NaN2015-07-09 13:38:0020157Thursday13Part ThreeRIVER ST42.25592648-71.12317207(42.25592648, -71.12317207)
319071I010370257-0003125Warrant ArrestsWARRANT ARRESTE13569NaN2016-05-31 19:35:0020165Tuesday19Part ThreeNEW WASHINGTON ST42.30233307-71.11156487(42.30233307, -71.11156487)
31907214205255003125Warrant ArrestsWARRANT ARRESTD4903NaN2015-06-22 00:12:0020156Monday0Part ThreeWASHINGTON ST42.33383935-71.08029038(42.33383935, -71.08029038)
\n", "

319073 rows × 17 columns

\n", "
" ], "text/plain": [ " INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP \\\n", "0 I182070945 00619 Larceny \n", "1 I182070943 01402 Vandalism \n", "2 I182070941 03410 Towed \n", "3 I182070940 03114 Investigate Property \n", "4 I182070938 03114 Investigate Property \n", "... ... ... ... \n", "319068 I050310906-00 03125 Warrant Arrests \n", "319069 I030217815-08 00111 Homicide \n", "319070 I030217815-08 03125 Warrant Arrests \n", "319071 I010370257-00 03125 Warrant Arrests \n", "319072 142052550 03125 Warrant Arrests \n", "\n", " OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING \\\n", "0 LARCENY ALL OTHERS D14 808 NaN \n", "1 VANDALISM C11 347 NaN \n", "2 TOWED MOTOR VEHICLE D4 151 NaN \n", "3 INVESTIGATE PROPERTY D4 272 NaN \n", "4 INVESTIGATE PROPERTY B3 421 NaN \n", "... ... ... ... ... \n", "319068 WARRANT ARREST D4 285 NaN \n", "319069 MURDER, NON-NEGLIGIENT MANSLAUGHTER E18 520 NaN \n", "319070 WARRANT ARREST E18 520 NaN \n", "319071 WARRANT ARREST E13 569 NaN \n", "319072 WARRANT ARREST D4 903 NaN \n", "\n", " OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART \\\n", "0 2018-09-02 13:00:00 2018 9 Sunday 13 Part One \n", "1 2018-08-21 00:00:00 2018 8 Tuesday 0 Part Two \n", "2 2018-09-03 19:27:00 2018 9 Monday 19 Part Three \n", "3 2018-09-03 21:16:00 2018 9 Monday 21 Part Three \n", "4 2018-09-03 21:05:00 2018 9 Monday 21 Part Three \n", "... ... ... ... ... ... ... \n", "319068 2016-06-05 17:25:00 2016 6 Sunday 17 Part Three \n", "319069 2015-07-09 13:38:00 2015 7 Thursday 13 Part One \n", "319070 2015-07-09 13:38:00 2015 7 Thursday 13 Part Three \n", "319071 2016-05-31 19:35:00 2016 5 Tuesday 19 Part Three \n", "319072 2015-06-22 00:12:00 2015 6 Monday 0 Part Three \n", "\n", " STREET Lat Long \\\n", "0 LINCOLN ST 42.35779134 -71.13937053 \n", "1 HECLA ST 42.30682138 -71.06030035 \n", "2 CAZENOVE ST 42.34658879 -71.07242943 \n", "3 NEWCOMB ST 42.33418175 -71.07866441 \n", "4 DELHI ST 42.27536542 -71.09036101 \n", "... ... ... ... \n", "319068 COVENTRY ST 42.33695098 -71.08574813 \n", "319069 RIVER ST 42.25592648 -71.12317207 \n", "319070 RIVER ST 42.25592648 -71.12317207 \n", "319071 NEW WASHINGTON ST 42.30233307 -71.11156487 \n", "319072 WASHINGTON ST 42.33383935 -71.08029038 \n", "\n", " Location \n", "0 (42.35779134, -71.13937053) \n", "1 (42.30682138, -71.06030035) \n", "2 (42.34658879, -71.07242943) \n", "3 (42.33418175, -71.07866441) \n", "4 (42.27536542, -71.09036101) \n", "... ... \n", "319068 (42.33695098, -71.08574813) \n", "319069 (42.25592648, -71.12317207) \n", "319070 (42.25592648, -71.12317207) \n", "319071 (42.30233307, -71.11156487) \n", "319072 (42.33383935, -71.08029038) \n", "\n", "[319073 rows x 17 columns]" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.compute()" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.preprocessing.text import Tokenizer" ] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 2.33 s\n" ] } ], "source": [ "%%time\n", "col_name = \"INCIDENT_NUMBER\"\n", "tk = Tokenizer(num_words=None, char_level=True, oov_token='UNK')\n", "X_train = df[col_name]\n", "tk.fit_on_texts(X_train)\n", "X_train = tk.texts_to_sequences(X_train)" ] }, { "cell_type": "code", "execution_count": 257, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2857944\n" ] }, { "ename": "ValueError", "evalue": "setting an array element with a sequence.", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;31mTypeError\u001b[0m: int() argument must be a string, a bytes-like object or a number, not 'list'", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetsizeof\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetsizeof\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: setting an array element with a sequence." ] } ], "source": [ "print(sys.getsizeof(X_train))\n", "arr = np.array(X_train, dtype=np.uint8) \n", "print(sys.getsizeof(arr))" ] }, { "cell_type": "code", "execution_count": 243, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "expected sequence object with len >= 0 or a single integer", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;31m# pdf = df[col_name].map(func).astype(dtype=a).compute()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: expected sequence object with len >= 0 or a single integer" ] } ], "source": [ "# %%time\n", "import numpy as np\n", "def func(value):\n", " return [ord(c) for c in value]\n", "\n", "a = np.ndarray(np.uint8)\n", "type(a)\n", "# pdf = df[col_name].map(func).astype(dtype=a).compute()" ] }, { "cell_type": "code", "execution_count": 233, "metadata": {}, "outputs": [], "source": [ "# pdf = df[col_name].compute()" ] }, { "cell_type": "code", "execution_count": 234, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[73, 49, 56, 50, 48, 55, 48, 57, 52, 53]" ] }, "execution_count": 234, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdf[0]\n" ] }, { "cell_type": "code", "execution_count": 235, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "192" ] }, "execution_count": 235, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sys\n", "sys.getsizeof(pdf[0])" ] }, { "cell_type": "code", "execution_count": 236, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "59" ] }, "execution_count": 236, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sys.getsizeof(\"I182070945\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }