{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Load data from a CSV file\n", "\n", "These examples illustrate how to load data from a CSV file using the Pandas and GeoPandas libraries." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## From latitude and longitude columns" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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incident_datetimeincident_dateincident_timeincident_yearincident_day_of_weekreport_datetimerow_idincident_idincident_numbercad_number...:@computed_region_qgnn_b9vv:@computed_region_26cr_cadq:@computed_region_ajp5_b2md:@computed_region_nqbw_i6c3:@computed_region_2dwj_jsy4:@computed_region_h4ep_8xdi:@computed_region_y6ts_4iup:@computed_region_jg9y_a9du:@computed_region_6pnf_4xz7geometry
02019-05-01T01:00:00.0002019-05-01T00:00:00.00001:002019Wednesday2019-06-12T20:27:00.00081097515200810975190424067191634131.0...10.07.035.0NaNNaNNaNNaNNaN1.0POINT (-122.49963 37.76257)
12019-06-22T07:45:00.0002019-06-22T00:00:00.00007:452019Saturday2019-06-22T08:05:00.00081465564020814655190450880191730737.0...1.010.034.01.0NaN1.0NaNNaN2.0POINT (-122.40816 37.78054)
22019-06-03T16:16:00.0002019-06-03T00:00:00.00016:162019Monday2019-06-03T16:16:00.00080769875000807698190397016191533509.0...2.09.01.0NaNNaNNaNNaNNaN2.0POINT (-122.39075 37.72160)
32018-11-16T16:34:00.0002018-11-16T00:00:00.00016:342018Friday2018-11-16T16:34:00.00073857915041738579180870806183202539.0...6.03.06.0NaN18.0NaNNaNNaN2.0POINT (-122.40488 37.79486)
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5 rows × 37 columns

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" ], "text/plain": [ " incident_datetime incident_date incident_time \\\n", "0 2019-05-01T01:00:00.000 2019-05-01T00:00:00.000 01:00 \n", "1 2019-06-22T07:45:00.000 2019-06-22T00:00:00.000 07:45 \n", "2 2019-06-03T16:16:00.000 2019-06-03T00:00:00.000 16:16 \n", "3 2018-11-16T16:34:00.000 2018-11-16T00:00:00.000 16:34 \n", "4 2019-05-27T02:25:00.000 2019-05-27T00:00:00.000 02:25 \n", "\n", " incident_year incident_day_of_week report_datetime row_id \\\n", "0 2019 Wednesday 2019-06-12T20:27:00.000 81097515200 \n", "1 2019 Saturday 2019-06-22T08:05:00.000 81465564020 \n", "2 2019 Monday 2019-06-03T16:16:00.000 80769875000 \n", "3 2018 Friday 2018-11-16T16:34:00.000 73857915041 \n", "4 2019 Monday 2019-05-27T02:55:00.000 80509204134 \n", "\n", " incident_id incident_number cad_number ... :@computed_region_qgnn_b9vv \\\n", "0 810975 190424067 191634131.0 ... 10.0 \n", "1 814655 190450880 191730737.0 ... 1.0 \n", "2 807698 190397016 191533509.0 ... 2.0 \n", "3 738579 180870806 183202539.0 ... 6.0 \n", "4 805092 190378555 191470256.0 ... 4.0 \n", "\n", " :@computed_region_26cr_cadq :@computed_region_ajp5_b2md \\\n", "0 7.0 35.0 \n", "1 10.0 34.0 \n", "2 9.0 1.0 \n", "3 3.0 6.0 \n", "4 6.0 13.0 \n", "\n", " :@computed_region_nqbw_i6c3 :@computed_region_2dwj_jsy4 \\\n", "0 NaN NaN \n", "1 1.0 NaN \n", "2 NaN NaN \n", "3 NaN 18.0 \n", "4 NaN NaN \n", "\n", " :@computed_region_h4ep_8xdi :@computed_region_y6ts_4iup \\\n", "0 NaN NaN \n", "1 1.0 NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " :@computed_region_jg9y_a9du :@computed_region_6pnf_4xz7 \\\n", "0 NaN 1.0 \n", "1 NaN 2.0 \n", "2 NaN 2.0 \n", "3 NaN 2.0 \n", "4 NaN 1.0 \n", "\n", " geometry \n", "0 POINT (-122.49963 37.76257) \n", "1 POINT (-122.40816 37.78054) \n", "2 POINT (-122.39075 37.72160) \n", "3 POINT (-122.40488 37.79486) \n", "4 POINT (-122.43056 37.79772) \n", "\n", "[5 rows x 37 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pandas import read_csv\n", "from geopandas import GeoDataFrame, points_from_xy\n", "\n", "remote_file_path = 'http://data.sfgov.org/resource/wg3w-h783.csv'\n", "\n", "df = read_csv(remote_file_path)\n", "\n", "# Clean rows where the `longitude` column is NULL\n", "df = df[df['longitude'].notna()]\n", "\n", "gdf = GeoDataFrame(df, geometry=points_from_xy(df['longitude'], df['latitude']))\n", "gdf.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " None\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " Static map image\n", " \n", " \n", "
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    the_geomcartodb_idfield_1nameaddressrevenueid_storegeometry
    00101000020E61000005EA27A6B607D52C01956F146E655...10Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.772APOINT (-73.95901 40.67109)
    10101000020E6100000B610E4A0847D52C0B532E197FA49...21607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.418BPOINT (-73.96122 40.57796)
    20101000020E6100000E5B8533A587F52C05726FC523F4F...3265th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.699CPOINT (-73.98976 40.61912)
    30101000020E61000008BA6B393C18152C08D62B9A5D550...43Bay Ridge Pkwy & 3rd Ave7419 3rd Avenue,Brooklyn, NY 112091185702.676DPOINT (-74.02744 40.63152)
    40101000020E6100000CEFC6A0E108052C080D4264EEE4B...54Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141148427.411EPOINT (-74.00098 40.59321)
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    " ], "text/plain": [ " the_geom cartodb_id field_1 \\\n", "0 0101000020E61000005EA27A6B607D52C01956F146E655... 1 0 \n", "1 0101000020E6100000B610E4A0847D52C0B532E197FA49... 2 1 \n", "2 0101000020E6100000E5B8533A587F52C05726FC523F4F... 3 2 \n", "3 0101000020E61000008BA6B393C18152C08D62B9A5D550... 4 3 \n", "4 0101000020E6100000CEFC6A0E108052C080D4264EEE4B... 5 4 \n", "\n", " name address \\\n", "0 Franklin Ave & Eastern Pkwy 341 Eastern Pkwy,Brooklyn, NY 11238 \n", "1 607 Brighton Beach Ave 607 Brighton Beach Avenue,Brooklyn, NY 11235 \n", "2 65th St & 18th Ave 6423 18th Avenue,Brooklyn, NY 11204 \n", "3 Bay Ridge Pkwy & 3rd Ave 7419 3rd Avenue,Brooklyn, NY 11209 \n", "4 Caesar's Bay Shopping Center 8973 Bay Parkway,Brooklyn, NY 11214 \n", "\n", " revenue id_store geometry \n", "0 1321040.772 A POINT (-73.95901 40.67109) \n", "1 1268080.418 B POINT (-73.96122 40.57796) \n", "2 1248133.699 C POINT (-73.98976 40.61912) \n", "3 1185702.676 D POINT (-74.02744 40.63152) \n", "4 1148427.411 E POINT (-74.00098 40.59321) " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cartoframes.utils import decode_geometry\n", "\n", "remote_file_path='http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_geocoded.csv'\n", "\n", "df = read_csv(remote_file_path)\n", "\n", "gdf = GeoDataFrame(df, geometry=decode_geometry(df['the_geom']))\n", "gdf.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " None\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " Static map image\n", " \n", " \n", "
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