{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

\"FT

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Frogtown DataLoad Workbook, 05/06/18

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By Frogtown Crusader (Abu Nayeem)

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is just clean coded process going over the uploading steps assuming all the preliminary steps after decoder are made\n", "\n", "\n", "## Table of contents \n", "* [Data Setup](#setup)\n", "* [Create Variables](#create)\n", "* [Intersection Table: Preparation](#intersection)\n", "* [Address Table: Preparation](#address)\n", "* [Preliminary Data Cleaning](#pre)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Setup " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n" ] }, { "data": { "text/html": [ "
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BlockCallDispCodeCallDispositionCaseCodeCountDateGridIncidentIncTypeNeighborhoodNNumTime
1VANBUREN AV & COMOAAdvised21165906261912021-08-11T23:47:34.00090.0DischargeWeapons, Discharging a Firearm in the City Limits7 - Thomas/Dale(Frogtown)72021-08-11T23:47:34.000
2THOMAS AV & GROTTOAAdvised21165903995412021-08-11T23:46:19.00088.0Proactive Police VisitProactive Police Visit7 - Thomas/Dale(Frogtown)72021-08-11T23:46:19.000
515X CHARLES AVAAdvised21165885180012021-08-11T23:13:25.00090.0NarcoticsNarcotics7 - Thomas/Dale(Frogtown)72021-08-11T23:13:25.000
655X CENTRAL AV WAAdvised21165880995412021-08-11T23:01:57.000109.0Proactive Police VisitProactive Police Visit8 - Summit/University82021-08-11T23:01:57.000
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" ], "text/plain": [ " Block CallDispCode CallDisposition Case Code Count \\\n", "1 VANBUREN AV & COMO A Advised 21165906 2619 1 \n", "2 THOMAS AV & GROTTO A Advised 21165903 9954 1 \n", "5 15X CHARLES AV A Advised 21165885 1800 1 \n", "6 55X CENTRAL AV W A Advised 21165880 9954 1 \n", "\n", " Date Grid Incident \\\n", "1 2021-08-11T23:47:34.000 90.0 Discharge \n", "2 2021-08-11T23:46:19.000 88.0 Proactive Police Visit \n", "5 2021-08-11T23:13:25.000 90.0 Narcotics \n", "6 2021-08-11T23:01:57.000 109.0 Proactive Police Visit \n", "\n", " IncType \\\n", "1 Weapons, Discharging a Firearm in the City Limits \n", "2 Proactive Police Visit \n", "5 Narcotics \n", "6 Proactive Police Visit \n", "\n", " Neighborhood NNum Time \n", "1 7 - Thomas/Dale(Frogtown) 7 2021-08-11T23:47:34.000 \n", "2 7 - Thomas/Dale(Frogtown) 7 2021-08-11T23:46:19.000 \n", "5 7 - Thomas/Dale(Frogtown) 7 2021-08-11T23:13:25.000 \n", "6 8 - Summit/University 8 2021-08-11T23:01:57.000 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline \n", "from IPython.display import HTML\n", "from IPython.display import display\n", "import requests # library to handle requests\n", "\n", "#Load Data\n", "#df_crime = pd.read_csv('Datasets/Crime_Incident_Report_-_Dataset.csv')\n", "\n", "#rename columns\n", "#cols= ['Case','Date','Time','Code','IncType','Incident','Grid','NNum','Neighborhood','Block','CallDispCode','CallDisposition', 'Count']\n", "#df_crime.columns= cols\n", "\n", "#selection for Frogtown and nearby area\n", "#df=df_crime.query('Grid in [66,67, 68, 86, 87,88,89, 90, 91, 92,106,107,108,109,110]')\n", "\n", "#Socarata upload Method\n", "from sodapy import Socrata\n", "\n", "#New Upload Method Get Information from Socrata API\n", "client = Socrata(\"information.stpaul.gov\", None)\n", "\n", "#Easier to bulk upload\n", "results = client.get(\"gppb-g9cg\", limit=1000000)\n", "df = pd.DataFrame.from_records(results)\n", "# Find Max Date Value\n", "results = client.get(\"gppb-g9cg\", limit=1)\n", "r_max = pd.DataFrame.from_records(results)\n", "\n", "#rename columns [Note the order of Columns have changed]\n", "cols= ['Block','CallDispCode','CallDisposition','Case','Code', 'Count','Date','Grid','Incident','IncType','Neighborhood','NNum','Time']\n", "df.columns= cols\n", "df=df.dropna()\n", "df = df.astype({\"Case\": int, \"Code\": int, \"Grid\":float, \"NNum\":int,\"Count\":int})\n", "#select respective Grids of interest\n", "df=df.query(\"Grid in (66,67, 68, 86, 87,88,89, 90, 91, 92,106,107,108,109,110)\")\n", "\n", "df.head(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create New Variables " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BlockCallDispCodeCallDispositionCaseCodeCountDateGridIncidentIncType...DayofWeekWeekendMonthDayDayYearDay_MaxTimeHourHourLateNightIntersection
1vanburen av & comoAAdvised21165906261912021-08-11T23:47:34.00090.0DischargeWeapons, Discharging a Firearm in the City Limits...208112232232021-08-11 23:47:342311
2thomas av & grottoAAdvised21165903995412021-08-11T23:46:19.00088.0Proactive Police VisitProactive Police Visit...208112232232021-08-11 23:46:192311
515x charles avAAdvised21165885180012021-08-11T23:13:25.00090.0NarcoticsNarcotics...208112232232021-08-11 23:13:252310
655x central av wAAdvised21165880995412021-08-11T23:01:57.000109.0Proactive Police VisitProactive Police Visit...208112232232021-08-11 23:01:572310
1264x central av wRRReport Written2116586266112021-08-11T21:54:00.000108.0TheftTheft, Bicycle Theft, Under $500...208112232232021-08-11 21:54:002100
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5 rows × 25 columns

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" ], "text/plain": [ " Block CallDispCode CallDisposition Case Code Count \\\n", "1 vanburen av & como A Advised 21165906 2619 1 \n", "2 thomas av & grotto A Advised 21165903 9954 1 \n", "5 15x charles av A Advised 21165885 1800 1 \n", "6 55x central av w A Advised 21165880 9954 1 \n", "12 64x central av w RR Report Written 21165862 661 1 \n", "\n", " Date Grid Incident \\\n", "1 2021-08-11T23:47:34.000 90.0 Discharge \n", "2 2021-08-11T23:46:19.000 88.0 Proactive Police Visit \n", "5 2021-08-11T23:13:25.000 90.0 Narcotics \n", "6 2021-08-11T23:01:57.000 109.0 Proactive Police Visit \n", "12 2021-08-11T21:54:00.000 108.0 Theft \n", "\n", " IncType ... DayofWeek Weekend \\\n", "1 Weapons, Discharging a Firearm in the City Limits ... 2 0 \n", "2 Proactive Police Visit ... 2 0 \n", "5 Narcotics ... 2 0 \n", "6 Proactive Police Visit ... 2 0 \n", "12 Theft, Bicycle Theft, Under $500 ... 2 0 \n", "\n", " Month Day DayYear Day_Max TimeHour Hour LateNight \\\n", "1 8 11 223 223 2021-08-11 23:47:34 23 1 \n", "2 8 11 223 223 2021-08-11 23:46:19 23 1 \n", "5 8 11 223 223 2021-08-11 23:13:25 23 1 \n", "6 8 11 223 223 2021-08-11 23:01:57 23 1 \n", "12 8 11 223 223 2021-08-11 21:54:00 21 0 \n", "\n", " Intersection \n", "1 1 \n", "2 1 \n", "5 0 \n", "6 0 \n", "12 0 \n", "\n", "[5 rows x 25 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Add Time Variables\n", "df= df[df.Case != 18254093] #messed up time variable\n", "\n", "#Convert Date to Datetime!\n", "from datetime import datetime\n", "\n", "df['DateTime']= pd.to_datetime(df['Date']) # Create new column called DateTime\n", "df['Year']= df['DateTime'].dt.year #create year column\n", "df['DayofWeek']=df['DateTime'].dt.dayofweek #create day of the week column where default 0=Monday\n", "df['Weekend'] = df['DayofWeek'].apply(lambda x: 1 if (x>4) else 0) #Create a weekend category\n", "df['Month'] = df['DateTime'].dt.month # Create Month Category\n", "df['Day'] = df['DateTime'].dt.day #Create Day of the Current month\n", "df['DayYear'] = df['DateTime'].dt.dayofyear #Create Day of the year (0-365)\n", "df['Day_Max'] = df.iloc[0,-1] #selects uptodate day; NOTE: the data is sorted chronologically\n", "\n", "#Hour Data\n", "df['TimeHour']= pd.to_datetime(df['Time'])\n", "df['Hour'] = df['TimeHour'].dt.hour.astype(int) #Create Hour Colum\n", "df['LateNight'] = df['Hour'].apply(lambda x: 1 if (x>21 or x<5) else 0) #Latenight designation from 10Pm to 6PM\n", "\n", "#Creating the intersection Column. Note: the Block column has the address information\n", "df.Block = df.Block.astype(str) #first change the type to string\n", "df['Block']= df['Block'].str.lower() #lowercase string to create uniformity\n", "\n", "#While scanning the data I noticed that all intersections had \"&\" \n", "df['Intersection'] = df['Block'].apply(lambda x: 1 if '&' in x else 0) #intersection\n", "\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prepare Intersection table " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The intersection table dimension are (13128, 25)\n" ] } ], "source": [ "#Load clean intersections key\n", "df_Intersection_key = pd.read_csv('Datasets/Intersection_key_clean.csv')\n", "\n", "# Create a new dateframe specifying only intersections from primary dataset\n", "dfI=df.query('Intersection ==1')\n", "print('The intersection table dimension are ' + str(dfI.shape))\n", "#print(dfI.Block.head(10))\n", "\n", "\n", "#Split the strings\n", "new=dfI['Block'].str.split(\"& \", n = 1, expand = True) \n", "dfI['Inter2']= new[1]\n", "new=dfI['Block'].str.split(\" \", n = 1, expand = True) #Note the code specifies the first time a space occured\n", "dfI['Inter1']=new[0]\n", "\n", "#Create the IndexKey; recall we prepared the IntersectionKey where it considers any order\n", "dfI['IndexKey']= dfI['Inter1']+ '_' + dfI['Inter2']\n", "dfI.reset_index()\n", "dfI=pd.merge(dfI, df_Intersection_key, on='IndexKey', how='left')\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BlockCallDispCodeCallDispositionCaseCodeCountDateGridIncidentIncType...MonthDayDayYearDay_MaxTimeHourHourLateNightIntersectionLatitudeLongitude
1grotto_thomasAAdvised21165903995412021-08-11T23:46:19.00088.0Proactive Police VisitProactive Police Visit...8112232232021-08-11 23:46:19231144.959356-93.131431
3rice_sherburneAAdvised21165585995412021-08-11T17:03:05.00091.0Proactive Police VisitProactive Police Visit...8112232232021-08-11 17:03:05170144.956848-93.105964
4hamline_universityRRReport Written21165556182012021-08-11T16:43:00.00086.0NarcoticsNarcotics, Possession of Synthetic Narcotic, D......8112232232021-08-11 16:43:00160144.955811-93.156832
5rice_sherburneAAdvised21165552995412021-08-11T16:39:05.00091.0Proactive Police VisitProactive Police Visit...8112232232021-08-11 16:39:05160144.956848-93.105964
6jackson_universityRRReport Written2116559471012021-08-11T15:41:00.00092.0Auto TheftMotor Vehicle Theft, Automobile...8112232232021-08-11 15:41:00150144.955956-93.096982
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5 rows × 27 columns

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" ], "text/plain": [ " Block CallDispCode CallDisposition Case Code Count \\\n", "1 grotto_thomas A Advised 21165903 9954 1 \n", "3 rice_sherburne A Advised 21165585 9954 1 \n", "4 hamline_university RR Report Written 21165556 1820 1 \n", "5 rice_sherburne A Advised 21165552 9954 1 \n", "6 jackson_university RR Report Written 21165594 710 1 \n", "\n", " Date Grid Incident \\\n", "1 2021-08-11T23:46:19.000 88.0 Proactive Police Visit \n", "3 2021-08-11T17:03:05.000 91.0 Proactive Police Visit \n", "4 2021-08-11T16:43:00.000 86.0 Narcotics \n", "5 2021-08-11T16:39:05.000 91.0 Proactive Police Visit \n", "6 2021-08-11T15:41:00.000 92.0 Auto Theft \n", "\n", " IncType ... Month Day DayYear \\\n", "1 Proactive Police Visit ... 8 11 223 \n", "3 Proactive Police Visit ... 8 11 223 \n", "4 Narcotics, Possession of Synthetic Narcotic, D... ... 8 11 223 \n", "5 Proactive Police Visit ... 8 11 223 \n", "6 Motor Vehicle Theft, Automobile ... 8 11 223 \n", "\n", " Day_Max TimeHour Hour LateNight Intersection Latitude \\\n", "1 223 2021-08-11 23:46:19 23 1 1 44.959356 \n", "3 223 2021-08-11 17:03:05 17 0 1 44.956848 \n", "4 223 2021-08-11 16:43:00 16 0 1 44.955811 \n", "5 223 2021-08-11 16:39:05 16 0 1 44.956848 \n", "6 223 2021-08-11 15:41:00 15 0 1 44.955956 \n", "\n", " Longitude \n", "1 -93.131431 \n", "3 -93.105964 \n", "4 -93.156832 \n", "5 -93.105964 \n", "6 -93.096982 \n", "\n", "[5 rows x 27 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Drop rows with missing coordinates\n", "dfI=dfI[dfI['Coordinates'].notnull()]\n", "\n", "# Separate Latitude and Longitude \n", "new=dfI['Coordinates'].str.split(\",\", n = 1, expand = True) \n", "# making seperate first name column from new data frame \n", "dfI['Latitude']= pd.to_numeric(new[0]) #pd.to_numeric convert it to float\n", "dfI['Longitude']= pd.to_numeric(new[1])\n", "\n", "#Renaming columns\n", "\n", "dfI['Block']=dfI['OutputKey'] #for practical purposes it makes sense\n", "Drop_col=['Inter2','Inter1', 'IndexKey', 'Coordinates', 'OutputKey']\n", "dfI_Final=dfI.drop(Drop_col, axis=1,)\n", "dfI_Final.head(5)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prepare Address Decoder Table \n", "\n", "It is so remarkably short" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#Split Data\n", "dfW=df.query('Intersection==0')\n", "\n", "#Load Complete Decoder Key\n", "df_C= pd.read_csv('Datasets/SemiKey.csv')\n", "df_C= df_C[['Block','Latitude','Longitude']]\n", "\n", "# Merge with the dataset and remove missing values\n", "dC=pd.merge(dfW, df_C, on='Block', how='left')\n", "dC=dC.fillna('Mi')\n", "dC=dC.query('Latitude != \"Mi\"')\n", "\n", "#Bringing the data back together \n", "fg= dfI.append(dC, ignore_index=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preliminary Pre-Cleaning steps \n", "\n", "It is good to do the data pre-cleaning steps here to reduce clutter on a working notebook. A few edits include renaming some values, clustering crimes together and creating some dummies. It is saved in a csv, which is used for execution" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#Few Quick Edits\n", "fg.CallDisposition.loc[(fg['CallDisposition'] == 'G - Gone on Arrival')] = 'Gone on Arrival'\n", "fg.CallDisposition.loc[(fg['CallDisposition'] == 'A - Advised')] = 'Advised'\n", "fg.CallDisposition.loc[(fg['CallDisposition'] == 'RR - Report Written')] = 'Report Written'\n", "fg.Incident.loc[(fg['Incident'] == 'Simple Asasult Dom.')] = 'Simple Assault Dom.'\n", "fg.Incident.loc[(fg['Incident'] == 'Graffiti')] = 'Vandalism'\n", "fg.Incident.loc[fg[\"Incident\"].isin([ \"Rape\",\"Agg. Assault\",'Homicide'])]= 'Violent'\n", "fg.Incident.loc[fg[\"Incident\"].isin([\"Simple Assault Dom.\",\"Agg. Assault Dom.\"])]= 'Domestic Assault'\n", "\n", "\n", "#[fg[\"Incident\"].isin([\"Simple Assault Dom.\", \"Rape\"])\n", "\n", "\n", "#Create a dummy for each crime category\n", "fg= pd.concat([fg,pd.get_dummies(fg['Incident'])], axis=1)\n", "fg= pd.concat([fg,pd.get_dummies(fg['CallDisposition'])], axis=1)\n", "\n", "fg.to_csv('Datasets/FGCrime_Final.csv', encoding='utf-8', index=False)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BlockCallDispCodeCallDispositionCaseCodeCoordinatesCountDateDateTimeDay...VandalismViolent10 - Como11 - Hamline/Midway13 - Union Park7 - Thomas/Dale(Frogtown)8 - Summit/UniversityAdvisedGone on ArrivalReport Written
0grotto_thomasAAdvised21165903995444.959356, -93.13143112021-08-11T23:46:19.0002021-08-11 23:46:1911...0000000100
1rice_sherburneAAdvised21165585995444.956848, -93.10596412021-08-11T17:03:05.0002021-08-11 17:03:0511...0000000100
2hamline_universityRRReport Written21165556182044.955811, -93.15683212021-08-11T16:43:00.0002021-08-11 16:43:0011...0000000001
3rice_sherburneAAdvised21165552995444.956848, -93.10596412021-08-11T16:39:05.0002021-08-11 16:39:0511...0000000100
4jackson_universityRRReport Written2116559471044.955956, -93.09698212021-08-11T15:41:00.0002021-08-11 15:41:0011...0000000001
5dale_thomasAAdvised21165391995444.959342, -93.12637112021-08-11T12:57:32.0002021-08-11 12:57:3211...0000000100
6rice_universityAAdvised21165325995444.955847, -93.10595912021-08-11T11:20:31.0002021-08-11 11:20:3111...0000000100
7linden_mtairyAAdvised21165189995444.959025, -93.09511112021-08-11T08:13:58.0002021-08-11 08:13:5811...0000000100
8rice_sherburneAAdvised21164616995444.956848, -93.10596412021-08-10T14:01:49.0002021-08-10 14:01:4910...0000000100
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6074 rows × 53 columns

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" ], "text/plain": [ " Block CallDispCode CallDisposition Case Code \\\n", "0 grotto_thomas A Advised 21165903 9954 \n", "1 rice_sherburne A Advised 21165585 9954 \n", "2 hamline_university RR Report Written 21165556 1820 \n", "3 rice_sherburne A Advised 21165552 9954 \n", "4 jackson_university RR Report Written 21165594 710 \n", "5 dale_thomas A Advised 21165391 9954 \n", "6 rice_university A Advised 21165325 9954 \n", "7 linden_mtairy A Advised 21165189 9954 \n", "8 rice_sherburne A Advised 21164616 9954 \n", "9 rice_charles A Advised 21164500 9954 \n", "10 arundel_vanburen A Advised 21164358 9954 \n", "11 victoria_vanburen RR Report Written 21164180 2619 \n", "12 rice_charles A Advised 21164176 9954 \n", "13 victoria_minnehaha RR Report Written 21164141 311 \n", "14 grotto_lafond A Advised 21163523 9954 \n", "15 linden_mtairy A Advised 21163303 9954 \n", "16 rice_como A Advised 21163263 9954 \n", "17 rice_sherburne A Advised 21163208 9954 \n", "18 rice_sherburne A Advised 21163179 9954 \n", "19 rice_sherburne A Advised 21163172 9954 \n", "20 victoria_charles RR Report Written 21163180 210 \n", "21 kent_edmund A Advised 21162826 9954 \n", "22 mackubin_sherburne A Advised 21162822 9954 \n", "23 arundel_university A Advised 21162803 9954 \n", "24 dale_fuller A Advised 21162789 9954 \n", "25 western_thomas A Advised 21162739 9954 \n", "26 western_thomas A Advised 21162730 9954 \n", "27 dale_university A Advised 21162704 9954 \n", "28 stanthony_ravoux RR Report Written 21815094 643 \n", "29 griggs_thomas A Advised 21162656 9954 \n", "... ... ... ... ... ... \n", "16707 27x lexington pa n A Advised 21001049 9954 \n", "16708 52x sherburne av RR Report Written 21001046 651 \n", "16709 58x dale st n A Advised 21001026 9954 \n", "16710 27x lexington pa n A Advised 21000853 9954 \n", "16711 46x charles av A Advised 21000708 9954 \n", "16712 28x ravoux st A Advised 21000678 9954 \n", "16713 95x lexington pa n A Advised 21000624 9954 \n", "16714 109x university av w A 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university av w A Advised 21000218 9954 \n", "16734 50x rice st A Advised 21000210 9954 \n", "16735 121x charles av A Advised 21000098 2619 \n", "16736 119x university av w A Advised 21000043 9954 \n", "\n", " Coordinates Count Date \\\n", "0 44.959356, -93.131431 1 2021-08-11T23:46:19.000 \n", "1 44.956848, -93.105964 1 2021-08-11T17:03:05.000 \n", "2 44.955811, -93.156832 1 2021-08-11T16:43:00.000 \n", "3 44.956848, -93.105964 1 2021-08-11T16:39:05.000 \n", "4 44.955956, -93.096982 1 2021-08-11T15:41:00.000 \n", "5 44.959342, -93.126371 1 2021-08-11T12:57:32.000 \n", "6 44.955847, -93.105959 1 2021-08-11T11:20:31.000 \n", "7 44.959025, -93.095111 1 2021-08-11T08:13:58.000 \n", "8 44.956848, -93.105964 1 2021-08-10T14:01:49.000 \n", "9 44.957614, -93.105951 1 2021-08-10T10:53:19.000 \n", "10 44.962067, -93.118676 1 2021-08-10T05:31:00.000 \n", "11 44.962078, -93.136498 1 2021-08-09T21:38:00.000 \n", "12 44.957614, -93.105951 1 2021-08-09T21:36:53.000 \n", "13 44.962988, -93.136503 1 2021-08-09T20:40:00.000 \n", "14 44.960253, -93.131436 1 2021-08-09T00:10:34.000 \n", "15 44.959025, -93.095111 1 2021-08-08T16:53:43.000 \n", "16 44.958975, -93.105941 1 2021-08-08T15:32:19.000 \n", "17 44.956848, -93.105964 1 2021-08-08T13:39:44.000 \n", "18 44.956848, -93.105964 1 2021-08-08T12:39:58.000 \n", "19 44.956848, -93.105964 1 2021-08-08T12:21:17.000 \n", "20 44.957542, -93.136495 1 2021-08-07T23:00:00.000 \n", "21 44.958457, -93.123799 1 2021-08-07T20:24:28.000 \n", "22 44.956661, -93.121237 1 2021-08-07T20:12:54.000 \n", "23 44.955853, -93.118679 1 2021-08-07T19:42:00.000 \n", "24 44.953949, -93.126372 1 2021-08-07T19:21:15.000 \n", "25 44.959379, -93.116175 1 2021-08-07T18:04:24.000 \n", "26 44.959379, -93.116175 1 2021-08-07T17:49:37.000 \n", "27 44.955828, -93.126376 1 2021-08-07T17:05:15.000 \n", "28 44.951390, -93.113338 1 2021-08-07T16:30:00.000 \n", "29 44.959361, -93.151729 1 2021-08-07T15:53:23.000 \n", "... ... ... ... \n", "16707 NaN 1 2021-01-02T18:48:53.000 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Vandalism Violent 10 - Como \\\n", "0 2021-08-11 23:46:19 11 ... 0 0 0 \n", "1 2021-08-11 17:03:05 11 ... 0 0 0 \n", "2 2021-08-11 16:43:00 11 ... 0 0 0 \n", "3 2021-08-11 16:39:05 11 ... 0 0 0 \n", "4 2021-08-11 15:41:00 11 ... 0 0 0 \n", "5 2021-08-11 12:57:32 11 ... 0 0 0 \n", "6 2021-08-11 11:20:31 11 ... 0 0 0 \n", "7 2021-08-11 08:13:58 11 ... 0 0 0 \n", "8 2021-08-10 14:01:49 10 ... 0 0 0 \n", "9 2021-08-10 10:53:19 10 ... 0 0 0 \n", "10 2021-08-10 05:31:00 10 ... 0 0 0 \n", "11 2021-08-09 21:38:00 9 ... 0 0 0 \n", "12 2021-08-09 21:36:53 9 ... 0 0 0 \n", "13 2021-08-09 20:40:00 9 ... 0 0 0 \n", "14 2021-08-09 00:10:34 9 ... 0 0 0 \n", "15 2021-08-08 16:53:43 8 ... 0 0 0 \n", "16 2021-08-08 15:32:19 8 ... 0 0 0 \n", "17 2021-08-08 13:39:44 8 ... 0 0 0 \n", "18 2021-08-08 12:39:58 8 ... 0 0 0 \n", "19 2021-08-08 12:21:17 8 ... 0 0 0 \n", "20 2021-08-07 23:00:00 7 ... 0 1 0 \n", "21 2021-08-07 20:24:28 7 ... 0 0 0 \n", "22 2021-08-07 20:12:54 7 ... 0 0 0 \n", "23 2021-08-07 19:42:00 7 ... 0 0 0 \n", "24 2021-08-07 19:21:15 7 ... 0 0 0 \n", "25 2021-08-07 18:04:24 7 ... 0 0 0 \n", "26 2021-08-07 17:49:37 7 ... 0 0 0 \n", "27 2021-08-07 17:05:15 7 ... 0 0 0 \n", "28 2021-08-07 16:30:00 7 ... 0 0 0 \n", "29 2021-08-07 15:53:23 7 ... 0 0 0 \n", "... ... ... ... ... ... ... \n", "16707 2021-01-02 18:48:53 2 ... 0 0 0 \n", "16708 2021-01-02 18:17:00 2 ... 0 0 0 \n", "16709 2021-01-02 18:16:14 2 ... 0 0 0 \n", "16710 2021-01-02 14:17:00 2 ... 0 0 0 \n", "16711 2021-01-02 08:28:39 2 ... 0 0 0 \n", "16712 2021-01-02 04:28:08 2 ... 0 0 0 \n", "16713 2021-01-02 01:07:36 2 ... 0 0 0 \n", "16714 2021-01-01 23:43:49 1 ... 0 0 0 \n", "16715 2021-01-01 23:41:15 1 ... 0 0 0 \n", "16716 2021-01-01 23:30:53 1 ... 0 0 0 \n", "16717 2021-01-01 23:29:38 1 ... 0 0 0 \n", "16718 2021-01-01 23:28:44 1 ... 0 0 0 \n", "16719 2021-01-01 23:27:39 1 ... 0 0 0 \n", "16720 2021-01-01 23:21:29 1 ... 0 0 0 \n", "16721 2021-01-01 22:32:41 1 ... 0 0 0 \n", "16722 2021-01-01 22:30:00 1 ... 0 0 0 \n", "16723 2021-01-01 18:07:56 1 ... 0 0 0 \n", "16724 2021-01-01 17:33:48 1 ... 0 0 0 \n", "16725 2021-01-01 16:44:38 1 ... 0 0 0 \n", "16726 2021-01-01 16:32:14 1 ... 0 0 0 \n", "16727 2021-01-01 15:25:45 1 ... 1 0 0 \n", "16728 2021-01-01 14:56:58 1 ... 0 0 0 \n", "16729 2021-01-01 14:26:21 1 ... 0 0 0 \n", "16730 2021-01-01 12:36:17 1 ... 0 0 0 \n", "16731 2021-01-01 11:57:10 1 ... 0 0 0 \n", "16732 2021-01-01 10:33:37 1 ... 0 0 0 \n", "16733 2021-01-01 10:33:31 1 ... 0 0 0 \n", "16734 2021-01-01 10:14:26 1 ... 0 0 0 \n", "16735 2021-01-01 03:11:57 1 ... 0 0 0 \n", "16736 2021-01-01 01:08:02 1 ... 0 0 0 \n", "\n", " 11 - Hamline/Midway 13 - Union Park 7 - Thomas/Dale(Frogtown) \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "5 0 0 0 \n", "6 0 0 0 \n", "7 0 0 0 \n", "8 0 0 0 \n", "9 0 0 0 \n", "10 0 0 0 \n", "11 0 0 0 \n", "12 0 0 0 \n", "13 0 0 0 \n", "14 0 0 0 \n", "15 0 0 0 \n", "16 0 0 0 \n", "17 0 0 0 \n", "18 0 0 0 \n", "19 0 0 0 \n", "20 0 0 0 \n", "21 0 0 0 \n", "22 0 0 0 \n", "23 0 0 0 \n", "24 0 0 0 \n", "25 0 0 0 \n", "26 0 0 0 \n", "27 0 0 0 \n", "28 0 0 0 \n", "29 0 0 0 \n", "... ... ... ... \n", "16707 0 0 0 \n", "16708 0 0 0 \n", "16709 0 0 0 \n", "16710 0 0 0 \n", "16711 0 0 0 \n", "16712 0 0 0 \n", "16713 0 0 0 \n", "16714 0 0 0 \n", "16715 0 0 0 \n", "16716 0 0 0 \n", "16717 0 0 0 \n", "16718 0 0 0 \n", "16719 0 0 0 \n", "16720 0 0 0 \n", "16721 0 0 0 \n", "16722 0 0 0 \n", "16723 0 0 0 \n", "16724 0 0 0 \n", "16725 0 0 0 \n", "16726 0 0 0 \n", "16727 0 0 0 \n", "16728 0 0 0 \n", "16729 0 0 0 \n", "16730 0 0 0 \n", "16731 0 0 0 \n", "16732 0 0 0 \n", "16733 0 0 0 \n", "16734 0 0 0 \n", "16735 0 0 0 \n", "16736 0 0 0 \n", "\n", " 8 - Summit/University Advised Gone on Arrival Report Written \n", "0 0 1 0 0 \n", "1 0 1 0 0 \n", "2 0 0 0 1 \n", "3 0 1 0 0 \n", "4 0 0 0 1 \n", "5 0 1 0 0 \n", "6 0 1 0 0 \n", "7 0 1 0 0 \n", "8 0 1 0 0 \n", "9 0 1 0 0 \n", "10 0 1 0 0 \n", "11 0 0 0 1 \n", "12 0 1 0 0 \n", "13 0 0 0 1 \n", "14 0 1 0 0 \n", "15 0 1 0 0 \n", "16 0 1 0 0 \n", "17 0 1 0 0 \n", "18 0 1 0 0 \n", "19 0 1 0 0 \n", "20 0 0 0 1 \n", "21 0 1 0 0 \n", "22 0 1 0 0 \n", "23 0 1 0 0 \n", "24 0 1 0 0 \n", "25 0 1 0 0 \n", "26 0 1 0 0 \n", "27 0 1 0 0 \n", "28 0 0 0 1 \n", "29 0 1 0 0 \n", "... ... ... ... ... \n", "16707 0 1 0 0 \n", "16708 0 0 0 1 \n", "16709 0 1 0 0 \n", "16710 0 1 0 0 \n", "16711 0 1 0 0 \n", "16712 0 1 0 0 \n", "16713 0 1 0 0 \n", "16714 0 1 0 0 \n", "16715 0 1 0 0 \n", "16716 0 1 0 0 \n", "16717 0 1 0 0 \n", "16718 0 1 0 0 \n", "16719 0 1 0 0 \n", "16720 0 1 0 0 \n", "16721 0 1 0 0 \n", "16722 0 0 0 1 \n", "16723 0 1 0 0 \n", "16724 0 1 0 0 \n", "16725 0 1 0 0 \n", "16726 0 0 1 0 \n", "16727 0 0 1 0 \n", "16728 0 1 0 0 \n", "16729 0 1 0 0 \n", "16730 0 1 0 0 \n", "16731 0 0 1 0 \n", "16732 0 1 0 0 \n", "16733 0 1 0 0 \n", "16734 0 1 0 0 \n", "16735 0 1 0 0 \n", "16736 0 1 0 0 \n", "\n", "[6074 rows x 53 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fg.query('Year==2021')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "unexpected EOF while parsing (, 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 Ward1.query('ElectionDescription in (\"11/08/2016 - STATE GENERAL\", \"08/09/2016 - STATE PRIMARY\",\"11/07/2017 - MUNICIPAL GENERAL\",\"11/07/2017 - SCHOOL DISTRICT GENERAL\" ,\"11/08/2016 - STATE GENERAL\",\"08/09/2016 - STATE PRIMARY\",\"11/03/2015 - MUNICIPAL GENERAL\",\"11/03/2015 - SCHOOL DISTRICT GENERAL\",\"11/08/2011 - MUNICIPAL GENERAL\",\"11/06/2018 - STATE GENERAL\",\"08/14/2018 - STATE PRIMARY\")'\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unexpected EOF while parsing\n" ] } ], "source": [ "Ward1.query('ElectionDescription in (\"11/08/2016 - STATE GENERAL\", \"08/09/2016 - STATE PRIMARY\",\"11/07/2017 - MUNICIPAL GENERAL\",\"11/07/2017 - SCHOOL DISTRICT GENERAL\" ,\"11/08/2016 - STATE GENERAL\",\"08/09/2016 - STATE PRIMARY\",\"11/03/2015 - MUNICIPAL GENERAL\",\"11/03/2015 - SCHOOL DISTRICT GENERAL\",\"11/08/2011 - MUNICIPAL GENERAL\",\"11/06/2018 - STATE GENERAL\",\"08/14/2018 - STATE PRIMARY\")' " ] }, { "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }