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

\"FT

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Frogtown, MN, Crime Map 06/17/20

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By Frogtown Crusader (Abu Nayeem)

" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_folding": [] }, "outputs": [], "source": [ "## Table of contents \n", "* [Purpose](#purpose)\n", "* [Data](#data)\n", "* [Audience Participation](#audience)\n", "* [Overview](#overview)\n", " * [Plot: Minneapolis Rental Building Age by Building Size](#age)\n", " * [Map: Minneapolis Tier 2 & 3 geo-coordinates](#tier)\n", "* [Landlord/ Housing Provider Breakdown](#owner)\n", " * [List: Dominant Providers](#big)\n", " * [Map: Properties by Single Owner](#prop)\n", "* [Plot: New Rental Housing Construction by Community](#new)\n", " * [Map: New Rental Housing Construction](#newmap)\n", "* [Interactive Community Data](#comm)\n", " * [Map: Powderhorn](#powder)\n", " * [Map: Northeast](#Northeast)\n", " * [Map: Near North](#Near North)\n", " * [Map: Camden](#Camden)\n", " * [Map: Calhoun Isle](#Calhoun Isle)\n", " * [Map: University](#University)\n", " * [Map: Southwest](#Southwest)\n", " * [Map: Central](#Central)\n", " * [Map: Nokomis](#Nokomis)\n", " * [Map: Longfellow](#Longfellow)\n", " * [Map: Phillips](#Phillips)\n", "* [Audience Participation Reminder](#reminder)\n", "* [Functions_Run](#runall)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents \n", "* [Purpose](#purpose)\n", "* [Data](#data)\n", "* [Frogtown All Crime](#all_crime)\n", " * [Graph: Frogtown Longitudinal Crime Uptodate](#fgd_crime)\n", " * [Table: Frogtown Longitudinal Crime Uptodate by Month](#fgd_month)\n", " * [Graph: Saint Paul Longitudinal Crime Uptodate](#sp_crime)\n", " * [Map: Frogtown 2018 Crime Map](#2018map)\n", " * [Map: Frogtown 2019 to Present Crime Map w/ Proactive visits](#2019map)\n", " * [Map: Frogtown Violent Crime 2017 to Present Crime Map](#violent)\n", " * [Map: Frogtown Multicrime Map](#multi)\n", "* [Frogtown Hotspots w/ map](#hotspot)\n", "* [Frogtown Shooting Report/ include Covid Breakdown](#discharge)\n", " * [Map: Frogtown Longitudinal Discharge Map Uptodate](#fgd_dis)\n", " * [Map: Frogtown Longitudinal Discharge Map on Daytime Uptodate](#fgd_day)\n", " * [Map: Frogtown Longitudinal Annual Discharge Map](#fgd_ann)\n", " * [Graph: Frogtown Longitudinal Firearms Uptodate](#firearm) \n", "* [Saint Paul Shooting Report](#sp_discharge) \n", "* [Concluding Remarks](#conclude)\n", "* [Functions](#func)\n", "* [Functions_Run](#runall)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Purpose \n", "\n", "As a Frogtown resident, it is important that residents are aware on what is happening around our community and how we address it. Every year, there is commotion about safety. Some community members feel that the community is getting worse, while others are saying its getting better. While the city and police department might be saying something totally different. In our current political climate, it's controversial to talk about crime given it's strong correlation with poverty. Unfortunately, this can disenfranchise some community members whom are victims of crime and live in heavy crime areas on a daily basis. These reports and graphs are met to just shed light on the issue and encourage community members to see and interact with the data themselves. Hopefully, the community and/or agencies will take action. **Note:** Parts of Midway, Rondo, and Union City are included. \n", "\n", "Some questions that you may consider:\n", "* What are vulnerable areas or hotspots in the community? \n", "* Are certain crimes more frequent in your area, and how should you or the community address it?\n", "* What are some trends in the your community?\n", "* What is the frequency of calling the police in nearby area? Is there over-reporting/ under-reporting?\n", "* How can we address these issues as a community (i.e. organizing; etc)?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Open Source Data Initiative\n", "\n", "It is important that data is accessible and provided to the public. This report and others will be available on Github allowing others to contribute, replicate, and use code for their own respective neighborhood. If anyone is interested in mapping out East Side, Payne Phalen, etc., please reach out to me. \n", "\n", "You can use the data provided by this report, but understand that I'm not an official agency and not liable for incorrect data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### About the Dataset: \n", "\n", "The [Crime Incident Report - Dataset](https://information.stpaul.gov/Public-Safety/Crime-Incident-Report-Dataset/gppb-g9cg) was obtained from the Saint Paul Website. It is publicly available. The report contains incidents from Aug 14 2014 through the most recent date, as released by the Saint Paul Police Department.\n", "\n", "A few notes about the dataset:\n", "* The dataset have several human errors such as mis-categorizing addresses and address being designated to the wrong police grid.\n", "* The dataset **DOES NOT PROVIDE EXACT ADDRESSES/ GEO-COORDINATES**. However, I've constructed an algorithm that get a reliable proxy address for most entries. \n", " * The process included entering coordinate manually; if any residents are interested in mapping out their own community please feel free to connect with me.\n", " * Google Maps geocoder was used\n", " * The algorithm **does not cover** the intersections of Capitol Heights and Mt. Airy region. Some data was excluded from region due to inadequate mapping. They have many intersections and curved streets; it would take time to geo-code it.\n", "* The analysis/algorithm is restricted near the Frogtown area.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Saint Paul Police grid w/ total crime numbers all years [excluding proactive visits] \n", "\n", "The dataset comprise this area.\n", "\n", "![title](Images/gridnum1.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Display Total Incidents of all years\n", "\n", "**NOTE:** The following changes were made in the crime category to consolidate categories\n", "* Graffiti was combined into Vandalism\n", "* Violent Crimes combined the categories: Rape, Homicide, Aggressive Assault\n", "* Domestic Assault includes both Simple and Aggressive Assaults \n", "* Community Engagement Events and Proactive Police Visit are not crimes. Proactive Police Visit is not response to 911 call. They will be excluded for vast majority of the analysis" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "List of Events in Frogtown (nearby) from 2019 to Present\n" ] }, { "data": { "text/plain": [ "Proactive Police Visit 9268\n", "Theft 1915\n", "Vandalism 730\n", "Auto Theft 693\n", "Narcotics 533\n", "Discharge 444\n", "Burglary 426\n", "Domestic Assault 386\n", "Community Engagement Event 198\n", "Robbery 178\n", "Violent 165\n", "Arson 26\n", "Name: Incident, dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print('List of Events in Frogtown (nearby) from 2019 to Present')\n", "fg.query('Year>=2019')['Incident'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Frogtown Yearly Crime Comparison Up to Current Date \n", "\n", "How much crime is there so far in comparison to the previous year on this date? Are certain areas increasing or decreasing? **Note:** the function can choose any day prior to the current date.\n", "\n", "We choose the max date for the dataset. We can choose an earlier date if desired" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph maps All incidents up to 5/12/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_toDate_Year_Crime(Incident='All',Day=Max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Frogtown Total Crimes toDate by Month \n", "\n", "What are some monthly trends" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This table maps All incidents up to Day 9/10/20XX\n" ] }, { "data": { "text/html": [ "
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Month1234...6789
Year2018201920202018201920202018201920202018...2020201820192020201820192020201820192020
CommunityGrid
Midway66.01001100112...2101041000
86.027141812131018141216...25193023192013762
Summit-University107.0182415171052281720...29241324192212914
108.010251923122215171520...23181528282228714
109.010386862556...51144255404
110.02217141917152281118...19351722191619925
Thomas-Frogtown67.01169971215497...71191610132422
68.05340368291...9815236223
87.027192533211314182719...263121243324271254
88.018262826193325153626...6740314636405317215
89.050324124203626143729...534637355446469315
90.032162218202029152716...672931932936576516
91.063441333414...11165163713103
92.00010011102...0020010000
Union Park106.033354923415743314934...184749154465169913
\n", "

15 rows × 27 columns

\n", "
" ], "text/plain": [ "Month 1 2 3 4 \\\n", "Year 2018 2019 2020 2018 2019 2020 2018 2019 2020 2018 \n", "Community Grid \n", "Midway 66.0 1 0 0 1 1 0 0 1 1 2 \n", " 86.0 27 14 18 12 13 10 18 14 12 16 \n", "Summit-University 107.0 18 24 15 17 10 5 22 8 17 20 \n", " 108.0 10 25 19 23 12 22 15 17 15 20 \n", " 109.0 10 3 8 6 8 6 2 5 5 6 \n", " 110.0 22 17 14 19 17 15 22 8 11 18 \n", "Thomas-Frogtown 67.0 11 6 9 9 7 12 15 4 9 7 \n", " 68.0 5 3 4 0 3 6 8 2 9 1 \n", " 87.0 27 19 25 33 21 13 14 18 27 19 \n", " 88.0 18 26 28 26 19 33 25 15 36 26 \n", " 89.0 50 32 41 24 20 36 26 14 37 29 \n", " 90.0 32 16 22 18 20 20 29 15 27 16 \n", " 91.0 6 3 4 4 1 3 3 3 4 14 \n", " 92.0 0 0 1 0 0 1 1 1 0 2 \n", "Union Park 106.0 33 35 49 23 41 57 43 31 49 34 \n", "\n", "Month ... 6 7 8 9 \n", "Year ... 2020 2018 2019 2020 2018 2019 2020 2018 2019 2020 \n", "Community Grid ... \n", "Midway 66.0 ... 2 1 0 1 0 4 1 0 0 0 \n", " 86.0 ... 25 19 30 23 19 20 13 7 6 2 \n", "Summit-University 107.0 ... 29 24 13 24 19 22 12 9 1 4 \n", " 108.0 ... 23 18 15 28 28 22 28 7 1 4 \n", " 109.0 ... 5 11 4 4 2 5 5 4 0 4 \n", " 110.0 ... 19 35 17 22 19 16 19 9 2 5 \n", "Thomas-Frogtown 67.0 ... 7 11 9 16 10 13 2 4 2 2 \n", " 68.0 ... 9 8 1 5 2 3 6 2 2 3 \n", " 87.0 ... 26 31 21 24 33 24 27 12 5 4 \n", " 88.0 ... 67 40 31 46 36 40 53 17 2 15 \n", " 89.0 ... 53 46 37 35 54 46 46 9 3 15 \n", " 90.0 ... 67 29 31 93 29 36 57 6 5 16 \n", " 91.0 ... 11 16 5 16 3 7 13 1 0 3 \n", " 92.0 ... 0 0 2 0 0 1 0 0 0 0 \n", "Union Park 106.0 ... 18 47 49 15 44 65 16 9 9 13 \n", "\n", "[15 rows x 27 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table_toDate_Month_Crime(Incident='All',Day=Max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Saint Paul Yearly Crime Comparison Up to Current Date \n", "\n", "How do we compare to our neighbors and what are some trends?" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge normalized incidents of Saint Paul neighborhoods within 132 days from date 5/12/20XX\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_toDate_Days_SPCrime_Norm(Incident='Discharge',Day=Max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Frogtown Annual Crime Map (Default 2020) \n", "At least four crimes must occur in one location to be displayed\n", "\n", "**NOTE: It's interactive, click on the circles for more details on the total number and type of crimes committed**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_Frogtown_year(2020)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Frogtown Violent Crimes Map from 2018 to Present \n", "\n", "Violent crimes are less salient or apparent to community members. We can see which areas/ blocks are safer. \n", "\n", "* Orange= 2018\n", "* Green= 2019\n", "* Purple= 2020" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_Frogtown_long_crime('Violent')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Frogtown 2020 Multi-Crime Map \n", "\n", "* Purple= Discharge\n", "* Green= Autotheft\n", "* Orange= Theft\n", "* Blue= Burglary\n", "* Brown= Narcotics\n", "* Red= Vandalism" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_multicrime_byYear(2020)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hotspots near Frogtown from 2019 to present \n", "\n", "Notice the address codes are masked and count is limited to 15" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Block_IntersectionCount
204130x university av w661
66262x rice st85
23117x charles av74
22515x sherburne av67
28127x lexington pa n63
170124x stanthony av53
163123x university av w51
113795x lexington pa n46
1249dale_university46
87375x milton st n44
38739x lexington pa n44
51050x rice st35
99683x university av w31
1302hamline_university30
23519x edmund av30
36537x lexington pa n29
29028x ravoux st26
79107x university av w26
1384rice_charles25
55754x university av w25
64961x university av w25
65362x aurora av25
47046x dale st n24
116097x university av w23
105788x university av w23
89876x university av w23
1196arundel_university21
1387rice_sherburne20
61959x university av w20
70965x galtier st19
47546x thomas av19
31631x dale st n19
1248dale_thomas19
117298x university av w19
188128x concordia av18
1339lexington_university18
44344x galtier st18
48247x marion st16
72966x thomas av16
52151x rice st16
42542x rice st16
65662x chatsworth st n16
81371x sherburne av16
1420victoria_university16
43843x university av w15
99113x university av w15
43543x lafond av15
74967x thomas av15
\n", "
" ], "text/plain": [ " Block_Intersection Count\n", "204 130x university av w 661\n", "662 62x rice st 85\n", "231 17x charles av 74\n", "225 15x sherburne av 67\n", "281 27x lexington pa n 63\n", "170 124x stanthony av 53\n", "163 123x university av w 51\n", "1137 95x lexington pa n 46\n", "1249 dale_university 46\n", "873 75x milton st n 44\n", "387 39x lexington pa n 44\n", "510 50x rice st 35\n", "996 83x university av w 31\n", "1302 hamline_university 30\n", "235 19x edmund av 30\n", "365 37x lexington pa n 29\n", "290 28x ravoux st 26\n", "79 107x university av w 26\n", "1384 rice_charles 25\n", "557 54x university av w 25\n", "649 61x university av w 25\n", "653 62x aurora av 25\n", "470 46x dale st n 24\n", "1160 97x university av w 23\n", "1057 88x university av w 23\n", "898 76x university av w 23\n", "1196 arundel_university 21\n", "1387 rice_sherburne 20\n", "619 59x university av w 20\n", "709 65x galtier st 19\n", "475 46x thomas av 19\n", "316 31x dale st n 19\n", "1248 dale_thomas 19\n", "1172 98x university av w 19\n", "188 128x concordia av 18\n", "1339 lexington_university 18\n", "443 44x galtier st 18\n", "482 47x marion st 16\n", "729 66x thomas av 16\n", "521 51x rice st 16\n", "425 42x rice st 16\n", "656 62x chatsworth st n 16\n", "813 71x sherburne av 16\n", "1420 victoria_university 16\n", "438 43x university av w 15\n", "99 113x university av w 15\n", "435 43x lafond av 15\n", "749 67x thomas av 15" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "B=fgc.query('Year in [2019,2020]')\n", "B=B[['Block','Count']].groupby(['Block']).sum().reset_index()\n", "B.columns=['Block_Intersection','Count']\n", "B.query('Count>14').sort_values(['Count'], ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Frogtown Hotspot Interactive map from 2019 to present \n", "\n", "* The bubbles are **not to scale** across the different categories to make this map more accessible. You may need to zoom in to distinguish points\n", "* The graph is **Interactive**; if you click on a point, it will list the number and type of incidents in that area\n", "* Legend\n", " * Orange: Number of Incidents greater than 10 and less than 20\n", " * Green: Number of Incidents greater than 20 and less than 50\n", " * Purple: Number of Incident greater than 50 \n" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_Frogtown_hot_spot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Frogtown Gun Discharge Reports\n", "\n", "\n", "We hear it **all** the time from many people that our awareness of shootings is based on connection to social media and officials suggesting it is safer now than years prior. Is this true? Well, let's find out. It's worth noting that weather conditions can influence the frequency of when shootings occur. Finally, the discharge category excludes a firearm being used to assist in another crime. \n", "\n", "**Why focus on shootings?**\n", "\n", "Shootings can harm innocent bystanders and creates a significant sense of fear because it can be heard from a distance. It is one of the more salient crimes. \n", "\n", "**How is a discharge report determined?** \n", "\n", "If a police report was made, that means there were 2 or more witnesses and/or evidence was found (spent shell casings, bullet holes in cars, homes, etc).\n", "\n", "First let's construct a table showing the total number of shootings up to current date in comparison to previous years. Notice that many grids are experiencing different trends, so it can be true that some neighbors are exposed to more shootings this year and others less.\n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph maps Discharge incidents up to 12/9/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_toDate_Year_Crime(Incident='Discharge',Day=Max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a results of the the Covid outbreak and civil unrest, I have broken the data into three stages: preCovid, Covid only, and post_GeorgeFloyd murder " ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge incidents of the Frogtown Grid within 73 days from date 3/14/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#PreCovid\n", "plot_toDate_Days_FGCrime(Incident='Discharge',Day=CV, CDate=CV)\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge incidents of the Frogtown Grid within 73 days from date 5/25/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Covid Only\n", "plot_toDate_Days_FGCrime(Incident='Discharge',Day=GE-CV, CDate=GE)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge incidents of the Frogtown Grid within 93 days from date 8/26/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#post-George Floyd\n", "plot_toDate_Days_FGCrime(Incident='Discharge',Day=Max-GE, CDate=Max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Shootings within last 120 days" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This map displays Discharge incidents within 60 days from date 8/26/20XX\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_Frogtown_long_crime_todate_2020(Incident='Discharge',Day=60, CDate=Max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The table below displays the number of shootings by grid in Frogtown broken by month over the last two years" ] }, { "cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This table maps Discharge incidents up to Day 6/10/20XX\n" ] }, { "data": { "text/html": [ "
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Month123456
Year201820192020201820192020201820192020201820192020201820192020201820192020
CommunityGrid
Midway66.0000000000100001001
86.0011001010011203006
Summit-University107.0123301102300157336
108.0025121210232015102
109.0000100001000004000
110.0101010000110100100
Thomas-Frogtown67.0000001002011210000
68.0101000005001000002
87.0133110113271247003
88.0233021105313419205
89.06191170180295410414
90.0000002112121101100
91.0000000110300012000
Union Park106.0100010000000002003
\n", "
" ], "text/plain": [ "Month 1 2 3 4 \\\n", "Year 2018 2019 2020 2018 2019 2020 2018 2019 2020 2018 \n", "Community Grid \n", "Midway 66.0 0 0 0 0 0 0 0 0 0 1 \n", " 86.0 0 1 1 0 0 1 0 1 0 0 \n", "Summit-University 107.0 1 2 3 3 0 1 1 0 2 3 \n", " 108.0 0 2 5 1 2 1 2 1 0 2 \n", " 109.0 0 0 0 1 0 0 0 0 1 0 \n", " 110.0 1 0 1 0 1 0 0 0 0 1 \n", "Thomas-Frogtown 67.0 0 0 0 0 0 1 0 0 2 0 \n", " 68.0 1 0 1 0 0 0 0 0 5 0 \n", " 87.0 1 3 3 1 1 0 1 1 3 2 \n", " 88.0 2 3 3 0 2 1 1 0 5 3 \n", " 89.0 6 1 9 1 1 7 0 1 8 0 \n", " 90.0 0 0 0 0 0 2 1 1 2 1 \n", " 91.0 0 0 0 0 0 0 1 1 0 3 \n", "Union Park 106.0 1 0 0 0 1 0 0 0 0 0 \n", "\n", "Month 5 6 \n", "Year 2019 2020 2018 2019 2020 2018 2019 2020 \n", "Community Grid \n", "Midway 66.0 0 0 0 0 1 0 0 1 \n", " 86.0 1 1 2 0 3 0 0 6 \n", "Summit-University 107.0 0 0 1 5 7 3 3 6 \n", " 108.0 3 2 0 1 5 1 0 2 \n", " 109.0 0 0 0 0 4 0 0 0 \n", " 110.0 1 0 1 0 0 1 0 0 \n", "Thomas-Frogtown 67.0 1 1 2 1 0 0 0 0 \n", " 68.0 0 1 0 0 0 0 0 2 \n", " 87.0 7 1 2 4 7 0 0 3 \n", " 88.0 1 3 4 1 9 2 0 5 \n", " 89.0 2 9 5 4 10 4 1 4 \n", " 90.0 2 1 1 0 1 1 0 0 \n", " 91.0 0 0 0 1 2 0 0 0 \n", "Union Park 106.0 0 0 0 0 2 0 0 3 " ] }, "execution_count": 196, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table_toDate_Month_Crime(Incident='Discharge',Day=Max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Frogtown UptoDate gun discharge map 2020 to Present (interactive) \n", "\n", "A police Grid can cover a lot of area. From the map we can spot parts of the community that are currently vulnerable and those in the past.\n", "\n", "* Orange= 2018\n", "* Green= 2019\n", "* Purple= 2020" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This map displays Discharge incidents up to 12/9/20XX\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_Frogtown_long_crime_todate(Incident='Discharge',Day=Max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Frogtown UptoDate Daytime Discharge Map \n", "\n", "Daytime shooting are particularly concerning to our community as there as there are greater frequency of innocent bystanders being harmed and can influence community members behavior to go outside or parents decision to let their kids play. \n", "\n", "* Orange= 2018\n", "* Green= 2019\n", "* Purple= 2020" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This map displays Discharge incidents up to 12/9/20XX from 7AM to 8PM\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_Frogtown_long_crime_daytime_todate(Incident='Discharge',Day=Max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Frogtown yearly gun discharge from 2018 to Present \n", "\n", "* Orange= 2018\n", "* Green= 2019\n", "* Purple= 2020" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_Frogtown_long_crime(Incident='Discharge')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Firearm Usage (Any crime involving it) \n", "\n", "Often firearms are used when committing another crime, but does not need to be fired. " ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The total crimes involving firearms in Frogtown Area\n" ] }, { "data": { "text/plain": [ "Discharge 1231\n", "Robbery 271\n", "Violent 251\n", "Name: Incident, dtype: int64" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print('The total crimes involving firearms in Frogtown Area')\n", "fgc_fire['Incident'].value_counts()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph maps Firearm incidents up to 1/29/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_toDate_Year_Firearm(Day=Max)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph maps Firearm incidents of Saint Paul neighborhoods up to 8/26/20XX\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_toDate_Year_SPFirearm()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saint Paul Discharge Incidents \n", "\n", "Similar to the Frogtown analysis, we will construct a normalized trend graph, and break the data to the Covid and civil unrest events\n", "\n", "What are some trends at the city-wide level? (normalized per 10000)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge normalized incidents of Saint Paul neighborhoods within 239 days from date 8/26/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_toDate_Days_SPCrime_Norm(Incident='Discharge')" ] }, { "cell_type": "code", "execution_count": 237, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge incidents of Saint Paul neighborhoods within 73 days from date 3/14/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#PreCovid\n", "plot_toDate_Days_SPCrime(Incident='Discharge',Day=CV, CDate=CV)" ] }, { "cell_type": "code", "execution_count": 233, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge incidents of Saint Paul neighborhoods within 73 days from date 5/25/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Covid\n", "plot_toDate_Days_SPCrime(Incident='Discharge',Day=GE-CV, CDate=GE)\n" ] }, { "cell_type": "code", "execution_count": 234, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge incidents of Saint Paul neighborhoods within 16 days from date 6/10/20XX\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Post: GeorgeFloyd\n", "plot_toDate_Days_SPCrime(Incident='Discharge',Day=Max-GE, CDate=Max)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Concluding Remarks \n", "\n", "From the data above, we can visually spot sections in the community and specific locations where crime is more frequent. It is important that we have a community discussion before taking any further action. If you have any question in regards to visualization and other things, please feel free to reach out to me." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions " ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Plot Multiple Crimes by Year" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "hidden": true }, "outputs": [], "source": [ "def plot_multicrime_byYear(Year=2019):\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " B= fgc[(fgc['Year'] == Year)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge']\n", "\n", " T=B.query('Theft==1')\n", " T=T[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", "\n", " V=B.query('Vandalism==1')\n", " V=V[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", "\n", " N=B.query('Narcotics==1')\n", " N=N[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", "\n", " A=B.query('Incident==\"Auto Theft\"')\n", " A=A[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", "\n", " D=B.query('Discharge==1')\n", " D=D[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", "\n", " Br=B.query('Burglary==1')\n", " Br=Br[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", "\n", "#Create Frogtown GeoMap\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", "\n", " for index, row in T.iterrows(): \n", " popup_text = \"Address: {}
total theft incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"],row['Count']) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count'] +3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", "\n", " for index, row in V.iterrows(): \n", " popup_text = \"Address: {}
total vandalism incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"],row['Count']) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count'] +3,\n", " color=\"#FF0000\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", "\n", " for index, row in N.iterrows(): \n", " popup_text = \"Address: {}
total narcotics incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"],row['Narcotics']) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Narcotics'] + 3,\n", " color=\"#654321\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", "\n", " for index, row in A.iterrows(): \n", " popup_text = \"Address: {}
total autotheft incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"],row['Count']) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count'] +3,\n", " color='#007849',\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in D.iterrows(): \n", " popup_text = \"Address: {}
total discharge incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"],row['Count']) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count'] +3,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", "\n", " for index, row in Br.iterrows(): \n", " popup_text = \"Address: {}
total burglary incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"],row['Count']) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count'] +3,\n", " color=\"#0000ff\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " return FG_map" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Plot Crime Map by Year" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "hidden": true }, "outputs": [], "source": [ "\n", "\n", "def plot_Frogtown_year(Year=2018):\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " B= fgc[(fgc['Year'] == Year)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson']\n", " B=B[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " B=B.query('Count>3')\n", " \n", " # for each row in the data, add a cicle marker\n", " for index, row in B.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " return FG_map\n" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "hidden": true }, "outputs": [], "source": [ "#interact(plot_Frogtown_year, Year=widgets.IntSlider(min=2014,max=2019,step=1,value=2018));" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Plot Crime Map by Year w/ proactive visits" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "hidden": true }, "outputs": [], "source": [ "def plot_Frogtown_year_proactive(Year=2018):\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " B= fgc[(fgc['Year'] == Year)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson']\n", " B=B[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " \n", " A= fgp[(fgp['Year'] == Year)]\n", " A= A.groupby(['Block','Latitude','Longitude']).size().reset_index()\n", " A.columns=['Block','Latitude','Longitude','Count'] \n", " \n", " for index, row in B.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", "\n", " for index, row in A.iterrows(): \n", " popup_text = \"Address: {}
total proactive calls: {}\"\n", " popup_text = popup_text.format(row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " \n", " return FG_map" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Plot Longitudinal Crime Compared by Year" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "hidden": true }, "outputs": [], "source": [ "def plot_Frogtown_long_crime(Incident='Discharge'):\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson','Year']\n", "\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " B17=B.query('Year == 2017')\n", " B17=B17[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " B18=B.query('Year == 2018')\n", " B18=B18[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " B19=B.query('Year == 2019')\n", " B19=B19[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", "\n", " for index, row in B17.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B18.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"], row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B19.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " \n", " return FG_map" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "hidden": true }, "outputs": [], "source": [ "#interact(plot_Frogtown_long_crime, Incident=['Discharge','Violent','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Robbery','Domestic Assault','Arson'])" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Plot Longitudinal Crime Map Compared by Year; todate" ] }, { "cell_type": "code", "execution_count": 247, "metadata": { "hidden": true }, "outputs": [], "source": [ "\n", "def plot_Frogtown_long_crime_todate(Incident='All',Day=Max):\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson','Year']\n", "\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " if Incident=='All':\n", " B= fgc\n", " else: \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " B17=B.query('Year == 2017')\n", " B17=B17[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " B18=B.query('Year == 2018')\n", " B18=B18[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " B19=B.query('Year == 2019')\n", " B19=B19[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " print('This map displays {} incidents up to {}20XX'.format(Incident,Date))\n", " for index, row in B17.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B18.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"], row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B19.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " \n", " return FG_map" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Plot Longitudinal Daytime Crime Map Compared by Year; todate" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hidden": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'Max' 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;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mplot_Frogtown_long_crime_daytime_todate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mIncident\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'All'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mDay\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mMax\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 Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n\u001b[0;32m 4\u001b[0m ,'Robbery','Domestic Assault','Violent','Arson','Year']\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'Max' is not defined" ] } ], "source": [ "\n", "def plot_Frogtown_long_crime_daytime_todate(Incident='All',Day=Max):\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson','Year']\n", "\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " if Incident=='All':\n", " B= fgc\n", " else: \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " B= B.query('Hour >= 6 and Hour <20')\n", " B17=B.query('Year == 2017')\n", " B17=B17[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " B18=B.query('Year == 2018')\n", " B18=B18[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " B19=B.query('Year == 2019')\n", " B19=B19[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " print('This map displays {} incidents up to {}20XX from 7AM to 8PM'.format(Incident,Date))\n", " for index, row in B17.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B18.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"], row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B19.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " \n", " return FG_map" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Crime Table toDate by Month " ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "hidden": true }, "outputs": [], "source": [ "#convert MOnth\n", "def Convert_Month(num):\n", " Months= ['January','February','March','April','May','June','July','August','September','October','November','December']\n", " for i in range(12):\n", " if num==(i+1):\n", " Xi=Months[i]\n", " return Xi \n", "\n", "def table_toDate_Month_Crime(Incident='All',Day=Max):\n", " if Incident=='All':\n", " B= fgc\n", " else: \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " B=B.query('Year >= 2017')\n", " Index= ['Year','Grid','Count','Month']\n", " C= B[Index].groupby(['Year','Grid','Month']).sum().reset_index()\n", " C['Month_Name']= C.Month.apply(Convert_Month) \n", " C['Community']= C.Grid.apply(commun)\n", " print('This table maps {} incidents up to Day {}20XX'.format(Incident,Date))\n", " return pd.pivot_table(C, values='Count', index=['Community','Grid'], columns=['Month', 'Year'], fill_value=0)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Crime Graph toDate by Year" ] }, { "cell_type": "code", "execution_count": 272, "metadata": { "hidden": true }, "outputs": [], "source": [ "\n", "#Crime Plot\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "\n", "def plot_toDate_Year_Crime(Incident='All',Day=Max):\n", " if Incident=='All':\n", " B= fgc\n", " else: \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B=B.query('Year >= 2017')\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Grid','Count']\n", " C= B[Index].groupby(['Year','Grid']).sum().reset_index()\n", " C['Community']= C.Grid.apply(commun) \n", " print('This graph maps {} incidents up to {}20XX'.format(Incident,Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community','Grid'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= Incident + ' Yearly Total Incidents UptoDate: '+ str(Date)+'20XX')\n", " return plt.show()\n", "\n", "# Firearm Plot\n", "def plot_toDate_Year_Firearm(Day=Max):\n", " B= fgc_fire[(fgc_fire['DayYear'] <= Day)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B.query('Year>2016')\n", " Index= ['Year','Grid','Count']\n", " C= B[Index].groupby(['Year','Grid']).sum().reset_index()\n", " C['Community']= C.Grid.apply(commun) \n", " print('This graph maps Firearm incidents up to {}20XX'.format(Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community','Grid'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Firearm Yearly Total Incidents UptoDate: ' + str(Date)+'20XX')\n", " return plt.show()\n" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Latenight Crime Plot by Year" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hidden": true }, "outputs": [], "source": [ "def plot_Frogtown_year_Latenight(Year=2018):\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " B= fgc[(fgc['Year'] == Year)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson']\n", " BM=B.query('LateNight ==0')\n", " BM=BM[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " BM=BM.query('Count>2')\n", " BL=B.query('LateNight ==1')\n", " BL=BL[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " BL=BL.query('Count>2')\n", " \n", " # for each row in the data, add a cicle marker\n", " for index, row in BM.iterrows(): \n", " popup_text = \"Year: {} Non-late
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", "\n", " for index, row in BL.iterrows(): \n", " popup_text = \"Year: {} Late
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " return FG_map\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "hidden": true }, "outputs": [], "source": [ "#plot_Frogtown_year_Latenight(2018)" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Frogtown Plot by Year and month" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "hidden": true }, "outputs": [], "source": [ "def plot_Frogtown_yearmonth(Year=2018,Month=1):\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " B= fgc[(fgc['Year'] == Year)]\n", " B= B[(B['Month'] == Month)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge']\n", " B=B[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " \n", "\n", " # for each row in the data, add a cicle marker\n", " for index, row in B.iterrows(): \n", " Other= row['Count'] - row['Theft'] - row['Vandalism'] - row['Narcotics'] -row['Auto Theft'] - row['Burglary'] - row['Discharge']\n", " popup_text = \"Year: {} Month: {}
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Other: {}\"\n", " popup_text = popup_text.format(str(Year), str(Month),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Narcotics'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], Other) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " return FG_map\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "hidden": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bc4b454da29041f4baa0cced57918dd0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(IntSlider(value=2018, description='Year', max=2019, min=2014), Dropdown(description='Mon…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot_Frogtown_yearmonth(2018,1)\n", "interact(plot_Frogtown_yearmonth, Year=widgets.IntSlider(min=2014,max=2019,step=1,value=2018),\\\n", " Month=[('January', 1), ('February', 2), ('March',3), ('April',4), ('May',5) ,('June',6),('July',7)]);" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Frogtown plot by year and hour" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hidden": true }, "outputs": [], "source": [ "def plot_Frogtown_yearhour(Year=2018,Hour=0):\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " B= fgc[(fgc['Year'] == Year)]\n", " B= B[(B['Hour'] == Hour)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge']\n", " B=B[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " \n", "\n", " # for each row in the data, add a cicle marker\n", " for index, row in B.iterrows(): \n", " Other= row['Count'] - row['Theft'] - row['Vandalism'] - row['Narcotics'] -row['Auto Theft'] - row['Burglary'] - row['Discharge']\n", " popup_text = \"Year: {} Hour: {}
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Other: {}\"\n", " popup_text = popup_text.format(str(Year), str(Hour),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Narcotics'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], Other) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " return FG_map" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Frogtown Hotspot Map" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "def plot_Frogtown_hot_spot(Year=2018):\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " B= fgc[(fgc['Year'] >= Year)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson']\n", " B=B[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " C=B.query('Count>9 and Count < 21')\n", " D=B.query('Count>20 and Count < 51')\n", " E=B.query('Count>50')\n", " \n", " # for each row in the data, add a cicle marker\n", " for index, row in C.iterrows(): \n", " popup_text = \"Year: 2018/19
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']/2,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in D.iterrows(): \n", " popup_text = \"Year: 2018/19
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']/5,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in E.iterrows(): \n", " popup_text = \"Year: 2018/19
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']/9,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", "\n", " return FG_map" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "#### Saint Paul Data Load and Crimetable to Date" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [ "#Read Data\n", "df = pd.read_csv('Datasets/Crime_Incident_Report_-_Dataset.csv')\n", "cols= ['Case','Date','Time','Code','IncType','Incident','Grid','NNum','Neighborhood','Block','CallDispCode','CallDisposition', 'Count']\n", "df.columns= cols\n", "\n", "#Add Time Variables\n", "df= df[df.Case != 18254093] #messed up time variable\n", "df['Date']= pd.to_datetime(df['Date'])\n", "df['Year']= df['Date'].dt.year\n", "df=df.query('Year > 2016')\n", "df['DayYear'] = df['Date'].dt.dayofyear\n", "df['Community']= df['Grid'].apply(commun)\n", "df= df.query('Code not in [9954,9959] and Community !=\"NaN\"')\n", "\n", "def plot_toDate_Year_SPCrime(Incident='All',Day=Max):\n", " if Incident=='All':\n", " B= df\n", " else: \n", " B= df[(df['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Community','Count']\n", " C= B[Index].groupby(['Year','Community']).sum().reset_index()\n", " print('This graph maps {} incidents of Saint Paul neighborhoods up to {}20XX'.format(Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= Incident + ' Yearly UptoDate Total Incidents')\n", " return plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions all \n", "\n", "Run to activate all functions" ] }, { "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" ] } ], "source": [ "from __future__ import print_function\n", "from ipywidgets import interact, interactive, fixed, interact_manual\n", "import ipywidgets as widgets\n", "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline \n", "import folium\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "#Upload Data\n", "fg = pd.read_csv('Datasets\\FGCrime_Final.csv')\n", "\n", "#Set max limit for uptodate function\n", "Max= fg.loc[1,'Day_Max']\n", "\n", "# Set a friendly Date variable\n", "fg['FDate']=fg['Month'].astype(str) + '/' + fg['Day'].astype(str) + '/'\n", "\n", "fgp= fg.query('Code in [9954]') # Specify proactive calls\n", "fgc= fg.query('Code not in [9954,9959]') #specify all crime related police visits\n", "fgc_Date= fgc[(fgc['DayYear'] <= Max)] #this specifies to date df\n", "fgc_fire=fgc[fgc['IncType'].str.contains(\"Firearm\")] #specifies all firearm\n", "\n", "def plot_multicrime_byYear(Year=2019):\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " B= fgc[(fgc['Year'] == Year)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge']\n", "\n", " T=B.query('Theft==1')\n", " T=T[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", "\n", " V=B.query('Vandalism==1')\n", " V=V[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", "\n", " N=B.query('Narcotics==1')\n", " N=N[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", "\n", " A=B.query('Incident==\"Auto Theft\"')\n", " A=A[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", "\n", " D=B.query('Discharge==1')\n", " D=D[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", "\n", " Br=B.query('Burglary==1')\n", " Br=Br[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", "\n", "#Create Frogtown GeoMap\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", "\n", " for index, row in T.iterrows(): \n", " popup_text = \"Address: {}
total theft incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"],row['Count']) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count'] +3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", "\n", " for index, row in V.iterrows(): \n", " popup_text = \"Address: {}
total vandalism incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"],row['Count']) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count'] +3,\n", " color=\"#FF0000\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", "\n", " for index, row in N.iterrows(): \n", " popup_text = \"Address: {}
total narcotics incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"],row['Narcotics']) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Narcotics'] + 3,\n", " color=\"#654321\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", "\n", " for index, row in A.iterrows(): \n", " popup_text = \"Address: {}
total autotheft incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"],row['Count']) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count'] +3,\n", " color='#007849',\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in D.iterrows(): \n", " popup_text = \"Address: {}
total discharge incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"],row['Count']) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count'] +3,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", "\n", " for index, row in Br.iterrows(): \n", " popup_text = \"Address: {}
total burglary incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"],row['Count']) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count'] +3,\n", " color=\"#0000ff\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " return FG_map\n", "\n", "def plot_Frogtown_year(Year=2018):\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " B= fgc[(fgc['Year'] == Year)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson']\n", " B=B[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " B=B.query('Count>3')\n", " \n", " # for each row in the data, add a cicle marker\n", " for index, row in B.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " return FG_map\n", "\n", "\n", "def plot_Frogtown_year_proactive(Year=2018):\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " B= fgc[(fgc['Year'] == Year)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson']\n", " B=B[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " \n", " A= fgp[(fgp['Year'] == Year)]\n", " A= A.groupby(['Block','Latitude','Longitude']).size().reset_index()\n", " A.columns=['Block','Latitude','Longitude','Count'] \n", " \n", " for index, row in B.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", "\n", " for index, row in A.iterrows(): \n", " popup_text = \"Address: {}
total proactive calls: {}\"\n", " popup_text = popup_text.format(row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " \n", " return FG_map\n", "\n", "def plot_Frogtown_long_crime(Incident='Discharge'):\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson','Year']\n", "\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " B17=B.query('Year == 2018')\n", " B17=B17[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " B18=B.query('Year == 2019')\n", " B18=B18[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " B19=B.query('Year == 2020')\n", " B19=B19[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", "\n", " for index, row in B17.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B18.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"], row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B19.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " \n", " return FG_map\n", "\n", "def plot_Frogtown_long_crime_todate(Incident='All',Day=Max):\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson','Year']\n", "\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " if Incident=='All':\n", " B= fgc\n", " else: \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " B17=B.query('Year == 2018')\n", " B17=B17[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " B18=B.query('Year == 2019')\n", " B18=B18[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " B19=B.query('Year == 2020')\n", " B19=B19[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " print('This map displays {} incidents up to {}20XX'.format(Incident,Date))\n", " for index, row in B17.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B18.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"], row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B19.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " \n", " return FG_map\n", "\n", "# Latest Shooting Data \n", "\n", "\n", "def plot_Frogtown_long_crime_todate_2020(Incident='All',Day=Max, CDate=Max):\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson','Year']\n", "\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " if Incident=='All':\n", " B= fgc\n", " else: \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == CDate)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " B=B.query('Year == 2020')\n", " B=B[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " print('This map displays {} incidents within {} days from date {}20XX'.format(Incident,Day,Date))\n", "\n", " \n", " for index, row in B.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " \n", " return FG_map\n", "\n", "def plot_toDate_Days_SPCrime(Incident='All',Day=Max, CDate=Max):\n", " if Incident=='All':\n", " B= df\n", " else: \n", " B= df[(df['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == CDate)]['FDate'][0:1].iloc[0,]\n", " Bd= CDate-Day\n", " B= B[(B['DayYear'] >= Bd)]\n", " B= B[(B['DayYear'] <= CDate)]\n", "\n", "\n", "\n", "#convert MOnth\n", "def Convert_Month(num):\n", " Months= ['January','February','March','April','May','June','July','August','September','October','November','December']\n", " for i in range(12):\n", " if num==(i+1):\n", " Xi=Months[i]\n", " return Xi\n", "\n", "#Community function\n", "def commun(x): \n", " if x in [67,68,87,88,89,90,91,92]:\n", " return 'Thomas-Frogtown'\n", " elif x in [5,6,7,8,25,26,27,28,45,46,47,48]:\n", " return 'Como'\n", " elif x in [107, 108, 109, 110,127,128,129,130]: \n", " return 'Summit-University'\n", " elif x in [101,102,103,104,105,106,122,123,124,125,126]: \n", " return 'Union Park'\n", " elif x in [63,64,65,66,83,84,85,86]: \n", " return 'Midway' \n", " elif x in [142,143,144,145,146,162,163,164,165,166]:\n", " return 'Macalester_Groveland'\n", " elif x in [182,183,184,185,186,202,203,204,205,206,223,224,225,242,243,244,245,246]:\n", " return 'Highland Park'\n", " elif x in [147,148,149,167,168]:\n", " return 'Summit Hill'\n", " elif x in [1,2,21,22,43,44,61,62,81,82]:\n", " return 'St. Anthony'\n", " elif x in [226,207,187,188,189,267,268,169,170,171,249,150,151,230]:\n", " return 'West 7th'\n", " elif x in [209,210,211,212,213,214,215,192,193,194,195,172,173,174,175]:\n", " return 'West Side'\n", " elif x in [111,112,131,132,133,152,153]:\n", " return 'Capital_River'\n", " elif x in [98,99,100,118,119,119,120,137,138,139,140,160,197,180,200,240,280]:\n", " return 'Battle_Creek'\n", " elif x in [76,95,96,97,115,116,117,138,114,136]:\n", " return 'Dayton Bluff' \n", " elif x in [9,10,11,12,29,30,31,32,49,50,51,52,269,69,70,71,72]:\n", " return 'North End'\n", " elif x in [13,14,15,16,33,34,35,36,53,54,55,56,73,74,75,93,94]:\n", " return 'Payne-Phalen'\n", " elif x in [17,18,19,20,37,38,39,40,56,57,58,59,60,77,78,79,80]:\n", " return 'Greater East Side'\n", " else: \n", " return 'NaN'\n", "\n", "def table_toDate_Month_Crime(Incident='All',Day=Max):\n", " if Incident=='All':\n", " B= fgc\n", " else: \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " B=B.query('Year >= 2018')\n", " Index= ['Year','Grid','Count','Month']\n", " C= B[Index].groupby(['Year','Grid','Month']).sum().reset_index()\n", " C['Month_Name']= C.Month.apply(Convert_Month) \n", " C['Community']= C.Grid.apply(commun)\n", " print('This table maps {} incidents up to Day {}20XX'.format(Incident,Date))\n", " return pd.pivot_table(C, values='Count', index=['Community','Grid'], columns=['Month', 'Year'], fill_value=0)\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "\n", "def plot_toDate_Year_Crime(Incident='All',Day=Max):\n", " if Incident=='All':\n", " B= fgc\n", " else: \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B=B.query('Year >= 2018')\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Grid','Count']\n", " C= B[Index].groupby(['Year','Grid']).sum().reset_index()\n", " C['Community']= C.Grid.apply(commun) \n", " print('This graph maps {} incidents up to {}20XX'.format(Incident,Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community','Grid'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= Incident + ' Yearly Total Incidents UptoDate: '+ str(Date)+'20XX')\n", " return plt.show()\n", "\n", "\n", "\n", "\n", "# Firearm Plot\n", "def plot_toDate_Year_Firearm(Day=Max):\n", " B= fgc_fire[(fgc_fire['DayYear'] <= Day)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B.query('Year>2017')\n", " Index= ['Year','Grid','Count']\n", " C= B[Index].groupby(['Year','Grid']).sum().reset_index()\n", " C['Community']= C.Grid.apply(commun) \n", " print('This graph maps Firearm incidents up to {}20XX'.format(Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community','Grid'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Firearm Yearly Total Incidents UptoDate: ' + str(Date)+'20XX')\n", " return plt.show()\n", "\n", "#4\n", "def plot_toDate_Year_Firearm(Day=Max):\n", " B= fgc_fire[(fgc_fire['DayYear'] <= Day)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B.query('Year>2017')\n", " Index= ['Year','Grid','Count']\n", " C= B[Index].groupby(['Year','Grid']).sum().reset_index()\n", " C['Community']= C.Grid.apply(commun) \n", " print('This graph maps Firearm incidents up to {}20XX'.format(Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community','Grid'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Firearm Yearly Total Incidents UptoDate: ' + str(Date)+'20XX')\n", " return plt.show()\n", "\n", "\n", "def plot_Frogtown_year_Latenight(Year=2018):\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " B= fgc[(fgc['Year'] == Year)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson']\n", " BM=B.query('LateNight ==0')\n", " BM=BM[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " BM=BM.query('Count>2')\n", " BL=B.query('LateNight ==1')\n", " BL=BL[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " BL=BL.query('Count>2')\n", " \n", " # for each row in the data, add a cicle marker\n", " for index, row in BM.iterrows(): \n", " popup_text = \"Year: {} Non-late
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", "\n", " for index, row in BL.iterrows(): \n", " popup_text = \"Year: {} Late
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " return FG_map\n", "\n", "\n", "def plot_Frogtown_yearmonth(Year=2018,Month=1):\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " B= fgc[(fgc['Year'] == Year)]\n", " B= B[(B['Month'] == Month)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge']\n", " B=B[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " \n", "\n", " # for each row in the data, add a cicle marker\n", " for index, row in B.iterrows(): \n", " Other= row['Count'] - row['Theft'] - row['Vandalism'] - row['Narcotics'] -row['Auto Theft'] - row['Burglary'] - row['Discharge']\n", " popup_text = \"Year: {} Month: {}
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Other: {}\"\n", " popup_text = popup_text.format(str(Year), str(Month),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Narcotics'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], Other) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " return FG_map\n", "\n", "\n", "def plot_Frogtown_yearhour(Year=2018,Hour=0):\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " B= fgc[(fgc['Year'] == Year)]\n", " B= B[(B['Hour'] == Hour)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge']\n", " B=B[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " \n", "\n", " # for each row in the data, add a cicle marker\n", " for index, row in B.iterrows(): \n", " Other= row['Count'] - row['Theft'] - row['Vandalism'] - row['Narcotics'] -row['Auto Theft'] - row['Burglary'] - row['Discharge']\n", " popup_text = \"Year: {} Hour: {}
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Other: {}\"\n", " popup_text = popup_text.format(str(Year), str(Hour),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Narcotics'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], Other) \n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " return FG_map\n", "\n", "\n", "def plot_Frogtown_long_crime_daytime_todate(Incident='All',Day=Max):\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson','Year']\n", "\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " if Incident=='All':\n", " B= fgc\n", " else: \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " B= B.query('Hour >= 6 and Hour <20')\n", " B17=B.query('Year == 2018')\n", " B17=B17[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " B18=B.query('Year == 2019')\n", " B18=B18[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " B19=B.query('Year == 2020')\n", " B19=B19[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " print('This map displays {} incidents up to {}20XX from 7AM to 8PM'.format(Incident,Date))\n", " for index, row in B17.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B18.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"], row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B19.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " \n", " return FG_map\n", "\n", "#hotspot map\n", "\n", "def plot_Frogtown_hot_spot(Year=2019):\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " B= fgc[(fgc['Year'] >= Year)]\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson']\n", " B=B[Index].groupby(['Block','Latitude','Longitude']).sum().reset_index()\n", " C=B.query('Count>9 and Count < 21')\n", " D=B.query('Count>20 and Count < 51')\n", " E=B.query('Count>50')\n", " \n", " # for each row in the data, add a cicle marker\n", " for index, row in C.iterrows(): \n", " popup_text = \"Year: 2019/20{}
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']/2,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in D.iterrows(): \n", " popup_text = \"Year: 2019/20
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']/5,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in E.iterrows(): \n", " popup_text = \"Year: 2019/20
Address: {}
total incidents: {}
Theft: {}
Vandalism: {}\\\n", "
Narcotics: {}
Auto Theft: {}
Burglary: {}
Discharge: {}
Robbery: {}\\\n", "
Domestic Assault: {}
Violent: {}
Arson: {}\"\n", " popup_text = popup_text.format(str(Year),row[\"Block\"], row['Count'], row['Theft'], row['Vandalism'], row['Robbery'],\\\n", " row['Auto Theft'], row['Burglary'], row['Discharge'], row['Domestic Assault'],\\\n", " row['Domestic Assault'],row['Violent'],row['Arson'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']/9,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", "\n", " return FG_map\n", "\n", "\n", "\n", "#Saint Paul Data\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", "#Easier to bulk upload\n", "results = client.get(\"gppb-g9cg\", limit=1000000)\n", "df = 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", "\n", "#Add Time Variables\n", "df= df[df.Case != 18254093] #messed up time variable\n", "df['Date']= pd.to_datetime(df['Date'])\n", "df['Year']= df['Date'].dt.year\n", "df=df.query('Year > 2017')\n", "\n", "df['DayYear'] = df['Date'].dt.dayofyear\n", "df['Community']= df['Grid'].apply(commun)\n", "df= df.query('Code not in [9954,9959] and Community !=\"NaN\"')\n", "\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", "\n", "df.Incident.loc[(df['Incident'] == 'Simple Asasult Dom.')] = 'Simple Assault Dom.'\n", "df.Incident.loc[(df['Incident'] == 'Graffiti')] = 'Vandalism'\n", "df.Incident.loc[df[\"Incident\"].isin([ \"Rape\",\"Agg. Assault\",'Homicide'])]= 'Violent'\n", "df.Incident.loc[df[\"Incident\"].isin([\"Simple Assault Dom.\",\"Agg. Assault Dom.\"])]= 'Domestic Assault'\n", "\n", "\n", "#Current Date Numeric \n", "Max= df.iloc[0,14]\n", "#GeorgeFloyd Death date\n", "GE= 146\n", "#Covid start Date\n", "CV= 73\n", "\n", "\n", "#Add Time Variables\n", "df= df[df.Case != 18254093] #messed up time variable\n", "df['Date']= pd.to_datetime(df['Date'])\n", "df['Year']= df['Date'].dt.year\n", "df=df.query('Year > 2017')\n", "df['DayYear'] = df['Date'].dt.dayofyear\n", "df['Community']= df['Grid'].apply(commun)\n", "df= df.query('Code not in [9954,9959] and Community !=\"NaN\"')\n", "df_fire=df[df['IncType'].str.contains(\"Firearm\")]\n", "\n", "#Normalized Population\n", "P1 = {'Community': ['Battle_Creek','Greater East Side','West Side','Dayton Bluff','Payne-Phalen','North End','Thomas-Frogtown','Summit-University','West 7th','Como','Midway','St. Anthony','Union Park','Macalester_Groveland','Highland Park','Summit Hill','Capital_River'],\n", " 'Pop': [23492,30101,15459,18294,32170,24677,15454,17935,11671,16739,12688,8239,16726,19885,23867,6961,8442]\n", " }\n", "Norm = pd.DataFrame(P1, columns = ['Community', 'Pop'])\n", "\n", "\n", "\n", "def plot_toDate_Days_SPCrime(Incident='All',Day=Max, CDate=Max):\n", " if Incident=='All':\n", " B= df\n", " else: \n", " B= df[(df['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == CDate)]['FDate'][0:1].iloc[0,]\n", " Bd= CDate-Day\n", " B= B[(B['DayYear'] >= Bd)]\n", " B= B[(B['DayYear'] <= CDate)]\n", " Index= ['Year','Community','Count']\n", " C= B[Index].groupby(['Year','Community']).sum().reset_index()\n", " print('This graph displays total {} incidents of Saint Paul neighborhoods within {} days from date {}20XX'.format(Incident,Day,Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= Incident + ' Yearly UptoDate Total Incidents') \n", " return plt.show()\n", "\n", "def plot_toDate_Days_FGCrime(Incident='All',Day=Max, CDate=Max):\n", " if Incident=='All':\n", " B= fgc\n", " else: \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == CDate)]['FDate'][0:1].iloc[0,]\n", " Bd= CDate-Day\n", " B= B[(B['DayYear'] >= Bd)]\n", " B= B[(B['DayYear'] <= CDate)]\n", " Index= ['Year','Grid','Count']\n", " C= B[Index].groupby(['Year','Grid']).sum().reset_index()\n", " C1=C.query('Year>2017')\n", " C1['Community']= C1.Grid.apply(commun) \n", " print('This graph displays total {} incidents of the Frogtown Grid within {} days from date {}20XX'.format(Incident,Day,Date))\n", " sns.set()\n", " pd.pivot_table(C1, values='Count', index=['Community','Grid'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= Incident + ' Yearly UptoDate Total Incidents') \n", " return plt.show()\n", "\n", "\n", "\n", "\n", "#Firearm\n", "\n", "def plot_toDate_Year_SPFirearm(Day=Max):\n", " B= df_fire\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Community','Count']\n", " C= B[Index].groupby(['Year','Community']).sum().reset_index()\n", " print('This graph maps Firearm incidents of Saint Paul neighborhoods up to {}20XX'.format(Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Firearm Yearly UptoDate Total Incidents')\n", " return plt.show()\n", "\n", "\n", "def plot_toDate_Days_SPCrime_Norm(Incident='All',Day=Max, CDate=Max):\n", " if Incident=='All':\n", " B= df\n", " else: \n", " B= df[(df['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == CDate)]['FDate'][0:1].iloc[0,]\n", " Bd= CDate-Day\n", " B= B[(B['DayYear'] >= Bd)]\n", " B= B[(B['DayYear'] <= CDate)]\n", " Index= ['Year','Community','Count']\n", " C= B[Index].groupby(['Year','Community']).sum().reset_index()\n", " C1=pd.merge(C, Norm, on='Community', how='left').reset_index()\n", " C1['Norm_Count']= (C1.Count/C1.Pop) *10000\n", " print('This graph displays total {} normalized incidents of Saint Paul neighborhoods within {} days from date {}20XX'.format(Incident,Day,Date))\n", " sns.set()\n", " pd.pivot_table(C1, values='Norm_Count', index=['Community'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= Incident + ' Yearly UptoDate Total Normalized per 10000 Persons') \n", " return plt.show()\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays Auto Accessory Theft incidents by cost for Saint Paul neighborhoods since 2020\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_auto=df[df['IncType'].str.contains(\"Auto Accessories\")]\n", "df_auto['Amount']='Between $500-1000'\n", "df_auto.Amount.loc[(df_auto['IncType'].str.contains(\"500\"))] = 'Under $500'\n", "df_auto.Amount.loc[(df_auto['IncType'].str.contains(\"Over\"))] = 'Over $1000'\n", "#df_auto.query('Year>2019')\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "def plot_SPAutoTheft(Day=Max):\n", " B= df_auto.query('Year>2019')\n", " #Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " #B= B[(B['DayYear'] <= Day)]\n", " Index= ['Amount','Community','Count']\n", " C= B[Index].groupby(['Amount','Community']).sum().reset_index()\n", " print('This graph displays Auto Accessory Theft incidents by cost for Saint Paul neighborhoods since 2020')\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community'], columns=['Amount'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Auto Accessory Theft Incidents')\n", " return plt.show()\n", "\n", "\n", "plot_SPAutoTheft()\n", "\n", "#fg.Incident.loc[(fg['Incident'] == 'Simple Asasult Dom.')] = 'Simple Assault Dom.'\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge normalized incidents of Saint Paul neighborhoods within 273 days from date 9/29/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_toDate_Days_SPCrime_Norm('Discharge')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge incidents of Saint Paul neighborhoods within 273 days from date 9/29/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_toDate_Days_SPCrime(Incident='Discharge')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Motor Vehicle Theft, Automobile', 'Motor Vehicle Theft',\n", " 'Motor Vehicle Theft, Trucks and Buses',\n", " 'Motor Vehicle Theft, All Other Vehicles',\n", " 'Att. Motor Vehicle Theft, Automobile',\n", " 'Att. Motor Vehicle Theft, Trucks and Buses',\n", " 'Att. Motor Vehicle Theft, All Other Vehicles'], dtype=object)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#a=df.query(\"Incident=='Auto Theft'\")\n", "Wa.IncType.unique()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph maps Discharge annual incidents of Saint Paul within 273 days from 9/29/20XX\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_toDate_Year_SPCityCrime(Incident='All',Day=Max):\n", " if Incident=='All':\n", " B= df\n", " else: \n", " B= df[(df['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Count']\n", " C= B[Index].groupby(['Year']).sum().reset_index()\n", " print('This graph maps {} annual incidents of Saint Paul within {} days from {}20XX'.format(Incident,Day,Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(6,4),title='St. Paul Total ' + Incident + ' Incidents up to 9/29/20XX')\n", " return plt.show()\n", "\n", "\n", "plot_toDate_Year_SPCityCrime(\"Discharge\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total incidents in Saint Paul within 273 days from date 9/29/20XX\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.Incident.loc[(df['Incident'] == 'Simple Asasult Dom.')] = 'Simple Assault Dom.'\n", "df.Incident.loc[(df['Incident'] == 'Graffiti')] = 'Vandalism'\n", "df.Incident.loc[df[\"Incident\"].isin([ \"Rape\",\"Agg. Assault\",'Homicide'])]= 'Violent'\n", "df.Incident.loc[df[\"Incident\"].isin([\"Simple Assault Dom.\",\"Agg. Assault Dom.\"])]= 'Domestic Assault'\n", "\n", "def plot_toDate_Days_SPTotalCrime(Day=Max, CDate=Max):\n", " B=df\n", " Date= fgc[(fgc['DayYear'] == CDate)]['FDate'][0:1].iloc[0,]\n", " Bd= CDate-Day\n", " B= B[(B['DayYear'] >= Bd)]\n", " B= B[(B['DayYear'] <= CDate)]\n", " mask = B['Incident'].isin(['Proactive Foot Patrol', 'Other'])\n", " B=B[~mask]\n", " Index= ['Year','Incident','Count']\n", " C= B[Index].groupby(['Year','Incident']).sum().reset_index()\n", " print('This graph displays total incidents in Saint Paul within {} days from date {}20XX'.format(Day,Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Incident'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Yearly UptoDate Total Incidents') \n", " return plt.show()\n", "\n", "plot_toDate_Days_SPTotalCrime()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total car jacking incidents in Saint Paul within 273 days from date 9/29/20XX\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "a=df[df['IncType'].str.contains(\"Automobile\")]\n", "a['Incident']= 'Car Jacking'\n", "\n", "def plot_toDate_Days_SPTotalCrime(Day=Max, CDate=Max):\n", " B=a\n", " Date= fgc[(fgc['DayYear'] == CDate)]['FDate'][0:1].iloc[0,]\n", " Bd= CDate-Day\n", " B= B[(B['DayYear'] >= Bd)]\n", " B= B[(B['DayYear'] <= CDate)]\n", " Index= ['Year','Incident','Count']\n", " C= B[Index].groupby(['Year','Incident']).sum().reset_index()\n", " print('This graph displays total car jacking incidents in Saint Paul within {} days from date {}20XX'.format(Day,Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Incident'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Yearly UptoDate Total Incidents') \n", " return plt.show()\n", "\n", "plot_toDate_Days_SPTotalCrime()\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LateNightCount
00184
11307
\n", "
" ], "text/plain": [ " LateNight Count\n", "0 0 184\n", "1 1 307" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#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", "df['LateNight'] = df['Hour'].apply(lambda x: 1 if (x>21 or x<5) else 0) #Latenight designation from 10Pm to 6PM\n", "\n", "\n", "a= df[(df['DayYear'] < 224)]\n", "a= a[(a['Incident'] =='Discharge' )]\n", "a= a[(a['Year'] == 2021)] \n", "\n", "a=a[['Hour','Count']].groupby(['Hour']).sum().reset_index()\n", "a.columns=['Hour','Count']\n", "\n", "\n", "b= df[(df['DayYear'] < 224)]\n", "b= b[(b['Incident'] =='Discharge' )]\n", "b= b[(b['Year'] == 2019)]\n", "\n", "\n", "b=b[['LateNight','Count']].groupby(['LateNight']).sum().reset_index()\n", "b.columns=['LateNight','Count']\n", "b\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Grid Count\n", "68 106.0 1\n", "401 86.0 1\n", "438 109.0 1\n", "500 88.0 1\n", "1298 86.0 1\n", "1560 110.0 1\n", "1878 89.0 1\n", "2278 108.0 1\n", "4164 88.0 1\n", "4440 92.0 1\n", "4450 86.0 1\n", "4489 88.0 1\n", "5653 106.0 1\n", "6086 107.0 1\n", "6266 87.0 1\n", "6728 106.0 1\n", "7072 106.0 1\n", "7154 91.0 1\n", "7866 86.0 1\n", "8296 89.0 1\n", "8544 109.0 1\n", "8892 86.0 1\n", "9359 90.0 1\n", "9367 92.0 1\n", "9518 67.0 1\n", "10235 91.0 1\n", "10453 66.0 1\n", "10602 87.0 1\n", "10649 91.0 1\n", "10949 108.0 1\n", "... ... ...\n", "38025 68.0 1\n", "38095 68.0 1\n", "38306 110.0 1\n", "38354 88.0 1\n", "38357 110.0 1\n", "38528 107.0 1\n", "38595 88.0 1\n", "38642 88.0 1\n", "38668 89.0 1\n", "38721 90.0 1\n", "38835 106.0 1\n", "38959 87.0 1\n", "39068 87.0 1\n", "39091 86.0 1\n", "39092 88.0 1\n", "39265 89.0 1\n", "39439 67.0 1\n", "39515 110.0 1\n", "39539 108.0 1\n", "39604 67.0 1\n", "39605 67.0 1\n", "39610 110.0 1\n", "39611 110.0 1\n", "39684 110.0 1\n", "39685 110.0 1\n", "39725 88.0 1\n", "39887 106.0 1\n", "39984 87.0 1\n", "40104 90.0 1\n", "40163 89.0 1\n", "\n", "[424 rows x 2 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n", "import matplotlib.cm as cm\n", "import matplotlib.colors as colors\n", "import folium # map rendering library\n", "\n", "sp_geo = r'Geo-Json\\Saint Paul Police Grid - Shapefile.geojson'\n", "\n", "df_auto=df[df['IncType'].str.contains(\"Auto Accessories\")]\n", "df_auto=df_auto.query(\"Year>2020\")\n", "#list(df_auto.columns)\n", "\n", "Features= ['Grid','Count']\n", "df1=df_auto[Features]\n", "\n", "# Create a sum and divide by Count; and \n", "N= df1.groupby(['Grid']).sum()\n", "\n", "N=N.reset_index()\n", "N.Grid = N.Grid.astype(int)\n", "N.Grid = N.Grid.astype(str)\n", "N\n", "\n", "#Create Saint Paul Population Map\n", "FG_map = folium.Map(location=[44.958326, -93.132926], zoom_start=12,tiles=\"OpenStreetMap\")\n", "\n", "\n", "FG_map.choropleth(\n", " geo_data=sp_geo,\n", " data=N,\n", " columns=['Grid','Count'],\n", " #nan_fill_color='purple',\n", " #nan_fill_opacity=0.4,\n", " key_on=\"feature.properties.gridnum\",\n", " fill_color='YlOrRd', \n", " fill_opacity=0.7, \n", " line_opacity=0.2,\n", " legend_name='Auto Accessory Thefts since 2021',\n", " highlight= True,\n", " name= 'Clean Map'\n", " \n", ")\n", "# display map\n", "FG_map\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This map displays Auto Accessory incidents in 2021\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Auto Accessories\n", "fgc_auto=fgc[fgc['IncType'].str.contains(\"Auto Accessories\")]\n", "fgc_auto['Amount']='Medium'\n", "fgc_auto.Amount.loc[(fgc_auto['IncType'].str.contains(\"500\"))] = 'Small'\n", "fgc_auto.Amount.loc[(fgc_auto['IncType'].str.contains(\"Over\"))] = 'Large'\n", "#df_auto.query('Year>2019')\n", "\n", "def plot_AutoAccesoriesTheft_todate(Day=Max):\n", " Index =['Block','Latitude','Longitude', 'Count','Amount','Year']\n", "\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " #Date= fgc_auto[(fgc_auto['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= fgc_auto\n", " B= B[(B['DayYear'] <= Day)]\n", " B17=B.query('Amount == \"Small\"')\n", " B17=B17[Index].groupby(['Amount','Block','Latitude','Longitude']).sum().reset_index()\n", " B18=B.query('Amount == \"Medium\"')\n", " B18=B18[Index].groupby(['Amount','Block','Latitude','Longitude']).sum().reset_index()\n", " B19=B.query('Amount == \"Large\"')\n", " B19=B19[Index].groupby(['Amount','Block','Latitude','Longitude']).sum().reset_index()\n", " print('This map displays Auto Accessory incidents in 2021')\n", " for index, row in B17.iterrows(): \n", " popup_text = \"Amount: Small
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B18.iterrows(): \n", " popup_text = \"Amount: Medium
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"], row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B19.iterrows(): \n", " popup_text = \"Amount: Large
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " \n", " return FG_map\n", "\n", "plot_AutoAccesoriesTheft_todate(20)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This map displays Auto Accessory incidents for Frogtown/Midway/Rondo neighborhood since 2020\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def plot_AutoAccesoriesTheft():\n", " Index =['Block','Latitude','Longitude', 'Count','Amount','Year']\n", "\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " #Date= fgc_auto[(fgc_auto['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= fgc_auto.query('Year>2019')\n", " B17=B.query('Amount == \"Small\"')\n", " B17=B17[Index].groupby(['Amount','Block','Latitude','Longitude']).sum().reset_index()\n", " B18=B.query('Amount == \"Medium\"')\n", " B18=B18[Index].groupby(['Amount','Block','Latitude','Longitude']).sum().reset_index()\n", " B19=B.query('Amount == \"Large\"')\n", " B19=B19[Index].groupby(['Amount','Block','Latitude','Longitude']).sum().reset_index()\n", " print('This map displays Auto Accessory incidents for Frogtown/Midway/Rondo neighborhood since 2020')\n", " for index, row in B17.iterrows(): \n", " popup_text = \"Amount: Small
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#E37222\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B18.iterrows(): \n", " popup_text = \"Amount: Medium
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"], row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#007849\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map)\n", " \n", " for index, row in B19.iterrows(): \n", " popup_text = \"Amount: Large
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " \n", " return FG_map\n", "\n", "plot_AutoAccesoriesTheft()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n" ] } ], "source": [ "from __future__ import print_function\n", "from ipywidgets import interact, interactive, fixed, interact_manual\n", "import ipywidgets as widgets\n", "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline \n", "import folium\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "\n", "#Saint Paul Data Socrata\n", "from sodapy import Socrata\n", "\n", "#New Upload Method Get Information from Socrata API\n", "client = Socrata(\"information.stpaul.gov\", None)\n", "#Easier to bulk upload\n", "results = client.get(\"gppb-g9cg\", limit=1000000)\n", "df = 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", "\n", "\n", "\n", "\n", "#convert MOnth\n", "def Convert_Month(num):\n", " Months= ['January','February','March','April','May','June','July','August','September','October','November','December']\n", " for i in range(12):\n", " if num==(i+1):\n", " Xi=Months[i]\n", " return Xi\n", "\n", "#Community function\n", "def commun(x): \n", " if x in [67,68,87,88,89,90,91,92]:\n", " return 'Thomas-Frogtown'\n", " elif x in [5,6,7,8,25,26,27,28,45,46,47,48]:\n", " return 'Como'\n", " elif x in [107, 108, 109, 110,127,128,129,130]: \n", " return 'Summit-University'\n", " elif x in [101,102,103,104,105,106,122,123,124,125,126]: \n", " return 'Union Park'\n", " elif x in [63,64,65,66,83,84,85,86]: \n", " return 'Midway' \n", " elif x in [142,143,144,145,146,162,163,164,165,166]:\n", " return 'Macalester_Groveland'\n", " elif x in [182,183,184,185,186,202,203,204,205,206,223,224,225,242,243,244,245,246]:\n", " return 'Highland Park'\n", " elif x in [147,148,149,167,168]:\n", " return 'Summit Hill'\n", " elif x in [1,2,21,22,43,44,61,62,81,82]:\n", " return 'St. Anthony'\n", " elif x in [226,207,187,188,189,267,268,169,170,171,249,150,151,230]:\n", " return 'West 7th'\n", " elif x in [209,210,211,212,213,214,215,192,193,194,195,172,173,174,175]:\n", " return 'West Side'\n", " elif x in [111,112,131,132,133,152,153]:\n", " return 'Capital_River'\n", " elif x in [98,99,100,118,119,119,120,137,138,139,140,160,197,180,200,240,280]:\n", " return 'Battle_Creek'\n", " elif x in [76,95,96,97,115,116,117,138,114,136]:\n", " return 'Dayton Bluff' \n", " elif x in [9,10,11,12,29,30,31,32,49,50,51,52,269,69,70,71,72]:\n", " return 'North End'\n", " elif x in [13,14,15,16,33,34,35,36,53,54,55,56,73,74,75,93,94]:\n", " return 'Payne-Phalen'\n", " elif x in [17,18,19,20,37,38,39,40,56,57,58,59,60,77,78,79,80]:\n", " return 'Greater East Side'\n", " else: \n", " return 'NaN'\n", "\n", "#Add Time Variables\n", "df= df[df.Case != 18254093] #messed up time variable\n", "df['Date']= pd.to_datetime(df['Date'])\n", "df['Year']= df['Date'].dt.year\n", "df=df.query('Year > 2017')\n", "df['DayYear'] = df['Date'].dt.dayofyear\n", "df['Community']= df['Grid'].apply(commun)\n", "df= df.query('Code not in [9954,9959] and Community !=\"NaN\"')\n", "df['Month'] = df['Date'].dt.month # Create Month Category\n", "df['Day'] = df['Date'].dt.day #Create Day of the Current month\n", "\n", "\n", "df\n", "#Current Date Numeric \n", "Max= df.iloc[0,14]\n", "#GeorgeFloyd Death date\n", "GE= 146\n", "#Covid start Date\n", "CV= 73\n", "\n", "\n", "def plot_toDate_Days_SPCrime(Incident='All',Day=Max, CDate=Max):\n", " if Incident=='All':\n", " B= df\n", " else: \n", " B= df[(df['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == CDate)]['FDate'][0:1].iloc[0,]\n", " Bd= CDate-Day\n", " B= B[(B['DayYear'] >= Bd)]\n", " B= B[(B['DayYear'] <= CDate)]\n", " Index= ['Year','Community','Count']\n", " C= B[Index].groupby(['Year','Community']).sum().reset_index()\n", " print('This graph displays total {} incidents of Saint Paul neighborhoods within {} days from date {}20XX'.format(Incident,Day,Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= Incident + ' Yearly UptoDate Total Incidents') \n", " return plt.show()\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Minneapolis" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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XYpublicaddresscaseNumberprecinctreportedDatereportedTimebeginDatereportedDateTimebeginTime...enteredDatecentergbsidcenterLongcenterLatcenterXcenterYneighborhoodlastchangedLastUpdateDateETLOBJECTID
0-93.26503344.9777600003XX 4TH ST SMP201924851612019/08/19 00:00:00+0002019/08/12 00:00:00+002019/08/19 00:00:00+000...2019/08/19 00:00:00+0018596.0-93.26503444.977764-103822165618021Downtown West2019/08/24 00:00:00+002019/08/25 08:15:46+001
1-93.24485744.9456480031XX 19TH AVE SMP201925334632019/08/23 00:00:00+0011502019/08/22 00:00:00+002019/08/23 11:50:00+001606...2019/08/24 00:00:00+0013244.0-93.24486344.945650-103799705612969Corcoran2019/08/24 00:00:00+002019/08/25 08:15:46+002
2-93.29320445.0305360041XX DUPONT AVE NMP201925339242019/08/23 00:00:00+0016062019/08/23 00:00:00+002019/08/23 16:06:00+001220...2019/08/24 00:00:00+0010605.0-93.29321145.030541-103853525626330Webber - Camden2019/08/24 00:00:00+002019/08/25 08:15:46+003
3-93.27880444.94834400001X LAKE ST WMP201925347052019/08/23 00:00:00+0014202019/08/23 00:00:00+002019/08/23 14:20:00+001330...2019/08/24 00:00:00+0021928.0-93.27880844.948347-103837495613393Whittier2019/08/24 00:00:00+002019/08/25 08:15:46+004
4-93.30520644.9484270018XX LAKE ST WMP201925348652019/08/23 00:00:00+0015132019/08/10 00:00:00+002019/08/23 15:13:00+00452...2019/08/24 00:00:00+0019969.0-93.30521044.948430-103866885613406ECCO2019/08/24 00:00:00+002019/08/25 08:15:46+005
5-93.27347744.9483510002XX LAKE ST EMP201925362232019/08/23 00:00:00+0015062019/07/20 00:00:00+002019/08/23 15:06:00+001...2019/08/24 00:00:00+0019103.0-93.27348444.948355-103831565613394Phillips West2019/08/24 00:00:00+002019/08/25 08:15:46+006
6-93.22687344.9932510003XX STINSON BLVD NEMP201925364922019/08/23 00:00:00+0020232019/08/23 00:00:00+002019/08/23 20:23:00+001545...2019/08/24 00:00:00+0017962.0-93.22687544.993251-103779685620459Mid - City Industrial2019/08/24 00:00:00+002019/08/25 08:15:46+007
7-93.30570044.9936060014XX NEWTON AVE NMP201925368542019/08/23 00:00:00+0017052019/07/22 00:00:00+002019/08/23 17:05:00+000...2019/08/24 00:00:00+0011496.0-93.30570844.993611-103867435620515Willard - Hay2019/08/24 00:00:00+002019/08/25 08:15:46+008
8-93.24004244.9808990011XX UNIVERSITY AVE SEMP201925370422019/08/23 00:00:00+0018372019/08/22 00:00:00+002019/08/23 18:37:00+001800...2019/08/24 00:00:00+0016644.0-93.24004444.980902-103794345618515Marcy Holmes2019/08/24 00:00:00+002019/08/25 08:15:46+009
9-93.29835244.9421130033XX HENNEPIN AVEMP201925370652019/08/23 00:00:00+0016532019/08/23 00:00:00+002019/08/23 16:53:00+00900...2019/08/24 00:00:00+0011735.0-93.29835644.942118-103859255612413ECCO2019/08/24 00:00:00+002019/08/25 08:15:46+0010
10-93.26755744.9260370042XX PORTLAND AVEMP201925371632019/08/24 00:00:00+00242019/08/22 00:00:00+002019/08/24 00:24:00+002100...2019/08/24 00:00:00+0013511.0-93.26756344.926042-103824975609885Regina2019/08/24 00:00:00+002019/08/25 08:15:46+0011
11-93.24911545.013157NaNMP201925372322019/08/23 00:00:00+0018042019/08/23 00:00:00+002019/08/23 18:04:00+001615...2019/08/24 00:00:00+00NaN-93.24912045.013160-103804445623593Holland2019/08/24 00:00:00+002019/08/25 08:15:46+0012
12-93.24568344.9634930009XX 19TH AVE SMP201925375132019/08/23 00:00:00+0017382019/08/22 00:00:00+002019/08/23 17:38:00+001800...2019/08/24 00:00:00+0018344.0-93.24568944.963496-103800625615776Seward2019/08/24 00:00:00+002019/08/25 08:15:46+0013
13-93.26967744.9777410005XX MARQUETTE AVEMP201925377112019/08/23 00:00:00+0019412019/08/21 00:00:00+002019/08/23 19:41:00+000...2019/08/24 00:00:00+0017245.0-93.26967844.977745-103827335618018Downtown West2019/08/24 00:00:00+002019/08/25 08:15:46+0014
14-93.30471245.0344080043XX MORGAN AVE NMP201925378042019/08/23 00:00:00+0018012019/08/23 00:00:00+002019/08/23 18:01:00+001700...2019/08/25 00:00:00+0010744.0-93.30471845.034411-103866335626940Webber - Camden2019/08/25 00:00:00+002019/08/25 08:15:46+0015
15-93.27960444.9600030023XX BLAISDELL AVEMP201925378352019/08/23 00:00:00+0020522019/08/23 00:00:00+002019/08/23 20:52:00+001804...2019/08/24 00:00:00+0016776.0-93.27960844.960009-103838385615227Whittier2019/09/04 00:00:00+002019/09/05 08:15:34+0016
16-93.31273444.9899410025XX 12TH AVE NMP201925384042019/08/23 00:00:00+0019202019/08/23 00:00:00+002019/08/23 19:20:00+001858...2019/08/24 00:00:00+0017991.0-93.31273744.989945-103875265619938Willard - Hay2019/08/24 00:00:00+002019/08/25 08:15:46+0017
17-93.29656444.924911NaNMP201925397652019/08/23 00:00:00+0021432019/08/23 00:00:00+002019/08/23 21:43:00+002000...2019/08/24 00:00:00+00NaN-93.29657044.924910-103857265609708East Harriet2019/08/24 00:00:00+002019/08/25 08:15:46+0018
18-93.26026344.975746NaNMP201925406212019/08/23 00:00:00+0023202019/08/23 00:00:00+002019/08/23 23:20:00+002215...2019/08/24 00:00:00+00NaN-93.26026044.975750-103816855617704Downtown East2019/08/24 00:00:00+002019/08/25 08:15:46+0019
19-93.26285044.9781480004XX 3RD ST SMP201925411812019/08/23 00:00:00+0023432019/08/23 00:00:00+002019/08/23 23:43:00+001430...2019/08/24 00:00:00+0018615.0-93.26285744.978154-103819735618082Downtown West2019/08/24 00:00:00+002019/08/25 08:15:46+0020
20-93.25551144.9600290013XX 23RD ST EMP201925423432019/08/24 00:00:00+002102019/08/24 00:00:00+002019/08/24 02:10:00+0052...2019/08/24 00:00:00+0016866.0-93.25551144.960035-103811565615231Ventura Village2019/08/24 00:00:00+002019/08/25 08:15:46+0021
21-93.27701744.978015NaNMP201925426812019/08/24 00:00:00+004232019/08/24 00:00:00+002019/08/24 04:23:00+00137...2019/08/24 00:00:00+00NaN-93.27703044.978020-103835505618061Downtown West2019/08/24 00:00:00+002019/08/25 08:15:46+0022
22-93.23078944.9468440028XX 31ST ST EMP201925427532019/08/24 00:00:00+003532019/08/24 00:00:00+002019/08/24 03:53:00+00150...2019/08/24 00:00:00+0011257.0-93.23079444.946849-103784045613157Longfellow2019/08/24 00:00:00+002019/08/25 08:15:46+0023
23-93.27770844.9705030013XX NICOLLET MALLMP201925429912019/08/24 00:00:00+005082019/08/24 00:00:00+002019/08/24 05:08:00+00200...2019/08/24 00:00:00+0016714.0-93.27771444.970506-103836275616879Loring Park2019/08/24 00:00:00+002019/08/25 08:15:46+0024
24-93.27313644.9808040004XX 1ST AVE NMP201925430712019/08/24 00:00:00+002112019/08/24 00:00:00+002019/08/24 02:11:00+00130...2019/08/24 00:00:00+0017121.0-93.27314144.980808-103831185618500Downtown West2019/08/31 00:00:00+002019/09/01 08:15:37+0025
25-93.27310944.9816430001XX 4TH ST NMP201925434412019/08/24 00:00:00+003502019/08/24 00:00:00+002019/08/24 03:50:00+00244...2019/08/24 00:00:00+0017122.0-93.27311644.981648-103831155618632Downtown West2019/08/26 00:00:00+002019/08/27 08:15:35+0026
26-93.27195944.98083000001X 4TH ST NMP201925439212019/08/24 00:00:00+005532019/08/24 00:00:00+002019/08/24 05:53:00+00245...2019/08/24 00:00:00+0017119.0-93.27196044.980833-103829875618504Downtown West2019/09/04 00:00:00+002019/09/05 08:15:34+0027
27-93.29212644.9222840044XX COLFAX AVE SMP201925443052019/08/24 00:00:00+006162019/08/24 00:00:00+002019/08/24 06:16:00+00453...2019/08/24 00:00:00+0020551.0-93.29212944.922286-103852325609295East Harriet2019/10/04 00:00:00+002019/10/05 08:15:39+0028
28-93.23244244.9823860006XX 15TH AVE SEMP201925451322019/08/24 00:00:00+0013232019/08/12 00:00:00+002019/08/24 13:23:00+001200...2019/08/24 00:00:00+0016600.0-93.23244844.982393-103785885618749Marcy Holmes2019/08/24 00:00:00+002019/08/25 08:15:46+0029
29-93.27880444.94834400001X LAKE ST WMP201925451552019/08/24 00:00:00+009232019/08/24 00:00:00+002019/08/24 09:23:00+00900...2019/08/24 00:00:00+0021928.0-93.27880844.948347-103837495613393Whittier2019/08/24 00:00:00+002019/08/25 08:15:46+0030
..................................................................
1963-93.26897744.9582550024XX 5TH AVE SMP20212326232021/02/02 00:00:00+0015142021/02/02 00:00:00+002021/02/02 15:14:00+001514...2021/02/03 00:00:00+0022549.0-93.26897844.958261-103826555614952Phillips West2021/02/03 00:00:00+002021/02/04 07:03:32+001964
1964-93.26774644.9492090029XX PORTLAND AVEMP20212328032021/02/02 00:00:00+0019242019/11/14 00:00:00+002021/02/02 19:24:00+001100...2021/02/03 00:00:00+0017502.0-93.26775144.949214-103825185613529Phillips West2021/02/03 00:00:00+002021/02/04 07:03:32+001965
1965-93.28101444.9744940012XX HENNEPIN AVEMP20212333012021/02/02 00:00:00+0017002021/02/02 00:00:00+002021/02/02 17:00:00+001652...2021/02/03 00:00:00+0016661.0-93.28102244.974496-103839955617507Loring Park2021/02/03 00:00:00+002021/02/04 07:03:32+001966
1966-93.28803944.9546380026XX LYNDALE AVE SMP20212334152021/02/02 00:00:00+0017252021/02/02 00:00:00+002021/02/02 17:25:00+001300...2021/02/03 00:00:00+0016678.0-93.28804844.954638-103847775614383Lowry Hill East2021/02/03 00:00:00+002021/02/04 07:03:32+001967
1967-93.31027244.933586NaNMP20212335152021/02/02 00:00:00+0017502021/02/02 00:00:00+002021/02/02 17:50:00+001700...2021/02/02 00:00:00+00NaN-93.31028044.933590-103872525611072East Harriet2021/02/03 00:00:00+002021/02/04 07:03:32+001968
1968-93.22904744.9609690029XX 22ND ST EMP20212337132021/02/02 00:00:00+0018272021/01/31 00:00:00+002021/02/02 18:27:00+000...2021/02/03 00:00:00+0017405.0-93.22905044.960974-103782105615379Seward2021/02/03 00:00:00+002021/02/04 07:03:32+001969
1969-93.23853344.9627490023XX FRANKLIN AVE EMP20212339832021/02/02 00:00:00+0018002021/02/01 00:00:00+002021/02/02 18:00:00+002200...2021/02/03 00:00:00+0018338.0-93.23854144.962752-103792665615659Seward2021/02/03 00:00:00+002021/02/04 07:03:32+001970
1970-93.28101444.9744940012XX HENNEPIN AVEMP20212340512021/02/02 00:00:00+0018352021/01/29 00:00:00+002021/02/02 18:35:00+001100...2021/02/03 00:00:00+0016661.0-93.28102244.974496-103839955617507Loring Park2021/02/05 00:00:00+002021/02/06 07:03:57+001971
1971-93.26191644.9804420004XX 2ND ST SMP20212344112021/02/02 00:00:00+0019062021/02/02 00:00:00+002021/02/02 19:06:00+00600...2021/02/03 00:00:00+0020491.0-93.26191644.980444-103818695618443Downtown West2021/02/03 00:00:00+002021/02/04 07:03:32+001972
1972-93.26311145.0105400022XX UNIVERSITY AVE NEMP20212344722021/02/02 00:00:00+0019122020/01/28 00:00:00+002021/02/02 19:12:00+00350...2021/02/03 00:00:00+0014501.0-93.26311445.010545-103820025623181Holland2021/02/03 00:00:00+002021/02/04 07:03:32+001973
1973-93.26263544.9456680031XX CHICAGO AVEMP20212348132021/02/02 00:00:00+0020132021/02/02 00:00:00+002021/02/02 20:13:00+001945...2021/02/03 00:00:00+0016376.0-93.26264244.945673-103819495612972Central2021/02/03 00:00:00+002021/02/04 07:03:32+001974
1974-93.25616744.967446NaNMP20212372212021/02/03 00:00:00+002502021/02/03 00:00:00+002021/02/03 02:50:00+00216...2021/02/03 00:00:00+00NaN-93.25617044.967450-103812295616398Elliot Park2021/02/03 00:00:00+002021/02/04 07:03:32+001975
1975-93.27274144.960899NaNMP202170074252021/01/29 00:00:00+0012452021/01/28 00:00:00+002021/01/29 12:45:00+002330...2021/02/03 00:00:00+00NaN-93.27274044.960900-103830745615368Whittier2021/02/04 00:00:00+002021/02/05 07:01:48+001976
1976-93.22568744.9205540045XX 32ND AVE SMP202170074432021/02/01 00:00:00+008332021/01/31 00:00:00+002021/02/01 08:33:00+0030...2021/02/03 00:00:00+0012954.0-93.22568844.920554-103778365609023Ericsson2021/02/03 00:00:00+002021/02/04 07:03:32+001977
1977-93.30838645.0268030039XX PENN AVE NMP202170074542021/02/01 00:00:00+008532021/01/29 00:00:00+002021/02/01 08:53:00+00900...2021/02/03 00:00:00+0010495.0-93.30839045.026805-103870425625742Victory2021/02/03 00:00:00+002021/02/04 07:03:32+001978
1978-93.26472844.98483300008X HENNEPIN AVEMP202170075012021/02/01 00:00:00+0010232021/01/30 00:00:00+002021/02/01 10:23:00+002200...2021/02/03 00:00:00+0015152.0-93.26473644.984837-103821825619134Downtown West2021/02/03 00:00:00+002021/02/04 07:03:32+001979
1979-93.29703144.9456550031XX GIRARD AVE SMP202170075552021/02/01 00:00:00+0011412020/07/10 00:00:00+002021/02/01 11:41:00+002100...2021/02/03 00:00:00+0014074.0-93.29703644.945660-103857785612970CARAG2021/02/04 00:00:00+002021/02/05 07:01:48+001980
1980-93.28936044.9492020029XX ALDRICH AVE SMP202170075752021/02/01 00:00:00+0011452021/01/29 00:00:00+002021/02/01 11:45:00+002000...2021/02/03 00:00:00+0021710.0-93.28936444.949203-103849245613528Lowry Hill East2021/02/03 00:00:00+002021/02/04 07:03:32+001981
1981-93.24249444.964014NaNMP202170076032021/02/01 00:00:00+0012052021/01/31 00:00:00+002021/02/01 12:05:00+001745...2021/02/03 00:00:00+00NaN-93.24250044.964020-103797075615858Seward2021/02/03 00:00:00+002021/02/04 07:03:32+001982
1982-93.25407444.9765020002XX 10TH AVE SMP202170076312021/02/01 00:00:00+0013022021/01/29 00:00:00+002021/02/01 13:02:00+001600...2021/02/03 00:00:00+0015555.0-93.25407744.976508-103809965617823Downtown East2021/02/03 00:00:00+002021/02/04 07:03:32+001983
1983-93.26384745.0030650003XX 15TH AVE NEMP202170076522021/02/01 00:00:00+0013542021/01/24 00:00:00+002021/02/01 13:54:00+001200...2021/02/03 00:00:00+0012779.0-93.26385645.003069-103820845622004Sheridan2021/02/03 00:00:00+002021/02/04 07:03:32+001984
1984-93.29203744.9367910036XX COLFAX AVE SMP202170076952021/02/01 00:00:00+0014162021/01/02 00:00:00+002021/02/01 14:16:00+005...2021/02/03 00:00:00+0020643.0-93.29204344.936791-103852225611576East Harriet2021/02/04 00:00:00+002021/02/05 07:01:48+001985
1985-93.22948744.9736550006XX WASHINGTON AVE SEMP202170077022021/02/01 00:00:00+0014512020/12/18 00:00:00+002021/02/01 14:51:00+001435...2021/02/03 00:00:00+0020281.0-93.22949444.973658-103782595617375University of Minnesota2021/02/03 00:00:00+002021/02/04 07:03:32+001986
1986-93.20827844.9497050029XX DORMAN AVEMP202170077132021/02/01 00:00:00+0014592021/01/28 00:00:00+002021/02/01 14:59:00+001000...2021/02/03 00:00:00+0011099.0-93.20828044.949706-103758985613607Cooper2021/02/03 00:00:00+002021/02/04 07:03:32+001987
1987-93.23646744.9807660013XX 4TH ST SEMP202170077222021/02/01 00:00:00+0015152021/01/30 00:00:00+002021/02/01 15:15:00+002200...2021/02/03 00:00:00+0016642.0-93.23647344.980767-103790365618494Marcy Holmes2021/02/03 00:00:00+002021/02/04 07:03:32+001988
1988-93.26263544.914295NaNMP202170077332021/02/01 00:00:00+0016092021/01/26 00:00:00+002021/02/01 16:09:00+002000...2021/02/03 00:00:00+00NaN-93.26264044.914300-103819495608039Northrop2021/02/03 00:00:00+002021/02/04 07:03:32+001989
1989-93.29432744.9587890024XX HENNEPIN AVEMP202170077452021/02/01 00:00:00+0016532021/02/01 00:00:00+002021/02/01 16:53:00+001200...2021/02/03 00:00:00+0018382.0-93.29433544.958795-103854775615036East Isles2021/02/04 00:00:00+002021/02/05 07:01:48+001990
1990-93.27178844.9773660006XX NICOLLET MALLMP202170077512021/02/01 00:00:00+0021562021/02/01 00:00:00+002021/02/01 21:56:00+001800...2021/02/03 00:00:00+0021933.0-93.27179644.977367-103829685617959Downtown West2021/02/03 00:00:00+002021/02/04 07:03:32+001991
1991-93.24984344.9600290023XX 17TH AVE SMP202170078232021/02/02 00:00:00+0010222021/02/01 00:00:00+002021/02/02 10:22:00+001600...2021/02/03 00:00:00+0021480.0-93.24984944.960034-103805255615231Ventura Village2021/02/03 00:00:00+002021/02/04 07:03:32+001992
1992-93.24695944.9836570006XX UNIVERSITY AVE SEMP202170078322021/02/02 00:00:00+0010512021/02/01 00:00:00+002021/02/02 10:51:00+002100...2021/02/03 00:00:00+0017977.0-93.24696144.983663-103802045618949Marcy Holmes2021/02/03 00:00:00+002021/02/04 07:03:32+001993
\n", "

49063 rows × 23 columns

\n", "
" ], "text/plain": [ " X Y publicaddress caseNumber \\\n", "0 -93.265033 44.977760 0003XX 4TH ST S MP2019248516 \n", "1 -93.244857 44.945648 0031XX 19TH AVE S MP2019253346 \n", "2 -93.293204 45.030536 0041XX DUPONT AVE N MP2019253392 \n", "3 -93.278804 44.948344 00001X LAKE ST W MP2019253470 \n", "4 -93.305206 44.948427 0018XX LAKE ST W MP2019253486 \n", "5 -93.273477 44.948351 0002XX LAKE ST E MP2019253622 \n", "6 -93.226873 44.993251 0003XX STINSON BLVD NE MP2019253649 \n", "7 -93.305700 44.993606 0014XX NEWTON AVE N MP2019253685 \n", "8 -93.240042 44.980899 0011XX UNIVERSITY AVE SE MP2019253704 \n", "9 -93.298352 44.942113 0033XX HENNEPIN AVE MP2019253706 \n", "10 -93.267557 44.926037 0042XX PORTLAND AVE MP2019253716 \n", "11 -93.249115 45.013157 NaN MP2019253723 \n", "12 -93.245683 44.963493 0009XX 19TH AVE S MP2019253751 \n", "13 -93.269677 44.977741 0005XX MARQUETTE AVE MP2019253771 \n", "14 -93.304712 45.034408 0043XX MORGAN AVE N MP2019253780 \n", "15 -93.279604 44.960003 0023XX BLAISDELL AVE MP2019253783 \n", "16 -93.312734 44.989941 0025XX 12TH AVE N MP2019253840 \n", "17 -93.296564 44.924911 NaN MP2019253976 \n", "18 -93.260263 44.975746 NaN MP2019254062 \n", "19 -93.262850 44.978148 0004XX 3RD ST S MP2019254118 \n", "20 -93.255511 44.960029 0013XX 23RD ST E MP2019254234 \n", "21 -93.277017 44.978015 NaN MP2019254268 \n", "22 -93.230789 44.946844 0028XX 31ST ST E MP2019254275 \n", "23 -93.277708 44.970503 0013XX NICOLLET MALL MP2019254299 \n", "24 -93.273136 44.980804 0004XX 1ST AVE N MP2019254307 \n", "25 -93.273109 44.981643 0001XX 4TH ST N MP2019254344 \n", "26 -93.271959 44.980830 00001X 4TH ST N MP2019254392 \n", "27 -93.292126 44.922284 0044XX COLFAX AVE S MP2019254430 \n", "28 -93.232442 44.982386 0006XX 15TH AVE SE MP2019254513 \n", "29 -93.278804 44.948344 00001X LAKE ST W MP2019254515 \n", "... ... ... ... ... \n", "1963 -93.268977 44.958255 0024XX 5TH AVE S MP202123262 \n", "1964 -93.267746 44.949209 0029XX PORTLAND AVE MP202123280 \n", "1965 -93.281014 44.974494 0012XX HENNEPIN AVE MP202123330 \n", "1966 -93.288039 44.954638 0026XX LYNDALE AVE S MP202123341 \n", "1967 -93.310272 44.933586 NaN MP202123351 \n", "1968 -93.229047 44.960969 0029XX 22ND ST E MP202123371 \n", "1969 -93.238533 44.962749 0023XX FRANKLIN AVE E MP202123398 \n", "1970 -93.281014 44.974494 0012XX HENNEPIN AVE MP202123405 \n", "1971 -93.261916 44.980442 0004XX 2ND ST S MP202123441 \n", "1972 -93.263111 45.010540 0022XX UNIVERSITY AVE NE MP202123447 \n", "1973 -93.262635 44.945668 0031XX CHICAGO AVE MP202123481 \n", "1974 -93.256167 44.967446 NaN MP202123722 \n", "1975 -93.272741 44.960899 NaN MP2021700742 \n", "1976 -93.225687 44.920554 0045XX 32ND AVE S MP2021700744 \n", "1977 -93.308386 45.026803 0039XX PENN AVE N MP2021700745 \n", "1978 -93.264728 44.984833 00008X HENNEPIN AVE MP2021700750 \n", "1979 -93.297031 44.945655 0031XX GIRARD AVE S MP2021700755 \n", "1980 -93.289360 44.949202 0029XX ALDRICH AVE S MP2021700757 \n", "1981 -93.242494 44.964014 NaN MP2021700760 \n", "1982 -93.254074 44.976502 0002XX 10TH AVE S MP2021700763 \n", "1983 -93.263847 45.003065 0003XX 15TH AVE NE MP2021700765 \n", "1984 -93.292037 44.936791 0036XX COLFAX AVE S MP2021700769 \n", "1985 -93.229487 44.973655 0006XX WASHINGTON AVE SE MP2021700770 \n", "1986 -93.208278 44.949705 0029XX DORMAN AVE MP2021700771 \n", "1987 -93.236467 44.980766 0013XX 4TH ST SE MP2021700772 \n", "1988 -93.262635 44.914295 NaN MP2021700773 \n", "1989 -93.294327 44.958789 0024XX HENNEPIN AVE MP2021700774 \n", "1990 -93.271788 44.977366 0006XX NICOLLET MALL MP2021700775 \n", "1991 -93.249843 44.960029 0023XX 17TH AVE S MP2021700782 \n", "1992 -93.246959 44.983657 0006XX UNIVERSITY AVE SE MP2021700783 \n", "\n", " precinct reportedDate reportedTime beginDate \\\n", "0 1 2019/08/19 00:00:00+00 0 2019/08/12 00:00:00+00 \n", "1 3 2019/08/23 00:00:00+00 1150 2019/08/22 00:00:00+00 \n", "2 4 2019/08/23 00:00:00+00 1606 2019/08/23 00:00:00+00 \n", "3 5 2019/08/23 00:00:00+00 1420 2019/08/23 00:00:00+00 \n", "4 5 2019/08/23 00:00:00+00 1513 2019/08/10 00:00:00+00 \n", "5 3 2019/08/23 00:00:00+00 1506 2019/07/20 00:00:00+00 \n", "6 2 2019/08/23 00:00:00+00 2023 2019/08/23 00:00:00+00 \n", "7 4 2019/08/23 00:00:00+00 1705 2019/07/22 00:00:00+00 \n", "8 2 2019/08/23 00:00:00+00 1837 2019/08/22 00:00:00+00 \n", "9 5 2019/08/23 00:00:00+00 1653 2019/08/23 00:00:00+00 \n", "10 3 2019/08/24 00:00:00+00 24 2019/08/22 00:00:00+00 \n", "11 2 2019/08/23 00:00:00+00 1804 2019/08/23 00:00:00+00 \n", "12 3 2019/08/23 00:00:00+00 1738 2019/08/22 00:00:00+00 \n", "13 1 2019/08/23 00:00:00+00 1941 2019/08/21 00:00:00+00 \n", "14 4 2019/08/23 00:00:00+00 1801 2019/08/23 00:00:00+00 \n", "15 5 2019/08/23 00:00:00+00 2052 2019/08/23 00:00:00+00 \n", "16 4 2019/08/23 00:00:00+00 1920 2019/08/23 00:00:00+00 \n", "17 5 2019/08/23 00:00:00+00 2143 2019/08/23 00:00:00+00 \n", "18 1 2019/08/23 00:00:00+00 2320 2019/08/23 00:00:00+00 \n", "19 1 2019/08/23 00:00:00+00 2343 2019/08/23 00:00:00+00 \n", "20 3 2019/08/24 00:00:00+00 210 2019/08/24 00:00:00+00 \n", "21 1 2019/08/24 00:00:00+00 423 2019/08/24 00:00:00+00 \n", "22 3 2019/08/24 00:00:00+00 353 2019/08/24 00:00:00+00 \n", "23 1 2019/08/24 00:00:00+00 508 2019/08/24 00:00:00+00 \n", "24 1 2019/08/24 00:00:00+00 211 2019/08/24 00:00:00+00 \n", "25 1 2019/08/24 00:00:00+00 350 2019/08/24 00:00:00+00 \n", "26 1 2019/08/24 00:00:00+00 553 2019/08/24 00:00:00+00 \n", "27 5 2019/08/24 00:00:00+00 616 2019/08/24 00:00:00+00 \n", "28 2 2019/08/24 00:00:00+00 1323 2019/08/12 00:00:00+00 \n", "29 5 2019/08/24 00:00:00+00 923 2019/08/24 00:00:00+00 \n", "... ... ... ... ... \n", "1963 3 2021/02/02 00:00:00+00 1514 2021/02/02 00:00:00+00 \n", "1964 3 2021/02/02 00:00:00+00 1924 2019/11/14 00:00:00+00 \n", "1965 1 2021/02/02 00:00:00+00 1700 2021/02/02 00:00:00+00 \n", "1966 5 2021/02/02 00:00:00+00 1725 2021/02/02 00:00:00+00 \n", "1967 5 2021/02/02 00:00:00+00 1750 2021/02/02 00:00:00+00 \n", "1968 3 2021/02/02 00:00:00+00 1827 2021/01/31 00:00:00+00 \n", "1969 3 2021/02/02 00:00:00+00 1800 2021/02/01 00:00:00+00 \n", "1970 1 2021/02/02 00:00:00+00 1835 2021/01/29 00:00:00+00 \n", "1971 1 2021/02/02 00:00:00+00 1906 2021/02/02 00:00:00+00 \n", "1972 2 2021/02/02 00:00:00+00 1912 2020/01/28 00:00:00+00 \n", "1973 3 2021/02/02 00:00:00+00 2013 2021/02/02 00:00:00+00 \n", "1974 1 2021/02/03 00:00:00+00 250 2021/02/03 00:00:00+00 \n", "1975 5 2021/01/29 00:00:00+00 1245 2021/01/28 00:00:00+00 \n", "1976 3 2021/02/01 00:00:00+00 833 2021/01/31 00:00:00+00 \n", "1977 4 2021/02/01 00:00:00+00 853 2021/01/29 00:00:00+00 \n", "1978 1 2021/02/01 00:00:00+00 1023 2021/01/30 00:00:00+00 \n", "1979 5 2021/02/01 00:00:00+00 1141 2020/07/10 00:00:00+00 \n", "1980 5 2021/02/01 00:00:00+00 1145 2021/01/29 00:00:00+00 \n", "1981 3 2021/02/01 00:00:00+00 1205 2021/01/31 00:00:00+00 \n", "1982 1 2021/02/01 00:00:00+00 1302 2021/01/29 00:00:00+00 \n", "1983 2 2021/02/01 00:00:00+00 1354 2021/01/24 00:00:00+00 \n", "1984 5 2021/02/01 00:00:00+00 1416 2021/01/02 00:00:00+00 \n", "1985 2 2021/02/01 00:00:00+00 1451 2020/12/18 00:00:00+00 \n", "1986 3 2021/02/01 00:00:00+00 1459 2021/01/28 00:00:00+00 \n", "1987 2 2021/02/01 00:00:00+00 1515 2021/01/30 00:00:00+00 \n", "1988 3 2021/02/01 00:00:00+00 1609 2021/01/26 00:00:00+00 \n", "1989 5 2021/02/01 00:00:00+00 1653 2021/02/01 00:00:00+00 \n", "1990 1 2021/02/01 00:00:00+00 2156 2021/02/01 00:00:00+00 \n", "1991 3 2021/02/02 00:00:00+00 1022 2021/02/01 00:00:00+00 \n", "1992 2 2021/02/02 00:00:00+00 1051 2021/02/01 00:00:00+00 \n", "\n", " reportedDateTime beginTime ... enteredDate \\\n", "0 2019/08/19 00:00:00+00 0 ... 2019/08/19 00:00:00+00 \n", "1 2019/08/23 11:50:00+00 1606 ... 2019/08/24 00:00:00+00 \n", "2 2019/08/23 16:06:00+00 1220 ... 2019/08/24 00:00:00+00 \n", "3 2019/08/23 14:20:00+00 1330 ... 2019/08/24 00:00:00+00 \n", "4 2019/08/23 15:13:00+00 452 ... 2019/08/24 00:00:00+00 \n", "5 2019/08/23 15:06:00+00 1 ... 2019/08/24 00:00:00+00 \n", "6 2019/08/23 20:23:00+00 1545 ... 2019/08/24 00:00:00+00 \n", "7 2019/08/23 17:05:00+00 0 ... 2019/08/24 00:00:00+00 \n", "8 2019/08/23 18:37:00+00 1800 ... 2019/08/24 00:00:00+00 \n", "9 2019/08/23 16:53:00+00 900 ... 2019/08/24 00:00:00+00 \n", "10 2019/08/24 00:24:00+00 2100 ... 2019/08/24 00:00:00+00 \n", "11 2019/08/23 18:04:00+00 1615 ... 2019/08/24 00:00:00+00 \n", "12 2019/08/23 17:38:00+00 1800 ... 2019/08/24 00:00:00+00 \n", "13 2019/08/23 19:41:00+00 0 ... 2019/08/24 00:00:00+00 \n", "14 2019/08/23 18:01:00+00 1700 ... 2019/08/25 00:00:00+00 \n", "15 2019/08/23 20:52:00+00 1804 ... 2019/08/24 00:00:00+00 \n", "16 2019/08/23 19:20:00+00 1858 ... 2019/08/24 00:00:00+00 \n", "17 2019/08/23 21:43:00+00 2000 ... 2019/08/24 00:00:00+00 \n", "18 2019/08/23 23:20:00+00 2215 ... 2019/08/24 00:00:00+00 \n", "19 2019/08/23 23:43:00+00 1430 ... 2019/08/24 00:00:00+00 \n", "20 2019/08/24 02:10:00+00 52 ... 2019/08/24 00:00:00+00 \n", "21 2019/08/24 04:23:00+00 137 ... 2019/08/24 00:00:00+00 \n", "22 2019/08/24 03:53:00+00 150 ... 2019/08/24 00:00:00+00 \n", "23 2019/08/24 05:08:00+00 200 ... 2019/08/24 00:00:00+00 \n", "24 2019/08/24 02:11:00+00 130 ... 2019/08/24 00:00:00+00 \n", "25 2019/08/24 03:50:00+00 244 ... 2019/08/24 00:00:00+00 \n", "26 2019/08/24 05:53:00+00 245 ... 2019/08/24 00:00:00+00 \n", "27 2019/08/24 06:16:00+00 453 ... 2019/08/24 00:00:00+00 \n", "28 2019/08/24 13:23:00+00 1200 ... 2019/08/24 00:00:00+00 \n", "29 2019/08/24 09:23:00+00 900 ... 2019/08/24 00:00:00+00 \n", "... ... ... ... ... \n", "1963 2021/02/02 15:14:00+00 1514 ... 2021/02/03 00:00:00+00 \n", "1964 2021/02/02 19:24:00+00 1100 ... 2021/02/03 00:00:00+00 \n", "1965 2021/02/02 17:00:00+00 1652 ... 2021/02/03 00:00:00+00 \n", "1966 2021/02/02 17:25:00+00 1300 ... 2021/02/03 00:00:00+00 \n", "1967 2021/02/02 17:50:00+00 1700 ... 2021/02/02 00:00:00+00 \n", "1968 2021/02/02 18:27:00+00 0 ... 2021/02/03 00:00:00+00 \n", "1969 2021/02/02 18:00:00+00 2200 ... 2021/02/03 00:00:00+00 \n", "1970 2021/02/02 18:35:00+00 1100 ... 2021/02/03 00:00:00+00 \n", "1971 2021/02/02 19:06:00+00 600 ... 2021/02/03 00:00:00+00 \n", "1972 2021/02/02 19:12:00+00 350 ... 2021/02/03 00:00:00+00 \n", "1973 2021/02/02 20:13:00+00 1945 ... 2021/02/03 00:00:00+00 \n", "1974 2021/02/03 02:50:00+00 216 ... 2021/02/03 00:00:00+00 \n", "1975 2021/01/29 12:45:00+00 2330 ... 2021/02/03 00:00:00+00 \n", "1976 2021/02/01 08:33:00+00 30 ... 2021/02/03 00:00:00+00 \n", "1977 2021/02/01 08:53:00+00 900 ... 2021/02/03 00:00:00+00 \n", "1978 2021/02/01 10:23:00+00 2200 ... 2021/02/03 00:00:00+00 \n", "1979 2021/02/01 11:41:00+00 2100 ... 2021/02/03 00:00:00+00 \n", "1980 2021/02/01 11:45:00+00 2000 ... 2021/02/03 00:00:00+00 \n", "1981 2021/02/01 12:05:00+00 1745 ... 2021/02/03 00:00:00+00 \n", "1982 2021/02/01 13:02:00+00 1600 ... 2021/02/03 00:00:00+00 \n", "1983 2021/02/01 13:54:00+00 1200 ... 2021/02/03 00:00:00+00 \n", "1984 2021/02/01 14:16:00+00 5 ... 2021/02/03 00:00:00+00 \n", "1985 2021/02/01 14:51:00+00 1435 ... 2021/02/03 00:00:00+00 \n", "1986 2021/02/01 14:59:00+00 1000 ... 2021/02/03 00:00:00+00 \n", "1987 2021/02/01 15:15:00+00 2200 ... 2021/02/03 00:00:00+00 \n", "1988 2021/02/01 16:09:00+00 2000 ... 2021/02/03 00:00:00+00 \n", "1989 2021/02/01 16:53:00+00 1200 ... 2021/02/03 00:00:00+00 \n", "1990 2021/02/01 21:56:00+00 1800 ... 2021/02/03 00:00:00+00 \n", "1991 2021/02/02 10:22:00+00 1600 ... 2021/02/03 00:00:00+00 \n", "1992 2021/02/02 10:51:00+00 2100 ... 2021/02/03 00:00:00+00 \n", "\n", " centergbsid centerLong centerLat centerX centerY \\\n", "0 18596.0 -93.265034 44.977764 -10382216 5618021 \n", "1 13244.0 -93.244863 44.945650 -10379970 5612969 \n", "2 10605.0 -93.293211 45.030541 -10385352 5626330 \n", "3 21928.0 -93.278808 44.948347 -10383749 5613393 \n", "4 19969.0 -93.305210 44.948430 -10386688 5613406 \n", "5 19103.0 -93.273484 44.948355 -10383156 5613394 \n", "6 17962.0 -93.226875 44.993251 -10377968 5620459 \n", "7 11496.0 -93.305708 44.993611 -10386743 5620515 \n", "8 16644.0 -93.240044 44.980902 -10379434 5618515 \n", "9 11735.0 -93.298356 44.942118 -10385925 5612413 \n", "10 13511.0 -93.267563 44.926042 -10382497 5609885 \n", "11 NaN -93.249120 45.013160 -10380444 5623593 \n", "12 18344.0 -93.245689 44.963496 -10380062 5615776 \n", "13 17245.0 -93.269678 44.977745 -10382733 5618018 \n", "14 10744.0 -93.304718 45.034411 -10386633 5626940 \n", "15 16776.0 -93.279608 44.960009 -10383838 5615227 \n", "16 17991.0 -93.312737 44.989945 -10387526 5619938 \n", "17 NaN -93.296570 44.924910 -10385726 5609708 \n", "18 NaN -93.260260 44.975750 -10381685 5617704 \n", "19 18615.0 -93.262857 44.978154 -10381973 5618082 \n", "20 16866.0 -93.255511 44.960035 -10381156 5615231 \n", "21 NaN -93.277030 44.978020 -10383550 5618061 \n", "22 11257.0 -93.230794 44.946849 -10378404 5613157 \n", "23 16714.0 -93.277714 44.970506 -10383627 5616879 \n", "24 17121.0 -93.273141 44.980808 -10383118 5618500 \n", "25 17122.0 -93.273116 44.981648 -10383115 5618632 \n", "26 17119.0 -93.271960 44.980833 -10382987 5618504 \n", "27 20551.0 -93.292129 44.922286 -10385232 5609295 \n", "28 16600.0 -93.232448 44.982393 -10378588 5618749 \n", "29 21928.0 -93.278808 44.948347 -10383749 5613393 \n", "... ... ... ... ... ... \n", "1963 22549.0 -93.268978 44.958261 -10382655 5614952 \n", "1964 17502.0 -93.267751 44.949214 -10382518 5613529 \n", "1965 16661.0 -93.281022 44.974496 -10383995 5617507 \n", "1966 16678.0 -93.288048 44.954638 -10384777 5614383 \n", "1967 NaN -93.310280 44.933590 -10387252 5611072 \n", "1968 17405.0 -93.229050 44.960974 -10378210 5615379 \n", "1969 18338.0 -93.238541 44.962752 -10379266 5615659 \n", "1970 16661.0 -93.281022 44.974496 -10383995 5617507 \n", "1971 20491.0 -93.261916 44.980444 -10381869 5618443 \n", "1972 14501.0 -93.263114 45.010545 -10382002 5623181 \n", "1973 16376.0 -93.262642 44.945673 -10381949 5612972 \n", "1974 NaN -93.256170 44.967450 -10381229 5616398 \n", "1975 NaN -93.272740 44.960900 -10383074 5615368 \n", "1976 12954.0 -93.225688 44.920554 -10377836 5609023 \n", "1977 10495.0 -93.308390 45.026805 -10387042 5625742 \n", "1978 15152.0 -93.264736 44.984837 -10382182 5619134 \n", "1979 14074.0 -93.297036 44.945660 -10385778 5612970 \n", "1980 21710.0 -93.289364 44.949203 -10384924 5613528 \n", "1981 NaN -93.242500 44.964020 -10379707 5615858 \n", "1982 15555.0 -93.254077 44.976508 -10380996 5617823 \n", "1983 12779.0 -93.263856 45.003069 -10382084 5622004 \n", "1984 20643.0 -93.292043 44.936791 -10385222 5611576 \n", "1985 20281.0 -93.229494 44.973658 -10378259 5617375 \n", "1986 11099.0 -93.208280 44.949706 -10375898 5613607 \n", "1987 16642.0 -93.236473 44.980767 -10379036 5618494 \n", "1988 NaN -93.262640 44.914300 -10381949 5608039 \n", "1989 18382.0 -93.294335 44.958795 -10385477 5615036 \n", "1990 21933.0 -93.271796 44.977367 -10382968 5617959 \n", "1991 21480.0 -93.249849 44.960034 -10380525 5615231 \n", "1992 17977.0 -93.246961 44.983663 -10380204 5618949 \n", "\n", " neighborhood lastchanged LastUpdateDateETL \\\n", "0 Downtown West 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "1 Corcoran 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "2 Webber - Camden 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "3 Whittier 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "4 ECCO 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "5 Phillips West 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "6 Mid - City Industrial 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "7 Willard - Hay 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "8 Marcy Holmes 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "9 ECCO 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "10 Regina 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "11 Holland 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "12 Seward 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "13 Downtown West 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "14 Webber - Camden 2019/08/25 00:00:00+00 2019/08/25 08:15:46+00 \n", "15 Whittier 2019/09/04 00:00:00+00 2019/09/05 08:15:34+00 \n", "16 Willard - Hay 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "17 East Harriet 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "18 Downtown East 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "19 Downtown West 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "20 Ventura Village 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "21 Downtown West 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "22 Longfellow 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "23 Loring Park 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "24 Downtown West 2019/08/31 00:00:00+00 2019/09/01 08:15:37+00 \n", "25 Downtown West 2019/08/26 00:00:00+00 2019/08/27 08:15:35+00 \n", "26 Downtown West 2019/09/04 00:00:00+00 2019/09/05 08:15:34+00 \n", "27 East Harriet 2019/10/04 00:00:00+00 2019/10/05 08:15:39+00 \n", "28 Marcy Holmes 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "29 Whittier 2019/08/24 00:00:00+00 2019/08/25 08:15:46+00 \n", "... ... ... ... \n", "1963 Phillips West 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1964 Phillips West 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1965 Loring Park 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1966 Lowry Hill East 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1967 East Harriet 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1968 Seward 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1969 Seward 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1970 Loring Park 2021/02/05 00:00:00+00 2021/02/06 07:03:57+00 \n", "1971 Downtown West 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1972 Holland 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1973 Central 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1974 Elliot Park 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1975 Whittier 2021/02/04 00:00:00+00 2021/02/05 07:01:48+00 \n", "1976 Ericsson 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1977 Victory 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1978 Downtown West 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1979 CARAG 2021/02/04 00:00:00+00 2021/02/05 07:01:48+00 \n", "1980 Lowry Hill East 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1981 Seward 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1982 Downtown East 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1983 Sheridan 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1984 East Harriet 2021/02/04 00:00:00+00 2021/02/05 07:01:48+00 \n", "1985 University of Minnesota 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1986 Cooper 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1987 Marcy Holmes 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1988 Northrop 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1989 East Isles 2021/02/04 00:00:00+00 2021/02/05 07:01:48+00 \n", "1990 Downtown West 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1991 Ventura Village 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "1992 Marcy Holmes 2021/02/03 00:00:00+00 2021/02/04 07:03:32+00 \n", "\n", " OBJECTID \n", "0 1 \n", "1 2 \n", "2 3 \n", "3 4 \n", "4 5 \n", "5 6 \n", "6 7 \n", "7 8 \n", "8 9 \n", "9 10 \n", "10 11 \n", "11 12 \n", "12 13 \n", "13 14 \n", "14 15 \n", "15 16 \n", "16 17 \n", "17 18 \n", "18 19 \n", "19 20 \n", "20 21 \n", "21 22 \n", "22 23 \n", "23 24 \n", "24 25 \n", "25 26 \n", "26 27 \n", "27 28 \n", "28 29 \n", "29 30 \n", "... ... \n", "1963 1964 \n", "1964 1965 \n", "1965 1966 \n", "1966 1967 \n", "1967 1968 \n", "1968 1969 \n", "1969 1970 \n", "1970 1971 \n", "1971 1972 \n", "1972 1973 \n", "1973 1974 \n", "1974 1975 \n", "1975 1976 \n", "1976 1977 \n", "1977 1978 \n", "1978 1979 \n", "1979 1980 \n", "1980 1981 \n", "1981 1982 \n", "1982 1983 \n", "1983 1984 \n", "1984 1985 \n", "1985 1986 \n", "1986 1987 \n", "1987 1988 \n", "1988 1989 \n", "1989 1990 \n", "1990 1991 \n", "1991 1992 \n", "1992 1993 \n", "\n", "[49063 rows x 23 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Minneapolis\n", "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", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "#Load Data\n", "mp_crime19 = pd.read_csv('Datasets/MCrime_2019.csv')\n", "mp_crime20 = pd.read_csv('Datasets/MCrime_2020.csv')\n", "mp_crime21 = pd.read_csv('Datasets/MCrime_2021.csv')\n", "\n", "mp_crime= pd.concat([mp_crime19, mp_crime20, mp_crime21], axis=0)\n", "\n", "#Index=['precinct', 'neighborhood', 'offense','description','reportedDate']\n", "#mp_crime=mp_crime[Index]\n", "mp_crime" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['X', 'Y', 'publicaddress', 'caseNumber', 'precinct', 'reportedDate',\n", " 'reportedTime', 'beginDate', 'reportedDateTime', 'beginTime', 'offense',\n", " 'description', 'UCRCode', 'enteredDate', 'centergbsid', 'centerLong',\n", " 'centerLat', 'centerX', 'centerY', 'neighborhood', 'lastchanged',\n", " 'LastUpdateDateETL', 'OBJECTID'],\n", " dtype='object')\n" ] } ], "source": [ "print(mp_crime.columns)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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XYpublicaddresscaseNumberprecinctreportedDatereportedTimebeginDatereportedDateTimebeginTime...lastchangedLastUpdateDateETLOBJECTIDDateYearDayYearMonthDayCountType
1-93.24485744.9456480031XX 19TH AVE SMP201925334632019/08/23 00:00:00+0011502019/08/22 00:00:00+002019/08/23 11:50:00+001606...2019/08/24 00:00:00+002019/08/25 08:15:46+0022019-08-23 00:00:00+00:0020192358231Blank
2-93.29320445.0305360041XX DUPONT AVE NMP201925339242019/08/23 00:00:00+0016062019/08/23 00:00:00+002019/08/23 16:06:00+001220...2019/08/24 00:00:00+002019/08/25 08:15:46+0032019-08-23 00:00:00+00:0020192358231Theft_InsideVehicle
6-93.22687344.9932510003XX STINSON BLVD NEMP201925364922019/08/23 00:00:00+0020232019/08/23 00:00:00+002019/08/23 20:23:00+001545...2019/08/24 00:00:00+002019/08/25 08:15:46+0072019-08-23 00:00:00+00:0020192358231Theft_Auto
7-93.30570044.9936060014XX NEWTON AVE NMP201925368542019/08/23 00:00:00+0017052019/07/22 00:00:00+002019/08/23 17:05:00+000...2019/08/24 00:00:00+002019/08/25 08:15:46+0082019-08-23 00:00:00+00:0020192358231Theft_Auto
8-93.24004244.9808990011XX UNIVERSITY AVE SEMP201925370422019/08/23 00:00:00+0018372019/08/22 00:00:00+002019/08/23 18:37:00+001800...2019/08/24 00:00:00+002019/08/25 08:15:46+0092019-08-23 00:00:00+00:0020192358231Theft_Auto
9-93.29835244.9421130033XX HENNEPIN AVEMP201925370652019/08/23 00:00:00+0016532019/08/23 00:00:00+002019/08/23 16:53:00+00900...2019/08/24 00:00:00+002019/08/25 08:15:46+00102019-08-23 00:00:00+00:0020192358231Theft_Auto
11-93.24911545.013157NaNMP201925372322019/08/23 00:00:00+0018042019/08/23 00:00:00+002019/08/23 18:04:00+001615...2019/08/24 00:00:00+002019/08/25 08:15:46+00122019-08-23 00:00:00+00:0020192358231Theft_MotorParts
12-93.24568344.9634930009XX 19TH AVE SMP201925375132019/08/23 00:00:00+0017382019/08/22 00:00:00+002019/08/23 17:38:00+001800...2019/08/24 00:00:00+002019/08/25 08:15:46+00132019-08-23 00:00:00+00:0020192358231Theft_Auto
13-93.26967744.9777410005XX MARQUETTE AVEMP201925377112019/08/23 00:00:00+0019412019/08/21 00:00:00+002019/08/23 19:41:00+000...2019/08/24 00:00:00+002019/08/25 08:15:46+00142019-08-23 00:00:00+00:0020192358231Theft_InsideVehicle
17-93.29656444.924911NaNMP201925397652019/08/23 00:00:00+0021432019/08/23 00:00:00+002019/08/23 21:43:00+002000...2019/08/24 00:00:00+002019/08/25 08:15:46+00182019-08-23 00:00:00+00:0020192358231Theft_InsideVehicle
18-93.26026344.975746NaNMP201925406212019/08/23 00:00:00+0023202019/08/23 00:00:00+002019/08/23 23:20:00+002215...2019/08/24 00:00:00+002019/08/25 08:15:46+00192019-08-23 00:00:00+00:0020192358231Blank
19-93.26285044.9781480004XX 3RD ST SMP201925411812019/08/23 00:00:00+0023432019/08/23 00:00:00+002019/08/23 23:43:00+001430...2019/08/24 00:00:00+002019/08/25 08:15:46+00202019-08-23 00:00:00+00:0020192358231Theft_InsideVehicle
23-93.27770844.9705030013XX NICOLLET MALLMP201925429912019/08/24 00:00:00+005082019/08/24 00:00:00+002019/08/24 05:08:00+00200...2019/08/24 00:00:00+002019/08/25 08:15:46+00242019-08-24 00:00:00+00:0020192368241Blank
28-93.23244244.9823860006XX 15TH AVE SEMP201925451322019/08/24 00:00:00+0013232019/08/12 00:00:00+002019/08/24 13:23:00+001200...2019/08/24 00:00:00+002019/08/25 08:15:46+00292019-08-24 00:00:00+00:0020192368241Theft_Auto
29-93.27880444.94834400001X LAKE ST WMP201925451552019/08/24 00:00:00+009232019/08/24 00:00:00+002019/08/24 09:23:00+00900...2019/08/24 00:00:00+002019/08/25 08:15:46+00302019-08-24 00:00:00+00:0020192368241Blank
30-93.24554944.9889050007XX 5TH AVE SEMP201925452322019/08/24 00:00:00+0013092019/08/24 00:00:00+002019/08/24 13:09:00+00200...2019/08/24 00:00:00+002019/08/25 08:15:46+00312019-08-24 00:00:00+00:0020192368241Theft_Auto
31-93.26374944.9332490038XX COLUMBUS AVEMP201925453232019/08/24 00:00:00+0010402019/08/23 00:00:00+002019/08/24 10:40:00+002200...2019/08/24 00:00:00+002019/08/25 08:15:46+00322019-08-24 00:00:00+00:0020192368241Theft_Auto
32-93.25367844.9582300024XX 15TH AVE SMP201925454132019/08/24 00:00:00+0010392019/08/23 00:00:00+002019/08/24 10:39:00+001800...2019/08/24 00:00:00+002019/08/25 08:15:46+00332019-08-24 00:00:00+00:0020192368241Theft_Auto
35-93.29258444.9915040011XX 7TH ST NMP201925457942019/08/24 00:00:00+0012052019/08/24 00:00:00+002019/08/24 12:05:00+00400...2019/08/24 00:00:00+002019/08/25 08:15:46+00362019-08-24 00:00:00+00:0020192368241Theft_Auto
39-93.21126044.96548200004X MELBOURNE AVE SEMP201925468822019/08/24 00:00:00+0013472019/08/23 00:00:00+002019/08/24 13:47:00+001200...2019/08/24 00:00:00+002019/08/25 08:15:46+00402019-08-24 00:00:00+00:0020192368241Theft_Auto
41-93.28132044.9528390027XX PILLSBURY AVEMP201925471852019/08/24 00:00:00+0014102019/08/23 00:00:00+002019/08/24 14:10:00+001900...2019/08/24 00:00:00+002019/08/25 08:15:46+00422019-08-24 00:00:00+00:0020192368241Theft_Auto
42-93.31465644.9870180008XX UPTON AVE NMP201925472142019/08/24 00:00:00+0014252019/08/18 00:00:00+002019/08/24 14:25:00+00800...2019/08/24 00:00:00+002019/08/25 08:15:46+00432019-08-24 00:00:00+00:0020192368241Blank
43-93.25000445.0167700008XX 27TH AVE NEMP201925472522019/08/24 00:00:00+0014282019/08/23 00:00:00+002019/08/24 14:28:00+002000...2019/08/24 00:00:00+002019/08/25 08:15:46+00442019-08-24 00:00:00+00:0020192368241Theft_InsideVehicle
48-93.21790844.9720470027XX UNIVERSITY AVE SEMP201925476122019/08/24 00:00:00+0016432019/08/24 00:00:00+002019/08/24 16:43:00+001500...2019/08/24 00:00:00+002019/08/25 08:15:46+00492019-08-24 00:00:00+00:0020192368241Blank
49-93.22478944.9736040009XX WASHINGTON AVE SEMP201925477722019/08/24 00:00:00+0017122019/08/24 00:00:00+002019/08/24 17:12:00+001535...2019/09/07 00:00:00+002019/09/08 08:15:27+00502019-08-24 00:00:00+00:0020192368241Blank
52-93.27790644.9332040038XX NICOLLET AVEMP201925483152019/08/24 00:00:00+0018002019/08/23 00:00:00+002019/08/24 18:00:00+001400...2019/08/24 00:00:00+002019/08/25 08:15:46+00532019-08-24 00:00:00+00:0020192368241Theft_Auto
53-93.25808944.976127NaNMP201925485312019/08/24 00:00:00+0019002019/08/24 00:00:00+002019/08/24 19:00:00+00900...2019/08/24 00:00:00+002019/08/25 08:15:46+00542019-08-24 00:00:00+00:0020192368241Theft_InsideVehicle
54-93.25618544.9314300039XX 13TH AVE SMP201925488232019/08/24 00:00:00+0017542019/08/23 00:00:00+002019/08/24 17:54:00+002100...2019/08/24 00:00:00+002019/08/25 08:15:46+00552019-08-24 00:00:00+00:0020192368241Theft_InsideVehicle
57-93.27385544.974977NaNMP201925493512019/08/24 00:00:00+0018242019/08/24 00:00:00+002019/08/24 18:24:00+001800...2019/08/24 00:00:00+002019/08/25 08:15:46+00582019-08-24 00:00:00+00:0020192368241Blank
59-93.25543044.9770870002XX 9TH AVE SMP201970520412019/08/23 00:00:00+0011022019/08/23 00:00:00+002019/08/23 11:02:00+00245...2019/08/24 00:00:00+002019/08/25 08:15:46+00602019-08-23 00:00:00+00:0020192358231Theft_InsideVehicle
..................................................................
1959-93.23715845.0104900022XX JOHNSON ST NEMP20212319222021/02/02 00:00:00+0012582021/01/29 00:00:00+002021/02/02 12:58:00+002030...2021/02/03 00:00:00+002021/02/04 07:03:32+0019602021-02-02 00:00:00+00:00202133221Theft_MotorParts
1960-93.25611345.0121660006XX 24TH AVE NEMP20212319822021/02/02 00:00:00+0013172021/01/29 00:00:00+002021/02/02 13:17:00+001800...2021/02/03 00:00:00+002021/02/04 07:03:32+0019612021-02-02 00:00:00+00:00202133221Blank
1961-93.20154044.9068070053XX RIVERVIEW RDMP20212323832021/02/02 00:00:00+0014382021/02/01 00:00:00+002021/02/02 14:38:00+002200...2021/02/03 00:00:00+002021/02/04 07:03:32+0019622021-02-02 00:00:00+00:00202133221Theft_Auto
1962-93.29509144.9577780024XX HENNEPIN AVEMP20212324552021/02/02 00:00:00+0014312020/02/02 00:00:00+002021/02/02 14:31:00+000...2021/02/04 00:00:00+002021/02/05 07:01:48+0019632021-02-02 00:00:00+00:00202133221Theft_MotorParts
1963-93.26897744.9582550024XX 5TH AVE SMP20212326232021/02/02 00:00:00+0015142021/02/02 00:00:00+002021/02/02 15:14:00+001514...2021/02/03 00:00:00+002021/02/04 07:03:32+0019642021-02-02 00:00:00+00:00202133221Theft_MotorParts
1966-93.28803944.9546380026XX LYNDALE AVE SMP20212334152021/02/02 00:00:00+0017252021/02/02 00:00:00+002021/02/02 17:25:00+001300...2021/02/03 00:00:00+002021/02/04 07:03:32+0019672021-02-02 00:00:00+00:00202133221Blank
1967-93.31027244.933586NaNMP20212335152021/02/02 00:00:00+0017502021/02/02 00:00:00+002021/02/02 17:50:00+001700...2021/02/03 00:00:00+002021/02/04 07:03:32+0019682021-02-02 00:00:00+00:00202133221Theft_InsideVehicle
1968-93.22904744.9609690029XX 22ND ST EMP20212337132021/02/02 00:00:00+0018272021/01/31 00:00:00+002021/02/02 18:27:00+000...2021/02/03 00:00:00+002021/02/04 07:03:32+0019692021-02-02 00:00:00+00:00202133221Theft_MotorParts
1969-93.23853344.9627490023XX FRANKLIN AVE EMP20212339832021/02/02 00:00:00+0018002021/02/01 00:00:00+002021/02/02 18:00:00+002200...2021/02/03 00:00:00+002021/02/04 07:03:32+0019702021-02-02 00:00:00+00:00202133221Theft_MotorParts
1970-93.28101444.9744940012XX HENNEPIN AVEMP20212340512021/02/02 00:00:00+0018352021/01/29 00:00:00+002021/02/02 18:35:00+001100...2021/02/05 00:00:00+002021/02/06 07:03:57+0019712021-02-02 00:00:00+00:00202133221Blank
1972-93.26311145.0105400022XX UNIVERSITY AVE NEMP20212344722021/02/02 00:00:00+0019122020/01/28 00:00:00+002021/02/02 19:12:00+00350...2021/02/03 00:00:00+002021/02/04 07:03:32+0019732021-02-02 00:00:00+00:00202133221Blank
1973-93.26263544.9456680031XX CHICAGO AVEMP20212348132021/02/02 00:00:00+0020132021/02/02 00:00:00+002021/02/02 20:13:00+001945...2021/02/03 00:00:00+002021/02/04 07:03:32+0019742021-02-02 00:00:00+00:00202133221Theft_InsideVehicle
1975-93.27274144.960899NaNMP202170074252021/01/29 00:00:00+0012452021/01/28 00:00:00+002021/01/29 12:45:00+002330...2021/02/04 00:00:00+002021/02/05 07:01:48+0019762021-01-29 00:00:00+00:002021291291Theft_MotorParts
1976-93.22568744.9205540045XX 32ND AVE SMP202170074432021/02/01 00:00:00+008332021/01/31 00:00:00+002021/02/01 08:33:00+0030...2021/02/03 00:00:00+002021/02/04 07:03:32+0019772021-02-01 00:00:00+00:00202132211Theft_MotorParts
1977-93.30838645.0268030039XX PENN AVE NMP202170074542021/02/01 00:00:00+008532021/01/29 00:00:00+002021/02/01 08:53:00+00900...2021/02/03 00:00:00+002021/02/04 07:03:32+0019782021-02-01 00:00:00+00:00202132211Theft_InsideVehicle
1978-93.26472844.98483300008X HENNEPIN AVEMP202170075012021/02/01 00:00:00+0010232021/01/30 00:00:00+002021/02/01 10:23:00+002200...2021/02/03 00:00:00+002021/02/04 07:03:32+0019792021-02-01 00:00:00+00:00202132211Blank
1979-93.29703144.9456550031XX GIRARD AVE SMP202170075552021/02/01 00:00:00+0011412020/07/10 00:00:00+002021/02/01 11:41:00+002100...2021/02/04 00:00:00+002021/02/05 07:01:48+0019802021-02-01 00:00:00+00:00202132211Blank
1980-93.28936044.9492020029XX ALDRICH AVE SMP202170075752021/02/01 00:00:00+0011452021/01/29 00:00:00+002021/02/01 11:45:00+002000...2021/02/03 00:00:00+002021/02/04 07:03:32+0019812021-02-01 00:00:00+00:00202132211Theft_InsideVehicle
1981-93.24249444.964014NaNMP202170076032021/02/01 00:00:00+0012052021/01/31 00:00:00+002021/02/01 12:05:00+001745...2021/02/03 00:00:00+002021/02/04 07:03:32+0019822021-02-01 00:00:00+00:00202132211Theft_MotorParts
1982-93.25407444.9765020002XX 10TH AVE SMP202170076312021/02/01 00:00:00+0013022021/01/29 00:00:00+002021/02/01 13:02:00+001600...2021/02/03 00:00:00+002021/02/04 07:03:32+0019832021-02-01 00:00:00+00:00202132211Theft_InsideVehicle
1983-93.26384745.0030650003XX 15TH AVE NEMP202170076522021/02/01 00:00:00+0013542021/01/24 00:00:00+002021/02/01 13:54:00+001200...2021/02/03 00:00:00+002021/02/04 07:03:32+0019842021-02-01 00:00:00+00:00202132211Theft_MotorParts
1984-93.29203744.9367910036XX COLFAX AVE SMP202170076952021/02/01 00:00:00+0014162021/01/02 00:00:00+002021/02/01 14:16:00+005...2021/02/04 00:00:00+002021/02/05 07:01:48+0019852021-02-01 00:00:00+00:00202132211Theft_MotorParts
1985-93.22948744.9736550006XX WASHINGTON AVE SEMP202170077022021/02/01 00:00:00+0014512020/12/18 00:00:00+002021/02/01 14:51:00+001435...2021/02/03 00:00:00+002021/02/04 07:03:32+0019862021-02-01 00:00:00+00:00202132211Blank
1986-93.20827844.9497050029XX DORMAN AVEMP202170077132021/02/01 00:00:00+0014592021/01/28 00:00:00+002021/02/01 14:59:00+001000...2021/02/03 00:00:00+002021/02/04 07:03:32+0019872021-02-01 00:00:00+00:00202132211Theft_MotorParts
1987-93.23646744.9807660013XX 4TH ST SEMP202170077222021/02/01 00:00:00+0015152021/01/30 00:00:00+002021/02/01 15:15:00+002200...2021/02/03 00:00:00+002021/02/04 07:03:32+0019882021-02-01 00:00:00+00:00202132211Blank
1988-93.26263544.914295NaNMP202170077332021/02/01 00:00:00+0016092021/01/26 00:00:00+002021/02/01 16:09:00+002000...2021/02/03 00:00:00+002021/02/04 07:03:32+0019892021-02-01 00:00:00+00:00202132211Theft_MotorParts
1989-93.29432744.9587890024XX HENNEPIN AVEMP202170077452021/02/01 00:00:00+0016532021/02/01 00:00:00+002021/02/01 16:53:00+001200...2021/02/04 00:00:00+002021/02/05 07:01:48+0019902021-02-01 00:00:00+00:00202132211Theft_InsideVehicle
1990-93.27178844.9773660006XX NICOLLET MALLMP202170077512021/02/01 00:00:00+0021562021/02/01 00:00:00+002021/02/01 21:56:00+001800...2021/02/03 00:00:00+002021/02/04 07:03:32+0019912021-02-01 00:00:00+00:00202132211Blank
1991-93.24984344.9600290023XX 17TH AVE SMP202170078232021/02/02 00:00:00+0010222021/02/01 00:00:00+002021/02/02 10:22:00+001600...2021/02/03 00:00:00+002021/02/04 07:03:32+0019922021-02-02 00:00:00+00:00202133221Blank
1992-93.24695944.9836570006XX UNIVERSITY AVE SEMP202170078322021/02/02 00:00:00+0010512021/02/01 00:00:00+002021/02/02 10:51:00+002100...2021/02/03 00:00:00+002021/02/04 07:03:32+0019932021-02-02 00:00:00+00:00202133221Theft_InsideVehicle
\n", "

31230 rows × 30 columns

\n", "
" ], "text/plain": [ " X Y publicaddress caseNumber \\\n", "1 -93.244857 44.945648 0031XX 19TH AVE S MP2019253346 \n", "2 -93.293204 45.030536 0041XX DUPONT AVE N MP2019253392 \n", "6 -93.226873 44.993251 0003XX STINSON BLVD NE MP2019253649 \n", "7 -93.305700 44.993606 0014XX NEWTON AVE N MP2019253685 \n", "8 -93.240042 44.980899 0011XX UNIVERSITY AVE SE MP2019253704 \n", "9 -93.298352 44.942113 0033XX HENNEPIN AVE MP2019253706 \n", "11 -93.249115 45.013157 NaN MP2019253723 \n", "12 -93.245683 44.963493 0009XX 19TH AVE S MP2019253751 \n", "13 -93.269677 44.977741 0005XX MARQUETTE AVE MP2019253771 \n", "17 -93.296564 44.924911 NaN MP2019253976 \n", "18 -93.260263 44.975746 NaN MP2019254062 \n", "19 -93.262850 44.978148 0004XX 3RD ST S MP2019254118 \n", "23 -93.277708 44.970503 0013XX NICOLLET MALL MP2019254299 \n", "28 -93.232442 44.982386 0006XX 15TH AVE SE MP2019254513 \n", "29 -93.278804 44.948344 00001X LAKE ST W MP2019254515 \n", "30 -93.245549 44.988905 0007XX 5TH AVE SE MP2019254523 \n", "31 -93.263749 44.933249 0038XX COLUMBUS AVE MP2019254532 \n", "32 -93.253678 44.958230 0024XX 15TH AVE S MP2019254541 \n", "35 -93.292584 44.991504 0011XX 7TH ST N MP2019254579 \n", "39 -93.211260 44.965482 00004X MELBOURNE AVE SE MP2019254688 \n", "41 -93.281320 44.952839 0027XX PILLSBURY AVE MP2019254718 \n", "42 -93.314656 44.987018 0008XX UPTON AVE N MP2019254721 \n", "43 -93.250004 45.016770 0008XX 27TH AVE NE MP2019254725 \n", "48 -93.217908 44.972047 0027XX UNIVERSITY AVE SE MP2019254761 \n", "49 -93.224789 44.973604 0009XX WASHINGTON AVE SE MP2019254777 \n", "52 -93.277906 44.933204 0038XX NICOLLET AVE MP2019254831 \n", "53 -93.258089 44.976127 NaN MP2019254853 \n", "54 -93.256185 44.931430 0039XX 13TH AVE S MP2019254882 \n", "57 -93.273855 44.974977 NaN MP2019254935 \n", "59 -93.255430 44.977087 0002XX 9TH AVE S MP2019705204 \n", "... ... ... ... ... \n", "1959 -93.237158 45.010490 0022XX JOHNSON ST NE MP202123192 \n", "1960 -93.256113 45.012166 0006XX 24TH AVE NE MP202123198 \n", "1961 -93.201540 44.906807 0053XX RIVERVIEW RD MP202123238 \n", "1962 -93.295091 44.957778 0024XX HENNEPIN AVE MP202123245 \n", "1963 -93.268977 44.958255 0024XX 5TH AVE S MP202123262 \n", "1966 -93.288039 44.954638 0026XX LYNDALE AVE S MP202123341 \n", "1967 -93.310272 44.933586 NaN MP202123351 \n", "1968 -93.229047 44.960969 0029XX 22ND ST E MP202123371 \n", "1969 -93.238533 44.962749 0023XX FRANKLIN AVE E MP202123398 \n", "1970 -93.281014 44.974494 0012XX HENNEPIN AVE MP202123405 \n", "1972 -93.263111 45.010540 0022XX UNIVERSITY AVE NE MP202123447 \n", "1973 -93.262635 44.945668 0031XX CHICAGO AVE MP202123481 \n", "1975 -93.272741 44.960899 NaN MP2021700742 \n", "1976 -93.225687 44.920554 0045XX 32ND AVE S MP2021700744 \n", "1977 -93.308386 45.026803 0039XX PENN AVE N MP2021700745 \n", "1978 -93.264728 44.984833 00008X HENNEPIN AVE MP2021700750 \n", "1979 -93.297031 44.945655 0031XX GIRARD AVE S MP2021700755 \n", "1980 -93.289360 44.949202 0029XX ALDRICH AVE S MP2021700757 \n", "1981 -93.242494 44.964014 NaN MP2021700760 \n", "1982 -93.254074 44.976502 0002XX 10TH AVE S MP2021700763 \n", "1983 -93.263847 45.003065 0003XX 15TH AVE NE MP2021700765 \n", "1984 -93.292037 44.936791 0036XX COLFAX AVE S MP2021700769 \n", "1985 -93.229487 44.973655 0006XX WASHINGTON AVE SE MP2021700770 \n", "1986 -93.208278 44.949705 0029XX DORMAN AVE MP2021700771 \n", "1987 -93.236467 44.980766 0013XX 4TH ST SE MP2021700772 \n", "1988 -93.262635 44.914295 NaN MP2021700773 \n", "1989 -93.294327 44.958789 0024XX HENNEPIN AVE MP2021700774 \n", "1990 -93.271788 44.977366 0006XX NICOLLET MALL MP2021700775 \n", "1991 -93.249843 44.960029 0023XX 17TH AVE S MP2021700782 \n", "1992 -93.246959 44.983657 0006XX UNIVERSITY AVE SE MP2021700783 \n", "\n", " precinct reportedDate reportedTime beginDate \\\n", "1 3 2019/08/23 00:00:00+00 1150 2019/08/22 00:00:00+00 \n", "2 4 2019/08/23 00:00:00+00 1606 2019/08/23 00:00:00+00 \n", "6 2 2019/08/23 00:00:00+00 2023 2019/08/23 00:00:00+00 \n", "7 4 2019/08/23 00:00:00+00 1705 2019/07/22 00:00:00+00 \n", "8 2 2019/08/23 00:00:00+00 1837 2019/08/22 00:00:00+00 \n", "9 5 2019/08/23 00:00:00+00 1653 2019/08/23 00:00:00+00 \n", "11 2 2019/08/23 00:00:00+00 1804 2019/08/23 00:00:00+00 \n", "12 3 2019/08/23 00:00:00+00 1738 2019/08/22 00:00:00+00 \n", "13 1 2019/08/23 00:00:00+00 1941 2019/08/21 00:00:00+00 \n", "17 5 2019/08/23 00:00:00+00 2143 2019/08/23 00:00:00+00 \n", "18 1 2019/08/23 00:00:00+00 2320 2019/08/23 00:00:00+00 \n", "19 1 2019/08/23 00:00:00+00 2343 2019/08/23 00:00:00+00 \n", "23 1 2019/08/24 00:00:00+00 508 2019/08/24 00:00:00+00 \n", "28 2 2019/08/24 00:00:00+00 1323 2019/08/12 00:00:00+00 \n", "29 5 2019/08/24 00:00:00+00 923 2019/08/24 00:00:00+00 \n", "30 2 2019/08/24 00:00:00+00 1309 2019/08/24 00:00:00+00 \n", "31 3 2019/08/24 00:00:00+00 1040 2019/08/23 00:00:00+00 \n", "32 3 2019/08/24 00:00:00+00 1039 2019/08/23 00:00:00+00 \n", "35 4 2019/08/24 00:00:00+00 1205 2019/08/24 00:00:00+00 \n", "39 2 2019/08/24 00:00:00+00 1347 2019/08/23 00:00:00+00 \n", "41 5 2019/08/24 00:00:00+00 1410 2019/08/23 00:00:00+00 \n", "42 4 2019/08/24 00:00:00+00 1425 2019/08/18 00:00:00+00 \n", "43 2 2019/08/24 00:00:00+00 1428 2019/08/23 00:00:00+00 \n", "48 2 2019/08/24 00:00:00+00 1643 2019/08/24 00:00:00+00 \n", "49 2 2019/08/24 00:00:00+00 1712 2019/08/24 00:00:00+00 \n", "52 5 2019/08/24 00:00:00+00 1800 2019/08/23 00:00:00+00 \n", "53 1 2019/08/24 00:00:00+00 1900 2019/08/24 00:00:00+00 \n", "54 3 2019/08/24 00:00:00+00 1754 2019/08/23 00:00:00+00 \n", "57 1 2019/08/24 00:00:00+00 1824 2019/08/24 00:00:00+00 \n", "59 1 2019/08/23 00:00:00+00 1102 2019/08/23 00:00:00+00 \n", "... ... ... ... ... \n", "1959 2 2021/02/02 00:00:00+00 1258 2021/01/29 00:00:00+00 \n", "1960 2 2021/02/02 00:00:00+00 1317 2021/01/29 00:00:00+00 \n", "1961 3 2021/02/02 00:00:00+00 1438 2021/02/01 00:00:00+00 \n", "1962 5 2021/02/02 00:00:00+00 1431 2020/02/02 00:00:00+00 \n", "1963 3 2021/02/02 00:00:00+00 1514 2021/02/02 00:00:00+00 \n", "1966 5 2021/02/02 00:00:00+00 1725 2021/02/02 00:00:00+00 \n", "1967 5 2021/02/02 00:00:00+00 1750 2021/02/02 00:00:00+00 \n", "1968 3 2021/02/02 00:00:00+00 1827 2021/01/31 00:00:00+00 \n", "1969 3 2021/02/02 00:00:00+00 1800 2021/02/01 00:00:00+00 \n", "1970 1 2021/02/02 00:00:00+00 1835 2021/01/29 00:00:00+00 \n", "1972 2 2021/02/02 00:00:00+00 1912 2020/01/28 00:00:00+00 \n", "1973 3 2021/02/02 00:00:00+00 2013 2021/02/02 00:00:00+00 \n", "1975 5 2021/01/29 00:00:00+00 1245 2021/01/28 00:00:00+00 \n", "1976 3 2021/02/01 00:00:00+00 833 2021/01/31 00:00:00+00 \n", "1977 4 2021/02/01 00:00:00+00 853 2021/01/29 00:00:00+00 \n", "1978 1 2021/02/01 00:00:00+00 1023 2021/01/30 00:00:00+00 \n", "1979 5 2021/02/01 00:00:00+00 1141 2020/07/10 00:00:00+00 \n", "1980 5 2021/02/01 00:00:00+00 1145 2021/01/29 00:00:00+00 \n", "1981 3 2021/02/01 00:00:00+00 1205 2021/01/31 00:00:00+00 \n", "1982 1 2021/02/01 00:00:00+00 1302 2021/01/29 00:00:00+00 \n", "1983 2 2021/02/01 00:00:00+00 1354 2021/01/24 00:00:00+00 \n", "1984 5 2021/02/01 00:00:00+00 1416 2021/01/02 00:00:00+00 \n", "1985 2 2021/02/01 00:00:00+00 1451 2020/12/18 00:00:00+00 \n", "1986 3 2021/02/01 00:00:00+00 1459 2021/01/28 00:00:00+00 \n", "1987 2 2021/02/01 00:00:00+00 1515 2021/01/30 00:00:00+00 \n", "1988 3 2021/02/01 00:00:00+00 1609 2021/01/26 00:00:00+00 \n", "1989 5 2021/02/01 00:00:00+00 1653 2021/02/01 00:00:00+00 \n", "1990 1 2021/02/01 00:00:00+00 2156 2021/02/01 00:00:00+00 \n", "1991 3 2021/02/02 00:00:00+00 1022 2021/02/01 00:00:00+00 \n", "1992 2 2021/02/02 00:00:00+00 1051 2021/02/01 00:00:00+00 \n", "\n", " reportedDateTime beginTime ... lastchanged \\\n", "1 2019/08/23 11:50:00+00 1606 ... 2019/08/24 00:00:00+00 \n", "2 2019/08/23 16:06:00+00 1220 ... 2019/08/24 00:00:00+00 \n", "6 2019/08/23 20:23:00+00 1545 ... 2019/08/24 00:00:00+00 \n", "7 2019/08/23 17:05:00+00 0 ... 2019/08/24 00:00:00+00 \n", "8 2019/08/23 18:37:00+00 1800 ... 2019/08/24 00:00:00+00 \n", "9 2019/08/23 16:53:00+00 900 ... 2019/08/24 00:00:00+00 \n", "11 2019/08/23 18:04:00+00 1615 ... 2019/08/24 00:00:00+00 \n", "12 2019/08/23 17:38:00+00 1800 ... 2019/08/24 00:00:00+00 \n", "13 2019/08/23 19:41:00+00 0 ... 2019/08/24 00:00:00+00 \n", "17 2019/08/23 21:43:00+00 2000 ... 2019/08/24 00:00:00+00 \n", "18 2019/08/23 23:20:00+00 2215 ... 2019/08/24 00:00:00+00 \n", "19 2019/08/23 23:43:00+00 1430 ... 2019/08/24 00:00:00+00 \n", "23 2019/08/24 05:08:00+00 200 ... 2019/08/24 00:00:00+00 \n", "28 2019/08/24 13:23:00+00 1200 ... 2019/08/24 00:00:00+00 \n", "29 2019/08/24 09:23:00+00 900 ... 2019/08/24 00:00:00+00 \n", "30 2019/08/24 13:09:00+00 200 ... 2019/08/24 00:00:00+00 \n", "31 2019/08/24 10:40:00+00 2200 ... 2019/08/24 00:00:00+00 \n", "32 2019/08/24 10:39:00+00 1800 ... 2019/08/24 00:00:00+00 \n", "35 2019/08/24 12:05:00+00 400 ... 2019/08/24 00:00:00+00 \n", "39 2019/08/24 13:47:00+00 1200 ... 2019/08/24 00:00:00+00 \n", "41 2019/08/24 14:10:00+00 1900 ... 2019/08/24 00:00:00+00 \n", "42 2019/08/24 14:25:00+00 800 ... 2019/08/24 00:00:00+00 \n", "43 2019/08/24 14:28:00+00 2000 ... 2019/08/24 00:00:00+00 \n", "48 2019/08/24 16:43:00+00 1500 ... 2019/08/24 00:00:00+00 \n", "49 2019/08/24 17:12:00+00 1535 ... 2019/09/07 00:00:00+00 \n", "52 2019/08/24 18:00:00+00 1400 ... 2019/08/24 00:00:00+00 \n", "53 2019/08/24 19:00:00+00 900 ... 2019/08/24 00:00:00+00 \n", "54 2019/08/24 17:54:00+00 2100 ... 2019/08/24 00:00:00+00 \n", "57 2019/08/24 18:24:00+00 1800 ... 2019/08/24 00:00:00+00 \n", "59 2019/08/23 11:02:00+00 245 ... 2019/08/24 00:00:00+00 \n", "... ... ... ... ... \n", "1959 2021/02/02 12:58:00+00 2030 ... 2021/02/03 00:00:00+00 \n", "1960 2021/02/02 13:17:00+00 1800 ... 2021/02/03 00:00:00+00 \n", "1961 2021/02/02 14:38:00+00 2200 ... 2021/02/03 00:00:00+00 \n", "1962 2021/02/02 14:31:00+00 0 ... 2021/02/04 00:00:00+00 \n", "1963 2021/02/02 15:14:00+00 1514 ... 2021/02/03 00:00:00+00 \n", "1966 2021/02/02 17:25:00+00 1300 ... 2021/02/03 00:00:00+00 \n", "1967 2021/02/02 17:50:00+00 1700 ... 2021/02/03 00:00:00+00 \n", "1968 2021/02/02 18:27:00+00 0 ... 2021/02/03 00:00:00+00 \n", "1969 2021/02/02 18:00:00+00 2200 ... 2021/02/03 00:00:00+00 \n", "1970 2021/02/02 18:35:00+00 1100 ... 2021/02/05 00:00:00+00 \n", "1972 2021/02/02 19:12:00+00 350 ... 2021/02/03 00:00:00+00 \n", "1973 2021/02/02 20:13:00+00 1945 ... 2021/02/03 00:00:00+00 \n", "1975 2021/01/29 12:45:00+00 2330 ... 2021/02/04 00:00:00+00 \n", "1976 2021/02/01 08:33:00+00 30 ... 2021/02/03 00:00:00+00 \n", "1977 2021/02/01 08:53:00+00 900 ... 2021/02/03 00:00:00+00 \n", "1978 2021/02/01 10:23:00+00 2200 ... 2021/02/03 00:00:00+00 \n", "1979 2021/02/01 11:41:00+00 2100 ... 2021/02/04 00:00:00+00 \n", "1980 2021/02/01 11:45:00+00 2000 ... 2021/02/03 00:00:00+00 \n", "1981 2021/02/01 12:05:00+00 1745 ... 2021/02/03 00:00:00+00 \n", "1982 2021/02/01 13:02:00+00 1600 ... 2021/02/03 00:00:00+00 \n", "1983 2021/02/01 13:54:00+00 1200 ... 2021/02/03 00:00:00+00 \n", "1984 2021/02/01 14:16:00+00 5 ... 2021/02/04 00:00:00+00 \n", "1985 2021/02/01 14:51:00+00 1435 ... 2021/02/03 00:00:00+00 \n", "1986 2021/02/01 14:59:00+00 1000 ... 2021/02/03 00:00:00+00 \n", "1987 2021/02/01 15:15:00+00 2200 ... 2021/02/03 00:00:00+00 \n", "1988 2021/02/01 16:09:00+00 2000 ... 2021/02/03 00:00:00+00 \n", "1989 2021/02/01 16:53:00+00 1200 ... 2021/02/04 00:00:00+00 \n", "1990 2021/02/01 21:56:00+00 1800 ... 2021/02/03 00:00:00+00 \n", "1991 2021/02/02 10:22:00+00 1600 ... 2021/02/03 00:00:00+00 \n", "1992 2021/02/02 10:51:00+00 2100 ... 2021/02/03 00:00:00+00 \n", "\n", " LastUpdateDateETL OBJECTID Date Year \\\n", "1 2019/08/25 08:15:46+00 2 2019-08-23 00:00:00+00:00 2019 \n", "2 2019/08/25 08:15:46+00 3 2019-08-23 00:00:00+00:00 2019 \n", "6 2019/08/25 08:15:46+00 7 2019-08-23 00:00:00+00:00 2019 \n", "7 2019/08/25 08:15:46+00 8 2019-08-23 00:00:00+00:00 2019 \n", "8 2019/08/25 08:15:46+00 9 2019-08-23 00:00:00+00:00 2019 \n", "9 2019/08/25 08:15:46+00 10 2019-08-23 00:00:00+00:00 2019 \n", "11 2019/08/25 08:15:46+00 12 2019-08-23 00:00:00+00:00 2019 \n", "12 2019/08/25 08:15:46+00 13 2019-08-23 00:00:00+00:00 2019 \n", "13 2019/08/25 08:15:46+00 14 2019-08-23 00:00:00+00:00 2019 \n", "17 2019/08/25 08:15:46+00 18 2019-08-23 00:00:00+00:00 2019 \n", "18 2019/08/25 08:15:46+00 19 2019-08-23 00:00:00+00:00 2019 \n", "19 2019/08/25 08:15:46+00 20 2019-08-23 00:00:00+00:00 2019 \n", "23 2019/08/25 08:15:46+00 24 2019-08-24 00:00:00+00:00 2019 \n", "28 2019/08/25 08:15:46+00 29 2019-08-24 00:00:00+00:00 2019 \n", "29 2019/08/25 08:15:46+00 30 2019-08-24 00:00:00+00:00 2019 \n", "30 2019/08/25 08:15:46+00 31 2019-08-24 00:00:00+00:00 2019 \n", "31 2019/08/25 08:15:46+00 32 2019-08-24 00:00:00+00:00 2019 \n", "32 2019/08/25 08:15:46+00 33 2019-08-24 00:00:00+00:00 2019 \n", "35 2019/08/25 08:15:46+00 36 2019-08-24 00:00:00+00:00 2019 \n", "39 2019/08/25 08:15:46+00 40 2019-08-24 00:00:00+00:00 2019 \n", "41 2019/08/25 08:15:46+00 42 2019-08-24 00:00:00+00:00 2019 \n", "42 2019/08/25 08:15:46+00 43 2019-08-24 00:00:00+00:00 2019 \n", "43 2019/08/25 08:15:46+00 44 2019-08-24 00:00:00+00:00 2019 \n", "48 2019/08/25 08:15:46+00 49 2019-08-24 00:00:00+00:00 2019 \n", "49 2019/09/08 08:15:27+00 50 2019-08-24 00:00:00+00:00 2019 \n", "52 2019/08/25 08:15:46+00 53 2019-08-24 00:00:00+00:00 2019 \n", "53 2019/08/25 08:15:46+00 54 2019-08-24 00:00:00+00:00 2019 \n", "54 2019/08/25 08:15:46+00 55 2019-08-24 00:00:00+00:00 2019 \n", "57 2019/08/25 08:15:46+00 58 2019-08-24 00:00:00+00:00 2019 \n", "59 2019/08/25 08:15:46+00 60 2019-08-23 00:00:00+00:00 2019 \n", "... ... ... ... ... \n", "1959 2021/02/04 07:03:32+00 1960 2021-02-02 00:00:00+00:00 2021 \n", "1960 2021/02/04 07:03:32+00 1961 2021-02-02 00:00:00+00:00 2021 \n", "1961 2021/02/04 07:03:32+00 1962 2021-02-02 00:00:00+00:00 2021 \n", "1962 2021/02/05 07:01:48+00 1963 2021-02-02 00:00:00+00:00 2021 \n", "1963 2021/02/04 07:03:32+00 1964 2021-02-02 00:00:00+00:00 2021 \n", "1966 2021/02/04 07:03:32+00 1967 2021-02-02 00:00:00+00:00 2021 \n", "1967 2021/02/04 07:03:32+00 1968 2021-02-02 00:00:00+00:00 2021 \n", "1968 2021/02/04 07:03:32+00 1969 2021-02-02 00:00:00+00:00 2021 \n", "1969 2021/02/04 07:03:32+00 1970 2021-02-02 00:00:00+00:00 2021 \n", "1970 2021/02/06 07:03:57+00 1971 2021-02-02 00:00:00+00:00 2021 \n", "1972 2021/02/04 07:03:32+00 1973 2021-02-02 00:00:00+00:00 2021 \n", "1973 2021/02/04 07:03:32+00 1974 2021-02-02 00:00:00+00:00 2021 \n", "1975 2021/02/05 07:01:48+00 1976 2021-01-29 00:00:00+00:00 2021 \n", "1976 2021/02/04 07:03:32+00 1977 2021-02-01 00:00:00+00:00 2021 \n", "1977 2021/02/04 07:03:32+00 1978 2021-02-01 00:00:00+00:00 2021 \n", "1978 2021/02/04 07:03:32+00 1979 2021-02-01 00:00:00+00:00 2021 \n", "1979 2021/02/05 07:01:48+00 1980 2021-02-01 00:00:00+00:00 2021 \n", "1980 2021/02/04 07:03:32+00 1981 2021-02-01 00:00:00+00:00 2021 \n", "1981 2021/02/04 07:03:32+00 1982 2021-02-01 00:00:00+00:00 2021 \n", "1982 2021/02/04 07:03:32+00 1983 2021-02-01 00:00:00+00:00 2021 \n", "1983 2021/02/04 07:03:32+00 1984 2021-02-01 00:00:00+00:00 2021 \n", "1984 2021/02/05 07:01:48+00 1985 2021-02-01 00:00:00+00:00 2021 \n", "1985 2021/02/04 07:03:32+00 1986 2021-02-01 00:00:00+00:00 2021 \n", "1986 2021/02/04 07:03:32+00 1987 2021-02-01 00:00:00+00:00 2021 \n", "1987 2021/02/04 07:03:32+00 1988 2021-02-01 00:00:00+00:00 2021 \n", "1988 2021/02/04 07:03:32+00 1989 2021-02-01 00:00:00+00:00 2021 \n", "1989 2021/02/05 07:01:48+00 1990 2021-02-01 00:00:00+00:00 2021 \n", "1990 2021/02/04 07:03:32+00 1991 2021-02-01 00:00:00+00:00 2021 \n", "1991 2021/02/04 07:03:32+00 1992 2021-02-02 00:00:00+00:00 2021 \n", "1992 2021/02/04 07:03:32+00 1993 2021-02-02 00:00:00+00:00 2021 \n", "\n", " DayYear Month Day Count Type \n", "1 235 8 23 1 Blank \n", "2 235 8 23 1 Theft_InsideVehicle \n", "6 235 8 23 1 Theft_Auto \n", "7 235 8 23 1 Theft_Auto \n", "8 235 8 23 1 Theft_Auto \n", "9 235 8 23 1 Theft_Auto \n", "11 235 8 23 1 Theft_MotorParts \n", "12 235 8 23 1 Theft_Auto \n", "13 235 8 23 1 Theft_InsideVehicle \n", "17 235 8 23 1 Theft_InsideVehicle \n", "18 235 8 23 1 Blank \n", "19 235 8 23 1 Theft_InsideVehicle \n", "23 236 8 24 1 Blank \n", "28 236 8 24 1 Theft_Auto \n", "29 236 8 24 1 Blank \n", "30 236 8 24 1 Theft_Auto \n", "31 236 8 24 1 Theft_Auto \n", "32 236 8 24 1 Theft_Auto \n", "35 236 8 24 1 Theft_Auto \n", "39 236 8 24 1 Theft_Auto \n", "41 236 8 24 1 Theft_Auto \n", "42 236 8 24 1 Blank \n", "43 236 8 24 1 Theft_InsideVehicle \n", "48 236 8 24 1 Blank \n", "49 236 8 24 1 Blank \n", "52 236 8 24 1 Theft_Auto \n", "53 236 8 24 1 Theft_InsideVehicle \n", "54 236 8 24 1 Theft_InsideVehicle \n", "57 236 8 24 1 Blank \n", "59 235 8 23 1 Theft_InsideVehicle \n", "... ... ... ... ... ... \n", "1959 33 2 2 1 Theft_MotorParts \n", "1960 33 2 2 1 Blank \n", "1961 33 2 2 1 Theft_Auto \n", "1962 33 2 2 1 Theft_MotorParts \n", "1963 33 2 2 1 Theft_MotorParts \n", "1966 33 2 2 1 Blank \n", "1967 33 2 2 1 Theft_InsideVehicle \n", "1968 33 2 2 1 Theft_MotorParts \n", "1969 33 2 2 1 Theft_MotorParts \n", "1970 33 2 2 1 Blank \n", "1972 33 2 2 1 Blank \n", "1973 33 2 2 1 Theft_InsideVehicle \n", "1975 29 1 29 1 Theft_MotorParts \n", "1976 32 2 1 1 Theft_MotorParts \n", "1977 32 2 1 1 Theft_InsideVehicle \n", "1978 32 2 1 1 Blank \n", "1979 32 2 1 1 Blank \n", "1980 32 2 1 1 Theft_InsideVehicle \n", "1981 32 2 1 1 Theft_MotorParts \n", "1982 32 2 1 1 Theft_InsideVehicle \n", "1983 32 2 1 1 Theft_MotorParts \n", "1984 32 2 1 1 Theft_MotorParts \n", "1985 32 2 1 1 Blank \n", "1986 32 2 1 1 Theft_MotorParts \n", "1987 32 2 1 1 Blank \n", "1988 32 2 1 1 Theft_MotorParts \n", "1989 32 2 1 1 Theft_InsideVehicle \n", "1990 32 2 1 1 Blank \n", "1991 33 2 2 1 Blank \n", "1992 33 2 2 1 Theft_InsideVehicle \n", "\n", "[31230 rows x 30 columns]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Check Out the variables\n", "\n", "df=mp_crime[mp_crime['description'].str.contains(\"THEFT\")]\n", "df['Type']='Blank'\n", "df.Type.loc[(df['description'].str.contains(\"THEFT FROM MOTR VEHC\"))] = 'Theft_InsideVehicle'\n", "df.Type.loc[(df['description'].str.contains(\"THEFT-MOTR VEH PARTS\"))] = 'Theft_MotorParts'\n", "df.Type.loc[(df['description'].str.contains(\"AUTOMOBILE THEFT\"))] = 'Theft_Auto'\n", "#mp_crime.offense.unique()\n", "#df[df['IncType'].str.contains(\"Theft\")].IncType.unique()\n", "#mp_crime[mp_crime['description'].str.contains(\"THEFT\")]\n", "df\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays AutoMobile Theft of Minneapolis up to 1/29/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "#a.description.unique()\n", "mp_crime['Date']= pd.to_datetime(mp_crime['reportedDate'])\n", "mp_crime['Year']= mp_crime['Date'].dt.year\n", "mp_crime['DayYear'] = mp_crime['Date'].dt.dayofyear\n", "mp_crime['Month'] = mp_crime['Date'].dt.month # Create Month Category\n", "mp_crime['Day'] = mp_crime['Date'].dt.day #Create Day of the Current month\n", "mp_crime['Count']=1\n", "df=mp_crime[mp_crime['description'].str.contains(\"THEFT-MOTR VEH PARTS\")]\n", "df_auto =df\n", "\n", "# Set a friendly Date variable\n", "df_auto['FDate']=df_auto['Month'].astype(str) + '/' + df_auto['Day'].astype(str) + '/'\n", "\n", "def plot_toDate_Year_MPAutoTheft(Day=29):\n", " B= df_auto.query('Year>2018')\n", " Date= df_auto[(df_auto['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Count']\n", " C= B[Index].groupby(['Year']).sum().reset_index()\n", " print('This graph displays AutoMobile Theft of Minneapolis up to {}20XX'.format(Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Motor Vehicles Parts Theft Incidents')\n", " return plt.show()\n", "\n", "plot_toDate_Year_MPAutoTheft()\n", "\n", "#df_auto.query('Year==2021 & precint==1')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays AutoMobile Theft of Minneapolis up to 6/29/20XX\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_toDate_Year_MPAutoTheft(Day=31):\n", " B= df_auto.query('Year>2018')\n", " Date= df_auto[(df_auto['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Count']\n", " C= B[Index].groupby(['Year']).sum().reset_index()\n", " print('This graph displays AutoMobile Theft of Minneapolis up to {}20XX'.format(Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Motor Vehicles Parts Theft Incidents')\n", " return plt.show()\n", "\n", "plot_toDate_Year_MPAutoTheft(181)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "Index1= ['Holland', 'Powderhorn Park', 'Central', 'Ventura Village',\n", " 'Seward', 'Minnehaha', 'Cooper', 'Standish', 'Elliot Park',\n", " 'Whittier', 'Phillips West', 'Corcoran', 'Hawthorne']\n", "\n", "df_auto1=df_auto[df_auto['neighborhood'].isin(Index1)]\n", "\n", "\n", "Index2=['Lowry Hill East', 'Jordan', 'Near - North',\n", " 'Mid - City Industrial', 'Prospect Park - East River Road',\n", " 'Longfellow', 'Waite Park', 'Northrop', 'Marshall Terrace']\n", "\n", "df_auto2=df_auto[df_auto['neighborhood'].isin(Index2)]\n", "\n", "Index3=['Lowry Hill', 'Marcy Holmes', 'Bryant', 'Armatage', 'Logan Park',\n", " 'Como', 'Hiawatha', 'East Phillips', 'ECCO', 'Midtown Phillips',\n", " 'Downtown West', 'Harrison', 'Field', 'Cleveland', 'North Loop']\n", "\n", "df_auto3=df_auto[df_auto['neighborhood'].isin(Index3)]\n", "\n", "Index4=['Ericsson', 'Keewaydin', 'Windom', 'East Isles', 'Willard - Hay',\n", " 'Loring Park', 'Bryn - Mawr', 'CARAG',\n", " \"Steven's Square - Loring Heights\", 'Lyndale', 'Diamond Lake']\n", "\n", "df_auto4=df_auto[df_auto['neighborhood'].isin(Index4)]\n", "\n", "Index5=['Bottineau', 'Nicollet Island - East Bank', 'Howe', 'McKinley',\n", " 'Morris Park', 'Page', 'Webber - Camden', 'Cedar Riverside',\n", " 'King Field', 'Folwell', 'Bancroft', 'Regina', 'Audubon Park']\n", "\n", "df_auto5=df_auto[df_auto['neighborhood'].isin(Index5)]\n", "\n", "Index6=['Wenonah', 'Windom Park', 'Tangletown', 'Victory', 'Lynnhurst',\n", " 'East Harriet', 'Hale', 'Fulton', 'West Calhoun', 'Lind - Bohanon',\n", " 'St. Anthony East', 'Cedar - Isles - Dean', 'Camden Industrial']\n", "\n", "df_auto6=df_auto[df_auto['neighborhood'].isin(Index6)]\n", "\n", "Index7=['Shingle Creek', 'Northeast Park', 'St. Anthony West', 'Kenny',\n", " 'Columbia Park', 'Kenwood', 'Beltrami', 'Sheridan',\n", " 'Downtown East', 'Linden Hills', 'Sumner - Glenwood',\n", " 'University of Minnesota']\n", "\n", "df_auto7=df_auto[df_auto['neighborhood'].isin(Index7)]\n" ] }, { "cell_type": "code", "execution_count": 56, "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", "
precinctneighborhoodoffensedescriptionreportedDateDateYearDayYearMonthDayCountFDate
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [precinct, neighborhood, offense, description, reportedDate, Date, Year, DayYear, Month, Day, Count, FDate]\n", "Index: []" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c=df_auto.query('Year<2020')\n", "c.query('neighborhood==\"Shingle Creek\"')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays \"Motor Vehicles Parts Theft\" incidents of Minneapolis neighborhoods.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_SPAutoTheftYear(Day=365):\n", " B= df_auto7.query('Year<2021')\n", " #Date= df_auto[(df_auto['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','neighborhood','Count']\n", " C= B[Index].groupby(['Year','neighborhood']).sum().reset_index()\n", " print('This graph displays \"Motor Vehicles Parts Theft\" incidents of Minneapolis neighborhoods.')\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['neighborhood'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Motor Vehicles Parts Theft Yearly Total Incidents')\n", " return plt.show()\n", "\n", "plot_SPAutoTheftYear()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "single positional indexer is out-of-bounds", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\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;32mreturn\u001b[0m \u001b[0mplt\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[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mplot_SPAutoTheftYear\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\u001b[0m in \u001b[0;36mplot_SPAutoTheftYear\u001b[1;34m(Day)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mplot_SPAutoTheftYear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDay\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m365\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 5\u001b[0m \u001b[0mB\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mdf_auto\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Year<2021'\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[0mDate\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mdf_auto\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_auto\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'DayYear'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m 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\u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1492\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAttributeError\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 1493\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1494\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\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 1495\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 1496\u001b[0m \u001b[1;31m# we by definition only have the 0th axis\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\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m 2141\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\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 2142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2143\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\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 2144\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[0;32m 2145\u001b[0m \u001b[1;32mreturn\u001b[0m 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2071\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\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 2072\u001b[0m \u001b[1;31m# a tuple should already have been caught by this point\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\\indexing.py\u001b[0m in \u001b[0;36m_validate_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 2137\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\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[0m_get_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\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 2138\u001b[0m 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matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "def plot_SPAutoTheftYear(Day=365):\n", " B= df_auto.query('Year<2021')\n", " Date= df_auto[(df_auto['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " #B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','neighborhood','Count']\n", " C= B[Index].groupby(['Year','neighborhood']).sum().reset_index()\n", " print('This graph displays \"Auto Accessory Thefts\" incidents of Saint Paul neighborhoods.')\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['neighborhood'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Auto Accessory Thefts Yearly Total Incidents')\n", " return plt.show()\n", "\n", "plot_SPAutoTheftYear()" ] }, { "cell_type": "code", "execution_count": 36, "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", "
precinctneighborhoodoffensedescriptionreportedDateDateYearDayYearMonthDayCountFDate
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [precinct, neighborhood, offense, description, reportedDate, Date, Year, DayYear, Month, Day, Count, FDate]\n", "Index: []" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_auto.query('Day>31')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "single positional indexer is out-of-bounds", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\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 12\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mplt\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[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mplot_toDate_Year_MPAutoTheft\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m365\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\u001b[0m in \u001b[0;36mplot_toDate_Year_MPAutoTheft\u001b[1;34m(Day)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mplot_toDate_Year_MPAutoTheft\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDay\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m31\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 2\u001b[0m \u001b[0mB\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mdf_auto\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Year>2018'\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[0mDate\u001b[0m\u001b[1;33m=\u001b[0m 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\u001b[0;36m_validate_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 2137\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\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[0m_get_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\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 2138\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0mlen_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2139\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"single positional indexer is out-of-bounds\"\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 2140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2141\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\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;31mIndexError\u001b[0m: single positional indexer is out-of-bounds" ] } ], "source": [ "def plot_toDate_Year_MPAutoTheft(Day=31):\n", " B= df_auto.query('Year>2018')\n", " Date= df_auto[(df_auto['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Count']\n", " C= B[Index].groupby(['Year']).sum().reset_index()\n", " print('This graph displays Motor Vehicles Parts Theft of Minneapolis up to {}20XX'.format(Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Motor Vehicles Parts Theft Incidents')\n", " return plt.show()\n", "\n", "plot_toDate_Year_MPAutoTheft(365)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'IncType'", "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 2656\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-> 2657\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[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 2658\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\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: 'IncType'", "\nDuring handling of the above exception, another exception occurred:\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_auto\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'IncType'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Auto Accessories\"\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 2\u001b[0m \u001b[0mdf_auto\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Amount'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Between $500-1000'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m 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2659\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[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\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 2660\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2661\u001b[0m \u001b[1;32mif\u001b[0m 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Accessories\")]\n", "df_auto['Amount']='Between $500-1000'\n", "df_auto.Amount.loc[(df_auto['IncType'].str.contains(\"500\"))] = 'Under $500'\n", "df_auto.Amount.loc[(df_auto['IncType'].str.contains(\"Over\"))] = 'Over $1000'\n", "#df_auto.query('Year>2019')\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "def plot_SPAutoTheft(Day=Max):\n", " B= df_auto.query('Year>2019')\n", " #Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " #B= B[(B['DayYear'] <= Day)]\n", " Index= ['Amount','Community','Count']\n", " C= B[Index].groupby(['Amount','Community']).sum().reset_index()\n", " print('This graph displays Auto Accessory Theft incidents by cost for Saint Paul neighborhoods since 2020')\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community'], columns=['Amount'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Auto Accessory Theft Incidents')\n", " return plt.show()\n", "\n", "\n", "plot_SPAutoTheft()\n", "\n", "#fg.Incident.loc[(fg['Incident'] == 'Simple Asasult Dom.')] = 'Simple Assault Dom.'\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays Auto Accessory Theft of Saint Paul up to 12/30/20XX\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'Month'", "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 2656\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-> 2657\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[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 2658\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\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: 'Month'", "\nDuring handling of the above exception, another exception occurred:\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# Set a friendly Date variable\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_auto\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'FDate'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf_auto\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Month'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'/'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdf_auto\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Day'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m 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2659\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[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\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 2660\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2661\u001b[0m \u001b[1;32mif\u001b[0m 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"df_auto['FDate']=df_auto['Month'].astype(str) + '/' + df_auto['Day'].astype(str) + '/'\n", "\n", "def plot_toDate_Year_SPAutoTheft(Day=Max):\n", " B= df_auto.query('Year>2018')\n", " Date= df_auto[(df_auto['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Community','Count']\n", " C= B[Index].groupby(['Year','Community']).sum().reset_index()\n", " print('This graph displays the Auto Accessory Theft of Saint Paul neighborhoods from {}20XX'.format(Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Auto Accessory Theft Yearly Total Incidents')\n", " return plt.show()\n", "\n", "plot_toDate_Year_SPAutoTheft()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays Auto Accessory Theft of Saint Paul up to 1/27/20XX\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Set a friendly Date variable\n", "df_auto['FDate']=df_auto['Month'].astype(str) + '/' + df_auto['Day'].astype(str) + '/'\n", "\n", "def plot_toDate_Year_SPAutoTheft(Day=Max):\n", " B= df_auto.query('Year>2018')\n", " Date= df_auto[(df_auto['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Count']\n", " C= B[Index].groupby(['Year']).sum().reset_index()\n", " print('This graph displays Auto Accessory Theft of Saint Paul up to {}20XX'.format(Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Auto Accessory Theft Incidents')\n", " return plt.show()\n", "\n", "plot_toDate_Year_SPAutoTheft()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearCount
02019242
120201867
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\n", "
" ], "text/plain": [ " Year Count\n", "0 2019 242\n", "1 2020 1867\n", "2 2021 247" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "def plot_toDate_Year_SPAutoTheft(Day=366):\n", " B= df_auto.query('Year>2018')\n", " #Date= df_auto[(df_auto['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Count']\n", " C= B[Index].groupby(['Year']).sum().reset_index()\n", " return C\n", "\n", "plot_toDate_Year_SPAutoTheft(365)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Link All" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays \"Auto Accessory Thefts\" incidents of Saint Paul neighborhoods.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_SPAutoTheftYear(Day=Max):\n", " B= df_auto.query('Year<2021')\n", " Date= df_auto[(df_auto['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " #B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Community','Count']\n", " C= B[Index].groupby(['Year','Community']).sum().reset_index()\n", " print('This graph displays \"Auto Accessory Thefts\" incidents of Saint Paul neighborhoods.')\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Community'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= 'Auto Accessory Thefts Yearly Total Incidents')\n", " return plt.show()\n", "\n", "plot_SPAutoTheftYear()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n" ] } ], "source": [ "#Saint Paul Data Socrata\n", "from sodapy import Socrata\n", "\n", "#New Upload Method Get Information from Socrata API\n", "client = Socrata(\"information.stpaul.gov\", None)\n", "#Easier to bulk upload\n", "results = client.get(\"gppb-g9cg\", limit=1000000)\n", "df = 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", "\n", "#Add Time Variables\n", "df= df[df.Case != 18254093] #messed up time variable\n", "df['Date']= pd.to_datetime(df['Date'])\n", "df['Year']= df['Date'].dt.year\n", "df=df.query('Year > 2017')\n", "df['DayYear'] = df['Date'].dt.dayofyear\n", "df['Community']= df['Grid'].apply(commun)\n", "df= df.query('Code not in [9954,9959] and Community !=\"NaN\"')\n", "\n", "\n", "#df.at[0,'DayYear']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Link to runall](#content)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge incidents of Saint Paul neighborhoods within 344 days from date 12/9/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_toDate_Days_SPCrime(Incident='Discharge') #All Days" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge incidents of Saint Paul neighborhoods within 73 days from date 3/14/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#PreCovid\n", "plot_toDate_Days_SPCrime(Incident='Discharge',Day=CV, CDate=CV)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge incidents of Saint Paul neighborhoods within 73 days from date 5/25/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "#Covid to Floyd's death\n", "plot_toDate_Days_SPCrime(Incident='Discharge',Day=GE-CV, CDate=GE)\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge incidents of Saint Paul neighborhoods within 100 days from date 12/9/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Post: GeorgeFloyd\n", "plot_toDate_Days_SPCrime(Incident='Discharge',Day=Max-244, CDate=Max)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def plot_toDate_Year_SPCityCrime(Incident='All',Day=Max):\n", " if Incident=='All':\n", " B= df\n", " else: \n", " B= df[(df['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Count']\n", " C= B[Index].groupby(['Year']).sum().reset_index()\n", " print('This graph maps {} annual incidents incidents of Saint Paul up to {}20XX'.format(Incident,Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(6,4),title= Incident + ' Annual Total Incidents')\n", " return plt.show()\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph maps Discharge annual incidents incidents of Saint Paul up to 12/9/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_toDate_Year_SPCityCrime('Discharge',344)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Discharge incidents per 10000 residents within 100 days from date 12/9/20XX\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "def plot_toDate_Days_SPCrime_Norm(Incident='All',Day=Max, CDate=Max):\n", " if Incident=='All':\n", " B= df\n", " else: \n", " B= df[(df['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == CDate)]['FDate'][0:1].iloc[0,]\n", " Bd= CDate-Day\n", " B= B[(B['DayYear'] >= Bd)]\n", " B= B[(B['DayYear'] <= CDate)]\n", " Index= ['Year','Community','Count']\n", " C= B[Index].groupby(['Year','Community']).sum().reset_index()\n", " C1=pd.merge(C, Norm, on='Community', how='left').reset_index()\n", " C1['Norm_Count']= (C1.Count/C1.Pop) *10000\n", " print('This graph displays total {} incidents per 10000 residents within {} days from date {}20XX'.format(Incident,Day,Date))\n", " sns.set()\n", " pd.pivot_table(C1, values='Norm_Count', index=['Community'], columns=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(12,10),title= Incident + ' Yearly UptoDate Normalized Total Incidents') \n", " return plt.show()\n", "\n", "\n", "plot_toDate_Days_SPCrime_Norm(Incident='Discharge', Day= Max-244)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearCount
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" ], "text/plain": [ " Year Count\n", "0 2018 1022\n", "1 2019 956\n", "2 2020 1755" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "B= df.query(\"Incident=='Discharge'\")\n", "B= B.query('Hour >= 6 and Hour <20')\n", "Index= ['Year','Count']\n", "C= B[Index].groupby(['Year']).sum().reset_index()\n", "C" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Narcotics', 'Vandalism', 'Auto Theft', 'Theft', 'Discharge',\n", " 'Robbery', 'Arson', 'Agg. Assault', 'Simple Asasult Dom.',\n", " 'Agg. Assault Dom.', 'Burglary', 'Rape', 'Graffiti', 'Homicide',\n", " 'Other'], dtype=object)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.Incident.unique()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "df.Incident.loc[(df['Incident'] == 'Simple Asasult Dom.')] = 'Simple Assault Dom.'\n", "df.Incident.loc[(df['Incident'] == 'Graffiti')] = 'Vandalism'\n", "df.Incident.loc[df[\"Incident\"].isin([ \"Rape\",\"Agg. Assault\",'Homicide'])]= 'Violent'\n", "df.Incident.loc[df[\"Incident\"].isin([\"Simple Assault Dom.\",\"Agg. Assault Dom.\"])]= 'Domestic Assault'" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays total Auto Theft incidents of Saint Paul neighborhoods within 344 days from date 12/9/20XX\n" ] }, { "data": { "image/png": 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BlockCallDispCodeCallDispositionCaseCodeCountDateGridIncidentIncTypeNeighborhoodNNumTimeYearDayYearCommunity
1769X CONWAY STAAdvised20261208180012020-12-09 23:28:49114.0NarcoticsNarcotics4 - Dayton's Bluff42020-12-09T23:28:49.0002020344Dayton Bluff
2317X CHARLES AVAAdvised20261188140012020-12-09 23:09:2290.0VandalismCriminal Damage to Property7 - Thomas/Dale(Frogtown)72020-12-09T23:09:22.0002020344Thomas-Frogtown
2830X SNELLING AV NRRReport Written20261169182012020-12-09 22:21:00105.0NarcoticsNarcotics, Possession of Synthetic Narcotic, D...13 - Union Park132020-12-09T22:21:00.0002020344Union Park
2951X STANTHONY AVRReport Written2026116770012020-12-09 22:16:23109.0Auto TheftMotor Vehicle Theft8 - Summit/University82020-12-09T22:16:23.0002020344Summit-University
38218X POWERS AVRRReport Written2026114866112020-12-09 21:37:00140.0TheftTheft, Bicycle Theft, Under $5001 - Conway/Battlecreek/Highwood12020-12-09T21:37:00.0002020344Battle_Creek
4147X MARION STAAdvised20261132140012020-12-09 21:08:21110.0VandalismCriminal Damage to Property8 - Summit/University82020-12-09T21:08:21.0002020344Summit-University
4517X CHARLES AVAAdvised20261141180012020-12-09 21:01:5790.0NarcoticsNarcotics7 - Thomas/Dale(Frogtown)72020-12-09T21:01:57.0002020344Thomas-Frogtown
46WESTERN AV N & UNIVERSITYGGone on Arrival20261115140012020-12-09 20:50:1890.0VandalismCriminal Damage to Property7 - Thomas/Dale(Frogtown)72020-12-09T20:50:18.0002020344Thomas-Frogtown
5573X PELHAM BDRRReport Written2026109871012020-12-09 20:14:0081.0Auto TheftMotor Vehicle Theft, Automobile12 - St. Anthony122020-12-09T20:14:00.0002020344St. Anthony
59158X RANDOLPH AVRRReport Written2026108763112020-12-09 20:02:00164.0TheftTheft, Shoplifting, Under $50014 - Macalester-Groveland142020-12-09T20:02:00.0002020344Macalester_Groveland
63MARYLAND AV W & NORTONAAdvised20261070261912020-12-09 19:38:2029.0DischargeWeapons, Discharging a Firearm in the City Limits6 - North End62020-12-09T19:38:20.0002020344North End
8016X 5 ST WRReport Written2026103760012020-12-09 18:49:23152.0TheftTheft, Except Auto Theft17 - Capitol River172020-12-09T18:49:23.0002020344Capital_River
81138X DANFORTH STAAdvised2026104564012020-12-09 18:49:1929.0TheftTheft, From Auto6 - North End62020-12-09T18:49:19.0002020344North End
9436X SPRING STAAdvised2026100864012020-12-09 18:28:26171.0TheftTheft, From Auto9 - West Seventh92020-12-09T18:28:26.0002020344West 7th
10358X MACKUBIN STAAdvised20260989180012020-12-09 18:16:5489.0NarcoticsNarcotics7 - Thomas/Dale(Frogtown)72020-12-09T18:16:54.0002020344Thomas-Frogtown
104133X FREMONT AVRRReport Written2026099865112020-12-09 18:12:0097.0TheftTheft, Auto Accessories, Under $5004 - Dayton's Bluff42020-12-09T18:12:00.0002020344Dayton Bluff
11044X MARYLAND AV WRReport Written2026097730012020-12-09 18:04:0249.0RobberyRobbery6 - North End62020-12-09T18:04:02.0002020344North End
125117X VANBUREN AVRRReport Written2026098371012020-12-09 17:30:0086.0Auto TheftMotor Vehicle Theft, Automobile11 - Hamline/Midway112020-12-09T17:30:00.0002020344Midway
13924X CONGRESS ST ERReport Written2026090430012020-12-09 17:05:00194.0RobberyRobbery3 - West Side32020-12-09T17:05:00.0002020344West Side
155211X IVY AV ERRReport Written2026086471012020-12-09 16:31:0040.0Auto TheftMotor Vehicle Theft, Automobile2 - Greater East Side22020-12-09T16:31:00.0002020344Greater East Side
16377X HYACINTH AV EAAdvised2026080264012020-12-09 15:17:5834.0TheftTheft, From Auto5 - Payne/Phalen52020-12-09T15:17:58.0002020344Payne-Phalen
16828X BURGESS STRRReport Written20260792141012020-12-09 14:52:0070.0VandalismCriminal Damage to Property (Misdemeanor, Unde...6 - North End62020-12-09T14:52:00.0002020344North End
17893X TUSCARORA AVRRReport Written20260754141012020-12-09 14:08:00187.0VandalismCriminal Damage to Property (Misdemeanor, Unde...9 - West Seventh92020-12-09T14:08:00.0002020344West 7th
187ARLINGTON AV E & WHITEBEARAAdvised2026074070012020-12-09 13:39:4319.0Auto TheftMotor Vehicle Theft2 - Greater East Side22020-12-09T13:39:43.0002020344Greater East Side
189143X FARRINGTON STRReport Written2026072064012020-12-09 13:29:0830.0TheftTheft, From Auto6 - North End62020-12-09T13:29:08.0002020344North End
19039X WABASHA ST NRRReport Written2026071863112020-12-09 13:26:00153.0TheftTheft, Shoplifting, Under $50017 - Capitol River172020-12-09T13:26:00.0002020344Capital_River
19111X 10 ST EAAdvised2026071660012020-12-09 13:25:51132.0TheftTheft, Except Auto Theft17 - Capitol River172020-12-09T13:25:51.0002020344Capital_River
192117X VANBUREN AVAAdvised2026071970012020-12-09 13:19:4386.0Auto TheftMotor Vehicle Theft11 - Hamline/Midway112020-12-09T13:19:43.0002020344Midway
198156X SHERWOOD AVAAdvised2026073564012020-12-09 12:14:0138.0TheftTheft, From Auto2 - Greater East Side22020-12-09T12:14:01.0002020344Greater East Side
199191X UNIVERSITY AV WRRReport Written2026068765112020-12-09 12:11:0083.0TheftTheft, Auto Accessories, Under $50011 - Hamline/Midway112020-12-09T12:11:00.0002020344Midway
...................................................
186502157X PACIFIC STRRReport Written1800029871012018-01-01 00:00:00118.0Auto TheftMotor Vehicle Theft, Automobile1 - Conway/Battlecreek/Highwood18:0020181Battle_Creek
186503183X MECHANIC AVAAdvised18000180140012018-01-01 00:00:0059.0VandalismCriminal Damage to Property2 - Greater East Side27:5420181Greater East Side
18650784X MARSHALL AVRRReport Written1800010786112018-01-01 00:00:00128.0Domestic AssaultAssault, Domestic, Opposite Sex8 - Summit/University83:0020181Summit-University
18650870X COMO AVRRReport Written18000078261912018-01-01 00:00:0048.0DischargeWeapons, Discharging a Firearm in the City Limits10 - Como102:3820181Como
186509127X BEECH STRRReport Written1800008171012018-01-01 00:00:0096.0Auto TheftMotor Vehicle Theft, Automobile4 - Dayton's Bluff42:1020181Dayton Bluff
186510114X HUDSON RDRRReport Written1800092864312018-01-01 00:00:00117.0TheftTheft, From Auto, Over $10004 - Dayton's Bluff42:0020181Dayton Bluff
186511183X MECHANIC AVRRReport Written1800006445112018-01-01 00:00:0059.0Domestic AssaultAggravated Assault, Domestic2 - Greater East Side21:4720181Greater East Side
186512159X RUTH STRRReport Written18000289141012018-01-01 00:00:0019.0VandalismCriminal Damage to Property (Misdemeanor, Unde...2 - Greater East Side21:3020181Greater East Side
1865137 ST E & EARLRRReport Written18000051261912018-01-01 00:00:0076.0DischargeWeapons, Discharging a Firearm in the City Limits4 - Dayton's Bluff41:2320181Dayton Bluff
186514220X 6 ST ERRReport Written18000050142012018-01-01 00:00:00100.0VandalismCriminal Damage to Property (Gross Mis., $250-...1 - Conway/Battlecreek/Highwood11:1320181Battle_Creek
186515117X WESTERN AV NAAdvised1800004870012018-01-01 00:00:0049.0Auto TheftMotor Vehicle Theft6 - North End61:1020181North End
186516102X ARKWRIGHT STRRReport Written1800020971012018-01-01 00:00:0053.0Auto TheftMotor Vehicle Theft, Automobile5 - Payne/Phalen51:0020181Payne-Phalen
1865176 ST E & SIBLEYRRReport Written1800004931412018-01-01 00:00:00133.0RobberyRobbery, Highway, By Strong Arm17 - Capitol River171:0020181Capital_River
186518123X 7 ST ERRReport Written18000016261912018-01-01 00:00:0076.0DischargeWeapons, Discharging a Firearm in the City Limits4 - Dayton's Bluff40:4820181Dayton Bluff
186520117X WESTERN AV NRRReport Written1800006071012018-01-01 00:00:0049.0Auto TheftMotor Vehicle Theft, Automobile6 - North End60:3020181North End
186521167X GURNEY STRRReport Written18000028261912018-01-01 00:00:0011.0DischargeWeapons, Discharging a Firearm in the City Limits6 - North End60:3020181North End
186524FRONT AV & RICERRReport Written18000017261912018-01-01 00:00:0051.0DischargeWeapons, Discharging a Firearm in the City Limits6 - North End60:1420181North End
18652552X ORANGE AV WRRReport Written18000014261912018-01-01 00:00:0029.0DischargeWeapons, Discharging a Firearm in the City Limits6 - North End60:0920181North End
18652668X VANBUREN AVRRReport Written18000006261912018-01-01 00:00:0088.0DischargeWeapons, Discharging a Firearm in the City Limits7 - Thomas/Dale(Frogtown)70:0320181Thomas-Frogtown
18652761X MAGNOLIA AV ERRReport Written18000007261912018-01-01 00:00:0054.0DischargeWeapons, Discharging a Firearm in the City Limits5 - Payne/Phalen50:0120181Payne-Phalen
18653034X CEDAR STRRReport Written18000457183512018-01-01 00:00:00153.0NarcoticsNarcotics, Possession of Marijuana17 - Capitol River1719:4920181Capital_River
186533EARL ST & MOUNDSRRReport Written1800046771012018-01-01 00:00:00136.0Auto TheftMotor Vehicle Theft, Automobile4 - Dayton's Bluff420:0220181Dayton Bluff
186534140X ARCADE STRRReport Written1800048171012018-01-01 00:00:0035.0Auto TheftMotor Vehicle Theft, Automobile5 - Payne/Phalen520:2720181Payne-Phalen
186536133X MINNEHAHA AV WRRReport Written1800067571012018-01-01 00:00:0086.0Auto TheftMotor Vehicle Theft, Automobile11 - Hamline/Midway1120:3020181Midway
186537133X MINNEHAHA AV WAAdvised1800049170012018-01-01 00:00:0086.0Auto TheftMotor Vehicle Theft11 - Hamline/Midway1120:3720181Midway
186538160X RICE STRRReport Written1800049869112018-01-01 00:00:0010.0TheftTheft, All Other, Under $5006 - North End620:4620181North End
1865397X LEECH STRRReport Written1800049443112018-01-01 00:00:00150.0Domestic AssaultAggravated Assault, Domestic9 - West Seventh921:3020181West 7th
18654190X MARYLAND AV ERRReport Written18000464141012018-01-01 00:00:0055.0VandalismCriminal Damage to Property (Misdemeanor, Unde...5 - Payne/Phalen522:0120181Payne-Phalen
186544125X VIRGINIA STRRReport Written1800051343212018-01-01 00:00:0030.0Domestic AssaultAggravated Assault, Domestic6 - North End622:1820181North End
186545117X EDMUND AVRRReport Written1880001652312018-01-01 00:00:0086.0BurglaryBurglary, No Forced Entry, Night, Garage11 - Hamline/Midway1122:3020181Midway
\n", "

72310 rows × 16 columns

\n", "
" ], "text/plain": [ " Block CallDispCode CallDisposition Case \\\n", "17 69X CONWAY ST A Advised 20261208 \n", "23 17X CHARLES AV A Advised 20261188 \n", "28 30X SNELLING AV N RR Report Written 20261169 \n", "29 51X STANTHONY AV R Report Written 20261167 \n", "38 218X POWERS AV RR Report Written 20261148 \n", "41 47X MARION ST A Advised 20261132 \n", "45 17X CHARLES AV A Advised 20261141 \n", "46 WESTERN AV N & UNIVERSITY G Gone on Arrival 20261115 \n", "55 73X PELHAM BD RR Report Written 20261098 \n", "59 158X RANDOLPH AV RR Report Written 20261087 \n", "63 MARYLAND AV W & NORTON A Advised 20261070 \n", "80 16X 5 ST W R Report Written 20261037 \n", "81 138X DANFORTH ST A Advised 20261045 \n", "94 36X SPRING ST A Advised 20261008 \n", "103 58X MACKUBIN ST A Advised 20260989 \n", "104 133X FREMONT AV RR Report Written 20260998 \n", "110 44X MARYLAND AV W R Report Written 20260977 \n", "125 117X VANBUREN AV RR Report Written 20260983 \n", "139 24X CONGRESS ST E R Report Written 20260904 \n", "155 211X IVY AV E RR Report Written 20260864 \n", "163 77X HYACINTH AV E A Advised 20260802 \n", "168 28X BURGESS ST RR Report Written 20260792 \n", "178 93X TUSCARORA AV RR Report Written 20260754 \n", "187 ARLINGTON AV E & WHITEBEAR A Advised 20260740 \n", "189 143X FARRINGTON ST R Report Written 20260720 \n", "190 39X WABASHA ST N RR Report Written 20260718 \n", "191 11X 10 ST E A Advised 20260716 \n", "192 117X VANBUREN AV A Advised 20260719 \n", "198 156X SHERWOOD AV A Advised 20260735 \n", "199 191X UNIVERSITY AV W RR Report Written 20260687 \n", "... ... ... ... ... \n", "186502 157X PACIFIC ST RR Report Written 18000298 \n", "186503 183X MECHANIC AV A Advised 18000180 \n", "186507 84X MARSHALL AV RR Report Written 18000107 \n", "186508 70X COMO AV RR Report Written 18000078 \n", "186509 127X BEECH ST RR Report Written 18000081 \n", "186510 114X HUDSON RD RR Report Written 18000928 \n", "186511 183X MECHANIC AV RR Report Written 18000064 \n", "186512 159X RUTH ST RR Report Written 18000289 \n", "186513 7 ST E & EARL RR Report Written 18000051 \n", "186514 220X 6 ST E RR Report Written 18000050 \n", "186515 117X WESTERN AV N A Advised 18000048 \n", "186516 102X ARKWRIGHT ST RR Report Written 18000209 \n", "186517 6 ST E & SIBLEY RR Report Written 18000049 \n", "186518 123X 7 ST E RR Report Written 18000016 \n", "186520 117X WESTERN AV N RR Report Written 18000060 \n", "186521 167X GURNEY ST RR Report Written 18000028 \n", "186524 FRONT AV & RICE RR Report Written 18000017 \n", "186525 52X ORANGE AV W RR Report Written 18000014 \n", "186526 68X VANBUREN AV RR Report Written 18000006 \n", "186527 61X MAGNOLIA AV E RR Report Written 18000007 \n", "186530 34X CEDAR ST RR Report Written 18000457 \n", "186533 EARL ST & MOUNDS RR Report Written 18000467 \n", "186534 140X ARCADE ST RR Report Written 18000481 \n", "186536 133X MINNEHAHA AV W RR Report Written 18000675 \n", "186537 133X MINNEHAHA AV W A Advised 18000491 \n", "186538 160X RICE ST RR Report Written 18000498 \n", "186539 7X LEECH ST RR Report Written 18000494 \n", "186541 90X MARYLAND AV E RR Report Written 18000464 \n", "186544 125X VIRGINIA ST RR Report Written 18000513 \n", "186545 117X EDMUND AV RR Report Written 18800016 \n", "\n", " Code Count Date Grid Incident \\\n", "17 1800 1 2020-12-09 23:28:49 114.0 Narcotics \n", "23 1400 1 2020-12-09 23:09:22 90.0 Vandalism \n", "28 1820 1 2020-12-09 22:21:00 105.0 Narcotics \n", "29 700 1 2020-12-09 22:16:23 109.0 Auto Theft \n", "38 661 1 2020-12-09 21:37:00 140.0 Theft \n", "41 1400 1 2020-12-09 21:08:21 110.0 Vandalism \n", "45 1800 1 2020-12-09 21:01:57 90.0 Narcotics \n", "46 1400 1 2020-12-09 20:50:18 90.0 Vandalism \n", "55 710 1 2020-12-09 20:14:00 81.0 Auto Theft \n", "59 631 1 2020-12-09 20:02:00 164.0 Theft \n", "63 2619 1 2020-12-09 19:38:20 29.0 Discharge \n", "80 600 1 2020-12-09 18:49:23 152.0 Theft \n", "81 640 1 2020-12-09 18:49:19 29.0 Theft \n", "94 640 1 2020-12-09 18:28:26 171.0 Theft \n", "103 1800 1 2020-12-09 18:16:54 89.0 Narcotics \n", "104 651 1 2020-12-09 18:12:00 97.0 Theft \n", "110 300 1 2020-12-09 18:04:02 49.0 Robbery \n", "125 710 1 2020-12-09 17:30:00 86.0 Auto Theft \n", "139 300 1 2020-12-09 17:05:00 194.0 Robbery \n", "155 710 1 2020-12-09 16:31:00 40.0 Auto Theft \n", "163 640 1 2020-12-09 15:17:58 34.0 Theft \n", "168 1410 1 2020-12-09 14:52:00 70.0 Vandalism \n", "178 1410 1 2020-12-09 14:08:00 187.0 Vandalism \n", "187 700 1 2020-12-09 13:39:43 19.0 Auto Theft \n", "189 640 1 2020-12-09 13:29:08 30.0 Theft \n", "190 631 1 2020-12-09 13:26:00 153.0 Theft \n", "191 600 1 2020-12-09 13:25:51 132.0 Theft \n", "192 700 1 2020-12-09 13:19:43 86.0 Auto Theft \n", "198 640 1 2020-12-09 12:14:01 38.0 Theft \n", "199 651 1 2020-12-09 12:11:00 83.0 Theft \n", "... ... ... ... ... ... \n", "186502 710 1 2018-01-01 00:00:00 118.0 Auto Theft \n", "186503 1400 1 2018-01-01 00:00:00 59.0 Vandalism \n", "186507 861 1 2018-01-01 00:00:00 128.0 Domestic Assault \n", "186508 2619 1 2018-01-01 00:00:00 48.0 Discharge \n", "186509 710 1 2018-01-01 00:00:00 96.0 Auto Theft \n", "186510 643 1 2018-01-01 00:00:00 117.0 Theft \n", "186511 451 1 2018-01-01 00:00:00 59.0 Domestic Assault \n", "186512 1410 1 2018-01-01 00:00:00 19.0 Vandalism \n", "186513 2619 1 2018-01-01 00:00:00 76.0 Discharge \n", "186514 1420 1 2018-01-01 00:00:00 100.0 Vandalism \n", "186515 700 1 2018-01-01 00:00:00 49.0 Auto Theft \n", "186516 710 1 2018-01-01 00:00:00 53.0 Auto Theft \n", "186517 314 1 2018-01-01 00:00:00 133.0 Robbery \n", "186518 2619 1 2018-01-01 00:00:00 76.0 Discharge \n", "186520 710 1 2018-01-01 00:00:00 49.0 Auto Theft \n", "186521 2619 1 2018-01-01 00:00:00 11.0 Discharge \n", "186524 2619 1 2018-01-01 00:00:00 51.0 Discharge \n", "186525 2619 1 2018-01-01 00:00:00 29.0 Discharge \n", "186526 2619 1 2018-01-01 00:00:00 88.0 Discharge \n", "186527 2619 1 2018-01-01 00:00:00 54.0 Discharge \n", "186530 1835 1 2018-01-01 00:00:00 153.0 Narcotics \n", "186533 710 1 2018-01-01 00:00:00 136.0 Auto Theft \n", "186534 710 1 2018-01-01 00:00:00 35.0 Auto Theft \n", "186536 710 1 2018-01-01 00:00:00 86.0 Auto Theft \n", "186537 700 1 2018-01-01 00:00:00 86.0 Auto Theft \n", "186538 691 1 2018-01-01 00:00:00 10.0 Theft \n", "186539 431 1 2018-01-01 00:00:00 150.0 Domestic Assault \n", "186541 1410 1 2018-01-01 00:00:00 55.0 Vandalism \n", "186544 432 1 2018-01-01 00:00:00 30.0 Domestic Assault \n", "186545 523 1 2018-01-01 00:00:00 86.0 Burglary \n", "\n", " IncType \\\n", "17 Narcotics \n", "23 Criminal Damage to Property \n", "28 Narcotics, Possession of Synthetic Narcotic, D... \n", "29 Motor Vehicle Theft \n", "38 Theft, Bicycle Theft, Under $500 \n", "41 Criminal Damage to Property \n", "45 Narcotics \n", "46 Criminal Damage to Property \n", "55 Motor Vehicle Theft, Automobile \n", "59 Theft, Shoplifting, Under $500 \n", "63 Weapons, Discharging a Firearm in the City Limits \n", "80 Theft, Except Auto Theft \n", "81 Theft, From Auto \n", "94 Theft, From Auto \n", "103 Narcotics \n", "104 Theft, Auto Accessories, Under $500 \n", "110 Robbery \n", "125 Motor Vehicle Theft, Automobile \n", "139 Robbery \n", "155 Motor Vehicle Theft, Automobile \n", "163 Theft, From Auto \n", "168 Criminal Damage to Property (Misdemeanor, Unde... \n", "178 Criminal Damage to Property (Misdemeanor, Unde... \n", "187 Motor Vehicle Theft \n", "189 Theft, From Auto \n", "190 Theft, Shoplifting, Under $500 \n", "191 Theft, Except Auto Theft \n", "192 Motor Vehicle Theft \n", "198 Theft, From Auto \n", "199 Theft, Auto Accessories, Under $500 \n", "... ... \n", "186502 Motor Vehicle Theft, Automobile \n", "186503 Criminal Damage to Property \n", "186507 Assault, Domestic, Opposite Sex \n", "186508 Weapons, Discharging a Firearm in the City Limits \n", "186509 Motor Vehicle Theft, Automobile \n", "186510 Theft, From Auto, Over $1000 \n", "186511 Aggravated Assault, Domestic \n", "186512 Criminal Damage to Property (Misdemeanor, Unde... \n", "186513 Weapons, Discharging a Firearm in the City Limits \n", "186514 Criminal Damage to Property (Gross Mis., $250-... \n", "186515 Motor Vehicle Theft \n", "186516 Motor Vehicle Theft, Automobile \n", "186517 Robbery, Highway, By Strong Arm \n", "186518 Weapons, Discharging a Firearm in the City Limits \n", "186520 Motor Vehicle Theft, Automobile \n", "186521 Weapons, Discharging a Firearm in the City Limits \n", "186524 Weapons, Discharging a Firearm in the City Limits \n", "186525 Weapons, Discharging a Firearm in the City Limits \n", "186526 Weapons, Discharging a Firearm in the City Limits \n", "186527 Weapons, Discharging a Firearm in the City Limits \n", "186530 Narcotics, Possession of Marijuana \n", "186533 Motor Vehicle Theft, Automobile \n", "186534 Motor Vehicle Theft, Automobile \n", "186536 Motor Vehicle Theft, Automobile \n", "186537 Motor Vehicle Theft \n", "186538 Theft, All Other, Under $500 \n", "186539 Aggravated Assault, Domestic \n", "186541 Criminal Damage to Property (Misdemeanor, Unde... \n", "186544 Aggravated Assault, Domestic \n", "186545 Burglary, No Forced Entry, Night, Garage \n", "\n", " Neighborhood NNum Time Year \\\n", "17 4 - Dayton's Bluff 4 2020-12-09T23:28:49.000 2020 \n", "23 7 - Thomas/Dale(Frogtown) 7 2020-12-09T23:09:22.000 2020 \n", "28 13 - Union Park 13 2020-12-09T22:21:00.000 2020 \n", "29 8 - Summit/University 8 2020-12-09T22:16:23.000 2020 \n", "38 1 - Conway/Battlecreek/Highwood 1 2020-12-09T21:37:00.000 2020 \n", "41 8 - Summit/University 8 2020-12-09T21:08:21.000 2020 \n", "45 7 - Thomas/Dale(Frogtown) 7 2020-12-09T21:01:57.000 2020 \n", "46 7 - Thomas/Dale(Frogtown) 7 2020-12-09T20:50:18.000 2020 \n", "55 12 - St. Anthony 12 2020-12-09T20:14:00.000 2020 \n", "59 14 - Macalester-Groveland 14 2020-12-09T20:02:00.000 2020 \n", "63 6 - North End 6 2020-12-09T19:38:20.000 2020 \n", "80 17 - Capitol River 17 2020-12-09T18:49:23.000 2020 \n", "81 6 - North End 6 2020-12-09T18:49:19.000 2020 \n", "94 9 - West Seventh 9 2020-12-09T18:28:26.000 2020 \n", "103 7 - Thomas/Dale(Frogtown) 7 2020-12-09T18:16:54.000 2020 \n", "104 4 - Dayton's Bluff 4 2020-12-09T18:12:00.000 2020 \n", "110 6 - North End 6 2020-12-09T18:04:02.000 2020 \n", "125 11 - Hamline/Midway 11 2020-12-09T17:30:00.000 2020 \n", "139 3 - West Side 3 2020-12-09T17:05:00.000 2020 \n", "155 2 - Greater East Side 2 2020-12-09T16:31:00.000 2020 \n", "163 5 - Payne/Phalen 5 2020-12-09T15:17:58.000 2020 \n", "168 6 - North End 6 2020-12-09T14:52:00.000 2020 \n", "178 9 - West Seventh 9 2020-12-09T14:08:00.000 2020 \n", "187 2 - Greater East Side 2 2020-12-09T13:39:43.000 2020 \n", "189 6 - North End 6 2020-12-09T13:29:08.000 2020 \n", "190 17 - Capitol River 17 2020-12-09T13:26:00.000 2020 \n", "191 17 - Capitol River 17 2020-12-09T13:25:51.000 2020 \n", "192 11 - Hamline/Midway 11 2020-12-09T13:19:43.000 2020 \n", "198 2 - Greater East Side 2 2020-12-09T12:14:01.000 2020 \n", "199 11 - Hamline/Midway 11 2020-12-09T12:11:00.000 2020 \n", "... ... ... ... ... \n", "186502 1 - Conway/Battlecreek/Highwood 1 8:00 2018 \n", "186503 2 - Greater East Side 2 7:54 2018 \n", "186507 8 - Summit/University 8 3:00 2018 \n", "186508 10 - Como 10 2:38 2018 \n", "186509 4 - Dayton's Bluff 4 2:10 2018 \n", "186510 4 - Dayton's Bluff 4 2:00 2018 \n", "186511 2 - Greater East Side 2 1:47 2018 \n", "186512 2 - Greater East Side 2 1:30 2018 \n", "186513 4 - Dayton's Bluff 4 1:23 2018 \n", "186514 1 - Conway/Battlecreek/Highwood 1 1:13 2018 \n", "186515 6 - North End 6 1:10 2018 \n", "186516 5 - Payne/Phalen 5 1:00 2018 \n", "186517 17 - Capitol River 17 1:00 2018 \n", "186518 4 - Dayton's Bluff 4 0:48 2018 \n", "186520 6 - North End 6 0:30 2018 \n", "186521 6 - North End 6 0:30 2018 \n", "186524 6 - North End 6 0:14 2018 \n", "186525 6 - North End 6 0:09 2018 \n", "186526 7 - Thomas/Dale(Frogtown) 7 0:03 2018 \n", "186527 5 - Payne/Phalen 5 0:01 2018 \n", "186530 17 - Capitol River 17 19:49 2018 \n", "186533 4 - Dayton's Bluff 4 20:02 2018 \n", "186534 5 - Payne/Phalen 5 20:27 2018 \n", "186536 11 - Hamline/Midway 11 20:30 2018 \n", "186537 11 - Hamline/Midway 11 20:37 2018 \n", "186538 6 - North End 6 20:46 2018 \n", "186539 9 - West Seventh 9 21:30 2018 \n", "186541 5 - Payne/Phalen 5 22:01 2018 \n", "186544 6 - North End 6 22:18 2018 \n", "186545 11 - Hamline/Midway 11 22:30 2018 \n", "\n", " DayYear Community \n", "17 344 Dayton Bluff \n", "23 344 Thomas-Frogtown \n", "28 344 Union Park \n", "29 344 Summit-University \n", "38 344 Battle_Creek \n", "41 344 Summit-University \n", "45 344 Thomas-Frogtown \n", "46 344 Thomas-Frogtown \n", "55 344 St. Anthony \n", "59 344 Macalester_Groveland \n", "63 344 North End \n", "80 344 Capital_River \n", "81 344 North End \n", "94 344 West 7th \n", "103 344 Thomas-Frogtown \n", "104 344 Dayton Bluff \n", "110 344 North End \n", "125 344 Midway \n", "139 344 West Side \n", "155 344 Greater East Side \n", "163 344 Payne-Phalen \n", "168 344 North End \n", "178 344 West 7th \n", "187 344 Greater East Side \n", "189 344 North End \n", "190 344 Capital_River \n", "191 344 Capital_River \n", "192 344 Midway \n", "198 344 Greater East Side \n", "199 344 Midway \n", "... ... ... \n", "186502 1 Battle_Creek \n", "186503 1 Greater East Side \n", "186507 1 Summit-University \n", "186508 1 Como \n", "186509 1 Dayton Bluff \n", "186510 1 Dayton Bluff \n", "186511 1 Greater East Side \n", "186512 1 Greater East Side \n", "186513 1 Dayton Bluff \n", "186514 1 Battle_Creek \n", "186515 1 North End \n", "186516 1 Payne-Phalen \n", "186517 1 Capital_River \n", "186518 1 Dayton Bluff \n", "186520 1 North End \n", "186521 1 North End \n", "186524 1 North End \n", "186525 1 North End \n", "186526 1 Thomas-Frogtown \n", "186527 1 Payne-Phalen \n", "186530 1 Capital_River \n", "186533 1 Dayton Bluff \n", "186534 1 Payne-Phalen \n", "186536 1 Midway \n", "186537 1 Midway \n", "186538 1 North End \n", "186539 1 West 7th \n", "186541 1 Payne-Phalen \n", "186544 1 North End \n", "186545 1 Midway \n", "\n", "[72310 rows x 16 columns]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearCount
02018267
12019221
22020352
\n", "
" ], "text/plain": [ " Year Count\n", "0 2018 267\n", "1 2019 221\n", "2 2020 352" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#dayshooting\n", "df['TimeHour']= pd.to_datetime(df['Time'])\n", "df['Hour'] = df['TimeHour'].dt.hour.astype(int) #Create Hour Colum\n", "B= df.query(\"Incident=='Discharge'\")\n", "B= B.query('Hour >= 6 and Hour <20')\n", "Index= ['Year','Count']\n", "C= B[Index].groupby(['Year']).sum().reset_index()\n", "C" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This graph displays daytime(7am to 8pm) Discharge annual incidents incidents of Saint Paul up to 12/9/20XX\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_toDate_Year_SPDayCrime(Incident='All',Day=Max):\n", " if Incident=='All':\n", " B= df\n", " B= B.query('Hour >= 6 and Hour <20')\n", " else: \n", " B= df[(df['Incident'] == Incident)]\n", " B= B.query('Hour >= 6 and Hour <20')\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " Index= ['Year','Count']\n", " C= B[Index].groupby(['Year']).sum().reset_index()\n", " print('This graph displays daytime(7am to 8pm) {} annual incidents incidents of Saint Paul up to {}20XX'.format(Incident,Date))\n", " sns.set()\n", " pd.pivot_table(C, values='Count', index=['Year'], \n", " fill_value=0).plot(kind= 'barh', \n", " figsize=(6,4),title= Incident + ' Annual Total Incidents')\n", " return plt.show()\n", "\n", "plot_toDate_Year_SPDayCrime(Incident='Discharge', Day=Max)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This map displays Discharge incidents up to 12/9/20XX\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def plot_Frogtown_long_crime_todate1(Incident='All',Day=Max):\n", " Index =['Block','Latitude','Longitude', 'Count','Theft','Vandalism','Narcotics','Auto Theft','Burglary','Discharge'\\\n", " ,'Robbery','Domestic Assault','Violent','Arson','Year']\n", "\n", " # generate a new map\n", " FG_map = folium.Map(location=[44.958326, -93.122926], zoom_start=14,tiles=\"OpenStreetMap\")\n", " \n", " #setup\n", " if Incident=='All':\n", " B= fgc\n", " else: \n", " B= fgc[(fgc['Incident'] == Incident)]\n", " Date= fgc[(fgc['DayYear'] == Day)]['FDate'][0:1].iloc[0,]\n", " B= B[(B['DayYear'] <= Day)]\n", " B19=B.query('Year == 2020')\n", " B19=B19[Index].groupby(['Year','Block','Latitude','Longitude']).sum().reset_index()\n", " print('This map displays {} incidents up to {}20XX'.format(Incident,Date))\n", " for index, row in B19.iterrows(): \n", " popup_text = \"Year: {}
Address: {}
total incidents: {}\"\n", " popup_text = popup_text.format(row[\"Year\"],row[\"Block\"], row['Count'])\n", " folium.CircleMarker(location=(row[\"Latitude\"],row[\"Longitude\"]),\n", " radius=row['Count']+3,\n", " color=\"#800080\",\n", " popup=popup_text,\n", " fill=True).add_to(FG_map) \n", " \n", " return FG_map\n", "\n", "\n", "plot_Frogtown_long_crime_todate1(Incident='Discharge',Day=Max)" ] }, { "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 }