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KapilKhanal/Police Data Challenge blog post.ipynb

Created December 26, 2018 05:39
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Police Data Challenge blog post.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Police Data Challenge](http://thisisstatistics.org/wp-content/uploads/2017/09/ASA_PDC_opt1_1140x350-1140x350.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <font color=green>**Police Data Challenge**</font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"As data collection and analysis become critical tools for policing, law enforcement agencies aren’t just working case by case. Police forces are working with statisticians in crime analyst roles to identify big-picture patterns in the numbers that are as critical as any other clue in the fight to keep communities safe. Together, they are encouraging joint problem solving, innovation, enhanced understanding, and accountability between communities and the law enforcement agencies that serve them.\n",
"\n",
">Using data available through the <font color = red>*Police Data Initiative*</font>, we had to analyze complex data sets from the <font color = red>Baltimore, Cincinnati and **Seattle** Police Departments</font>, and also recommend innovative solutions to enhance public safety. Although not required, teams may identify and utilize external data sets.\n",
"\n",
"**How Does the Contest Work?** \n",
"Teams of 2-5 high school or college undergrad students in the U.S. and Canada can submit an entry. Each team must complete the declaration of intent form by 11:59 PM on Friday, Oct. 20 and must submit their presentation by 11:59 PM EDT on Friday, Nov. 3 to be eligible. Submissions will require a short essay describing the team’s process and presenting their analysis and recommendations via a PowerPoint presentation.\n",
"\n",
"> Awards will be given in three categories (1) **<font color = green>Best Overall Analysis**</font>,<font color = purple>(*Spoiler Alert*)</font> (2) Best Visualization, and (3) Best Use of External Data.\n",
"**--------------------------------------------------------------------------------------------------------------------**\n",
"\n",
"This was also a Midterm Project for our <font,color=magenta>**Data Visualization Class**</font> taught by <font,color=green>[**Prof.Silas Bergen**](http://driftlessdata.space)</font>. We would like to thank him for this huge midterm project(*maybe the first time i am thankful for giving us a huge project and pushing us to think creative and practical*) and ofcourse his creativity and innovative thinking\n",
"\n",
"My team,<font color= purple>**Jimmy Hickey**,**Luke Peacock**,*and me* **Kapil Khanal**</font> representing <font color=purple>**WINONA STATE UNIVERSITY**</font>\n",
"\n",
"I would definetly like to thank *StackOverflow*,*Pandas Documentation*,*DataQuest*,*tab completion in Jupyter notebook* for code snippets and tutorials and **F<font color=red>.</font>R<font color=magenta>.</font>I<font color=purple>.</font>E<font color=blue>.</font>N<font color=green>.</font>D<font color=violet>.</font>S** for being there for me during computation time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Let's dive in...."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#Import Everythng that is necessary\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"import matplotlib.dates as mdates\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2728: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
}
],
"source": [
"#Had to name like this... dsci_midterm.csv..LOL....Any WSU data science student knows the mess..in their machine.\n",
"#CSV,CSV everywhere !!\n",
"crime_data = pd.read_csv(\"dsci_midterm.csv\") "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CAD CDW ID</th>\n",
" <th>CAD Event Number</th>\n",
" <th>General Offense Number</th>\n",
" <th>Event Clearance Code</th>\n",
" <th>Event Clearance Description</th>\n",
" <th>Event Clearance SubGroup</th>\n",
" <th>Event Clearance Group</th>\n",
" <th>Event Clearance Date</th>\n",
" <th>Hundred Block Location</th>\n",
" <th>District/Sector</th>\n",
" <th>Zone/Beat</th>\n",
" <th>Census Tract</th>\n",
" <th>Longitude</th>\n",
" <th>Latitude</th>\n",
" <th>Incident Location</th>\n",
" <th>Initial Type Description</th>\n",
" <th>Initial Type Subgroup</th>\n",
" <th>Initial Type Group</th>\n",
" <th>At Scene Time</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>15736</td>\n",
" <td>10000246357</td>\n",
" <td>2010246357</td>\n",
" <td>242.0</td>\n",
" <td>FIGHT DISTURBANCE</td>\n",
" <td>DISTURBANCES</td>\n",
" <td>DISTURBANCES</td>\n",
" <td>07/17/2010 08:49:00 PM</td>\n",
" <td>3XX BLOCK OF PINE ST</td>\n",
" <td>M</td>\n",
" <td>M2</td>\n",
" <td>8100.2001</td>\n",
" <td>-122.338147</td>\n",
" <td>47.610975</td>\n",
" <td>(47.610975163, -122.338146748)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15737</td>\n",
" <td>10000246471</td>\n",
" <td>2010246471</td>\n",
" <td>65.0</td>\n",
" <td>THEFT - MISCELLANEOUS</td>\n",
" <td>THEFT</td>\n",
" <td>OTHER PROPERTY</td>\n",
" <td>07/17/2010 08:50:00 PM</td>\n",
" <td>36XX BLOCK OF DISCOVERY PARK BLVD</td>\n",
" <td>Q</td>\n",
" <td>Q1</td>\n",
" <td>5700.1012</td>\n",
" <td>-122.404613</td>\n",
" <td>47.658325</td>\n",
" <td>(47.658324899, -122.404612874)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>15738</td>\n",
" <td>10000246255</td>\n",
" <td>2010246255</td>\n",
" <td>250.0</td>\n",
" <td>MISCHIEF, NUISANCE COMPLAINTS</td>\n",
" <td>NUISANCE, MISCHIEF COMPLAINTS</td>\n",
" <td>NUISANCE, MISCHIEF</td>\n",
" <td>07/17/2010 08:55:00 PM</td>\n",
" <td>21XX BLOCK OF 3RD AVE</td>\n",
" <td>M</td>\n",
" <td>M2</td>\n",
" <td>7200.2025</td>\n",
" <td>-122.342843</td>\n",
" <td>47.613551</td>\n",
" <td>(47.613551471, -122.342843234)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>15739</td>\n",
" <td>10000246473</td>\n",
" <td>2010246473</td>\n",
" <td>460.0</td>\n",
" <td>TRAFFIC (MOVING) VIOLATION</td>\n",
" <td>TRAFFIC RELATED CALLS</td>\n",
" <td>TRAFFIC RELATED CALLS</td>\n",
" <td>07/17/2010 09:00:00 PM</td>\n",
" <td>7XX BLOCK OF ROY ST</td>\n",
" <td>D</td>\n",
" <td>D1</td>\n",
" <td>7200.1002</td>\n",
" <td>-122.341847</td>\n",
" <td>47.625401</td>\n",
" <td>(47.625401388, -122.341846999)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>15740</td>\n",
" <td>10000246330</td>\n",
" <td>2010246330</td>\n",
" <td>250.0</td>\n",
" <td>MISCHIEF, NUISANCE COMPLAINTS</td>\n",
" <td>NUISANCE, MISCHIEF COMPLAINTS</td>\n",
" <td>NUISANCE, MISCHIEF</td>\n",
" <td>07/17/2010 09:00:00 PM</td>\n",
" <td>9XX BLOCK OF ALOHA ST</td>\n",
" <td>D</td>\n",
" <td>D1</td>\n",
" <td>6700.1009</td>\n",
" <td>-122.339709</td>\n",
" <td>47.627425</td>\n",
" <td>(47.627424837, -122.339708605)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CAD CDW ID CAD Event Number General Offense Number Event Clearance Code \\\n",
"0 15736 10000246357 2010246357 242.0 \n",
"1 15737 10000246471 2010246471 65.0 \n",
"2 15738 10000246255 2010246255 250.0 \n",
"3 15739 10000246473 2010246473 460.0 \n",
"4 15740 10000246330 2010246330 250.0 \n",
"\n",
" Event Clearance Description Event Clearance SubGroup \\\n",
"0 FIGHT DISTURBANCE DISTURBANCES \n",
"1 THEFT - MISCELLANEOUS THEFT \n",
"2 MISCHIEF, NUISANCE COMPLAINTS NUISANCE, MISCHIEF COMPLAINTS \n",
"3 TRAFFIC (MOVING) VIOLATION TRAFFIC RELATED CALLS \n",
"4 MISCHIEF, NUISANCE COMPLAINTS NUISANCE, MISCHIEF COMPLAINTS \n",
"\n",
" Event Clearance Group Event Clearance Date \\\n",
"0 DISTURBANCES 07/17/2010 08:49:00 PM \n",
"1 OTHER PROPERTY 07/17/2010 08:50:00 PM \n",
"2 NUISANCE, MISCHIEF 07/17/2010 08:55:00 PM \n",
"3 TRAFFIC RELATED CALLS 07/17/2010 09:00:00 PM \n",
"4 NUISANCE, MISCHIEF 07/17/2010 09:00:00 PM \n",
"\n",
" Hundred Block Location District/Sector Zone/Beat Census Tract \\\n",
"0 3XX BLOCK OF PINE ST M M2 8100.2001 \n",
"1 36XX BLOCK OF DISCOVERY PARK BLVD Q Q1 5700.1012 \n",
"2 21XX BLOCK OF 3RD AVE M M2 7200.2025 \n",
"3 7XX BLOCK OF ROY ST D D1 7200.1002 \n",
"4 9XX BLOCK OF ALOHA ST D D1 6700.1009 \n",
"\n",
" Longitude Latitude Incident Location \\\n",
"0 -122.338147 47.610975 (47.610975163, -122.338146748) \n",
"1 -122.404613 47.658325 (47.658324899, -122.404612874) \n",
"2 -122.342843 47.613551 (47.613551471, -122.342843234) \n",
"3 -122.341847 47.625401 (47.625401388, -122.341846999) \n",
"4 -122.339709 47.627425 (47.627424837, -122.339708605) \n",
"\n",
" Initial Type Description Initial Type Subgroup Initial Type Group \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"\n",
" At Scene Time \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Let's peek at the data\n",
"crime_data.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CAD CDW ID</th>\n",
" <th>CAD Event Number</th>\n",
" <th>General Offense Number</th>\n",
" <th>Event Clearance Code</th>\n",
" <th>Event Clearance Description</th>\n",
" <th>Event Clearance SubGroup</th>\n",
" <th>Event Clearance Group</th>\n",
" <th>Event Clearance Date</th>\n",
" <th>Hundred Block Location</th>\n",
" <th>District/Sector</th>\n",
" <th>Zone/Beat</th>\n",
" <th>Census Tract</th>\n",
" <th>Longitude</th>\n",
" <th>Latitude</th>\n",
" <th>Incident Location</th>\n",
" <th>Initial Type Description</th>\n",
" <th>Initial Type Subgroup</th>\n",
" <th>Initial Type Group</th>\n",
" <th>At Scene Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1444259</th>\n",
" <td>2088572</td>\n",
" <td>17000333842</td>\n",
" <td>2017333842</td>\n",
" <td>280.0</td>\n",
" <td>SUSPICIOUS PERSON</td>\n",
" <td>SUSPICIOUS CIRCUMSTANCES</td>\n",
" <td>SUSPICIOUS CIRCUMSTANCES</td>\n",
" <td>09/08/2017 02:06:17 AM</td>\n",
" <td>62XX BLOCK OF 13 AV S</td>\n",
" <td>O</td>\n",
" <td>O3</td>\n",
" <td>10900.2089</td>\n",
" <td>-122.316110</td>\n",
" <td>47.547516</td>\n",
" <td>(47.547516, -122.31611)</td>\n",
" <td>UNKNOWN - COMPLAINT OF UNKNOWN NATURE</td>\n",
" <td>SUSPICIOUS CIRCUMSTANCES</td>\n",
" <td>SUSPICIOUS CIRCUMSTANCES</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1444260</th>\n",
" <td>2088573</td>\n",
" <td>17000333823</td>\n",
" <td>2017333823</td>\n",
" <td>245.0</td>\n",
" <td>DISTURBANCE, OTHER</td>\n",
" <td>DISTURBANCES</td>\n",
" <td>DISTURBANCES</td>\n",
" <td>09/08/2017 02:13:38 AM</td>\n",
" <td>46XX BLOCK OF S MORGAN ST</td>\n",
" <td>S</td>\n",
" <td>S2</td>\n",
" <td>11101.1002</td>\n",
" <td>-122.274994</td>\n",
" <td>47.544250</td>\n",
" <td>(47.54425, -122.274994)</td>\n",
" <td>FIGHT - JO - PHYSICAL (NO WEAPONS)</td>\n",
" <td>DISTURBANCES</td>\n",
" <td>DISTURBANCES</td>\n",
" <td>09/08/2017 01:26:38 AM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1444261</th>\n",
" <td>2088522</td>\n",
" <td>17000333768</td>\n",
" <td>2017333768</td>\n",
" <td>161.0</td>\n",
" <td>TRESPASS</td>\n",
" <td>TRESPASS</td>\n",
" <td>TRESPASS</td>\n",
" <td>09/08/2017 12:13:15 AM</td>\n",
" <td>1XX BLOCK OF MERCER ST</td>\n",
" <td>Q</td>\n",
" <td>Q3</td>\n",
" <td>7000.3021</td>\n",
" <td>-122.354750</td>\n",
" <td>47.624577</td>\n",
" <td>(47.624577, -122.35475)</td>\n",
" <td>TRESPASS</td>\n",
" <td>TRESPASS</td>\n",
" <td>TRESPASS</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1444262</th>\n",
" <td>2088590</td>\n",
" <td>17000333779</td>\n",
" <td>2017333779</td>\n",
" <td>430.0</td>\n",
" <td>MOTOR VEHICLE COLLISION</td>\n",
" <td>TRAFFIC RELATED CALLS</td>\n",
" <td>MOTOR VEHICLE COLLISION INVESTIGATION</td>\n",
" <td>09/08/2017 02:41:47 AM</td>\n",
" <td>1XX BLOCK OF BROADWAY E</td>\n",
" <td>E</td>\n",
" <td>E1</td>\n",
" <td>7402.1010</td>\n",
" <td>-122.320860</td>\n",
" <td>47.619324</td>\n",
" <td>(47.619324, -122.32086)</td>\n",
" <td>MOTOR VEHICLE COLLISION, HIT AND RUN</td>\n",
" <td>MOTOR VEHICLE COLLISION INVESTIGATION</td>\n",
" <td>TRAFFIC RELATED CALLS</td>\n",
" <td>09/08/2017 12:12:32 AM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1444263</th>\n",
" <td>2088591</td>\n",
" <td>17000333762</td>\n",
" <td>2017333762</td>\n",
" <td>280.0</td>\n",
" <td>SUSPICIOUS PERSON</td>\n",
" <td>SUSPICIOUS CIRCUMSTANCES</td>\n",
" <td>SUSPICIOUS CIRCUMSTANCES</td>\n",
" <td>09/08/2017 02:42:57 AM</td>\n",
" <td>2XX BLOCK OF LAKE WASHINGTON BV E</td>\n",
" <td>C</td>\n",
" <td>C3</td>\n",
" <td>7800.1016</td>\n",
" <td>-122.281586</td>\n",
" <td>47.620106</td>\n",
" <td>(47.620106, -122.281586)</td>\n",
" <td>SUSPICIOUS STOP - OFFICER INITIATED ONVIEW</td>\n",
" <td>SUSPICIOUS CIRCUMSTANCES</td>\n",
" <td>SUSPICIOUS CIRCUMSTANCES</td>\n",
" <td>09/07/2017 11:34:38 PM</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CAD CDW ID CAD Event Number General Offense Number \\\n",
"1444259 2088572 17000333842 2017333842 \n",
"1444260 2088573 17000333823 2017333823 \n",
"1444261 2088522 17000333768 2017333768 \n",
"1444262 2088590 17000333779 2017333779 \n",
"1444263 2088591 17000333762 2017333762 \n",
"\n",
" Event Clearance Code Event Clearance Description \\\n",
"1444259 280.0 SUSPICIOUS PERSON \n",
"1444260 245.0 DISTURBANCE, OTHER \n",
"1444261 161.0 TRESPASS \n",
"1444262 430.0 MOTOR VEHICLE COLLISION \n",
"1444263 280.0 SUSPICIOUS PERSON \n",
"\n",
" Event Clearance SubGroup Event Clearance Group \\\n",
"1444259 SUSPICIOUS CIRCUMSTANCES SUSPICIOUS CIRCUMSTANCES \n",
"1444260 DISTURBANCES DISTURBANCES \n",
"1444261 TRESPASS TRESPASS \n",
"1444262 TRAFFIC RELATED CALLS MOTOR VEHICLE COLLISION INVESTIGATION \n",
"1444263 SUSPICIOUS CIRCUMSTANCES SUSPICIOUS CIRCUMSTANCES \n",
"\n",
" Event Clearance Date Hundred Block Location \\\n",
"1444259 09/08/2017 02:06:17 AM 62XX BLOCK OF 13 AV S \n",
"1444260 09/08/2017 02:13:38 AM 46XX BLOCK OF S MORGAN ST \n",
"1444261 09/08/2017 12:13:15 AM 1XX BLOCK OF MERCER ST \n",
"1444262 09/08/2017 02:41:47 AM 1XX BLOCK OF BROADWAY E \n",
"1444263 09/08/2017 02:42:57 AM 2XX BLOCK OF LAKE WASHINGTON BV E \n",
"\n",
" District/Sector Zone/Beat Census Tract Longitude Latitude \\\n",
"1444259 O O3 10900.2089 -122.316110 47.547516 \n",
"1444260 S S2 11101.1002 -122.274994 47.544250 \n",
"1444261 Q Q3 7000.3021 -122.354750 47.624577 \n",
"1444262 E E1 7402.1010 -122.320860 47.619324 \n",
"1444263 C C3 7800.1016 -122.281586 47.620106 \n",
"\n",
" Incident Location Initial Type Description \\\n",
"1444259 (47.547516, -122.31611) UNKNOWN - COMPLAINT OF UNKNOWN NATURE \n",
"1444260 (47.54425, -122.274994) FIGHT - JO - PHYSICAL (NO WEAPONS) \n",
"1444261 (47.624577, -122.35475) TRESPASS \n",
"1444262 (47.619324, -122.32086) MOTOR VEHICLE COLLISION, HIT AND RUN \n",
"1444263 (47.620106, -122.281586) SUSPICIOUS STOP - OFFICER INITIATED ONVIEW \n",
"\n",
" Initial Type Subgroup Initial Type Group \\\n",
"1444259 SUSPICIOUS CIRCUMSTANCES SUSPICIOUS CIRCUMSTANCES \n",
"1444260 DISTURBANCES DISTURBANCES \n",
"1444261 TRESPASS TRESPASS \n",
"1444262 MOTOR VEHICLE COLLISION INVESTIGATION TRAFFIC RELATED CALLS \n",
"1444263 SUSPICIOUS CIRCUMSTANCES SUSPICIOUS CIRCUMSTANCES \n",
"\n",
" At Scene Time \n",
"1444259 NaN \n",
"1444260 09/08/2017 01:26:38 AM \n",
"1444261 NaN \n",
"1444262 09/08/2017 12:12:32 AM \n",
"1444263 09/07/2017 11:34:38 PM "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Woah !! What is up with that NaN..Let's check if its like that all across dataset\n",
"crime_data.tail()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CAD CDW ID object\n",
"CAD Event Number int64\n",
"General Offense Number int64\n",
"Event Clearance Code float64\n",
"Event Clearance Description object\n",
"Event Clearance SubGroup object\n",
"Event Clearance Group object\n",
"Event Clearance Date object\n",
"Hundred Block Location object\n",
"District/Sector object\n",
"Zone/Beat object\n",
"Census Tract float64\n",
"Longitude float64\n",
"Latitude float64\n",
"Incident Location object\n",
"Initial Type Description object\n",
"Initial Type Subgroup object\n",
"Initial Type Group object\n",
"At Scene Time object\n",
"dtype: object"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"crime_data.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Time info are not in a datetime object.Right now, I am thinking to change those to proper type.*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I right away started splitting time in ':' and started pulling cash one of them separately which was cumbersome until i read somewhere that i can change it to datetime and pull time/hour/second as necessary from there..which was cool.\n",
"\n",
"```python\n",
"\n",
"***Terrible way of doing things***\n",
"\n",
"crime_data[\"Event_Hour\"] = crime_data[\"Event_Time\"].apply(lambda x: x.split(\":\"))\n",
"crime_data[\"Scene_Hour\"] = crime_data[\"Scene_Time\"].apply(lambda x: x.split(\":\"))\n",
"\n",
"\n",
"crime_data[\"event_hour\"] = [list_time[0] for list_time in crime_data[\"Event_Hour\"]]\n",
"crime_data[\"scene_hour\"] = [list_time[0] for list_time in crime_data[\"Scene_Hour\"]]\n",
"\n",
"\n",
"crime_data[\"scene_hour\"] =crime_data[\"scene_hour\"].replace('nan',None)\n",
"crime_data[\"scene_hour\"] = crime_data[\"scene_hour\"].dropna()\n",
"crime_data[\"scene_hour\"]'\n",
"\n",
"```\n",
"So, i tried that...DATETIME MODULE to the RESCUE"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"crime_data[\"Event_timing\"] = pd.to_datetime(crime_data[\"Event Clearance Date\"],errors = 'ignore')\n",
"crime_data[\"Scene_timing\"] = pd.to_datetime(crime_data[\"At Scene Time\"],errors = 'ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Would be interesting to see the response time.... For Now,Let's just compute it..I am hoping it will do proper subtract as it's datetime object* Else, We could always do heavy lifting...through python toolbox like split,lambdas and others...i did that before i actually did datetime object...i am still learning so...don't blame me! plz !!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"crime_data[\"response_time\"] = crime_data[\"Event_timing\"] - crime_data[\"Scene_timing\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There were one or more rows where there was no data for either scene time or very rarely event clearance type...So we will get some NA's...*kinda like hollywood superheroes... Tricky Business*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Let's slice and dice the time information**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"crime_data[\"Event_Date\"] = crime_data[\"Event_timing\"].dt.date\n",
"crime_data[\"Event_Time\"] = crime_data[\"Event_timing\"].dt.time\n",
"crime_data[\"Scene_Date\"] = crime_data[\"Scene_timing\"].dt.date\n",
"crime_data[\"Scene_Time\"] = crime_data[\"Scene_timing\"].dt.time \n"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"by_crime_group = crime_data.groupby([\"Scene_Date\"]).count()\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"<bound method DataFrame.reset_index of CAD CDW ID CAD Event Number General Offense Number \\\n",
"Scene_Date \n",
"2009-06-19 1 1 1 \n",
"2009-06-21 1 1 1 \n",
"2009-06-26 1 1 1 \n",
"2009-08-22 1 1 1 \n",
"2009-11-28 1 1 1 \n",
"2009-12-22 1 1 1 \n",
"2009-12-31 1 1 1 \n",
"2010-01-01 2 2 2 \n",
"2010-01-02 1 1 1 \n",
"2010-01-04 2 2 2 \n",
"2010-01-05 1 1 1 \n",
"2010-01-06 2 2 2 \n",
"2010-01-07 1 1 1 \n",
"2010-01-08 3 3 3 \n",
"2010-01-09 1 1 1 \n",
"2010-05-21 1 1 1 \n",
"2010-08-12 1 1 1 \n",
"2010-08-17 1 1 1 \n",
"2010-09-02 1 1 1 \n",
"2010-09-21 1 1 1 \n",
"2010-09-30 1 1 1 \n",
"2010-10-12 1 1 1 \n",
"2010-12-01 3 3 3 \n",
"2010-12-02 2 2 2 \n",
"2010-12-03 3 3 3 \n",
"2010-12-04 2 2 2 \n",
"2010-12-05 1 1 1 \n",
"2010-12-07 1 1 1 \n",
"2010-12-09 1 1 1 \n",
"2010-12-10 2 2 2 \n",
"... ... ... ... \n",
"2017-09-11 356 356 356 \n",
"2017-09-12 321 321 321 \n",
"2017-09-13 363 363 363 \n",
"2017-09-14 434 434 434 \n",
"2017-09-15 345 345 345 \n",
"2017-09-16 348 348 348 \n",
"2017-09-17 319 319 319 \n",
"2017-09-18 327 327 327 \n",
"2017-09-19 322 322 322 \n",
"2017-09-20 293 293 293 \n",
"2017-09-21 312 312 312 \n",
"2017-09-22 367 367 367 \n",
"2017-09-23 292 292 292 \n",
"2017-09-24 291 291 291 \n",
"2017-09-25 325 325 325 \n",
"2017-09-26 342 342 342 \n",
"2017-09-27 345 345 345 \n",
"2017-09-28 358 358 358 \n",
"2017-09-29 344 344 344 \n",
"2017-09-30 295 295 295 \n",
"2017-10-01 315 315 315 \n",
"2017-10-02 350 350 350 \n",
"2017-10-03 318 318 318 \n",
"2017-10-04 346 346 346 \n",
"2017-10-05 375 375 375 \n",
"2017-10-06 364 364 364 \n",
"2017-10-07 311 311 311 \n",
"2017-10-08 281 281 281 \n",
"2017-10-09 299 299 299 \n",
"2017-10-10 142 142 142 \n",
"\n",
" Event Clearance Code Event Clearance Description \\\n",
"Scene_Date \n",
"2009-06-19 1 1 \n",
"2009-06-21 1 1 \n",
"2009-06-26 1 1 \n",
"2009-08-22 1 1 \n",
"2009-11-28 1 1 \n",
"2009-12-22 1 1 \n",
"2009-12-31 1 1 \n",
"2010-01-01 2 2 \n",
"2010-01-02 1 1 \n",
"2010-01-04 2 2 \n",
"2010-01-05 1 1 \n",
"2010-01-06 2 2 \n",
"2010-01-07 1 1 \n",
"2010-01-08 3 3 \n",
"2010-01-09 1 1 \n",
"2010-05-21 1 1 \n",
"2010-08-12 1 1 \n",
"2010-08-17 1 1 \n",
"2010-09-02 1 1 \n",
"2010-09-21 1 1 \n",
"2010-09-30 1 1 \n",
"2010-10-12 1 1 \n",
"2010-12-01 3 3 \n",
"2010-12-02 2 2 \n",
"2010-12-03 3 3 \n",
"2010-12-04 2 2 \n",
"2010-12-05 1 1 \n",
"2010-12-07 1 1 \n",
"2010-12-09 1 1 \n",
"2010-12-10 2 2 \n",
"... ... ... \n",
"2017-09-11 356 356 \n",
"2017-09-12 321 321 \n",
"2017-09-13 363 363 \n",
"2017-09-14 434 434 \n",
"2017-09-15 345 345 \n",
"2017-09-16 348 348 \n",
"2017-09-17 319 319 \n",
"2017-09-18 327 327 \n",
"2017-09-19 322 322 \n",
"2017-09-20 293 293 \n",
"2017-09-21 312 312 \n",
"2017-09-22 367 367 \n",
"2017-09-23 292 292 \n",
"2017-09-24 291 291 \n",
"2017-09-25 325 325 \n",
"2017-09-26 342 342 \n",
"2017-09-27 345 345 \n",
"2017-09-28 358 358 \n",
"2017-09-29 344 344 \n",
"2017-09-30 295 295 \n",
"2017-10-01 315 315 \n",
"2017-10-02 350 350 \n",
"2017-10-03 318 318 \n",
"2017-10-04 346 346 \n",
"2017-10-05 375 375 \n",
"2017-10-06 364 364 \n",
"2017-10-07 311 311 \n",
"2017-10-08 281 281 \n",
"2017-10-09 299 299 \n",
"2017-10-10 142 142 \n",
"\n",
" Event Clearance SubGroup Event Clearance Group \\\n",
"Scene_Date \n",
"2009-06-19 1 1 \n",
"2009-06-21 1 1 \n",
"2009-06-26 1 1 \n",
"2009-08-22 1 1 \n",
"2009-11-28 1 1 \n",
"2009-12-22 1 1 \n",
"2009-12-31 1 1 \n",
"2010-01-01 2 2 \n",
"2010-01-02 1 1 \n",
"2010-01-04 2 2 \n",
"2010-01-05 1 1 \n",
"2010-01-06 2 2 \n",
"2010-01-07 1 1 \n",
"2010-01-08 3 3 \n",
"2010-01-09 1 1 \n",
"2010-05-21 1 1 \n",
"2010-08-12 1 1 \n",
"2010-08-17 1 1 \n",
"2010-09-02 1 1 \n",
"2010-09-21 1 1 \n",
"2010-09-30 1 1 \n",
"2010-10-12 1 1 \n",
"2010-12-01 3 3 \n",
"2010-12-02 2 2 \n",
"2010-12-03 3 3 \n",
"2010-12-04 2 2 \n",
"2010-12-05 1 1 \n",
"2010-12-07 1 1 \n",
"2010-12-09 1 1 \n",
"2010-12-10 2 2 \n",
"... ... ... \n",
"2017-09-11 356 356 \n",
"2017-09-12 321 321 \n",
"2017-09-13 363 363 \n",
"2017-09-14 434 434 \n",
"2017-09-15 345 345 \n",
"2017-09-16 348 348 \n",
"2017-09-17 319 319 \n",
"2017-09-18 327 327 \n",
"2017-09-19 322 322 \n",
"2017-09-20 293 293 \n",
"2017-09-21 312 312 \n",
"2017-09-22 367 367 \n",
"2017-09-23 292 292 \n",
"2017-09-24 291 291 \n",
"2017-09-25 325 325 \n",
"2017-09-26 342 342 \n",
"2017-09-27 345 345 \n",
"2017-09-28 358 358 \n",
"2017-09-29 344 344 \n",
"2017-09-30 295 295 \n",
"2017-10-01 315 315 \n",
"2017-10-02 350 350 \n",
"2017-10-03 318 318 \n",
"2017-10-04 346 346 \n",
"2017-10-05 375 375 \n",
"2017-10-06 364 364 \n",
"2017-10-07 311 311 \n",
"2017-10-08 281 281 \n",
"2017-10-09 299 299 \n",
"2017-10-10 142 142 \n",
"\n",
" Event Clearance Date Hundred Block Location District/Sector \\\n",
"Scene_Date \n",
"2009-06-19 1 1 1 \n",
"2009-06-21 1 1 1 \n",
"2009-06-26 1 1 1 \n",
"2009-08-22 1 1 1 \n",
"2009-11-28 1 1 1 \n",
"2009-12-22 1 1 1 \n",
"2009-12-31 1 1 1 \n",
"2010-01-01 2 2 2 \n",
"2010-01-02 1 1 1 \n",
"2010-01-04 2 2 2 \n",
"2010-01-05 1 1 1 \n",
"2010-01-06 2 2 2 \n",
"2010-01-07 1 1 1 \n",
"2010-01-08 3 3 3 \n",
"2010-01-09 1 1 1 \n",
"2010-05-21 1 1 1 \n",
"2010-08-12 1 1 1 \n",
"2010-08-17 1 1 1 \n",
"2010-09-02 1 1 1 \n",
"2010-09-21 1 1 1 \n",
"2010-09-30 1 1 1 \n",
"2010-10-12 1 1 1 \n",
"2010-12-01 3 3 3 \n",
"2010-12-02 2 2 2 \n",
"2010-12-03 3 3 3 \n",
"2010-12-04 2 2 2 \n",
"2010-12-05 1 1 1 \n",
"2010-12-07 1 1 1 \n",
"2010-12-09 1 1 1 \n",
"2010-12-10 2 2 2 \n",
"... ... ... ... \n",
"2017-09-11 356 356 356 \n",
"2017-09-12 321 321 321 \n",
"2017-09-13 363 363 363 \n",
"2017-09-14 434 434 434 \n",
"2017-09-15 345 345 345 \n",
"2017-09-16 348 348 348 \n",
"2017-09-17 319 319 319 \n",
"2017-09-18 327 327 327 \n",
"2017-09-19 322 322 322 \n",
"2017-09-20 293 293 293 \n",
"2017-09-21 312 312 312 \n",
"2017-09-22 367 367 367 \n",
"2017-09-23 292 292 292 \n",
"2017-09-24 291 291 291 \n",
"2017-09-25 325 325 325 \n",
"2017-09-26 342 342 342 \n",
"2017-09-27 345 345 345 \n",
"2017-09-28 358 358 358 \n",
"2017-09-29 344 344 344 \n",
"2017-09-30 295 295 295 \n",
"2017-10-01 315 315 315 \n",
"2017-10-02 350 350 350 \n",
"2017-10-03 318 318 318 \n",
"2017-10-04 346 346 346 \n",
"2017-10-05 375 375 375 \n",
"2017-10-06 364 364 364 \n",
"2017-10-07 311 311 311 \n",
"2017-10-08 281 281 281 \n",
"2017-10-09 299 299 299 \n",
"2017-10-10 142 142 142 \n",
"\n",
" ... Initial Type Description Initial Type Subgroup \\\n",
"Scene_Date ... \n",
"2009-06-19 ... 1 1 \n",
"2009-06-21 ... 1 1 \n",
"2009-06-26 ... 1 1 \n",
"2009-08-22 ... 1 1 \n",
"2009-11-28 ... 1 1 \n",
"2009-12-22 ... 1 1 \n",
"2009-12-31 ... 1 1 \n",
"2010-01-01 ... 2 2 \n",
"2010-01-02 ... 1 1 \n",
"2010-01-04 ... 2 2 \n",
"2010-01-05 ... 1 1 \n",
"2010-01-06 ... 2 2 \n",
"2010-01-07 ... 1 1 \n",
"2010-01-08 ... 3 3 \n",
"2010-01-09 ... 1 1 \n",
"2010-05-21 ... 1 1 \n",
"2010-08-12 ... 1 1 \n",
"2010-08-17 ... 1 1 \n",
"2010-09-02 ... 1 1 \n",
"2010-09-21 ... 1 1 \n",
"2010-09-30 ... 1 1 \n",
"2010-10-12 ... 1 1 \n",
"2010-12-01 ... 3 3 \n",
"2010-12-02 ... 2 2 \n",
"2010-12-03 ... 3 3 \n",
"2010-12-04 ... 2 2 \n",
"2010-12-05 ... 1 1 \n",
"2010-12-07 ... 1 1 \n",
"2010-12-09 ... 1 1 \n",
"2010-12-10 ... 2 2 \n",
"... ... ... ... \n",
"2017-09-11 ... 356 356 \n",
"2017-09-12 ... 321 321 \n",
"2017-09-13 ... 363 363 \n",
"2017-09-14 ... 434 434 \n",
"2017-09-15 ... 345 345 \n",
"2017-09-16 ... 348 348 \n",
"2017-09-17 ... 319 319 \n",
"2017-09-18 ... 327 327 \n",
"2017-09-19 ... 322 322 \n",
"2017-09-20 ... 293 293 \n",
"2017-09-21 ... 312 312 \n",
"2017-09-22 ... 367 367 \n",
"2017-09-23 ... 292 292 \n",
"2017-09-24 ... 291 291 \n",
"2017-09-25 ... 325 325 \n",
"2017-09-26 ... 342 342 \n",
"2017-09-27 ... 345 345 \n",
"2017-09-28 ... 358 358 \n",
"2017-09-29 ... 344 344 \n",
"2017-09-30 ... 295 295 \n",
"2017-10-01 ... 315 315 \n",
"2017-10-02 ... 350 350 \n",
"2017-10-03 ... 318 318 \n",
"2017-10-04 ... 346 346 \n",
"2017-10-05 ... 375 375 \n",
"2017-10-06 ... 364 364 \n",
"2017-10-07 ... 311 311 \n",
"2017-10-08 ... 281 281 \n",
"2017-10-09 ... 299 299 \n",
"2017-10-10 ... 142 142 \n",
"\n",
" Initial Type Group At Scene Time Event_timing Scene_timing \\\n",
"Scene_Date \n",
"2009-06-19 1 1 1 1 \n",
"2009-06-21 1 1 1 1 \n",
"2009-06-26 1 1 1 1 \n",
"2009-08-22 1 1 1 1 \n",
"2009-11-28 1 1 1 1 \n",
"2009-12-22 1 1 1 1 \n",
"2009-12-31 1 1 1 1 \n",
"2010-01-01 2 2 2 2 \n",
"2010-01-02 1 1 1 1 \n",
"2010-01-04 2 2 2 2 \n",
"2010-01-05 1 1 1 1 \n",
"2010-01-06 2 2 2 2 \n",
"2010-01-07 1 1 1 1 \n",
"2010-01-08 3 3 3 3 \n",
"2010-01-09 1 1 1 1 \n",
"2010-05-21 1 1 1 1 \n",
"2010-08-12 1 1 1 1 \n",
"2010-08-17 1 1 1 1 \n",
"2010-09-02 1 1 1 1 \n",
"2010-09-21 1 1 1 1 \n",
"2010-09-30 1 1 1 1 \n",
"2010-10-12 1 1 1 1 \n",
"2010-12-01 3 3 3 3 \n",
"2010-12-02 2 2 2 2 \n",
"2010-12-03 3 3 3 3 \n",
"2010-12-04 2 2 2 2 \n",
"2010-12-05 1 1 1 1 \n",
"2010-12-07 1 1 1 1 \n",
"2010-12-09 1 1 1 1 \n",
"2010-12-10 2 2 2 2 \n",
"... ... ... ... ... \n",
"2017-09-11 356 356 356 356 \n",
"2017-09-12 321 321 321 321 \n",
"2017-09-13 363 363 363 363 \n",
"2017-09-14 434 434 434 434 \n",
"2017-09-15 345 345 345 345 \n",
"2017-09-16 348 348 348 348 \n",
"2017-09-17 319 319 319 319 \n",
"2017-09-18 327 327 327 327 \n",
"2017-09-19 322 322 322 322 \n",
"2017-09-20 293 293 293 293 \n",
"2017-09-21 312 312 312 312 \n",
"2017-09-22 367 367 367 367 \n",
"2017-09-23 292 292 292 292 \n",
"2017-09-24 291 291 291 291 \n",
"2017-09-25 325 325 325 325 \n",
"2017-09-26 342 342 342 342 \n",
"2017-09-27 345 345 345 345 \n",
"2017-09-28 358 358 358 358 \n",
"2017-09-29 344 344 344 344 \n",
"2017-09-30 295 295 295 295 \n",
"2017-10-01 315 315 315 315 \n",
"2017-10-02 350 350 350 350 \n",
"2017-10-03 318 318 318 318 \n",
"2017-10-04 346 346 346 346 \n",
"2017-10-05 375 375 375 375 \n",
"2017-10-06 364 364 364 364 \n",
"2017-10-07 311 311 311 311 \n",
"2017-10-08 281 281 281 281 \n",
"2017-10-09 299 299 299 299 \n",
"2017-10-10 142 142 142 142 \n",
"\n",
" response_time Event_Date Event_Time Scene_Time \n",
"Scene_Date \n",
"2009-06-19 1 1 1 1 \n",
"2009-06-21 1 1 1 1 \n",
"2009-06-26 1 1 1 1 \n",
"2009-08-22 1 1 1 1 \n",
"2009-11-28 1 1 1 1 \n",
"2009-12-22 1 1 1 1 \n",
"2009-12-31 1 1 1 1 \n",
"2010-01-01 2 2 2 2 \n",
"2010-01-02 1 1 1 1 \n",
"2010-01-04 2 2 2 2 \n",
"2010-01-05 1 1 1 1 \n",
"2010-01-06 2 2 2 2 \n",
"2010-01-07 1 1 1 1 \n",
"2010-01-08 3 3 3 3 \n",
"2010-01-09 1 1 1 1 \n",
"2010-05-21 1 1 1 1 \n",
"2010-08-12 1 1 1 1 \n",
"2010-08-17 1 1 1 1 \n",
"2010-09-02 1 1 1 1 \n",
"2010-09-21 1 1 1 1 \n",
"2010-09-30 1 1 1 1 \n",
"2010-10-12 1 1 1 1 \n",
"2010-12-01 3 3 3 3 \n",
"2010-12-02 2 2 2 2 \n",
"2010-12-03 3 3 3 3 \n",
"2010-12-04 2 2 2 2 \n",
"2010-12-05 1 1 1 1 \n",
"2010-12-07 1 1 1 1 \n",
"2010-12-09 1 1 1 1 \n",
"2010-12-10 2 2 2 2 \n",
"... ... ... ... ... \n",
"2017-09-11 356 356 356 356 \n",
"2017-09-12 321 321 321 321 \n",
"2017-09-13 363 363 363 363 \n",
"2017-09-14 434 434 434 434 \n",
"2017-09-15 345 345 345 345 \n",
"2017-09-16 348 348 348 348 \n",
"2017-09-17 319 319 319 319 \n",
"2017-09-18 327 327 327 327 \n",
"2017-09-19 322 322 322 322 \n",
"2017-09-20 293 293 293 293 \n",
"2017-09-21 312 312 312 312 \n",
"2017-09-22 367 367 367 367 \n",
"2017-09-23 292 292 292 292 \n",
"2017-09-24 291 291 291 291 \n",
"2017-09-25 325 325 325 325 \n",
"2017-09-26 342 342 342 342 \n",
"2017-09-27 345 345 345 345 \n",
"2017-09-28 358 358 358 358 \n",
"2017-09-29 344 344 344 344 \n",
"2017-09-30 295 295 295 295 \n",
"2017-10-01 315 315 315 315 \n",
"2017-10-02 350 350 350 350 \n",
"2017-10-03 318 318 318 318 \n",
"2017-10-04 346 346 346 346 \n",
"2017-10-05 375 375 375 375 \n",
"2017-10-06 364 364 364 364 \n",
"2017-10-07 311 311 311 311 \n",
"2017-10-08 281 281 281 281 \n",
"2017-10-09 299 299 299 299 \n",
"2017-10-10 142 142 142 142 \n",
"\n",
"[1945 rows x 25 columns]>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_crime_group.reset_index"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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UaMd1x4xusOlGmfQ5GNcKrW0bunYObVf2o2W7HIcV7bbep0BbZZfj\nupQ5hzZNV7bpphBoAQcwKBSa1oVA2/fzCNEf0QrtuJ5DS5fj6hWt0LbR5bhrIarNQBtdxrx5/mWq\ninY5zvpOGfcKbcrhMYCuYFAoNI0uxxh3nENbHxfa2BVNn0NbpEIb6NrxSJWjHOd9TdH19fGPx8/H\ndlCovG3p2o8LdaNCCziALsfoGgItMFS0Qpu0jVOhHU9VjnLcZJfjK67wb+lyPJS0/vOcJ/uHPwxH\nSeYc2nRUaAEHMCgUmmbb5bjNNgBVqKJKWnRQqCqmrfK5VSHQFpP1vdpmt/WkQPvEE9Lvf+//7Xqg\nrbNCm3V/dBnT09KOO9ovuyvrvC0EWsABNhXacf8yQ7XKdplssg1A27o0KFSbyrxuV19z3bo+KNTS\npcO/u9ZjLGkd1N0D4pZbpIcfjn8sT6DNeozPzFCpQOt53tMl/U7Sv0ialHSmpGlJN0k6whgz5Xne\nsZJePXj8KGPMtaVaDIwhm3NogSq1dQ7twoXSjTdKe+zBzhrtsgmp4c9BlV2OXa3QcqBdTpe7HAfH\nIWlcr9DazitL9NqzYUUDbVZbsvbZfVf4HFrP81aW9BVJwW8zJ0o6xhizm6RZkvb3PG9HSXMl7Szp\nAEmnlmsuMJ4Y5RhNs+1yXPV29+IXSy9/uXTTTWzTaFebg0KVqSC58LlxoY1dUUWF9tFHpcsuK9cO\nm22yz4G2Kg8+aDedzQ9qXXlNXVBmUKjPSTpd0n2D/79Q0uWDv38qaS9Ju0r6uTFm2hhzl6TZnudt\nWGKZwFiiyzGa1lagvf12//auu9im4Y48FdpXvcpufi5iUKhy6qrQ7rmn/++3v81uQ94fWcLTdy3Q\ntjnKcZLgvNi4+Zap0No81meFAq3neW+V9A9jzCWhu2cZY4LVuFjSOpLWlvRYaJrgfgA50OUYTWur\ny3FaG4Am1TUolM1Bq+sV2iKBtgtdpdtWNtCGhbehYNCm227Lfl6ZbbJrxyNJ6+6vf5Uuvrj59sS5\n7DLpgx/0/05q7513SjfcEP9Y0n1dey/qVvQc2kMlTXuet5ek7SV9U9LTQ4+vJelRSYsGf0fvT+R5\n3jxJxxZsF9BLaRXaQFd2yOiHtiq0edoAdEWeQaHKVoq6jEGhqle0Qpt2zncRXetyPDkpXX65tNtu\n0iqrxE+Ttj5e85rin9uqnXCC9NnPJi9jiy3i72egqKFCFVpjzO7GmLnGmD0k3SDpzZJ+6nneHoNJ\n9pE0X9KVkvb2PG/C87zNJE0YYxZkzHueMWZW+J+kLYu0E+gLmwrtuH6JoR5tB1qbihdQhTKhssig\nUC94QfZ8qdA2qwvrLEkVFdqiy8s7vyYD7QknSHvtJX30o8nTJHU57qoquhwHxq1CW+Yc2qj3Sfq4\n53lXSVpF0nnGmN/JD7ZXSTpf0hEVLg8YGzbn0AJVsu1yXOfyXTgAwXgrEobf8hbpaU8r/vwuK3MO\nrauvuUp1j3Js871d5hzaJn9g/81v/Ntf/zp5mrztqKtCW+S5Nm2hQjtU+jq0gyptYG7M4/MkzSu7\nHGCcMcoxuqqpLsds32ha3u6I4ceXLJHmz5d23XXmQf7EhLTPPtI55yTP8/Wvl770pWra3SRXuxy3\nsex77pEOOUQ68URphx2Sp2u6Qps2v4cf9gfue9GLpGXLpGuukbbaajhNkz+wB69vIqU0lxXw06av\nks0lj6LLL/v+jVuxo8oKLYCaMCgUmta1Lsf/+Ec9ywHKSOpyfPDB0u67Sz/8Yfxzsj4/a65ZbTu7\npmuBtg0f+5hfXXzDG4b3dfk6tFNT0rbbSjvtJN17r7TzztLcudL//u9wmia7HNsE2vA6OOus7HnW\nVaFNu/Zs0jJsrldLhXaIQAs4gEGh0LS2A23d8way5Nn+otM++aR/mzQyaZ0/RLpQ6exaoG1j2cF+\nPRxc6hrlOG3+acsL+/OfpQce8P8+4wzpj3/0/w66/krdC7ThdlxzTfY862q37Y8LeQNt1n3jhEAL\nOIBBodA023NoCbQYZ0kV2kD4YDt4PPxZ6ts5tGF5q119eM11aLpCm+Sww4Z/h3+oeeqp4d95A+30\ntHTggdLZZ+dvT95AW1YT59CGp7PppsxnZohACziAQaHQtLYrtAwKhbbZbH+2n5PofXV+frpS6XQp\n0HbluyarQpsnpFZdoQ0L/7geDrR5j0fuvls691zpzW/O9zwpf5djG21XaMPyVmiXLRv9/7gdGxJo\nAQcwKBSa1nagrXveQKCK7SzpB5ikamzWwWbft30CbbGqXRODQuUNtEH3eqlYhbaoIPDZVmjL/kCV\n9NjkpPS610k/+EGx+SZNl/cc2re+VbrpJrvl9BGBFnAAXY7RNLocY9zZjHKc1eX44ov9EYuTzpOk\nQms3fZ/l+XEjT5fjrB9YygiHyDJdjsvI2+W4ih4Xca66SrrgAum1r02epsg5tLYjIwduvFF6/vOl\nRx7J97y+KH3ZHgD1o8sxmlakK2XVyx/XA1w0q4ptOWlbvfpq//aqq+IrtH3bxl0NtF15H6qs3DdV\noTVm+HcbgTbuh/7oNJK/bvP8IGCrqqA8NVVuUKjAvff6t+N2bEiFFnBAWoU20JUdMvqhiS5jbc4b\nCKQNzPTXv9rPI217XbFidFCoOrscu/C56Vqg7Yq6L9tT1Tm03/xm/P15Q1SZ97zpCm3SY2nLD9j8\nuBANsGnPietyHFi8OP45553nX/e46sHCuoJACzjApkLLwQDq1HSXYyq06ILbb69mPknnz9Ll2G76\nunEObTWaPB6pI9AWUdWPBOEfvYL/FxEE2mi73vAG6ZxzpJtvLjbfriPQAg6wOYcWqFLbg0IRaNE2\nm3Now4/leTzr89P3bb9soL31Vumxx6prT5vC+/C6K7Q2ymx7TQ4KVcdle4pUaKsMtF/+8vD/aefQ\nFqnQBpYuzW6Liwi0gAMY5RhNazvQ1j1voAq2n4PouXzjftmeyy9Pf26axx+XPE/aaqt8bUvT5e+a\nKiu0VYWvJHkDbdEqpNSdy/ZU1eX4iCNG/5+2bn7+8+THglGnk97r6OV9+oJACziALsdoWnR7YpRj\njJusz0B02iIVWttluyjtNXzuc/mmD1u0yL9dsCB/m7ro9tulr33N/7sLFdoy8vYYK9PGvlVozzpr\n9P9pgfbss7PnOzkpHXSQ9ItfjN4fvsxSnxBoAQfYDAoFVKntCi1djtGUKrpH2gTawLicQ1vXc+s4\nzabtc2jf/nb/tmygvfPO4d9UaKsNtEls1mmR4F5m3UjSffdJ3/mO9K//Ono/XY4BtCatQhvg4B9V\najvQ1j1vIEuV29+4nENbZhCeNgNtl+VZp694xfDvp54qNgBQnwJtlVXqMqMcF1mnv/lNsflmLeuJ\nJ/K3xQUEWsABQYU26Ytz3HbwqJ9toG2zDUBX5OlybHPZnrJtcdG4VWjj5K3QfuADw+6nUaedJm2z\njXTppenzr1LeQFtnl+Ppaekvf8k3zy5VaNPsv3/yY1k/EqQNNuUyAi3ggMnJ9Oqs1J0dMvopaadN\nl2P01fR09sGqbU+FvF2OXd32Xa3QtrG+bV9H0jqdnvbPRX7zm2c+FnbVVfnaVWZd2Hwewo/VWaE9\n6yz760gHqjiHtuwI3jbmzEmeb9w6vfXW9Mf7gEALOGDFivRAO2uWuwdAcBNdjjFu0g5Uiw4K1bdz\naMOaOH/RZUXO3U4b9MmmCtjUObS2/va34svKCrRpIwEnKfLao89pItDmDd6eN/y7jcHCmkCgBRww\nOZk+IBRdjlG1rp5Dy7aOpuQ58Ms7KNQ4nENb13PrOCDvyvrO0+U4WmlLWi9NvjabLsfBY3feKb3x\njXbzXbJEuvvu0fuC158UaIsMolmkQhu9P+59qHqbLfOeUqEF0JqsCq3UnR0y+qnpy/YkBQQCLZpi\ns23nqbIldTuuahlFp69LXe3uyuurQ55Aa1uhbXL7ydPl+NBD7ee7zTbSZpuNDmiUVaHNOmZKa1se\nNu9DXRXaIvMl0AJojU2Fts87eTSv7QptUqBlO0fVbCsvVXU5znsOrUvbfBPn0Pa5m3acshXasC5d\ntueyy+znG1yOaPHi4X1ZgfbMM+3nbyPpNdkE2qortOedJx17bLHn0uUYQGtszqEFmtRWl+O+7ozR\nPXkGJsuaLrzd2nQ5zjPvstNXqYll9+U7wLYHSpXn0MY5+WTplFOKPTeq7sAc92NQlcc/VZxD20SF\nVpI+8QkqtGEFCvIAmsYox2hbV7oc9+VgFt0X3dbqGBQqbfq05bqACm31kgJtuHIZtWzZ8O+47e6o\no/zbd71r5jLysqnQTk5K991XbP5tBlrbCm3cdMFI1FUrEk77ug+lQgs4gC7HaFrbXY6T5t3XnTHa\nU6bLcfixPINC2cwzzzRlpq+Sq4NCdUVWhXbJEumhh/y/w2Fm7bWT18unPpWvDVUE2rvv9ucTdBUO\n+4//kDbfvNj849ZP2xVam0r51VcXa0+W8I8VtvpaoSXQAg6gyzFc8rWvSV/8Yvn5EGjRhKrOoc1a\nRlyoreMc2q78uEmFNp8VK7ID7d57Sxtt5H8PLlo0Op3Nd+OyZdKDD5ZrZ5og0B5zjHTIIdIWW8yc\n5txzi88//Bqb3g4eeCD+/qq6fmd55jNn3vfUU/nn09d9KIEWcEBWhVbqxg4Z/VGmQvv2t0vveU/5\n5RNo0QTbroRZz81Toa3zHNo2uTooVBcsXx5/f9L34JZbjt6XFLjCDjxQ2njj0dGCbZZnKxzIv/Wt\n4vNJEhdom/hB/9FHpa9+Nf4xm3Noq3DGGdIjj4zeV+THCSq0AFpjU6Ht604e3dD0ObRJ8ybQomq2\nlzspcn5deBlNnUPr6r6gzS7Hbayz6DI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uckJRQei27X59773SXXdJ++8/vC94j2xHRu4T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"text/plain": [
"<matplotlib.figure.Figure at 0x11b356518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(by_crime_group.index ,by_crime_group[\"Event Clearance Group\"],color = 'blue')\n",
"plt.title(\"Count of crime across years\")\n",
"fig_size = plt.rcParams[\"figure.figsize\"]\n",
"fig_size = [14,10]\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = fig_size\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What happened between 2013 ans 2015? let's look at the data !"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CAD CDW ID</th>\n",
" <th>CAD Event Number</th>\n",
" <th>General Offense Number</th>\n",
" <th>Event Clearance Code</th>\n",
" <th>Event Clearance Description</th>\n",
" <th>Event Clearance SubGroup</th>\n",
" <th>Event Clearance Group</th>\n",
" <th>Event Clearance Date</th>\n",
" <th>Hundred Block Location</th>\n",
" <th>District/Sector</th>\n",
" <th>...</th>\n",
" <th>Initial Type Description</th>\n",
" <th>Initial Type Subgroup</th>\n",
" <th>Initial Type Group</th>\n",
" <th>At Scene Time</th>\n",
" <th>Event_timing</th>\n",
" <th>Scene_timing</th>\n",
" <th>response_time</th>\n",
" <th>Event_Date</th>\n",
" <th>Event_Time</th>\n",
" <th>Scene_Time</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Scene_Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-08-09</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-12</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-02-15</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-13</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-14</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-15</th>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-16</th>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-17</th>\n",
" <td>255</td>\n",
" <td>255</td>\n",
" <td>255</td>\n",
" <td>239</td>\n",
" <td>239</td>\n",
" <td>239</td>\n",
" <td>239</td>\n",
" <td>238</td>\n",
" <td>255</td>\n",
" <td>255</td>\n",
" <td>...</td>\n",
" <td>255</td>\n",
" <td>255</td>\n",
" <td>255</td>\n",
" <td>255</td>\n",
" <td>238</td>\n",
" <td>255</td>\n",
" <td>238</td>\n",
" <td>238</td>\n",
" <td>238</td>\n",
" <td>255</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" CAD CDW ID CAD Event Number General Offense Number \\\n",
"Scene_Date \n",
"2013-08-09 1 1 1 \n",
"2014-01-12 1 1 1 \n",
"2014-02-15 2 2 2 \n",
"2014-07-13 1 1 1 \n",
"2014-07-14 5 5 5 \n",
"2014-07-15 4 4 4 \n",
"2014-07-16 12 12 12 \n",
"2014-07-17 255 255 255 \n",
"\n",
" Event Clearance Code Event Clearance Description \\\n",
"Scene_Date \n",
"2013-08-09 1 1 \n",
"2014-01-12 1 1 \n",
"2014-02-15 2 2 \n",
"2014-07-13 1 1 \n",
"2014-07-14 5 5 \n",
"2014-07-15 4 4 \n",
"2014-07-16 12 12 \n",
"2014-07-17 239 239 \n",
"\n",
" Event Clearance SubGroup Event Clearance Group \\\n",
"Scene_Date \n",
"2013-08-09 1 1 \n",
"2014-01-12 1 1 \n",
"2014-02-15 2 2 \n",
"2014-07-13 1 1 \n",
"2014-07-14 5 5 \n",
"2014-07-15 4 4 \n",
"2014-07-16 12 12 \n",
"2014-07-17 239 239 \n",
"\n",
" Event Clearance Date Hundred Block Location District/Sector \\\n",
"Scene_Date \n",
"2013-08-09 1 1 0 \n",
"2014-01-12 1 1 1 \n",
"2014-02-15 2 2 2 \n",
"2014-07-13 1 1 1 \n",
"2014-07-14 5 5 5 \n",
"2014-07-15 4 4 4 \n",
"2014-07-16 12 12 12 \n",
"2014-07-17 238 255 255 \n",
"\n",
" ... Initial Type Description Initial Type Subgroup \\\n",
"Scene_Date ... \n",
"2013-08-09 ... 1 1 \n",
"2014-01-12 ... 1 1 \n",
"2014-02-15 ... 2 2 \n",
"2014-07-13 ... 1 1 \n",
"2014-07-14 ... 5 5 \n",
"2014-07-15 ... 4 4 \n",
"2014-07-16 ... 12 12 \n",
"2014-07-17 ... 255 255 \n",
"\n",
" Initial Type Group At Scene Time Event_timing Scene_timing \\\n",
"Scene_Date \n",
"2013-08-09 1 1 1 1 \n",
"2014-01-12 1 1 1 1 \n",
"2014-02-15 2 2 2 2 \n",
"2014-07-13 1 1 1 1 \n",
"2014-07-14 5 5 5 5 \n",
"2014-07-15 4 4 4 4 \n",
"2014-07-16 12 12 12 12 \n",
"2014-07-17 255 255 238 255 \n",
"\n",
" response_time Event_Date Event_Time Scene_Time \n",
"Scene_Date \n",
"2013-08-09 1 1 1 1 \n",
"2014-01-12 1 1 1 1 \n",
"2014-02-15 2 2 2 2 \n",
"2014-07-13 1 1 1 1 \n",
"2014-07-14 5 5 5 5 \n",
"2014-07-15 4 4 4 4 \n",
"2014-07-16 12 12 12 12 \n",
"2014-07-17 238 238 238 255 \n",
"\n",
"[8 rows x 25 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Looking at the data if that's true...\n",
"by_crime_group #set index first\n",
"by_crime_group[(by_crime_group.index.astype(str) > '2013-02-09') & (by_crime_group.index.astype(str) <= '2014-07-17')]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What are the top most crime description??**"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SUSPICIOUS PERSON 162841\n",
"DISTURBANCE, OTHER 140965\n",
"TRAFFIC (MOVING) VIOLATION 106723\n",
"PARKING VIOLATION (EXCEPT ABANDONED VEHICLES) 104212\n",
"LIQUOR VIOLATION - INTOXICATED PERSON 59213\n",
"SUSPICIOUS VEHICLE 47912\n",
"THEFT - CAR PROWL 44446\n",
"MOTOR VEHICLE COLLISION 40687\n",
"MISCHIEF, NUISANCE COMPLAINTS 40585\n",
"THEFT - MISCELLANEOUS 40242\n",
"Name: Event Clearance Description, dtype: int64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"crime_data[\"Event Clearance Description\"].value_counts()[0:10] #Top Crime types"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
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Iiq9/3ZJAIiIiIiIiIiIq3piwIiIiIiIiIiKiYoUJKyIi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\nhAmltplMJk2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"text/plain": [
"<matplotlib.figure.Figure at 0x10a91cc18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plotdat(data,cat):\n",
" l=data.groupby(cat).size().sort_values(ascending = True)\n",
" \n",
" fig=plt.figure(figsize=(20,10))\n",
" plt.yticks(fontsize=8)\n",
" l.plot(kind='bar',fontsize=12,color='k')\n",
" plt.xlabel('Crime types')\n",
" plt.ylabel('Number of reports',fontsize=16)\n",
" plt.savefig(\"Group_Frequency.png\")\n",
" \n",
"plotdat(crime_data,'Event Clearance Group')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Our first inclination was to distill the crimes into smaller, more meaningful categories than was provided in the dataset. \n",
"Dealing with all the crime granularities is cumbersome and hard to get inference.**\n",
"These categories are:\n",
"+ <font color = blue>**Disturbance/Traffic**</font>\n",
"+ <font color = green>**Property**</font>\n",
"+ <font color = red>**Miscellaneous**</font>\n",
"+ <font color = purple>**Health/Drug**</font>\n",
"+ <font color = blue+yellow>**Sexual/Harassment**</font>\n",
"+ <font color = green+blue>**Emergency/Violent**</font>\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"#First lets map with only Initials..Who wants to type all the word?\n",
"crime_type_map = {\"ACCIDENT INVESTIGATION\": \"D\",\n",
"\"ANIMAL COMPLAINTS\": \"D\",\n",
"\"ARREST\": \"M\",\n",
"\"ASSAULTS\": \"E\",\n",
"\"AUTO THEFTS\": \"P\",\n",
"\"BEHAVIORAL HEALTH\": \"H\",\n",
"\"BIKE\": \"P\",\n",
"\"BURGLARY\": \"P\",\n",
"\"CAR PROWL\": \"S\",\n",
"\"DISTURBANCES\": \"D\",\n",
"\"DRIVE BY (NO INJURY)\": \"D\",\n",
"\"FAILURE TO REGISTER (SEX OFFENDER)\": \"M\",\n",
"\"FALSE ALACAD\": \"M\",\n",
"\"FALSE ALARMS\": \"M\",\n",
"\"FRAUD CALLS\": \"D\",\n",
"\"HARBOR CALLS\": \"E\",\n",
"\"HAZARDS\": \"M\",\n",
"\"HOMICIDE\": \"E\",\n",
"\"LEWD CONDUCT\": \"S\",\n",
"\"LIQUOR VIOLATIONS\": \"H\",\n",
"\"MENTAL HEALTH\": \"H\",\n",
"\"MISCELLANEOUS MISDEMEANORS\": \"M\",\n",
"\"MOTOR VEHICLE COLLISION INVESTIGATION\": \"D\",\n",
"\"NARCOTICS COMPLAINTS\": \"H\",\n",
"\"NUISANCE, MISCHIEF\": \"M\",\n",
"\"NUISANCE, MISCHIEF \": \"M\",\n",
"\"OTHER PROPERTY\": \"P\",\n",
"\"OTHER VICE\": \"M\",\n",
"\"PERSON DOWN/INJURY\": \"E\",\n",
"\"PERSONS - LOST, FOUND, MISSING\": \"E\",\n",
"\"PROPERTY - MISSING, FOUND\": \"E\",\n",
"\"PROPERTY DAMAGE\": \"P\",\n",
"\"PROSTITUTION\": \"S\",\n",
"\"PROWLER\": \"S\",\n",
"\"PUBLIC GATHERINGS\": \"D\",\n",
"\"RECKLESS BURNING\": \"P\",\n",
"\"ROBBERY\": \"P\",\n",
"\"SHOPLIFTING\": \"P\",\n",
"\"SUSPICIOUS CIRCUMSTANCES\": \"D\",\n",
"\"THREATS, HARASSMENT\": \"S\",\n",
"\"TRAFFIC RELATED CALLS\": \"D\",\n",
"\"TRESPASS\": \"P\",\n",
"\"VICE CALLS\": \"M\",\n",
"\"WEAPONS CALLS\": \"E\"}"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"crime_data[\"crime_group\"] = crime_data[\"Event Clearance Group\"].map(crime_type_map)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Disturbance/ Traffic 777939\n",
"Property 233790\n",
"Miscellaneous 147620\n",
"Health/Drug 137000\n",
"Sexual/Harassment 74139\n",
"Emergency/ Violent 62891\n",
"Name: crime_group, dtype: int64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Now here, should type the full word..\n",
"crime_data[\"Event Clearance Group\"].isnull\n",
"\n",
"\n",
"\n",
"group_mapping = {\"D\":\"Disturbance/ Traffic\",\n",
"\"E\":\"Emergency/ Violent\",\n",
"\"H\":\"Health/Drug\",\n",
"\"P\":\"Property\",\n",
"\"M\": \"Miscellaneous\",\n",
"\"S\":\"Sexual/Harassment\"}\n",
"\n",
"crime_data[\"crime_group\"] = crime_data[\"crime_group\"].map(group_mapping)\n",
"#Count of crime groups\n",
"crime_data[\"crime_group\"].value_counts()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Let's see the Graphics....*"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda/lib/python3.6/site-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
" stat_data = remove_na(group_data)\n"
]
},
{
"data": {
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cKJvNpuHDh2vYsGGyWq32udn5+cc//qEFCxZIkkqVKiWTyaQGDRooMTFRkhQX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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a94b4a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.countplot(x = \"crime_group\",data = crime_data)\n",
"g.figure.set_size_inches(12,8)\n",
"sns.despine()\n",
"g.set(xlabel='Crime types grouped', ylabel='Total count of crime groups')\n",
"g.figure.savefig('crime_group.png')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda/lib/python3.6/site-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
" stat_data = remove_na(group_data)\n"
]
},
{
"data": {
"image/png": 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XQ4cOVYMGDeR0OvXtt9/q9ddf11133eXt/gBIenid3bJa/+/xVMtqAbgxeRQepk6dqrlz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\nEcIDAAAw8v8BSI9NNt0QDv4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e413668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#What are the top five disricts?\n",
"crime_data[\"District/Sector\"].value_counts()\n",
"sns.countplot(x=crime_data[\"District/Sector\"],data = crime_data)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda/lib/python3.6/site-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
" stat_data = remove_na(group_data)\n"
]
},
{
"data": {
"image/png": 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m6p///KcWL16s9957Tw6HQ5LUunVrlZeXq6Kios42CrXtHo+nXt/zycjIUGZmpn1PBgAA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\nAAAwHoEGAAAYj0ADAACMR6ABAADG+1/K12xu0FkOQAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109fd8208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Which District have high number of Violent Crimes\n",
"sns.countplot(x = crime_data[crime_data[\"crime_group\"]==\"Emergency/ Violent\"][\"District/Sector\"],data = crime_data)\n",
"sns.despine()\n",
"\n",
"#sns.set(style='ticks')\n",
"\n",
"\n",
"plt.tight_layout()\n",
"\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### Analyzing crime type by Month"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"#Getting Months\n",
"crime_data[\"month\"] =[list_month.month for list_month in pd.to_datetime(crime_data[\"Event_Date\"])]\n",
"\n",
"map_month = {1.0:'Jan',2.0:'Feb',3.0:'March',4.0:'April',5.0:'May',6.0:'June',7.0:'July',8.0:'Aug',9.0:'Sept',10.0:'Oct',11.0:'Nov',12.0:'Dec'}\n",
"\n",
"crime_data[\"month\"] = crime_data[\"month\"].map(map_month)\n"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(crime_data[\"Scene_Time\"])"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 NaN\n",
"1 NaN\n",
"2 NaN\n",
"3 NaN\n",
"4 NaN\n",
"5 NaN\n",
"6 NaN\n",
"7 NaN\n",
"8 NaN\n",
"9 NaN\n",
"10 NaN\n",
"11 NaN\n",
"12 NaN\n",
"13 NaN\n",
"14 NaN\n",
"15 NaN\n",
"16 NaN\n",
"17 NaN\n",
"18 NaN\n",
"19 NaN\n",
"20 NaN\n",
"21 NaN\n",
"22 NaN\n",
"23 NaN\n",
"24 NaN\n",
"25 NaN\n",
"26 NaN\n",
"27 NaN\n",
"28 NaN\n",
"29 NaN\n",
" ... \n",
"1444234 18.0\n",
"1444235 18.0\n",
"1444236 17.0\n",
"1444237 NaN\n",
"1444238 NaN\n",
"1444239 NaN\n",
"1444240 19.0\n",
"1444241 NaN\n",
"1444242 NaN\n",
"1444243 NaN\n",
"1444244 NaN\n",
"1444245 19.0\n",
"1444246 20.0\n",
"1444247 NaN\n",
"1444248 16.0\n",
"1444249 NaN\n",
"1444250 NaN\n",
"1444251 NaN\n",
"1444252 NaN\n",
"1444253 NaN\n",
"1444254 16.0\n",
"1444255 16.0\n",
"1444256 16.0\n",
"1444257 NaN\n",
"1444258 22.0\n",
"1444259 NaN\n",
"1444260 1.0\n",
"1444261 NaN\n",
"1444262 0.0\n",
"1444263 23.0\n",
"Name: Hour of Day, Length: 1444264, dtype: float64"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Getting hours\n",
"crime_data[\"Scene_Time\"] = crime_data[\"Scene_Time\"].astype(str)\n",
"crime_data[\"Hour of Day\"] = pd.to_datetime(crime_data[\"Scene_Time\"]).apply(lambda time: time.hour)\n",
"crime_data[\"Hour of Day\"]\n"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"#Getting Day of Week\n",
"crime_data[\"Day of week\"] = pd.to_datetime(crime_data[\"Scene_Date\"]).apply(lambda time: time.dayofweek)\n"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"#Mapping to meaningful words\n",
"day_map = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}\n",
"\n",
"\n",
"\n",
"crime_data[\"Day of week\"] = crime_data[\"Day of week\"].map(day_map)\n",
"\n",
"\n",
"#by_dayHour = crime_data.groupby(by=['Day of week','Hour of Day']).count()['Event Clearance Group']"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 NaN\n",
"1 NaN\n",
"2 NaN\n",
"3 NaN\n",
"4 NaN\n",
"5 NaN\n",
"6 NaN\n",
"7 NaN\n",
"8 NaN\n",
"9 NaN\n",
"10 NaN\n",
"11 NaN\n",
"12 NaN\n",
"13 NaN\n",
"14 NaN\n",
"15 NaN\n",
"16 NaN\n",
"17 NaN\n",
"18 NaN\n",
"19 NaN\n",
"20 NaN\n",
"21 NaN\n",
"22 NaN\n",
"23 NaN\n",
"24 NaN\n",
"25 NaN\n",
"26 NaN\n",
"27 NaN\n",
"28 NaN\n",
"29 NaN\n",
" ... \n",
"1444234 6PM\n",
"1444235 6PM\n",
"1444236 5PM\n",
"1444237 NaN\n",
"1444238 NaN\n",
"1444239 NaN\n",
"1444240 7PM\n",
"1444241 NaN\n",
"1444242 NaN\n",
"1444243 NaN\n",
"1444244 NaN\n",
"1444245 7PM\n",
"1444246 8PM\n",
"1444247 NaN\n",
"1444248 4PM\n",
"1444249 NaN\n",
"1444250 NaN\n",
"1444251 NaN\n",
"1444252 NaN\n",
"1444253 NaN\n",
"1444254 4PM\n",
"1444255 4PM\n",
"1444256 4PM\n",
"1444257 NaN\n",
"1444258 10PM\n",
"1444259 NaN\n",
"1444260 1AM\n",
"1444261 NaN\n",
"1444262 12AM\n",
"1444263 11PM\n",
"Name: Hour of Day, Length: 1444264, dtype: object"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Time_mapped = {0 :\"12AM\",1:\"1AM\",2:\"2AM\",3:\"3AM\",4:\"4AM\",5:\"5AM\",6:\"6AM\",7:\"7AM\",8:\"8AM\",9:\"9AM\",10:\"10AM\",11:\"11AM\",12:\"12PM\",13:\"1PM\",14:\"2PM\",15:\"3PM\",16:\"4PM\",17:\"5PM\",18:\"6PM\",19:\"7PM\",20:\"8PM\",21:\"9PM\",22:\"10PM\",23:\"11PM\"}\n",
"crime_data[\"Hour of Day\"] = crime_data[\"Hour of Day\"].map(Time_mapped)\n",
"crime_data[\"Hour of Day\"]\n"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Hour of Day</th>\n",
" <th>0.0</th>\n",
" <th>1.0</th>\n",
" <th>2.0</th>\n",
" <th>3.0</th>\n",
" <th>4.0</th>\n",
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" <th>19.0</th>\n",
" <th>20.0</th>\n",
" <th>21.0</th>\n",
" <th>22.0</th>\n",
" <th>23.0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Day of week</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Fri</th>\n",
" <td>2027</td>\n",
" <td>1753</td>\n",
" <td>1369</td>\n",
" <td>677</td>\n",
" <td>1096</td>\n",
" <td>845</td>\n",
" <td>1056</td>\n",
" <td>1840</td>\n",
" <td>1970</td>\n",
" <td>2398</td>\n",
" <td>...</td>\n",
" <td>3088</td>\n",
" <td>3259</td>\n",
" <td>3347</td>\n",
" <td>3145</td>\n",
" <td>2621</td>\n",
" <td>2184</td>\n",
" <td>3912</td>\n",
" <td>3195</td>\n",
" <td>2920</td>\n",
" <td>2667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mon</th>\n",
" <td>1950</td>\n",
" <td>1704</td>\n",
" <td>1346</td>\n",
" <td>823</td>\n",
" <td>1389</td>\n",
" <td>1058</td>\n",
" <td>1202</td>\n",
" <td>2086</td>\n",
" <td>2475</td>\n",
" <td>2658</td>\n",
" <td>...</td>\n",
" <td>3462</td>\n",
" <td>3549</td>\n",
" <td>3426</td>\n",
" <td>3285</td>\n",
" <td>2685</td>\n",
" <td>2225</td>\n",
" <td>3905</td>\n",
" <td>2908</td>\n",
" <td>2373</td>\n",
" <td>1964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sat</th>\n",
" <td>2520</td>\n",
" <td>2636</td>\n",
" <td>2219</td>\n",
" <td>1036</td>\n",
" <td>1500</td>\n",
" <td>1028</td>\n",
" <td>925</td>\n",
" <td>1349</td>\n",
" <td>1528</td>\n",
" <td>1838</td>\n",
" <td>...</td>\n",
" <td>2775</td>\n",
" <td>2880</td>\n",
" <td>2708</td>\n",
" <td>2778</td>\n",
" <td>2460</td>\n",
" <td>1980</td>\n",
" <td>3794</td>\n",
" <td>3839</td>\n",
" <td>3858</td>\n",
" <td>3313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sun</th>\n",
" <td>2968</td>\n",
" <td>3144</td>\n",
" <td>2499</td>\n",
" <td>1282</td>\n",
" <td>1781</td>\n",
" <td>1093</td>\n",
" <td>1026</td>\n",
" <td>1372</td>\n",
" <td>1529</td>\n",
" <td>1797</td>\n",
" <td>...</td>\n",
" <td>2827</td>\n",
" <td>2820</td>\n",
" <td>2787</td>\n",
" <td>2760</td>\n",
" <td>2366</td>\n",
" <td>2064</td>\n",
" <td>3699</td>\n",
" <td>3363</td>\n",
" <td>3009</td>\n",
" <td>2372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thu</th>\n",
" <td>1728</td>\n",
" <td>1550</td>\n",
" <td>1295</td>\n",
" <td>645</td>\n",
" <td>1124</td>\n",
" <td>890</td>\n",
" <td>1140</td>\n",
" <td>1915</td>\n",
" <td>2132</td>\n",
" <td>2540</td>\n",
" <td>...</td>\n",
" <td>3131</td>\n",
" <td>3500</td>\n",
" <td>3314</td>\n",
" <td>3188</td>\n",
" <td>2714</td>\n",
" <td>2079</td>\n",
" <td>3895</td>\n",
" <td>3558</td>\n",
" <td>3179</td>\n",
" <td>2386</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tue</th>\n",
" <td>1566</td>\n",
" <td>1449</td>\n",
" <td>1165</td>\n",
" <td>677</td>\n",
" <td>1142</td>\n",
" <td>973</td>\n",
" <td>1164</td>\n",
" <td>1833</td>\n",
" <td>2282</td>\n",
" <td>2515</td>\n",
" <td>...</td>\n",
" <td>3256</td>\n",
" <td>3539</td>\n",
" <td>3441</td>\n",
" <td>3362</td>\n",
" <td>2747</td>\n",
" <td>2124</td>\n",
" <td>3972</td>\n",
" <td>3581</td>\n",
" <td>2940</td>\n",
" <td>2250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wed</th>\n",
" <td>1770</td>\n",
" <td>1557</td>\n",
" <td>1121</td>\n",
" <td>655</td>\n",
" <td>1094</td>\n",
" <td>895</td>\n",
" <td>1128</td>\n",
" <td>1940</td>\n",
" <td>2179</td>\n",
" <td>2475</td>\n",
" <td>...</td>\n",
" <td>3258</td>\n",
" <td>3359</td>\n",
" <td>3440</td>\n",
" <td>3359</td>\n",
" <td>2750</td>\n",
" <td>2046</td>\n",
" <td>3696</td>\n",
" <td>2916</td>\n",
" <td>2413</td>\n",
" <td>2047</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>7 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
"Hour of Day 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 ... \\\n",
"Day of week ... \n",
"Fri 2027 1753 1369 677 1096 845 1056 1840 1970 2398 ... \n",
"Mon 1950 1704 1346 823 1389 1058 1202 2086 2475 2658 ... \n",
"Sat 2520 2636 2219 1036 1500 1028 925 1349 1528 1838 ... \n",
"Sun 2968 3144 2499 1282 1781 1093 1026 1372 1529 1797 ... \n",
"Thu 1728 1550 1295 645 1124 890 1140 1915 2132 2540 ... \n",
"Tue 1566 1449 1165 677 1142 973 1164 1833 2282 2515 ... \n",
"Wed 1770 1557 1121 655 1094 895 1128 1940 2179 2475 ... \n",
"\n",
"Hour of Day 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 \n",
"Day of week \n",
"Fri 3088 3259 3347 3145 2621 2184 3912 3195 2920 2667 \n",
"Mon 3462 3549 3426 3285 2685 2225 3905 2908 2373 1964 \n",
"Sat 2775 2880 2708 2778 2460 1980 3794 3839 3858 3313 \n",
"Sun 2827 2820 2787 2760 2366 2064 3699 3363 3009 2372 \n",
"Thu 3131 3500 3314 3188 2714 2079 3895 3558 3179 2386 \n",
"Tue 3256 3539 3441 3362 2747 2124 3972 3581 2940 2250 \n",
"Wed 3258 3359 3440 3359 2750 2046 3696 2916 2413 2047 \n",
"\n",
"[7 rows x 24 columns]"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_dayHour = crime_data.groupby(by=['Day of week','Hour of Day']).count()['Event Clearance Group']\n",
"by_dayHour = by_dayHour.unstack()\n",
"by_dayHour\n"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Hour of Day</th>\n",
" <th>0.0</th>\n",
" <th>1.0</th>\n",
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" <th>3.0</th>\n",
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" <th>23.0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Day of week</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Fri</th>\n",
" <td>2027</td>\n",
" <td>1753</td>\n",
" <td>1369</td>\n",
" <td>677</td>\n",
" <td>1096</td>\n",
" <td>845</td>\n",
" <td>1056</td>\n",
" <td>1840</td>\n",
" <td>1970</td>\n",
" <td>2398</td>\n",
" <td>...</td>\n",
" <td>3088</td>\n",
" <td>3259</td>\n",
" <td>3347</td>\n",
" <td>3145</td>\n",
" <td>2621</td>\n",
" <td>2184</td>\n",
" <td>3912</td>\n",
" <td>3195</td>\n",
" <td>2920</td>\n",
" <td>2667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mon</th>\n",
" <td>1950</td>\n",
" <td>1704</td>\n",
" <td>1346</td>\n",
" <td>823</td>\n",
" <td>1389</td>\n",
" <td>1058</td>\n",
" <td>1202</td>\n",
" <td>2086</td>\n",
" <td>2475</td>\n",
" <td>2658</td>\n",
" <td>...</td>\n",
" <td>3462</td>\n",
" <td>3549</td>\n",
" <td>3426</td>\n",
" <td>3285</td>\n",
" <td>2685</td>\n",
" <td>2225</td>\n",
" <td>3905</td>\n",
" <td>2908</td>\n",
" <td>2373</td>\n",
" <td>1964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sat</th>\n",
" <td>2520</td>\n",
" <td>2636</td>\n",
" <td>2219</td>\n",
" <td>1036</td>\n",
" <td>1500</td>\n",
" <td>1028</td>\n",
" <td>925</td>\n",
" <td>1349</td>\n",
" <td>1528</td>\n",
" <td>1838</td>\n",
" <td>...</td>\n",
" <td>2775</td>\n",
" <td>2880</td>\n",
" <td>2708</td>\n",
" <td>2778</td>\n",
" <td>2460</td>\n",
" <td>1980</td>\n",
" <td>3794</td>\n",
" <td>3839</td>\n",
" <td>3858</td>\n",
" <td>3313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sun</th>\n",
" <td>2968</td>\n",
" <td>3144</td>\n",
" <td>2499</td>\n",
" <td>1282</td>\n",
" <td>1781</td>\n",
" <td>1093</td>\n",
" <td>1026</td>\n",
" <td>1372</td>\n",
" <td>1529</td>\n",
" <td>1797</td>\n",
" <td>...</td>\n",
" <td>2827</td>\n",
" <td>2820</td>\n",
" <td>2787</td>\n",
" <td>2760</td>\n",
" <td>2366</td>\n",
" <td>2064</td>\n",
" <td>3699</td>\n",
" <td>3363</td>\n",
" <td>3009</td>\n",
" <td>2372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thu</th>\n",
" <td>1728</td>\n",
" <td>1550</td>\n",
" <td>1295</td>\n",
" <td>645</td>\n",
" <td>1124</td>\n",
" <td>890</td>\n",
" <td>1140</td>\n",
" <td>1915</td>\n",
" <td>2132</td>\n",
" <td>2540</td>\n",
" <td>...</td>\n",
" <td>3131</td>\n",
" <td>3500</td>\n",
" <td>3314</td>\n",
" <td>3188</td>\n",
" <td>2714</td>\n",
" <td>2079</td>\n",
" <td>3895</td>\n",
" <td>3558</td>\n",
" <td>3179</td>\n",
" <td>2386</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tue</th>\n",
" <td>1566</td>\n",
" <td>1449</td>\n",
" <td>1165</td>\n",
" <td>677</td>\n",
" <td>1142</td>\n",
" <td>973</td>\n",
" <td>1164</td>\n",
" <td>1833</td>\n",
" <td>2282</td>\n",
" <td>2515</td>\n",
" <td>...</td>\n",
" <td>3256</td>\n",
" <td>3539</td>\n",
" <td>3441</td>\n",
" <td>3362</td>\n",
" <td>2747</td>\n",
" <td>2124</td>\n",
" <td>3972</td>\n",
" <td>3581</td>\n",
" <td>2940</td>\n",
" <td>2250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wed</th>\n",
" <td>1770</td>\n",
" <td>1557</td>\n",
" <td>1121</td>\n",
" <td>655</td>\n",
" <td>1094</td>\n",
" <td>895</td>\n",
" <td>1128</td>\n",
" <td>1940</td>\n",
" <td>2179</td>\n",
" <td>2475</td>\n",
" <td>...</td>\n",
" <td>3258</td>\n",
" <td>3359</td>\n",
" <td>3440</td>\n",
" <td>3359</td>\n",
" <td>2750</td>\n",
" <td>2046</td>\n",
" <td>3696</td>\n",
" <td>2916</td>\n",
" <td>2413</td>\n",
" <td>2047</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>7 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
"Hour of Day 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 ... \\\n",
"Day of week ... \n",
"Fri 2027 1753 1369 677 1096 845 1056 1840 1970 2398 ... \n",
"Mon 1950 1704 1346 823 1389 1058 1202 2086 2475 2658 ... \n",
"Sat 2520 2636 2219 1036 1500 1028 925 1349 1528 1838 ... \n",
"Sun 2968 3144 2499 1282 1781 1093 1026 1372 1529 1797 ... \n",
"Thu 1728 1550 1295 645 1124 890 1140 1915 2132 2540 ... \n",
"Tue 1566 1449 1165 677 1142 973 1164 1833 2282 2515 ... \n",
"Wed 1770 1557 1121 655 1094 895 1128 1940 2179 2475 ... \n",
"\n",
"Hour of Day 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 \n",
"Day of week \n",
"Fri 3088 3259 3347 3145 2621 2184 3912 3195 2920 2667 \n",
"Mon 3462 3549 3426 3285 2685 2225 3905 2908 2373 1964 \n",
"Sat 2775 2880 2708 2778 2460 1980 3794 3839 3858 3313 \n",
"Sun 2827 2820 2787 2760 2366 2064 3699 3363 3009 2372 \n",
"Thu 3131 3500 3314 3188 2714 2079 3895 3558 3179 2386 \n",
"Tue 3256 3539 3441 3362 2747 2124 3972 3581 2940 2250 \n",
"Wed 3258 3359 3440 3359 2750 2046 3696 2916 2413 2047 \n",
"\n",
"[7 rows x 24 columns]"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_dayHour.reindex()"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
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MsVUJ3IxZzRcMh4b6843Vr0Wdyxcu7Evcxbff1Ze4QzOa/yOw7P7+fMYL71zUl7h/u+W+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\nJEnSiPh/PQe3y4jy7b0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b968908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16,8))\n",
"crime_map = sns.heatmap(by_dayHour,cbar_kws={'label': 'Number of 911 Calls Grouped'}) #Use Scene Time \n",
"crime_map\n",
"\n",
"crime_map.set_title('911 Call frequency Heatmap for Hours of Day')\n",
"crime_map.figure.savefig(\"Crime_Group_frequency_latest.png\")\n",
"\n",
"plt.show()\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Clearly we can see PMs are more prone to have more Calls."
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"by_month = crime_data.groupby(by=['Day of week','month']).count()['Event Clearance Group']\n",
"by_month.reset_index()\n",
"by_month = by_month.unstack()"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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MWiVJktTz+mp6gCRJ0mTkklftmbRKkiQ1zDmt7Zm0SpIkNczVA9pzTqskSZJ6npVWSZKk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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c411128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"by_month.set_index([\"Jan\",\"Feb\",\"March\",\"April\",\"May\",\"June\",\"July\",\"Aug\",\"Sept\",\"Oct\",\"Nov\",\"Dec\"])\n",
"by_month.reindex([\"Jan\",\"Feb\",\"March\",\"April\",\"May\",\"June\",\"July\",\"Aug\",\"Sept\",\"Oct\",\"Nov\",\"Dec\"])\n",
"by_month\n",
"\n",
"plt.figure(figsize=(12,6))\n",
"\n",
"crime_map_month = sns.heatmap(by_month,cbar_kws={'label': 'Number of 911 Calls'})\n",
"crime_map_month\n",
"\n",
"crime_map_month.set_title('911 Call frequency Heatmap for day of Month')\n",
"crime_map_month.figure.savefig(\"Crime_frequency_month.png\")\n",
"plt.show()\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ASSAULTS, OTHER 24188\n",
"PROPERTY - FOUND (FOLLOW UP TO SPD CASE) 13905\n",
"CASUALTY (NON CRIMINAL/TRAFFIC) - MAN DOWN, SICK PERSONS, INJURED, DOA) 8314\n",
"MISSING PERSON 5635\n",
"PERSON WITH A GUN 2152\n",
"PROPERTY - MISSING 1896\n",
"CASUALTY - DRUG RELATED (OVERDOSE, OTHER) 1551\n",
"PERSON WITH A WEAPON (NOT GUN) 1420\n",
"FOUND PERSON 1033\n",
"ASSAULTS, FIREARM INVOLVED 763\n",
"Name: Event Clearance Description, dtype: int64"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"group=crime_data[crime_data['crime_group']=='Emergency/ Violent']\n",
"c = group['Event Clearance Description'].value_counts()\n",
"c.sort_values(ascending=False)\n",
"c.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>month</th>\n",
" <th>April</th>\n",
" <th>Aug</th>\n",
" <th>Dec</th>\n",
" <th>Feb</th>\n",
" <th>Jan</th>\n",
" <th>July</th>\n",
" <th>June</th>\n",
" <th>March</th>\n",
" <th>May</th>\n",
" <th>Nov</th>\n",
" <th>Oct</th>\n",
" <th>Sept</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Day of week</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Fri</th>\n",
" <td>273</td>\n",
" <td>443</td>\n",
" <td>320</td>\n",
" <td>295</td>\n",
" <td>332</td>\n",
" <td>439</td>\n",
" <td>327</td>\n",
" <td>264</td>\n",
" <td>362</td>\n",
" <td>317</td>\n",
" <td>411</td>\n",
" <td>529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mon</th>\n",
" <td>256</td>\n",
" <td>505</td>\n",
" <td>362</td>\n",
" <td>248</td>\n",
" <td>324</td>\n",
" <td>362</td>\n",
" <td>341</td>\n",
" <td>260</td>\n",
" <td>378</td>\n",
" <td>325</td>\n",
" <td>448</td>\n",
" <td>463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sat</th>\n",
" <td>317</td>\n",
" <td>513</td>\n",
" <td>333</td>\n",
" <td>306</td>\n",
" <td>296</td>\n",
" <td>484</td>\n",
" <td>328</td>\n",
" <td>280</td>\n",
" <td>382</td>\n",
" <td>381</td>\n",
" <td>399</td>\n",
" <td>566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sun</th>\n",
" <td>317</td>\n",
" <td>498</td>\n",
" <td>354</td>\n",
" <td>300</td>\n",
" <td>299</td>\n",
" <td>469</td>\n",
" <td>348</td>\n",
" <td>246</td>\n",
" <td>344</td>\n",
" <td>386</td>\n",
" <td>430</td>\n",
" <td>593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thu</th>\n",
" <td>273</td>\n",
" <td>433</td>\n",
" <td>315</td>\n",
" <td>279</td>\n",
" <td>288</td>\n",
" <td>367</td>\n",
" <td>353</td>\n",
" <td>314</td>\n",
" <td>327</td>\n",
" <td>366</td>\n",
" <td>389</td>\n",
" <td>484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tue</th>\n",
" <td>268</td>\n",
" <td>475</td>\n",
" <td>351</td>\n",
" <td>284</td>\n",
" <td>354</td>\n",
" <td>362</td>\n",
" <td>355</td>\n",
" <td>288</td>\n",
" <td>350</td>\n",
" <td>351</td>\n",
" <td>359</td>\n",
" <td>522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wed</th>\n",
" <td>272</td>\n",
" <td>491</td>\n",
" <td>329</td>\n",
" <td>282</td>\n",
" <td>303</td>\n",
" <td>418</td>\n",
" <td>354</td>\n",
" <td>320</td>\n",
" <td>319</td>\n",
" <td>340</td>\n",
" <td>379</td>\n",
" <td>443</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"month April Aug Dec Feb Jan July June March May Nov Oct Sept\n",
"Day of week \n",
"Fri 273 443 320 295 332 439 327 264 362 317 411 529\n",
"Mon 256 505 362 248 324 362 341 260 378 325 448 463\n",
"Sat 317 513 333 306 296 484 328 280 382 381 399 566\n",
"Sun 317 498 354 300 299 469 348 246 344 386 430 593\n",
"Thu 273 433 315 279 288 367 353 314 327 366 389 484\n",
"Tue 268 475 351 284 354 362 355 288 350 351 359 522\n",
"Wed 272 491 329 282 303 418 354 320 319 340 379 443"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"group=crime_data[crime_data['crime_group']=='Emergency/ Violent']\n",
"by_month_violent = group.groupby(by=['Day of week','month']).count()[\"Event Clearance Group\"]\n",
"by_month_violent.reset_index()\n",
"by_month_violent = by_month_violent.unstack()\n",
"by_month_violent"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [
{
"data": {
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D6NjCT3vKxHfqIg/8886mQ+jY+b+7duI7dZn1t4mJ79RFll5z1aZD6NhNJ/914jt1kaVX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\nAvBM4P+AyzLzvLqOJ0mS1Ku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"text/plain": [
"<matplotlib.figure.Figure at 0x11293b9e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"by_month_violent.set_index([\"Jan\",\"Feb\",\"March\",\"April\",\"May\",\"June\",\"July\",\"Aug\",\"Sept\",\"Oct\",\"Nov\",\"Dec\"])\n",
"\n",
"\n",
"plt.figure(figsize=(12,6))\n",
"\n",
"crime_map_month_violent = sns.heatmap(by_month_violent,cbar_kws={'label': 'Number of 911 Calls'})\n",
"crime_map_month_violent\n",
"\n",
"crime_map_month_violent.set_title('911 [Violent/Emergency only] Call frequency Heatmap for day of Month')\n",
"crime_map_month_violent.figure.savefig(\"Crime_frequency_month_violent.png\")\n",
"plt.show()\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"gun_related = [\"PERSON WITH A GUN\",\"ASSAULTS, FIREARM INVOLVED\"]\n",
"gun = pd.DataFrame(crime_data[(crime_data[\"Event Clearance Description\"]==\"ASSAULTS, FIREARM INVOLVED\") | (crime_data[\"Event Clearance Description\"]==\"PERSON WITH A GUN\")])\n",
"gun[\"year\"] = [x.year for x in pd.to_datetime(gun[\"Scene_Date\"])]\n"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
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" \n",
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" if (window.Bokeh !== undefined) {\n",
" document.getElementById(\"44a7e710-166f-4242-9a17-ac86c3d4a119\").textContent = \"BokehJS successfully loaded.\";\n",
" } else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
" \n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"44a7e710-166f-4242-9a17-ac86c3d4a119\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid '44a7e710-166f-4242-9a17-ac86c3d4a119' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
" \n",
" var js_urls = [];\n",
" \n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" (function() {\n",
" var fn = function() {\n",
" var docs_json = 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\",\"dtype\":\"float64\",\"shape\":[2915]},\"lon\":{\"__ndarray__\":\"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" var render_items = [{\"docid\":\"8fa69125-de0a-43bc-8cf4-cc82c74ec469\",\"elementid\":\"44a7e710-166f-4242-9a17-ac86c3d4a119\",\"modelid\":\"3c47ed94-c527-4ee4-962c-3b8edca17ee5\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"44a7e710-166f-4242-9a17-ac86c3d4a119\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:/anaconda/lib/python3.6/site-packages/bokeh/core/validation/check.py:W-1005 (SNAPPED_TOOLBAR_ANNOTATIONS): Snapped toolbars and annotations on the same side MAY overlap visually: GMapPlot(id='2e944da3-5ae5-423c-ab87-627c7f1c9912', ...)\n",
"WARNING:/anaconda/lib/python3.6/site-packages/bokeh/core/validation/check.py:W-1005 (SNAPPED_TOOLBAR_ANNOTATIONS): Snapped toolbars and annotations on the same side MAY overlap visually: GMapPlot(id='d9dd0aa7-0cdb-440b-a95b-ffe4c560ba87', ...)\n",
"WARNING:/anaconda/lib/python3.6/site-packages/bokeh/core/validation/check.py:W-1005 (SNAPPED_TOOLBAR_ANNOTATIONS): Snapped toolbars and annotations on the same side MAY overlap visually: GMapPlot(id='3c47ed94-c527-4ee4-962c-3b8edca17ee5', ...)\n"
]
}
],
"source": [
"from bokeh.io import output_file, output_notebook, show\n",
"from bokeh.models import (\n",
" GMapPlot, GMapOptions, ColumnDataSource, Circle, LogColorMapper, BasicTicker, ColorBar,\n",
" DataRange1d, PanTool, WheelZoomTool, BoxSelectTool\n",
")\n",
"from bokeh.models.mappers import ColorMapper, LinearColorMapper\n",
"from bokeh.palettes import Viridis5\n",
"\n",
"\n",
"map_options = GMapOptions(lat=47.5982623, lng=-122.3415519, map_type=\"roadmap\", zoom=6) #latitutde and longitude of seattle\n",
"\n",
"plot = GMapPlot(\n",
" x_range=DataRange1d(), y_range=DataRange1d(), map_options=map_options\n",
")\n",
"plot.title.text = \"Gun related crimes on Different location Seattle-City\"\n",
"\n",
"# For GMaps to function, Google requires you obtain and enable an API key:\n",
"#\n",
"# https://developers.google.com/maps/documentation/javascript/get-api-key\n",
"#\n",
"# Replace the value below with your personal API key:\n",
"plot.api_key = \"AIzaSyBSbve1oDm-jmjK0OkPndn2o7U9obp9yfQ\"\n",
"\n",
"source = ColumnDataSource(\n",
" data=dict(\n",
" lat=gun[\"Latitude\"],\n",
" lon=gun[\"Longitude\"],\n",
" size=gun['year'],\n",
" color=gun['year']\n",
" )\n",
")\n",
"\n",
"color_mapper = LinearColorMapper(palette=Viridis5)\n",
"\n",
"circle = Circle(x=\"lon\", y=\"lat\", fill_alpha=0.7, line_color='red')\n",
"plot.add_glyph(source, circle)\n",
"\n",
"color_bar = ColorBar(color_mapper=color_mapper, ticker=BasicTicker(),\n",
" label_standoff=12, border_line_color=None, location=(0,0))\n",
"plot.add_layout(color_bar, 'right')\n",
"\n",
"plot.add_tools(PanTool(), WheelZoomTool(), BoxSelectTool())\n",
"output_file(\"gmap_plot.html\")\n",
"output_notebook()\n",
"\n",
"show(plot)\n",
"\n",
"#You Might need to Zoom it and orient it properly"
]
},
{
"attachments": {
"image.png": {
"image/png": 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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"References:\n",
"\n",
"![image.png](attachment:image.png)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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