mapping-crime-in-boston.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import re #regular expression matching operations\n",
    "\n",
    "import folium #maping crime on the map\n",
    "from folium.plugins import HeatMap, MarkerCluster #making maping visually appealing\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston_coords = (42.361529, -71.054859)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (6) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "crimedf = pd.read_csv('https://data.boston.gov/dataset/6220d948-eae2-4e4b-8723-2dc8e67722a3/resource/12cb3883-56f5-47de-afa5-3b1cf61b257b/download/tmpsh2y6rpr.csv')\n",
    "\n",
    "offense_codes = pd.read_excel(\"https://data.boston.gov/dataset/6220d948-eae2-4e4b-8723-2dc8e67722a3/resource/3aeccf51-a231-4555-ba21-74572b4c33d6/download/rmsoffensecodes.xlsx\")\n",
    "\n",
    "field_notes = pd.read_excel(\"https://data.boston.gov/dataset/6220d948-eae2-4e4b-8723-2dc8e67722a3/resource/9c30453a-fefa-4fe0-b51a-5fc09b0f4655/download/rmscrimeincidentfieldexplanation.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>OFFENSE_CODE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>426839.000000</td>\n",
       "      <td>426839.000000</td>\n",
       "      <td>426839.000000</td>\n",
       "      <td>426839.000000</td>\n",
       "      <td>399635.000000</td>\n",
       "      <td>399635.000000</td>\n",
       "      <td>426839.000000</td>\n",
       "      <td>426839.000000</td>\n",
       "      <td>426839.000000</td>\n",
       "      <td>426839.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2327.707632</td>\n",
       "      <td>2017.092391</td>\n",
       "      <td>6.646239</td>\n",
       "      <td>13.116880</td>\n",
       "      <td>42.221633</td>\n",
       "      <td>-70.920431</td>\n",
       "      <td>2017.092391</td>\n",
       "      <td>6.646239</td>\n",
       "      <td>2.946769</td>\n",
       "      <td>13.116880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1181.577366</td>\n",
       "      <td>1.283724</td>\n",
       "      <td>3.279190</td>\n",
       "      <td>6.298591</td>\n",
       "      <td>2.084323</td>\n",
       "      <td>3.371583</td>\n",
       "      <td>1.283724</td>\n",
       "      <td>3.279190</td>\n",
       "      <td>1.964510</td>\n",
       "      <td>6.298591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>111.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-71.178674</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1102.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>42.297105</td>\n",
       "      <td>-71.097223</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3001.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>42.325538</td>\n",
       "      <td>-71.077619</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3201.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>42.348546</td>\n",
       "      <td>-71.062583</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3831.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>42.395042</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        OFFENSE_CODE           YEAR          MONTH           HOUR  \\\n",
       "count  426839.000000  426839.000000  426839.000000  426839.000000   \n",
       "mean     2327.707632    2017.092391       6.646239      13.116880   \n",
       "std      1181.577366       1.283724       3.279190       6.298591   \n",
       "min       111.000000    2015.000000       1.000000       0.000000   \n",
       "25%      1102.000000    2016.000000       4.000000       9.000000   \n",
       "50%      3001.000000    2017.000000       7.000000      14.000000   \n",
       "75%      3201.000000    2018.000000       9.000000      18.000000   \n",
       "max      3831.000000    2019.000000      12.000000      23.000000   \n",
       "\n",
       "                 Lat           Long           year          month  \\\n",
       "count  399635.000000  399635.000000  426839.000000  426839.000000   \n",
       "mean       42.221633     -70.920431    2017.092391       6.646239   \n",
       "std         2.084323       3.371583       1.283724       3.279190   \n",
       "min        -1.000000     -71.178674    2015.000000       1.000000   \n",
       "25%        42.297105     -71.097223    2016.000000       4.000000   \n",
       "50%        42.325538     -71.077619    2017.000000       7.000000   \n",
       "75%        42.348546     -71.062583    2018.000000       9.000000   \n",
       "max        42.395042      -1.000000    2019.000000      12.000000   \n",
       "\n",
       "           dayofweek           hour  \n",
       "count  426839.000000  426839.000000  \n",
       "mean        2.946769      13.116880  \n",
       "std         1.964510       6.298591  \n",
       "min         0.000000       0.000000  \n",
       "25%         1.000000       9.000000  \n",
       "50%         3.000000      14.000000  \n",
       "75%         5.000000      18.000000  \n",
       "max         6.000000      23.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimedf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "crimedf[\"datetime\"] = pd.to_datetime(crimedf[\"OCCURRED_ON_DATE\"])\n",
    "\n",
    "crimedf['year'] = crimedf['datetime'].dt.year\n",
    "crimedf['month'] = crimedf['datetime'].dt.month\n",
    "crimedf['dayofweek'] = crimedf['datetime'].dt.dayofweek\n",
    "crimedf['hour'] = crimedf['datetime'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "crimedf[\"crime_type\"] = crimedf[\"OFFENSE_DESCRIPTION\"].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "values_crime = crimedf[\"crime_type\"].value_counts() \n",
    "labels_crime = crimedf[\"crime_type\"].value_counts().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SICK/INJURED/MEDICAL - PERSON                   26360\n",
       "INVESTIGATE PERSON                              24759\n",
       "M/V - LEAVING SCENE - PROPERTY DAMAGE           21741\n",
       "ASSAULT SIMPLE - BATTERY                        19888\n",
       "VANDALISM                                       19739\n",
       "                                                ...  \n",
       "DRUGS - GLUE INHALATION                             1\n",
       "KILLING OF FELON BY POLICE                          1\n",
       "FIREARM/WEAPON - CARRY - SELL - RENT                1\n",
       "FRAUD - FALSE PRETENSE                              1\n",
       "A&B HANDS, FEET, ETC.  - MED. ATTENTION REQ.        1\n",
       "Name: crime_type, Length: 244, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values_crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalIndex(['SICK/INJURED/MEDICAL - PERSON', 'INVESTIGATE PERSON',\n",
       "                  'M/V - LEAVING SCENE - PROPERTY DAMAGE',\n",
       "                  'ASSAULT SIMPLE - BATTERY', 'VANDALISM', 'VERBAL DISPUTE',\n",
       "                  'INVESTIGATE PROPERTY', 'TOWED MOTOR VEHICLE',\n",
       "                  'PROPERTY - LOST', 'THREATS TO DO BODILY HARM',\n",
       "                  ...\n",
       "                  'AUTO THEFT OTHER', 'B&E NON-RESIDENCE NIGHT - FORCE',\n",
       "                  'B&E RESIDENCE DAY - NO FORCE', 'ROBBERY - KNIFE - STREET',\n",
       "                  'CHILD ABUSE', 'DRUGS - GLUE INHALATION',\n",
       "                  'KILLING OF FELON BY POLICE',\n",
       "                  'FIREARM/WEAPON - CARRY - SELL - RENT',\n",
       "                  'FRAUD - FALSE PRETENSE',\n",
       "                  'A&B HANDS, FEET, ETC.  - MED. ATTENTION REQ.'],\n",
       "                 categories=['A&B HANDS, FEET, ETC.  - MED. ATTENTION REQ.', 'A&B ON POLICE OFFICER', 'ABDUCTION - INTICING', 'AFFRAY', 'AIRCRAFT INCIDENTS', 'ANIMAL ABUSE', 'ANIMAL CONTROL - DOG BITES - ETC.', 'ANIMAL INCIDENTS', ...], ordered=False, dtype='category', length=244)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>INCIDENT_NUMBER</th>\n",
       "      <th>OFFENSE_CODE</th>\n",
       "      <th>OFFENSE_CODE_GROUP</th>\n",
       "      <th>OFFENSE_DESCRIPTION</th>\n",
       "      <th>DISTRICT</th>\n",
       "      <th>REPORTING_AREA</th>\n",
       "      <th>SHOOTING</th>\n",
       "      <th>OCCURRED_ON_DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>...</th>\n",
       "      <th>STREET</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Location</th>\n",
       "      <th>datetime</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "      <th>crime_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>I192078638</td>\n",
       "      <td>423</td>\n",
       "      <td>Aggravated Assault</td>\n",
       "      <td>ASSAULT - AGGRAVATED</td>\n",
       "      <td>A1</td>\n",
       "      <td>75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-29 04:46:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>CAUSEWAY ST</td>\n",
       "      <td>42.364331</td>\n",
       "      <td>-71.063193</td>\n",
       "      <td>(42.36433126, -71.06319318)</td>\n",
       "      <td>2019-09-29 04:46:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>ASSAULT - AGGRAVATED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>I192078613</td>\n",
       "      <td>423</td>\n",
       "      <td>Aggravated Assault</td>\n",
       "      <td>ASSAULT - AGGRAVATED</td>\n",
       "      <td>A7</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-29 02:30:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>CHELSEA ST</td>\n",
       "      <td>42.379124</td>\n",
       "      <td>-71.028082</td>\n",
       "      <td>(42.37912370, -71.02808210)</td>\n",
       "      <td>2019-09-29 02:30:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>ASSAULT - AGGRAVATED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>I192078613</td>\n",
       "      <td>802</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT SIMPLE - BATTERY</td>\n",
       "      <td>A7</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-29 02:30:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>CHELSEA ST</td>\n",
       "      <td>42.379124</td>\n",
       "      <td>-71.028082</td>\n",
       "      <td>(42.37912370, -71.02808210)</td>\n",
       "      <td>2019-09-29 02:30:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>ASSAULT SIMPLE - BATTERY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>I192078608</td>\n",
       "      <td>801</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT - SIMPLE</td>\n",
       "      <td>E18</td>\n",
       "      <td>501</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-29 01:35:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>CANTERBURY ST</td>\n",
       "      <td>42.278688</td>\n",
       "      <td>-71.117344</td>\n",
       "      <td>(42.27868789, -71.11734430)</td>\n",
       "      <td>2019-09-29 01:35:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>ASSAULT - SIMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>I192078603</td>\n",
       "      <td>802</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT SIMPLE - BATTERY</td>\n",
       "      <td>A7</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-29 01:30:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>BENNINGTON ST</td>\n",
       "      <td>42.375470</td>\n",
       "      <td>-71.037302</td>\n",
       "      <td>(42.37546983, -71.03730221)</td>\n",
       "      <td>2019-09-29 01:30:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>ASSAULT SIMPLE - BATTERY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426793</th>\n",
       "      <td>I110177502-00</td>\n",
       "      <td>802</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "      <td>B2</td>\n",
       "      <td>318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-10-02 21:00:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>HOMESTEAD ST</td>\n",
       "      <td>42.311277</td>\n",
       "      <td>-71.089093</td>\n",
       "      <td>(42.31127726, -71.08909334)</td>\n",
       "      <td>2015-10-02 21:00:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426794</th>\n",
       "      <td>I110177502-00</td>\n",
       "      <td>802</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "      <td>B2</td>\n",
       "      <td>318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-10-02 21:00:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>HOMESTEAD ST</td>\n",
       "      <td>42.311277</td>\n",
       "      <td>-71.089093</td>\n",
       "      <td>(42.31127726, -71.08909334)</td>\n",
       "      <td>2015-10-02 21:00:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426809</th>\n",
       "      <td>I090317057-00</td>\n",
       "      <td>403</td>\n",
       "      <td>Aggravated Assault</td>\n",
       "      <td>ASSAULT &amp; BATTERY D/W - OTHER</td>\n",
       "      <td>B3</td>\n",
       "      <td>458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-20 11:15:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>BLUE HILL AVE</td>\n",
       "      <td>42.301897</td>\n",
       "      <td>-71.085549</td>\n",
       "      <td>(42.30189690, -71.08554944)</td>\n",
       "      <td>2015-11-20 11:15:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>ASSAULT &amp; BATTERY D/W - OTHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426823</th>\n",
       "      <td>I070720870-00</td>\n",
       "      <td>802</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "      <td>B2</td>\n",
       "      <td>318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-12-13 00:00:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>BROOKLEDGE ST</td>\n",
       "      <td>42.309563</td>\n",
       "      <td>-71.089902</td>\n",
       "      <td>(42.30956305, -71.08990197)</td>\n",
       "      <td>2018-12-13 00:00:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426824</th>\n",
       "      <td>I070720870-00</td>\n",
       "      <td>802</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "      <td>B2</td>\n",
       "      <td>318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-12-13 00:00:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>BROOKLEDGE ST</td>\n",
       "      <td>42.309563</td>\n",
       "      <td>-71.089902</td>\n",
       "      <td>(42.30956305, -71.08990197)</td>\n",
       "      <td>2018-12-13 00:00:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32264 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       INCIDENT_NUMBER  OFFENSE_CODE  OFFENSE_CODE_GROUP  \\\n",
       "12          I192078638           423  Aggravated Assault   \n",
       "25          I192078613           423  Aggravated Assault   \n",
       "26          I192078613           802      Simple Assault   \n",
       "31          I192078608           801      Simple Assault   \n",
       "36          I192078603           802      Simple Assault   \n",
       "...                ...           ...                 ...   \n",
       "426793   I110177502-00           802      Simple Assault   \n",
       "426794   I110177502-00           802      Simple Assault   \n",
       "426809   I090317057-00           403  Aggravated Assault   \n",
       "426823   I070720870-00           802      Simple Assault   \n",
       "426824   I070720870-00           802      Simple Assault   \n",
       "\n",
       "                  OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING  \\\n",
       "12               ASSAULT - AGGRAVATED       A1             75      NaN   \n",
       "25               ASSAULT - AGGRAVATED       A7             17      NaN   \n",
       "26           ASSAULT SIMPLE - BATTERY       A7             17      NaN   \n",
       "31                   ASSAULT - SIMPLE      E18            501      NaN   \n",
       "36           ASSAULT SIMPLE - BATTERY       A7             23      NaN   \n",
       "...                               ...      ...            ...      ...   \n",
       "426793              ASSAULT & BATTERY       B2            318      NaN   \n",
       "426794              ASSAULT & BATTERY       B2            318      NaN   \n",
       "426809  ASSAULT & BATTERY D/W - OTHER       B3            458      NaN   \n",
       "426823              ASSAULT & BATTERY       B2            318      NaN   \n",
       "426824              ASSAULT & BATTERY       B2            318      NaN   \n",
       "\n",
       "           OCCURRED_ON_DATE  YEAR  MONTH  ...         STREET        Lat  \\\n",
       "12      2019-09-29 04:46:00  2019      9  ...    CAUSEWAY ST  42.364331   \n",
       "25      2019-09-29 02:30:00  2019      9  ...     CHELSEA ST  42.379124   \n",
       "26      2019-09-29 02:30:00  2019      9  ...     CHELSEA ST  42.379124   \n",
       "31      2019-09-29 01:35:00  2019      9  ...  CANTERBURY ST  42.278688   \n",
       "36      2019-09-29 01:30:00  2019      9  ...  BENNINGTON ST  42.375470   \n",
       "...                     ...   ...    ...  ...            ...        ...   \n",
       "426793  2015-10-02 21:00:00  2015     10  ...   HOMESTEAD ST  42.311277   \n",
       "426794  2015-10-02 21:00:00  2015     10  ...   HOMESTEAD ST  42.311277   \n",
       "426809  2015-11-20 11:15:00  2015     11  ...  BLUE HILL AVE  42.301897   \n",
       "426823  2018-12-13 00:00:00  2018     12  ...  BROOKLEDGE ST  42.309563   \n",
       "426824  2018-12-13 00:00:00  2018     12  ...  BROOKLEDGE ST  42.309563   \n",
       "\n",
       "             Long                     Location            datetime  year  \\\n",
       "12     -71.063193  (42.36433126, -71.06319318) 2019-09-29 04:46:00  2019   \n",
       "25     -71.028082  (42.37912370, -71.02808210) 2019-09-29 02:30:00  2019   \n",
       "26     -71.028082  (42.37912370, -71.02808210) 2019-09-29 02:30:00  2019   \n",
       "31     -71.117344  (42.27868789, -71.11734430) 2019-09-29 01:35:00  2019   \n",
       "36     -71.037302  (42.37546983, -71.03730221) 2019-09-29 01:30:00  2019   \n",
       "...           ...                          ...                 ...   ...   \n",
       "426793 -71.089093  (42.31127726, -71.08909334) 2015-10-02 21:00:00  2015   \n",
       "426794 -71.089093  (42.31127726, -71.08909334) 2015-10-02 21:00:00  2015   \n",
       "426809 -71.085549  (42.30189690, -71.08554944) 2015-11-20 11:15:00  2015   \n",
       "426823 -71.089902  (42.30956305, -71.08990197) 2018-12-13 00:00:00  2018   \n",
       "426824 -71.089902  (42.30956305, -71.08990197) 2018-12-13 00:00:00  2018   \n",
       "\n",
       "       month dayofweek  hour                     crime_type  \n",
       "12         9         6     4           ASSAULT - AGGRAVATED  \n",
       "25         9         6     2           ASSAULT - AGGRAVATED  \n",
       "26         9         6     2       ASSAULT SIMPLE - BATTERY  \n",
       "31         9         6     1               ASSAULT - SIMPLE  \n",
       "36         9         6     1       ASSAULT SIMPLE - BATTERY  \n",
       "...      ...       ...   ...                            ...  \n",
       "426793    10         4    21              ASSAULT & BATTERY  \n",
       "426794    10         4    21              ASSAULT & BATTERY  \n",
       "426809    11         4    11  ASSAULT & BATTERY D/W - OTHER  \n",
       "426823    12         3     0              ASSAULT & BATTERY  \n",
       "426824    12         3     0              ASSAULT & BATTERY  \n",
       "\n",
       "[32264 rows x 23 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Too many to work with so let us just stick with Auto Theft\n",
    "\n",
    "crime_assault = crimedf[crimedf[\"OFFENSE_DESCRIPTION\"].str.contains('ASSAULT', flags = re.IGNORECASE, regex = True)] #Step 1.\n",
    "\n",
    "values_assault = crime_assault[\"OFFENSE_DESCRIPTION\"].value_counts() #Step 2.\n",
    "\n",
    "crime_assault"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCIDENT_NUMBER</th>\n",
       "      <th>OFFENSE_CODE</th>\n",
       "      <th>OFFENSE_CODE_GROUP</th>\n",
       "      <th>OFFENSE_DESCRIPTION</th>\n",
       "      <th>DISTRICT</th>\n",
       "      <th>REPORTING_AREA</th>\n",
       "      <th>SHOOTING</th>\n",
       "      <th>OCCURRED_ON_DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>...</th>\n",
       "      <th>STREET</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Location</th>\n",
       "      <th>datetime</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "      <th>crime_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>I192078638</td>\n",
       "      <td>423</td>\n",
       "      <td>Aggravated Assault</td>\n",
       "      <td>ASSAULT - AGGRAVATED</td>\n",
       "      <td>A1</td>\n",
       "      <td>75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-29 04:46:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>CAUSEWAY ST</td>\n",
       "      <td>42.364331</td>\n",
       "      <td>-71.063193</td>\n",
       "      <td>(42.36433126, -71.06319318)</td>\n",
       "      <td>2019-09-29 04:46:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>ASSAULT - AGGRAVATED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>I192078613</td>\n",
       "      <td>423</td>\n",
       "      <td>Aggravated Assault</td>\n",
       "      <td>ASSAULT - AGGRAVATED</td>\n",
       "      <td>A7</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-29 02:30:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>CHELSEA ST</td>\n",
       "      <td>42.379124</td>\n",
       "      <td>-71.028082</td>\n",
       "      <td>(42.37912370, -71.02808210)</td>\n",
       "      <td>2019-09-29 02:30:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>ASSAULT - AGGRAVATED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>I192078613</td>\n",
       "      <td>802</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT SIMPLE - BATTERY</td>\n",
       "      <td>A7</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-29 02:30:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>CHELSEA ST</td>\n",
       "      <td>42.379124</td>\n",
       "      <td>-71.028082</td>\n",
       "      <td>(42.37912370, -71.02808210)</td>\n",
       "      <td>2019-09-29 02:30:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>ASSAULT SIMPLE - BATTERY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>I192078608</td>\n",
       "      <td>801</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT - SIMPLE</td>\n",
       "      <td>E18</td>\n",
       "      <td>501</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-29 01:35:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>CANTERBURY ST</td>\n",
       "      <td>42.278688</td>\n",
       "      <td>-71.117344</td>\n",
       "      <td>(42.27868789, -71.11734430)</td>\n",
       "      <td>2019-09-29 01:35:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>ASSAULT - SIMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>I192078603</td>\n",
       "      <td>802</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT SIMPLE - BATTERY</td>\n",
       "      <td>A7</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-09-29 01:30:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>BENNINGTON ST</td>\n",
       "      <td>42.375470</td>\n",
       "      <td>-71.037302</td>\n",
       "      <td>(42.37546983, -71.03730221)</td>\n",
       "      <td>2019-09-29 01:30:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>ASSAULT SIMPLE - BATTERY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426793</th>\n",
       "      <td>I110177502-00</td>\n",
       "      <td>802</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "      <td>B2</td>\n",
       "      <td>318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-10-02 21:00:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>HOMESTEAD ST</td>\n",
       "      <td>42.311277</td>\n",
       "      <td>-71.089093</td>\n",
       "      <td>(42.31127726, -71.08909334)</td>\n",
       "      <td>2015-10-02 21:00:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426794</th>\n",
       "      <td>I110177502-00</td>\n",
       "      <td>802</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "      <td>B2</td>\n",
       "      <td>318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-10-02 21:00:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>HOMESTEAD ST</td>\n",
       "      <td>42.311277</td>\n",
       "      <td>-71.089093</td>\n",
       "      <td>(42.31127726, -71.08909334)</td>\n",
       "      <td>2015-10-02 21:00:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426809</th>\n",
       "      <td>I090317057-00</td>\n",
       "      <td>403</td>\n",
       "      <td>Aggravated Assault</td>\n",
       "      <td>ASSAULT &amp; BATTERY D/W - OTHER</td>\n",
       "      <td>B3</td>\n",
       "      <td>458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-20 11:15:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>BLUE HILL AVE</td>\n",
       "      <td>42.301897</td>\n",
       "      <td>-71.085549</td>\n",
       "      <td>(42.30189690, -71.08554944)</td>\n",
       "      <td>2015-11-20 11:15:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>ASSAULT &amp; BATTERY D/W - OTHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426823</th>\n",
       "      <td>I070720870-00</td>\n",
       "      <td>802</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "      <td>B2</td>\n",
       "      <td>318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-12-13 00:00:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>BROOKLEDGE ST</td>\n",
       "      <td>42.309563</td>\n",
       "      <td>-71.089902</td>\n",
       "      <td>(42.30956305, -71.08990197)</td>\n",
       "      <td>2018-12-13 00:00:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426824</th>\n",
       "      <td>I070720870-00</td>\n",
       "      <td>802</td>\n",
       "      <td>Simple Assault</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "      <td>B2</td>\n",
       "      <td>318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-12-13 00:00:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>BROOKLEDGE ST</td>\n",
       "      <td>42.309563</td>\n",
       "      <td>-71.089902</td>\n",
       "      <td>(42.30956305, -71.08990197)</td>\n",
       "      <td>2018-12-13 00:00:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>ASSAULT &amp; BATTERY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32264 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       INCIDENT_NUMBER  OFFENSE_CODE  OFFENSE_CODE_GROUP  \\\n",
       "12          I192078638           423  Aggravated Assault   \n",
       "25          I192078613           423  Aggravated Assault   \n",
       "26          I192078613           802      Simple Assault   \n",
       "31          I192078608           801      Simple Assault   \n",
       "36          I192078603           802      Simple Assault   \n",
       "...                ...           ...                 ...   \n",
       "426793   I110177502-00           802      Simple Assault   \n",
       "426794   I110177502-00           802      Simple Assault   \n",
       "426809   I090317057-00           403  Aggravated Assault   \n",
       "426823   I070720870-00           802      Simple Assault   \n",
       "426824   I070720870-00           802      Simple Assault   \n",
       "\n",
       "                  OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING  \\\n",
       "12               ASSAULT - AGGRAVATED       A1             75      NaN   \n",
       "25               ASSAULT - AGGRAVATED       A7             17      NaN   \n",
       "26           ASSAULT SIMPLE - BATTERY       A7             17      NaN   \n",
       "31                   ASSAULT - SIMPLE      E18            501      NaN   \n",
       "36           ASSAULT SIMPLE - BATTERY       A7             23      NaN   \n",
       "...                               ...      ...            ...      ...   \n",
       "426793              ASSAULT & BATTERY       B2            318      NaN   \n",
       "426794              ASSAULT & BATTERY       B2            318      NaN   \n",
       "426809  ASSAULT & BATTERY D/W - OTHER       B3            458      NaN   \n",
       "426823              ASSAULT & BATTERY       B2            318      NaN   \n",
       "426824              ASSAULT & BATTERY       B2            318      NaN   \n",
       "\n",
       "           OCCURRED_ON_DATE  YEAR  MONTH  ...         STREET        Lat  \\\n",
       "12      2019-09-29 04:46:00  2019      9  ...    CAUSEWAY ST  42.364331   \n",
       "25      2019-09-29 02:30:00  2019      9  ...     CHELSEA ST  42.379124   \n",
       "26      2019-09-29 02:30:00  2019      9  ...     CHELSEA ST  42.379124   \n",
       "31      2019-09-29 01:35:00  2019      9  ...  CANTERBURY ST  42.278688   \n",
       "36      2019-09-29 01:30:00  2019      9  ...  BENNINGTON ST  42.375470   \n",
       "...                     ...   ...    ...  ...            ...        ...   \n",
       "426793  2015-10-02 21:00:00  2015     10  ...   HOMESTEAD ST  42.311277   \n",
       "426794  2015-10-02 21:00:00  2015     10  ...   HOMESTEAD ST  42.311277   \n",
       "426809  2015-11-20 11:15:00  2015     11  ...  BLUE HILL AVE  42.301897   \n",
       "426823  2018-12-13 00:00:00  2018     12  ...  BROOKLEDGE ST  42.309563   \n",
       "426824  2018-12-13 00:00:00  2018     12  ...  BROOKLEDGE ST  42.309563   \n",
       "\n",
       "             Long                     Location            datetime  year  \\\n",
       "12     -71.063193  (42.36433126, -71.06319318) 2019-09-29 04:46:00  2019   \n",
       "25     -71.028082  (42.37912370, -71.02808210) 2019-09-29 02:30:00  2019   \n",
       "26     -71.028082  (42.37912370, -71.02808210) 2019-09-29 02:30:00  2019   \n",
       "31     -71.117344  (42.27868789, -71.11734430) 2019-09-29 01:35:00  2019   \n",
       "36     -71.037302  (42.37546983, -71.03730221) 2019-09-29 01:30:00  2019   \n",
       "...           ...                          ...                 ...   ...   \n",
       "426793 -71.089093  (42.31127726, -71.08909334) 2015-10-02 21:00:00  2015   \n",
       "426794 -71.089093  (42.31127726, -71.08909334) 2015-10-02 21:00:00  2015   \n",
       "426809 -71.085549  (42.30189690, -71.08554944) 2015-11-20 11:15:00  2015   \n",
       "426823 -71.089902  (42.30956305, -71.08990197) 2018-12-13 00:00:00  2018   \n",
       "426824 -71.089902  (42.30956305, -71.08990197) 2018-12-13 00:00:00  2018   \n",
       "\n",
       "       month dayofweek  hour                     crime_type  \n",
       "12         9         6     4           ASSAULT - AGGRAVATED  \n",
       "25         9         6     2           ASSAULT - AGGRAVATED  \n",
       "26         9         6     2       ASSAULT SIMPLE - BATTERY  \n",
       "31         9         6     1               ASSAULT - SIMPLE  \n",
       "36         9         6     1       ASSAULT SIMPLE - BATTERY  \n",
       "...      ...       ...   ...                            ...  \n",
       "426793    10         4    21              ASSAULT & BATTERY  \n",
       "426794    10         4    21              ASSAULT & BATTERY  \n",
       "426809    11         4    11  ASSAULT & BATTERY D/W - OTHER  \n",
       "426823    12         3     0              ASSAULT & BATTERY  \n",
       "426824    12         3     0              ASSAULT & BATTERY  \n",
       "\n",
       "[32264 rows x 23 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_assault"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>OFFENSE_DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ASSAULT SIMPLE - BATTERY</th>\n",
       "      <td>19888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASSAULT - AGGRAVATED - BATTERY</th>\n",
       "      <td>6673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASSAULT - AGGRAVATED</th>\n",
       "      <td>3998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>1705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                OFFENSE_DESCRIPTION\n",
       "ASSAULT SIMPLE - BATTERY                      19888\n",
       "ASSAULT - AGGRAVATED - BATTERY                 6673\n",
       "ASSAULT - AGGRAVATED                           3998\n",
       "other                                          1705"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for key,value in values_assault.iteritems(): #Step 3.\n",
    "#    if value < 1500:\n",
    "#       values_assault= pd.DataFrame(values_assault.rename({key: \"other\"}))\n",
    "\n",
    "values_assault = values_assault.groupby(values_assault.index).sum().sort_values(\"OFFENSE_DESCRIPTION\",ascending=False) #Step 4.\n",
    "\n",
    "labels_assault = values_assault.index #Step 5.\n",
    "\n",
    "values_assault"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f8739602e50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(num = None, figsize = (10, 8))\n",
    "\n",
    "plt.scatter(\"Long\", \"Lat\", data = crime_assault, c = 'y', alpha = 0.5, edgecolor = 'black', s = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f8749c63110>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\"Long\", \"Lat\", data = crimedf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid()\n",
    "plt.xlabel('long')\n",
    "plt.ylabel('lat')\n",
    "plt.title('Boston Crime')\n",
    "plt.tight_layout()\n",
    "plt.axis('tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "map = folium.Map(location = boston_coords, zoom_start = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=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 onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f9618e7c5d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "crimedf_19 = crimedf[ crimedf['YEAR'] == 2019 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>INCIDENT_NUMBER</th>\n",
       "      <th>OFFENSE_CODE</th>\n",
       "      <th>OFFENSE_CODE_GROUP</th>\n",
       "      <th>OFFENSE_DESCRIPTION</th>\n",
       "      <th>DISTRICT</th>\n",
       "      <th>REPORTING_AREA</th>\n",
       "      <th>SHOOTING</th>\n",
       "      <th>OCCURRED_ON_DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>UCR_PART</th>\n",
       "      <th>STREET</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I192082859</td>\n",
       "      <td>724</td>\n",
       "      <td>Auto Theft</td>\n",
       "      <td>AUTO THEFT</td>\n",
       "      <td>E18</td>\n",
       "      <td>519</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-10-13 09:28:24</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>9</td>\n",
       "      <td>Part One</td>\n",
       "      <td>LINCOLN ST</td>\n",
       "      <td>42.259518</td>\n",
       "      <td>-71.121563</td>\n",
       "      <td>(42.25951765, -71.12156299)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I192082751</td>\n",
       "      <td>724</td>\n",
       "      <td>Auto Theft</td>\n",
       "      <td>AUTO THEFT</td>\n",
       "      <td>E18</td>\n",
       "      <td>493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-10-12 20:11:26</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>20</td>\n",
       "      <td>Part One</td>\n",
       "      <td>METROPOLITAN AVE</td>\n",
       "      <td>42.262092</td>\n",
       "      <td>-71.116710</td>\n",
       "      <td>(42.26209214, -71.11670964)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I192082680</td>\n",
       "      <td>727</td>\n",
       "      <td>Auto Theft</td>\n",
       "      <td>AUTO THEFT - LEASED/RENTED VEHICLE</td>\n",
       "      <td>D14</td>\n",
       "      <td>794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-10-12 15:12:43</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>15</td>\n",
       "      <td>Part One</td>\n",
       "      <td>ALLSTON ST</td>\n",
       "      <td>42.352375</td>\n",
       "      <td>-71.135096</td>\n",
       "      <td>(42.35237455, -71.13509584)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I192082577</td>\n",
       "      <td>724</td>\n",
       "      <td>Auto Theft</td>\n",
       "      <td>AUTO THEFT</td>\n",
       "      <td>D4</td>\n",
       "      <td>130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-10-12 04:41:52</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>4</td>\n",
       "      <td>Part One</td>\n",
       "      <td>SAINT JAMES AVE</td>\n",
       "      <td>42.349476</td>\n",
       "      <td>-71.076402</td>\n",
       "      <td>(42.34947586, -71.07640150)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I192079582</td>\n",
       "      <td>727</td>\n",
       "      <td>Auto Theft</td>\n",
       "      <td>AUTO THEFT - LEASED/RENTED VEHICLE</td>\n",
       "      <td>A15</td>\n",
       "      <td>47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-10-02 08:08:49</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>Part One</td>\n",
       "      <td>N MEAD ST</td>\n",
       "      <td>42.381846</td>\n",
       "      <td>-71.066551</td>\n",
       "      <td>(42.38184582, -71.06655134)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426833</th>\n",
       "      <td>I020695210-00</td>\n",
       "      <td>1843</td>\n",
       "      <td>Drug Violation</td>\n",
       "      <td>DRUGS - POSS CLASS B - INTENT TO MFR DIST DISP</td>\n",
       "      <td>A1</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-23 09:00:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>9</td>\n",
       "      <td>Part Two</td>\n",
       "      <td>NEW SUDBURY ST</td>\n",
       "      <td>42.361839</td>\n",
       "      <td>-71.059765</td>\n",
       "      <td>(42.36183857, -71.05976489)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426834</th>\n",
       "      <td>I020691165-00</td>\n",
       "      <td>3125</td>\n",
       "      <td>Warrant Arrests</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>A1</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-23 08:00:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>8</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>NEW SUDBURY ST</td>\n",
       "      <td>42.361839</td>\n",
       "      <td>-71.059765</td>\n",
       "      <td>(42.36183857, -71.05976489)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426835</th>\n",
       "      <td>I020674147-00</td>\n",
       "      <td>3125</td>\n",
       "      <td>Warrant Arrests</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>A1</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-23 08:00:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>8</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>NEW SUDBURY ST</td>\n",
       "      <td>42.361839</td>\n",
       "      <td>-71.059765</td>\n",
       "      <td>(42.36183857, -71.05976489)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426836</th>\n",
       "      <td>I020669075-00</td>\n",
       "      <td>3125</td>\n",
       "      <td>Warrant Arrests</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>A1</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-23 08:00:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>8</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>NEW SUDBURY ST</td>\n",
       "      <td>42.361839</td>\n",
       "      <td>-71.059765</td>\n",
       "      <td>(42.36183857, -71.05976489)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426837</th>\n",
       "      <td>I020662996-00</td>\n",
       "      <td>3125</td>\n",
       "      <td>Warrant Arrests</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>A1</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-23 08:56:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>8</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>NEW SUDBURY ST</td>\n",
       "      <td>42.361839</td>\n",
       "      <td>-71.059765</td>\n",
       "      <td>(42.36183857, -71.05976489)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73586 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       INCIDENT_NUMBER  OFFENSE_CODE OFFENSE_CODE_GROUP  \\\n",
       "0           I192082859           724         Auto Theft   \n",
       "1           I192082751           724         Auto Theft   \n",
       "2           I192082680           727         Auto Theft   \n",
       "3           I192082577           724         Auto Theft   \n",
       "4           I192079582           727         Auto Theft   \n",
       "...                ...           ...                ...   \n",
       "426833   I020695210-00          1843     Drug Violation   \n",
       "426834   I020691165-00          3125    Warrant Arrests   \n",
       "426835   I020674147-00          3125    Warrant Arrests   \n",
       "426836   I020669075-00          3125    Warrant Arrests   \n",
       "426837   I020662996-00          3125    Warrant Arrests   \n",
       "\n",
       "                                   OFFENSE_DESCRIPTION DISTRICT  \\\n",
       "0                                           AUTO THEFT      E18   \n",
       "1                                           AUTO THEFT      E18   \n",
       "2                   AUTO THEFT - LEASED/RENTED VEHICLE      D14   \n",
       "3                                           AUTO THEFT       D4   \n",
       "4                   AUTO THEFT - LEASED/RENTED VEHICLE      A15   \n",
       "...                                                ...      ...   \n",
       "426833  DRUGS - POSS CLASS B - INTENT TO MFR DIST DISP       A1   \n",
       "426834                                  WARRANT ARREST       A1   \n",
       "426835                                  WARRANT ARREST       A1   \n",
       "426836                                  WARRANT ARREST       A1   \n",
       "426837                                  WARRANT ARREST       A1   \n",
       "\n",
       "       REPORTING_AREA SHOOTING     OCCURRED_ON_DATE  YEAR  MONTH DAY_OF_WEEK  \\\n",
       "0                 519      NaN  2019-10-13 09:28:24  2019     10      Sunday   \n",
       "1                 493      NaN  2019-10-12 20:11:26  2019     10    Saturday   \n",
       "2                 794      NaN  2019-10-12 15:12:43  2019     10    Saturday   \n",
       "3                 130      NaN  2019-10-12 04:41:52  2019     10    Saturday   \n",
       "4                  47      NaN  2019-10-02 08:08:49  2019     10   Wednesday   \n",
       "...               ...      ...                  ...   ...    ...         ...   \n",
       "426833             77      NaN  2019-05-23 09:00:00  2019      5    Thursday   \n",
       "426834             77      NaN  2019-05-23 08:00:00  2019      5    Thursday   \n",
       "426835             77      NaN  2019-05-23 08:00:00  2019      5    Thursday   \n",
       "426836             77      NaN  2019-05-23 08:00:00  2019      5    Thursday   \n",
       "426837             77      NaN  2019-05-23 08:56:00  2019      5    Thursday   \n",
       "\n",
       "        HOUR    UCR_PART            STREET        Lat       Long  \\\n",
       "0          9    Part One        LINCOLN ST  42.259518 -71.121563   \n",
       "1         20    Part One  METROPOLITAN AVE  42.262092 -71.116710   \n",
       "2         15    Part One        ALLSTON ST  42.352375 -71.135096   \n",
       "3          4    Part One   SAINT JAMES AVE  42.349476 -71.076402   \n",
       "4          8    Part One         N MEAD ST  42.381846 -71.066551   \n",
       "...      ...         ...               ...        ...        ...   \n",
       "426833     9    Part Two    NEW SUDBURY ST  42.361839 -71.059765   \n",
       "426834     8  Part Three    NEW SUDBURY ST  42.361839 -71.059765   \n",
       "426835     8  Part Three    NEW SUDBURY ST  42.361839 -71.059765   \n",
       "426836     8  Part Three    NEW SUDBURY ST  42.361839 -71.059765   \n",
       "426837     8  Part Three    NEW SUDBURY ST  42.361839 -71.059765   \n",
       "\n",
       "                           Location  \n",
       "0       (42.25951765, -71.12156299)  \n",
       "1       (42.26209214, -71.11670964)  \n",
       "2       (42.35237455, -71.13509584)  \n",
       "3       (42.34947586, -71.07640150)  \n",
       "4       (42.38184582, -71.06655134)  \n",
       "...                             ...  \n",
       "426833  (42.36183857, -71.05976489)  \n",
       "426834  (42.36183857, -71.05976489)  \n",
       "426835  (42.36183857, -71.05976489)  \n",
       "426836  (42.36183857, -71.05976489)  \n",
       "426837  (42.36183857, -71.05976489)  \n",
       "\n",
       "[73586 rows x 17 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimedf_19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'crimedf_19' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-fa68842bd606>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcrimedf_19\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'crimedf_19' is not defined"
     ]
    }
   ],
   "source": [
    "crimedf_19.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxrows = 2000\n",
    "\n",
    "map = folium.Map(location = boston_coords, zoom_start = 12)\n",
    "\n",
    "for each in crimedf_19[0:maxrows].iterrows():\n",
    "    MarkerCluster(\n",
    "        location = [each[1]['Lat'],each[1]['Long']], Cluster = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=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 onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f95a8aaa310>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = folium.Map(\n",
    "    location=[46.1991, -122.1889],\n",
    "    tiles='Stamen Terrain',\n",
    "    zoom_start=13\n",
    ")\n",
    "\n",
    "m.add_child(folium.LatLngPopup())\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=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 onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f95d9812ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group by neighborhood\n",
    "boston = crime_input.groupby('PdDistrict').count()\n",
    "sf = pd.DataFrame(sf,columns=['Category'])  # remove unneeded columns\n",
    "sf.reset_index(inplace=True)   # default index, otherwise groupby column becomes index\n",
    "sf.rename(columns={'PdDistrict':'Neighborhood','Category':'Count'}, inplace=True)\n",
    "sf.sort_values(by='Count', inplace=True, ascending=False)\n",
    "#print(sf)\n",
    "\n",
    "# San Francisco latitude and longitude values\n",
    "latitude = 37.77\n",
    "longitude = -122.42\n",
    "sf_neighborhood_geo = '../datasets/san-francisco.geojson'\n",
    "\n",
    "# Create map\n",
    "sf_map = folium.Map(\n",
    "       location=[latitude,longitude],\n",
    "       zoom_start=12)\n",
    "\n",
    "# Use json file  TEST based on class\n",
    "sf_map.choropleth(\n",
    "       geo_data=sf_neighborhood_geo,\n",
    "       data=sf,\n",
    "       columns=['Neighborhood','Count'],\n",
    "       key_on='feature.properties.DISTRICT',\n",
    "       fill_color='YlOrRd',\n",
    "       fill_opacity='0.7',\n",
    "       line_opacity='0.2',\n",
    "       legend_name='Crime Rate in San Francisco, by Neighborhood')\n",
    "\n",
    "# display the map\n",
    "sf_map"
   ]
  }
 ],
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