THE NEW BOSTON CRIME FROM 2015-2018.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0d907327",
   "metadata": {},
   "source": [
    "Boston crime incidence data is analyzed to obtain insights on crime occuring in Boston. This insight helps the law enforcement agency to take precautionary action and when properly done, it reduces the crime rate of boston."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe4dd328",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "835e35b3",
   "metadata": {},
   "source": [
    "imported the excel file into pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae230b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "crime=pd.read_excel('crimeb.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c526ee1b",
   "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>I182070945</td>\n",
       "      <td>619</td>\n",
       "      <td>Larceny</td>\n",
       "      <td>LARCENY ALL OTHERS</td>\n",
       "      <td>D14</td>\n",
       "      <td>808</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-09-02 13:00:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>13</td>\n",
       "      <td>Part One</td>\n",
       "      <td>LINCOLN ST</td>\n",
       "      <td>42.357791</td>\n",
       "      <td>-71.139371</td>\n",
       "      <td>(42.35779134, -71.13937053)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I182070943</td>\n",
       "      <td>1402</td>\n",
       "      <td>Vandalism</td>\n",
       "      <td>VANDALISM</td>\n",
       "      <td>C11</td>\n",
       "      <td>347</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-21 00:00:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td>Part Two</td>\n",
       "      <td>HECLA ST</td>\n",
       "      <td>42.306821</td>\n",
       "      <td>-71.060300</td>\n",
       "      <td>(42.30682138, -71.06030035)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I182070941</td>\n",
       "      <td>3410</td>\n",
       "      <td>Towed</td>\n",
       "      <td>TOWED MOTOR VEHICLE</td>\n",
       "      <td>D4</td>\n",
       "      <td>151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-09-03 19:27:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>Monday</td>\n",
       "      <td>19</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>CAZENOVE ST</td>\n",
       "      <td>42.346589</td>\n",
       "      <td>-71.072429</td>\n",
       "      <td>(42.34658879, -71.07242943)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I182070940</td>\n",
       "      <td>3114</td>\n",
       "      <td>Investigate Property</td>\n",
       "      <td>INVESTIGATE PROPERTY</td>\n",
       "      <td>D4</td>\n",
       "      <td>272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-09-03 21:16:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>Monday</td>\n",
       "      <td>21</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>NEWCOMB ST</td>\n",
       "      <td>42.334182</td>\n",
       "      <td>-71.078664</td>\n",
       "      <td>(42.33418175, -71.07866441)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I182070938</td>\n",
       "      <td>3114</td>\n",
       "      <td>Investigate Property</td>\n",
       "      <td>INVESTIGATE PROPERTY</td>\n",
       "      <td>B3</td>\n",
       "      <td>421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-09-03 21:05:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>Monday</td>\n",
       "      <td>21</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>DELHI ST</td>\n",
       "      <td>42.275365</td>\n",
       "      <td>-71.090361</td>\n",
       "      <td>(42.27536542, -71.09036101)</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>319068</th>\n",
       "      <td>I050310906-00</td>\n",
       "      <td>3125</td>\n",
       "      <td>Warrant Arrests</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>D4</td>\n",
       "      <td>285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-05 17:25:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>17</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>COVENTRY ST</td>\n",
       "      <td>42.336951</td>\n",
       "      <td>-71.085748</td>\n",
       "      <td>(42.33695098, -71.08574813)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319069</th>\n",
       "      <td>I030217815-08</td>\n",
       "      <td>111</td>\n",
       "      <td>Homicide</td>\n",
       "      <td>MURDER, NON-NEGLIGIENT MANSLAUGHTER</td>\n",
       "      <td>E18</td>\n",
       "      <td>520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-07-09 13:38:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>13</td>\n",
       "      <td>Part One</td>\n",
       "      <td>RIVER ST</td>\n",
       "      <td>42.255926</td>\n",
       "      <td>-71.123172</td>\n",
       "      <td>(42.25592648, -71.12317207)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319070</th>\n",
       "      <td>I030217815-08</td>\n",
       "      <td>3125</td>\n",
       "      <td>Warrant Arrests</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>E18</td>\n",
       "      <td>520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-07-09 13:38:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>13</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>RIVER ST</td>\n",
       "      <td>42.255926</td>\n",
       "      <td>-71.123172</td>\n",
       "      <td>(42.25592648, -71.12317207)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319071</th>\n",
       "      <td>I010370257-00</td>\n",
       "      <td>3125</td>\n",
       "      <td>Warrant Arrests</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>E13</td>\n",
       "      <td>569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-05-31 19:35:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>19</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>NEW WASHINGTON ST</td>\n",
       "      <td>42.302333</td>\n",
       "      <td>-71.111565</td>\n",
       "      <td>(42.30233307, -71.11156487)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319072</th>\n",
       "      <td>142052550</td>\n",
       "      <td>3125</td>\n",
       "      <td>Warrant Arrests</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>D4</td>\n",
       "      <td>903</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-06-22 00:12:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>WASHINGTON ST</td>\n",
       "      <td>42.333839</td>\n",
       "      <td>-71.080290</td>\n",
       "      <td>(42.33383935, -71.08029038)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>319073 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       INCIDENT_NUMBER  OFFENSE_CODE    OFFENSE_CODE_GROUP  \\\n",
       "0           I182070945           619               Larceny   \n",
       "1           I182070943          1402             Vandalism   \n",
       "2           I182070941          3410                 Towed   \n",
       "3           I182070940          3114  Investigate Property   \n",
       "4           I182070938          3114  Investigate Property   \n",
       "...                ...           ...                   ...   \n",
       "319068   I050310906-00          3125       Warrant Arrests   \n",
       "319069   I030217815-08           111              Homicide   \n",
       "319070   I030217815-08          3125       Warrant Arrests   \n",
       "319071   I010370257-00          3125       Warrant Arrests   \n",
       "319072       142052550          3125       Warrant Arrests   \n",
       "\n",
       "                        OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING  \\\n",
       "0                        LARCENY ALL OTHERS      D14            808      NaN   \n",
       "1                                 VANDALISM      C11            347      NaN   \n",
       "2                       TOWED MOTOR VEHICLE       D4            151      NaN   \n",
       "3                      INVESTIGATE PROPERTY       D4            272      NaN   \n",
       "4                      INVESTIGATE PROPERTY       B3            421      NaN   \n",
       "...                                     ...      ...            ...      ...   \n",
       "319068                       WARRANT ARREST       D4            285      NaN   \n",
       "319069  MURDER, NON-NEGLIGIENT MANSLAUGHTER      E18            520      NaN   \n",
       "319070                       WARRANT ARREST      E18            520      NaN   \n",
       "319071                       WARRANT ARREST      E13            569      NaN   \n",
       "319072                       WARRANT ARREST       D4            903      NaN   \n",
       "\n",
       "          OCCURRED_ON_DATE  YEAR  MONTH DAY_OF_WEEK  HOUR    UCR_PART  \\\n",
       "0      2018-09-02 13:00:00  2018      9      Sunday    13    Part One   \n",
       "1      2018-08-21 00:00:00  2018      8     Tuesday     0    Part Two   \n",
       "2      2018-09-03 19:27:00  2018      9      Monday    19  Part Three   \n",
       "3      2018-09-03 21:16:00  2018      9      Monday    21  Part Three   \n",
       "4      2018-09-03 21:05:00  2018      9      Monday    21  Part Three   \n",
       "...                    ...   ...    ...         ...   ...         ...   \n",
       "319068 2016-06-05 17:25:00  2016      6      Sunday    17  Part Three   \n",
       "319069 2015-07-09 13:38:00  2015      7    Thursday    13    Part One   \n",
       "319070 2015-07-09 13:38:00  2015      7    Thursday    13  Part Three   \n",
       "319071 2016-05-31 19:35:00  2016      5     Tuesday    19  Part Three   \n",
       "319072 2015-06-22 00:12:00  2015      6      Monday     0  Part Three   \n",
       "\n",
       "                   STREET        Lat       Long                     Location  \n",
       "0              LINCOLN ST  42.357791 -71.139371  (42.35779134, -71.13937053)  \n",
       "1                HECLA ST  42.306821 -71.060300  (42.30682138, -71.06030035)  \n",
       "2             CAZENOVE ST  42.346589 -71.072429  (42.34658879, -71.07242943)  \n",
       "3              NEWCOMB ST  42.334182 -71.078664  (42.33418175, -71.07866441)  \n",
       "4                DELHI ST  42.275365 -71.090361  (42.27536542, -71.09036101)  \n",
       "...                   ...        ...        ...                          ...  \n",
       "319068        COVENTRY ST  42.336951 -71.085748  (42.33695098, -71.08574813)  \n",
       "319069           RIVER ST  42.255926 -71.123172  (42.25592648, -71.12317207)  \n",
       "319070           RIVER ST  42.255926 -71.123172  (42.25592648, -71.12317207)  \n",
       "319071  NEW WASHINGTON ST  42.302333 -71.111565  (42.30233307, -71.11156487)  \n",
       "319072      WASHINGTON ST  42.333839 -71.080290  (42.33383935, -71.08029038)  \n",
       "\n",
       "[319073 rows x 17 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28a63d35",
   "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>I182070945</td>\n",
       "      <td>619</td>\n",
       "      <td>Larceny</td>\n",
       "      <td>LARCENY ALL OTHERS</td>\n",
       "      <td>D14</td>\n",
       "      <td>808</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-09-02 13:00:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>13</td>\n",
       "      <td>Part One</td>\n",
       "      <td>LINCOLN ST</td>\n",
       "      <td>42.357791</td>\n",
       "      <td>-71.139371</td>\n",
       "      <td>(42.35779134, -71.13937053)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I182070943</td>\n",
       "      <td>1402</td>\n",
       "      <td>Vandalism</td>\n",
       "      <td>VANDALISM</td>\n",
       "      <td>C11</td>\n",
       "      <td>347</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-21 00:00:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td>Part Two</td>\n",
       "      <td>HECLA ST</td>\n",
       "      <td>42.306821</td>\n",
       "      <td>-71.060300</td>\n",
       "      <td>(42.30682138, -71.06030035)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I182070941</td>\n",
       "      <td>3410</td>\n",
       "      <td>Towed</td>\n",
       "      <td>TOWED MOTOR VEHICLE</td>\n",
       "      <td>D4</td>\n",
       "      <td>151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-09-03 19:27:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>Monday</td>\n",
       "      <td>19</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>CAZENOVE ST</td>\n",
       "      <td>42.346589</td>\n",
       "      <td>-71.072429</td>\n",
       "      <td>(42.34658879, -71.07242943)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I182070940</td>\n",
       "      <td>3114</td>\n",
       "      <td>Investigate Property</td>\n",
       "      <td>INVESTIGATE PROPERTY</td>\n",
       "      <td>D4</td>\n",
       "      <td>272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-09-03 21:16:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>Monday</td>\n",
       "      <td>21</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>NEWCOMB ST</td>\n",
       "      <td>42.334182</td>\n",
       "      <td>-71.078664</td>\n",
       "      <td>(42.33418175, -71.07866441)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I182070938</td>\n",
       "      <td>3114</td>\n",
       "      <td>Investigate Property</td>\n",
       "      <td>INVESTIGATE PROPERTY</td>\n",
       "      <td>B3</td>\n",
       "      <td>421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-09-03 21:05:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>Monday</td>\n",
       "      <td>21</td>\n",
       "      <td>Part Three</td>\n",
       "      <td>DELHI ST</td>\n",
       "      <td>42.275365</td>\n",
       "      <td>-71.090361</td>\n",
       "      <td>(42.27536542, -71.09036101)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  INCIDENT_NUMBER  OFFENSE_CODE    OFFENSE_CODE_GROUP   OFFENSE_DESCRIPTION  \\\n",
       "0      I182070945           619               Larceny    LARCENY ALL OTHERS   \n",
       "1      I182070943          1402             Vandalism             VANDALISM   \n",
       "2      I182070941          3410                 Towed   TOWED MOTOR VEHICLE   \n",
       "3      I182070940          3114  Investigate Property  INVESTIGATE PROPERTY   \n",
       "4      I182070938          3114  Investigate Property  INVESTIGATE PROPERTY   \n",
       "\n",
       "  DISTRICT REPORTING_AREA SHOOTING    OCCURRED_ON_DATE  YEAR  MONTH  \\\n",
       "0      D14            808      NaN 2018-09-02 13:00:00  2018      9   \n",
       "1      C11            347      NaN 2018-08-21 00:00:00  2018      8   \n",
       "2       D4            151      NaN 2018-09-03 19:27:00  2018      9   \n",
       "3       D4            272      NaN 2018-09-03 21:16:00  2018      9   \n",
       "4       B3            421      NaN 2018-09-03 21:05:00  2018      9   \n",
       "\n",
       "  DAY_OF_WEEK  HOUR    UCR_PART       STREET        Lat       Long  \\\n",
       "0      Sunday    13    Part One   LINCOLN ST  42.357791 -71.139371   \n",
       "1     Tuesday     0    Part Two     HECLA ST  42.306821 -71.060300   \n",
       "2      Monday    19  Part Three  CAZENOVE ST  42.346589 -71.072429   \n",
       "3      Monday    21  Part Three   NEWCOMB ST  42.334182 -71.078664   \n",
       "4      Monday    21  Part Three     DELHI ST  42.275365 -71.090361   \n",
       "\n",
       "                      Location  \n",
       "0  (42.35779134, -71.13937053)  \n",
       "1  (42.30682138, -71.06030035)  \n",
       "2  (42.34658879, -71.07242943)  \n",
       "3  (42.33418175, -71.07866441)  \n",
       "4  (42.27536542, -71.09036101)  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8247d7a7",
   "metadata": {},
   "source": [
    "using isnull to check how many null value present in the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7325d1f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INCIDENT_NUMBER             0\n",
       "OFFENSE_CODE                0\n",
       "OFFENSE_CODE_GROUP          0\n",
       "OFFENSE_DESCRIPTION         0\n",
       "DISTRICT                 1765\n",
       "REPORTING_AREA              0\n",
       "SHOOTING               318054\n",
       "OCCURRED_ON_DATE            0\n",
       "YEAR                        0\n",
       "MONTH                       0\n",
       "DAY_OF_WEEK                 0\n",
       "HOUR                        0\n",
       "UCR_PART                   90\n",
       "STREET                  10871\n",
       "Lat                     19999\n",
       "Long                    19999\n",
       "Location                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf8fd6c",
   "metadata": {},
   "source": [
    "FOR ALL SHOOTING THAT IS EMPTY, FILL IT UP WITH 0 MEANING NO SHOOTING HAPPENED THERE AND FOR EVERY ROW WITH 'Y' REPLACE WITH 1\n",
    "SINCE THE DATA SAID 'Y' MEANS SHOOTING OCCURRED "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6f185a10",
   "metadata": {},
   "outputs": [],
   "source": [
    "crime['SHOOTING']= crime['SHOOTING'].fillna(0)\n",
    "crime['SHOOTING'].replace('Y',1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ece99fd",
   "metadata": {},
   "source": [
    "DROP EVERY OTHER EMPTY ROW TO HAVE A CLEAN DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a2e38db",
   "metadata": {},
   "outputs": [],
   "source": [
    "crime.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c20ef37a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INCIDENT_NUMBER        0\n",
       "OFFENSE_CODE           0\n",
       "OFFENSE_CODE_GROUP     0\n",
       "OFFENSE_DESCRIPTION    0\n",
       "DISTRICT               0\n",
       "REPORTING_AREA         0\n",
       "SHOOTING               0\n",
       "OCCURRED_ON_DATE       0\n",
       "YEAR                   0\n",
       "MONTH                  0\n",
       "DAY_OF_WEEK            0\n",
       "HOUR                   0\n",
       "UCR_PART               0\n",
       "STREET                 0\n",
       "Lat                    0\n",
       "Long                   0\n",
       "Location               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c84a58f0",
   "metadata": {},
   "source": [
    "CHECK THE TOTAL INFORMATION OF THE DATA USING INFO(). CHECKING THE COLUMN NAME, THE TYPE OF DATA OBTAINED IN THE COLUMN, HOW MANY ROWS AND NULL VALUES "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6bc3173",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 296573 entries, 0 to 319072\n",
      "Data columns (total 17 columns):\n",
      " #   Column               Non-Null Count   Dtype         \n",
      "---  ------               --------------   -----         \n",
      " 0   INCIDENT_NUMBER      296573 non-null  object        \n",
      " 1   OFFENSE_CODE         296573 non-null  int64         \n",
      " 2   OFFENSE_CODE_GROUP   296573 non-null  object        \n",
      " 3   OFFENSE_DESCRIPTION  296573 non-null  object        \n",
      " 4   DISTRICT             296573 non-null  object        \n",
      " 5   REPORTING_AREA       296573 non-null  object        \n",
      " 6   SHOOTING             296573 non-null  int64         \n",
      " 7   OCCURRED_ON_DATE     296573 non-null  datetime64[ns]\n",
      " 8   YEAR                 296573 non-null  int64         \n",
      " 9   MONTH                296573 non-null  int64         \n",
      " 10  DAY_OF_WEEK          296573 non-null  object        \n",
      " 11  HOUR                 296573 non-null  int64         \n",
      " 12  UCR_PART             296573 non-null  object        \n",
      " 13  STREET               296573 non-null  object        \n",
      " 14  Lat                  296573 non-null  float64       \n",
      " 15  Long                 296573 non-null  float64       \n",
      " 16  Location             296573 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(5), object(9)\n",
      "memory usage: 40.7+ MB\n"
     ]
    }
   ],
   "source": [
    "crime.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b062fcf5",
   "metadata": {},
   "source": [
    "CHECK THE STATISTICS WITH '.DESCRIBE() OF THE DATA TO UNDERSTAND ITS DISTRIBUTION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b2d10d10",
   "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_CODE</th>\n",
       "      <th>SHOOTING</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>296573.000000</td>\n",
       "      <td>296573.000000</td>\n",
       "      <td>296573.000000</td>\n",
       "      <td>296573.000000</td>\n",
       "      <td>296573.000000</td>\n",
       "      <td>296573.000000</td>\n",
       "      <td>296573.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2293.856737</td>\n",
       "      <td>0.003274</td>\n",
       "      <td>2016.550954</td>\n",
       "      <td>6.611856</td>\n",
       "      <td>13.124576</td>\n",
       "      <td>42.300079</td>\n",
       "      <td>-71.046943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1182.909512</td>\n",
       "      <td>0.057126</td>\n",
       "      <td>1.001118</td>\n",
       "      <td>3.279314</td>\n",
       "      <td>6.278383</td>\n",
       "      <td>0.981040</td>\n",
       "      <td>1.586477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>111.000000</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>802.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>42.297521</td>\n",
       "      <td>-71.097223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2907.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>42.325574</td>\n",
       "      <td>-71.077562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3201.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>42.348624</td>\n",
       "      <td>-71.062529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3831.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>42.395042</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        OFFENSE_CODE       SHOOTING           YEAR          MONTH  \\\n",
       "count  296573.000000  296573.000000  296573.000000  296573.000000   \n",
       "mean     2293.856737       0.003274    2016.550954       6.611856   \n",
       "std      1182.909512       0.057126       1.001118       3.279314   \n",
       "min       111.000000       0.000000    2015.000000       1.000000   \n",
       "25%       802.000000       0.000000    2016.000000       4.000000   \n",
       "50%      2907.000000       0.000000    2017.000000       7.000000   \n",
       "75%      3201.000000       0.000000    2017.000000       9.000000   \n",
       "max      3831.000000       1.000000    2018.000000      12.000000   \n",
       "\n",
       "                HOUR            Lat           Long  \n",
       "count  296573.000000  296573.000000  296573.000000  \n",
       "mean       13.124576      42.300079     -71.046943  \n",
       "std         6.278383       0.981040       1.586477  \n",
       "min         0.000000      -1.000000     -71.178674  \n",
       "25%         9.000000      42.297521     -71.097223  \n",
       "50%        14.000000      42.325574     -71.077562  \n",
       "75%        18.000000      42.348624     -71.062529  \n",
       "max        23.000000      42.395042      -1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71274be4",
   "metadata": {},
   "source": [
    "TOTAL NUMBER OF SHOOTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "58e0e764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "971"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime['SHOOTING'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02bd078d",
   "metadata": {},
   "source": [
    "EXPLORING THE DATA BY GROUPING THE SHOOTING INTO YEARS TO UNDERSTAND THE SHOOTING PER YEAR "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "58d8ed5e",
   "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_CODE</th>\n",
       "      <th>SHOOTING</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEAR</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>2018</th>\n",
       "      <td>141822607</td>\n",
       "      <td>169</td>\n",
       "      <td>283196</td>\n",
       "      <td>795421</td>\n",
       "      <td>2.569542e+06</td>\n",
       "      <td>-4.315833e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>111595060</td>\n",
       "      <td>178</td>\n",
       "      <td>470094</td>\n",
       "      <td>666387</td>\n",
       "      <td>2.164921e+06</td>\n",
       "      <td>-3.636112e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>209300743</td>\n",
       "      <td>267</td>\n",
       "      <td>598184</td>\n",
       "      <td>1205624</td>\n",
       "      <td>3.872388e+06</td>\n",
       "      <td>-6.504058e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>217577564</td>\n",
       "      <td>357</td>\n",
       "      <td>609424</td>\n",
       "      <td>1224963</td>\n",
       "      <td>3.938209e+06</td>\n",
       "      <td>-6.614602e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      OFFENSE_CODE  SHOOTING   MONTH     HOUR           Lat          Long\n",
       "YEAR                                                                     \n",
       "2018     141822607       169  283196   795421  2.569542e+06 -4.315833e+06\n",
       "2015     111595060       178  470094   666387  2.164921e+06 -3.636112e+06\n",
       "2016     209300743       267  598184  1205624  3.872388e+06 -6.504058e+06\n",
       "2017     217577564       357  609424  1224963  3.938209e+06 -6.614602e+06"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shooting_rate=crime.groupby('YEAR').sum()\n",
    "shooting_rate.sort_values('SHOOTING',ascending=True, inplace=True)\n",
    "shooting_rate.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76850ac9",
   "metadata": {},
   "source": [
    "TO KNOW THE MOST COMMITTED CRIME, I USED VALUE COUNT AND ILOC TO GET THE 10 MOST COMMITTED CRIME."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "866a6b65",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_offence=crime.OFFENSE_DESCRIPTION.value_counts().iloc[:10].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6f5b1d98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "THREATS TO DO BODILY HARM                 8786\n",
       "LARCENY THEFT FROM BUILDING               8870\n",
       "INVESTIGATE PROPERTY                     10594\n",
       "TOWED MOTOR VEHICLE                      10736\n",
       "VERBAL DISPUTE                           12947\n",
       "ASSAULT SIMPLE - BATTERY                 13869\n",
       "M/V - LEAVING SCENE - PROPERTY DAMAGE    14370\n",
       "VANDALISM                                14597\n",
       "SICK/INJURED/MEDICAL - PERSON            17820\n",
       "INVESTIGATE PERSON                       17973\n",
       "Name: OFFENSE_DESCRIPTION, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_offence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a3e501",
   "metadata": {},
   "source": [
    "TO EXPLORE IT MORE, TURN THE 10 MOST COMMITTED CRIME INTO A DATA OF ITS OWN NAMED (FIRST_10_CRIME). \n",
    "NOW USED THE FIRST_10_CRIME AND THE ILOC DATA TO MAKE A BAR CHART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c1d1ce48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('THREATS TO DO BODILY HARM',\n",
       " 'LARCENY THEFT FROM BUILDING',\n",
       " 'INVESTIGATE PROPERTY',\n",
       " 'TOWED MOTOR VEHICLE',\n",
       " 'VERBAL DISPUTE',\n",
       " 'ASSAULT SIMPLE - BATTERY ',\n",
       " 'VANDALISM',\n",
       " 'M/V - LEAVING SCENE - PROPERTY DAMAGE',\n",
       " 'SICK/INJURED/MEDICAL - PERSON',\n",
       " 'INVESTIGATE PERSON')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_10_crime=(\"THREATS TO DO BODILY HARM\",\"LARCENY THEFT FROM BUILDING\", \"INVESTIGATE PROPERTY\",\"TOWED MOTOR VEHICLE\", \"VERBAL DISPUTE\",\"ASSAULT SIMPLE - BATTERY \", \"VANDALISM\",\"M/V - LEAVING SCENE - PROPERTY DAMAGE\",\"SICK/INJURED/MEDICAL - PERSON\", \"INVESTIGATE PERSON\")\n",
    "first_10_crime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b9ec25e",
   "metadata": {},
   "source": [
    "USING MATPLOT TO PLOT THE BAR CHART NAMING IT CRIMES THAT ARE FREQUENTLY COMMITTED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "df07cfb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(13,8))\n",
    "plt.xlabel(\"THEIR NUMBERS\", fontsize=20)\n",
    "plt.ylabel(\"HIGHEST CRIMES COMMITTED\", fontsize=14)\n",
    "plt.title(\"CRIMES THAT ARE FREQUENTLY COMMITTED\", fontsize=20)\n",
    "plt.barh(first_10_crime,most_offence, color=\"gray\", ec=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae29ceca",
   "metadata": {},
   "source": [
    "INVESTIGATE PERSON AND SICK/INJURED/MEDICAL - PERSON ARE LEADING THE RACE IN THE  MOST COMMITTED CRIME DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e847090",
   "metadata": {},
   "source": [
    "GETTING THE STREET THAT ARE CRIME PRONE USING ILOC, MAKING THE STREET A DATA OF ITS OWN AND PLOTTING IT IN A BAR CHART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "252c5bc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HYDE PARK AVE         3401\n",
       "COMMONWEALTH AVE      3865\n",
       "HARRISON AVE          4274\n",
       "CENTRE ST             4284\n",
       "MASSACHUSETTS AVE     4392\n",
       "TREMONT ST            4663\n",
       "DORCHESTER AVE        5007\n",
       "BOYLSTON ST           6939\n",
       "BLUE HILL AVE         6988\n",
       "WASHINGTON ST        14107\n",
       "Name: STREET, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_offence1=crime.STREET.value_counts().iloc[:10].sort_values()\n",
    "most_offence1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cdc0300a",
   "metadata": {},
   "outputs": [],
   "source": [
    "street=('HYDE PARK AVE','COMMONWEALTH AVE', 'CENTRE ST','HARRISON AVE', 'MASSACHUSETTS AVE', 'TREMONT ST', 'DORCHESTER AVE', 'BOYLSTON ST', 'BLUE HILL AVE', 'WASHINGTON ST', )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7d230ead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,6))\n",
    "plt.xlabel(\"THEIR NUMBERS\", fontsize=12)\n",
    "plt.ylabel(\"STREET WITH HIGHEST CRIME RATE\", fontsize=12)\n",
    "plt.title(\"STREETS WITH HIGH CRIME RATE\", fontsize=15)\n",
    "plt.barh(street,most_offence1, color=\"red\", ec=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2254015",
   "metadata": {},
   "source": [
    "WASHINGTON STREET AND BLUE HILL AVENUE IS LEADING THE RACE IN THE STREET WITH HIGHEST CRIME RATE. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab6a6eb",
   "metadata": {},
   "source": [
    "LOOKING AT THE SHOOTING DISTRIBUTION IN THE FOUR YEARS, MORE SHOOTING HAPPENED IN 2017 THAT ANY OTHER YEAR. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4f785821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'YEAR WITH HIGHEST SHOOTING'}, xlabel='YEAR'>"
      ]
     },
     "execution_count": 27,
     "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": [
    "shooting_rate['SHOOTING'].head(4).plot.bar(color='red', title=\"YEAR WITH HIGHEST SHOOTING\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b00160a6",
   "metadata": {},
   "source": [
    "EXPLORING THE MONTH WITH HIGH CRIME RATE. \n",
    "FIRST USE ILOC AND VALUE COUNT TO GET THE MONTH WITH HIGH CRIME RATE. \n",
    "MAKE THE MONTHS WITH HIGHEST CRIME A DATA OF ITS OWN \n",
    "PLOT THE BAR CHART OF THE MONTH VALUE COUNT TO THE NEW DATA CREATED.\n",
    "\n",
    "OBSERVATION: JUNE, JULY AND AUGUST HAS THE HIGHEST AMOUNT OF CRIME WHILE NOVEMBER, JANUARY AND MARCH HAS\n",
    "THE LOWEST AMOUNT OF CRIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a7049a72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11    22091\n",
       "1     22187\n",
       "3     22273\n",
       "4     22410\n",
       "10    23874\n",
       "5     24219\n",
       "9     24662\n",
       "6     28496\n",
       "7     32050\n",
       "8     32205\n",
       "Name: MONTH, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month=crime.MONTH.value_counts().iloc[:10].sort_values()\n",
    "month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b49466b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "month_of_crime=['nov', 'jan', 'mar','apr', 'oct', 'may', 'sep', 'jun', 'jul', 'aug']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5b52fecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,6))\n",
    "plt.xlabel(\"THE MONTHS\", fontsize=12)\n",
    "plt.ylabel(\"THEIR NUMBERS\", fontsize=12)\n",
    "plt.title('MONTHS WITH HIGH CRIME RATE', fontsize=15)\n",
    "plt.bar(month_of_crime,month, color=\"green\", ec=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b37263",
   "metadata": {},
   "source": [
    "EXPLORING THE DAY WITH HIGH CRIME RATE. FIRST USE ILOC AND VALUE COUNT TO GET THE DAY AND THEIR CRIME RATE. \n",
    "MAKE THE DAYS WITH THEIR CRIME RATE A DATA OF ITS OWN PLOT THE BAR CHART OF THE DAYS VALUE COUNT TO THE NEW DATA CREATED.\n",
    "\n",
    "OBSERVATION: FRIDAY, WEDNESDAY AND THURSDAY HAS THE HIGHEST AMOUNT OF CRIME WHILE SUNDAY,\n",
    "SATURDAY AND MONDAY HAS THE LOWEST AMOUNT OF CRIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7208c1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sunday       37677\n",
       "Saturday     41569\n",
       "Monday       42469\n",
       "Tuesday      42997\n",
       "Thursday     43283\n",
       "Wednesday    43445\n",
       "Friday       45133\n",
       "Name: DAY_OF_WEEK, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day=crime.DAY_OF_WEEK.value_counts().iloc[:10].sort_values()\n",
    "day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "36a39e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "day_with_high_crime=['sun', 'sat', 'mon', 'tue', 'thur', 'wed', 'fri']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4dca35e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,6))\n",
    "plt.xlabel(\"THE DAYS\", fontsize=12)\n",
    "plt.ylabel(\"THEIR NUMBERS\", fontsize=12)\n",
    "plt.title('DAYS WITH HIGH CRIME RATE', fontsize=15)\n",
    "plt.bar(day_with_high_crime,day, color=\"black\", ec=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d119acb",
   "metadata": {},
   "source": [
    "EXPLORING THE TIME WITH HIGH CRIME RATE. FIRST USE ILOC AND VALUE COUNT TO GET THE TIME AND THEIR CRIME RATE.\n",
    "MAKE THE TIME WITH THEIR CRIME RATE A DATA OF ITS OWN PLOT THE BAR CHART OF THE TIME VALUE COUNT TO THE NEW DATA CREATED.\n",
    "\n",
    "OBSERVATION: 4PM-6PM HAS THE HIGHEST AMOUNT OF CRIME "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cbf26113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15    15401\n",
       "11    15414\n",
       "10    15420\n",
       "13    15769\n",
       "14    16026\n",
       "19    16288\n",
       "12    17558\n",
       "16    18420\n",
       "18    18617\n",
       "17    18982\n",
       "Name: HOUR, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time=crime.HOUR.value_counts().iloc[:10].sort_values()\n",
    "time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4134664b",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_of_occurrance=['10am', '3pm', '11am', '1pm', '2pm', '7pm', '12pm', '4pm', '6pm', '5pm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9823491a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,6))\n",
    "plt.xlabel(\"THE HOURS\", fontsize=12)\n",
    "plt.ylabel(\"THEIR NUMBERS\", fontsize=12)\n",
    "plt.title('HOURS WITH HIGH CRIME RATE', fontsize=15)\n",
    "plt.bar(time_of_occurrance,time, color=\"red\", ec=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05c49db",
   "metadata": {},
   "source": [
    "DOWNLOADING THE FILE FOR DASHBOARDING BUILDDING IN POWER BI "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e60e246f",
   "metadata": {},
   "outputs": [],
   "source": [
    "crime.to_csv('C:\\\\Users\\\\user\\\\Downloads\\\\crime2.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}