Baltimore_Crime_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CrimeDate</th>\n",
       "      <th>CrimeTime</th>\n",
       "      <th>CrimeCode</th>\n",
       "      <th>Location</th>\n",
       "      <th>Description</th>\n",
       "      <th>Weapon</th>\n",
       "      <th>Post</th>\n",
       "      <th>District</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Location 1</th>\n",
       "      <th>Total Incidents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/24/2012</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>9S</td>\n",
       "      <td>2100 VINE ST</td>\n",
       "      <td>Outside</td>\n",
       "      <td>FIREARM</td>\n",
       "      <td>714.0</td>\n",
       "      <td>WESTERN</td>\n",
       "      <td>Penrose/Fayette Street Outreach</td>\n",
       "      <td>(39.2899300000, -76.6503900000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/07/2016</td>\n",
       "      <td>23:59:00</td>\n",
       "      <td>9S</td>\n",
       "      <td>700 E EAGER ST</td>\n",
       "      <td>Inside</td>\n",
       "      <td>FIREARM</td>\n",
       "      <td>311.0</td>\n",
       "      <td>EASTERN</td>\n",
       "      <td>Johnston Square</td>\n",
       "      <td>(39.3013900000, -76.6071300000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06/15/2015</td>\n",
       "      <td>23:59:00</td>\n",
       "      <td>1F</td>\n",
       "      <td>2700 HARLEM AV</td>\n",
       "      <td>Outside</td>\n",
       "      <td>FIREARM</td>\n",
       "      <td>721.0</td>\n",
       "      <td>WESTERN</td>\n",
       "      <td>Mosher</td>\n",
       "      <td>(39.2955400000, -76.6617100000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09/12/2014</td>\n",
       "      <td>23:59:00</td>\n",
       "      <td>9S</td>\n",
       "      <td>4000 PALL MALL RD</td>\n",
       "      <td>Inside</td>\n",
       "      <td>FIREARM</td>\n",
       "      <td>533.0</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>Greenspring</td>\n",
       "      <td>(39.3356800000, -76.6599800000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06/15/2013</td>\n",
       "      <td>23:59:00</td>\n",
       "      <td>9S</td>\n",
       "      <td>400 BRUNSWICK ST</td>\n",
       "      <td>Outside</td>\n",
       "      <td>FIREARM</td>\n",
       "      <td>841.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Millhill</td>\n",
       "      <td>(39.2806100000, -76.6601500000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CrimeDate CrimeTime CrimeCode           Location Description   Weapon  \\\n",
       "0  09/24/2012  00:00:00        9S       2100 VINE ST     Outside  FIREARM   \n",
       "1  01/07/2016  23:59:00        9S     700 E EAGER ST      Inside  FIREARM   \n",
       "2  06/15/2015  23:59:00        1F     2700 HARLEM AV     Outside  FIREARM   \n",
       "3  09/12/2014  23:59:00        9S  4000 PALL MALL RD      Inside  FIREARM   \n",
       "4  06/15/2013  23:59:00        9S   400 BRUNSWICK ST     Outside  FIREARM   \n",
       "\n",
       "    Post      District                     Neighborhood  \\\n",
       "0  714.0       WESTERN  Penrose/Fayette Street Outreach   \n",
       "1  311.0       EASTERN                  Johnston Square   \n",
       "2  721.0       WESTERN                           Mosher   \n",
       "3  533.0      NORTHERN                      Greenspring   \n",
       "4  841.0  SOUTHWESTERN                         Millhill   \n",
       "\n",
       "                        Location 1  Total Incidents  \n",
       "0  (39.2899300000, -76.6503900000)                1  \n",
       "1  (39.3013900000, -76.6071300000)                1  \n",
       "2  (39.2955400000, -76.6617100000)                1  \n",
       "3  (39.3356800000, -76.6599800000)                1  \n",
       "4  (39.2806100000, -76.6601500000)                1  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baltimore_Crime_data = pd.read_csv('BPD_Part_1_Victim_Based_Crime_Data (1).csv')\n",
    "baltimore_Crime_data_table = pd.DataFrame(baltimore_Crime_data)\n",
    "baltimore_Crime_data_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CrimeDate</th>\n",
       "      <th>CrimeTime</th>\n",
       "      <th>CrimeCode</th>\n",
       "      <th>Location</th>\n",
       "      <th>Description</th>\n",
       "      <th>Weapon</th>\n",
       "      <th>Post</th>\n",
       "      <th>District</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Location 1</th>\n",
       "      <th>Total Incidents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>264491</th>\n",
       "      <td>01/05/2011</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>7A</td>\n",
       "      <td>4100 BRENDAN AV</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>432.0</td>\n",
       "      <td>NORTHEASTERN</td>\n",
       "      <td>Belair-Edison</td>\n",
       "      <td>(39.3141400000, -76.5664700000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264492</th>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>4E</td>\n",
       "      <td>5500 BOWLEYS LA</td>\n",
       "      <td>I</td>\n",
       "      <td>HANDS</td>\n",
       "      <td>443.0</td>\n",
       "      <td>NORTHEASTERN</td>\n",
       "      <td>Frankford</td>\n",
       "      <td>(39.3189200000, -76.5516100000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264493</th>\n",
       "      <td>01/02/2011</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>6D</td>\n",
       "      <td>3700 BANCROFT RD</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632.0</td>\n",
       "      <td>NORTHWESTERN</td>\n",
       "      <td>Glen</td>\n",
       "      <td>(39.3589600000, -76.6993600000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264494</th>\n",
       "      <td>01/01/2011</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>6G</td>\n",
       "      <td>0 E CROSS ST</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>942.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Federal Hill</td>\n",
       "      <td>(39.2769600000, -76.6133800000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264495</th>\n",
       "      <td>01/01/2011</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>6E</td>\n",
       "      <td>2700 HARLEM AV</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>721.0</td>\n",
       "      <td>WESTERN</td>\n",
       "      <td>Mosher</td>\n",
       "      <td>(39.2956000000, -76.6615600000)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CrimeDate CrimeTime CrimeCode          Location Description Weapon  \\\n",
       "264491  01/05/2011  00:00:00        7A   4100 BRENDAN AV           O    NaN   \n",
       "264492  01/04/2011  00:00:00        4E   5500 BOWLEYS LA           I  HANDS   \n",
       "264493  01/02/2011  00:00:00        6D  3700 BANCROFT RD           O    NaN   \n",
       "264494  01/01/2011  00:00:00        6G      0 E CROSS ST           I    NaN   \n",
       "264495  01/01/2011  00:00:00        6E    2700 HARLEM AV           O    NaN   \n",
       "\n",
       "         Post      District   Neighborhood                       Location 1  \\\n",
       "264491  432.0  NORTHEASTERN  Belair-Edison  (39.3141400000, -76.5664700000)   \n",
       "264492  443.0  NORTHEASTERN      Frankford  (39.3189200000, -76.5516100000)   \n",
       "264493  632.0  NORTHWESTERN           Glen  (39.3589600000, -76.6993600000)   \n",
       "264494  942.0      SOUTHERN   Federal Hill  (39.2769600000, -76.6133800000)   \n",
       "264495  721.0       WESTERN         Mosher  (39.2956000000, -76.6615600000)   \n",
       "\n",
       "        Total Incidents  \n",
       "264491                1  \n",
       "264492                1  \n",
       "264493                1  \n",
       "264494                1  \n",
       "264495                1  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baltimore_Crime_data_table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 264496 entries, 0 to 264495\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   CrimeDate        264496 non-null  object \n",
      " 1   CrimeTime        264496 non-null  object \n",
      " 2   CrimeCode        264496 non-null  object \n",
      " 3   Location         262692 non-null  object \n",
      " 4   Description      260445 non-null  object \n",
      " 5   Weapon           89833 non-null   object \n",
      " 6   Post             264286 non-null  float64\n",
      " 7   District         264435 non-null  object \n",
      " 8   Neighborhood     262623 non-null  object \n",
      " 9   Location 1       262694 non-null  object \n",
      " 10  Total Incidents  264496 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(9)\n",
      "memory usage: 22.2+ MB\n"
     ]
    }
   ],
   "source": [
    "baltimore_Crime_data_table.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "baltimore_Crime_data_table_2 = baltimore_Crime_data_table.query('CrimeDate.str.contains(\"2016\")', engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "baltimore_Crime_data_table.drop(labels = baltimore_Crime_data_table_2.index.values , axis = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "244423\n"
     ]
    }
   ],
   "source": [
    "print(sum(baltimore_Crime_data_table['Total Incidents']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "District_wise_collect = baltimore_Crime_data_table.groupby(['District'] ).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "District_wise_collect.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IERExkXSTSO6S9DXgLcCPJK3a5XoREbEC6CYhvAX4MTDb9h+BKcA/jraSpI0k/VTSjZKul/TRUn64pLskXV1er2tb51BJCyTdLGn3tvIdJF1b5h0nSaV8VUnfKeWXS5o5pk8fERE9GzWR2P4TcC/w8lK0FPh9F9teCnzc9guAnYADJG1V5h1re9vy+hFAmTcH2BqYDZwgaVJZ/kRgLrBlec0u5fsDD9reAjgWOKqLuCIiYhx102vrM8AngUNL0SrAN0dbz/Yi21eV6SXAjcD0EVbZAzjT9uO2bwUWADtKmgZMtn2pbQOnA3u2rXNamT4L2K11tRIREYPRTdXWm4E3AY8C2L4bWHMsOylVTtsBl5eiD0u6RtIpktYpZdOBO9tWW1jKppfpoeXLrGN7KfAQsG6H/c+VNF/S/MWLF48l9IiIGEU3ieQv5UrAAJJWH8sOJK0BnA0caPthqmqqzYFtgUXA0a1FO6zuEcpHWmfZAvsk27Nsz5o6depYwo+IiFF0k0jmlV5ba0t6P/AT4OvdbFzSKlRJ5L9sfw/A9j22n7D9ZNnOjmXxhcBGbavPAO4u5TM6lC+zjqSVgbWAB7qJLSIixseo45HY/oKk1wAPA88DPm37wtHWK20VJwM32j6mrXya7UXl7ZuB68r0ecC3JB0DbEjVqH6F7SckLZG0E1XV2L7Al9vW2Q+4FNgbuLhcPUU0WsZIib8lXQ1sZftCSZe3lpc0xfZov/x3Bt4FXCvp6lJ2GPA2SdtSVUHdBnyg7ON6SfOAG6h6fB1gu3Uz5IeAU4HVgPPLC6pEdYakBVRXInO6+TwRETF+Rk0kkj4A/CvwZ+BJqnYJA5uNtJ7tX9K5DeNHI6xzBHBEh/L5wDYdyh8D9hkpjoiI6K9urkgOBra2fV+/g4mIiImnm8b2/wX+1O9AIiJiYurmiuRQ4JLSRvJ4q9D2R/oWVURETBjdJJKvARcD11K1kURERDylm0Sy1PZBfY8kIiImpG7aSH5aHjEyTdKU1qvvkUVExITQzRXJ28u/h7aVjdr9NyIiVgzd3Nm+6SACiYiIiWnYRCJpV9sXS9qr0/zWs7MiImLFNtIVySupemu9scM8A0kkEbFCyTPSOhs2kdj+jKSVgPNtzxtgTBERMYGM2GurPOr9wwOKJSIiJqBuuv9eKOlgSRul+29ERAzVTfff95Z/D2grS/ffiIgA0v03IiJ6NGzVlqR3SnpXh/L3S3p7p3UiImLFM1IbyceB73co/06ZFxERMWIimWR7ydBC2w8Dq4y24dI4/1NJN0q6XtJHS/kUSRdK+n35d522dQ6VtEDSzZJ2byvfQdK1Zd5xZTx4JK0q6Tul/HJJM8fw2SMiYhyM1EayiqTVbT/aXihpTeBZXWx7KfBx21eVda6UdCHwbuAi20dKOgQ4BPikpK2oxlzfGtgQ+Imk55Zx208E5gKXUQ3VO5tq3Pb9gQdtbyFpDnAU8NZuP/zfgtwgFRF1G+mK5GTgrPZf+WX6zDJvRLYX2b6qTC8BbgSmA3sAp5XFTgP2LNN7AGfaftz2rcACYEdJ04DJti+1beD0Ieu0tnUWsFvraiUiIgZjpDvbvyDpEeDnktag6vL7KHCk7RPHspOSgLYDLgc2sL2o7GORpPXLYtOprjhaFpayv5bpoeWtde4s21oq6SFgXWCZ8eUlzaW6omHjjTceS+gRETGKEbv/2v4q8NWSSNSpzWQ0Zd2zgQNtPzzCBUOnGR6hfKR1li2wTwJOApg1a9Yz5kdExPLr5oZEbD+yPBuXtApVEvmvtqcF3yNpWrkamQbcW8oXAhu1rT4DuLuUz+hQ3r7OQkkrA2sBDyxPrBERE1ET2km7eUTKciltFScDN9o+pm3WecB+ZXo/4Ny28jmlJ9amwJbAFaUabImknco29x2yTmtbewMXl3aUiIgYkJHGI9nH9nclbVoav8dqZ+BdwLWSri5lhwFHAvMk7Q/cAewDYPt6SfOAG6h6fB1QemwBfAg4FViNqrfW+aX8ZOAMSQuorkTmLEecERHRg5Gqtg4FvktVNbX9WDds+5d0bsMA2G2YdY4AjuhQPh/YpkP5Y5REFBER9Rgpkdwv6afAppLOGzrT9pv6F1ZEREwUIyWS11NdiZwBHD2YcCIiYqIZ6T6SvwCXSXqZ7cXl7nQvbw+uiIj429RNr60NJP0GuA64QdKVkp7RXhERESumbhLJScBBtjexvTHVk39P6m9YERExUXSTSFa3/dPWG9s/A1bvW0QRETGhdHNn+y2S/pmq0R3gncDy3FcSERF/g7q5InkvMBX4XnmtB7ynn0FFRMTE0c2Y7Q8CHxlALBERMQH17VlbERGxYkgiiYiIniSRRERET0ZNJJJmSDpH0mJJ90g6W9KM0daLiIgVQzdXJN+gGvdjGtXQtj8oZREREV0lkqm2v2F7aXmdStUdOCIioqtEcp+kd0qaVF7vBO7vd2ARETExdHtD4luAPwCLqIa0fW8/g4qIiImjmxsS7wAyiFVERHQ00pjtnx5hPdv+7EgblnQK8AbgXtvblLLDgfcDi8tih9n+UZl3KLA/8ATwEds/LuU78PR47T8CPmrbklYFTgd2oKpqe6vt20aKKSIixt9IVVuPdnhB9cf+k11s+1RgdofyY21vW16tJLIVMAfYuqxzgqRJZfkTgbnAluXV2ub+wIO2twCOBY7qIqaIiBhnwyYS20e3XlTjj6xG9bDGM4HNRtuw7f8BHugyjj2AM20/bvtWYAGwo6RpwGTbl9o21RXInm3rnFamzwJ2k6Qu9xcREeNkxMZ2SVMk/RtwDVU12Pa2P2n73h72+WFJ10g6RdI6pWw6cGfbMgtL2fQyPbR8mXVsLwUeAtYd5nPMlTRf0vzFixd3WiQiIpbTsIlE0ueBXwNLgBfaPrw8CbgXJwKbA9tS9QA7urW7Dst6hPKR1nlmoX2S7Vm2Z02dmltgIiLG00hXJB8HNgQ+Bdwt6eHyWiLp4eXZme17bD9h+0ng68COZdZCYKO2RWcAd5fyGR3Kl1lH0srAWnRflRYREeNkpDaSlWyvZntN25PbXmvanrw8OyttHi1vBq4r0+cBcyStKmlTqkb1K2wvApZI2qm0f+wLnNu2zn5lem/g4tKOEhERA9TNULvLRdK3gVcB60laCHwGeJWkbamqoG4DPgBg+3pJ84AbgKXAAbafKJv6EE93/z2/vABOBs6QtIDqSmROvz5LREQMr2+JxPbbOhSfPMLyRwBHdCifD2zTofwxYJ9eYoyIiN5lPJKIiOhJEklERPQkiSQiInqSRBIRET1JIomIiJ4kkURERE+SSCIioidJJBER0ZMkkoiI6EkSSURE9CSJJCIiepJEEhERPUkiiYiIniSRRERET5JIIiKiJ0kkERHRkySSiIjoSd8SiaRTJN0r6bq2simSLpT0+/LvOm3zDpW0QNLNknZvK99B0rVl3nFl7HbK+O7fKeWXS5rZr88SERHD6+cVyanA7CFlhwAX2d4SuKi8R9JWVGOub13WOUHSpLLOicBcYMvyam1zf+BB21sAxwJH9e2TRETEsPqWSGz/D/DAkOI9gNPK9GnAnm3lZ9p+3PatwAJgR0nTgMm2L7Vt4PQh67S2dRawW+tqJSIiBmfQbSQb2F4EUP5dv5RPB+5sW25hKZtepoeWL7OO7aXAQ8C6nXYqaa6k+ZLmL168eJw+SkREQHMa2ztdSXiE8pHWeWahfZLtWbZnTZ06dTlDjIiITgadSO4p1VWUf+8t5QuBjdqWmwHcXcpndChfZh1JKwNr8cyqtIiI6LNBJ5LzgP3K9H7AuW3lc0pPrE2pGtWvKNVfSyTtVNo/9h2yTmtbewMXl3aUiIgYoJX7tWFJ3wZeBawnaSHwGeBIYJ6k/YE7gH0AbF8vaR5wA7AUOMD2E2VTH6LqAbYacH55AZwMnCFpAdWVyJx+fZaIiBhe3xKJ7bcNM2u3YZY/AjiiQ/l8YJsO5Y9RElFERNSnKY3tERExQSWRRERET5JIIiKiJ0kkERHRkySSiIjoSRJJRET0JIkkIiJ6kkQSERE9SSKJiIieJJFERERPkkgiIqInSSQREdGTJJKIiOhJEklERPQkiSQiInqSRBIRET1JIomIiJ7Ukkgk3SbpWklXS5pfyqZIulDS78u/67Qtf6ikBZJulrR7W/kOZTsLJB1XxnWPiIgBqvOKZBfb29qeVd4fAlxke0vgovIeSVtRjce+NTAbOEHSpLLOicBcYMvymj3A+CMigmZVbe0BnFamTwP2bCs/0/bjtm8FFgA7SpoGTLZ9qW0Dp7etExERA1JXIjFwgaQrJc0tZRvYXgRQ/l2/lE8H7mxbd2Epm16mh5ZHRMQArVzTfne2fbek9YELJd00wrKd2j08QvkzN1Alq7kAG2+88VhjjYiIEdRyRWL77vLvvcA5wI7APaW6ivLvvWXxhcBGbavPAO4u5TM6lHfa30m2Z9meNXXq1PH8KBERK7yBJxJJq0taszUN/B/gOuA8YL+y2H7AuWX6PGCOpFUlbUrVqH5Fqf5aImmn0ltr37Z1IiJiQOqo2toAOKf01F0Z+Jbt/0/Sr4F5kvYH7gD2AbB9vaR5wA3AUuAA20+UbX0IOBVYDTi/vCIiYoAGnkhs3wK8uEP5/cBuw6xzBHBEh/L5wDbjHWNERHSvSd1/IyJiAkoiiYiIniSRRERET5JIIiKiJ0kkERHRkySSiIjoSRJJRET0JIkkIiJ6kkQSERE9SSKJiIieJJFERERPkkgiIqInSSQREdGTJJKIiOhJEklERPQkiSQiInqSRBIRET1JIomIiJ5M+EQiabakmyUtkHRI3fFERKxoJnQikTQJ+ArwWmAr4G2Stqo3qoiIFcuETiTAjsAC27fY/gtwJrBHzTFFRKxQZLvuGJabpL2B2bbfV96/C3ip7Q8PWW4uMLe8fR5w8ziHsh5w3zhvsxdNiwcSUzeaFg8kpm40LR7oT0yb2J7aacbK47yjQVOHsmdkRtsnASf1LQhpvu1Z/dr+WDUtHkhM3WhaPJCYutG0eGDwMU30qq2FwEZt72cAd9cUS0TECmmiJ5JfA1tK2lTSs4A5wHk1xxQRsUKZ0FVbtpdK+jDwY2AScIrt62sIpW/VZsupafFAYupG0+KBxNSNpsUDA45pQje2R0RE/SZ61VZERNQsiSQiInqSRBIRET1JIhknkg6sO4aIiDqksX2cSLrD9sZ1xxGdSfr0CLNt+7MDCya6knM2cUzo7r8N0+ku+/7vVFrC03fzt2Iw1bl9lu2Bn2NJt7LsEwbU9t62Nx90TMCjHcqeA7wPWBcY6B+lJh6jBsbUqHMGzTtGTfn+J5GMn1ou7Wyv2f5e0prA/wU+AJxTR0zA0EczrAS8BTgY+M3gwwHbR7emyzH6KPBeqgd9Hj3cen3UuGNEw2Jq4DmD5h2jRnz/k0jGYEj2X2YW1S+l2khaGzgQ2Bf4FvAS2/fXEUtrv5JWAt4F/CNwNfB62zfUEVOJZwpwEPAO4DRge9sP1hFLE49RE2Nq0jmDZh6jEs/a1Pj9TyIZg6HZvwkkrQd8HHgrcAqwne2Hao5pFapfjh8DfgnsYft/a47p88BeVHf8vtD2IzXH08Rj1KiYmnbOoJHHqBHf/zS290jS6sCewNttv76G/T8KLAa+ASwZOt/2MTXEtBBYCnwRuKNDTN+rIaYngcep4npGHbftyQOOp4nHqFExNe2clZiadowa8f3PFclyKA+IfB3wdmA2cDbw1ZrC+TxPf8macsX0E6qYXlxe7QwM/I+k7aZ1dW/cMaJhMTXwnEHDjhEN+f7nimQMJL0GeBuwO/BT4DvAl23PrDOu4Uha3Xanni9RlLrlA2wfUXcs0Z2cs+4M8vvfxIzfZD8GNgdebvudtn8APFlzTEiaLmlWuVJC0vqSPgf8vsaYJpX629b7Z0maK+nGmuLZSNJJkn4o6X2SniPpaKpjtH5NMTXqGDUtpiaesxJXY45R2X/t3/8kkrHZAbgM+ImkCyXtT/X4+tqUO+qvBr4MXCZpP+BGYDWqeOuIaQ7wAHCNpJ9L2gW4BXgtVe+bOpxONejZl4Gtqc7jhlSNuB8ddDBNPEYNjKlR5wyad4wa8/23nddyvICdgeOBRcD5wNya4rgBmFKmNwb+AuxU87G5DtiiTG9P1WD65ppj+u2Q9/cAq+YYNTempp2zhh6jRnz/c0WynGz/yvaHgenAscBragrlMdsPlJjuAH5n+7KaYmn5i+0FJaargFtt13Vz5FMkrSNpSrk34Q/Ac9reD1oTj1HjYmrYOYPmHaNGfP/Ta2uMJE0HpgHX2P4LsB7wKqorlDrMkHRc2/v129/b/kgNMa0v6aC292u0v3cNXZKBtYArWfZRNleVfw1sNuB4mniMmhZT084ZNO8YNeL7n0QyBqU+8p+ABcCqkr4EHENVl1tLewTVnbXtrqwlimV9nWW7Ig59P3BuXs+6xh2jDjHUGlMDzxk07BjRkO9/uv+OgaQbqHpsPSBpY6qE8ooGVCV1JGll20vrjqMJJL3T9jfL9M62f9U278O2j68vuugk56w3g/z+p41kbBpRH9lO0i/bps8YMvuKAYfTimNe2/RRQ+ZdMPiIgOp5TS1fHjLvvYMMBJp5jBoYU6POGTTvGDXl+59EMjYzJB3XelHqI9ve12H1tumth8yr5dH2wJZt00M7IUwdZCBtNMx0p/eD0MRj1LSYmnbOoHnHqBHf/7SRjE0j6iOHGKlusq56y6bHNDSGOmJq+jEay7x+ado5G22/dR+jscwbV0kkY/M824fVHcQQa0t6M9XV5dqS9irlour1UofnSNquxLRamVZ5rVZTTM+XdE2JYfMyTXlfR++fJh6jpsXUtHMGzTtGjfj+p7F9DCRdZXv7uuNoJ+kbI823/Z5BxdIi6acjzbe9y6BiaZG0yUjzbd8+qFigsceoUTE17ZxBI49RI77/SSRjIOm3VPeMdKx7bDXED5KkvVzDI8dHImmnujshNF0Tj1ETY2qaph2jpnz/k0jGQNLjwF10TiS2PfDL7YZeJTUxppFGt7QHPx5JE49Ro2Jq2jmDRh6jRsSTNpKxucH2dnUHMQHU1aNmWG4b3VLSbxpwHht3jGhYTA08Z9CwY9QUSSQT3/PbGiHbtX61vWjQAQGbSjpvuJm23zTIYDqFUPP+oZnHqIkxPbX7GvfdrmnHqBHf/ySSsflS3QF0cCvwxrqDGGIxcHTdQTRcE49RE2NqmqYdo0Z8/5NIxuaVkl4xzDzb3n+g0VT+UkfvlVE8YvvndQfRrq1bJCzbTRKoZYz0xh0jGhZTA88ZNOwY0ZDvfxLJ2PywQ9nGwIHUN8DVr0ZfZOBurTuADtp/tf18yPs6xtpu4jFqWkxNO2fQvGPUiO9/em0tJ0mbAYcBr6Aaj+Tk8lj5QcfxRqpH2t9e3n8a+HvgduCjtgf+H1/SS4A7bf+hvN+3LabD0026sceoUTE17ZxBI49RI77/edbWGEl6gaRvAj8AfglsZfvEOpJIcQRVvS2S3gC8k+qBducBX60ppq9RjdRGqQo8kupR+w8BJ9UU06dq2u9wmniMmhZT084ZNO8YNeL7n6qtMZD0XWAW8AXgY8ATwGSp6hFYx6/Iarf+U5nei+rK6ErgSkn/t4Z4ACa1HYu3AifZPhs4W9LVNcXUNE08Rk2MqWmadowa8f1PIhmbl1DVzR4MfLyUtfqV1zVimyStAfwJ2A04oW3es2uIB2BS21gIuwFz2+bV9X+uEd0k2zTxGDUtpqadM2jeMWrE9z+JZAwaOmLbF4GrgYeBG23PBygPk1tUU0zfBn4u6T7gz8AvSkxbUFUB1KER3STbNPEYNS2mpp0zaN4x+iIN+P6nsX0M1NAR21SNI78+8FvbT5ay/wdYxfadNcW0E9XY9hfYfrSUPRdY3fZvaoinKXdGP2WEY7SG7atGXHnwMQ38vDXxnEHzztsw3/9pVN//OwYRQxrbx6ZxI7YB2L7L9m/a/hNtDrwPOL/GmC6zfU7bF211YEfg32oKqWM3SUnPk/T1QQcjadfWMaL6IwCA7d8BMwcdT9v+m3TeGtG1tV3Tzlv5cXtXSfJ/1xbPImBgd9knkYxNE0dsq3YuTZN0oKQrgOup7mt5W80xPUvSnqqGJ11EVYdbV0+yr0u6QNJ1kv5N0gaSzgYuAm6oIZ4vtE2fPWRerb2VGnTebpV0UNvrY5LeJWnTGmJpadp5a8SP2ySSsWnciG2S3i/pYqobttajuhJZZPtfbF9bU0yvkXQKVR333sAZwAO232P7B3XERNU181tUfewXA1cBtwBb2D62hnga96OkgedtDWDNttdkql6T50uaU0M80Lzz1oh40tg+Nk0cse0rwKXA29sa2upu+PoxVSPky1s3REmq+zllq9o+tUzfLOlg4BDbT9QUT+N+lNCw82b7XzqVS5oC/AQ4c7ARAc07b42IJ4lkbF4MbAAMbcDeBLh78OEAsCGwD3CMpA2AecAqNcXSsgMwB/iJpFuovvB1PUKm5dl6elhUgEeAF6ncBFRDI+lm5SmyapumvK+r6qaJ5+0ZbD/QOm81aNp5a8SP2/TaGgNJPwQOs33NkPJZwGds19pVUdIMqj8EbwOeA5zjmseYl7RziefvqbopnmN74HcAa+QhUm1714EFA0h65Ujz634wYFPOWyeSdgU+NehzVvbdqPMm6WNUT9h4EPhrh3gG8kDHJJIxkHSd7W2GmXet7RcOOqbhSHoe8Fbb/1p3LACSVgJeQxVTbT3cmkLSnsAltu+tO5aR1HneJF3LM6tnplBd/e9r+6ZBxlNi2pMGnTdJXwBeBjwfuAa4hKq326WDfNJGqrbGZqQ7RVcbWBRtJH3C9n+U6X1sfxfA9s2SarmzvdP9NqVr8o8lbVlHTCWW9YEDgK2p/kDdAHylpj8K7wS+IulPVF/8S4Bf2b6+hliARp63Nwx5b+D+VtfkmjTqvNk+GKqedlQdEV5G1Vvr65L+aHurQcSRXltj82tJ7x9aKGl/4Moa4oGqKqvl0CHzZg8ykDaN6JLYrlTV/Lq8PR34Zpm+oswbKNt7255O9Wv/AuBFwOmSFkv60aDjKRp13mzfXqpmNqO6w/2NVPe01Kah5w2qH7KTgbXK627g8kHtPFckY3MgcI6kd/B04pgFPAt4c00xNaL73wj7bUpMRwN7Drk7+1xJ51A90fWldQRl+7Zy5bhaebWm69Co81bu2P4e8BjV903AWyQdBbzZ9l2DjqmlKedN0klUV9hLqBLHJcAxth8cZBxJJGNg+x7gZZJ2AVptJf9t++I6wxpmutP7QWliTJM7PeLD9tWS1hx0MJIOo7oTeSpwM3AZcDwwN12Sn3I8cGJbt23gqTFATgD2GHRADTxvGwOrAr8H7gIWAn8cdBBpbJ/gJD0BPEr1a201qqeAUt4/2/bAuwKX+uMFJYbNy3Qrps1sr15DTDcCLxv6S63ck3CJ7ecPOJ6bqLog/5DqV+Tltut6WGMrpkadN0k3237eWOf1OaYmnjdRXZW8rLy2AR6ganD/zEBiSCKJ8SZpk5HmD6pLYjtJc4H3Uw0B0LpnZAfgKOAU21+rIaYpPP3l34nqTu7fUiW2b9QQT6POm6QFtrfoUL4S8LtO8wahaeetLa4ZwM4lrjcA69peeyD7TiKJFYWqEeQ+QfXrDapnkn2+xse2ACBpZaqk9grgA8Cmtht3I+CgSfoisDpw4JCHSB4LPGb7IzWG14jzJukjVIljZ6r7SH5F9aSLXwHXth7k2vc4kkhivElaQuc69daARJMHHFLjSHoTT/8B2JoqqV3SetleXENMjTpvklYBPge8h2oMclM9ReI0qhuDBz68ddPOm6RjeLoLcl3jDyWRRH+pIWNKSPr0CLNt+7MDCwaQ9D2evnnsyjr+KI6kCedN0kt4uvF4C2AXqiqbm4DDB3nDXVtMjT5vdUkiib6SdJXt7RsQx8c7FK8O7E9Vl7zGgENqtCacN0lXAa8uz9Z6BdWzv/4B2BZ4ge2964wvnpbuv7FCsH10a7p09/0oVZXJmVT3mAxU06qRGmpS21XHW4GTbJ8NnC3p6joCynnrLIkkxp2kvdrerj3kPba/N+CQgKd62xwEvIOqnn37Qd+41WL7qXtXmlCNVOJo2nmbJGll20upBtea2zavlr9dTTxvTZBEEv3Q/hTknw95b6q7lQdK0ueBvagGuHqh7UcGHcMImlK/3LTz9m3g55LuA/5MNVYKkrYAar13o2jKeatd2khi3Enaq66rjuFIehJ4HFjKsn8Aaq+SaEJ7RImjiedtJ2AacEFbF+DnAmt48GPIDI2tEeetCZJIYtzlCza6IdVGX6C6UfIpdfxBz3kbXRPPWxOkaiuiHk2rRoru5Lx1kCuSGHdtz2x6xiyqaqQXDTikxmloNVLO2yiaeN6aIIkkxp2k64HXDTe/jmdtNU0Tq5Fy3kbXxPPWBKnain74S/7oTEg5b7FckkiiH37VqbCMI3+w7WeMMrkCer6kazqU11mN1PG8xTKaeN5ql0QS/fB1SRcAGwLfpxq29QSqUQgHfhd5Q93Ksg21TXCrpPbhdg3cB/zS9q01xdQ0TTxvtUsiiX44CTiR6nHWs6nG//gW8A7bj9UZWIM0sRqp0/PGZgL/JOlw22cOOJ4mauJ5q10a22PcSbra9rZt7+8EZtY4hGzjSDre9oc7lDeu+q88WuYnaWQe/ryt6HJFEv3wbEnbUdUbQzU06YvKkKDUfUdyQ0yY6r/y9F2NvuQKIdV/HSSRRD8sAo5pe/+HtvcGdh14RM0zYar/JO0K1PJwywZK9V8HqdqKqEETq/8kXcszH0Q4Bbgb2Nf2TYOPamJY0av/ckUSfSFpfeAAquFIDdwAfMX2vbUG1hxNrP57w5D3Bu5vPSwxhreiV//liiTGnaSdqappTgWupPpjuT2wH1XVzQp/v4Kkn44w27Zrq/6TtAttPwBsjxRr8FT136fqPG91SiKJcSfpMuBDtn8zpHxb4Gu2X1pLYDEiSdOpHjr4GMv+AFgNeLPtu2oMrxFS/ddZEkmMO0k32N5qrPNWNE2r/pN0DnCu7VOHlO8L/L3tPeqIq0kkbTKkKNV/wEp1BxB/kyRpnQ6FU8j/OeCp6r9fl7enA98s01eUeXXYamgSAbB9OvD8wYfTPLZvLzckbkZ1h/sbgR3rjap+uSKJcSdpLvB+qkF/Wo3GOwBHAafY/lpdsTVFE6v/JC2wvUWH8pWA33Wat6JJ9V9nSSTRF5LeAHyCqtoG4Hrg87Z/UF9UzdHE6j9JXwRWBw5sG9Z2deBY4DHbHxl0TE2T6r/OkkgiaiDpRuBlth8cUj4FuMT2wKuSJK0CfA54D3A7Vf3/JsBpwGG2/zLomJpG0s22nzfWeX/rch9JjDtJnx5htm1/dmDBNNexwAWSOlX/HVtTTNtSPYHg08AWwC5U95Y8i+qO7gdqiqtJJnUqLNV/HeetCHJFEuNO0sc7FK8O7A+sa7vTYyZWOE2r/pN0FfDqcnPdK4AzgX+gSjAvsL13HXE1Sar/Oksiib6StCbwUaokMg84One3N5Ok39p+cZn+CrDY9uHl/TKPdFlRpfqvs1RtRV+Uuv6DgHdQfcm2H9oesCJraPXfJEkr214K7AbMbZuXvxWVbUn13zOkT3+MO0mfp7pHYgnwQtuHJ4k8w6MdXlBduX2yppi+Dfxc0rnAn4FfAEjaAnioppia5mvA47b/DKwDHFLKHqJ6ovMKKVVbMe4kPQk8Dixl2cdJtMa1nlxLYA3VpOo/STsB04AL2toAnguskXFkUv03nFyuxriznSvdLjSx+s/2ZR3KfldHLA2V6r8OVtgPHlGnUv23F1V1yAttP1JzSNGdVvXffaT67ymp2oqoQar/Jq5U/z1TEklERPQkddkREdGTJJKIiOhJEklEDyQ9IelqSddL+q2kg8pzl5A0S9JxI6w7U9LbR5i/oaSzRtn/gZKes/yfIKJ3aSOJ6IGkR1rPDisjHn4L+JXtz3Sx7quAg22/ocO8VhfT0bZxGzDL9n1jDD1i3CSRRPSgPZGU95tR3dW/HvBKSqKQ9ErgS2UxA68ALgReANxKdR/Jg8DrgWdTPRjwvcAPbW8jaRLVk4F3L+t/naqH1xeAm4H7gFcDJwOzyjKn2K7rScKxAsl9JBHjyPYtpWpr/SGzDgYOsP0rSWtQjbB3CG1XJJLeDfwd8KLyBN6ZbevPBTYFtrO9VNKUssxBwC6275O0AzDd9jZle2v375NGPC1tJBHjTx3KfgUcI+kjwNojVFtdaLvTg/9eDXy1td4wy9wCbCbpy5JmAw8vR+wRY5ZEEjGOStXWE8Ayz8qyfSTwPqqxvS+TNNwIiI8OUy6WvXHxGcrjVV4M/Aw4APjPrgOP6EESScQ4kTQV+CpwvIc0Pkra3Pa1to8C5gPPp3o68ppdbv4C4IOSVi7bm1LKn9qGpPWAlWyfDfwzsH2PHymiK2kjiejNapKuBlahetzJGVTjVQx1oKRdqK5WbgDOB54Elkr6LXAqVWP7cP4TeC5wjaS/UjW2H0/1rK7zJS0CDgS+0ep+DBza0yeL6FJ6bUVERE9StRURET1JIomIiJ4kkURERE+SSCIioidJJBER0ZMkkoiI6EkSSURE9OT/B7vjdmW/a0aeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(District_wise_collect['District'], District_wise_collect['Total Incidents'], width = 0.3)\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('District vs No of Crimes')\n",
    "plt.ylabel('No of Crimes')\n",
    "plt.xlabel('Districts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>These are the number of crimes in each District of Baltimore</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "weapons_used = baltimore_Crime_data_table.groupby(['Weapon']).sum()\n",
    "weapons_used.reset_index(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "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.bar(weapons_used['Weapon'],weapons_used['Total Incidents'], width = 0.3)\n",
    "plt.title('Weapons used')\n",
    "plt.ylabel('No of Crimes')\n",
    "plt.xlabel('Weapons')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Most of the Crimes are comitted by Hands</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "Crime_code = baltimore_Crime_data_table.groupby(['CrimeCode']).sum()\n",
    "Crime_code.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_crime = Crime_code.loc[Crime_code['Total Incidents'] == max(Crime_code['Total Incidents'])]\n",
    "min_crime = Crime_code.loc[Crime_code['Total Incidents'] == min(Crime_code['Total Incidents'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "Crimecode_list = max_crime['CrimeCode'].to_list()\n",
    "Crimecode_list1 = min_crime['CrimeCode'].to_list()\n",
    "final_Crimecode_list = Crimecode_list + Crimecode_list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "Crime_Incidents = max_crime['Total Incidents'].to_list()\n",
    "Crime_Incidents1 = min_crime['Total Incidents'].to_list()\n",
    "final_Incidents_list = Crime_Incidents +  Crime_Incidents1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "Crime_by_Neighbourhood = baltimore_Crime_data_table.groupby(['Neighborhood']).sum()\n",
    "Crime_by_Neighbourhood.reset_index(inplace = True)\n",
    "Crime_by_Neighbourhood.columns = Crime_by_Neighbourhood.columns.str.replace(' ', '')\n",
    "max_Crime_neighbourhood = Crime_by_Neighbourhood.loc[Crime_by_Neighbourhood['TotalIncidents'] == max(Crime_by_Neighbourhood['TotalIncidents'])]\n",
    "min_Crime_neighbourhood = Crime_by_Neighbourhood.loc[Crime_by_Neighbourhood['TotalIncidents'] == min(Crime_by_Neighbourhood['TotalIncidents'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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.bar(final_Crimecode_list,final_Incidents_list, width = 0.3)\n",
    "plt.title('Max and Min type of Crimes')\n",
    "plt.ylabel('No of Crimes')\n",
    "plt.xlabel('Crime Code')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>The maximum type of crime is 4E , and minimum are 3LK and 8DO</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum Crime takes place in Downtown Neighbourhood and the total no of crimes are 8194\n"
     ]
    }
   ],
   "source": [
    "print('The maximum Crime takes place in', max_Crime_neighbourhood['Neighborhood'].iloc[0], 'Neighbourhood ' 'and the total no of crimes are',max_Crime_neighbourhood['TotalIncidents'].iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum Crime and safest Neighbourhood is Dundalk Marine Terminal and the total no of crimes are 1\n"
     ]
    }
   ],
   "source": [
    "print('The minimum Crime and safest Neighbourhood is', min_Crime_neighbourhood['Neighborhood'].iloc[0], 'and the total no of crimes are',min_Crime_neighbourhood['TotalIncidents'].iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To view all the data please execute this cell\n"
     ]
    }
   ],
   "source": [
    "print('To view all the data please execute this cell')\n",
    "#Crime_by_Neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "baltimore_Crime_data_table['dateandtime'] = baltimore_Crime_data_table['CrimeDate'] + baltimore_Crime_data_table['CrimeTime']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "baltimore_Crime_data_table['dateandtime'] = pd.to_datetime(baltimore_Crime_data_table['dateandtime'],format = '%m/%d/%Y%H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "baltimore_Crime_data_table['dateandtime'] = baltimore_Crime_data_table['dateandtime'].replace(['08/03/20110'],'08/03/201100:00:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#baltimore_Crime_data_table.set_index(keys = 'dateandtime',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "baltimore_Crime_data_table.sort_values(['dateandtime'],axis = 0,ascending=False, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "Incident_Description = baltimore_Crime_data_table.groupby(['Description']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>Description</th>\n",
       "      <th>Post</th>\n",
       "      <th>Total Incidents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>62256369.0</td>\n",
       "      <td>122854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Inside</td>\n",
       "      <td>295577.0</td>\n",
       "      <td>571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>O</td>\n",
       "      <td>56687727.0</td>\n",
       "      <td>114224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Outside</td>\n",
       "      <td>1598616.0</td>\n",
       "      <td>2788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Description        Post  Total Incidents\n",
       "0           I  62256369.0           122854\n",
       "1      Inside    295577.0              571\n",
       "2           O  56687727.0           114224\n",
       "3     Outside   1598616.0             2788"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Incident_Description.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "inside_Dataframe = Incident_Description.iloc[[0, 1]].reset_index()\n",
    "outside_Dataframe =Incident_Description.iloc[[2, 3]].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "Total_outside = outside_Dataframe['Total Incidents'].sum()\n",
    "Total_inside = inside_Dataframe['Total Incidents'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "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.bar(['Outside','Inside'],[Total_outside,Total_inside], width = 0.3 , align = 'center')\n",
    "plt.title('Crime Description')\n",
    "plt.ylabel('No of Crimes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "baltimore_Crime_data_table['CrimeTime'] = pd.to_datetime(baltimore_Crime_data_table['CrimeTime'],format = '%H:%M:%S')\n",
    "#baltimore_Crime_data_table['CrimeTime'] = baltimore_Crime_data_table['CrimeTime'].replace(['0'],'00:00:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "baltimore_Crime_data_table['hour'] = baltimore_Crime_data_table['CrimeTime'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "Most_crime_happen_at = baltimore_Crime_data_table.groupby(['hour']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "Most_crime_happen_at.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "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.plot(Most_crime_happen_at['hour'],Most_crime_happen_at['Total Incidents'])\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Total No of Crimes')\n",
    "plt.title('No of Crimes during the day')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "baltimore_Crime_data_table['Year'] = baltimore_Crime_data_table['dateandtime'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "Year_wise_data = baltimore_Crime_data_table.groupby(['Year']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "Year_wise_data.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "Year_wise_data.drop(['hour'], inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = Year_wise_data['Year']\n",
    "values = [2011,2012,2013,2014,2015]\n",
    "plt.plot(Year_wise_data['Year'],Year_wise_data['Total Incidents'])\n",
    "plt.title('Year wise Crime data')\n",
    "plt.xlabel('Year')\n",
    "plt.xticks(x,values)\n",
    "plt.ylabel('Total no of crimes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "Neighbourhood_year_data = baltimore_Crime_data_table.groupby(['Neighborhood','Year']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Neighborhood</th>\n",
       "      <th>Year</th>\n",
       "      <th>Post</th>\n",
       "      <th>Total Incidents</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abell</td>\n",
       "      <td>2011</td>\n",
       "      <td>46170.0</td>\n",
       "      <td>90</td>\n",
       "      <td>1124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abell</td>\n",
       "      <td>2012</td>\n",
       "      <td>65151.0</td>\n",
       "      <td>127</td>\n",
       "      <td>1645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abell</td>\n",
       "      <td>2013</td>\n",
       "      <td>58995.0</td>\n",
       "      <td>115</td>\n",
       "      <td>1526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abell</td>\n",
       "      <td>2014</td>\n",
       "      <td>75924.0</td>\n",
       "      <td>148</td>\n",
       "      <td>1799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abell</td>\n",
       "      <td>2015</td>\n",
       "      <td>56430.0</td>\n",
       "      <td>110</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>York-Homeland</td>\n",
       "      <td>2011</td>\n",
       "      <td>7815.0</td>\n",
       "      <td>15</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>York-Homeland</td>\n",
       "      <td>2012</td>\n",
       "      <td>12504.0</td>\n",
       "      <td>24</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1381</th>\n",
       "      <td>York-Homeland</td>\n",
       "      <td>2013</td>\n",
       "      <td>14588.0</td>\n",
       "      <td>28</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>York-Homeland</td>\n",
       "      <td>2014</td>\n",
       "      <td>15630.0</td>\n",
       "      <td>30</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>York-Homeland</td>\n",
       "      <td>2015</td>\n",
       "      <td>20840.0</td>\n",
       "      <td>40</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1384 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Neighborhood  Year     Post  Total Incidents  hour\n",
       "0             Abell  2011  46170.0               90  1124\n",
       "1             Abell  2012  65151.0              127  1645\n",
       "2             Abell  2013  58995.0              115  1526\n",
       "3             Abell  2014  75924.0              148  1799\n",
       "4             Abell  2015  56430.0              110  1444\n",
       "...             ...   ...      ...              ...   ...\n",
       "1379  York-Homeland  2011   7815.0               15   197\n",
       "1380  York-Homeland  2012  12504.0               24   326\n",
       "1381  York-Homeland  2013  14588.0               28   338\n",
       "1382  York-Homeland  2014  15630.0               30   403\n",
       "1383  York-Homeland  2015  20840.0               40   629\n",
       "\n",
       "[1384 rows x 5 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Neighbourhood_year_data.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "Neighbourhood_year_data.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mapping(Neighbourhood):\n",
    "    if Neighbourhood in Neighbourhood_year_data['Neighborhood'].tolist():\n",
    "        yo = list(set(Neighbourhood_year_data['Year']))\n",
    "        yo.sort()\n",
    "        list_index = Neighbourhood_year_data.index[Neighbourhood_year_data['Neighborhood'] == Neighbourhood].tolist()\n",
    "        plt.bar(yo ,Neighbourhood_year_data['Total Incidents'][list_index].tolist())\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the Neighbourhood for which you want yearly data for Abell\n"
     ]
    },
    {
     "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": [
    "Neighbor = input('Write the Neighbourhood for which you want yearly data for')\n",
    "mapping(Neighbor)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}