crime_rates_women.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE/UT</th>\n",
       "      <th>DISTRICT</th>\n",
       "      <th>Year</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault on women</th>\n",
       "      <th>Kidnapping and Abduction</th>\n",
       "      <th>Dacoity</th>\n",
       "      <th>Robbery</th>\n",
       "      <th>Arson</th>\n",
       "      <th>Hurt</th>\n",
       "      <th>Prevention of atrocities (POA) Act</th>\n",
       "      <th>Protection of Civil Rights (PCR) Act</th>\n",
       "      <th>Other Crimes Against SCs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>ADILABAD</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>ANANTAPUR</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>CHITTOOR</td>\n",
       "      <td>2001</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>CUDDAPAH</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         STATE/UT       DISTRICT  Year  Murder  Assault on women  \\\n",
       "0  ANDHRA PRADESH       ADILABAD  2001       0                 1   \n",
       "1  ANDHRA PRADESH      ANANTAPUR  2001       0                 4   \n",
       "2  ANDHRA PRADESH       CHITTOOR  2001       3                 3   \n",
       "3  ANDHRA PRADESH       CUDDAPAH  2001       0                 3   \n",
       "4  ANDHRA PRADESH  EAST GODAVARI  2001       1                 3   \n",
       "\n",
       "   Kidnapping and Abduction  Dacoity  Robbery  Arson  Hurt  \\\n",
       "0                         4        0        0      0     3   \n",
       "1                         0        0        0      0    49   \n",
       "2                         0        0        0      0    38   \n",
       "3                         0        0        0      0    20   \n",
       "4                         0        0        0      0     3   \n",
       "\n",
       "   Prevention of atrocities (POA) Act  Protection of Civil Rights (PCR) Act  \\\n",
       "0                                   0                                    15   \n",
       "1                                  21                                     0   \n",
       "2                                  36                                     0   \n",
       "3                                  52                                     0   \n",
       "4                                  12                                    63   \n",
       "\n",
       "   Other Crimes Against SCs  \n",
       "0                        32  \n",
       "1                        53  \n",
       "2                        34  \n",
       "3                        25  \n",
       "4                         7  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/crime_by_district.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data['DISTRICT'] != 'TOTAL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "assault_by_district = data.groupby('DISTRICT')['Assault on women'].count().to_frame().reset_index().sort_values(by='Assault on women', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "lowest_crime_rate_districts = assault_by_district[assault_by_district['Assault on women'] == 1]['DISTRICT'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "District which has the most crime rates in India is: G.R.P.\n",
      "\n",
      "Districts which has the lowest crime rates in India are :\n",
      "['A and N ISLANDS', 'VILUPPURAM', 'AGAR', 'AMETHI', 'DISCOM', 'RAILWAYSKMR', 'AMROHA', 'I&P HARYANA', 'LONGDING', 'GUMALA', 'S.T.F.', 'C.B.KASHMIR', 'SAMBHAL', 'MAHENDERGARH', 'HAPUR', 'N.C. HILLS', 'SHAMLI', 'D&N HAVELI', 'GARO HILLS SOUTH W.', 'HISAR', 'GARO HILLS NORTH', 'KHASI HILLS SOUTH W.', 'G.R.P. AJMER', 'I.G.I.AIRPORT', 'RAILWAYS KMR', 'SPL NARCOTIC', 'METRO RAIL', 'NORTH WEST', 'NORTH EAST', 'MIDNAPUR', 'KASGANJ', 'JAINTIA HILLS EAST', 'JAINTIA HILLS WEST', 'G.R.P.(RLY)', 'TRAFFIC PS', 'G.R.P. JODHPUR', 'SOUTH WEST']\n"
     ]
    }
   ],
   "source": [
    "print('District which has the most crime rates in India is: G.R.P.\\n')\n",
    "print(f'Districts which has the lowest crime rates in India are :\\n{lowest_crime_rate_districts}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DATA CLEANING**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE/UT</th>\n",
       "      <th>DISTRICT</th>\n",
       "      <th>Year</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault on women</th>\n",
       "      <th>Kidnapping and Abduction</th>\n",
       "      <th>Dacoity</th>\n",
       "      <th>Robbery</th>\n",
       "      <th>Arson</th>\n",
       "      <th>Hurt</th>\n",
       "      <th>Prevention of atrocities (POA) Act</th>\n",
       "      <th>Protection of Civil Rights (PCR) Act</th>\n",
       "      <th>Other Crimes Against SCs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>ADILABAD</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>ANANTAPUR</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>CHITTOOR</td>\n",
       "      <td>2001</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>CUDDAPAH</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         STATE/UT       DISTRICT  Year  Murder  Assault on women  \\\n",
       "0  ANDHRA PRADESH       ADILABAD  2001       0                 1   \n",
       "1  ANDHRA PRADESH      ANANTAPUR  2001       0                 4   \n",
       "2  ANDHRA PRADESH       CHITTOOR  2001       3                 3   \n",
       "3  ANDHRA PRADESH       CUDDAPAH  2001       0                 3   \n",
       "4  ANDHRA PRADESH  EAST GODAVARI  2001       1                 3   \n",
       "\n",
       "   Kidnapping and Abduction  Dacoity  Robbery  Arson  Hurt  \\\n",
       "0                         4        0        0      0     3   \n",
       "1                         0        0        0      0    49   \n",
       "2                         0        0        0      0    38   \n",
       "3                         0        0        0      0    20   \n",
       "4                         0        0        0      0     3   \n",
       "\n",
       "   Prevention of atrocities (POA) Act  Protection of Civil Rights (PCR) Act  \\\n",
       "0                                   0                                    15   \n",
       "1                                  21                                     0   \n",
       "2                                  36                                     0   \n",
       "3                                  52                                     0   \n",
       "4                                  12                                    63   \n",
       "\n",
       "   Other Crimes Against SCs  \n",
       "0                        32  \n",
       "1                        53  \n",
       "2                        34  \n",
       "3                        25  \n",
       "4                         7  "
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data of same cities merged which was different due to\n",
    "# CASE discrepency (Like \"DELHI\" and \"delhi\")\n",
    "data['STATE/UT'] = data['STATE/UT'].str.upper() \n",
    "data['DISTRICT'] = data['DISTRICT'].str.upper() \n",
    "data.loc[ data[\"STATE/UT\"] == \"DELHI UT\",\"STATE/UT\"] = \"DELHI\"\n",
    "data.loc[ data[\"STATE/UT\"] == \"A&N ISLANDS\",\"STATE/UT\"] = \"A & N ISLANDS\"\n",
    "data.loc[ data[\"STATE/UT\"] == \"D&N HAVELI\",\"STATE/UT\"] = \"D & N HAVELI\"\n",
    "data.head() # Cleaned Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DATA VISUALISATION**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Total Crimes']  = data.sum(axis=1, numeric_only=True)\n",
    "data[\"Year\"] = data[\"Year\"].astype(int)\n",
    "data[\"Total Crimes\"] = data[\"Total Crimes\"].astype(int)\n",
    "data[\"Total Crimes\"] = data['Total Crimes'] - data['Year']\n",
    "data[\"Year\"] = data[\"Year\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    State = input(\"Input State to be analysed\")\n",
    "    State = State.upper()\n",
    "    state_df = data[data[\"STATE/UT\"] == State]\n",
    "    state_df.head()\n",
    "    result = state_df.groupby(\"Year\")[\"Total Crimes\"].sum()\n",
    "    Yearly_Crimes = {}\n",
    "    for year, records in result.items():\n",
    "        Yearly_Crimes[year] = records\n",
    "    Yearly_Crimes = pd.DataFrame({'Year' : Yearly_Crimes.keys() , 'Total Crimes' : Yearly_Crimes.values()})\n",
    "    base_color = sb.color_palette()[0]\n",
    "    sb.barplot(x = \"Year\",y = \"Total Crimes\",data = Yearly_Crimes,color = base_color)\n",
    "    plt.title(\"Increase in Crimes over the years in {state}\".format(state = State.lower()))\n",
    "except:\n",
    "    print(\"State Not Found\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.7 64-bit",
   "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": "3.10.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "2dcff298e6fc9f300c172f449ac5b974b753c4428b95c0ffa294019aef922779"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}