crime rates against sc and other backward classes#2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4aaed169",
   "metadata": {},
   "source": [
    "# What are the crime rates against sc and other backward classes in different states of India"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2fc9e398",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fdca5f29",
   "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>STATE/UT</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>2001</td>\n",
       "      <td>45</td>\n",
       "      <td>69</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>518</td>\n",
       "      <td>950</td>\n",
       "      <td>312</td>\n",
       "      <td>1006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>2002</td>\n",
       "      <td>60</td>\n",
       "      <td>98</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>568</td>\n",
       "      <td>830</td>\n",
       "      <td>459</td>\n",
       "      <td>1336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>2003</td>\n",
       "      <td>33</td>\n",
       "      <td>79</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>615</td>\n",
       "      <td>1234</td>\n",
       "      <td>165</td>\n",
       "      <td>1386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>2004</td>\n",
       "      <td>39</td>\n",
       "      <td>66</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>474</td>\n",
       "      <td>1319</td>\n",
       "      <td>68</td>\n",
       "      <td>1234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>2005</td>\n",
       "      <td>37</td>\n",
       "      <td>74</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>459</td>\n",
       "      <td>1244</td>\n",
       "      <td>61</td>\n",
       "      <td>1212</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>420 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           STATE/UT  Year  Murder  Assault on women  Kidnapping and Abduction  \\\n",
       "0    ANDHRA PRADESH  2001      45                69                        22   \n",
       "1    ANDHRA PRADESH  2002      60                98                        18   \n",
       "2    ANDHRA PRADESH  2003      33                79                        27   \n",
       "3    ANDHRA PRADESH  2004      39                66                        28   \n",
       "4    ANDHRA PRADESH  2005      37                74                        21   \n",
       "..              ...   ...     ...               ...                       ...   \n",
       "415      PUDUCHERRY  2008       0                 0                         0   \n",
       "416      PUDUCHERRY  2009       0                 0                         0   \n",
       "417      PUDUCHERRY  2010       1                 0                         1   \n",
       "418      PUDUCHERRY  2011       0                 0                         0   \n",
       "419      PUDUCHERRY  2012       2                 0                         0   \n",
       "\n",
       "     Dacoity  Robbery  Arson  Hurt  Prevention of atrocities (POA) Act  \\\n",
       "0          3        2      6   518                                 950   \n",
       "1          0        4     12   568                                 830   \n",
       "2          1       15      4   615                                1234   \n",
       "3          0        7     20   474                                1319   \n",
       "4          0        0      9   459                                1244   \n",
       "..       ...      ...    ...   ...                                 ...   \n",
       "415        0        0      0     0                                   2   \n",
       "416        0        0      0     0                                   3   \n",
       "417        0        0      0     1                                   2   \n",
       "418        0        0      0     0                                   1   \n",
       "419        0        0      0     0                                   1   \n",
       "\n",
       "     Protection of Civil Rights (PCR) Act  Other Crimes Against SCs  \n",
       "0                                     312                      1006  \n",
       "1                                     459                      1336  \n",
       "2                                     165                      1386  \n",
       "3                                      68                      1234  \n",
       "4                                      61                      1212  \n",
       "..                                    ...                       ...  \n",
       "415                                    27                         0  \n",
       "416                                    26                         0  \n",
       "417                                    26                         0  \n",
       "418                                    15                         2  \n",
       "419                                    20                         1  \n",
       "\n",
       "[420 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"data\\crime_by_state_rt.csv\").copy()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "85fbcade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(420, 12)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0db50629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATE/UT                                0\n",
       "Year                                    0\n",
       "Murder                                  0\n",
       "Assault on women                        0\n",
       "Kidnapping and Abduction                0\n",
       "Dacoity                                 0\n",
       "Robbery                                 0\n",
       "Arson                                   0\n",
       "Hurt                                    0\n",
       "Prevention of atrocities (POA) Act      0\n",
       "Protection of Civil Rights (PCR) Act    0\n",
       "Other Crimes Against SCs                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # nice we don't have to deal with any null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc4c9a4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 420 entries, 0 to 419\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                Non-Null Count  Dtype \n",
      "---  ------                                --------------  ----- \n",
      " 0   STATE/UT                              420 non-null    object\n",
      " 1   Year                                  420 non-null    int64 \n",
      " 2   Murder                                420 non-null    int64 \n",
      " 3   Assault on women                      420 non-null    int64 \n",
      " 4   Kidnapping and Abduction              420 non-null    int64 \n",
      " 5   Dacoity                               420 non-null    int64 \n",
      " 6   Robbery                               420 non-null    int64 \n",
      " 7   Arson                                 420 non-null    int64 \n",
      " 8   Hurt                                  420 non-null    int64 \n",
      " 9   Prevention of atrocities (POA) Act    420 non-null    int64 \n",
      " 10  Protection of Civil Rights (PCR) Act  420 non-null    int64 \n",
      " 11  Other Crimes Against SCs              420 non-null    int64 \n",
      "dtypes: int64(11), object(1)\n",
      "memory usage: 39.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13212722",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2c0b7d8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ANDHRA PRADESH', 'UTTARAKHAND', 'PUNJAB', 'RAJASTHAN', 'SIKKIM',\n",
       "       'TAMIL NADU', 'TRIPURA', 'UTTAR PRADESH', 'WEST BENGAL', 'NAGALAND',\n",
       "       'A & N ISLANDS', 'CHANDIGARH', 'D & N HAVELI', 'DAMAN & DIU', 'DELHI',\n",
       "       'LAKSHADWEEP', 'ODISHA', 'MIZORAM', 'ARUNACHAL PRADESH',\n",
       "       'HIMACHAL PRADESH', 'ASSAM', 'BIHAR', 'CHHATTISGARH', 'GOA', 'GUJARAT',\n",
       "       'HARYANA', 'JAMMU & KASHMIR', 'MEGHALAYA', 'JHARKHAND', 'KARNATAKA',\n",
       "       'KERALA', 'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'PUDUCHERRY'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states=df['STATE/UT'].value_counts().keys()\n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf9e3601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Murder',\n",
       " 'Assault on women',\n",
       " 'Kidnapping and Abduction',\n",
       " 'Dacoity',\n",
       " 'Robbery',\n",
       " 'Arson',\n",
       " 'Hurt',\n",
       " 'Other Crimes Against SCs']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n",
    "col=[]\n",
    "for i in df.columns:\n",
    "    if (i !='STATE/UT') & (i!='Prevention of atrocities (POA) Act') & (i!='Protection of Civil Rights (PCR) Act')&(i!='Year'):\n",
    "        col.append(i)\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6f33114f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ANDHRA PRADESH': {'Murder': 556,\n",
       "  'Assault on women': 1110,\n",
       "  'Kidnapping and Abduction': 238,\n",
       "  'Dacoity': 9,\n",
       "  'Robbery': 39,\n",
       "  'Arson': 136,\n",
       "  'Hurt': 7248,\n",
       "  'Other Crimes Against SCs': 17412},\n",
       " 'UTTARAKHAND': {'Murder': 50,\n",
       "  'Assault on women': 65,\n",
       "  'Kidnapping and Abduction': 27,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 1,\n",
       "  'Arson': 4,\n",
       "  'Hurt': 160,\n",
       "  'Other Crimes Against SCs': 45},\n",
       " 'PUNJAB': {'Murder': 32,\n",
       "  'Assault on women': 115,\n",
       "  'Kidnapping and Abduction': 18,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 1,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 134,\n",
       "  'Other Crimes Against SCs': 502},\n",
       " 'RAJASTHAN': {'Murder': 630,\n",
       "  'Assault on women': 1909,\n",
       "  'Kidnapping and Abduction': 365,\n",
       "  'Dacoity': 1,\n",
       "  'Robbery': 33,\n",
       "  'Arson': 481,\n",
       "  'Hurt': 5984,\n",
       "  'Other Crimes Against SCs': 37653},\n",
       " 'SIKKIM': {'Murder': 8,\n",
       "  'Assault on women': 17,\n",
       "  'Kidnapping and Abduction': 8,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 4,\n",
       "  'Arson': 2,\n",
       "  'Hurt': 39,\n",
       "  'Other Crimes Against SCs': 18},\n",
       " 'TAMIL NADU': {'Murder': 352,\n",
       "  'Assault on women': 259,\n",
       "  'Kidnapping and Abduction': 62,\n",
       "  'Dacoity': 5,\n",
       "  'Robbery': 15,\n",
       "  'Arson': 89,\n",
       "  'Hurt': 2269,\n",
       "  'Other Crimes Against SCs': 4227},\n",
       " 'TRIPURA': {'Murder': 4,\n",
       "  'Assault on women': 15,\n",
       "  'Kidnapping and Abduction': 6,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 1,\n",
       "  'Arson': 1,\n",
       "  'Hurt': 64,\n",
       "  'Other Crimes Against SCs': 84},\n",
       " 'UTTAR PRADESH': {'Murder': 3577,\n",
       "  'Assault on women': 3625,\n",
       "  'Kidnapping and Abduction': 2225,\n",
       "  'Dacoity': 55,\n",
       "  'Robbery': 214,\n",
       "  'Arson': 715,\n",
       "  'Hurt': 4889,\n",
       "  'Other Crimes Against SCs': 34294},\n",
       " 'WEST BENGAL': {'Murder': 2,\n",
       "  'Assault on women': 19,\n",
       "  'Kidnapping and Abduction': 1,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 1,\n",
       "  'Hurt': 26,\n",
       "  'Other Crimes Against SCs': 148},\n",
       " 'NAGALAND': {'Murder': 12,\n",
       "  'Assault on women': 6,\n",
       "  'Kidnapping and Abduction': 1,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 0,\n",
       "  'Other Crimes Against SCs': 31},\n",
       " 'A & N ISLANDS': {'Murder': 0,\n",
       "  'Assault on women': 0,\n",
       "  'Kidnapping and Abduction': 0,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 0,\n",
       "  'Other Crimes Against SCs': 0},\n",
       " 'CHANDIGARH': {'Murder': 0,\n",
       "  'Assault on women': 2,\n",
       "  'Kidnapping and Abduction': 0,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 0,\n",
       "  'Other Crimes Against SCs': 4},\n",
       " 'D & N HAVELI': {'Murder': 0,\n",
       "  'Assault on women': 0,\n",
       "  'Kidnapping and Abduction': 2,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 0,\n",
       "  'Other Crimes Against SCs': 5},\n",
       " 'DAMAN & DIU': {'Murder': 0,\n",
       "  'Assault on women': 0,\n",
       "  'Kidnapping and Abduction': 0,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 0,\n",
       "  'Other Crimes Against SCs': 5},\n",
       " 'DELHI': {'Murder': 1,\n",
       "  'Assault on women': 0,\n",
       "  'Kidnapping and Abduction': 0,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 0,\n",
       "  'Other Crimes Against SCs': 10},\n",
       " 'LAKSHADWEEP': {'Murder': 0,\n",
       "  'Assault on women': 0,\n",
       "  'Kidnapping and Abduction': 0,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 0,\n",
       "  'Other Crimes Against SCs': 0},\n",
       " 'ODISHA': {'Murder': 158,\n",
       "  'Assault on women': 574,\n",
       "  'Kidnapping and Abduction': 135,\n",
       "  'Dacoity': 18,\n",
       "  'Robbery': 67,\n",
       "  'Arson': 170,\n",
       "  'Hurt': 3281,\n",
       "  'Other Crimes Against SCs': 4274},\n",
       " 'MIZORAM': {'Murder': 0,\n",
       "  'Assault on women': 0,\n",
       "  'Kidnapping and Abduction': 1,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 1,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 0,\n",
       "  'Other Crimes Against SCs': 0},\n",
       " 'ARUNACHAL PRADESH': {'Murder': 1,\n",
       "  'Assault on women': 0,\n",
       "  'Kidnapping and Abduction': 0,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 1,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 0,\n",
       "  'Other Crimes Against SCs': 0},\n",
       " 'HIMACHAL PRADESH': {'Murder': 10,\n",
       "  'Assault on women': 93,\n",
       "  'Kidnapping and Abduction': 2,\n",
       "  'Dacoity': 8,\n",
       "  'Robbery': 6,\n",
       "  'Arson': 1,\n",
       "  'Hurt': 11,\n",
       "  'Other Crimes Against SCs': 329},\n",
       " 'ASSAM': {'Murder': 57,\n",
       "  'Assault on women': 57,\n",
       "  'Kidnapping and Abduction': 96,\n",
       "  'Dacoity': 14,\n",
       "  'Robbery': 33,\n",
       "  'Arson': 25,\n",
       "  'Hurt': 341,\n",
       "  'Other Crimes Against SCs': 318},\n",
       " 'BIHAR': {'Murder': 286,\n",
       "  'Assault on women': 284,\n",
       "  'Kidnapping and Abduction': 127,\n",
       "  'Dacoity': 29,\n",
       "  'Robbery': 77,\n",
       "  'Arson': 309,\n",
       "  'Hurt': 4524,\n",
       "  'Other Crimes Against SCs': 3814},\n",
       " 'CHHATTISGARH': {'Murder': 117,\n",
       "  'Assault on women': 661,\n",
       "  'Kidnapping and Abduction': 43,\n",
       "  'Dacoity': 1,\n",
       "  'Robbery': 13,\n",
       "  'Arson': 17,\n",
       "  'Hurt': 743,\n",
       "  'Other Crimes Against SCs': 2672},\n",
       " 'GOA': {'Murder': 0,\n",
       "  'Assault on women': 1,\n",
       "  'Kidnapping and Abduction': 0,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 1,\n",
       "  'Other Crimes Against SCs': 13},\n",
       " 'GUJARAT': {'Murder': 204,\n",
       "  'Assault on women': 325,\n",
       "  'Kidnapping and Abduction': 301,\n",
       "  'Dacoity': 85,\n",
       "  'Robbery': 214,\n",
       "  'Arson': 115,\n",
       "  'Hurt': 3271,\n",
       "  'Other Crimes Against SCs': 5399},\n",
       " 'HARYANA': {'Murder': 128,\n",
       "  'Assault on women': 447,\n",
       "  'Kidnapping and Abduction': 221,\n",
       "  'Dacoity': 1,\n",
       "  'Robbery': 10,\n",
       "  'Arson': 32,\n",
       "  'Hurt': 524,\n",
       "  'Other Crimes Against SCs': 1148},\n",
       " 'JAMMU & KASHMIR': {'Murder': 3,\n",
       "  'Assault on women': 7,\n",
       "  'Kidnapping and Abduction': 0,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 1,\n",
       "  'Hurt': 7,\n",
       "  'Other Crimes Against SCs': 12},\n",
       " 'MEGHALAYA': {'Murder': 0,\n",
       "  'Assault on women': 0,\n",
       "  'Kidnapping and Abduction': 0,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 0,\n",
       "  'Other Crimes Against SCs': 0},\n",
       " 'JHARKHAND': {'Murder': 65,\n",
       "  'Assault on women': 129,\n",
       "  'Kidnapping and Abduction': 44,\n",
       "  'Dacoity': 16,\n",
       "  'Robbery': 2,\n",
       "  'Arson': 22,\n",
       "  'Hurt': 912,\n",
       "  'Other Crimes Against SCs': 1507},\n",
       " 'KARNATAKA': {'Murder': 302,\n",
       "  'Assault on women': 329,\n",
       "  'Kidnapping and Abduction': 70,\n",
       "  'Dacoity': 17,\n",
       "  'Robbery': 19,\n",
       "  'Arson': 20,\n",
       "  'Hurt': 1939,\n",
       "  'Other Crimes Against SCs': 7455},\n",
       " 'KERALA': {'Murder': 36,\n",
       "  'Assault on women': 782,\n",
       "  'Kidnapping and Abduction': 23,\n",
       "  'Dacoity': 5,\n",
       "  'Robbery': 9,\n",
       "  'Arson': 21,\n",
       "  'Hurt': 2023,\n",
       "  'Other Crimes Against SCs': 2231},\n",
       " 'MADHYA PRADESH': {'Murder': 1062,\n",
       "  'Assault on women': 4107,\n",
       "  'Kidnapping and Abduction': 510,\n",
       "  'Dacoity': 9,\n",
       "  'Robbery': 96,\n",
       "  'Arson': 446,\n",
       "  'Hurt': 9993,\n",
       "  'Other Crimes Against SCs': 30721},\n",
       " 'MAHARASHTRA': {'Murder': 243,\n",
       "  'Assault on women': 979,\n",
       "  'Kidnapping and Abduction': 151,\n",
       "  'Dacoity': 122,\n",
       "  'Robbery': 97,\n",
       "  'Arson': 109,\n",
       "  'Hurt': 766,\n",
       "  'Other Crimes Against SCs': 5354},\n",
       " 'MANIPUR': {'Murder': 0,\n",
       "  'Assault on women': 0,\n",
       "  'Kidnapping and Abduction': 0,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 0,\n",
       "  'Other Crimes Against SCs': 0},\n",
       " 'PUDUCHERRY': {'Murder': 4,\n",
       "  'Assault on women': 0,\n",
       "  'Kidnapping and Abduction': 1,\n",
       "  'Dacoity': 0,\n",
       "  'Robbery': 0,\n",
       "  'Arson': 0,\n",
       "  'Hurt': 5,\n",
       "  'Other Crimes Against SCs': 7}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now making a final Dictionary for which will contain crimes related to each state\n",
    "Ans={}\n",
    "for i in states:\n",
    "    temp=df[df['STATE/UT']==i]\n",
    "    dic={}\n",
    "    for k in col:\n",
    "        dic[k]=temp[k].sum()\n",
    "    Ans[i]=dic\n",
    "Ans        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "22d079e6",
   "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>STATE/UT</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",
       "      <th>Total crimes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>2001</td>\n",
       "      <td>45</td>\n",
       "      <td>69</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>518</td>\n",
       "      <td>950</td>\n",
       "      <td>312</td>\n",
       "      <td>1006</td>\n",
       "      <td>1671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>2002</td>\n",
       "      <td>60</td>\n",
       "      <td>98</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>568</td>\n",
       "      <td>830</td>\n",
       "      <td>459</td>\n",
       "      <td>1336</td>\n",
       "      <td>2096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>2003</td>\n",
       "      <td>33</td>\n",
       "      <td>79</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>615</td>\n",
       "      <td>1234</td>\n",
       "      <td>165</td>\n",
       "      <td>1386</td>\n",
       "      <td>2160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>2004</td>\n",
       "      <td>39</td>\n",
       "      <td>66</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>474</td>\n",
       "      <td>1319</td>\n",
       "      <td>68</td>\n",
       "      <td>1234</td>\n",
       "      <td>1868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>2005</td>\n",
       "      <td>37</td>\n",
       "      <td>74</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>459</td>\n",
       "      <td>1244</td>\n",
       "      <td>61</td>\n",
       "      <td>1212</td>\n",
       "      <td>1812</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>420 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           STATE/UT  Year  Murder  Assault on women  Kidnapping and Abduction  \\\n",
       "0    ANDHRA PRADESH  2001      45                69                        22   \n",
       "1    ANDHRA PRADESH  2002      60                98                        18   \n",
       "2    ANDHRA PRADESH  2003      33                79                        27   \n",
       "3    ANDHRA PRADESH  2004      39                66                        28   \n",
       "4    ANDHRA PRADESH  2005      37                74                        21   \n",
       "..              ...   ...     ...               ...                       ...   \n",
       "415      PUDUCHERRY  2008       0                 0                         0   \n",
       "416      PUDUCHERRY  2009       0                 0                         0   \n",
       "417      PUDUCHERRY  2010       1                 0                         1   \n",
       "418      PUDUCHERRY  2011       0                 0                         0   \n",
       "419      PUDUCHERRY  2012       2                 0                         0   \n",
       "\n",
       "     Dacoity  Robbery  Arson  Hurt  Prevention of atrocities (POA) Act  \\\n",
       "0          3        2      6   518                                 950   \n",
       "1          0        4     12   568                                 830   \n",
       "2          1       15      4   615                                1234   \n",
       "3          0        7     20   474                                1319   \n",
       "4          0        0      9   459                                1244   \n",
       "..       ...      ...    ...   ...                                 ...   \n",
       "415        0        0      0     0                                   2   \n",
       "416        0        0      0     0                                   3   \n",
       "417        0        0      0     1                                   2   \n",
       "418        0        0      0     0                                   1   \n",
       "419        0        0      0     0                                   1   \n",
       "\n",
       "     Protection of Civil Rights (PCR) Act  Other Crimes Against SCs  \\\n",
       "0                                     312                      1006   \n",
       "1                                     459                      1336   \n",
       "2                                     165                      1386   \n",
       "3                                      68                      1234   \n",
       "4                                      61                      1212   \n",
       "..                                    ...                       ...   \n",
       "415                                    27                         0   \n",
       "416                                    26                         0   \n",
       "417                                    26                         0   \n",
       "418                                    15                         2   \n",
       "419                                    20                         1   \n",
       "\n",
       "     Total crimes  \n",
       "0            1671  \n",
       "1            2096  \n",
       "2            2160  \n",
       "3            1868  \n",
       "4            1812  \n",
       "..            ...  \n",
       "415             0  \n",
       "416             0  \n",
       "417             3  \n",
       "418             2  \n",
       "419             3  \n",
       "\n",
       "[420 rows x 13 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total crimes']=(df['Murder'])+(df['Assault on women'])+(df['Kidnapping and Abduction'])+(df['Dacoity'])+(df['Robbery'])+(df['Arson'])+(df['Hurt'])+(df['Other Crimes Against SCs'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9e40efc5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ANDHRA PRADESH': 26748,\n",
       " 'UTTARAKHAND': 352,\n",
       " 'PUNJAB': 802,\n",
       " 'RAJASTHAN': 47056,\n",
       " 'SIKKIM': 96,\n",
       " 'TAMIL NADU': 7278,\n",
       " 'TRIPURA': 175,\n",
       " 'UTTAR PRADESH': 49594,\n",
       " 'WEST BENGAL': 197,\n",
       " 'NAGALAND': 50,\n",
       " 'A & N ISLANDS': 0,\n",
       " 'CHANDIGARH': 6,\n",
       " 'D & N HAVELI': 7,\n",
       " 'DAMAN & DIU': 5,\n",
       " 'DELHI': 11,\n",
       " 'LAKSHADWEEP': 0,\n",
       " 'ODISHA': 8677,\n",
       " 'MIZORAM': 2,\n",
       " 'ARUNACHAL PRADESH': 2,\n",
       " 'HIMACHAL PRADESH': 460,\n",
       " 'ASSAM': 941,\n",
       " 'BIHAR': 9450,\n",
       " 'CHHATTISGARH': 4267,\n",
       " 'GOA': 15,\n",
       " 'GUJARAT': 9914,\n",
       " 'HARYANA': 2511,\n",
       " 'JAMMU & KASHMIR': 30,\n",
       " 'MEGHALAYA': 0,\n",
       " 'JHARKHAND': 2697,\n",
       " 'KARNATAKA': 10151,\n",
       " 'KERALA': 5130,\n",
       " 'MADHYA PRADESH': 46944,\n",
       " 'MAHARASHTRA': 7821,\n",
       " 'MANIPUR': 0,\n",
       " 'PUDUCHERRY': 17}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total crimes for each state\n",
    "total={}\n",
    "for i in states:\n",
    "    temp=df[df['STATE/UT']==i]\n",
    "    total[i]=temp['Total crimes'].sum()\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f3bcbb8e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A & N ISLANDS': 0,\n",
       " 'LAKSHADWEEP': 0,\n",
       " 'MEGHALAYA': 0,\n",
       " 'MANIPUR': 0,\n",
       " 'MIZORAM': 2,\n",
       " 'ARUNACHAL PRADESH': 2,\n",
       " 'DAMAN & DIU': 5,\n",
       " 'CHANDIGARH': 6,\n",
       " 'D & N HAVELI': 7,\n",
       " 'DELHI': 11,\n",
       " 'GOA': 15,\n",
       " 'PUDUCHERRY': 17,\n",
       " 'JAMMU & KASHMIR': 30,\n",
       " 'NAGALAND': 50,\n",
       " 'SIKKIM': 96,\n",
       " 'TRIPURA': 175,\n",
       " 'WEST BENGAL': 197,\n",
       " 'UTTARAKHAND': 352,\n",
       " 'HIMACHAL PRADESH': 460,\n",
       " 'PUNJAB': 802,\n",
       " 'ASSAM': 941,\n",
       " 'HARYANA': 2511,\n",
       " 'JHARKHAND': 2697,\n",
       " 'CHHATTISGARH': 4267,\n",
       " 'KERALA': 5130,\n",
       " 'TAMIL NADU': 7278,\n",
       " 'MAHARASHTRA': 7821,\n",
       " 'ODISHA': 8677,\n",
       " 'BIHAR': 9450,\n",
       " 'GUJARAT': 9914,\n",
       " 'KARNATAKA': 10151,\n",
       " 'ANDHRA PRADESH': 26748,\n",
       " 'MADHYA PRADESH': 46944,\n",
       " 'RAJASTHAN': 47056,\n",
       " 'UTTAR PRADESH': 49594}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total=dict(sorted(total.items(), key=lambda item: item[1]))\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4c71302d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "plt.bar(total.keys(),total.values())\n",
    "plt.xticks(rotation=85)\n",
    "plt.title('Total Crimes vs State')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf0a436c",
   "metadata": {},
   "source": [
    "# Observation 1 - Uttar Pradesh , Rajasthan , MP ,Andhra Pradesh ,Karnataka are the top 5 cities with most no of crimes against Backward Classes\n",
    "# Mizoram,Manipur,Meghalaya Lakshadweep are some states with almost zero crimes agnaist Backward Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8af69a3c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ANDHRA PRADESH': 1110,\n",
       " 'UTTARAKHAND': 65,\n",
       " 'PUNJAB': 115,\n",
       " 'RAJASTHAN': 1909,\n",
       " 'SIKKIM': 17,\n",
       " 'TAMIL NADU': 259,\n",
       " 'TRIPURA': 15,\n",
       " 'UTTAR PRADESH': 3625,\n",
       " 'WEST BENGAL': 19,\n",
       " 'NAGALAND': 6,\n",
       " 'A & N ISLANDS': 0,\n",
       " 'CHANDIGARH': 2,\n",
       " 'D & N HAVELI': 0,\n",
       " 'DAMAN & DIU': 0,\n",
       " 'DELHI': 0,\n",
       " 'LAKSHADWEEP': 0,\n",
       " 'ODISHA': 574,\n",
       " 'MIZORAM': 0,\n",
       " 'ARUNACHAL PRADESH': 0,\n",
       " 'HIMACHAL PRADESH': 93,\n",
       " 'ASSAM': 57,\n",
       " 'BIHAR': 284,\n",
       " 'CHHATTISGARH': 661,\n",
       " 'GOA': 1,\n",
       " 'GUJARAT': 325,\n",
       " 'HARYANA': 447,\n",
       " 'JAMMU & KASHMIR': 7,\n",
       " 'MEGHALAYA': 0,\n",
       " 'JHARKHAND': 129,\n",
       " 'KARNATAKA': 329,\n",
       " 'KERALA': 782,\n",
       " 'MADHYA PRADESH': 4107,\n",
       " 'MAHARASHTRA': 979,\n",
       " 'MANIPUR': 0,\n",
       " 'PUDUCHERRY': 0}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "women={}\n",
    "for i in states:\n",
    "    temp=df[df['STATE/UT']==i]\n",
    "    women[i]=temp['Assault on women'].sum()\n",
    "women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "78ac0bb0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A & N ISLANDS': 0,\n",
       " 'D & N HAVELI': 0,\n",
       " 'DAMAN & DIU': 0,\n",
       " 'DELHI': 0,\n",
       " 'LAKSHADWEEP': 0,\n",
       " 'MIZORAM': 0,\n",
       " 'ARUNACHAL PRADESH': 0,\n",
       " 'MEGHALAYA': 0,\n",
       " 'MANIPUR': 0,\n",
       " 'PUDUCHERRY': 0,\n",
       " 'GOA': 1,\n",
       " 'CHANDIGARH': 2,\n",
       " 'NAGALAND': 6,\n",
       " 'JAMMU & KASHMIR': 7,\n",
       " 'TRIPURA': 15,\n",
       " 'SIKKIM': 17,\n",
       " 'WEST BENGAL': 19,\n",
       " 'ASSAM': 57,\n",
       " 'UTTARAKHAND': 65,\n",
       " 'HIMACHAL PRADESH': 93,\n",
       " 'PUNJAB': 115,\n",
       " 'JHARKHAND': 129,\n",
       " 'TAMIL NADU': 259,\n",
       " 'BIHAR': 284,\n",
       " 'GUJARAT': 325,\n",
       " 'KARNATAKA': 329,\n",
       " 'HARYANA': 447,\n",
       " 'ODISHA': 574,\n",
       " 'CHHATTISGARH': 661,\n",
       " 'KERALA': 782,\n",
       " 'MAHARASHTRA': 979,\n",
       " 'ANDHRA PRADESH': 1110,\n",
       " 'RAJASTHAN': 1909,\n",
       " 'UTTAR PRADESH': 3625,\n",
       " 'MADHYA PRADESH': 4107}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "women=dict(sorted(women.items(), key=lambda item: item[1]))\n",
    "women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "99b101d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "women1=list(women.keys())[-6:-1]\n",
    "women2=list(women.values())[-6:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8c48e38e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(women2,labels=women1,autopct='%.1f%%')\n",
    "plt.title('Crime Against Women')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20bfa98",
   "metadata": {},
   "source": [
    "# Observation 2- We can see the top 5 states with most crimes against women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a91916b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year=list(df['Year'].unique())\n",
    "year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d45c49b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2001': 19755,\n",
       " '2002': 21719,\n",
       " '2003': 17570,\n",
       " '2004': 17632,\n",
       " '2005': 17339,\n",
       " '2006': 18084,\n",
       " '2007': 20006,\n",
       " '2008': 21686,\n",
       " '2009': 22207,\n",
       " '2010': 22081,\n",
       " '2011': 22310,\n",
       " '2012': 21017}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_total={}\n",
    "for i in year:\n",
    "    temp=df[df['Year']==i]\n",
    "    year_total[str(i)]=temp['Total crimes'].sum()\n",
    "year_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b1ac36da",
   "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(year_total.keys(),year_total.values())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fe31ecd",
   "metadata": {},
   "source": [
    "# Observation 3 - Crimes against Backward classes decreased from 2001 to 2003 and remained still for 4 years till 2006\n",
    "# But from 2006 crimes significantly increased"
   ]
  }
 ],
 "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
}