EDA_ASBD_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w_MPSkA00-u7",
    "outputId": "f70efa2c-cc89-4b37-96e5-ce456cafb658"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting geopandas\n",
      "  Downloading geopandas-0.10.2-py2.py3-none-any.whl (1.0 MB)\n",
      "\u001b[K     |████████████████████████████████| 1.0 MB 10.3 MB/s \n",
      "\u001b[?25hCollecting pyproj>=2.2.0\n",
      "  Downloading pyproj-3.2.1-cp37-cp37m-manylinux2010_x86_64.whl (6.3 MB)\n",
      "\u001b[K     |████████████████████████████████| 6.3 MB 61.9 MB/s \n",
      "\u001b[?25hCollecting fiona>=1.8\n",
      "  Downloading Fiona-1.8.21-cp37-cp37m-manylinux2014_x86_64.whl (16.7 MB)\n",
      "\u001b[K     |████████████████████████████████| 16.7 MB 228 kB/s \n",
      "\u001b[?25hRequirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.3.5)\n",
      "Requirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.1.post1)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (57.4.0)\n",
      "Collecting munch\n",
      "  Downloading munch-2.5.0-py2.py3-none-any.whl (10 kB)\n",
      "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2021.10.8)\n",
      "Collecting cligj>=0.5\n",
      "  Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)\n",
      "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (7.1.2)\n",
      "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (21.4.0)\n",
      "Collecting click-plugins>=1.0\n",
      "  Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)\n",
      "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.15.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (1.21.6)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2022.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2.8.2)\n",
      "Installing collected packages: munch, cligj, click-plugins, pyproj, fiona, geopandas\n",
      "Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.8.21 geopandas-0.10.2 munch-2.5.0 pyproj-3.2.1\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "!pip install geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "id": "fp9zn-lF51ut"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import statistics\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "id": "orKHZeku6669",
    "outputId": "2ff6764f-c285-4360-cf61-ddcb7e8832c4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-3469eed4-4518-4f23-824e-8e7a361ff2e2\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-3469eed4-4518-4f23-824e-8e7a361ff2e2\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving 03_District_wise_crimes_committed_against_children_2001_2012.csv to 03_District_wise_crimes_committed_against_children_2001_2012.csv\n"
     ]
    }
   ],
   "source": [
    "from google.colab import files                                                   \n",
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "id": "wZ5v6AWDm_oV",
    "outputId": "d23829ef-f413-478f-bb8b-77806874a3a8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-14a506cb-bd65-4d3a-af91-ef877f2d66b6\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-14a506cb-bd65-4d3a-af91-ef877f2d66b6\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving 03_District_wise_crimes_committed_against_children_2013.csv to 03_District_wise_crimes_committed_against_children_2013 (1).csv\n"
     ]
    }
   ],
   "source": [
    "from google.colab import files                                                   \n",
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 627
    },
    "id": "MnqQ7uY2nJeI",
    "outputId": "1263137c-35f9-434b-bd16-47451abf70a9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-acf2bd60-d537-4472-ab43-6c1816b07ee6\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>DISTRICT</th>\n",
       "      <th>Year</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Rape</th>\n",
       "      <th>Kidnapping and Abduction</th>\n",
       "      <th>Foeticide</th>\n",
       "      <th>Abetment of suicide</th>\n",
       "      <th>Exposure and abandonment</th>\n",
       "      <th>Procuration of minor girls</th>\n",
       "      <th>Buying of girls for prostitution</th>\n",
       "      <th>Selling of girls for prostitution</th>\n",
       "      <th>Prohibition of child marriage act</th>\n",
       "      <th>Other Crimes</th>\n",
       "      <th>Total</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>ANANTAPUR</td>\n",
       "      <td>2001</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>CHITTOOR</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>CUDDAPAH</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9010</th>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9011</th>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9012</th>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>9013</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>9014</th>\n",
       "      <td>PUDUCHERRY</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32</td>\n",
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      ],
      "text/plain": [
       "            STATE/UT       DISTRICT  Year  Murder  Rape  \\\n",
       "0     ANDHRA PRADESH       ADILABAD  2001     0.0   0.0   \n",
       "1     ANDHRA PRADESH      ANANTAPUR  2001    19.0  12.0   \n",
       "2     ANDHRA PRADESH       CHITTOOR  2001     0.0   0.0   \n",
       "3     ANDHRA PRADESH       CUDDAPAH  2001     0.0   0.0   \n",
       "4     ANDHRA PRADESH  EAST GODAVARI  2001     0.0   0.0   \n",
       "...              ...            ...   ...     ...   ...   \n",
       "9010     LAKSHADWEEP    LAKSHADWEEP  2012     0.0   0.0   \n",
       "9011     LAKSHADWEEP          TOTAL  2012     0.0   0.0   \n",
       "9012      PUDUCHERRY       KARAIKAL  2012     0.0   3.0   \n",
       "9013      PUDUCHERRY     PUDUCHERRY  2012     0.0   4.0   \n",
       "9014      PUDUCHERRY          TOTAL  2012     0.0   7.0   \n",
       "\n",
       "      Kidnapping and Abduction  Foeticide  Abetment of suicide  \\\n",
       "0                          0.0        0.0                  0.0   \n",
       "1                         29.0        0.0                  6.0   \n",
       "2                          0.0        0.0                  0.0   \n",
       "3                          0.0        0.0                  0.0   \n",
       "4                          0.0        0.0                  0.0   \n",
       "...                        ...        ...                  ...   \n",
       "9010                       0.0        0.0                  0.0   \n",
       "9011                       0.0        0.0                  0.0   \n",
       "9012                       2.0        0.0                  0.0   \n",
       "9013                      16.0        0.0                  0.0   \n",
       "9014                      18.0        0.0                  0.0   \n",
       "\n",
       "      Exposure and abandonment  Procuration of minor girls  \\\n",
       "0                          0.0                         0.0   \n",
       "1                          0.0                         0.0   \n",
       "2                          0.0                         0.0   \n",
       "3                          0.0                         0.0   \n",
       "4                          0.0                         0.0   \n",
       "...                        ...                         ...   \n",
       "9010                       0.0                         0.0   \n",
       "9011                       0.0                         0.0   \n",
       "9012                       1.0                         0.0   \n",
       "9013                       2.0                         0.0   \n",
       "9014                       3.0                         0.0   \n",
       "\n",
       "      Buying of girls for prostitution  Selling of girls for prostitution  \\\n",
       "0                                  0.0                                0.0   \n",
       "1                                  0.0                                0.0   \n",
       "2                                  0.0                                0.0   \n",
       "3                                  0.0                                0.0   \n",
       "4                                  0.0                                0.0   \n",
       "...                                ...                                ...   \n",
       "9010                               0.0                                0.0   \n",
       "9011                               0.0                                0.0   \n",
       "9012                               0.0                                0.0   \n",
       "9013                               0.0                                0.0   \n",
       "9014                               0.0                                0.0   \n",
       "\n",
       "      Prohibition of child marriage act  Other Crimes  Total  \n",
       "0                                   0.0           0.0      0  \n",
       "1                                   0.0           0.0     66  \n",
       "2                                   0.0           0.0      0  \n",
       "3                                   0.0           0.0      0  \n",
       "4                                   0.0           0.0      0  \n",
       "...                                 ...           ...    ...  \n",
       "9010                                0.0           0.0      0  \n",
       "9011                                0.0           0.0      0  \n",
       "9012                                0.0           1.0      7  \n",
       "9013                                1.0           2.0     25  \n",
       "9014                                1.0           3.0     32  \n",
       "\n",
       "[9015 rows x 15 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('03_District_wise_crimes_committed_against_children_2001_2012.csv')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
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     "height": 627
    },
    "id": "PAAxYgyv6s2F",
    "outputId": "b693bdd1-cb84-4825-9c38-9c777093dd1c"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>STATE/UT</th>\n",
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       "      <th>Other murder</th>\n",
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       "      <th>Foeticide</th>\n",
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       "      <th>Other Crimes</th>\n",
       "      <th>Total</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
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       "      <td>87</td>\n",
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       "      <th>2</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>CHITTOOR</td>\n",
       "      <td>2013</td>\n",
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       "      <td>7</td>\n",
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       "      <th>3</th>\n",
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       "      <td>CUDDAPAH</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
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       "      <td>14</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>CYBERABAD</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>PUDUCHERRY</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
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       "      <td>Puducherry</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
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       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
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       "      border-radius: 50%;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-85533717-4ff5-400e-8ccf-f76d40731b1d button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-85533717-4ff5-400e-8ccf-f76d40731b1d');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
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       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           STATE/UT     DISTRICT  Year  Infanticid  Other murder  Rape  \\\n",
       "0    Andhra Pradesh     ADILABAD  2013           0             1    21   \n",
       "1    Andhra Pradesh    ANANTAPUR  2013           0             1    15   \n",
       "2    Andhra Pradesh     CHITTOOR  2013           0             6     1   \n",
       "3    Andhra Pradesh     CUDDAPAH  2013           2             0    14   \n",
       "4    Andhra Pradesh    CYBERABAD  2013           1             8    45   \n",
       "..              ...          ...   ...         ...           ...   ...   \n",
       "818     Lakshadweep  LAKSHADWEEP  2013           0             0     0   \n",
       "819     Lakshadweep        TOTAL  2013           0             0     0   \n",
       "820      Puducherry     KARAIKAL  2013           0             0     4   \n",
       "821      Puducherry   PUDUCHERRY  2013           0             0     5   \n",
       "822      Puducherry        TOTAL  2013           0             0     9   \n",
       "\n",
       "     Kidnapping and Abduction  Foeticide  Abetment of suicide  \\\n",
       "0                           9          0                    0   \n",
       "1                          68          0                    3   \n",
       "2                           0          0                    0   \n",
       "3                          32          0                    0   \n",
       "4                          69          2                    0   \n",
       "..                        ...        ...                  ...   \n",
       "818                         0          0                    0   \n",
       "819                         0          0                    0   \n",
       "820                         0          0                    0   \n",
       "821                        34          0                    0   \n",
       "822                        34          0                    0   \n",
       "\n",
       "     Exposure and abandonment  Procuration of minor girls  \\\n",
       "0                           0                           0   \n",
       "1                           0                           0   \n",
       "2                           0                           0   \n",
       "3                           0                           0   \n",
       "4                           2                           9   \n",
       "..                        ...                         ...   \n",
       "818                         0                           0   \n",
       "819                         0                           0   \n",
       "820                         0                           0   \n",
       "821                         1                           0   \n",
       "822                         1                           0   \n",
       "\n",
       "     Buying of girls for prostitution  Selling of girls for prostitution  \\\n",
       "0                                   0                                  0   \n",
       "1                                   0                                  0   \n",
       "2                                   0                                  0   \n",
       "3                                   0                                  0   \n",
       "4                                   0                                  0   \n",
       "..                                ...                                ...   \n",
       "818                                 0                                  0   \n",
       "819                                 0                                  0   \n",
       "820                                 0                                  0   \n",
       "821                                 0                                  0   \n",
       "822                                 0                                  0   \n",
       "\n",
       "     Prohibition of child marriage act  Other Crimes  Total  \n",
       "0                                    1             1     33  \n",
       "1                                    0             0     87  \n",
       "2                                    0             0      7  \n",
       "3                                    1             0     49  \n",
       "4                                    1            19    156  \n",
       "..                                 ...           ...    ...  \n",
       "818                                  0             0      0  \n",
       "819                                  0             0      0  \n",
       "820                                  0             0      4  \n",
       "821                                  1             2     43  \n",
       "822                                  1             2     47  \n",
       "\n",
       "[823 rows x 16 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('03_District_wise_crimes_committed_against_children_2013.csv')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SWBp35Ob79FO",
    "outputId": "27a613d3-e26f-4825-b396-a20a82dd0620"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9015 entries, 0 to 9014\n",
      "Data columns (total 15 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   STATE/UT                           9015 non-null   object \n",
      " 1   DISTRICT                           9015 non-null   object \n",
      " 2   Year                               9015 non-null   int64  \n",
      " 3   Murder                             9004 non-null   float64\n",
      " 4   Rape                               9004 non-null   float64\n",
      " 5   Kidnapping and Abduction           9004 non-null   float64\n",
      " 6   Foeticide                          9004 non-null   float64\n",
      " 7   Abetment of suicide                9004 non-null   float64\n",
      " 8   Exposure and abandonment           9004 non-null   float64\n",
      " 9   Procuration of minor girls         9004 non-null   float64\n",
      " 10  Buying of girls for prostitution   9004 non-null   float64\n",
      " 11  Selling of girls for prostitution  9004 non-null   float64\n",
      " 12  Prohibition of child marriage act  9004 non-null   float64\n",
      " 13  Other Crimes                       9004 non-null   float64\n",
      " 14  Total                              9015 non-null   int64  \n",
      "dtypes: float64(11), int64(2), object(2)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mVstdk_ouJWP",
    "outputId": "452612f6-dee0-400e-d6f6-ac630d82736a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATE/UT                              0\n",
       "DISTRICT                              0\n",
       "Year                                  0\n",
       "Murder                               11\n",
       "Rape                                 11\n",
       "Kidnapping and Abduction             11\n",
       "Foeticide                            11\n",
       "Abetment of suicide                  11\n",
       "Exposure and abandonment             11\n",
       "Procuration of minor girls           11\n",
       "Buying of girls for prostitution     11\n",
       "Selling of girls for prostitution    11\n",
       "Prohibition of child marriage act    11\n",
       "Other Crimes                         11\n",
       "Total                                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YmLLtZuLwP-v",
    "outputId": "c47b7431-8d18-4dc2-bcd3-c7245c03f0ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.  19.   2.   1.   3.   5.  35.   8.   6.   9.   4.  17.  78.   7.\n",
      "  31.  12.  76. 103.  30.  13. 203.  20.  24.  18.  21.  32.  16.  15.\n",
      "  27.  25. 489.  22.  43.  10.  28.  68.  64.  11.  94.  14. 248.  45.\n",
      " 424.  29.  55. 119.  49.  23.  47. 165. 206.  26.  60. 458.  71.  44.\n",
      "  74.  54.  51. 144. 187.  42.  33. 528.  41.  57.  37.  38. 150. 192.\n",
      " 390.  34.  61.  48.  87.  63. 160. 207.  77. 426.  62.  88.  83.  53.\n",
      " 213.  89. 397.  56. 116.  52.  59.  84. 107. 178.  97.  91. 376. 126.\n",
      "  67. 127. 182. 372.  69. 202.  66. 214.  82.  80. 346. 101. 124. 123.\n",
      " 204. 108. 326.  46.  39. 113.  nan 129.  40. 140. 212.  95.  36. 463.]\n"
     ]
    }
   ],
   "source": [
    "unique_murders = df1[\"Murder\"].unique()\n",
    "print(unique_murders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GV9DL_fuwmI4",
    "outputId": "f2f39156-a56e-4e67-b0f1-fbc8960ac806"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,  19.,   2.,   1.,   3.,   5.,  35.,   8.,   6.,   9.,   4.,\n",
       "        17.,  78.,   7.,  31.,  12.,  76., 103.,  30.,  13., 203.,  20.,\n",
       "        24.,  18.,  21.,  32.,  16.,  15.,  27.,  25., 489.,  22.,  43.,\n",
       "        10.,  28.,  68.,  64.,  11.,  94.,  14., 248.,  45., 424.,  29.,\n",
       "        55., 119.,  49.,  23.,  47., 165., 206.,  26.,  60., 458.,  71.,\n",
       "        44.,  74.,  54.,  51., 144., 187.,  42.,  33., 528.,  41.,  57.,\n",
       "        37.,  38., 150., 192., 390.,  34.,  61.,  48.,  87.,  63., 160.,\n",
       "       207.,  77., 426.,  62.,  88.,  83.,  53., 213.,  89., 397.,  56.,\n",
       "       116.,  52.,  59.,  84., 107., 178.,  97.,  91., 376., 126.,  67.,\n",
       "       127., 182., 372.,  69., 202.,  66., 214.,  82.,  80., 346., 101.,\n",
       "       124., 123., 204., 108., 326.,  46.,  39., 113.,  nan, 129.,  40.,\n",
       "       140., 212.,  95.,  36., 463.])"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values = ['nan','NAN','na','nil','NaN','n/a']\n",
    "df1 = df1.replace(missing_values,np.NaN)\n",
    "df1[\"Murder\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "id": "eVvN60QEuWI3"
   },
   "outputs": [],
   "source": [
    "df1['Murder'] = df1['Murder'].fillna(df1.groupby('STATE/UT')['Murder'].transform('median'))\n",
    "df1['Rape'] = df1['Rape'].fillna(df1.groupby('STATE/UT')['Rape'].transform('mean'))\n",
    "df1['Kidnapping and Abduction'] = df1['Kidnapping and Abduction'].fillna(df1.groupby('STATE/UT')['Kidnapping and Abduction'].transform('median'))\n",
    "df1['Foeticide'] = df1['Foeticide'].fillna(df1.groupby('STATE/UT')['Foeticide'].transform('median'))\n",
    "df1['Abetment of suicide'] = df1['Abetment of suicide'].fillna(df1.groupby('STATE/UT')['Abetment of suicide'].transform('median'))\n",
    "df1['Exposure and abandonment'] = df1['Exposure and abandonment'].fillna(df1.groupby('STATE/UT')['Exposure and abandonment'].transform('median'))\n",
    "df1['Procuration of minor girls'] = df1['Procuration of minor girls'].fillna(df1.groupby('STATE/UT')['Procuration of minor girls'].transform('median'))\n",
    "df1['Buying of girls for prostitution'] = df1['Buying of girls for prostitution'].fillna(df1.groupby('STATE/UT')['Buying of girls for prostitution'].transform('median'))\n",
    "df1['Selling of girls for prostitution'] = df1['Selling of girls for prostitution'].fillna(df1.groupby('STATE/UT')['Selling of girls for prostitution'].transform('median'))\n",
    "df1['Prohibition of child marriage act'] = df1['Prohibition of child marriage act'].fillna(df1.groupby('STATE/UT')['Prohibition of child marriage act'].transform('median'))\n",
    "df1['Other Crimes'] = df1['Other Crimes'].fillna(df1.groupby('STATE/UT')['Other Crimes'].transform('median'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "STXJh5W50pud",
    "outputId": "8ea3e58f-3d72-4ab3-bea4-d572d0106a23"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.  19.   2.   1.   3.   5.  35.   8.   6.   9.   4.  17.  78.   7.\n",
      "  31.  12.  76. 103.  30.  13. 203.  20.  24.  18.  21.  32.  16.  15.\n",
      "  27.  25. 489.  22.  43.  10.  28.  68.  64.  11.  94.  14. 248.  45.\n",
      " 424.  29.  55. 119.  49.  23.  47. 165. 206.  26.  60. 458.  71.  44.\n",
      "  74.  54.  51. 144. 187.  42.  33. 528.  41.  57.  37.  38. 150. 192.\n",
      " 390.  34.  61.  48.  87.  63. 160. 207.  77. 426.  62.  88.  83.  53.\n",
      " 213.  89. 397.  56. 116.  52.  59.  84. 107. 178.  97.  91. 376. 126.\n",
      "  67. 127. 182. 372.  69. 202.  66. 214.  82.  80. 346. 101. 124. 123.\n",
      " 204. 108. 326.  46.  39. 113. 129.  40. 140. 212.  95.  36. 463.]\n"
     ]
    }
   ],
   "source": [
    "unique_murders = df1[\"Murder\"].unique()\n",
    "print(unique_murders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qHTv73n5wK-p",
    "outputId": "78f7a8a1-f045-44c6-e65f-7c37e6a10365"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATE/UT                             0\n",
       "DISTRICT                             0\n",
       "Year                                 0\n",
       "Murder                               0\n",
       "Rape                                 0\n",
       "Kidnapping and Abduction             0\n",
       "Foeticide                            0\n",
       "Abetment of suicide                  0\n",
       "Exposure and abandonment             0\n",
       "Procuration of minor girls           0\n",
       "Buying of girls for prostitution     0\n",
       "Selling of girls for prostitution    0\n",
       "Prohibition of child marriage act    0\n",
       "Other Crimes                         0\n",
       "Total                                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RVAjEr7Xxak4",
    "outputId": "17e5a5db-cb38-476f-cf08-0f1961ecf8ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9015 entries, 0 to 9014\n",
      "Data columns (total 15 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   STATE/UT                           9015 non-null   object \n",
      " 1   DISTRICT                           9015 non-null   object \n",
      " 2   Year                               9015 non-null   int64  \n",
      " 3   Murder                             9015 non-null   float64\n",
      " 4   Rape                               9015 non-null   float64\n",
      " 5   Kidnapping and Abduction           9015 non-null   float64\n",
      " 6   Foeticide                          9015 non-null   float64\n",
      " 7   Abetment of suicide                9015 non-null   float64\n",
      " 8   Exposure and abandonment           9015 non-null   float64\n",
      " 9   Procuration of minor girls         9015 non-null   float64\n",
      " 10  Buying of girls for prostitution   9015 non-null   float64\n",
      " 11  Selling of girls for prostitution  9015 non-null   float64\n",
      " 12  Prohibition of child marriage act  9015 non-null   float64\n",
      " 13  Other Crimes                       9015 non-null   float64\n",
      " 14  Total                              9015 non-null   int64  \n",
      "dtypes: float64(11), int64(2), object(2)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5tH41o3NreeW",
    "outputId": "d7fdbd1b-e4e7-4da6-fcb0-9cdb3ec61735"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 823 entries, 0 to 822\n",
      "Data columns (total 16 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   STATE/UT                           823 non-null    object \n",
      " 1   DISTRICT                           823 non-null    object \n",
      " 2   Year                               823 non-null    int64  \n",
      " 3   Infanticid                         823 non-null    float64\n",
      " 4   Other murder                       823 non-null    float64\n",
      " 5   Rape                               823 non-null    float64\n",
      " 6   Kidnapping and Abduction           823 non-null    float64\n",
      " 7   Foeticide                          823 non-null    float64\n",
      " 8   Abetment of suicide                823 non-null    float64\n",
      " 9   Exposure and abandonment           823 non-null    float64\n",
      " 10  Procuration of minor girls         823 non-null    float64\n",
      " 11  Buying of girls for prostitution   823 non-null    float64\n",
      " 12  Selling of girls for prostitution  823 non-null    float64\n",
      " 13  Prohibition of child marriage act  823 non-null    float64\n",
      " 14  Other Crimes                       823 non-null    float64\n",
      " 15  Total                              823 non-null    int64  \n",
      "dtypes: float64(12), int64(2), object(2)\n",
      "memory usage: 103.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data_types_dict = {'Infanticid':float,'Other murder':float,'Rape':float,'Kidnapping and Abduction':float,'Foeticide':float, 'Abetment of suicide':float, 'Exposure and abandonment':float, 'Procuration of minor girls': float, 'Buying of girls for prostitution':float, 'Selling of girls for prostitution':float, 'Prohibition of child marriage act':float, 'Other Crimes':float} \n",
    "df2 = df2.astype(data_types_dict)\n",
    "data_types_dict1 = {'Total':float}\n",
    "df1 = df1.astype(data_types_dict1)\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "id": "Nq1FWPpEp3xe"
   },
   "outputs": [],
   "source": [
    "df2 = df2.drop(['Other murder','Infanticid'],axis=1)\n",
    "df1 = df1.drop(['Murder'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pl5nptvH7_RC",
    "outputId": "f8671e82-c9b7-4ad0-af83-1206d0a21ccf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9015, 14)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1.shape"
   ]
  },
  {
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   "metadata": {
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    "id": "shlg7X6aneo-",
    "outputId": "3fc2dfa4-a1c5-4e65-d727-816cc49a3571"
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    {
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     "execution_count": 242,
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    "id": "RO_cG9aX1UdZ",
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       "      <th></th>\n",
       "      <th>STATE/UT</th>\n",
       "      <th>DISTRICT</th>\n",
       "      <th>Year</th>\n",
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       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
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       "      <th>819</th>\n",
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       "      <td>0.0</td>\n",
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      "text/plain": [
       "           STATE/UT       DISTRICT  Year  Rape  Kidnapping and Abduction  \\\n",
       "0    ANDHRA PRADESH       ADILABAD  2001   0.0                       0.0   \n",
       "1    ANDHRA PRADESH      ANANTAPUR  2001  12.0                      29.0   \n",
       "2    ANDHRA PRADESH       CHITTOOR  2001   0.0                       0.0   \n",
       "3    ANDHRA PRADESH       CUDDAPAH  2001   0.0                       0.0   \n",
       "4    ANDHRA PRADESH  EAST GODAVARI  2001   0.0                       0.0   \n",
       "..              ...            ...   ...   ...                       ...   \n",
       "818     Lakshadweep    LAKSHADWEEP  2013   0.0                       0.0   \n",
       "819     Lakshadweep          TOTAL  2013   0.0                       0.0   \n",
       "820      Puducherry       KARAIKAL  2013   4.0                       0.0   \n",
       "821      Puducherry     PUDUCHERRY  2013   5.0                      34.0   \n",
       "822      Puducherry          TOTAL  2013   9.0                      34.0   \n",
       "\n",
       "     Foeticide  Abetment of suicide  Exposure and abandonment  \\\n",
       "0          0.0                  0.0                       0.0   \n",
       "1          0.0                  6.0                       0.0   \n",
       "2          0.0                  0.0                       0.0   \n",
       "3          0.0                  0.0                       0.0   \n",
       "4          0.0                  0.0                       0.0   \n",
       "..         ...                  ...                       ...   \n",
       "818        0.0                  0.0                       0.0   \n",
       "819        0.0                  0.0                       0.0   \n",
       "820        0.0                  0.0                       0.0   \n",
       "821        0.0                  0.0                       1.0   \n",
       "822        0.0                  0.0                       1.0   \n",
       "\n",
       "     Procuration of minor girls  Buying of girls for prostitution  \\\n",
       "0                           0.0                               0.0   \n",
       "1                           0.0                               0.0   \n",
       "2                           0.0                               0.0   \n",
       "3                           0.0                               0.0   \n",
       "4                           0.0                               0.0   \n",
       "..                          ...                               ...   \n",
       "818                         0.0                               0.0   \n",
       "819                         0.0                               0.0   \n",
       "820                         0.0                               0.0   \n",
       "821                         0.0                               0.0   \n",
       "822                         0.0                               0.0   \n",
       "\n",
       "     Selling of girls for prostitution  Prohibition of child marriage act  \\\n",
       "0                                  0.0                                0.0   \n",
       "1                                  0.0                                0.0   \n",
       "2                                  0.0                                0.0   \n",
       "3                                  0.0                                0.0   \n",
       "4                                  0.0                                0.0   \n",
       "..                                 ...                                ...   \n",
       "818                                0.0                                0.0   \n",
       "819                                0.0                                0.0   \n",
       "820                                0.0                                0.0   \n",
       "821                                0.0                                1.0   \n",
       "822                                0.0                                1.0   \n",
       "\n",
       "     Other Crimes  Total  \n",
       "0             0.0    0.0  \n",
       "1             0.0   66.0  \n",
       "2             0.0    0.0  \n",
       "3             0.0    0.0  \n",
       "4             0.0    0.0  \n",
       "..            ...    ...  \n",
       "818           0.0    0.0  \n",
       "819           0.0    0.0  \n",
       "820           0.0    4.0  \n",
       "821           2.0   43.0  \n",
       "822           2.0   47.0  \n",
       "\n",
       "[9838 rows x 14 columns]"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df_final = pd.concat([df1,df2],axis=0)\n",
    "df_final"
   ]
  },
  {
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   "execution_count": 244,
   "metadata": {
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    "outputId": "1fce6e3f-bc1b-4e54-96ad-fc3e54c4fa56"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9838 entries, 0 to 822\n",
      "Data columns (total 14 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   STATE/UT                           9838 non-null   object \n",
      " 1   DISTRICT                           9838 non-null   object \n",
      " 2   Year                               9838 non-null   int64  \n",
      " 3   Rape                               9838 non-null   float64\n",
      " 4   Kidnapping and Abduction           9838 non-null   float64\n",
      " 5   Foeticide                          9838 non-null   float64\n",
      " 6   Abetment of suicide                9838 non-null   float64\n",
      " 7   Exposure and abandonment           9838 non-null   float64\n",
      " 8   Procuration of minor girls         9838 non-null   float64\n",
      " 9   Buying of girls for prostitution   9838 non-null   float64\n",
      " 10  Selling of girls for prostitution  9838 non-null   float64\n",
      " 11  Prohibition of child marriage act  9838 non-null   float64\n",
      " 12  Other Crimes                       9838 non-null   float64\n",
      " 13  Total                              9838 non-null   float64\n",
      "dtypes: float64(11), int64(1), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_final.info()"
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  },
  {
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   "execution_count": 248,
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    "id": "zDp58xAKLOzv",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning:\n",
      "\n",
      "In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'No of Cases vs Year')"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.stem( df_final.Year,df_final.Total)\n",
    "plt.ylabel('Total Cases against children')   \n",
    "plt.xlabel('Year')\n",
    "plt.title('No of Cases vs Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "colab": {
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    },
    "id": "VhA2CW0fPZx5",
    "outputId": "65783866-e58f-476e-938e-fe82bd4c590c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Buying by State STATE/UT\n",
      "A & N ISLANDS        0.0\n",
      "A&N Islands          0.0\n",
      "ANDHRA PRADESH       0.1\n",
      "ARUNACHAL PRADESH    0.0\n",
      "ASSAM                0.0\n",
      "                    ... \n",
      "UTTARAKHAND          0.0\n",
      "Uttar Pradesh        0.0\n",
      "Uttarakhand          0.0\n",
      "WEST BENGAL          0.6\n",
      "West Bengal          0.1\n",
      "Name: Buying of girls for prostitution, Length: 70, dtype: float64\n",
      "Mean Selling by State STATE/UT\n",
      "A & N ISLANDS        0.0\n",
      "A&N Islands          0.0\n",
      "ANDHRA PRADESH       0.2\n",
      "ARUNACHAL PRADESH    0.0\n",
      "ASSAM                0.0\n",
      "                    ... \n",
      "UTTARAKHAND          0.0\n",
      "Uttar Pradesh        0.0\n",
      "Uttarakhand          0.0\n",
      "WEST BENGAL          3.8\n",
      "West Bengal          4.6\n",
      "Name: Selling of girls for prostitution, Length: 70, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "meanbuying =round(df_final.groupby('STATE/UT')['Buying of girls for prostitution'].mean(),1)\n",
    "print(\"Mean Buying by State\",meanbuying)\n",
    "meanselling = round(df_final.groupby('STATE/UT')['Selling of girls for prostitution'].mean(),1)\n",
    "print(\"Mean Selling by State\", meanselling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "id": "Hdli8yX5T3fi"
   },
   "outputs": [],
   "source": [
    "g= pd.DataFrame(df_final.groupby(['STATE/UT'])['Total'].sum().reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "hBPN2o6YTg05",
    "outputId": "41081fce-237e-434d-fb25-5bcae77d9a32"
   },
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "fig= px.bar(g,x='STATE/UT',y='Total',color_discrete_sequence=['blue'])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
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       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-d13ed8ae-b8b6-4d8b-8bfd-3cb3b0051018');\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
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       "        }\n",
       "      </script>\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "             STATE/UT    Total\n",
       "0       A & N ISLANDS    490.0\n",
       "1         A&N Islands    108.0\n",
       "2      ANDHRA PRADESH  33158.0\n",
       "3   ARUNACHAL PRADESH    500.0\n",
       "4               ASSAM   3440.0\n",
       "..                ...      ...\n",
       "65        UTTARAKHAND   1375.0\n",
       "66      Uttar Pradesh  19714.0\n",
       "67        Uttarakhand    464.0\n",
       "68        WEST BENGAL  13516.0\n",
       "69        West Bengal   5060.0\n",
       "\n",
       "[70 rows x 2 columns]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g1= pd.DataFrame(df_final.groupby(['STATE/UT'])['Total'].sum().reset_index())\n",
    "g1.columns=['STATE/UT','Total']\n",
    "g1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "id": "P_vo1NXv7qzx"
   },
   "outputs": [],
   "source": [
    "shp_gdf = gpd.read_file(\"India_State_Boundary.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "CQBT4M6R8X3r",
    "outputId": "13a017b2-79f8-44cc-baf9-c88b2c1cc9e4"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>State</td>\n",
       "      <td>MULTIPOLYGON (((88.01861 21.57278, 88.01889 21...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andaman &amp; Nicobar</td>\n",
       "      <td>Union Territory</td>\n",
       "      <td>MULTIPOLYGON (((92.90124 12.91071, 92.90157 12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>Union Territory</td>\n",
       "      <td>POLYGON ((76.77232 30.79420, 76.77286 30.79343...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daman and Diu and Dadra and Nagar Haveli</td>\n",
       "      <td>Union Territory</td>\n",
       "      <td>MULTIPOLYGON (((72.96339 20.33227, 72.96390 20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>Union Territory</td>\n",
       "      <td>POLYGON ((77.10591 28.87005, 77.10668 28.86989...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((76.57526 30.10063, 76.57645 30.10152...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((87.69613 24.16027, 87.69625 24.15974...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>State</td>\n",
       "      <td>MULTIPOLYGON (((77.33232 18.45086, 77.33267 18...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((74.98896 12.79553, 74.98953 12.79554...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lakshadweep</td>\n",
       "      <td>Union Territory</td>\n",
       "      <td>MULTIPOLYGON (((73.08025 8.32651, 73.08029 8.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((78.37211 26.86406, 78.37423 26.86091...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>State</td>\n",
       "      <td>MULTIPOLYGON (((74.38926 22.03241, 74.38942 22...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Puducherry</td>\n",
       "      <td>Union Territory</td>\n",
       "      <td>MULTIPOLYGON (((79.75669 11.00258, 79.75682 11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Tamilnadu</td>\n",
       "      <td>State</td>\n",
       "      <td>MULTIPOLYGON (((80.30358 13.47307, 80.30577 13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chhattishgarh</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((83.32707 24.10232, 83.32939 24.10130...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Telengana</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((78.33565 19.88358, 78.33688 19.88300...</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((84.67571 19.16721, 84.67725 19.16670...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Goa</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((73.87042 15.78117, 73.87097 15.78029...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((76.79412 33.25569, 76.79482 33.25557...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Punjab</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((75.83876 32.51269, 75.84094 32.51158...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((73.97266 30.19800, 73.97266 30.19800...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((72.26126 21.49999, 72.25911 21.49999...</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((79.06793 31.46153, 79.07046 31.46010...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((77.59189 30.40632, 77.59337 30.40621...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sikkim</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((88.61635 28.12763, 88.62302 28.12625...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Assam</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((93.24961 24.97312, 93.25244 24.97428...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((96.08538 29.45928, 96.09262 29.45613...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nagaland</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((95.19272 27.02710, 95.19475 27.01442...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Manipur</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((94.57415 25.69043, 94.57852 25.68761...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Mizoram</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((92.76384 24.52098, 92.76422 24.51967...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Tripura</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((92.16949 24.53175, 92.16961 24.53168...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((91.82617 26.11925, 91.82728 26.11914...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((84.10880 27.52173, 84.10896 27.52131...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Ladakh</td>\n",
       "      <td>Union Territory</td>\n",
       "      <td>POLYGON ((76.80933 33.24349, 76.79356 33.25175...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>Union Territory</td>\n",
       "      <td>POLYGON ((76.80933 33.24349, 76.80894 33.24367...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Odisha</td>\n",
       "      <td>State</td>\n",
       "      <td>POLYGON ((87.47639 21.64343, 87.47362 21.63205...</td>\n",
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      "text/plain": [
       "                                        Name             Type  \\\n",
       "0                                West Bengal            State   \n",
       "1                          Andaman & Nicobar  Union Territory   \n",
       "2                                 Chandigarh  Union Territory   \n",
       "3   Daman and Diu and Dadra and Nagar Haveli  Union Territory   \n",
       "4                                      Delhi  Union Territory   \n",
       "5                                    Haryana            State   \n",
       "6                                  Jharkhand            State   \n",
       "7                                  Karnataka            State   \n",
       "8                                     Kerala            State   \n",
       "9                                Lakshadweep  Union Territory   \n",
       "10                            Madhya Pradesh            State   \n",
       "11                               Maharashtra            State   \n",
       "12                                Puducherry  Union Territory   \n",
       "13                                 Tamilnadu            State   \n",
       "14                             Chhattishgarh            State   \n",
       "15                                 Telengana            State   \n",
       "16                            Andhra Pradesh            State   \n",
       "17                                       Goa            State   \n",
       "18                          Himachal Pradesh            State   \n",
       "19                                    Punjab            State   \n",
       "20                                 Rajasthan            State   \n",
       "21                                   Gujarat            State   \n",
       "22                               Uttarakhand            State   \n",
       "23                             Uttar Pradesh            State   \n",
       "24                                    Sikkim            State   \n",
       "25                                     Assam            State   \n",
       "26                         Arunachal Pradesh            State   \n",
       "27                                  Nagaland            State   \n",
       "28                                   Manipur            State   \n",
       "29                                   Mizoram            State   \n",
       "30                                   Tripura            State   \n",
       "31                                 Meghalaya            State   \n",
       "32                                     Bihar            State   \n",
       "33                                    Ladakh  Union Territory   \n",
       "34                         Jammu and Kashmir  Union Territory   \n",
       "35                                    Odisha            State   \n",
       "\n",
       "                                             geometry  \n",
       "0   MULTIPOLYGON (((88.01861 21.57278, 88.01889 21...  \n",
       "1   MULTIPOLYGON (((92.90124 12.91071, 92.90157 12...  \n",
       "2   POLYGON ((76.77232 30.79420, 76.77286 30.79343...  \n",
       "3   MULTIPOLYGON (((72.96339 20.33227, 72.96390 20...  \n",
       "4   POLYGON ((77.10591 28.87005, 77.10668 28.86989...  \n",
       "5   POLYGON ((76.57526 30.10063, 76.57645 30.10152...  \n",
       "6   POLYGON ((87.69613 24.16027, 87.69625 24.15974...  \n",
       "7   MULTIPOLYGON (((77.33232 18.45086, 77.33267 18...  \n",
       "8   POLYGON ((74.98896 12.79553, 74.98953 12.79554...  \n",
       "9   MULTIPOLYGON (((73.08025 8.32651, 73.08029 8.3...  \n",
       "10  POLYGON ((78.37211 26.86406, 78.37423 26.86091...  \n",
       "11  MULTIPOLYGON (((74.38926 22.03241, 74.38942 22...  \n",
       "12  MULTIPOLYGON (((79.75669 11.00258, 79.75682 11...  \n",
       "13  MULTIPOLYGON (((80.30358 13.47307, 80.30577 13...  \n",
       "14  POLYGON ((83.32707 24.10232, 83.32939 24.10130...  \n",
       "15  POLYGON ((78.33565 19.88358, 78.33688 19.88300...  \n",
       "16  POLYGON ((84.67571 19.16721, 84.67725 19.16670...  \n",
       "17  POLYGON ((73.87042 15.78117, 73.87097 15.78029...  \n",
       "18  POLYGON ((76.79412 33.25569, 76.79482 33.25557...  \n",
       "19  POLYGON ((75.83876 32.51269, 75.84094 32.51158...  \n",
       "20  POLYGON ((73.97266 30.19800, 73.97266 30.19800...  \n",
       "21  POLYGON ((72.26126 21.49999, 72.25911 21.49999...  \n",
       "22  POLYGON ((79.06793 31.46153, 79.07046 31.46010...  \n",
       "23  POLYGON ((77.59189 30.40632, 77.59337 30.40621...  \n",
       "24  POLYGON ((88.61635 28.12763, 88.62302 28.12625...  \n",
       "25  POLYGON ((93.24961 24.97312, 93.25244 24.97428...  \n",
       "26  POLYGON ((96.08538 29.45928, 96.09262 29.45613...  \n",
       "27  POLYGON ((95.19272 27.02710, 95.19475 27.01442...  \n",
       "28  POLYGON ((94.57415 25.69043, 94.57852 25.68761...  \n",
       "29  POLYGON ((92.76384 24.52098, 92.76422 24.51967...  \n",
       "30  POLYGON ((92.16949 24.53175, 92.16961 24.53168...  \n",
       "31  POLYGON ((91.82617 26.11925, 91.82728 26.11914...  \n",
       "32  POLYGON ((84.10880 27.52173, 84.10896 27.52131...  \n",
       "33  POLYGON ((76.80933 33.24349, 76.79356 33.25175...  \n",
       "34  POLYGON ((76.80933 33.24349, 76.80894 33.24367...  \n",
       "35  POLYGON ((87.47639 21.64343, 87.47362 21.63205...  "
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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   ]
  },
  {
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    "id": "nG9wJ0jJ_tmD",
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       "                                                     Type  \\\n",
       "Name                                                        \n",
       "West Bengal                                         State   \n",
       "Andaman & Nicobar                         Union Territory   \n",
       "Chandigarh                                Union Territory   \n",
       "Daman and Diu and Dadra and Nagar Haveli  Union Territory   \n",
       "Delhi                                     Union Territory   \n",
       "Haryana                                             State   \n",
       "Jharkhand                                           State   \n",
       "Karnataka                                           State   \n",
       "Kerala                                              State   \n",
       "Lakshadweep                               Union Territory   \n",
       "Madhya Pradesh                                      State   \n",
       "Maharashtra                                         State   \n",
       "Puducherry                                Union Territory   \n",
       "Tamilnadu                                           State   \n",
       "Chhattishgarh                                       State   \n",
       "Telengana                                           State   \n",
       "Andhra Pradesh                                      State   \n",
       "Goa                                                 State   \n",
       "Himachal Pradesh                                    State   \n",
       "Punjab                                              State   \n",
       "Rajasthan                                           State   \n",
       "Gujarat                                             State   \n",
       "Uttarakhand                                         State   \n",
       "Uttar Pradesh                                       State   \n",
       "Sikkim                                              State   \n",
       "Assam                                               State   \n",
       "Arunachal Pradesh                                   State   \n",
       "Nagaland                                            State   \n",
       "Manipur                                             State   \n",
       "Mizoram                                             State   \n",
       "Tripura                                             State   \n",
       "Meghalaya                                           State   \n",
       "Bihar                                               State   \n",
       "Ladakh                                    Union Territory   \n",
       "Jammu and Kashmir                         Union Territory   \n",
       "Odisha                                              State   \n",
       "\n",
       "                                                                                   geometry  \\\n",
       "Name                                                                                          \n",
       "West Bengal                               MULTIPOLYGON (((88.01861 21.57278, 88.01889 21...   \n",
       "Andaman & Nicobar                         MULTIPOLYGON (((92.90124 12.91071, 92.90157 12...   \n",
       "Chandigarh                                POLYGON ((76.77232 30.79420, 76.77286 30.79343...   \n",
       "Daman and Diu and Dadra and Nagar Haveli  MULTIPOLYGON (((72.96339 20.33227, 72.96390 20...   \n",
       "Delhi                                     POLYGON ((77.10591 28.87005, 77.10668 28.86989...   \n",
       "Haryana                                   POLYGON ((76.57526 30.10063, 76.57645 30.10152...   \n",
       "Jharkhand                                 POLYGON ((87.69613 24.16027, 87.69625 24.15974...   \n",
       "Karnataka                                 MULTIPOLYGON (((77.33232 18.45086, 77.33267 18...   \n",
       "Kerala                                    POLYGON ((74.98896 12.79553, 74.98953 12.79554...   \n",
       "Lakshadweep                               MULTIPOLYGON (((73.08025 8.32651, 73.08029 8.3...   \n",
       "Madhya Pradesh                            POLYGON ((78.37211 26.86406, 78.37423 26.86091...   \n",
       "Maharashtra                               MULTIPOLYGON (((74.38926 22.03241, 74.38942 22...   \n",
       "Puducherry                                MULTIPOLYGON (((79.75669 11.00258, 79.75682 11...   \n",
       "Tamilnadu                                 MULTIPOLYGON (((80.30358 13.47307, 80.30577 13...   \n",
       "Chhattishgarh                             POLYGON ((83.32707 24.10232, 83.32939 24.10130...   \n",
       "Telengana                                 POLYGON ((78.33565 19.88358, 78.33688 19.88300...   \n",
       "Andhra Pradesh                            POLYGON ((84.67571 19.16721, 84.67725 19.16670...   \n",
       "Goa                                       POLYGON ((73.87042 15.78117, 73.87097 15.78029...   \n",
       "Himachal Pradesh                          POLYGON ((76.79412 33.25569, 76.79482 33.25557...   \n",
       "Punjab                                    POLYGON ((75.83876 32.51269, 75.84094 32.51158...   \n",
       "Rajasthan                                 POLYGON ((73.97266 30.19800, 73.97266 30.19800...   \n",
       "Gujarat                                   POLYGON ((72.26126 21.49999, 72.25911 21.49999...   \n",
       "Uttarakhand                               POLYGON ((79.06793 31.46153, 79.07046 31.46010...   \n",
       "Uttar Pradesh                             POLYGON ((77.59189 30.40632, 77.59337 30.40621...   \n",
       "Sikkim                                    POLYGON ((88.61635 28.12763, 88.62302 28.12625...   \n",
       "Assam                                     POLYGON ((93.24961 24.97312, 93.25244 24.97428...   \n",
       "Arunachal Pradesh                         POLYGON ((96.08538 29.45928, 96.09262 29.45613...   \n",
       "Nagaland                                  POLYGON ((95.19272 27.02710, 95.19475 27.01442...   \n",
       "Manipur                                   POLYGON ((94.57415 25.69043, 94.57852 25.68761...   \n",
       "Mizoram                                   POLYGON ((92.76384 24.52098, 92.76422 24.51967...   \n",
       "Tripura                                   POLYGON ((92.16949 24.53175, 92.16961 24.53168...   \n",
       "Meghalaya                                 POLYGON ((91.82617 26.11925, 91.82728 26.11914...   \n",
       "Bihar                                     POLYGON ((84.10880 27.52173, 84.10896 27.52131...   \n",
       "Ladakh                                    POLYGON ((76.80933 33.24349, 76.79356 33.25175...   \n",
       "Jammu and Kashmir                         POLYGON ((76.80933 33.24349, 76.80894 33.24367...   \n",
       "Odisha                                    POLYGON ((87.47639 21.64343, 87.47362 21.63205...   \n",
       "\n",
       "                                            Total  \n",
       "Name                                               \n",
       "West Bengal                                5060.0  \n",
       "Andaman & Nicobar                             NaN  \n",
       "Chandigarh                                  426.0  \n",
       "Daman and Diu and Dadra and Nagar Haveli      NaN  \n",
       "Delhi                                         NaN  \n",
       "Haryana                                    3280.0  \n",
       "Jharkhand                                   258.0  \n",
       "Karnataka                                  2706.0  \n",
       "Kerala                                     3754.0  \n",
       "Lakshadweep                                   0.0  \n",
       "Madhya Pradesh                            16494.0  \n",
       "Maharashtra                               12820.0  \n",
       "Puducherry                                   94.0  \n",
       "Tamilnadu                                     NaN  \n",
       "Chhattishgarh                                 NaN  \n",
       "Telengana                                     NaN  \n",
       "Andhra Pradesh                             5152.0  \n",
       "Goa                                         540.0  \n",
       "Himachal Pradesh                            856.0  \n",
       "Punjab                                     2672.0  \n",
       "Rajasthan                                  5776.0  \n",
       "Gujarat                                    4152.0  \n",
       "Uttarakhand                                 464.0  \n",
       "Uttar Pradesh                             19714.0  \n",
       "Sikkim                                       78.0  \n",
       "Assam                                      1036.0  \n",
       "Arunachal Pradesh                           164.0  \n",
       "Nagaland                                     16.0  \n",
       "Manipur                                     246.0  \n",
       "Mizoram                                     156.0  \n",
       "Tripura                                     200.0  \n",
       "Meghalaya                                   366.0  \n",
       "Bihar                                      3160.0  \n",
       "Ladakh                                        NaN  \n",
       "Jammu and Kashmir                             NaN  \n",
       "Odisha                                     2246.0  "
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge =shp_gdf.set_index('Name').join(g1.set_index('STATE/UT'))\n",
    "merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 580
    },
    "id": "LPtLbmvWPUfT",
    "outputId": "04f7866d-332f-4c4f-efc3-f78cf45c8930"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1, figsize=(10,10))\n",
    "\n",
    "ax.set_title('State-wise Rape-Cases Reported',\n",
    "             fontdict={'fontsize': '15', 'fontweight' : '3'})\n",
    "fig = merge.plot(column='Total', cmap='Reds', linewidth=0.5, ax=ax, edgecolor='0.2',legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "V1B6M3goM-fC",
    "outputId": "d533157b-a82d-4366-a924-41bc94e4436e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<body>\n",
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "Rape = df_final['Rape'].sum()\n",
    "KA = df_final['Kidnapping and Abduction'].sum()\n",
    "F = df_final['Foeticide'].sum()\n",
    "AS = df_final['Abetment of suicide'].sum()\n",
    "EA = df_final['Exposure and abandonment'].sum()\n",
    "PMG = df_final['Procuration of minor girls'].sum()\n",
    "BGP = df_final['Buying of girls for prostitution'].sum()\n",
    "SGP = df_final['Selling of girls for prostitution'].sum()\n",
    "PFMA = df_final['Prohibition of child marriage act'].sum()\n",
    "OC = df_final['Other Crimes'].sum()\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "grp = ['Rape','Kidnapping and Abduction','Foeticide','Abetment of suicide','Exposure and abandonment','Buying of girls for prostitution','Selling of girls for prostitution','Prohibition of child marriage act','Other Crimes']\n",
    "group_vals = [Rape,KA,F,AS,EA,PMG,BGP,SGP,PFMA,OC]\n",
    "\n",
    "fig = go.Figure(data=[go.Pie(labels=grp, values=group_vals,sort=True,\n",
    "                            marker=dict(colors=px.colors.qualitative.G10),textfont_size=12)])\n",
    "\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "WcW-jEiVB46q",
    "outputId": "a6d498b1-23e9-44d8-f4c7-7dc1d9512498"
   },
   "outputs": [
    {
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     "metadata": {},
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   "source": [
    "g2 = pd.DataFrame(df_final.groupby(['Year'])['Kidnapping and Abduction'].sum().reset_index())\n",
    "g2.columns = ['Year','Kidnapping and Abduction']\n",
    "\n",
    "fig = px.bar(g2,x='Year',y='Kidnapping and Abduction',color_discrete_sequence=['black'])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
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    "id": "TI7XajcnCohy",
    "outputId": "d2ae8b3d-7827-4945-8e7e-898b8260d24d"
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   "outputs": [
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       "</body>\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g3 = pd.DataFrame(df_final.groupby(['STATE/UT'])['Kidnapping and Abduction'].sum().reset_index())\n",
    "fig = px.scatter(g3, y='Kidnapping and Abduction', x='STATE/UT',\n",
    "             orientation='h',color_discrete_sequence=[\"red\"])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 621
    },
    "id": "MoMcIYuhDH1O",
    "outputId": "e33ba8cd-895c-4b7b-9a75-b839b6a15619"
   },
   "outputs": [
    {
     "data": {
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mkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAwmkAEADLbQQFZV66rqzKr6VFVdUlUPr6pDq+pdVXXp9Puu07pVVa+sqi1VdWFVPWiRtQEArBaL3kP2iiR/2933TfJDSS5JckqSc7v76CTnTs+T5HFJjp5+TkrymgXXBgCwKiwskFXVIUl+LMlrk6S7v9Hd1yY5Nsnp02qnJ3ni9PjYJK/vmQ8kWVdVhy2qPgCA1aK6ezETVz0wyWlJPpnZ3rELkpycZGt3r5vWqSTXdPe6qnpbkhd19/umZecmeV53n7/TvCdltgct69evf/DmzZsXUv92F23dtuI5HnD4ISue46qrt+XKG8fXMQ96On8r7al+7shndP5WQ0/1c0cH0md0tfRjbzZt2nRBd2/Y1bKDF/i+Byd5UJJf6e4PVtUr8r8PTyZJururap8SYXefllnQy4YNG3rjxo1zKnfXTjjlnBXPcdlxG1c8x6vOOCunXrSyP9c86pgHPZ2/lfZUP3fkMzp/q6Gn+rmjA+kzulr6sRKLPIfs8iSXd/cHp+dnZhbQrtx+KHL6fdW0fGuSI5e8/ohpDADggLawQNbdX0zy+ar6vmno0Zkdvjw7yfHT2PFJzpoen53kWdPVlsck2dbdVyyqPgCA1WKRhyyT5FeSnFFVt03y2STPziwEvrmqTkzyuSRPndZ9e5LHJ9mS5GvTugAAB7yFBrLu/liSXZ289uhdrNtJnrPIegAAViN36gcAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhsoYGsqi6rqouq6mNVdf40dmhVvauqLp1+33Uar6p6ZVVtqaoLq+pBi6wNAGC12B97yDZ19wO7e8P0/JQk53b30UnOnZ4nyeOSHD39nJTkNfuhNgCA4UYcsjw2yenT49OTPHHJ+Ot75gNJ1lXVYQPqAwDYr6q7Fzd51b8kuSZJJ/mT7j6tqq7t7nXT8kpyTXevq6q3JXlRd79vWnZukud19/k7zXlSZnvQsn79+gdv3rx5YfUnyUVbt614jgccfsiK57jq6m258sbxdcyDns7fSnuqnzvyGZ2/1dBT/dzRgfQZXS392JtNmzZdsOSI4Q4OXvB7P7K7t1bVdyV5V1V9aunC7u6q2qdE2N2nJTktSTZs2NAbN26cW7G7csIp56x4jsuO27jiOV51xlk59aKV/bnmUcc86On8rbSn+rkjn9H5Ww091c8dHUif0dXSj5VY6CHL7t46/b4qyVuTPDTJldsPRU6/r5pW35rkyCUvP2IaAwA4oO01kFXVS6rqLlV1m6o6t6q+VFXPWMbr7lhVd97+OMlPJvlEkrOTHD+tdnySs6bHZyd51nS15TFJtnX3FbdgmwAAblWWs4fsJ7v7q0l+OsllSe6T5DeW8br1Sd5XVR9P8qEk53T33yZ5UZLHVNWlSX5iep4kb0/y2SRbkvzPJP9xH7YDAOBWazkHfrev81NJ3tLd22bn4u9Zd382yQ/tYvwrSR69i/FO8pxl1AMAcEBZTiB723Qy/o1J/kNV3SPJTYstCwBg7djrIcvuPiXJjyTZ0N3fTPK1zO4ZBgDAHCznpP47ZHY+1/Y7598zyS7voQEAwL5bzkn9r0vyjcz2kiWzW1H8/sIqAgBYY5YTyL6nu1+S5JtJ0t1fS7L3s/oBAFiW5QSyb1TV7TP7+qNU1fck+fpCqwIAWEOWc5Xl85P8bZIjq+qMJI9IcsIiiwIAWEv2Gsi6+11V9ZEkx2R2qPLk7v7ywisDAFgjlnOV5SOS3NTd5yRZl+S3qupeC68MAGCNWM45ZK9J8rWq+qEkv5bkM0lev9CqAADWkOUEsm9NX2t0bJI/6u4/SnLnxZYFALB2LOek/uuq6jeTPCPJj1XVdyS5zWLLAgBYO5azh+znM7vNxYnd/cUkRyR56UKrAgBYQ5ZzleUXk7xsyfN/jXPIAADmZjlXWR5TVR+uquur6htV9e2q2rY/igMAWAuWc8jy1UmenuTSJLdP8gtJ/niRRQEArCXLCWTp7i1JDurub3f365I8drFlAQCsHcu5yvJrVXXbJB+rqpckuSLLDHIAAOzdcoLVM6f1fjnJDUmOTPJziywKAGAt2e0esqq6R5J7dPcnp6GbkvxeVf1AEif1AwDMyZ72kL0qyd13MX5oklcsphwAgLVnT4HsPt393p0Hu/sfkvzg4koCAFhb9hTI9vR9lb46CQBgTvYUyLZU1eN3HqyqxyX57OJKAgBYW/Z024tfTXJOVT01yQXT2IYkD0/y04suDABgrdjtHrLuvjTJA5K8J8lR0897kvxgd//z/igOAGAt2OONYbv760let59qAQBYk9xxHwBgMIEMAGCw3Qayqjp3+v3i/VcOAMDas6dzyA6rqh9J8oSq2pykli7s7o8stDIAgDViT4Hsd5L8dpIjkrxsp2Wd5FGLKgoAYC3ZbSDr7jOTnFlVv93dL9iPNQEArCl7vO1FknT3C6rqCUl+bBo6r7vfttiyAADWjr1eZVlVL0xycpJPTj8nV9UfLrowAIC1Yq97yJL8VJIHdvfNSVJVpyf5aJLfWmRhAABrxXLvQ7ZuyeNDFlEIAMBatZw9ZC9M8tGqendmt774sSSnLLQqAIA1ZDkn9b+xqs5L8pBp6Hnd/cWFVgUAsIYsZw9ZuvuKJGcvuBYAgDXJd1kCAAwmkAEADLbHQFZVB1XVp/ZXMQAAa9EeA1l3fzvJp6vqu/dTPQAAa85yTuq/a5KLq+pDSW7YPtjdT1hYVQAAa8hyAtlvL7wKAIA1bDn3IXtPVd0rydHd/XdVdYckBy2+NACAtWE5Xy7+i0nOTPIn09DhSf56kUUBAKwly7ntxXOSPCLJV5Okuy9N8l2LLAoAYC1ZTiD7end/Y/uTqjo4SS+uJACAtWU5gew9VfVbSW5fVY9J8pYk/+9iywIAWDuWE8hOSfKlJBcl+aUkb0/yXxdZFADAWrKcqyxvrqrTk3wws0OVn+5uhywBAOZkr4Gsqn4qyf9I8pkkleTeVfVL3f03iy4OAGAtWM4hy1OTbOrujd3940k2JXn5ct9g+j7Mj1bV26bn966qD1bVlqp6U1Xddhr/zun5lmn5Ufu+OQAAtz7LCWTXdfeWJc8/m+S6fXiPk5NcsuT5i5O8vLvvk+SaJCdO4ycmuWYaf/m0HgDAAW+3gayqnlRVT0pyflW9vapOqKrjM7vC8sPLmbyqjkjyU0n+1/S8kjwqsxvNJsnpSZ44PT52ep5p+aOn9QEADmi1u/Pzq+p1e3phdz97r5NXnZnkhUnunOS5SU5I8oFpL1iq6sgkf9Pd96+qTyR5bHdfPi37TJKHdfeXd5rzpCQnJcn69esfvHnz5r2VsSIXbd224jkecPghK57jqqu35cobx9cxD3o6fyvtqX7uyGd0/lZDT/VzRwfSZ3S19GNvNm3adEF3b9jVst2e1L+cwLUnVfXTSa7q7guqauNK5lqqu09LclqSbNiwoTdunNvUu3TCKeeseI7Ljtu44jledcZZOfWi5XwX/GLrmAc9nb+V9lQ/d+QzOn+roaf6uaMD6TO6WvqxEsu5yvLeSX4lyVFL1+/uJ+zlpY9I8oSqenyS2yW5S5JXJFlXVQd397eSHJFk67T+1iRHJrl8+jaAQ5J8ZZ+2BgDgVmg5sfavk7w2s3PHbl7uxN39m0l+M0mmPWTP7e7jquotSZ6cZHOS45OcNb3k7On5+6flf+9+ZwDAWrCcQHZTd79yju/5vCSbq+r3k3w0s7CX6fefV9WWJFcnedoc3xMAYNVaTiB7RVU9P8k7k3x9+2B3f2S5b9Ld5yU5b3r82SQP3cU6NyV5ynLnBAA4UCwnkD0gyTMzu13F9kOWPT0HAGCFlhPInpLk33b3NxZdDADAWrScO/V/Ism6RRcCALBWLWcP2bokn6qqD2fHc8j2dtsLAACWYTmB7PkLrwIAYA3bayDr7vfsj0IAANaq5dyp/7rMrqpMktsmuU2SG7r7LossDABgrVjOHrI7b39cVZXk2CTHLLIoAIC1ZDlXWf7/euavk/y7BdUDALDmLOeQ5ZOWPP2OJBuS3LSwigAA1pjlXGX5M0sefyvJZZkdtgQAYA6Wcw7Zs/dHIQAAa9VuA1lV/c4eXtfd/YIF1AMAsObsaQ/ZDbsYu2OSE5PcLYlABgAwB7sNZN196vbHVXXnJCcneXaSzUlO3d3rAADYN3s8h6yqDk3ya0mOS3J6kgd19zX7ozAAgLViT+eQvTTJk5KcluQB3X39fqsKAGAN2dONYX89yT2T/NckX6iqr04/11XVV/dPeQAAB749nUO2T3fxBwDglhG6AAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAZbWCCrqttV1Yeq6uNVdXFV/d40fu+q+mBVbamqN1XVbafx75yeb5mWH7Wo2gAAVpNF7iH7epJHdfcPJXlgksdW1TFJXpzk5d19nyTXJDlxWv/EJNdM4y+f1gMAOOAtLJD1zPXT09tMP53kUUnOnMZPT/LE6fGx0/NMyx9dVbWo+gAAVovq7sVNXnVQkguS3CfJHyV5aZIPTHvBUlVHJvmb7r5/VX0iyWO7+/Jp2WeSPKy7v7zTnCclOSlJ1q9f/+DNmzcvrP4kuWjrthXP8YDDD1nxHFddvS1X3ji+jnnQ0/lbaU/1c0c+o/O3Gnqqnzs6kD6jq6Ufe7Np06YLunvDrpYdvMg37u5vJ3lgVa1L8tYk953DnKclOS1JNmzY0Bs3blzplHt0winnrHiOy47buOI5XnXGWTn1opX9ueZRxzzo6fyttKf6uSOf0flbDT3Vzx0dSJ/R1dKPldgvV1l297VJ3p3k4UnWVdX2v94RSbZOj7cmOTJJpuWHJPnK/qgPAGCkRV5leY9pz1iq6vZJHpPkksyC2ZOn1Y5Pctb0+Ozpeablf9+LPJ4KALBKLPKQ5WFJTp/OI/uOJG/u7rdV1SeTbK6q30/y0SSvndZ/bZI/r6otSa5O8rQF1gYAsGosLJB194VJfngX459N8tBdjN+U5CmLqgcAYLVyp34AgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMsC+MKIAAAzgSURBVACAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMgCAwQQyAIDBFhbIqurIqnp3VX2yqi6uqpOn8UOr6l1Vden0+67TeFXVK6tqS1VdWFUPWlRtAACrySL3kH0rya939/2SHJPkOVV1vySnJDm3u49Ocu70PEkel+To6eekJK9ZYG0AAKvGwgJZd1/R3R+ZHl+X5JIkhyc5Nsnp02qnJ3ni9PjYJK/vmQ8kWVdVhy2qPgCA1aK6e/FvUnVUkvcmuX+Sf+3uddN4Jbmmu9dV1duSvKi73zctOzfJ87r7/J3mOimzPWhZv379gzdv3rzQ2i/aum3Fczzg8ENWPMdVV2/LlTeOr2Me9HT+VtpT/dyRz+j8rYae6ueODqTP6Grpx95s2rTpgu7esKtlBy/6zavqTkn+MsmvdvdXZxlspru7qvYpEXb3aUlOS5INGzb0xo0b51jt/+mEU85Z8RyXHbdxxXO86oyzcupFK/tzzaOOedDT+VtpT/VzRz6j87caeqqfOzqQPqOrpR8rsdCrLKvqNpmFsTO6+6+m4Su3H4qcfl81jW9NcuSSlx8xjQEAHNAWeZVlJXltkku6+2VLFp2d5Pjp8fFJzloy/qzpastjkmzr7isWVR8AwGqxyEOWj0jyzCQXVdXHprHfSvKiJG+uqhOTfC7JU6dlb0/y+CRbknwtybMXWBsAwKqxsEA2nZxfu1n86F2s30mes6h6AABWK3fqBwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGEwgAwAYTCADABhMIAMAGGxhgayq/rSqrqqqTywZO7Sq3lVVl06/7zqNV1W9sqq2VNWFVfWgRdUFALDaLHIP2Z8leexOY6ckObe7j05y7vQ8SR6X5Ojp56Qkr1lgXQAAq8rCAll3vzfJ1TsNH5vk9Onx6UmeuGT89T3zgSTrquqwRdUGALCaVHcvbvKqo5K8rbvvPz2/trvXTY8ryTXdva6q3pbkRd39vmnZuUme193n72LOkzLbi5b169c/ePPmzQurP0ku2rptxXM84PBDVjzHVVdvy5U3jq9jHvR0/lbaU/3ckc/o/K2Gnurnjg6kz+hq6cfebNq06YLu3rCrZQcv/N13o7u7qvY5DXb3aUlOS5INGzb0xo0b513aDk445ZwVz3HZcRtXPMerzjgrp160sj/XPOqYBz2dv5X2VD935DM6f6uhp/q5owPpM7pa+rES+/sqyyu3H4qcfl81jW9NcuSS9Y6YxgAADnj7O5CdneT46fHxSc5aMv6s6WrLY5Js6+4r9nNtAABDLOyQZVW9McnGJHevqsuTPD/Ji5K8uapOTPK5JE+dVn97kscn2ZLka0mevai6AABWm4UFsu5++m4WPXoX63aS5yyqFgCA1cyd+gEABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAYTyAAABhPIAAAGE8gAAAZbVYGsqh5bVZ+uqi1VdcroegAA9odVE8iq6qAkf5TkcUnul+TpVXW/sVUBACzeqglkSR6aZEt3f7a7v5Fkc5JjB9cEALBw1d2ja0iSVNWTkzy2u39hev7MJA/r7l/eab2Tkpw0Pf2+JJ/er4WOc/ckXx5dxAFGT+dLP+dPT+dLP+dPT/fNvbr7HrtacPD+rmSluvu0JKeNrmN/q6rzu3vD6DoOJHo6X/o5f3o6X/o5f3o6P6vpkOXWJEcueX7ENAYAcEBbTYHsw0mOrqp7V9VtkzwtydmDawIAWLhVc8iyu79VVb+c5B1JDkryp9198eCyVpM1d5h2P9DT+dLP+dPT+dLP+dPTOVk1J/UDAKxVq+mQJQDAmiSQAQAMJpANUlVHVtW7q+qTVXVxVZ08jR9aVe+qqkun33edxquqXjl9rdSFVfWgafyBVfX+aY4Lq+rnR27XSPPq6ZL57lJVl1fVq0dsz2jz7GdVfXdVvbOqLpnmO2rMVo01556+ZJrjkmmdGrVdI92Cnt53+jfz61X13J3mWvNf3zevfu5uHvagu/0M+ElyWJIHTY/vnOSfM/vKqJckOWUaPyXJi6fHj0/yN0kqyTFJPjiNf2+So6fH90xyRZJ1o7fv1tzTJfO9Iskbkrx69Lbd2vuZ5Lwkj5ke3ynJHUZv3625p0l+JMk/ZnYB1EFJ3p9k4+jtu5X09LuSPCTJHyR57pJ5DkrymST/Nsltk3w8yf1Gb9+tuJ+7nGf09q3mH3vIBunuK7r7I9Pj65JckuTwzL4u6vRptdOTPHF6fGyS1/fMB5Ksq6rDuvufu/vSaZ4vJLkqyS7vAnygm1dPk6SqHpxkfZJ37sdNWFXm1c+afSftwd39rmmu67v7a/tzW1aLOX5GO8ntMgsO35nkNkmu3G8bsorsa0+7+6ru/nCSb+40la/vy/z6uYd52A2BbBWYDt/8cJIPJlnf3VdMi76YWShIZh/kzy952eXZ6cNdVQ/N7B/ozyyw3FuFlfS0qr4jyalJdjicsZat8DP6vUmuraq/qqqPVtVLq+qg/VL4KraSnnb3+5O8O7M94lckeUd3X7Ifyl7VltnT3dnrv7FrzQr7ubt52A2BbLCqulOSv0zyq9391aXLursz+3/Cy5nnsCR/nuTZ3X3z3Au9FZlDT/9jkrd39+ULKvFWZQ79PDjJj2YWcB+S2SGhE+Zf6a3HSntaVfdJ8v2ZfaPJ4UkeVVU/uqBybxXm9W8pM3P8b9Nu52FHAtlAVXWbzD6oZ3T3X03DVy45bHZYZocgkz18tVRV3SXJOUn+y3RYY82aU08fnuSXq+qyJP89ybOq6kX7ofxVZ079vDzJx6ZDQd9K8tdJdriAYi2ZU09/NskHpsO/12d2ntnD90f9q9E+9nR3fH3fZE793N087IZANsh0RdRrk1zS3S9bsujsJMdPj49PctaS8WdNV10dk2Rbd19Rs6+Zemtm55mcuZ/KX5Xm1dPuPq67v7u7j8psr87ru3vNXXE1r35m9rVo66pq+7mNj0ryyYVvwCo0x57+a5Ifr6qDp//o/Xhm5+isObegp7vj6/syv37uYR52Z15XB/jZt58kj8xsl++FST42/Tw+yd2SnJvk0iR/l+TQaf1K8keZnR92UZIN0/gzMjuZ8mNLfh44evtuzT3dac4TsnavspxbP5M8ZprnoiR/luS2o7fv1tzTzK4I/JPMQtgnk7xs9Lbdinr6bzLba/vVJNdOj+8yLXt8ZlcDfiazIw7Dt+/W2s/dzTN6+1bzj69OAgAYzCFLAIDBBDIAgMEEMgCAwQQyAIDBBDIAgMEEMmBNmO7l9b6qetySsadU1d+OrAsgidteAGtHVd0/yVsy+169g5N8NMlju3ufv/+1qg7u2TcPAKyYQAasKVX1kiQ3JLnj9PteSe6f5DZJfre7z5q+DPnPp3WS5Je7+5+qamOSFyS5Jsl9u/t792/1wIFKIAPWlKq6Y5KPJPlGkrclubi7/6Kq1iX5UGZ7zzrJzd19U1UdneSN3b1hCmTnJLl/d//LmC0ADkQHjy4AYH/q7huq6k1Jrk/y1CQ/U1XPnRbfLsl3J/lCkldX1QOTfDvJ0j1hHxLGgHkTyIC16Obpp5L8XHd/eunCqvrdJFcm+aHMLn66acniG/ZTjcAa4ipLYC17R5JfqapKkqr64Wn8kCRXdPfNSZ6Z2Zd5AyyMQAasZS/I7GT+C6vq4ul5kvxxkuOr6uNJ7ht7xYAFc1I/AMBg9pABAAwmkAEADCaQAQAMJpABAAwmkAEADCaQAQAMJpABAAz2/wEfiA8dVdAldQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final['Year'].hist(bins=30,figsize=(10, 10),align = 'right')\n",
    "plt.title(\"Number of Cases vs Year\")\n",
    "plt.ylabel('Number of Cases')\n",
    "plt.xlabel('Year');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 700
    },
    "id": "_3eaY33fDbOb",
    "outputId": "43a9408e-6099-4e3a-d2db-d109a005685c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(12,10)})\n",
    "sns.distplot(df_final['Year'], bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "6wkvI7RQDvx5",
    "outputId": "c0951998-f6e3-4e61-ba21-b3058a989d59"
   },
   "outputs": [
    {
     "data": {
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   "source": [
    "grp = ['Rape','Kidnapping and Abduction','Foeticide','Abetment of suicide','Exposure and abandonment','Buying of girls for prostitution','Selling of girls for prostitution','Prohibition of child marriage act','Other Crimes']\n",
    "group_vals = [Rape,KA,F,AS,EA,PMG,BGP,SGP,PFMA,OC]\n",
    "\n",
    "fig = go.Figure(go.Funnel(\n",
    "    y = grp,\n",
    "    x = group_vals,\n",
    "    textposition = \"inside\",\n",
    "    textinfo = \"value+percent initial\",\n",
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    "    connector = {\"line\": {\"color\": \"royalblue\", \"dash\": \"dot\", \"width\": 5}}))\n",
    "\n",
    "fig.show()"
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  }
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