CrimeAgainstWomen2001_2014.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CrimeAgainstWomen2001-2014",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1IgHx5tOpzVh-CRjDYdY40EMGzjE8nsa4",
      "authorship_tag": "ABX9TyMKMym9ub//uWwer3Mo5hNq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mkaustubh/data-analytics/blob/main/CrimeAgainstWomen2001_2014.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**This is a [Crime Against Women 2001-2014](https://github.com/mkaustubh/data-analytics) notebook. You can reference the dataset at [this link](https://www.kaggle.com/greeshmagirish/crime-against-women-20012014-india).**\n",
        "___\n"
      ],
      "metadata": {
        "id": "GzOELXdZMMcZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YQnCLkSepVQK"
      },
      "outputs": [],
      "source": [
        "!pip install -q kaggle\n",
        "! mkdir ~/.kaggle\n",
        "!cp /content/drive/MyDrive/ColabNotebooks/kaggle.json ~/.kaggle/kaggle.json\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "# !kaggle datasets list"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d greeshmagirish/crime-against-women-20012014-india"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2uEzZYpaqxZa",
        "outputId": "e92689c8-6d43-4a5b-fe4a-b483c9093360"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading crime-against-women-20012014-india.zip to /content\n",
            "\r  0% 0.00/174k [00:00<?, ?B/s]\n",
            "\r100% 174k/174k [00:00<00:00, 5.72MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip crime-against-women-20012014-india"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "noJYliljq8Hf",
        "outputId": "bb65c412-e642-42bd-a8f5-b0c93dbedb82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  crime-against-women-20012014-india.zip\n",
            "  inflating: crimes_against_women_2001-2014.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "pgpUmvMFrEmm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Data"
      ],
      "metadata": {
        "id": "jK-hcVAPuXZe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Reading the csv file."
      ],
      "metadata": {
        "id": "adAx-BfXuZHv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "crimes_df = pd.read_csv('/content/crimes_against_women_2001-2014.csv')\n",
        "crimes_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "Cn3MxFn4uRIa",
        "outputId": "7bcced5a-6ab6-48c7-e6e7-7f950231620f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0b271498-d040-443c-81a8-c5f550c22bb4\">\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>Unnamed: 0</th>\n",
              "      <th>STATE/UT</th>\n",
              "      <th>DISTRICT</th>\n",
              "      <th>Year</th>\n",
              "      <th>Rape</th>\n",
              "      <th>Kidnapping and Abduction</th>\n",
              "      <th>Dowry Deaths</th>\n",
              "      <th>Assault on women with intent to outrage her modesty</th>\n",
              "      <th>Insult to modesty of Women</th>\n",
              "      <th>Cruelty by Husband or his Relatives</th>\n",
              "      <th>Importation of Girls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>ADILABAD</td>\n",
              "      <td>2001</td>\n",
              "      <td>50</td>\n",
              "      <td>30</td>\n",
              "      <td>16</td>\n",
              "      <td>149</td>\n",
              "      <td>34</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>ANANTAPUR</td>\n",
              "      <td>2001</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>118</td>\n",
              "      <td>24</td>\n",
              "      <td>154</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>CHITTOOR</td>\n",
              "      <td>2001</td>\n",
              "      <td>27</td>\n",
              "      <td>34</td>\n",
              "      <td>14</td>\n",
              "      <td>112</td>\n",
              "      <td>83</td>\n",
              "      <td>186</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>CUDDAPAH</td>\n",
              "      <td>2001</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>17</td>\n",
              "      <td>126</td>\n",
              "      <td>38</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>EAST GODAVARI</td>\n",
              "      <td>2001</td>\n",
              "      <td>23</td>\n",
              "      <td>26</td>\n",
              "      <td>12</td>\n",
              "      <td>109</td>\n",
              "      <td>58</td>\n",
              "      <td>247</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b271498-d040-443c-81a8-c5f550c22bb4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0b271498-d040-443c-81a8-c5f550c22bb4 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-0b271498-d040-443c-81a8-c5f550c22bb4');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Unnamed: 0  ... Importation of Girls\n",
              "0           0  ...                    0\n",
              "1           1  ...                    0\n",
              "2           2  ...                    0\n",
              "3           3  ...                    0\n",
              "4           4  ...                    0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Let us find out the number of rows and column of the dataset"
      ],
      "metadata": {
        "id": "2fT8r3FtuoGK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "crimes_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wswzUjBlujMD",
        "outputId": "50f091d2-7e49-440f-8b49-9093b7f68125"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10677, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preparation and Cleaning"
      ],
      "metadata": {
        "id": "OMIOsiMWurfa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### To start with the very basic of data cleaning, let's find out if any of the columns have any Null or missing values"
      ],
      "metadata": {
        "id": "W2uDt4ZIuvWR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "overall_crime = crimes_df.isna().sum()"
      ],
      "metadata": {
        "id": "FMXL_MxTupuX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "overall_crime"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0G10TDDVuxE1",
        "outputId": "2c6e0cda-72d8-454e-b21d-eb84e24a781a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0                                             0\n",
              "STATE/UT                                               0\n",
              "DISTRICT                                               0\n",
              "Year                                                   0\n",
              "Rape                                                   0\n",
              "Kidnapping and Abduction                               0\n",
              "Dowry Deaths                                           0\n",
              "Assault on women with intent to outrage her modesty    0\n",
              "Insult to modesty of Women                             0\n",
              "Cruelty by Husband or his Relatives                    0\n",
              "Importation of Girls                                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### None of the column has any Null values."
      ],
      "metadata": {
        "id": "A-CNAJgwu1Hu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Now let's find the total number of 'Unique' districts, where the crimes have been committed"
      ],
      "metadata": {
        "id": "7w5JbZS2u2-A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "districts = len(crimes_df.DISTRICT.unique())\n",
        "districts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjwuquoJuyzs",
        "outputId": "15efcd4d-f368-4d29-885c-54b6639e6b66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1605"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### But there are 718 districts in India, in total, which means there is messy or false datas in a huge amount, in this case, we better drop the column \"District\" and also \"Unnamed: 0\", as it is of no use, in our data analysis process."
      ],
      "metadata": {
        "id": "TOzBUKzVu74Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "crimes_df.drop(['DISTRICT', 'Unnamed: 0'], axis = 1, inplace=True)"
      ],
      "metadata": {
        "id": "YLsdkhqSu5ve"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crimes_df.rename( columns = {'Kidnapping and Abduction':'Kidnapping_Abduction','Dowry Deaths':'Dowry_Deaths',\n",
        "                             'Assault on women with intent to outrage her modesty':'Hurting_of_womens_modesty',\n",
        "                             'Insult to modesty of Women':'Insult_to_womens_modesty',\n",
        "                             'Cruelty by Husband or his Relatives':'Domestic_Cruelty',\n",
        "                             'Importation of Girls':'Importation_of_Girls'}, inplace = True)"
      ],
      "metadata": {
        "id": "olBheTE5u9zJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crimes_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uX3dYzKwu-9k",
        "outputId": "d7deb4dd-6448-4e91-9518-7a450f0f844a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1e6665fe-4e7e-4d9f-b37d-2c39576195d1\">\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>Year</th>\n",
              "      <th>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Hurting_of_womens_modesty</th>\n",
              "      <th>Insult_to_womens_modesty</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>2001</td>\n",
              "      <td>50</td>\n",
              "      <td>30</td>\n",
              "      <td>16</td>\n",
              "      <td>149</td>\n",
              "      <td>34</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>2001</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>118</td>\n",
              "      <td>24</td>\n",
              "      <td>154</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>2001</td>\n",
              "      <td>27</td>\n",
              "      <td>34</td>\n",
              "      <td>14</td>\n",
              "      <td>112</td>\n",
              "      <td>83</td>\n",
              "      <td>186</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>2001</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>17</td>\n",
              "      <td>126</td>\n",
              "      <td>38</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>2001</td>\n",
              "      <td>23</td>\n",
              "      <td>26</td>\n",
              "      <td>12</td>\n",
              "      <td>109</td>\n",
              "      <td>58</td>\n",
              "      <td>247</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e6665fe-4e7e-4d9f-b37d-2c39576195d1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1e6665fe-4e7e-4d9f-b37d-2c39576195d1 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-1e6665fe-4e7e-4d9f-b37d-2c39576195d1');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         STATE/UT  Year  ...  Domestic_Cruelty  Importation_of_Girls\n",
              "0  ANDHRA PRADESH  2001  ...               175                     0\n",
              "1  ANDHRA PRADESH  2001  ...               154                     0\n",
              "2  ANDHRA PRADESH  2001  ...               186                     0\n",
              "3  ANDHRA PRADESH  2001  ...                57                     0\n",
              "4  ANDHRA PRADESH  2001  ...               247                     0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Now, let's start with analysing the datas of the column \"STATE/UT\", for that let's find out the names of all the states/UT through .unique()"
      ],
      "metadata": {
        "id": "e7m1S-JGvCgi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "crimes_df['STATE/UT'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJEzj0RtvA6S",
        "outputId": "dfd6888e-5496-40ba-e19a-08e808576c17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR',\n",
              "       'CHHATTISGARH', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH',\n",
              "       'JAMMU & KASHMIR', 'JHARKHAND', 'KARNATAKA', 'KERALA',\n",
              "       'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA', 'MIZORAM',\n",
              "       'NAGALAND', 'ODISHA', 'PUNJAB', 'RAJASTHAN', 'SIKKIM',\n",
              "       'TAMIL NADU', 'TRIPURA', 'UTTAR PRADESH', 'UTTARAKHAND',\n",
              "       'WEST BENGAL', 'A & N ISLANDS', 'CHANDIGARH', 'D & N HAVELI',\n",
              "       'DAMAN & DIU', 'DELHI', 'LAKSHADWEEP', 'PUDUCHERRY',\n",
              "       'Andhra Pradesh', 'Arunachal Pradesh', 'Assam', 'Bihar',\n",
              "       'Chhattisgarh', 'Goa', 'Gujarat', 'Haryana', 'Himachal Pradesh',\n",
              "       'Jammu & Kashmir', 'Jharkhand', 'Karnataka', 'Kerala',\n",
              "       'Madhya Pradesh', 'Maharashtra', 'Manipur', 'Meghalaya', 'Mizoram',\n",
              "       'Nagaland', 'Odisha', 'Punjab', 'Rajasthan', 'Sikkim',\n",
              "       'Tamil Nadu', 'Tripura', 'Uttar Pradesh', 'Uttarakhand',\n",
              "       'West Bengal', 'A&N Islands', 'Chandigarh', 'D&N Haveli',\n",
              "       'Daman & Diu', 'Delhi UT', 'Lakshadweep', 'Puducherry',\n",
              "       'Telangana', 'A & N Islands'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### We can see from above that there are lot many repeated datas, like some of them are repeated again by using capital letters and some of them have issues with space too, like A&N Islands and also Delhi has been repeated again by mentioning it as Delhi UT"
      ],
      "metadata": {
        "id": "Egi45NvQvL8S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fist we will remove all the repeated uppercase values\n",
        "def remove_uppercase(r):\n",
        "    r = r['STATE/UT'].strip()\n",
        "    r = r.upper()\n",
        "    return r\n",
        "crimes_df['STATE/UT'] = crimes_df.apply(remove_uppercase, axis=1)\n",
        "\n",
        "#Now use replace function to replace the other type of repeated datas as dicussed above\n",
        "crimes_df['STATE/UT'].replace(\"A&N ISLANDS\", \"A & N ISLANDS\", inplace = True)\n",
        "crimes_df['STATE/UT'].replace(\"D&N HAVELI\", \"D & N HAVELI\", inplace = True)\n",
        "crimes_df['STATE/UT'].replace(\"DELHI UT\", \"DELHI\", inplace = True)"
      ],
      "metadata": {
        "id": "IV5pn7BxvHuT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Let's go through the datas now!"
      ],
      "metadata": {
        "id": "xFmj07pvvQFa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "crimes_df['STATE/UT'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VY5CRk3dvP3i",
        "outputId": "53ec8626-085b-4d92-81c1-8e1fc3177666"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR',\n",
              "       'CHHATTISGARH', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH',\n",
              "       'JAMMU & KASHMIR', 'JHARKHAND', 'KARNATAKA', 'KERALA',\n",
              "       'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA', 'MIZORAM',\n",
              "       'NAGALAND', 'ODISHA', 'PUNJAB', 'RAJASTHAN', 'SIKKIM',\n",
              "       'TAMIL NADU', 'TRIPURA', 'UTTAR PRADESH', 'UTTARAKHAND',\n",
              "       'WEST BENGAL', 'A & N ISLANDS', 'CHANDIGARH', 'D & N HAVELI',\n",
              "       'DAMAN & DIU', 'DELHI', 'LAKSHADWEEP', 'PUDUCHERRY', 'TELANGANA'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Let's check the total number of States+UT"
      ],
      "metadata": {
        "id": "3S9OOeqbvUzB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(crimes_df['STATE/UT'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzywhCBrvTaK",
        "outputId": "0f208757-1b86-4856-a13e-890474b63223"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Which is coming out perfect, hence we are done with our data cleaning process of our dataset"
      ],
      "metadata": {
        "id": "G0ViqXqVvaWH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Analysis and Visualization"
      ],
      "metadata": {
        "id": "cLKoWZkIvcKu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Let us find out the total population of women over the years, 2001-2014, who has been a victim of the crime based on their gender."
      ],
      "metadata": {
        "id": "14jiVQInvdlR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "victims_raped = crimes_df.Rape.sum()\n",
        "victims_kidnapped_abducted = crimes_df.Kidnapping_Abduction.sum()\n",
        "dowery_death = crimes_df.Dowry_Deaths.sum()\n",
        "modesty_assault = crimes_df.Hurting_of_womens_modesty.sum()\n",
        "insult_to_modesty = crimes_df.Insult_to_womens_modesty.sum()\n",
        "domestic_violence = crimes_df.Domestic_Cruelty.sum()\n",
        "girls_imported = crimes_df.Importation_of_Girls.sum()"
      ],
      "metadata": {
        "id": "1zxqnNnzvXUT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_population_of_victim_overall = victims_raped + victims_raped + dowery_death +modesty_assault+ insult_to_modesty + domestic_violence+ girls_imported\n",
        "total_population_of_victim_overall"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y6-ReeE_ve70",
        "outputId": "19f29794-50cd-44ee-a69c-ca9a48f176f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5194570"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### This above analysis potrays a heartbreaking situation of women in our society, as more than 5 million number of females, over the years 2001-2014, have been a victim of assault, violance, rape or even death, in India alone.  "
      ],
      "metadata": {
        "id": "oBRj79h8vh_T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Now let us analyse the all the cases sepately by using bar graph. \n",
        "Note - For our ease, we are doing the analysis for six caterories, excluding the \"Insult_to_modesty_of_Women\" column."
      ],
      "metadata": {
        "id": "h3a0GWAqvifk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(2, 3, figsize=(25, 12))\n",
        "\n",
        "axes[0,0].set_title(\"Chart of rape cases in India in 2001-2014\")\n",
        "axes[0,0].bar(crimes_df.Year, crimes_df.Rape, color = 'black');\n",
        "plt.xlabel('Year') #X-axis\n",
        "plt.ylabel('Cases of Rape in India') #Y-axis\n",
        "\n",
        "axes[0,1].set_title(\"Chart of Kidnapping and Abduction cases in India in 2001-2014\")\n",
        "axes[0,1].bar(crimes_df.Year, crimes_df.Kidnapping_Abduction, color = 'violet');\n",
        "plt.xlabel('Year') #X-axis\n",
        "plt.ylabel('Cases of Kidnapping and Abduction in India') #Y-axis\n",
        "\n",
        "axes[0,2].set_title(\"Chart of Dowry death cases in India in 2001-2014\")\n",
        "axes[0,2].bar(crimes_df.Year, crimes_df.Dowry_Deaths, color = 'navy');\n",
        "plt.xlabel('Year') #X-axis\n",
        "plt.ylabel('Cases of Dowry deaths in India') #Y-axis\n",
        "\n",
        "axes[1,0].set_title(\"Chart of Assault to her modesty in 2001-2014\")\n",
        "axes[1,0].bar(crimes_df.Year, crimes_df.Hurting_of_womens_modesty, color = 'cyan');\n",
        "plt.xlabel('Year') #X-axis\n",
        "plt.ylabel('Cases of Assaulting a women for her modesty in India') #Y-axis\n",
        "\n",
        "axes[1,1].set_title(\"Chart of Domestic Violence cases in India in 2001-2014\")\n",
        "axes[1,1].bar(crimes_df.Year, crimes_df.Domestic_Cruelty, color = 'orange');\n",
        "plt.xlabel('Year') #X-axis\n",
        "plt.ylabel('Cases of Domestic Violence in India') #Y-axis\n",
        "\n",
        "axes[1,2].set_title(\"Chart of Importation of girls in India in 2001-2014\")\n",
        "axes[1,2].bar(crimes_df.Year, crimes_df.Importation_of_Girls, color = 'red');\n",
        "plt.xlabel('Year') #X-axis\n",
        "plt.ylabel('Cases ofImportation of girls in India') #Y-axis"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "7f5UazVgvgTQ",
        "outputId": "23d2f292-01c7-416e-d77d-8d143ad3b5a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Cases ofImportation of girls in India')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABacAAALJCAYAAACgIJ+tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZxlRX338c9XR0AFYZAJD5sOETRBn4g6osZoVBSRmKCJEtToqCRoxLgnLk8QEDGauEQTNcGAQlyQoEZUFImKiVGUwQUFNYwsAWQZmWFR3MDf80dVw6Xp23vfnun5vF+vfvW9dc6pU3c7Ved36lSlqpAkSZIkSZIkaZTusNgFkCRJkiRJkiRtfgxOS5IkSZIkSZJGzuC0JEmSJEmSJGnkDE5LkiRJkiRJkkbO4LQkSZIkSZIkaeQMTkuSJEmSJEmSRs7gtIZKcmSS9y92OcakeW+SDUm+ttjl2VQk+XGSX1+E/Z6Z5E/742ck+ews8/mnJIfPb+kkLQVLrZ6a6niXpJLsMbdSzk2SRyT5/mKWYTqSPDvJl+aw/cVJHjuD9Rfks0ny6SSr5zvfjdVi1fnjvy+zbTttKr8PSRNbau2KjdngueKmlPeoLGZ9MtimmUu9nOS8JI+a18JpyTI4vZlL8vQka3oj/Ip+EvQ7C7SvOZ0oAr8DPA7Ytar2madiLXlVtXVVXTjT7ZKs7BXTsnkowweqar9Zbvv8qjp6NtsmeXOSC5LckOR7SZ41bvneSc5JcmP/v/fAsiR5U5Jr+t+bkmRg+bFJvp/kV0mePUU57p3k40nWJVmf5PQk9xm3zkuTXJnk+iTHJ9lyYNnRSb6d5KYkR06yn+M3huCVNJ+WUj01QQDsbkn+O8lHkmwxl+PdqFTVf1XVfaZec+OXZOv+vfr0YpcFJg6KVNUTquqExSrTqM2xzp+3YMRs205z+X0k+b0kX0pybW8P/EuSbQaWb9nr+ev78peN237f3ta5MckXktxzYNlBSb7cl505jbLMpf306L7/65JcPMk+fre3WV4/vXdImh9LsF1xc38tP05yUVow+95z2OcmZaK6cymYY30y1+/dYDlmXS9X1X2r6syZbpfk15J8KMkPe13y30keMm6dpye5JMlPkvx7ku0Hlm2f5GN92SVJnj6wbKckp/a8K8nKKcoy67o5yRZJTknr8FAZEqjv6303yWUzfa+WEoPTm7H+w/l74A3AjsA9gHcBBy7AvuYc4ATuCVxcVT8Z4T61afsJ8PvAtsBq4O1JfhtaJQB8HHg/sBw4Afh4Twc4FHgScH/gt3o+zxvI+1vAC4CvT6Mc2wGnAveh/da+1vdNL8vjgVcB+9K+578OHDWw/Vrgr4BPDdtBb1TfaxplkTYZS7meSrIc+BxwCfDHVfWLedi/ZuaPgJ8Dj0vyfxa7MNrsbQu8HtgZ+E1gF+DvBpYfCexJO848GvirJPsDJNkB+ChwOLA9sAb48MC262nH0jdOsyxzaT/9BDge+MthmSe5E/B24KvTLI80L5Zou+IrVbU17ff6WOCnwDlJ7jcP+5+Q59laYFsDZwMPotVpJwCfSrI1QJL7Av8MPJP2O76R9jse807gF33ZM4B3920AfgV8htYGnI5Z183dl4A/Aa6cZB9/CaybZnmWrqrybzP8o/3Ifgw8dZJ1jgROBk4EbgDOA1YNLH8V8IO+7HzgyQPLng38N/A24BrgI8DPgJv7fq8dss+daUG89bSA3J/19EPGbX/UBNuO3+fracG6z/fnPwI+AGw3sM3FwKt7+TcA7wW2Glj+ROCbwLXAl4HfmuT9ui9wRi/7VcBrevo+wFd6HlcA/whs0Zell/dq4Hrg28D9+rItgTcD/9vz+yfgzn3ZDsAne57rgf8C7jCkXAXs0R+/j3aw/lT/3L4K3GvIdiv7tsumsy3tqv73gOv6a/wi8KcDn82XBtZ9O3Bpf83nAI+Y5H19H/D6/vhRwGXAy/t7dgXwnBl8708FXt4f7wdcDmRg+f8C+/fHXwYOHVh2CHDWBHl+CXj2DH9/2/f39u79+QeBNwws3xe4coLt3g8cOUH6MuAbtCD6LZ+3f/5tyn8s3XrqS8AKWt3yXgaO3YPHu/78L/tx7ofAc5nB8Xyy42x/306hBa9uoF1ku//A8osZUjfSj8Pj1n0FcC7t+P9hbluP/tXAa/jTyY5RwHOA7/YyXQg8b2DZo5jk+A/cvX8u19MuAB7NQL0zZH+fB47pr/8V45YNfQ+m8dmcSa//Bj/3gee3ay8A+9NOpH7Zvz/fGp8XrVPJX9MuaFxN+95v25et7GVYTavLfgT8v0le+52Bt/S8rqN9L8faGP9GO4m6DvhP4L4D2x3Q35MbaHXoKwaWDW0zAa/s698AfB/Yd0i53scs6/xx79Wcvi/jPs/fo9Wx19N+U0dOUoZHMYPfxxTfzz8Evj3w/IfAfgPPjwZO6o8PBb48sOyutADVb4zL80+BM6ez/3HbTbv9NJD2WFpQbaL8XgX8LeOOef75t5B/LOF2xQTpnwROGXj+B/21XEs7Vv5mT38O8ImB9S4A/m3g+aXA3v1xAYf1dS6itUHeMm6/pwIvHfI6h54r9uXPpbUBNgCnA/ccWDZhm4bJ686j++dxA/BZYIdJPvcDafXX9f3z3X/g/RnWLhl6Pt4/04/QAo4XAS8a2G4f2gXE62ltgLcOKdOjmGV9Mv57MdW2TN3eHKuXl/fXvK5/Tp+k9eof9r5eDDx2Or+tafx+rwce1B+/AfjgwLJ79e/BNrT67xfAvQeW/yvwxnH5Leuvc+UMjyPTrpvHbXcZ8KgJ0nfv37EnDH7em+OfPac3Xw8DtgI+NsV6fwCcxK09P/9xYNkPgEfQKvqjgPcn2Wlg+UNoB/EdaVeLnk+/sltV2w3Z30m0H+7OwFOANyR5TFUdN277I4ZsP7jPY2jB37/h1itdu9EOjIOeATyedlC7N+3EjyQPoPX8eB7tJOafgVMzMNzCmH5rx3/QrsLtDOxB6xEHrUHxUloF9jBa4PEFfdl+wCP7frcFDqI1ZqD1brk3sHfPbxfgtX3Zy/v7tKK/1tfQDq7TcTDt81pOawAdM83thm470GPnr/vr/AHw8EnyOZv2uranBWb/LclW0yzD/6G9V7vQGm3v7D0QJ5XkzsCDaRUhtODAudVrhe7cnj62/FsDy741sGyuHkkLPo991hPta8ckd59mfi8F/rOqzp2n8kkbg6VaT21PO2n6CvDcqvrVRCv1XhevoJ3M7UkL9Iw32fF8quPsgbQg5Njyf++9GcdMWDcOcRDtBHF32kWyZw+8hpf1su9BO9GazNW0AOfdaCeEb0vywIHlkx3/30k7id+JdmL13Ml21Ic8eBTtovUHgGdNsNqw9sF0Ppth+52wvVBVn6GdbH24f3/uP8Hmz+5/j6bdYbM1t/2+Q7sF/D60tsZrk/zmkKK8mdYj6bdp34G/ovUmAvh0f12/Rgvcf2Bgu+NoJ+fbAPejBfgnbTOlDWP1QuDBfbvH005Yp2NWdf40tp3J9+UntO/HdrRA9Z8nedI0ywBDfh/T8Eh6m6WXeyeGt0tu046o1svyB8xDu2UW7aep8rsn7f1+3VzLJs3QUm1XTOSjvZz0IT4+BLyEdu54GvCJfrfDF4FHJLlDkp2BLWjvE2nj7m9N+32PeVJ/jXvRerM+Lckd+vo70OrDD44vzFTnikkOpJ3P/mEv43/1Mo+ZsE0zRd35dFpb4tf663rFRG9Ukn1oAdO/pH3mj+TWOmqydsmE5+P9/fgE7Zi8C60+fkm/UxZaoP3tVXU3Wvvi5InKNcRs65Oh286wTXMH2sX6e9LuOvgpt2+HTGay39ZQaUNHbUFr68Lt67wf0APS/e+mqvqfgSzm+zx+unXzdPwD7bvz03kq3ybL4PTm6+7Aj6rqpinW+1JVnVZVN9OuON1ywK+qf6uqH1bVr6rqw7SrqINjYf2wqv6hqm6qqil/bEl2o1VSr6yqn1XVN4F/YeITxmFus8+qWltVZ1TVz6tqHfBW4HfHbfOPVXVpVa2nndg/racfCvxzVX21qm6uNubjz4GHTrDfJ9KCjW/pZb+hqr4KUFXnVNVZvUwX007YxsrwS9oVvt+g9UD5blVdkSR9/y+tqvVVdQOt4j14YLudaFeUf1ltTKrpBqc/VlVf65/9B2gV/XQN2/YA4LyqOqWqfkm7XW7orStV9f6quqa/J2+h9RKf7phavwRe11/3abSr5NPZ9p9olcXp/fnWtCvHg66jfR4TLb8O2Lp/NrOWZFfaSfHgWJET7YuBskyW3260YMBrp1pX2sQs1XpqN1rD+X1THLcPAt5bVd/pgaYjJ1hn6PF8GsfZcwaO2W+lnbAP1m/D6saJvKO/z+tpJ2Rj5Rh7DedV1Y1DXsMtqupTVfWDar5I6+n0iIFVJjz+J7kj7fbM11bVT6rqO7ST5sk8kxZgO592onTfHmAdNOw9mM5nM8zQ9sI0PIPWw+rCqvoxrWf3weNurz6qt3++Ravzbhfk7ifOzwVeXFWX9zbOl6vq5wBVdXwv18/7a7t/km375r8E9kpyt6raUFVjQ1tN1ma6mfb92yvJnarq4n4iOR2zrfOHbjvT70tVnVlV3+7HkXNpAZPxbcnJDPt9DJXkcbRe8GN1+9b9//i2wrA2y/jlczHT9tNU3gEc3r/D0igt1XbFRH5IC+QC/DHwqWrnxL+kXZy8M/Db1cbWv4F2XHok7Xf+wyS/QTvO/Vfd9iL63/Rz059W1ddov/19+7KDaXdmXDVBeaY6V3x+z/u7/fN5A7B3v5g123PH91bV//TP4WSGH3sPAY7v78+ver34vb7fydolw87HHwysqKrXVdUv+nv8Hm57Hr9Hkh2q6sdVddYUr2PQjOuTaWw77TZN/ww+UlU3VotPHMPM6sOhv61hktytr3tUVY3VPZPVQ1vTellPtGxOZlE3T5Xfk4E7VtVUF8w2CwanN1/XADtMY7yowUrjRmCrsW2SPCvJN/vg8NfSetDsMLD+pTMs087AWCB2zCW0K47TdZt9JtkxyUlJLk9yPW1YhB0m2eaSXg5oVwRfPvb6+mvcbWD5oN1oV4BvJ20yvE/2AfKvp1W2OwBU1edpVwzfCVydNsne3WhXYO9CGy9sbN+f6enQxjlaC3w2yYVJXjXVGzNg/Ge69bAVZ7Dtzgy8j71iHvr5J3lFH/T/uv7atuX2n8sw14xrVE75GpL8He37eVAvG7ST1LuNW/VutAbaRMvvBvx4YPvJ9vfjgb97DKSvoDVq3lVVg70BJtoXA2WZzN/TTr7HV9DSpm6p1lPfovVQ+fQEwdDx+xpfP4039Hg+jePs4DH7V9zaa+t2y7lt3TiRadUNTPF+J3lCkrPSJo69lnYyO1jmYcf/FbTbM6d6vwY9i94juKoup/UeWz1unWHvwXQ+m2GGthemYedx+7qE9rp3HEibTh2/A+1ixO3KkeSOSd6Y5Ae9zXLxwDbQgroHAJck+WKSh/X0oW2mqlpL67F3JK2tc1LvoTcdM67zp7HtjL4vSR6SNsHfuiTX0YIo022zwAzbXUkeSusZ+JS6tefXWCB3fFthWJtl/PLJ9vdPA22W14xbNpv202T7+n1gmx7Uk0ZtqbYrJrILbaiJsX3ccozrdf6lA/v4Iu1Ookf2x2fSAo6/258PGv/6TqD1EKf//9ch5ZnqXPGetLHtx97X9bQ7oHeBWZ87TvfYO9l5/GTtkmHn4/cEdh5XH76GW+vqQ2idFL6X5OwkT5zidczmNc1k22m3aZLcJck/p00yeD1t6K/t+kXf2ZRhq8l+j2l37nyCNrTm3wwsmqwemksd9YiB+vC8cctmUzdPtq+70oa3etFU624uDE5vvr5C69Eyk9sSb9GvYr6Hdpvm3avdpvQdWiUyZnwQb6qg3g+B7TMw+yntdpHLZ1C08ft4Q0/7v9VunfmTcWWEViEN7u+H/fGlwDFVtd3A313GBRUZWPfXh5Tp3bTxtfbsZXjNYBmq6h1V9SDa7VH3pt1S9CParR33Hdj3ttUmu6Baj6aXV9Wv026PeVmSfVk8VzDwPiYJt31fGVj2CNrtwwcBy/t35zpu/7nMiyRH0cZw2q+qBq+ingf8Vi/rmN/i1ttWz+O2V3PvP7BsUtVuKxv7+99ejuW0wPSpVTV+KJWJ9nVV3Trsx2T2Bf6uX/wYq/C/koFZiaVN1FKtp6iqt9OGbjojwycsus1xte9nWqZ5nB08Zt8B2JVb67/bLOe2deNMXNHznSjP8WXekjY+45uBHXuZT2N6dcM64Cam+X6lTey2J/DqgWPnQ4CnjztJGvYeTPXZ/IR2gXnM4GSLk7UXpvP9u+e4/d5EG7NyJn5EG9Jiokl0n04b8uWxtJP/lT09AFV1dlUdSLtN+t+59XbkSdtMVfXBqvqdXv4C3jTDMs+nGX1faCejpwK7VdW2tJ7EC9VmeUDf13Oramx4OKpqA+17N6xdcpt2RD/pvRfTaLdU1fMH2ixvGMhjtu2nyewLrBr43f0x7Xb3j0+xnTQflmy7YgJPpg2NMbaPW+qOgfO0sX2MBacf0R9/keHB6fGv5/3AgUnuTxtC89+HlGeqc8VLaUNGDdYhd66qL0+jTTPdu4eHuZQJ6sOp2iWTnI9fClw07rVsU1UH9O0uqKqn0erRNwGn9GP2YplJe/PltB7rD+lxjUf29HmvE/v7/++0zhPPG7d4fJ3367Te9P/T/5Yl2XNg/Wmdx/fe72P14S1Dc8yhbp7MnrQ21n/1+vCjwE69flw5je2XHIPTm6ney/K1tPH3ntSvgt2pXx3822lkcVdaRbAOIMlzaFeOJ3MVsGtunc17fJkupU2g8zdJtkryW7Qri++f3qua0Da0K1rXJdmFiWcOPyzJrkm2B/4ft85u/h7g+b3HTJLcNcnvjWs8jPkk7WDykrTxFbdJ8pCBMlwP/DjtFqk/H9soyYN7/neincz+DPhVv6L9Htq4Vr/W190lfayqJE9Mskev2K+j3TI74bilI/Ip2i3Rf9hP7F/EbU/GB21DOylcR6s4Xsvtr27OiySvpp1oP3aCQO+ZtPftRf0ze2FP/3z/fyKtkbFLWg+vl9MmhBjLe4u08VsD3Kl/Zyc8pqb1hj8d+O+qmqiX+4nAIUn2SrIdbTy2wX3dqe/rDrT3bKuBK9T3plWCe3Pr7Vm/z9Tj6UkbtaVeT1XV39LGHfyPtDF5xzsZeHY/LtwFmMlYk9M5zj5o4Jj9EtoJ++CtpcPqxpk4GXhOkt/sr+HwSdbdgnZisQ64KckTaPMyTKnfHvpR4Mj+PdmL2/eCHrSaNiHhXtx67Lwf7TbnJwysN+w9mOqz+Sbwh70se9C+I2Mmay9cBawcVpfQhpN4aZLd02asHxtnc6pb1G+jtzGOB96aZOe03tIP6yeC29C+C9fQAuyDwcotkjwjybbVbsu+nlvbHkPbTEnuk+QxPf+f0S6+L1qbZRbfl21oPRt/ljY26YJc/E27UPUZ4C+q6hMTrHIi8NdJlvf25J9xa1vhY8D9kvxRby+8ljZszfd63nfs6cuAO/Tj151uv4tbyjLr9lPa2LVbAXdqT7PVwDH1cG6dT2Vv2sn+e2hjuUoLaqm3K/rvfPck/0ALNh/VF50M/F6Sffvv/uW04/yX+/Iv0uYyuHNVXUYLau9PGwblG5Pts69/Nq3H9Edq+FAmU50r/hPtgvF9+2vZNslT+7Kp2jRT1Z1TOY7WVtm3H7926cfYSdslk5yPfw24Ickrk9y5fy73S/Lgvt2fJFnR6+Jre3aLeR4/k/bmNrQ6/NreNppJ23Ta+vf0lL6v1XX7+Vk+APx+Wk/nu9LmMPhov2DwE1od/7reFnk47aL7vw7kvxXtswXYMpPMfTXHupleT47lv0X/nYd2YWs3bq0P/5T2Xd6bmd+BsSQYnN6MVRuv6WW0QNg62o/ghQy/4jm47fm0Wd6/QvsR/V/abLiT+TztKtKVSX40ZJ2n0a4g/ZDW0D6iqv5jqvJM4ijggbQK41O0A9V4H6T1aL2QdkvP6wGqag3t4PKPtNlo1zJk0oF+K9bjaEHBK2njjz26L34FrYF/A60BPniCf7eetoF2C801tFuEoM1svxY4K+22mf/g1rG19uzPf0z7DN5VVV+Y8t1YIFX1I+CptJ6A1/TyDfs+nE47wP8P7TX/jIU7AL+BdvV3bcbdslpVv6D1nHgWrWHwXOBJPR3a2OCfAL5Nqzw+1dPGfJZWYf42cGx//Egm9mTa+GPPyQRDflSbzONvgS/QZry/hNtW9u/p+T+NFiD5KW28VKrq6qq6cuyvr/+jSRqH0iZjqddTVXU0bWzJzyW517hln6YN2/N5Wl3w+dvnMNR0jrMfp/Va3EA7nvxhDziOmbBunIn+Gt5BO7at5dbg988nWPcG2snqyb1MT6cFrqbrhbRbVK+knRS8d6KV+gnCQcA/DB47q+oi2onLYJByWPtgqs/mbbSJea6i3fJ8y4SCU7QX/q3/vybJ17m943sZ/xO4iPa5/sWwN2QKr6DVb2fTbp9+E+284ETad+Zy4Hxue8EC2nfl4t4ueT5tHOyp2kxb0toHP6K95l+jjZe9mKb1feleQDvJvYEW2JrJ5FUz8XLakCPHZeLbio+gfQ8voQWT/q63H6g2r8of0cb/3EC7E+DggW2fSWs7vJvWO/KntLbFMHNpPz2y538at06Y9dm+7Q3j2iw/BX5SbQxUacEt0XbFw5L8mHbB8Eza+eWDq+rbvdzfp909/A+04/DvA78/9putNkTBj+k9rfudEhfSOtXcPI39n0B7L4YN6THluWK1MXffBJzU65fvcOvF4qnaNFPVnZOqNnb2c2h193W04+s9p9EumfB8vL9nT6QFGS+ivef/QrsbCVrg/7z+mb0dOHgxz9tm2N78e9qF/B/R2gefWaBi/TbtPdyPFggfq4ce0ct8Hq0N8gHapJXb0OrqMS/o5byadmH/z/s2Y37KrUNyfI/JJyOcdd3cfb/nvwvtu/xT2vfrpnH14XpaJ8Urp/m7W3JS055DTVp6klwM/OkcA+CSJG0ykhwJ7FFVfzJk+cUsQN2Y5DdpJ5xbzrS3ryRJ2vgkeSStp/c9y+CSpFmy57QkSZIWRJIn91sal9N6RX3CwLQkSZu+PvzCi4F/MTAtaS4MTkuSJGmhPI92W+UPaOMx/vnkq0uSpI1dvxvqWmAn2nAPkjRrDushSZIkSZIkSRo5e05LkiRJkiRJkkZu2WIXYLZ22GGHWrly5WIXQ5K0RJ1zzjk/qqoVi12OTZ31tSRpIVlfzw/ra0nSQhtWZ2+ywemVK1eyZs2axS6GJGmJSnLJYpdhKbC+liQtJOvr+WF9LUlaaMPqbIf1kCRJkiRJkiSNnMFpSZIkSZIkSdLIGZyWJEmSJEmSJI2cwWlJkiRJkiRJ0sgZnJYkSZIkSZIkjZzBaUmSJEmSJEnSyBmcliRJkiRpkSXZLckXkpyf5LwkL+7pRya5PMk3+98BA9u8OsnaJN9P8viB9P172tokr1qM1yNJ0nQsW+wCSJIkSZIkbgJeXlVfT7INcE6SM/qyt1XVmwdXTrIXcDBwX2Bn4D+S3LsvfifwOOAy4Owkp1bV+SN5FZIkzYDBaUmSJEmSFllVXQFc0R/fkOS7wC6TbHIgcFJV/Ry4KMlaYJ++bG1VXQiQ5KS+rsFpSdJGx2E9JEmSJEnaiCRZCTwA+GpPemGSc5Mcn2R5T9sFuHRgs8t62rD08fs4NMmaJGvWrVs3z69AkqTpMTgtSZIkSdJGIsnWwEeAl1TV9cC7gXsBe9N6Vr9lPvZTVcdW1aqqWrVixYr5yFKSpBlzWA9JkiRJkjYCSe5EC0x/oKo+ClBVVw0sfw/wyf70cmC3gc137WlMki5J0kbFntOSJEmSJC2yJAGOA75bVW8dSN9pYLUnA9/pj08FDk6yZZLdgT2BrwFnA3sm2T3JFrRJE08dxWuQJGmm7DktSVoU7fxrflXVvOcpSdLmbMPRGxYk3+WHL596pc3Pw4FnAt9O8s2e9hrgaUn2Bgq4GHgeQFWdl+Rk2kSHNwGHVdXNAEleCJwO3BE4vqrOG+ULkSSNVnLUguRbdcSC5DtoTsHpJBcDNwA3AzdV1aok2wMfBlbSKs6DqmpDvwr8duAA4Ebg2VX19Z7PauCve7avr6oT5lIuSZIkSZI2JVX1JWCiq/enTbLNMcAxE6SfNtl2kiRtLOZjWI9HV9XeVbWqP38V8Lmq2hP4XH8O8ATabUZ7AofSJnWgB7OPAB4C7AMcMTD7sCRJkiRJkiRpCVqIMacPBMZ6Pp8APGkg/cRqzgK262NnPR44o6rWV9UG4Axg/wUolyRJkiRJkiRpIzHX4HQBn01yTpJDe9qOVXVFf3wlsGN/vAtw6cC2l/W0Yem3k+TQJGuSrFm3bt0ciy5JkiRJkiRJWixznRDxd6rq8iS/BpyR5HuDC6uqkszb7FRVdSxwLMCqVauc9UqSJEmSJEmSNlFz6jldVZf3/1cDH6ONGX1VH66D/v/qvvrlwG4Dm+/a04alS5IkSZIkSZKWqFkHp5PcNck2Y4+B/YDvAKcCq/tqq4GP98enAs9K81Dguj78x+nAfkmW94kQ9+tpkiRJkiRJkqQlai7DeuwIfCzJWD4frKrPJDkbODnJIcAlwEF9/dOAA4C1wI3AcwCqan2So4Gz+3qvq6r1cyiXJEmSJEmSJGkjN+vgdFVdCNx/gvRrgH0nSC/gsCF5HQ8cP9uySJIkSZIkSZI2LXMac1qSJEmSJEmSpNkwOC1JkiRJkiRJGjmD05IkSZIkSZKkkTM4LUnSEpHk+CRXJ/nOQNr2Sc5IckH/v7ynJ8k7kqxNcm6SBw5ss7qvf0GS1QPpD0ry7b7NO9JnRZYkSZIkaTYMTkuStHS8D9h/XNqrgM9V1Z7A5/pzgCcAe/a/Q4F3QwtmA0cADwH2AY4YC2j3df5sYLvx+5IkSZIkadqWLXYBJEnS/Kiq/0yyclzygcCj+uMTgDOBV/b0E6uqgLOSbJdkp77uGVW1HiDJGcD+Sc4E7lZVZ/X0E4EnAZ9euFckSZIkSZum5KgFybfqiAXJd7EYnJYkaWnbscnsJFMAACAASURBVKqu6I+vBHbsj3cBLh1Y77KeNln6ZROk306SQ2m9sbnHPe4xx+JLkiRJWsoM4m7eHNZDkqTNRO8lXSPYz7FVtaqqVq1YsWKhdydJkiRJ2kQZnJYkaWm7qg/XQf9/dU+/HNhtYL1de9pk6btOkC5JkiRJ0qwYnJYkaWk7FVjdH68GPj6Q/qw0DwWu68N/nA7sl2R5nwhxP+D0vuz6JA9NEuBZA3lJkiRJkjRjjjktSdISkeRDtAkNd0hyGXAE8Ebg5CSHAJcAB/XVTwMOANYCNwLPAaiq9UmOBs7u671ubHJE4AXA+4A70yZCdDJESZIkSdKsGZyWJGmJqKqnDVm07wTrFnDYkHyOB46fIH0NcL+5lFGSJEmSpDEO6yFJkiRJkiRJGjl7TkuSJEnSJmTD0RsWJN/lhy9fkHwlSZKGsee0JEmSJEmSJGnk7DktSZIkSZIkaclLjlqQfKuOWJB8NwcGpyVJkiRJkqRZMNgpzY3BaUmSJEmSJGkjZyBcS5HBaUmSJEmSJEm3MBCuUTE4LUmSJEmS5oUBLUnSTNxhsQsgSZIkSZIkSdr8GJyWJEmSJEmSJI2cwWlJkiRJkiRJ0sgZnJYkSZIkSZIkjZzBaUmSJEmSJEnSyBmcliRJkiRJkiSNnMFpSZIkSZIkSdLIGZyWJEmSJEmSJI2cwWlJkiRJkiRJ0sgZnJYkSZIkSZIkjZzBaUmSJEmSJEnSyC1b7AJIkjYeSeY9z6qa9zwlSZIkSdKmz57TkiRJkiRJkqSRMzgtSZIkSZIkSRo5g9OSJEmSJEmSpJFzzGlJkiRJkiQtGclRC5Jv1RELkq+0ObPntCRJkiRJkiRp5AxOS5IkSZIkSZJGzuC0JEmSJEmSJGnkDE5LkiRJkiRJkkbO4LQkSZIkSYssyW5JvpDk/CTnJXlxT98+yRlJLuj/l/f0JHlHkrVJzk3ywIG8Vvf1L0iyerFekyRJUzE4LUmSJEnS4rsJeHlV7QU8FDgsyV7Aq4DPVdWewOf6c4AnAHv2v0OBd0MLZgNHAA8B9gGOGAtoS5K0sTE4LUmSJEnSIquqK6rq6/3xDcB3gV2AA4ET+monAE/qjw8ETqzmLGC7JDsBjwfOqKr1VbUBOAPYf4QvRZKkaTM4LUmSJEnSRiTJSuABwFeBHavqir7oSmDH/ngX4NKBzS7racPSx+/j0CRrkqxZt27dvJZfkqTpMjgtSZIkSdJGIsnWwEeAl1TV9YPLqqqAmo/9VNWxVbWqqlatWLFiPrKUJGnGDE5LkiRJkrQRSHInWmD6A1X10Z58VR+ug/7/6p5+ObDbwOa79rRh6ZIkbXTmHJxOcsck30jyyf589yRf7TMGfzjJFj19y/58bV++ciCPV/f07yd5/FzLJEmSJEnSpiRJgOOA71bVWwcWnQqs7o9XAx8fSH9WmocC1/XhP04H9kuyvE+EuF9PkyRpozMfPadfTJuoYcybgLdV1R7ABuCQnn4IsKGnv62vR599+GDgvrRJGt6V5I7zUC5JkiRJkjYVDweeCTwmyTf73wHAG4HHJbkAeGx/DnAacCGwFngP8AKAqloPHA2c3f9e19MkSdroLJvLxkl2BX4POAZ4Wb/S+xjg6X2VE4AjgXfTZhI+sqefAvxjX/9A4KSq+jlwUZK1wD7AV+ZSNkmSJEmSNhVV9SUgQxbvO8H6BRw2JK/jgePnr3SSJC2Mufac/nvgr4Bf9ed3B66tqpv688FZgW+ZMbgvv66vP62ZhMHZhCVJkiRJkiRpqZh1cDrJE4Grq+qceSzPpJxNWJIkSZIkSZKWhrkM6/Fw4A/6GFhbAXcD3g5sl2RZ7x09OCvw2IzBlyVZBmwLXIMzCUuSJEmSJEnSZmfWPaer6tVVtWtVraRNaPj5qnoG8AXgKX218TMJj80w/JS+fvX0g5NsmWR3YE/ga7MtlyRJkiRJkiRp4zenCRGHeCVwUpLXA98AjuvpxwH/2ic8XE8LaFNV5yU5GTgfuAk4rKpuXoBySZIkSZIkSZI2EvMSnK6qM4Ez++MLgX0mWOdnwFOHbH8McMx8lEWSJEmSJEmStPFbiJ7TkiRJkiRJCyo5akHyrTpiQfKVJN3erMecliRJm44kL01yXpLvJPlQkq2S7J7kq0nWJvlwki36ulv252v78pUD+by6p38/yeMX6/VIkiRJkjZ9BqclSVrikuwCvAhYVVX3A+5Im/vhTcDbqmoPYANwSN/kEGBDT39bX48ke/Xt7gvsD7wryR1H+VokSZIkSUuHwWlJkjYPy4A7J1kG3AW4AngMcEpffgLwpP74wP6cvnzfJOnpJ1XVz6vqImAtE8wzIUmSJEnSdBicliRpiauqy4E3A/9LC0pfB5wDXFtVN/XVLgN26Y93AS7t297U17/7YPoE29wiyaFJ1iRZs27duvl/QZIkSZKkJcHgtCRJS1yS5bRez7sDOwN3pQ3LsSCq6tiqWlVVq1asWLFQu5EkSZIkbeIMTkuStPQ9FrioqtZV1S+BjwIPB7brw3wA7Apc3h9fDuwG0JdvC1wzmD7BNpIkSZIkzciyqVeRJEmbuP8FHprkLsBPgX2BNcAXgKcAJwGrgY/39U/tz7/Sl3++qirJqcAHk7yV1gN7T+Bro3whkrSx2nD0hgXJd/nhyxckX0mSpI2BwWlJkpa4qvpqklOArwM3Ad8AjgU+BZyU5PU97bi+yXHAvyZZC6wHDu75nJfkZOD8ns9hVXXzSF+MJEmSJGnJMDgtSdJmoKqOAI4Yl3whsM8E6/4MeOqQfI4Bjpn3AkqSJEmSNjuOOS1JkiRJkiRJGjl7TkuSJEmSJGlBJUctSL7tBkFJmyp7TkuSJEmSJEmSRs7gtCRJkiRJkiRp5AxOS5IkSZIkSZJGzuC0JEmSJEmSJGnkDE5LkiRJkiRJkkZu2WIXQJIkSZIWwoajN8x7nssPXz7veUqSJG2u7DktSZIkSZIkSRo5g9OSJEmSJEmSpJEzOC1JkiRJkiRJGjmD05IkSZIkSZKkkXNCREmSJEmSpI1EctSC5Ft1xILkK0lzYc9pSZIkSZIkSdLIGZyWJEmSJEmSJI2cwWlJkiRJkiRJ0sg55rQkbeSSzHueVTXveUqSJEmSJM2EPaclSZIkSZIkSSNnz2lJmgV7M0uSJEmSJM2NPaclSZIkSZIkSSNncFqSJEmSJEmSNHIGpyVJkiRJkiRJI2dwWpIkSZIkSZI0cganJUmSJEmSJEkjZ3BakiRJkiRJkjRyBqclSZIkSZIkSSNncFqSJEmSJEmSNHIGpyVJkiRJkiRJI7dssQsgSZIkSZKk0UuOWpB8q45YkHwlLT32nJYkSZIkSZIkjZzBaUmSJEmSJEnSyBmcliRJkiRJkiSNnMFpSZIkSZIWWZLjk1yd5DsDaUcmuTzJN/vfAQPLXp1kbZLvJ3n8QPr+PW1tkleN+nVIkjQTTogoaUlIsiD5VtWC5CtJkiSN8z7gH4ETx6W/rarePJiQZC/gYOC+wM7AfyS5d1/8TuBxwGXA2UlOrarzF7LgkiTNlsFpSZIkSZIWWVX9Z5KV01z9QOCkqvo5cFGStcA+fdnaqroQIMlJfV2D05KkjZLDekiSJEmStPF6YZJz+7Afy3vaLsClA+tc1tOGpd9OkkOTrEmyZt26dQtRbkmSpmRwWpIkSZKkjdO7gXsBewNXAG+Zr4yr6tiqWlVVq1asWDFf2UqSNCOzDk4n2SrJ15J8K8l5SY7q6bsn+WqffOHDSbbo6Vv252v78pUDeU04kYMkSZIkSZurqrqqqm6uql8B7+HWoTsuB3YbWHXXnjYsXZKkjdJcek7/HHhMVd2fdhV3/yQPBd5Em7BhD2ADcEhf/xBgQ09/W19v/EQO+wPvSnLHOZRLkiRJkqRNXpKdBp4+GfhOf3wqcHDvBLY7sCfwNeBsYM/eaWwL2rn2qaMssyRJMzHr4HQ1P+5P79T/CngMcEpPPwF4Un98YH9OX75vkjAwkUNVXQQMTuQgSZIkSdKSl+RDwFeA+yS5LMkhwN8m+XaSc4FHAy8FqKrzgJNpEx1+Bjis97C+CXghcDrwXeDkvq4kSRulZXPZuPdwPgfYA3gn8APg2l4hwm0nX7hlYoaquinJdcDde/pZA9lOOmEDcCjAPe5xj7kUXdqotOs086+qFiTf6Vqqr0uSJEmab1X1tAmSj5tk/WOAYyZIPw04bR6LJknSgpnThIj9yuzetHGs9gF+Y15KNXx/TtggSZIkSZIkSUvAnILTY6rqWuALwMOA7ZKM9cgenHzhlokZ+vJtgWtwwgZJkiRJkiRJ2uzMOjidZEWS7frjOwOPo41p9QXgKX211cDH++NT+3P68s9Xuzd/2EQOkiRJkiRJkqQlai49p3cCvtAnZjgbOKOqPgm8EnhZkrW0MaXHxsg6Drh7T38Z8CoYPpHDHMqlJSzJgvxtTnwPpc1Tku2SnJLke0m+m+RhSbZPckaSC/r/5X3dJHlHkrVJzk3ywIF8Vvf1L0iyevgeJUmSJEma3KwnRKyqc4EHTJB+IW386fHpPwOeOiSvCSdykCRJ8+btwGeq6ilJtgDuArwG+FxVvTHJq2gXjl8JPIF2J9OewEOAdwMPSbI9cASwCijgnCSnVtWG0b8cSZIkSdKmbl7GnNb02GN10+LnJWmpSLIt8Ej63UxV9Ys+X8SBwAl9tROAJ/XHBwInVnMWbT6JnYDH0+6UWt8D0mcA+4/wpUiSJEmSlpBZ95yWxixUwLUNSS5Jmge7A+uA9ya5P3AO8GJgx6q6oq9zJbBjf7wLcOnA9pf1tGHpkiRJkiTN2Gbfc9resZKkzcAy4IHAu6vqAcBP6HM/jOmTFM/LVcEkhyZZk2TNunXr5iNLSZIkSdIStNkHpyVJ2gxcBlxWVV/tz0+hBauv6sN10P9f3ZdfDuw2sP2uPW1Y+m1U1bFVtaqqVq1YsWJeX4gkSZIkaekwOC1J0hJXVVcClya5T0/aFzgfOBVY3dNWAx/vj08FnpXmocB1ffiP04H9kixPshzYr6dJkiRJkjRjjjktSdLm4S+ADyTZArgQeA7tIvXJSQ4BLgEO6uueBhwArAVu7OtSVeuTHA2c3dd7XVWtH91LkCRJkiQtJQanJUnaDFTVN4FVEyzad4J1CzhsSD7HA8fPb+kkSZIkSZsjh/WQJEmSJEmSJI2cwWlJkiRJkiRJ0sg5rMcSlWRB8m13ekuSJEmSJEnS3BicliRJkiRJmkRy1ILkW3XEguQrSZsKh/WQJEmSJEmSJI2cwWlJkiRJkiRJ0sgZnJYkSZIkSZIkjZzBaUmSJEmSJEnSyBmcliRJkiRJkiSN3LLFLoAkSZKkzceGozfMe57LD18+73lKkiRp4dlzWpIkSZIkSZI0cganJUmSJEmSJEkjZ3BakiRJkiRJkjRyBqclSZIkSZIkSSNncFqSJEmSJEmSNHIGpyVJkiRJkiRJI2dwWpIkSZIkSZI0cganJUmSJEmSJEkjZ3BakiRJkiRJkjRyyxa7AJIkSZIW14ajN8x7nssPXz7veUqSJGlpsee0JEmSJEmSJGnkDE5LkiRJkiRJkkbO4LQkSZIkSZIkaeQMTkuSJEmSJEmSRs7gtCRJkiRJkiRp5AxOS5IkSZIkSZJGzuC0JEmSJEmSJGnkDE5LkiRJkiRJkkbO4LQkSZIkSZIkaeQMTkuSJEmSJEmSRs7gtCRJkiRJkiRp5AxOS5IkSZIkSZJGzuC0JEmSJEmSJGnkDE5LkiRJkiRJkkbO4LQkSZIkSZIkaeQMTkuSJEmSJEmSRm7ZYhdAkiRJ0u1tOHrDvOe5/PDl856npPmR5HjgicDVVXW/nrY98GFgJXAxcFBVbUgS4O3AAcCNwLOr6ut9m9XAX/dsX19VJ4zydUiSNBP2nJYkSZIkafG9D9h/XNqrgM9V1Z7A5/pzgCcAe/a/Q4F3wy3B7COAhwD7AEck8aqUJGmjZXBakiRJkqRFVlX/Cawfl3wgMNbz+QTgSQPpJ1ZzFrBdkp2AxwNnVNX6qtoAnMHtA96SJG00Zh2cTrJbki8kOT/JeUle3NO3T3JGkgv6/+U9PUnekWRtknOTPHAgr9V9/Qv6LUiSJEmSJG3udqyqK/rjK4Ed++NdgEsH1ruspw1LlyRpozSXntM3AS+vqr2AhwKHJdkLbzuSJEmSJGleVVUBNV/5JTk0yZoka9atWzdf2UqSNCOzDk5X1RVjEy5U1Q3Ad2lXZL3tSJIkSZKkubuqnzfT/1/d0y8HdhtYb9eeNiz9dqrq2KpaVVWrVqxYMe8FlyRpOuZlzOkkK4EHAF/F244kSZIkSZoPpwJjQ1+uBj4+kP6sPnzmQ4Hr+nn46cB+SZb3O5L362mSJG2Uls01gyRbAx8BXlJV1ye5ZVlVVZJ5ve2INiQI97jHPeYrW0mSJEmSFlWSDwGPAnZIchlt+Ms3AicnOQS4BDior34acACwFrgReA5AVa1PcjRwdl/vdVU1fpJFSZI2GnMKTie5Ey0w/YGq+mhPvirJTlV1xQxuO3rUuPQzJ9pfVR0LHAuwatWqeQt6S5K01CW5I7AGuLyqnphkd+Ak4O7AOcAzq+oXSbYETgQeBFwD/HFVXdzzeDVwCHAz8KKqsieWJEnzpKqeNmTRvhOsW8BhQ/I5Hjh+HosmSdKCmfWwHmldpI8DvltVbx1Y5G1HkiRtfF5Mmx9izJuAt1XVHsAGWtCZ/n9DT39bX48+6fHBwH1pc0O8qwe8JUmSJEmalbmMOf1w4JnAY5J8s/8dQLvt6HFJLgAe259Du+3oQtptR+8BXgDttiNg7Lajs/G2I0mS5lWSXYHfA/6lPw/wGOCUvsr4CYzHJjY+Bdi3r38gcFJV/byqLqLV5/uM5hVIkiRJkpaiWQ/rUVVfAjJksbcdSZK08fh74K+AbfrzuwPXVtVN/fngZMS3TFRcVTclua6vvwtw1kCeTmAsSZIkSZqTufScliRJG7kkTwSurqpzRrjPQ5OsSbJm3bp1o9qtJEmSJGkTY3BakqSl7eHAHyS5mDYB4mOAtwPbJRm7g2pskmIYmMC4L9+WNjHisImNb6eqjq2qVVW1asWKFfP7aiRJkiRJS4bBaUmSlrCqenVV7VpVK2kTGn6+qp4BfAF4Sl9t/ATGYxMbP6WvXz394CRbJtkd2BP42ohehiRJkiRpCZr1mNOSJGmT9krgpCSvB74BHNfTjwP+NclaYD0toE1VnZfkZOB84CbgsKq6efTFliRJkiQtFQanJUnaTFTVmcCZ/fGFwD4TrPMz4KlDtj8GOGbhSihJkiRJ2pw4rIckSZIkSZIkaeQMTkuSJEmSJEmSRs7gtCRJkiRJkiRp5AxOS5IkSZIkSZJGzuC0JEmSJEmSJGnkDE5LkiRJkiRJkkbO4LQkSZIkSZIkaeQMTkuSJEmSJEmSRs7gtCRJkiRJkiRp5AxOS5IkSZIkSZJGzuC0JEmSJEmSJGnkDE5LkiRJkiRJkkZu2WIXQJIkSdpUbDh6w7znufzw5fOepyRJkrQpsOe0JEmSJEmSJGnkDE5LkiRJkiRJkkbO4LQkSZIkSZIkaeQMTkuSJEmSJEmSRs7gtCRJkiRJkiRp5AxOS5IkSZIkSZJGzuC0JEmSJEmSJGnkDE5LkiRJkiRJkkbO4LQkSZIkSZIkaeQMTkuSJEmSJEmSRs7gtCRJkiRJkiRp5JYtdgEkSZKmY8PRG+Y9z+WHL5/3PCVJkiRJ02PPaUmSJEmSJEnSyBmcliRJkiRJkiSNnMFpSZIkSZIkSdLIGZyWJEmSJEmSJI2cwWlJkiRJkiRJ0sgZnJYkSZIkSZIkjZzBaUmSJEmSJEnSyBmcliRJkiRJkiSN3LLFLoAkSZI0FxuO3jDveS4/fPm85ylJkiTptuw5LUmSJEmSJEkaOYPTkiRJkiRJkqSRMzgtSZIkSZIkSRo5g9OSJEmSJEmSpJEzOC1JkiRJkiRJGrlli10ASZIkjcaGozfMe57LD18+73lKkiRJ2jwYnJYkSRrHIK4kSZIkLTyD05IkSZp3Bvglaf4kuRi4AbgZuKmqViXZHvgwsBK4GDioqjYkCfB24ADgRuDZVfX1xSi3JElTmdOY00mOT3J1ku8MpG2f5IwkF/T/y3t6kvx/9u48bJKqPNj4/cCAssoAE8Iy4xAFFU1UMiLGJSiKQFQwKrIIIxIniRI1agRN8oHggkZNRA0RAzIoyiYGVBQRJcZEEFAEAZERgZlhWGQHkfX5/jjnZWpeut+1l7d77t919dXdp7Zzeqmn6qmqU0dHxJKIuCwitm9Ms7COf01ELJxOnSRJkiRJGkIvzcznZOaC+v5Q4LzM3AY4r74H2A3Ypj4WAcf0vKaSJE3QdG+IeAKw66iySQXIerT3MOD5wA7AYSMJbUmSJEmS1NIewOL6ejGwZ6P8xCwuADaKiM37UUFJksYzreR0Zv4QuH1U8WQD5CuBczPz9sy8AziXxye8JUmSJElaXSXw3Yi4JCIW1bLNMnNFfX0TsFl9vSWwtDHtslq2iohYFBEXR8TFt956a7fqLUnSmKZ75nQrkw2QEwqcYPCUJGkqImJuRPwgIq6MiCsi4p213K64JEkaDC/KzO0pVyS/PSJe0hyYmUlJYE9YZh6bmQsyc8GcOXM6WFVJkiauG8npx0wlQI4zP4OnJEmT9zDwnszcDtiRslO7HXbFJUnSQMjM5fX5FuDrlDh880h3HfX5ljr6cmBuY/KtapkkSTNON5LTkw2QBk5JkrooM1dk5k/r63uAqyhXKdkVlyRJM1xErBcRG4y8BnYBfgGcBYxcxbQQOLO+Pgs4oF4JtSNwV+PqZkmSZpRuJKcnGyDPAXaJiNn17KtdapkkSeqwiJgPPBe4kC51xWU3XJIkddRmwI8i4ufAT4BvZeZ3gKOAV0TENcDL63uAs4FrgSXAF4C39b7KkiRNzKzpTBwRXwV2AjaNiGWUS32PAk6NiIOA64G96uhnA7tTAuTvgAMBMvP2iDgSuKiOd0Rmjr7JoiRJmqaIWB/4GvCuzLw7Ih4blpkZER3piiszjwWOBViwYEHHuveSJGl1lJnXAs9uUX4bsHOL8gTe3oOqSZI0bdNKTmfmPm0GTSpAZubxwPHTqYskSWovItaiJKZPyswzavHNEbF5Zq6YRFdcO40qP7+b9ZYkSZIkDa+u3hBRkiT1X5RTpI8DrsrMTzUG2RWXJEmSJKlvpnXmtCRJGggvBPYHLo+IS2vZB7ArLkmSJElSH5mcliRpyGXmj4BoM9iuuCRJkiRJfWFyWpIkqY/uOPKOjs9z9j/P7vg8JUmSJKnT7HNakiRJkiRJktRzJqclSZIkSZIkST1nclqSJEmSJEmS1HMmpyVJkiRJkiRJPWdyWpIkSZIkSZLUcyanJUmSJEmSJEk9Z3JakiRJkiRJktRzJqclSZIkSZIkST1nclqSJEmSJEmS1HMmpyVJkiRJkiRJPWdyWpIkSZIkSZLUcyanJUmSJEmSJEk9Z3JakiRJkiRJktRzJqclSZIkSZIkST1nclqSJEmSJEmS1HMmpyVJkiRJkiRJPWdyWpIkSZIkSZLUcyanJUmSJEmSJEk9Z3JakiRJkiRJktRzJqclSZIkSZIkST1nclqSJEmSJEmS1HMmpyVJkiRJkiRJPWdyWpIkSZIkSZLUcyanJUmSJEmSJEk9Z3JakiRJkiRJktRzJqclSZIkSZIkST1nclqSJEmSJEmS1HMmpyVJkiRJkiRJPWdyWpIkSZIkSZLUcyanJUmSJEmSJEk9Z3JakiRJkiRJktRzJqclSZIkSZIkST1nclqSJEmSJEmS1HMmpyVJkiRJkiRJPWdyWpIkSZIkSZLUcyanJUmSJEmSJEk9Z3JakiRJkiRJktRzJqclSZIkSZIkST1nclqSJEmSJEmS1HMmpyVJkiRJkiRJPWdyWpIkSZIkSZLUcyanJUmSJEmSJEk9Z3JakiRJkiRJktRzMyY5HRG7RsTVEbEkIg7td30kSdLjGa8lSRoMxmxJ0iCYEcnpiFgT+BywG7AdsE9EbNffWkmSpCbjtSRJg8GYLUkaFDMiOQ3sACzJzGsz80HgZGCPPtdJkiStyngtSdJgMGZLkgZCZGa/60BEvB7YNTP/qr7fH3h+Zh48arxFwKL69mnA1cCmwG97WN1esV2DxXYNFts1WPrVridn5pw+LHfGMl63ZLsGz7C2zXYNFtvVOcbrFiYSs9vEa/D3OWhs12CxXYPFdnVWy5g9qw8VmbLMPBY4tlkWERdn5oI+ValrbNdgsV2DxXYNlmFt1zAzXg++YW0XDG/bbNdgsV2aCVrFaxje79F2DRbbNVhs12CZae2aKd16LAfmNt5vVcskSdLMYbyWJGkwGLMlSQNhpiSnLwK2iYitI2JtYG/grD7XSZIkrcp4LUnSYDBmS5IGwozo1iMzH46Ig4FzgDWB4zPziglO/rjLkIaE7Rostmuw2K7BMqztGjjG65Zs1+AZ1rbZrsFiu9RVxuyWbNdgsV2DxXYNlhnVrhlxQ0RJkiRJkiRJ0uplpnTrIUmSJEmSJElajZicliRJkiRJkiT13IxLTkfE3Ij4QURcGRFXRMQ7a/nGEXFuRFxTn2fX8oiIoyNiSURcFhHbN+a1sI5/TUQs7Febal060q6IeE5E/LjO47KIeOMwtKsxvw0jYllEfLYf7WnUo5O/w3kR8d2IuKrOb35/WtXxdn28zuOqOk4MULueXv9HD0TEe0fNa9eIuLq2+dB+tKdRl460q918+qmT31kdvmZE/CwivtnrtqzOOrxOMWYPSLsa8zNmd1GH22XM7rJOtavdfPqlk99XHW687oMOr0+M1wPSrsb8jNdd1OF2Ga+7rFPtajeffunk91WH9zZeZ+aMegCbA9vX1xsAvwK2Az4OHFrLDwU+Vl/vDnwbCGBH4MJavjFwbX2eXV/PHoJ2bQts123fJQAAIABJREFUU19vAawANhr0djXm92ngK8Bnh+F3WIedD7yivl4fWHfQ2wX8GfC/lJurrAn8GNhpgNr1B8DzgA8D723MZ03g18AfAWsDPwe2G4J2tZxPv9rVybY15vfuuu74Zj/btbo9OrhOMWYPULsa8zNmD0C7MGYPWrtmVMzuVLsa8zNeD8b3aLw2Xs/odmG8nontMl4PQbsa8+tpvO7LhzbJD/hM4BXA1cDmjQ/96vr688A+jfGvrsP3AT7fKF9lvH4/ptquFvP5OTWQzoTHdNoF/ClwMvBm+hw4O/g73A74Ub/r34V2vQC4BFgHWBe4GHhGv9sz0XY1xjucVQPMC4BzGu/fD7y/3+2Zbrvazaff7elU24CtgPOAl+HO7oz+HsdYpxizB6xdGLMHqV3G7AFqV7v59Ls9nWgXxusZ85jG+sR4PWDtwng9SO0yXg9Qu9rNp9/t6US76EO8nnHdejTVSzOeC1wIbJaZK+qgm4DN6ustgaWNyZbVsnblfTfNdjXnswPlyNOvu1jdCZtOuyJiDeCTwOMuJ+i3aX5f2wJ3RsQZ9ZKIf4mINXtS8XFMp12Z+WPgB5SzClZQgs1VPaj2uCbYrnYGfb0x2fnMCB1o278B7wMe7Ub9NDHGbGP2TGDMNmZ3uIpTMqwx23g9HIzXxuuZwHhtvO5wFafEeN1Wz+P1jE1OR8T6wNeAd2Xm3c1hWVL52ZeKTVOn2hURmwNfAg7MzL5v4HWgXW8Dzs7MZV2q4pR0oF2zgBdTNgieR7mU5c2dr+nkTLddEfFU4BmUI2pbAi+LiBd3qboT5npj6vPplw78Fl8F3JKZl3SvlhqP/71x52PM7gFjdtvpjdk9NKwx23g9HPzfjTsf43UPGK/bTm+87iHjddvp+xKvZ2RyOiLWonyYJ2XmGbX45hosRoLGLbV8OTC3MflWtaxded90qF1ExIbAt4B/zMwLelH3sXSoXS8ADo6I64BPAAdExFE9qH5bHWrXMuDSzLw2Mx8G/gtY5QYVvdahdr0WuCAz783Meyl9Zr2gF/VvZ5LtamfQ1xuTnU9fdahtLwReU9cdJ1M24r7cpSqrBWM2YMw2ZneJMXtMg77emOx8+sZ4PRyM14Dx2njdJcbrMQ36emOy8+mbQY7XMy45HREBHAdclZmfagw6C1hYXy+k9J8yUn5AFDsCd9VT1s8BdomI2VHuRrlLLeuLTrUrItYGvg6cmJmn96j6bXWqXZm5X2bOy8z5lCOgJ2Zm3+7i2sHf4UXARhExp473MuDKrjegjQ626wbgzyNiVl0B/jnQt0uOptCudi4CtomIret/be86j77oVLvGmE/fdKptmfn+zNyqrjv2Br6fmW/qQpXVgjHbmI0xu2uM2cbsmRCzjdfDwXhtvMZ43TXGa+O18boDcgZ01N18AC+inGZ+GXBpfewObELpkPsa4HvAxnX8AD5H6RPqcmBBY15vAZbUx4HD0C7gTcBDjXlcCjxn0Ns1ap5vpv93Eu7k7/AVdT6XAycAaw96uyh33P08JVheCXxqwL6vP6Qccb8buLO+3rAO251yZ9tfU86cGPh2tZvPMLRt1Dx3whsszfTv0ZhtzJ7R7cKYPRPbZczuY8zu5PfVmOdOGK9n+vdovDZez+h2Ybyeie0yXhuvp/yIukBJkiRJkiRJknpmxnXrIUmSJEmSJEkafianJUmSJEmSJEk9Z3JakiRJkiRJktRzJqclSZIkSZIkST1nclqSJEmSJEmS1HMmpyVJkiRJkiRJPWdyWpIkSZIkSZLUcyanJUmSJEmSJEk9Z3JakiRJkiRJktRzJqclSZIkSZIkST1nclqSJEmSJEmS1HMmpyVJkiRJkiRJPWdyWpIkSZIkSZLUcyanJUmSJEmSJEk9Z3JakiRJkiRJktRzJqclSZIkSZIkST1nclqSJEmSJEmS1HMmpyVJkiRJkiRJPWdyWpIkSZIkSZLUcyanJUmSJEmSJEk9Z3JakiRJkiRJktRzJqclSZIkSZIkST1nclqSJEmSJEmS1HMmpyVJkiRJkiRJPWdyWpIkSZIkSZLUcyanJUmSJEmSJEk9Z3JakiRJkiRJktRzJqdXExFxeER8ud/1GBHFFyPijoj4Sb/rM56I2CkilvVweW+OiB/1anmd1OvPqk0d9ouI7/azDpIGn7GzdyLiPyLin7sw3ysiYqcJjNf32DUIImJeRNwbEWv2YdnXRcTL6+sPRMR/TnE+346IhZ2tnaRBZ8zvr4h4cURc3YflPi0iLo2IeyLiHZOcdtyYGBHnR8RfdXq+3WKsVb+YnB4iEbFvRFxcV2Qr6grhRV1a1nSTpy8CXgFslZk7jLGcnSIiI+KQaSyr45or7TbD3cmdhoiYX7/3WVOZPjNPysxdprjshRFxSUTcHRHLIuLjzXpExMYR8fWIuC8iro+IfUdNv28tvy8i/isiNm4MO7j+Rx+IiBPGqccTIuK4Oq976kbTbqPG2TkifhkRv4uIH0TEkxvD9oqI/6vDzh9jOQfUz3pSG03SsBim2Fnn/0hty70R8Zu6Y7vtNJbZca0+h8z8m8w8cpLz+U5EHNGifI+IuCkiZmXmMzPz/GlWWVVm3pCZ62fmI5OdtpPbRpn5kcycUtzKzN0yc/Fkp+tAXH5CRBxfty9uioh3N4atHRGn1+3LjHEOqETEjhFxbkTcHhG3RsRpEbF5Y3hExMci4rb6+FhERGP4sRFxdUQ8GhFvHmM5501ne0yaaYYw5s+Ik5mmsn6v65anjrzPzP/JzKd1vnbjeh/wg8zcIDOPnsyE04mJ3ZqvsdZYO6hMTg+J+qf7N+AjwGbAPODfgT26sKxO/GmeDFyXmfeNM95C4HbggA4sc7W0uq/kpmBd4F3ApsDzgZ2B9zaGfw54kPI/2w84JiKeCVCfPw/sX4f/jvI/HHEj8CHg+AnUYxawFPhz4EnAPwGnRsT8uqxNgTOAfwY2Bi4GTmlMfztlnXBUuwVExGzgA8AVE6iPNHSGNHb+ODPXp6w3Xg7cD1wSEc/qwPJnmsXAm5o7AtX+wEmZ+XAf6qThNd24fDiwDeV//FLgfRGxa2P4j4A3ATdNoC6zgWOB+XV+9wBfbAxfBOwJPBv4E+DVwF83hv8ceBvw03YLiIj9gLUmUBdpIAxpzO+7IdjXfDJd2BeqiUvzbZNnrF1dZaaPAX9Q/rT3Am8YY5zDgVOBEyl/qiuABY3hhwK/rsOuBF7bGPZm4H+BfwVuA74G/B54pC73zjbL3AI4i5IkWwK8tZYfNGr6D7aZfr1an70pycBmfZ8IfLnW507gImCzRn2vrdP+Btivlj8F+H6d5rfAScBGjXkm8NTG+xOAD9XXOwHL6usvAY9SdvjvBd7Xot7313HurY8tgCdQNohurI9/A57Qpu1vpqw4PwHcUdux26jv/DhgBbCckvBcs8339aE2v4fT6md4D3A5sC3wfuAWSkDYZbzvsg5bp35Wd9Tfzj+MfFaNab8G3Frb8Y7GsB0oAeVu4GbgU7X8hvp9jHx+f16X/ceNaf+Akvyd0+7zG/Xd/g1wTf29fA6ICf6/3g18o/HdPghs2xj+JeCo+vojwFcaw55Sx99g1Dw/BJwwhf/6ZcDr6utFwP+1+N09fdQ0fwWc32Z+/0EJmOcDf9XtdZUPHzPpwRDGztHrvkb5N4HTG+9fU9tyZ/3/P6Mx7DrKevwy4D5KrNkM+HZt5/eA2Y3xdwT+r87r58BOo+qzSjwGntHqc6ARc+v7PYBLKfHh18CuLdq1DnAX8JJG2ew6/2c32vPy+rptHKYR5xvfQ7vYNd7vYi5lx+nW+t1/tjHsLcBVlJh5DvDkMX5/L2p8tkuBN9fyvwB+Vj+bpcDhjWnG2j4aa9vhqcB/18/zt8Apbeo0nxJTZ9X35wNHUn7r9wDfBTZtM+3oz3jMaSkHGa6vbfnHUd/l4cCXG+OeRtnRvAv4IfDMMT7X86kxj3G2tyawHplwXKb85prbVkcCJ7eY5zIa/6MJ1mN74J7G+/8DFjXeHwRc0GK6H438rkaVPwn4FeX//dj37cPHoD5YDWI+k4jfrFyXL6rrphXAexvzGjdeAodQ1run0Xrfdwfgx5RYtAL4LLB2nccP6/Lvq+O/kcfHiGdQ1td31u/iNY1hJ1D2575V23Yh8JQxvtuW2z2U3MAj9bO+l8Y+XmParWt9Rz7Dz1HjD61j4ofrb+F+Smw9n5Uxx1hrrB29XjLW1odHcobDCyg7I18fZ7zXACcDG1GC4Gcbw34NvJjyB/kg8OXmJQuUM0ivpQS4N1ESfT/OcrnJRm2WdzLlT78F8HrgIxHxssw8btT0h7WZ/i8pQeI0yg7cwsawhbWuc4FN6vzuj4j1gKMpK7sNgD+j7NwCBPDRWp9n1GkPb7PstjJzf0ry9NW1/h8fNfw+YDfgxjp8/cy8kbKy3xF4DuXo2g6UI4HtPB+4mnIG78eB4xpnh50APEwJcM8FdqEkIZvTjnxfH24z/1dTEquzKTu551CuptgSOIJyBvCIlt9lHXYYJQn7FOCVNL6nerT4G5SExZaUs5DfFRGvrKN8Gvh0Zm5Ypz+1lr+kPm9UP7//rnV4U6NO+wDnZeatbdo32quA51GOau5V6zoRL2Hl0fRtgYcz81eN4T8HnllfP7O+ByAzf01NZk9wWW1FxGZ1PiN1Gb2s+yj/42c+fuqW89sBWEBJUEuro2GNna2cUetJlC4+vkq5QmQOcDbwjYhYuzH+6yiXEm9LiRXfplxlMYcSJ95R57UlZcfwQ5SzV94LfC0i5rSLx5l51XifQ10/nUjZyd6Ish6+bvR4mXk/JW40r67aC/hlZv589PhMMA5PIHZBm99FlP4hv0nZ0Ztfpz+5Dtujfo5/WT/L/6F8F49TL1H9NvCZOu5zWLk9c19t80aURPXfRsSedVjL7aM67ATabzscSdlhnQ1sVZc7UfsCB1IOGq/NqlcbTWnaiNgOOIay07xFbctWY8zn25Qzpf6AcobSSZOow1jbW21NJi7XK5U2bw5n1e2H6WpuqzyuLlNY1kcon/9EziyTBsHqEvMnFL8bXkpZd+4CHBIru60cL17+ISXuP5kSj1rt+z4C/D1l3foCSix9G0BmjuzrPbuO3zzzlYhYixKHv0tZr/8dcFJENLv92JvyPcymJPZb7vOOtd2TmS+jxOKDaz1+1WIWXwF+QolDh1Pi0lj2pyRQN6BsCzQZa421TcbaBpPTw2ET4Lc5/uWrP8rMs7P0XfQlSqABIDNPy8wbM/PRGhyuoQShETdm5mcy8+G6MzimiJgLvBA4JDN/n5mXAv/J5LrnWEg5mvgIJSjsXQMVwEOUdj81Mx/JzEsy8+467FHgWRGxTmauyMwrahuXZOa5mflATWh+inJGbq/sBxyRmbfU5X+QsYPb9Zn5hdr+xZQV7WZ1Bb078K7MvC8zb6Ecpd+7Me1Evq//ycxz6u/mNEqwPiozH6JsKM2PiI0m8F3uBXw4M2/PzKWUZMSI51HObD4iMx/MzGuBLzTq+hDw1IjYNDPvzcwLxvg8FgP7NILY/pTf8UQdlZl3ZuYNwA8oG1tjioi3UBK4n6hF61POVGu6i7LxMTL8rjGGT0n93Z8ELM7MX053WTV58u+UDbFHp1M3aYANa+xs5UbKTiSUs5O+VePhQ5T12zqU5PGIz2TmzZm5nLLTdmFm/iwzf0/ZsX9uHe9NwNn183k0M8+lXA2zex3eMh5PwEHA8bWOj2bm8sa6b7TFwOsj4on1/QG1rJWJxuHxYhe0/13sQNnB+4cao3+fmSN9gv4N8NHMvKr+7j4CPKfZV2LDvsD3MvOrmflQZt5Wfw9k5vmZeXn9bC6j7HSPbM+03D6awLbDQ5Qkwxaj6jwRX8zMXzUOFowbXycw7euBb2bmDzPzAcrlu23jVWYen5n31HEPB54dEU+aYB1abm+NNcEU4vL6jfejh01LRPwJ8P8oB3NGjK7LXcD6E0wELKCshyaTNJFmutUl5k80fo/4YI0Jl1O6K9inlo8XLx8FDqv71S3bWuPPBfXzuI5y4tNE9713pKzHjqpx+PuUA7/7NMb5emb+pH6nJ9E+9kxku6eliJhH2Sb4f7UeP6IctBjLCZl5RW33Q6OGGWuNtSPzM9aOYnJ6ONwGbDqB/p6aR2R+BzxxZJooN0W7NCLujIg7gWdRjmqNWDrJOm0B3J6Z9zTKrqecQTSuGqxfysqjcWdSjnb/RX3/JcqZvidHxI1Rblq3VpYjZ2+k7ACuiIhvRcTT6zw3i4iTI2J5RNxNuey12cZu24JVj55eX8vaeez7yszf1ZfrUwLaWpT2jXxfn6ccwRwxke/r5sbr+ykbbI803o8sb7zvcotRy2u28cnAFiP1rHX9ACsD0UGUI6G/jIiLIuJV7SqbmRdSfrc71e/0qYy/cdA0+ve/frsRAepZaB+lnPX321p8L7DhqFE3pFwiNZHhYy3v27HyJmb7NcrXoPzeHwQObkwy5WVRzlq4LMc+GCANu6GLnWPYknLJ8MgyHltPZzlAtXTUMkbHh9HvR9afTwbeMGod/yJg87Hi8QTMpZwFM666Y/dbYM+IeAolUfCVNqNPNA6PF7ug/e9iLmUHrFUC5MnApxvzvJ1yVVer77ftZxARz49yA6BbI+Iuymc88rtruX3E+NsO76t1+UlEXFEPzk7UpOLrBKddZdui/p5uazWDiFgzIo6KiF/X7bvr6qCJbuO1295qaYpx+d7G+9HDxhQR8xrbB/eOGvZUypls78zM/xmjLhsC92aW64jHWNYalIPX75xAEk8aJKtLzJ9o/B4xeh9uJCaOFy9vrQnvtiJi24j4ZpSb0t1NOSA70fXyFsDSXPUkmtGfzURjz0S2e8aqx+2N2ADjf89jDTfWYqw11rZmcno4/Bh4gNIZ+6TVM3a+QPnTb5LlsqNfUFacI0b/wcb8w1HP0oqI5lGqeZQ+Didif8rv8xsRcRPlEqknUruMqGcRfTAzt6Mc9XwV9ShzlrOBX0E5GvfL2jYoATEp/RZvSDnjq9nG31FuhjfiD8eo33jtbzX8RsrO4Yh5tWyyllK+700zc6P62DAzm5eQjFe/yRjvu1xB2YluDmvW9TeNem6U5U7IuwNk5jWZuQ9l5/hjwOlRLgVvV//FlO9tf0r/qWNuFE1VlJsmfIHSdcvljUG/AmZFxDaNsmez8vKeK2icYRERf0Tps63VJWKryHJH45FL4U6q0wcr+4t73aij76OXtR6la5SJnJm4M/DaurF4E+U/9MmI+Ow400nDZBhjZzuvpZxBNbKMx2JRXc/MneIylgJfGrWOXy8zj4Ix4/F4n8NSyvpsok6kbAO8CTgnM29uM95E4/CYsWsCdZ/XJgGyFPjrUfNdJzP/r8247T6Dr1AOzs7NzCdRumcKGHP7aMxth8y8KTPfmplbUG7m8+91Z6xfVtm2iIh1KWc+trIvpY/yl1Mut58/MlmnKzXVuJyZd1Da9OzG+M3th7Yy84bG9sFjO/J1HfQ94MjMHH0l2Sp1meiyKDvWC4BT6vbBRbV8WUS8eALTSzPV6hTzJ2P0PtxITBwvXk6krcdQYv82dd/7A0x8vXwjMDdWvaHgVD+b6Wz3rKB8R80cwdx2I1dtv3dj7cQYa1dPJqeHQGbeRbnE4HMRsWdErBsRa0XEbhHx8fGmp3Qin5Qb9xARB1KOBI/lZmCrWLWPymadllI6iP9oRDwxymUQB1HOVp6IhZTLh57TeLwO2D0iNomIl0bEH0fpnuBuyiUyj0Y5O3qPupJ6gHI0a+SI6wb1/V1R+sn8h1UXyaXAvvWo4K6MfdnRzcAfjTN8k1j1MpevAv8UpS/OTSnf2UQ/j8dk5gpKX1WfjIgNI2KNiHhKRHSli5IJfJenAu+PiNkRsRWlT7ARPwHuiYhDImKd+tk+KyKeBxARb4qIOfUI9p11mkcpv8VHefxn/GVKkuVNlGREx0XpS/skSiD8SXNYPZp8BnBERKwXES+kBOmRQHUS8OqIeHH9DR4BnDFyRkREzIpy6fmawJr18xzrDI5jKP2jvzoff8nc1ymXy7+uzvP/Uc6G/mVd1pq1fBawRl3WSLc4b67zHflvXUz5v/3jJD4qaaANaex8TF0HbB0Rn6HcHOeDddCpwF9ExM51nfAeSrxslSAdz5cp67xXjqxzImKniNhqnHg85udA2SE5sNZxjYjYMsY+6/pEys7SW2nfpQdMPA6PGbvG8RPKjtFRNU48scYKKEnk90fEMwEi4kkR8YY28zkJeHlE7FVjxyYRMXIZ7gaUM7l+H6V/7n1HJmq3fTTetkNEvKHGcCg3K0rGuLS3B04HXhURL6q/kyNov9+yAeU3dhvlJIOPdLFeU47LlN/pP9XtpadTfq8njEwcEU+Ild3TrF1/Oy13+ut27PcpN9tsde+IE4F31//OFpT/eXNZa9dlBbBWXdYalEuSt2Dl9sHIAZk/pdxwTBpIwx7zp+Gf62fxTEqfxCN9P092v7XVvu8GlDh0b13n/W2LadrtT49cMfu++j3tROlD++SJN+0xU97uyczrKftJh9f15gtqPabEWDthxtrVkMnpIZGZnwTeTblRwa2UM2QOBv5rAtNeCXySckT5ZuCPKXdzHcv3KUeFboqI37YZZx/KEbUbKSuRwzLze+PVJyJ2pBzd/Fw9ujjyOItys4N9KGc1n04JeFdR7nr7Jcpv+t11mbdTEswjgfCDlDus3kW5gdMZoxb9TkqwuZPSz9ZYn91HKSu9OyPicTcjqCvHrwLX1nG2oNww6mLK3WYvp9xE4EPjfR5tHEC5mcGVlMB2OuXMtG4Z67v8IOVSqd9QdnwfO6KYpZuQV1FWur+hXHr9n5SjrQC7AldEuXTm08DemXl/vdTnw8D/1s9vxzq/pZTPLVl5FmCn/XOt39mx8tKebzeGv43ST9ktlO/4b3Nlv+ZXUC6vPqkO36COP+KfKJfUHUpJsN9Pm5tiRjlK+9eUz+6mGNXlR5b+315H+ZzuoNxkotkf6v51/sdQbt5yP/WsxSx9bz/236JcLnV33XCXVhvDFDsbXlDXqXdT7pS+IfC8katAMvNqyvrnM5R18qspG/8PTmIZ1HktpRyg+wArP79/oMTiseLxmJ9DPTB4IKVP5LsoMb5Vv8wj419H2clcj7G7e5pQHJ5A7GqrTvtqStdTN1BudPXGOuzrlKuETo5ySewvKDeRajWfGyg7K++hfH6XsvLsnLdRDpLeQ9kpO7UxabvtIxh72+F5wIX1t3MW5VLTa8drb7fUePp2ylniKyj1XdZm9BMp2yHLKW3rSpdVHYjLh1G6arme8r38S2Z+pzH8akqs3pLSNcv9tP/d/xUlqXN4tL4M+fOUm4ldTvmdfYtVb3T93Tr/PwOOra9fkkVz+2DkptM3T2UdIc0kQxrzp+u/KfvX5wGfyMzv1vJJ7be22fd9L+Xg6T2UfZBTRk12OLC4jr/XqPk9SImlu1Fi8L8DB2T7+0+01YHtnv0oN3S8jfIZnEJJ0k6FsXYcxtrVN9ZGjt0diiTNKBFxPOWGIy2TupIkSZKk1iJiPuXg61ppn7eTEhGnAL/MzMP6XRdpmIx3QwBJmjHqhtRf8vg7TUuSJEmS1DG1S6/bKcn8XShXjB3V10pJQ8huPSQNhIg4knK5zL9k5m/6XR9JkiRJ0lD7Q0o3afcCR1O6dPxZX2skDSG79ZAkSZIkSZIk9ZxnTkuSJEmSJEmSem5g+5zedNNNc/78+f2uhiRpSF1yySW/zcw5/a7HoDNeS5K6yXjdGcZrSVK3tYvZA5ucnj9/PhdffHG/qyFJGlIRcX2/6zAMjNeSpG4yXneG8VqS1G3tYrbdekiSJEmSJEmSes7ktCRJkiRJkiSp50xOS5IkSZIkSZJ6zuS0JEmSJEmSJKnnTE5LkiRJkiRJknrO5LQkSZIkSZIkqedMTkuSJEmSJEmSes7ktCRJkiRJkiSp50xOS5IkSZIkSZJ6btzkdEQcHxG3RMQvGmUbR8S5EXFNfZ5dyyMijo6IJRFxWURs35hmYR3/mohY2Cj/04i4vE5zdEREpxspSZIkSZIkSZpZJnLm9AnArqPKDgXOy8xtgPPqe4DdgG3qYxFwDJRkNnAY8HxgB+CwkYR2HeetjelGL0uSJEmSJEmSNGTGTU5n5g+B20cV7wEsrq8XA3s2yk/M4gJgo4jYHHglcG5m3p6ZdwDnArvWYRtm5gWZmcCJjXlJkiRJkiRJkobUrClOt1lmrqivbwI2q6+3BJY2xltWy8YqX9aivKWIWEQ5I5t58+ZNseqSpJmgG304ZRfmKUnSau0rXep1cV+j9tDqVk+d6W9GkobRtG+IWM947kmUyMxjM3NBZi6YM2dOLxYpSZIkSZIkSeqCqSanb65dclCfb6nly4G5jfG2qmVjlW/VolySJEmSJEmSNMSmmpw+C1hYXy8EzmyUHxDFjsBdtfuPc4BdImJ2vRHiLsA5ddjdEbFjRARwQGNekiRJkiRJkqQhNW6f0xHxVWAnYNOIWAYcBhwFnBoRBwHXA3vV0c8GdgeWAL8DDgTIzNsj4kjgojreEZk5cpPFtwEnAOsA364PSZIkSZIkSdIQGzc5nZn7tBm0c4txE3h7m/kcDxzfovxi4Fnj1UOSJEmSJEmSNDymfUNESZIkSZIkSZImy+S0JEmSJEkDKCL+PiKuiIhfRMRXI+KJEbF1RFwYEUsi4pSIWLvf9ZQkqR2T05IkSZIkDZiI2BJ4B7AgM58FrAnsDXwM+NfMfCpwB3BQ/2opSdLYTE5LkiRJkjSYZgHrRMQsYF1gBfAy4PQ6fDGwZ5/qJknSuExOS5IkSZI0YDJzOfAJ4AZKUvou4BLgzsx8uI62DNiyPzWUJGl8JqclSZIkSRowETEb2APYGtgCWA/YdRLTL4qIiyPi4ltvvbVLtZQkaWwmpyVJkiRJGjzbySJDAAAgAElEQVQvB36Tmbdm5kPAGcALgY1qNx8AWwHLW02cmcdm5oLMXDBnzpze1FiSpFFMTkuSJEmSNHhuAHaMiHUjIoCdgSuBHwCvr+MsBM7sU/0kSRqXyWlJkiRJkgZMZl5IufHhT4HLKfv3xwKHAO+OiCXAJsBxfaukJEnjmDX+KJIkSZIkaabJzMOAw0YVXwvs0IfqSJI0aZ45LUmSJEmSJEnqOZPTkiRJkiRJkqSeMzktSZIkSZIkSeo5k9OSJEmSJEmSpJ4zOS1JkiRJkiRJ6jmT05IkSZIkSZKknjM5LUmSJEmSJEnqOZPTkiRJkiRJkqSeMzktSZIkSZIkSeo5k9OSJEmSJEmSpJ4zOS1JkiRJkiRJ6jmT05IkSZIkSZKknjM5LUmSJEmSJEnqOZPTkiRJkiRJkqSeMzktSZIkSZIkSeq5Wf2ugCRJkiRJkvogojvzzezOfCUNHc+cliRJkiRJkiT1nMlpSZIkSZIkSVLPmZyWJEmSJEmSJPWcyWlJkiRJkiRJUs+ZnJYkSZIkSZIk9ZzJaUmShkBEzI2IH0TElRFxRUS8s5ZvHBHnRsQ19Xl2LY+IODoilkTEZRGxfWNeC+v410TEwkb5n0bE5XWaoyO6dXt3SZIkSdLqwOS0JEnD4WHgPZm5HbAj8PaI2A44FDgvM7cBzqvvAXYDtqmPRcAxUJLZwGHA84EdgMNGEtp1nLc2ptu1B+2SJEmSJA0pk9OSJA2BzFyRmT+tr+8BrgK2BPYAFtfRFgN71td7ACdmcQGwUURsDrwSODczb8/MO4BzgV3rsA0z84LMTODExrwkSZIkSZo0k9OSJA2ZiJgPPBe4ENgsM1fUQTcBm9XXWwJLG5Mtq2VjlS9rUd5q+Ysi4uKIuPjWW2+dVlskSZIkScPL5LQkSUMkItYHvga8KzPvbg6rZzxnt+uQmcdm5oLMXDBnzpxuL06SpNVSRDwtIi5tPO6OiHe1u9+EJEkz0ax+V0CSJHVGRKxFSUyflJln1OKbI2LzzFxRu+a4pZYvB+Y2Jt+qli0HdhpVfn4t36rF+JIkgK906R6x+3b9mKIGVGZeDTwHICLWpMTlr7PyfhNHRcSh9f0hfauoJEljMDktSdIQiIgAjgOuysxPNQadBSwEjqrPZzbKD46Ikyk3P7yrJrDPAT7SOMtqF+D9mXl7PSNrR0p3IQcAn+l6wyRpOrqRMDZZrJlpZ+DXmXl9ROzBygPNiykHmU1OS5JmJJPTkiQNhxcC+wOXR8SltewDlKT0qRFxEHA9sFcddjawO7AE+B1wIEBNQh8JXFTHOyIzb6+v3wacAKwDfLs+JElS/+0NfLW+bne/CUmSZhyT05IkDYHM/BHQ7hTBnVuMn8Db28zreOD4FuUXA8+aRjUlSVKHRcTawGuA948elpkZES1P94+IRcAigHnz5nW1jpIkteMNESVJkiRJGly7AT/NzJvr+5vrfSYYdb+JVXgDY0nSTGByWpIkSZKkwbUPK7v0gJX3m4BV7zchSdKMY3JakiRJkqQ+iogdI+KiiLg3Ih6MiEci4u4JTLce8ArgjEbxUcArIuIa4OX1vSRJM5J9TkuSJEmS1F+fpdzU8DRgAXAAsO14E2XmfcAmo8puo8X9JiRJmok8c1qS9JjowkOSJEnjy8wlwJqZ+UhmfhHYtd91kiSp2zxzWpIkSZKk/vpdRKwNXBoRHwdW4MlkkqTVgMFOkiRJkqT+2h9YEzgYuA+YC7yurzWSJKkHPHNakiRJkqQ+yszr68v7gQ/2sy6SJPXStM6cjoi/j4grIuIXEfHViHhiRGwdERdGxJKIOKVemkREPKG+X1KHz2/M5/21/OqIeOX0miRJkiRJ0swXEafW58sj4rLRj37XT5KkbpvymdMRsSXwDmC7zLy/BtW9gd2Bf83MkyPiP4CDgGPq8x2Z+dSI2Bv4GPDGiNiuTvdMYAvgexGxbWY+Mq2WSZIkSZI0s72zPr+qr7WQJKlPptvn9CxgnYiYBaxLuWnDy4DT6/DFwJ719R71PXX4zhERtfzkzHwgM38DLAF2mGa9JEmSJEma0TJzRX2+vtWj3/WTJKnbpnzmdGYuj4hPADdQ+sX6LnAJcGdmPlxHWwZsWV9vCSyt0z4cEXcBm9TyCxqzbk6ziohYBCwCmDdv3lSrLkmSJElS30XEPUC2G56ZG/awOpIk9dx0uvWYTTnreWvgTuA0YNcO1aulzDwWOBZgwYIFbQO4JEmSJEkzXWZuABARR1KuRP4SEMB+wOZ9rJokST0xnW49Xg78JjNvzcyHgDOAFwIb1W4+ALYCltfXy4G5AHX4k4DbmuUtppEkSZIkadi9JjP/PTPvycy7M/MYyslgkiQNtekkp28AdoyIdWvf0TsDVwI/AF5fx1kInFlfn1XfU4d/PzOzlu8dEU+IiK2BbYCfTKNekiRJkiQNkvsiYr+IWDMi1oiI/YD7+l0pSZK6bcrJ6cy8kHJjw58Cl9d5HQscArw7IpZQ+pQ+rk5yHLBJLX83cGidzxXAqZTE9neAt2fmI1OtlyRJkiRJA2ZfYC/g5vp4Qy2TJGmoTbnPaYDMPAw4bFTxtcAOLcb9PSXAtprPh4EPT6cukiRJkiQNosy8DrvxkCSthqaVnJYkSZIk9dhXojvz3dd7zvdLRMwB3grMp7Gfnplv6VedJEnqBZPTkiRJkiT115nA/wDfA+zmUpK02jA5LUmSJElSf62bmYf0uxKSJPXalG+IKEmSJEmSOuKbEbF7vyshSVKveea0JEmSpN7pRn/J9pWswfdO4AMR8QDwEBBAZuaG/a2WJEndZXJakiRJkqQ+yswN+l0HSZL6weS0JEmSJEl9EBHbjzU8M3/aq7pIktQPJqclSZIkSeqPT44xLIGX9aoikiT1g8lpSZIkSZL6IDNf2u86SJLUT2v0uwKSJEmSJEmSpNWPyWlJkiRJkiRJUs+ZnJYkSZIkSZIk9Zx9TkuSJEmS1GcRsSXwZBr76Zn5w3Gm2Qj4T+BZlBsovgW4GjgFmA9cB+yVmXd0pdKSJE2TyWlJkiRJkvooIj4GvBG4EnikFicwZnIa+DTwncx8fUSsDawLfAA4LzOPiohDgUOBQ7pTc0mSpsfktCRJkiRJ/bUn8LTMfGCiE0TEk4CXAG8GyMwHgQcjYg9gpzraYuB8TE5LkmYo+5yWJEmSJKm/rgXWmuQ0WwO3Al+MiJ9FxH9GxHrAZpm5oo5zE7BZB+spSVJHeea0JEmSJEn99Tvg0og4D3js7OnMfMcY08wCtgf+LjMvjIhPU7rweExmZkRkq4kjYhGwCGDevHnTrL4kSVNjclqSJEmSpP46qz4mYxmwLDMvrO9PpySnb46IzTNzRURsDtzSauLMPBY4FmDBggUtE9iSJHWbyWlJkiRJkvooMxdPYZqbImJpRDwtM68GdqbcUPFKYCFwVH0+s6OVlSSpg0xO91B0ab4e4pYkSZKkwRMRp2bmXhFxOS127TLzT8aZxd8BJ0XE2pR+qw+k3Fvq1Ig4CLge2KvD1ZYkqWNW++S0CWNJkiRJUp+8sz6/aioTZ+alwIIWg3aeco0kSeqh1T45LUmSJElSP2Tmivp8fb/rIklSP6zR7wpIkiRJkiRJklY/JqclSZIkSZIkST1ntx6SJEnS6u4rXbgTy77ehUWaioiYDczNzMv6XRdJkrrNM6clSZIkSeqjiDg/IjaMiI2BnwJfiIhP9btekiR1m8npIRVdekiSJEmSOu5JmXk38JfAiZn5fODlfa6TJEldZ3JakiRJkqT+mhURmwN7Ad/sd2UkSeoVk9OSJEmSJPXXEcA5wJLMvCgi/gi4ps91kiSp67whojQDdKvLFG9DJEmSJM18mXkacFrj/bXA6/pXI2nARZf2stO9bKnTTE5LkiRJktQHEfEZxjinJDPf0cPqSJLUcyanNW2e9StJkiRJU3JxvysgSVI/mZyWNBR6fZCkG8ubCcuSJElS72Tm4ohYE/hYZr633/WRJKnXvCGiJEmSJEl9kpmPAC/sdz0kSeoHz5yWJEmSJKm/Lo2Isyg3RbxvpDAzz+hflSRJ6j7PnJYkaUhExPERcUtE/KJRdnhELI+IS+tj98aw90fEkoi4OiJe2SjftZYtiYhDG+VbR8SFtfyUiFi7d62TJGmoPRG4DXgZ8Or6eFVfayRJUg945rQkScPjBOCzwImjyv81Mz/RLIiI7YC9gWcCWwDfi4ht6+DPAa8AlgEXRcRZmXkl8LE6r5Mj4j+Ag4BjutUYSZJWF5l5YL/rIElSP5icliRpSGTmDyNi/gRH3wM4OTMfAH4TEUuAHeqwJZl5LUBEnAzsERFXUc7m2reOsxg4HJPTkiRNWUS8LzM/HhGfocU9qzPzHX2oliRJPWNyWgMlujTfx20FDjE/Q2m1dHBEHABcDLwnM+8AtgQuaIyzrJYBLB1V/nxgE+DOzHy4xfiriIhFwCKAefPmdaoNkiQNo6vq88V9rYUkSX1iclqSpOF2DHAk5RjSkcAngbd0c4GZeSxwLMCCBQs8diVJUhuZ+Y36vLjfdZEkqR9MTkuSNMQy8+aR1xHxBeCb9e1yYG5j1K1qGW3KbwM2iohZ9ezp5viSJGkaIuIbPP5ixLsoZ1R/PjN/3/taSZLUfWv0uwKSJKl7ImLzxtvXAr+or88C9o6IJ0TE1sA2wE+Ai4BtImLriFibctPEszIzgR8Ar6/TLwTO7EUbJElaDVwL3At8oT7uBu4Btq3vJUkaSp45Lalr7N9a6q2I+CqwE7BpRCwDDgN2iojnUP461wF/DZCZV0TEqcCVwMPA2zPzkTqfg4FzgDWB4zPzirqIQ4CTI+JDwM+A43rUNEmSht2fZebzGu+/EREXZebzIuKKtlNJkjTgTE5LkjQkMnOfFsVtE8iZ+WHgwy3KzwbOblF+LbDDdOooSZJaWj8i5mXmDQARMQ9Yvw57sH/VkiSpu0xOS2141q8kSZKkHnkP8KOI+DVlV2Rr4G0RsR7gzRIlSUPL5LQkSZIkSX2UmWdHxDbA02vR1Y2bIP5bn6olSVLXmZyWJEmSJKnPMvMB4OeTmSYirqPcOPER4OHMXBARGwOnAPMp95vYKzPv6GhlJUnqkDWmM3FEbBQRp0fELyPiqoh4QURsHBHnRsQ19Xl2HTci4uiIWBIRl0XE9o35LKzjXxMRC6fbKEmSJEmSVhMvzcznZOaC+v5Q4LzM3AY4r76XJGlGmlZyGvg08J3MfDrwbOAq2gfC3YBt6mMRcAxAPap7GPB8yk2WDhtJaEuSJEmSNKwi4oX1+QkdnO0erOynejGwZwfnLUlSR005OR0RTwJeAhwHkJkPZuadtA+EewAnZnEBsFFEbA68Ejg3M2+vlxqdC+w61XpJkiRJkjQgjq7PP57i9Al8NyIuiYhFtWyzzFxRX98EbDadCkqS1E3T6XN6a+BW4IsR8WzgEuCdtA+EWwJLG9Mvq2Xtyh+nBttFAPPmzZtG1SVJkiRJ6ruHIuJYYMuIOHr0wMx8xzjTvygzl0fEHwDnRsQvR02fEZGtJnT/WpI0E0ynW49ZwPbAMZn5XOA+RvVllZlJOZLbEZl5bGYuyMwFc+bM6dRsJUmSJEnqh1cB3wd+Tznha/RjTJm5vD7fAnyd0lXmzfUqZerzLW2mdf9aktR30zlzehmwLDMvrO9PpySnb46IzTNzxahAuByY25h+q1q2HNhpVPn506iXJEmSJEkzXmb+Fjg5Iq7KzJ9PZtqIWA9YIzPvqa93AY4AzgIWAkfV5zM7XG1JkjpmymdOZ+b/b+/Ooy2rqzuBfzeTMwJKCDIEbDGGtuOQimI00aAg4tiJGueSkODqaIJtTKLp1a3RuKLpxClGI2mIGEU0URvaidAIsUmcCgcUaARRW2gQIgIOHSOy+497auWC9Yqi3r33vPvq81nrrHvO757ze3vXq3q73n7n/c5VSb5eVT85DD0yyYX510KY3LwQnp7kOTVxWJLrh+U/zkhyZFXtOTwI8chhDAAAAHYE36yq91fV1cP23qra/1au2SfJuVX1+SSfSvLB7v5IJk3pI6rqkiSPGo4BYE1azZ3TSfKbSd5ZVbsluSzJMZk0vN9TVccm+VqSpw7nfijJ0UkuTfK94dx097VV9coknx7Oe0V3X7vKuAAAYLmdUrOf8xkzW3EPmK2/SnJKkqcMx88axo5Y6YLuvizJ/bYw/s1Mbh4DgDVvVc3p7v5ckg1beOtHCuGw/vTzV5jnpCQnrSYWAAAAWFI/1t1/NXX8tqp64WjRAMCCrOaBiAAAAMDq/VNVPauqdh62ZyX55thBAcC8aU4DAADAuH41kyUxr0pyZZInZ1gKEwDWs9WuOQ0AAACsQnd/LckTxo4DABbNndMAAAAAACyc5jQAAAAAAAunOQ0AAAAAwMJZcxoAAABGVFW3S/LLSQ7K1Pfp3f2KsWICgEXQnAYAAIBxnZbk+iTnJfn+yLEAwMJoTgMAAMC49u/uo8YOAgAWzZrTAAAAMK5/rKp/N3YQALBo7pwGAACAcT0syXOr6iuZLOtRSbq7f3rcsABgvjSnAQAAYFyPGTsAgNFUzWfe7vnMy0xZ1gMAAABG1N1fS7JHkscP2x7DGACsa5rTAAAAMKKqOj7JO5P82LC9o6p+c9yoAGD+LOsBAAAA4zo2yYO7+7tJUlWvSfLxJH82alQAMGfunAYAAIBxVZIfTh3/cBgDgHXNndMAAAAwrr9K8smqev9w/KQkJ44YDwAshOY0AAAAjKi7X1tV5yR52DB0THd/dsSQAGAhNKcBAABgBFW1e3ffUFV7JfnqsG1+b6/uvnas2ABgETSnAQAAYBynJHlckvOS9NR4Dcf3HCMoAFgUzWkAAAAYQXc/bng9eOxYAGAMO40dAAAAAOzIquqsbRmDpVY1nw1Yau6cBgAAgBFU1e2T3DHJ3atqz0yW80iS3ZPsN1pgALAgmtMAAAAwjucleWGSe2Sy7vTm5vQNSd40VlAAsCiW9QAAAIARdPcbhvWmX9zd9+zug4ftft29Tc3pqtq5qj5bVR8Yjg+uqk9W1aVV9e6q2m2uSQDAKmhOAwAAwIi6+8+q6r5V9dSqes7mbRsvPz7JRVPHr0nyuu6+V5JvJTl21vECwKxY1gMAALbVKXN48NIzevZzAkulql6W5BFJDk3yoSSPSXJukrffynX7J3lsklcleVFVVZLDkzxjOOXkJC9P8pZ5xA0Aq+XOaQAAABjXk5M8MslV3X1Mkvslues2XPf6JL+b5Kbh+G5JruvuG4fjy+PBigCsYZrTAAAAMK7/1903JbmxqnZPcnWSA7Z2QVU9LsnV3X3e9nzAqjquqjZV1aZrrrlme6YAgFWzrAcAAACMa1NV7ZHkL5Ocl+Q7ST5+K9c8NMkTquroJLdPsnuSNyTZo6p2Ge6e3j/JFVu6uLtPSHJCkmzYsMH6QgCMwp3TAAAAMKLu/o3uvq67/yLJEUk2Dst7bO2al3b3/t19UJKnJflodz8zydmZLBOSJBuTnDbH0AFgVTSnAQAAYERVddbm/e7+anefPz12G/1eJg9HvDSTNahPnEWMwAqq5rPBDsKyHgAAADCCqrp9kjsmuXtV7Zlkc0dq99yGBxl29zlJzhn2L0vyoJkGCgBzojkNAAAA43hekhcmuUcma01vbk7fkORNYwUFAIuiOQ0AAAAj6O43VNWbkvx+d79y7HgAYNGsOQ0AAAAj6e4fJvmlseNYStb6BVh6mtMAAAAwrrOq6perdEYB2LFoTgMAAMC4npfkb5L8S1XdUFXfrqobxg4KAObNmtMAAAAwou6+y9gxcCvmdVN793zmBVgS7pwGgHWiqk6qqqur6otTY3tV1ZlVdcnwuucwXlX1xqq6tKrOr6oHTl2zcTj/kqraODX+M1X1heGaN/rVYwCYnap6QlX9ybA9bux4AGARNKcBYP14W5KjbjH2kiRndfchSc4ajpPkMUkOGbbjkrwlmTSzk7wsyYOTPCjJyzY3tIdzfn3qult+LABgO1TVq5Mcn+TCYTu+qv5o3KgAYP40pwFgnejujyW59hbDT0xy8rB/cpInTY2/vSc+kWSPqto3yaOTnNnd13b3t5KcmeSo4b3du/sT3d1J3j41FwCwOkcnOaK7T+rukzL5AfBjR46JsVTNZwNYgzSnAWB926e7rxz2r0qyz7C/X5KvT513+TC2tfHLtzD+I6rquKraVFWbrrnmmtVnAAA7hj2m9u86WhQAsEAeiAgAO4ju7qqa+1N3uvuEJCckyYYNGzzlBwBu3R8l+WxVnZ2kkvxC/nUpLgBYtzSnAWB9+0ZV7dvdVw5Lc1w9jF+R5ICp8/Yfxq5I8ohbjJ8zjO+/hfMBgFXq7ndV1TlJfjZJJ/m97r5q3KgAYP4s6wEA69vpSTYO+xuTnDY1/pyaOCzJ9cPyH2ckObKq9hwehHhkkjOG926oqsOqqpI8Z2ouAGD1HpLJD4gfMewDwLrnzmkAWCeq6l2ZfEN796q6PMnLkrw6yXuq6tgkX0vy1OH0D2Xy8KVLk3wvyTFJ0t3XVtUrk3x6OO8V3b35IYu/keRtSe6Q5MPDBgCsUlW9Ocm9krxrGHpeVT2qu58/YlgAMHea0wCwTnT301d465FbOLeTbPEb3u4+KclJWxjflOS+q4kRANiiw5P81FCfU1UnJ7lg3JAAYP5WvaxHVe1cVZ+tqg8MxwdX1Ser6tKqendV7TaM3244vnR4/6CpOV46jF9cVY9ebUwAAACwRC5NcuDU8QHDGACsa7NYc/r4JBdNHb8myeu6+15JvpXk2GH82CTfGsZfN5yXqjo0ydOS/NskRyV5c1XtPIO4AAAAYBncJclFVXXO8GDEC5PsXlWnV9Xp44YGAPOzqmU9qmr/JI9N8qokLxoekHR4kmcMp5yc5OVJ3pLkicN+kvxtkjcN5z8xyand/f0kX6mqS5M8KMnHVxMbAAAALIn/MnYAADCG1a45/fokv5vJT3mT5G5JruvuG4fjy5PsN+zvl+TrSdLdN1bV9cP5+yX5xNSc09fcTFUdl+S4JDnwwAO3dAoAAAAsle7++ySpqt0z9X361EOJAWBd2u5lParqcUmu7u7zZhjPVnX3Cd29obs37L333ov6sAAAADA3VXVcVV2V5Pwkm5KcN7wCwLq2mjunH5rkCVV1dJLbJ9k9yRuS7FFVuwx3T++f5Irh/CsyeajD5VW1S5K7Jvnm1Phm09cAAMDWnVKzn/MZPfs5AVb2O0nu293/NHYgALBI233ndHe/tLv37+6DMnmg4Ue7+5lJzk7y5OG0jUlOG/ZPH44zvP/R7u5h/GlVdbuqOjjJIUk+tb1xAQAAwJL5cpLvjR0EACzaatec3pLfS3JqVf1hks8mOXEYPzHJXw8PPLw2k4Z2uvuCqnpPJk8jvjHJ87v7h3OICwAAANailyb5x6r6ZJLvbx7s7t8aLySAdajm8Bt3SdJ+6257zaQ53d3nJDln2L8syYO2cM4/J3nKCte/KsmrZhELAAAALJm3Jvloki8kuWnkWABgYeZx5zQAAACw7Xbt7heNHQQALJrmNAAAs+chhQC3xYer6rgk/yM3X9bj2q1dVFW3T/KxJLfL5Pv7v+3ulw3Pczo1yd2SnJfk2d39L/MKHlgQS1KwDmlOAwAAwLiePry+dGqsk9zzVq77fpLDu/s7VbVrknOr6sNJXpTkdd19alX9RZJjk7xl1kEDwGppTgMAAMCIuvvg7byuk3xnONx12DrJ4UmeMYyfnOTl0ZwGYA3SnAYAAIARVNUvbe397n7fNsyxcyZLd9wryZ8n+XKS67r7xuGUy5Pst8pQAWAuNKcBAHYU1oEGWGsev5X3OsmtNqe7+4dJ7l9VeyR5f5L7bMsHHta4Pi5JDjzwwG25BABmTnMaAAAAxvG57n5DVT2su89dzUTdfV1VnZ3kIUn2qKpdhrun909yxRbOPyHJCUmyYcMGP2kEYBQ7jR0AAAAA7KCOGV7fuD0XV9Xewx3Tqao7JDkiyUVJzk7y5OG0jUlOW2WcADAX7pwGABiTpTYAdmQXVdUlSe5RVedPjVcmzzv86Vu5ft8kJw/rTu+U5D3d/YGqujDJqVX1h0k+m+TEeQQPAKulOQ0AAAAj6O6nV9WPJzkjyRO24/rzkzxgC+OXJXnQ6iMEgPnSnAYAAICRdPdVSe5XVbslufcwfHF3/2DEsABgITSnAQAAYERV9fAkb0/y1UyW9DigqjZ298dGDQwA5kxzGgAAAMb12iRHdvfFSVJV907yriQ/M2pUADBnO40dAAAAAOzgdt3cmE6S7v5Skl1HjAcAFsKd0wAAADCuTVX135K8Yzh+ZpJNI8YDAAuhOQ0AAADj+g9Jnp/kt4bj/5XkzeOFAwCLoTkNAAAAI+ru72ey7vRrx44FABZJcxoAWA6n1OznfEbPfk4AAAC2iQciAgAAAACwcJrTAAAAsEZU1U5VtfvYcQDAImhOAwAAwIiq6pSq2r2q7pTki0kurKrfGTsuAJg3zWkAAAAY16HdfUOSJyX5cJKDkzx73JAAYP48EBEA4JY8fBGAxdq1qnbNpDn9pu7+QVUpHACse+6cBgAAgHG9NclXk9wpyceq6ieS3DBqRACwAO6cBgAAgBF19xuTvHFq6GtV9YtjxQMAi+LOaQAAABhRVe1TVSdW1YeH40OTbBw5LACYO81pAAAAGNfbkpyR5B7D8ZeSvHC0aABgQTSnAQAAYFx37+73JLkpSbr7xiQ/HDckAJg/zWkAAAAY13er6m5JOkmq6rAk148bEgDMnwciAgAAwLhelOT0JP+mqv4hyd5JnjxuSAAwf5rTAAAAMKLu/kxVPTzJTyapJBd39w9GDgsA5s6yHgAAADCiqnpKkjt09wVJnpTk3VX1wJHDAoC505wGAACAcf3n7v52VT0sySOTnJjkLSPHBABzpzkNAAAA4/rh8Pa3uQ0AABFRSURBVPrYJH/Z3R9MstuI8QDAQmhOAwAAwLiuqKq3JvmVJB+qqtvF9+sA7AAUOwAAABjXU5OckeTR3X1dkr2S/M7WLqiqA6rq7Kq6sKouqKrjh/G9qurMqrpkeN1z/uEDwPbRnAYAAIARdff3uvt9Sa6vqgOT7Jrkf9/KZTcm+e3uPjTJYUmeX1WHJnlJkrO6+5AkZw3HALAmaU4DAADAiKrqCVV1SZKvJPn74fXDW7umu6/s7s8M+99OclGS/ZI8McnJw2knJ3nSvOIGgNXSnAYAAIBxvTKTu5+/1N0HJ3lUkk9s68VVdVCSByT5ZJJ9uvvK4a2rkuwz00gBYIY0pwEAAGBcP+jubybZqap26u6zk2zYlgur6s5J3pvkhd19w/R73d1JeoXrjquqTVW16Zprrlll+ACwfTSnAQAAYFzXDU3mjyV5Z1W9Icl3b+2iqto1k8b0O4c1q5PkG1W17/D+vkmu3tK13X1Cd2/o7g177733TJIAgNtKcxoAAADG9cQk30vyH5N8JMmXkzx+axdUVSU5MclF3f3aqbdOT7Jx2N+Y5LSZRwsAM6I5DQAAACOoqntV1UO7+7vdfVN339jdJyf5TJI9buXyhyZ5dpLDq+pzw3Z0klcnOWJ4wOKjhmMAWJM0pwFgB1BVX62qLwzfuG4axvaqqjOr6pLhdc9hvKrqjVV1aVWdX1UPnJpn43D+JVW1caWPBwBsk9cnuWEL49cP762ou8/t7urun+7u+w/bh7r7m939yO4+pLsf1d3XziVyAJgBzWkA2HH84vCN6+YHLL0kyVndfUiSs4bjJHlMkkOG7bgkb0kmzewkL0vy4CQPSvKyzQ1tAGC77NPdX7jl4DB20OLDAYDF0pwGgB3XE5OcPOyfnORJU+Nv74lPJNljeKDSo5Oc2d3Xdve3kpyZ5KhFBw0A68jWlu64w8KiAICRaE4DwI6hk/xdVZ1XVccNY/t095XD/lVJ9hn290vy9alrLx/GVhq/mao6rqo2VdWma665ZpY5AMB6s6mqfv2Wg1X1a0nOGyEeAFio7W5OV9UBVXV2VV1YVRdU1fHDuPUrAWDteVh3PzCTJTueX1W/MP1md3cmDexV6+4TuntDd2/Ye++9ZzElAKxXL0xyTFWdU1V/Omx/n+TYJMePHBsAzN1q7py+Mclvd/ehSQ7L5BvdQ2P9SgBYc7r7iuH16iTvz6TmfmNYriPD69XD6VckOWDq8v2HsZXGAYDt0N3f6O6fS/IHSb46bH/Q3Q/p7qvGjA0AFmG7m9PdfWV3f2bY/3aSizL51V7rVwLAGlJVd6qqu2zeT3Jkki8mOT3J5t9Y2pjktGH/9CTPGX7r6bAk1w/Lf5yR5Miq2nP4QfKRwxgAsArdfXZ3/9mwfXTseABgUXaZxSRVdVCSByT5ZOa0fuXwcY7L5K7rHHjggbMIHQB2BPskeX9VJZPaf0p3f6SqPp3kPVV1bJKvJXnqcP6Hkhyd5NIk30tyTJJ097VV9coknx7Oe0V3X7u4NAAAAFhPVt2crqo7J3lvkhd29w3DN75JJutXVtVM1q8c5jshyQlJsmHDhpnNCwDrWXdfluR+Wxj/ZpJHbmG8kzx/hblOSnLSrGMEAABgx7OaNadTVbtm0ph+Z3e/bxi2fiUAAAAAAFu13c3pmtwifWKSi7r7tVNvWb8SAAAAAICtWs2yHg9N8uwkX6iqzw1jv5/k1bF+JQAAAAAAW7HdzenuPjdJrfC29SsBAAAAAFjRqtacBgAAAACA7aE5DQAAAADAwmlOAwAAAACwcJrTAAAAAAAsnOY0AAAAAAALpzkNAAAAAMDCaU4DAAAAALBwmtMAAAAAACyc5jQAAAAAAAunOQ0AAAAAwMJpTgMAAAAAsHCa0wAAAAAALJzmNAAAAAAAC6c5DQAAAEuoqk6qqqur6otTY3tV1ZlVdcnwuueYMQLA1mhOAwAAwHJ6W5KjbjH2kiRndfchSc4ajgFgTdKcBgAAgCXU3R9Lcu0thp+Y5ORh/+QkT1poUABwG2hOAwAAwPqxT3dfOexflWSfMYMBgK3RnAYAAIB1qLs7SW/pvao6rqo2VdWma665ZsGRAcCE5jQAAACsH9+oqn2TZHi9eksndfcJ3b2huzfsvffeCw0QADbTnAYAAID14/QkG4f9jUlOGzEWANiqXcYOAAAAALjtqupdSR6R5O5VdXmSlyV5dZL3VNWxSb6W5KnjRQiwA6uaz7y9xdWalpbmNAAAACyh7n76Cm89cqGBAMB2sqwHAAAAAAALpzkNAAAAAMDCWdYDAAAAAPhX1ktmQdw5DQAAAADAwmlOAwAAAACwcJrTAAAAAAAsnOY0AAAAAAALpzkNAAAAAMDCaU4DAAAAALBwmtMAAAAAACyc5jQAAAAAAAunOQ0AAAAAwMLtMnYAAAAAAABsp6r5zNs9n3mnuHMaAAAAAICF05wGAAAAAGDhNKcBAAAAAFg4zWkAAAAAABZOcxoAAAAAgIXTnAYAAAAAYOE0pwEAAAAAWDjNaQAAAAAAFk5zGgAAAACAhdOcBgAAAABg4TSnAQAAAABYOM1pAAAAAAAWTnMaAAAAAICFWzPN6ao6qqourqpLq+olY8cDAPwo9RoAloOaDcAyWBPN6araOcmfJ3lMkkOTPL2qDh03KgBgmnoNAMtBzQZgWayJ5nSSByW5tLsv6+5/SXJqkieOHBMAcHPqNQAsBzUbgKVQ3T12DKmqJyc5qrt/bTh+dpIHd/cLbnHecUmOGw5/MsnFSe6e5J8WGO6iyGu5yGu5yGu5jJXXT3T33iN83DVLvd4ieS2f9ZqbvJaLvGZHvd6CbanZK9TrxN/PZSOv5SKv5SKv2dpizd5lhEC2W3efkOSE6bGq2tTdG0YKaW7ktVzktVzktVzWa17rmXq9/NZrXsn6zU1ey0VerAVbqtfJ+v08ymu5yGu5yGu5rLW81sqyHlckOWDqeP9hDABYO9RrAFgOajYAS2GtNKc/neSQqjq4qnZL8rQkp48cEwBwc+o1ACwHNRuApbAmlvXo7hur6gVJzkiyc5KTuvuCbbz8R34NaZ2Q13KR13KR13JZr3ktHfV6i+S1fNZrbvJaLvJirtTsLZLXcpHXcpHXcllTea2JByICAAAAALBjWSvLegAAAAAAsAPRnAYAAAAAYOHWXHO6qg6oqrOr6sKquqCqjh/G96qqM6vqkuF1z2G8quqNVXVpVZ1fVQ+cmmvjcP4lVbVxrJyGWGaSV1Xdv6o+PsxxflX9ynrIa2q+3avq8qp60xj5TMUxy7+HB1bV31XVRcN8B42T1czz+uNhjouGc2qJ8rrP8O/o+1X14lvMdVRVXTzk/JIx8pmKZSZ5rTTPmGb5ORve37mqPltVH1h0LjuyGX9NUbOXJK+p+dTsOZpxXmr2nM0qr5XmGcssP1/D++r1CGb89US9XpK8puZTr+doxnmp13M2q7xWmmcss/x8De8vtl5395rakuyb5IHD/l2SfCnJoUn+OMlLhvGXJHnNsH90kg8nqSSHJfnkML5XksuG1z2H/T3XQV73TnLIsH+PJFcm2WPZ85qa7w1JTknypvXw93B475wkRwz7d05yx2XPK8nPJfmHTB6usnOSjyd5xBLl9WNJfjbJq5K8eGqenZN8Ock9k+yW5PNJDl0HeW1xnrHymmVuU/O9aPja8YEx89rRthl+TVGzlyivqfnU7CXIK2r2suW1pmr2rPKamk+9Xo7Po3qtXq/pvKJer8W81Ot1kNfUfAut16P8od3GP+DTkhyR5OIk+079oV887L81ydOnzr94eP/pSd46NX6z88betjevLczz+QyFdC1sq8kryc8kOTXJczNy4Zzh38NDk5w7dvxzyOshSc5Lcockd0yyKclPjZ3PtuY1dd7Lc/MC85AkZ0wdvzTJS8fOZ7V5rTTP2PnMKrck+yc5K8nh8c3umv48buVripq9ZHlFzV6mvNTsJcprpXnGzmcWeUW9XjPbKr6eqNdLllfU62XKS71eorxWmmfsfGaRV0ao12tuWY9pw69mPCDJJ5Ps091XDm9dlWSfYX+/JF+fuuzyYWyl8dGtMq/peR6UyU+evjzHcLfZavKqqp2S/GmSH/l1grGt8vN17yTXVdX7hl+J+K9VtfNCAr8Vq8mruz+e5OxM7iq4MpNic9ECwr5V25jXSpb968ZtnWdNmEFur0/yu0lumkd8bBs1W81eC9RsNXvGIW6X9Vqz1ev1Qb1Wr9cC9Vq9nnGI20W9XtHC6/WabU5X1Z2TvDfJC7v7hun3etLK71ECW6VZ5VVV+yb56yTHdPfo/8GbQV6/keRD3X35nELcLjPIa5ckP5/Jfwh+NpNfZXnu7CO9bVabV1XdK8lPZfITtf2SHF5VPz+ncLeZrxvbP89YZvB38XFJru7u8+YXJbfGv71bnUfNXgA1e8Xr1ewFWq81W71eH/y7u9V51OsFUK9XvF69XiD1esXrR6nXa7I5XVW7ZvKH+c7uft8w/I2hWGwuGlcP41ckOWDq8v2HsZXGRzOjvFJVuyf5YJL/1N2fWETsWzOjvB6S5AVV9dUkf5LkOVX16gWEv6IZ5XV5ks9192XdfWOS/57kZg+oWLQZ5fXvk3yiu7/T3d/JZM2shywi/pXcxrxWsuxfN27rPKOaUW4PTfKE4WvHqZn8J+4dcwqZLVCzk6jZavacqNlbtexfN27rPKNRr9cH9TqJeq1ez4l6vVXL/nXjts4zmmWu12uuOV1VleTEJBd192un3jo9ycZhf2Mm66dsHn9OTRyW5PrhlvUzkhxZVXvW5GmURw5jo5hVXlW1W5L3J3l7d//tgsJf0azy6u5ndveB3X1QJj8BfXt3j/YU1xn+Pfx0kj2qau/hvMOTXDj3BFYww7z+T5KHV9UuwxfAhycZ7VeOtiOvlXw6ySFVdfDwb+1pwxyjmFVeW5lnNLPKrbtf2t37D187npbko939rDmEzBao2Wp21Oy5UbPV7LVQs9Xr9UG9Vq+jXs+Neq1eq9cz0Gtgoe7pLcnDMrnN/Pwknxu2o5PcLZMFuS9J8j+T7DWcX0n+PJM1ob6QZMPUXL+a5NJhO2Y95JXkWUl+MDXH55Lcf9nzusWcz834TxKe5d/DI4Z5vpDkbUl2W/a8Mnni7lszKZYXJnntkn2+fjyTn7jfkOS6YX/34b2jM3my7ZczuXNi6fNaaZ71kNst5nxEPGBprX8e1Ww1e03nFTV7LealZo9Ys2f5+Zqa8xFRr9f651G9Vq/XdF5Rr9diXuq1er3dWw0fEAAAAAAAFmbNLesBAAAAAMD6pzkNAAAAAMDCaU4DAAAAALBwmtMAAAAAACyc5jQAAAAAAAunOQ0AAADADq8mzq2qx0yNPaWqPjJmXLCeVXePHQMAAAAAjK6q7pvkb5I8IMkuST6b5Kju/vJ2zLVLd9844xBhXdGcBgAAAIBBVf1xku8mudPw+hNJ7ptk1yQv7+7TquqgJH89nJMkL+juf6yqRyR5ZZJvJblPd997sdHDctGcBgAAAIBBVd0pyWeS/EuSDyS5oLvfUVV7JPlUJndVd5Kbuvufq+qQJO/q7g1Dc/qDSe7b3V8ZJwNYHruMHQAAAAAArBXd/d2qeneS7yR5apLHV9WLh7dvn+TAJP83yZuq6v5Jfphk+g7pT2lMw7bRnAYAAACAm7tp2CrJL3f3xdNvVtXLk3wjyf2S7JTkn6fe/u6CYoSlt9PYAQAAAADAGnVGkt+sqkqSqnrAMH7XJFd2901Jnp1k55Hig6WmOQ0AAAAAW/bKTB6EeH5VXTAcJ8mbk2ysqs8nuU/cLQ3bxQMRAQAAAABYOHdOAwAAAACwcJrTAAAAAAAsnOY0AAAAAAALpzkNAAAAAMDCaU4DAAAAALBwmtMAAAAAACyc5jQAAAAAAAv3/wEw+19K3ooJ0wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1800x864 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### There are two things to be concluded from the above bar chart -\n",
        "\n",
        "####      1) The cases have incraesed over the years.\n",
        "\n",
        "####       2) 2014 has been the year, where violance against women was reported the maximum, under each of the cases like, rape, domestic violence etc, which can also be proved below, which can also be cross examined with the code below."
      ],
      "metadata": {
        "id": "5Ycz1tiGvqIF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "count_df = crimes_df.groupby('Year')[['STATE/UT']].count()\n",
        "count_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "Lsz1XOKvvll6",
        "outputId": "a53f99cc-874e-427c-e4c2-759a20dff75c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7ba01f41-3a0d-48e8-bdcd-667eb1aa767d\">\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>837</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ba01f41-3a0d-48e8-bdcd-667eb1aa767d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7ba01f41-3a0d-48e8-bdcd-667eb1aa767d 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-7ba01f41-3a0d-48e8-bdcd-667eb1aa767d');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      STATE/UT\n",
              "Year          \n",
              "2001       716\n",
              "2002       719\n",
              "2003       728\n",
              "2004       729\n",
              "2005       733\n",
              "2006       740\n",
              "2007       743\n",
              "2008       761\n",
              "2009       767\n",
              "2010       779\n",
              "2011       791\n",
              "2012       811\n",
              "2013       823\n",
              "2014       837"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Let us use seaborn to take help of heatmap to represent how more cases from each state started coming up more frequently with each passing year."
      ],
      "metadata": {
        "id": "ysZH4gywvvqe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.title(\"Total cases reported from every state Year wise\")\n",
        "sns.heatmap(count_df);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "_m66578-vsLN",
        "outputId": "73c84d28-c696-4653-93cc-df6b60bb9cce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### This heatmap shows how more number of state's name started appearing on the dataset, for reporting crimes and sadly the crime only increased with the passing year."
      ],
      "metadata": {
        "id": "rKJ49dv_vygv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### It gives us the conclusion that overall(cases under each category), from every state, has increased with time and not decreased."
      ],
      "metadata": {
        "id": "5pIYzxRgv3xs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Asking and Answering Questions"
      ],
      "metadata": {
        "id": "U9H3iP5kv88U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### As part of this data analysis, it is very crucial to raise question and find answer to them. Here we will try to find out some of the most essential questions, that will help us in drawing a major conclusion from our dataset.\n",
        "\n",
        "##### Note - We will be focusing on four major categories for the rest of the analysis, excluding \"Assault_for_her_modesty\" and \"Insult_to_modesty_of_Women\"."
      ],
      "metadata": {
        "id": "l1kcsNltv-wP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "crimes_df = crimes_df.drop(['Hurting_of_womens_modesty', 'Insult_to_womens_modesty'], axis=1)"
      ],
      "metadata": {
        "id": "HaUk4xrLvxCb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Q . Create a dataframe containing 10 highest reported rape cases in India, in the span of year 2001-2014."
      ],
      "metadata": {
        "id": "Uo7aMZmYwCTU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_rape_cases = crimes_df.sort_values('Rape', ascending = False).head(10)\n",
        "max_rape_cases"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "DzV5afIhwA5C",
        "outputId": "0f32bdaa-7574-40d9-9409-10bf7612efdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2942315e-a189-4496-a81b-26e94d4d8f59\">\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>Year</th>\n",
              "      <th>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10244</th>\n",
              "      <td>MADHYA PRADESH</td>\n",
              "      <td>2014</td>\n",
              "      <td>5076</td>\n",
              "      <td>5688</td>\n",
              "      <td>733</td>\n",
              "      <td>6451</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9426</th>\n",
              "      <td>MADHYA PRADESH</td>\n",
              "      <td>2013</td>\n",
              "      <td>4335</td>\n",
              "      <td>2873</td>\n",
              "      <td>776</td>\n",
              "      <td>4988</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10445</th>\n",
              "      <td>RAJASTHAN</td>\n",
              "      <td>2014</td>\n",
              "      <td>3759</td>\n",
              "      <td>4421</td>\n",
              "      <td>408</td>\n",
              "      <td>15905</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10595</th>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>2014</td>\n",
              "      <td>3467</td>\n",
              "      <td>10626</td>\n",
              "      <td>2469</td>\n",
              "      <td>10471</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10291</th>\n",
              "      <td>MAHARASHTRA</td>\n",
              "      <td>2014</td>\n",
              "      <td>3438</td>\n",
              "      <td>2457</td>\n",
              "      <td>279</td>\n",
              "      <td>7696</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8611</th>\n",
              "      <td>MADHYA PRADESH</td>\n",
              "      <td>2012</td>\n",
              "      <td>3425</td>\n",
              "      <td>1127</td>\n",
              "      <td>743</td>\n",
              "      <td>3988</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7810</th>\n",
              "      <td>MADHYA PRADESH</td>\n",
              "      <td>2011</td>\n",
              "      <td>3406</td>\n",
              "      <td>1088</td>\n",
              "      <td>811</td>\n",
              "      <td>3732</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9628</th>\n",
              "      <td>RAJASTHAN</td>\n",
              "      <td>2013</td>\n",
              "      <td>3285</td>\n",
              "      <td>4047</td>\n",
              "      <td>453</td>\n",
              "      <td>15094</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7025</th>\n",
              "      <td>MADHYA PRADESH</td>\n",
              "      <td>2010</td>\n",
              "      <td>3135</td>\n",
              "      <td>1030</td>\n",
              "      <td>892</td>\n",
              "      <td>3756</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9472</th>\n",
              "      <td>MAHARASHTRA</td>\n",
              "      <td>2013</td>\n",
              "      <td>3063</td>\n",
              "      <td>1874</td>\n",
              "      <td>320</td>\n",
              "      <td>8542</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2942315e-a189-4496-a81b-26e94d4d8f59')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2942315e-a189-4496-a81b-26e94d4d8f59 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-2942315e-a189-4496-a81b-26e94d4d8f59');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             STATE/UT  Year  ...  Domestic_Cruelty  Importation_of_Girls\n",
              "10244  MADHYA PRADESH  2014  ...              6451                     0\n",
              "9426   MADHYA PRADESH  2013  ...              4988                     7\n",
              "10445       RAJASTHAN  2014  ...             15905                     0\n",
              "10595   UTTAR PRADESH  2014  ...             10471                     0\n",
              "10291     MAHARASHTRA  2014  ...              7696                     1\n",
              "8611   MADHYA PRADESH  2012  ...              3988                     6\n",
              "7810   MADHYA PRADESH  2011  ...              3732                    45\n",
              "9628        RAJASTHAN  2013  ...             15094                     1\n",
              "7025   MADHYA PRADESH  2010  ...              3756                     5\n",
              "9472      MAHARASHTRA  2013  ...              8542                     0\n",
              "\n",
              "[10 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### We see from the analysis, the top states that reported the maximum number of rape cases, along with the year, in which they occured.\n",
        "#### Where, Madhya Pradesh reported the maximum rape cases in the year 2014."
      ],
      "metadata": {
        "id": "cp2tFri1wH14"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q. Create a dataframe containing 10 highest reported deaths caused by Dowry cases in India, in the span of year 2001-2014."
      ],
      "metadata": {
        "id": "pHEBCJA7wI1K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_dowry_death_cases = crimes_df.sort_values('Dowry_Deaths', ascending = False).head(10)\n",
        "max_dowry_death_cases"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "o03pODtnwFFm",
        "outputId": "7101d48f-9a68-4008-94ce-5d45c46584cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-85b80e88-2eae-48fd-9327-fa7ab63591ae\">\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>Year</th>\n",
              "      <th>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10595</th>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>2014</td>\n",
              "      <td>3467</td>\n",
              "      <td>10626</td>\n",
              "      <td>2469</td>\n",
              "      <td>10471</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9760</th>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>2013</td>\n",
              "      <td>3050</td>\n",
              "      <td>9737</td>\n",
              "      <td>2335</td>\n",
              "      <td>8781</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8132</th>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>2011</td>\n",
              "      <td>2042</td>\n",
              "      <td>7525</td>\n",
              "      <td>2322</td>\n",
              "      <td>7121</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8938</th>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>2012</td>\n",
              "      <td>1963</td>\n",
              "      <td>7910</td>\n",
              "      <td>2244</td>\n",
              "      <td>7661</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5796</th>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>2008</td>\n",
              "      <td>1871</td>\n",
              "      <td>4439</td>\n",
              "      <td>2237</td>\n",
              "      <td>8312</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6563</th>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>2009</td>\n",
              "      <td>1759</td>\n",
              "      <td>5078</td>\n",
              "      <td>2232</td>\n",
              "      <td>8566</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7342</th>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>2010</td>\n",
              "      <td>1563</td>\n",
              "      <td>5468</td>\n",
              "      <td>2217</td>\n",
              "      <td>7978</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>650</th>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>2001</td>\n",
              "      <td>1958</td>\n",
              "      <td>2879</td>\n",
              "      <td>2211</td>\n",
              "      <td>7365</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5040</th>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>2007</td>\n",
              "      <td>1648</td>\n",
              "      <td>3363</td>\n",
              "      <td>2076</td>\n",
              "      <td>7650</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1366</th>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>2002</td>\n",
              "      <td>1415</td>\n",
              "      <td>2298</td>\n",
              "      <td>1893</td>\n",
              "      <td>5679</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85b80e88-2eae-48fd-9327-fa7ab63591ae')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-85b80e88-2eae-48fd-9327-fa7ab63591ae 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-85b80e88-2eae-48fd-9327-fa7ab63591ae');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            STATE/UT  Year  ...  Domestic_Cruelty  Importation_of_Girls\n",
              "10595  UTTAR PRADESH  2014  ...             10471                     0\n",
              "9760   UTTAR PRADESH  2013  ...              8781                     0\n",
              "8132   UTTAR PRADESH  2011  ...              7121                     0\n",
              "8938   UTTAR PRADESH  2012  ...              7661                     0\n",
              "5796   UTTAR PRADESH  2008  ...              8312                     0\n",
              "6563   UTTAR PRADESH  2009  ...              8566                     0\n",
              "7342   UTTAR PRADESH  2010  ...              7978                     0\n",
              "650    UTTAR PRADESH  2001  ...              7365                     0\n",
              "5040   UTTAR PRADESH  2007  ...              7650                     0\n",
              "1366   UTTAR PRADESH  2002  ...              5679                     0\n",
              "\n",
              "[10 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### From our analysis, we observe that the highest reported dowry death was in Uttar Pradesh in 2014, with number of reports being 2469.\n",
        "\n",
        "##### One more here to be observed as well is that, Uttar Pradesh is the ONLY state that appears in this list."
      ],
      "metadata": {
        "id": "wD0OXpTywQ1R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q. Create a dataframe containing 10 highest reported Domestic Violance cases in India, in the span of year 2001-2014."
      ],
      "metadata": {
        "id": "drYFElPgwRYR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_domestic_violance_cases = crimes_df.sort_values('Domestic_Cruelty', ascending = False).head(10)\n",
        "max_domestic_violance_cases"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "KcKSPq9qwLys",
        "outputId": "95e2bc0f-dd8b-43d6-ac65-7579902a4e3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-652373d8-6ecd-454c-b594-429d259b8a8d\">\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>Year</th>\n",
              "      <th>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10640</th>\n",
              "      <td>WEST BENGAL</td>\n",
              "      <td>2014</td>\n",
              "      <td>1466</td>\n",
              "      <td>4976</td>\n",
              "      <td>501</td>\n",
              "      <td>23278</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8982</th>\n",
              "      <td>WEST BENGAL</td>\n",
              "      <td>2012</td>\n",
              "      <td>2046</td>\n",
              "      <td>4168</td>\n",
              "      <td>593</td>\n",
              "      <td>19865</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8172</th>\n",
              "      <td>WEST BENGAL</td>\n",
              "      <td>2011</td>\n",
              "      <td>2363</td>\n",
              "      <td>3711</td>\n",
              "      <td>510</td>\n",
              "      <td>19772</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9804</th>\n",
              "      <td>WEST BENGAL</td>\n",
              "      <td>2013</td>\n",
              "      <td>1685</td>\n",
              "      <td>3830</td>\n",
              "      <td>481</td>\n",
              "      <td>18116</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7381</th>\n",
              "      <td>WEST BENGAL</td>\n",
              "      <td>2010</td>\n",
              "      <td>2311</td>\n",
              "      <td>2764</td>\n",
              "      <td>507</td>\n",
              "      <td>17796</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6602</th>\n",
              "      <td>WEST BENGAL</td>\n",
              "      <td>2009</td>\n",
              "      <td>2336</td>\n",
              "      <td>2187</td>\n",
              "      <td>506</td>\n",
              "      <td>16112</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10445</th>\n",
              "      <td>RAJASTHAN</td>\n",
              "      <td>2014</td>\n",
              "      <td>3759</td>\n",
              "      <td>4421</td>\n",
              "      <td>408</td>\n",
              "      <td>15905</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9628</th>\n",
              "      <td>RAJASTHAN</td>\n",
              "      <td>2013</td>\n",
              "      <td>3285</td>\n",
              "      <td>4047</td>\n",
              "      <td>453</td>\n",
              "      <td>15094</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9050</th>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>2013</td>\n",
              "      <td>1635</td>\n",
              "      <td>1595</td>\n",
              "      <td>492</td>\n",
              "      <td>15084</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5835</th>\n",
              "      <td>WEST BENGAL</td>\n",
              "      <td>2008</td>\n",
              "      <td>2263</td>\n",
              "      <td>1907</td>\n",
              "      <td>451</td>\n",
              "      <td>13663</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-652373d8-6ecd-454c-b594-429d259b8a8d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-652373d8-6ecd-454c-b594-429d259b8a8d 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-652373d8-6ecd-454c-b594-429d259b8a8d');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             STATE/UT  Year  ...  Domestic_Cruelty  Importation_of_Girls\n",
              "10640     WEST BENGAL  2014  ...             23278                     4\n",
              "8982      WEST BENGAL  2012  ...             19865                    12\n",
              "8172      WEST BENGAL  2011  ...             19772                     0\n",
              "9804      WEST BENGAL  2013  ...             18116                     9\n",
              "7381      WEST BENGAL  2010  ...             17796                     8\n",
              "6602      WEST BENGAL  2009  ...             16112                     5\n",
              "10445       RAJASTHAN  2014  ...             15905                     0\n",
              "9628        RAJASTHAN  2013  ...             15094                     1\n",
              "9050   ANDHRA PRADESH  2013  ...             15084                     0\n",
              "5835      WEST BENGAL  2008  ...             13663                     5\n",
              "\n",
              "[10 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### According to our analysis, we see the maximum cases of Domestic Violance cases came from West Bengal in the year 2014, with number of cases reported being 23278."
      ],
      "metadata": {
        "id": "krlA48PNwVYp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q. Create a dataframe containing 10 highest reported Importation cases in India, in the span of year 2001-2014."
      ],
      "metadata": {
        "id": "1JarzpQDwW3T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_importation_case = crimes_df.sort_values('Importation_of_Girls', ascending = False).head(10)\n",
        "max_importation_case"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "pQUSiuBPwTrA",
        "outputId": "de921a6c-2674-41bd-9711-66b084df7eab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0b229413-cc9d-4612-bc77-e668ceab6fe5\">\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>Year</th>\n",
              "      <th>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>BIHAR</td>\n",
              "      <td>2001</td>\n",
              "      <td>888</td>\n",
              "      <td>518</td>\n",
              "      <td>859</td>\n",
              "      <td>1558</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3013</th>\n",
              "      <td>BIHAR</td>\n",
              "      <td>2005</td>\n",
              "      <td>1147</td>\n",
              "      <td>929</td>\n",
              "      <td>1014</td>\n",
              "      <td>1574</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3597</th>\n",
              "      <td>WEST BENGAL</td>\n",
              "      <td>2005</td>\n",
              "      <td>1686</td>\n",
              "      <td>1039</td>\n",
              "      <td>446</td>\n",
              "      <td>6936</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3590</th>\n",
              "      <td>WEST BENGAL</td>\n",
              "      <td>2005</td>\n",
              "      <td>148</td>\n",
              "      <td>97</td>\n",
              "      <td>48</td>\n",
              "      <td>545</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4486</th>\n",
              "      <td>BIHAR</td>\n",
              "      <td>2007</td>\n",
              "      <td>1555</td>\n",
              "      <td>1260</td>\n",
              "      <td>1172</td>\n",
              "      <td>1635</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3005</th>\n",
              "      <td>BIHAR</td>\n",
              "      <td>2005</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>40</td>\n",
              "      <td>73</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7810</th>\n",
              "      <td>MADHYA PRADESH</td>\n",
              "      <td>2011</td>\n",
              "      <td>3406</td>\n",
              "      <td>1088</td>\n",
              "      <td>811</td>\n",
              "      <td>3732</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3746</th>\n",
              "      <td>BIHAR</td>\n",
              "      <td>2006</td>\n",
              "      <td>1232</td>\n",
              "      <td>1084</td>\n",
              "      <td>1188</td>\n",
              "      <td>1689</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>BIHAR</td>\n",
              "      <td>2001</td>\n",
              "      <td>27</td>\n",
              "      <td>11</td>\n",
              "      <td>90</td>\n",
              "      <td>152</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5378</th>\n",
              "      <td>JHARKHAND</td>\n",
              "      <td>2008</td>\n",
              "      <td>791</td>\n",
              "      <td>499</td>\n",
              "      <td>266</td>\n",
              "      <td>851</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b229413-cc9d-4612-bc77-e668ceab6fe5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0b229413-cc9d-4612-bc77-e668ceab6fe5 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-0b229413-cc9d-4612-bc77-e668ceab6fe5');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            STATE/UT  Year  ...  Domestic_Cruelty  Importation_of_Girls\n",
              "115            BIHAR  2001  ...              1558                    83\n",
              "3013           BIHAR  2005  ...              1574                    74\n",
              "3597     WEST BENGAL  2005  ...              6936                    61\n",
              "3590     WEST BENGAL  2005  ...               545                    60\n",
              "4486           BIHAR  2007  ...              1635                    56\n",
              "3005           BIHAR  2005  ...                73                    48\n",
              "7810  MADHYA PRADESH  2011  ...              3732                    45\n",
              "3746           BIHAR  2006  ...              1689                    42\n",
              "102            BIHAR  2001  ...               152                    39\n",
              "5378       JHARKHAND  2008  ...               851                    39\n",
              "\n",
              "[10 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### According to our analysis, maximum of Importation of girls has been reported in Bihar in the year 2011"
      ],
      "metadata": {
        "id": "yRCFH7Fvwacg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q. Find out the total number of cases, in span of 2001-2014 under each category, state wise."
      ],
      "metadata": {
        "id": "V1ZQdqPbwbQk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "counts_df = crimes_df.groupby('STATE/UT')[['Rape', 'Kidnapping_Abduction', 'Dowry_Deaths','Domestic_Cruelty', 'Importation_of_Girls']].sum()\n",
        "counts_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3OAPqx9ywYkI",
        "outputId": "add160bc-6e23-441b-a536-c2ac1051be19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6106f11a-cc0b-4ec1-ae02-459b29403bb3\">\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>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>STATE/UT</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A &amp; N ISLANDS</th>\n",
              "      <td>336</td>\n",
              "      <td>212</td>\n",
              "      <td>20</td>\n",
              "      <td>288</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ANDHRA PRADESH</th>\n",
              "      <td>32150</td>\n",
              "      <td>34504</td>\n",
              "      <td>13844</td>\n",
              "      <td>280906</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ARUNACHAL PRADESH</th>\n",
              "      <td>1316</td>\n",
              "      <td>1470</td>\n",
              "      <td>6</td>\n",
              "      <td>476</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ASSAM</th>\n",
              "      <td>40190</td>\n",
              "      <td>62074</td>\n",
              "      <td>3268</td>\n",
              "      <td>115300</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BIHAR</th>\n",
              "      <td>30758</td>\n",
              "      <td>57086</td>\n",
              "      <td>32206</td>\n",
              "      <td>69770</td>\n",
              "      <td>904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHANDIGARH</th>\n",
              "      <td>770</td>\n",
              "      <td>1682</td>\n",
              "      <td>90</td>\n",
              "      <td>2080</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHHATTISGARH</th>\n",
              "      <td>29308</td>\n",
              "      <td>11808</td>\n",
              "      <td>2758</td>\n",
              "      <td>23436</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D &amp; N HAVELI</th>\n",
              "      <td>132</td>\n",
              "      <td>224</td>\n",
              "      <td>2</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DAMAN &amp; DIU</th>\n",
              "      <td>60</td>\n",
              "      <td>44</td>\n",
              "      <td>6</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DELHI</th>\n",
              "      <td>20312</td>\n",
              "      <td>46586</td>\n",
              "      <td>3758</td>\n",
              "      <td>42834</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GOA</th>\n",
              "      <td>1062</td>\n",
              "      <td>640</td>\n",
              "      <td>38</td>\n",
              "      <td>532</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GUJARAT</th>\n",
              "      <td>11644</td>\n",
              "      <td>34670</td>\n",
              "      <td>1108</td>\n",
              "      <td>146468</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HARYANA</th>\n",
              "      <td>17110</td>\n",
              "      <td>20016</td>\n",
              "      <td>7372</td>\n",
              "      <td>68414</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HIMACHAL PRADESH</th>\n",
              "      <td>4674</td>\n",
              "      <td>4116</td>\n",
              "      <td>112</td>\n",
              "      <td>7796</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JAMMU &amp; KASHMIR</th>\n",
              "      <td>7038</td>\n",
              "      <td>21164</td>\n",
              "      <td>294</td>\n",
              "      <td>5390</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JHARKHAND</th>\n",
              "      <td>22826</td>\n",
              "      <td>14186</td>\n",
              "      <td>7896</td>\n",
              "      <td>23910</td>\n",
              "      <td>298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KARNATAKA</th>\n",
              "      <td>15056</td>\n",
              "      <td>16262</td>\n",
              "      <td>7016</td>\n",
              "      <td>72706</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KERALA</th>\n",
              "      <td>20030</td>\n",
              "      <td>4452</td>\n",
              "      <td>700</td>\n",
              "      <td>111626</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAKSHADWEEP</th>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MADHYA PRADESH</th>\n",
              "      <td>90996</td>\n",
              "      <td>35608</td>\n",
              "      <td>21090</td>\n",
              "      <td>102816</td>\n",
              "      <td>134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAHARASHTRA</th>\n",
              "      <td>48974</td>\n",
              "      <td>30368</td>\n",
              "      <td>9696</td>\n",
              "      <td>193202</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MANIPUR</th>\n",
              "      <td>1068</td>\n",
              "      <td>2606</td>\n",
              "      <td>6</td>\n",
              "      <td>578</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MEGHALAYA</th>\n",
              "      <td>2642</td>\n",
              "      <td>670</td>\n",
              "      <td>36</td>\n",
              "      <td>460</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIZORAM</th>\n",
              "      <td>2070</td>\n",
              "      <td>30</td>\n",
              "      <td>8</td>\n",
              "      <td>134</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NAGALAND</th>\n",
              "      <td>562</td>\n",
              "      <td>190</td>\n",
              "      <td>2</td>\n",
              "      <td>32</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ODISHA</th>\n",
              "      <td>30480</td>\n",
              "      <td>25588</td>\n",
              "      <td>10782</td>\n",
              "      <td>49206</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PUDUCHERRY</th>\n",
              "      <td>208</td>\n",
              "      <td>306</td>\n",
              "      <td>56</td>\n",
              "      <td>234</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PUNJAB</th>\n",
              "      <td>14656</td>\n",
              "      <td>15096</td>\n",
              "      <td>3524</td>\n",
              "      <td>30840</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RAJASTHAN</th>\n",
              "      <td>45684</td>\n",
              "      <td>66278</td>\n",
              "      <td>11854</td>\n",
              "      <td>262200</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SIKKIM</th>\n",
              "      <td>570</td>\n",
              "      <td>180</td>\n",
              "      <td>4</td>\n",
              "      <td>108</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TAMIL NADU</th>\n",
              "      <td>16660</td>\n",
              "      <td>30908</td>\n",
              "      <td>5060</td>\n",
              "      <td>45524</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TELANGANA</th>\n",
              "      <td>1958</td>\n",
              "      <td>1422</td>\n",
              "      <td>578</td>\n",
              "      <td>12738</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TRIPURA</th>\n",
              "      <td>5060</td>\n",
              "      <td>2202</td>\n",
              "      <td>752</td>\n",
              "      <td>16086</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UTTAR PRADESH</th>\n",
              "      <td>51150</td>\n",
              "      <td>135906</td>\n",
              "      <td>57256</td>\n",
              "      <td>193738</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UTTARAKHAND</th>\n",
              "      <td>3752</td>\n",
              "      <td>6484</td>\n",
              "      <td>1974</td>\n",
              "      <td>9756</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WEST BENGAL</th>\n",
              "      <td>47876</td>\n",
              "      <td>61158</td>\n",
              "      <td>12308</td>\n",
              "      <td>344124</td>\n",
              "      <td>254</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6106f11a-cc0b-4ec1-ae02-459b29403bb3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6106f11a-cc0b-4ec1-ae02-459b29403bb3 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-6106f11a-cc0b-4ec1-ae02-459b29403bb3');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                    Rape  ...  Importation_of_Girls\n",
              "STATE/UT                  ...                      \n",
              "A & N ISLANDS        336  ...                     0\n",
              "ANDHRA PRADESH     32150  ...                    34\n",
              "ARUNACHAL PRADESH   1316  ...                     0\n",
              "ASSAM              40190  ...                    22\n",
              "BIHAR              30758  ...                   904\n",
              "CHANDIGARH           770  ...                     0\n",
              "CHHATTISGARH       29308  ...                    12\n",
              "D & N HAVELI         132  ...                     0\n",
              "DAMAN & DIU           60  ...                     0\n",
              "DELHI              20312  ...                     2\n",
              "GOA                 1062  ...                     0\n",
              "GUJARAT            11644  ...                     0\n",
              "HARYANA            17110  ...                     4\n",
              "HIMACHAL PRADESH    4674  ...                     0\n",
              "JAMMU & KASHMIR     7038  ...                     0\n",
              "JHARKHAND          22826  ...                   298\n",
              "KARNATAKA          15056  ...                    94\n",
              "KERALA             20030  ...                     0\n",
              "LAKSHADWEEP           20  ...                     0\n",
              "MADHYA PRADESH     90996  ...                   134\n",
              "MAHARASHTRA        48974  ...                     6\n",
              "MANIPUR             1068  ...                     0\n",
              "MEGHALAYA           2642  ...                     8\n",
              "MIZORAM             2070  ...                     6\n",
              "NAGALAND             562  ...                     2\n",
              "ODISHA             30480  ...                    36\n",
              "PUDUCHERRY           208  ...                     0\n",
              "PUNJAB             14656  ...                     4\n",
              "RAJASTHAN          45684  ...                    14\n",
              "SIKKIM               570  ...                     0\n",
              "TAMIL NADU         16660  ...                    30\n",
              "TELANGANA           1958  ...                     0\n",
              "TRIPURA             5060  ...                     0\n",
              "UTTAR PRADESH      51150  ...                     6\n",
              "UTTARAKHAND         3752  ...                     2\n",
              "WEST BENGAL        47876  ...                   254\n",
              "\n",
              "[36 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q. Find out the top 5 states, where maximum numbers of cases has been reported in TOTAL in span of 2001-2014, each category wise.\n",
        "\n",
        "##### For \"Rape\" case -"
      ],
      "metadata": {
        "id": "X-5AM7jMwfuL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "counts_df.sort_values(by = 'Rape', ascending = False).head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "1dqJE9aVwdrK",
        "outputId": "14177746-07ab-437a-cdcd-f29955644458"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b200a2b7-d624-43bc-9442-c5b6f084606e\">\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>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>STATE/UT</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MADHYA PRADESH</th>\n",
              "      <td>90996</td>\n",
              "      <td>35608</td>\n",
              "      <td>21090</td>\n",
              "      <td>102816</td>\n",
              "      <td>134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UTTAR PRADESH</th>\n",
              "      <td>51150</td>\n",
              "      <td>135906</td>\n",
              "      <td>57256</td>\n",
              "      <td>193738</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAHARASHTRA</th>\n",
              "      <td>48974</td>\n",
              "      <td>30368</td>\n",
              "      <td>9696</td>\n",
              "      <td>193202</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WEST BENGAL</th>\n",
              "      <td>47876</td>\n",
              "      <td>61158</td>\n",
              "      <td>12308</td>\n",
              "      <td>344124</td>\n",
              "      <td>254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RAJASTHAN</th>\n",
              "      <td>45684</td>\n",
              "      <td>66278</td>\n",
              "      <td>11854</td>\n",
              "      <td>262200</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b200a2b7-d624-43bc-9442-c5b6f084606e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b200a2b7-d624-43bc-9442-c5b6f084606e 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-b200a2b7-d624-43bc-9442-c5b6f084606e');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 Rape  ...  Importation_of_Girls\n",
              "STATE/UT               ...                      \n",
              "MADHYA PRADESH  90996  ...                   134\n",
              "UTTAR PRADESH   51150  ...                     6\n",
              "MAHARASHTRA     48974  ...                     6\n",
              "WEST BENGAL     47876  ...                   254\n",
              "RAJASTHAN       45684  ...                    14\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Madhya Pradesh has reported the highest number of rape cases in TOTAL in span of 2001-2014, where UP, Maharastra, West Bengal and Rajasthan follows the list.\n",
        "\n",
        "##### For Kidnapping and abduction case -"
      ],
      "metadata": {
        "id": "IcLJx_DfxPqZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "counts_df.sort_values(by = 'Kidnapping_Abduction', ascending = False).head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "uW4Rue8_w4qZ",
        "outputId": "3b97ece3-8d0a-4612-d0d8-0beca61c63d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e6ec840a-b3c1-4366-8d32-cddd1ab461ed\">\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>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>STATE/UT</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>UTTAR PRADESH</th>\n",
              "      <td>51150</td>\n",
              "      <td>135906</td>\n",
              "      <td>57256</td>\n",
              "      <td>193738</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RAJASTHAN</th>\n",
              "      <td>45684</td>\n",
              "      <td>66278</td>\n",
              "      <td>11854</td>\n",
              "      <td>262200</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ASSAM</th>\n",
              "      <td>40190</td>\n",
              "      <td>62074</td>\n",
              "      <td>3268</td>\n",
              "      <td>115300</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WEST BENGAL</th>\n",
              "      <td>47876</td>\n",
              "      <td>61158</td>\n",
              "      <td>12308</td>\n",
              "      <td>344124</td>\n",
              "      <td>254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BIHAR</th>\n",
              "      <td>30758</td>\n",
              "      <td>57086</td>\n",
              "      <td>32206</td>\n",
              "      <td>69770</td>\n",
              "      <td>904</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6ec840a-b3c1-4366-8d32-cddd1ab461ed')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e6ec840a-b3c1-4366-8d32-cddd1ab461ed 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-e6ec840a-b3c1-4366-8d32-cddd1ab461ed');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                Rape  ...  Importation_of_Girls\n",
              "STATE/UT              ...                      \n",
              "UTTAR PRADESH  51150  ...                     6\n",
              "RAJASTHAN      45684  ...                    14\n",
              "ASSAM          40190  ...                    22\n",
              "WEST BENGAL    47876  ...                   254\n",
              "BIHAR          30758  ...                   904\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Uttar Pradesh has reported the highest number of cses under \"Kidnapping and Abduction\" in TOTAL in span of 2001-2014, where Rajasthan, Assam, West Bengal and Bihar follows the list.\n",
        "\n",
        "##### For cases of deaths due to dowry -"
      ],
      "metadata": {
        "id": "swJk_7FFxUX6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "counts_df.sort_values(by = 'Dowry_Deaths', ascending = False).head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "XRlPSZMBxRvW",
        "outputId": "413c12ec-514a-4bed-acbb-a73e08fc86e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2aea201b-dc24-4f2d-a745-83934ac2e1a9\">\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>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>STATE/UT</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>UTTAR PRADESH</th>\n",
              "      <td>51150</td>\n",
              "      <td>135906</td>\n",
              "      <td>57256</td>\n",
              "      <td>193738</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BIHAR</th>\n",
              "      <td>30758</td>\n",
              "      <td>57086</td>\n",
              "      <td>32206</td>\n",
              "      <td>69770</td>\n",
              "      <td>904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MADHYA PRADESH</th>\n",
              "      <td>90996</td>\n",
              "      <td>35608</td>\n",
              "      <td>21090</td>\n",
              "      <td>102816</td>\n",
              "      <td>134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ANDHRA PRADESH</th>\n",
              "      <td>32150</td>\n",
              "      <td>34504</td>\n",
              "      <td>13844</td>\n",
              "      <td>280906</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WEST BENGAL</th>\n",
              "      <td>47876</td>\n",
              "      <td>61158</td>\n",
              "      <td>12308</td>\n",
              "      <td>344124</td>\n",
              "      <td>254</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2aea201b-dc24-4f2d-a745-83934ac2e1a9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2aea201b-dc24-4f2d-a745-83934ac2e1a9 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-2aea201b-dc24-4f2d-a745-83934ac2e1a9');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 Rape  ...  Importation_of_Girls\n",
              "STATE/UT               ...                      \n",
              "UTTAR PRADESH   51150  ...                     6\n",
              "BIHAR           30758  ...                   904\n",
              "MADHYA PRADESH  90996  ...                   134\n",
              "ANDHRA PRADESH  32150  ...                    34\n",
              "WEST BENGAL     47876  ...                   254\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Uttar Pradesh has reported the highest number of Deaths caused by Dowry cases in TOTAL in span of 2001-2014, where Bihar, Madhya Pradesh, Andhra Pradesh and  West Bengal follows the list.\n",
        "\n",
        "##### For  Domestic Violance case -"
      ],
      "metadata": {
        "id": "_rrU-dPqxziQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "counts_df.sort_values(by = 'Domestic_Cruelty', ascending = False).head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "TeXLuFmHxYkq",
        "outputId": "10f8ae19-5251-4469-9fca-47ce4fcb3c36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d339ed7f-334f-4a8c-a3d1-79d265b3c5f0\">\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>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>STATE/UT</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>WEST BENGAL</th>\n",
              "      <td>47876</td>\n",
              "      <td>61158</td>\n",
              "      <td>12308</td>\n",
              "      <td>344124</td>\n",
              "      <td>254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ANDHRA PRADESH</th>\n",
              "      <td>32150</td>\n",
              "      <td>34504</td>\n",
              "      <td>13844</td>\n",
              "      <td>280906</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RAJASTHAN</th>\n",
              "      <td>45684</td>\n",
              "      <td>66278</td>\n",
              "      <td>11854</td>\n",
              "      <td>262200</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UTTAR PRADESH</th>\n",
              "      <td>51150</td>\n",
              "      <td>135906</td>\n",
              "      <td>57256</td>\n",
              "      <td>193738</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAHARASHTRA</th>\n",
              "      <td>48974</td>\n",
              "      <td>30368</td>\n",
              "      <td>9696</td>\n",
              "      <td>193202</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d339ed7f-334f-4a8c-a3d1-79d265b3c5f0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d339ed7f-334f-4a8c-a3d1-79d265b3c5f0 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-d339ed7f-334f-4a8c-a3d1-79d265b3c5f0');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 Rape  ...  Importation_of_Girls\n",
              "STATE/UT               ...                      \n",
              "WEST BENGAL     47876  ...                   254\n",
              "ANDHRA PRADESH  32150  ...                    34\n",
              "RAJASTHAN       45684  ...                    14\n",
              "UTTAR PRADESH   51150  ...                     6\n",
              "MAHARASHTRA     48974  ...                     6\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### West Bengal has reported the highest number of cases of Domestic Violence in TOTAL in span of 2001-2014, where Andhra Pradesh, Rajsthan, Uttar Pradesh and  Maharashtra follows the list.\n",
        "\n",
        "\n",
        "##### For Importation of Girls case -"
      ],
      "metadata": {
        "id": "n9iusrEnx5PI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "counts_df.sort_values(by = 'Importation_of_Girls', ascending = False).head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "oSLKJpGUx2EG",
        "outputId": "8da07cdc-8922-40ab-d6d9-10f39e2c8076"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c7e4f747-c860-4e58-9317-9e7215703c02\">\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>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>STATE/UT</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>BIHAR</th>\n",
              "      <td>30758</td>\n",
              "      <td>57086</td>\n",
              "      <td>32206</td>\n",
              "      <td>69770</td>\n",
              "      <td>904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JHARKHAND</th>\n",
              "      <td>22826</td>\n",
              "      <td>14186</td>\n",
              "      <td>7896</td>\n",
              "      <td>23910</td>\n",
              "      <td>298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WEST BENGAL</th>\n",
              "      <td>47876</td>\n",
              "      <td>61158</td>\n",
              "      <td>12308</td>\n",
              "      <td>344124</td>\n",
              "      <td>254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MADHYA PRADESH</th>\n",
              "      <td>90996</td>\n",
              "      <td>35608</td>\n",
              "      <td>21090</td>\n",
              "      <td>102816</td>\n",
              "      <td>134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KARNATAKA</th>\n",
              "      <td>15056</td>\n",
              "      <td>16262</td>\n",
              "      <td>7016</td>\n",
              "      <td>72706</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7e4f747-c860-4e58-9317-9e7215703c02')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c7e4f747-c860-4e58-9317-9e7215703c02 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-c7e4f747-c860-4e58-9317-9e7215703c02');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 Rape  ...  Importation_of_Girls\n",
              "STATE/UT               ...                      \n",
              "BIHAR           30758  ...                   904\n",
              "JHARKHAND       22826  ...                   298\n",
              "WEST BENGAL     47876  ...                   254\n",
              "MADHYA PRADESH  90996  ...                   134\n",
              "KARNATAKA       15056  ...                    94\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Bihar has reported the highest number of Importation of girl's cases in TOTAL in span of 2001-2014, where Jharkhand, West Bengal, Madhya Pradesh and  Karnataka follows the list."
      ],
      "metadata": {
        "id": "E__FU8UPyRVb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Q: Which state has featured in both the lists of \"Maximum number of rape cases\" and \"Maximum number of Importation cases\"."
      ],
      "metadata": {
        "id": "DllkkSFhyR9T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_importation_case = max_importation_case.merge(max_rape_cases)\n",
        "max_importation_case"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "8SY_FSViyPru",
        "outputId": "20954fc5-a15e-4dc2-eb6b-0703e8e934f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-987e4d01-ff79-4eb2-a001-66cfb05043d9\">\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>Year</th>\n",
              "      <th>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MADHYA PRADESH</td>\n",
              "      <td>2011</td>\n",
              "      <td>3406</td>\n",
              "      <td>1088</td>\n",
              "      <td>811</td>\n",
              "      <td>3732</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-987e4d01-ff79-4eb2-a001-66cfb05043d9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-987e4d01-ff79-4eb2-a001-66cfb05043d9 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-987e4d01-ff79-4eb2-a001-66cfb05043d9');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         STATE/UT  Year  ...  Domestic_Cruelty  Importation_of_Girls\n",
              "0  MADHYA PRADESH  2011  ...              3732                    45\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### It is Madhya Pradesh, who has maximum cases reported in both the categories.\n",
        "\n",
        "#### Q: Which state has featured in both the lists of \"Maximum number of rape cases\" and \"Maximum number of Deaths due to Dowry cases\"."
      ],
      "metadata": {
        "id": "dBNKEj6IyYF4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_dowry_death_cases = max_dowry_death_cases.merge(max_rape_cases)\n",
        "max_dowry_death_cases"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "CySSZi59yVmC",
        "outputId": "d2bd6732-0598-435f-c0b1-a53b75293b90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-fbba3e0e-aa3a-426a-89b0-05988a4a1221\">\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>Year</th>\n",
              "      <th>Rape</th>\n",
              "      <th>Kidnapping_Abduction</th>\n",
              "      <th>Dowry_Deaths</th>\n",
              "      <th>Domestic_Cruelty</th>\n",
              "      <th>Importation_of_Girls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>UTTAR PRADESH</td>\n",
              "      <td>2014</td>\n",
              "      <td>3467</td>\n",
              "      <td>10626</td>\n",
              "      <td>2469</td>\n",
              "      <td>10471</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fbba3e0e-aa3a-426a-89b0-05988a4a1221')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fbba3e0e-aa3a-426a-89b0-05988a4a1221 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-fbba3e0e-aa3a-426a-89b0-05988a4a1221');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        STATE/UT  Year  ...  Domestic_Cruelty  Importation_of_Girls\n",
              "0  UTTAR PRADESH  2014  ...             10471                     0\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### We conclude, it is Uttar Pradesh, who has reported maximum cases in both the given categories."
      ],
      "metadata": {
        "id": "t2N-wGRZybnx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inferences and Conclusion"
      ],
      "metadata": {
        "id": "RQ8tpS-ayfdB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### The main aim of the project was to analyse the situation of women in the year 2001-2014. \n",
        "#### We also did a deep analysis through charts and by raising important questions. Let us go through some of the important analysis, we have done through thid project -\n",
        "            \n",
        "          1)More than 5 million females has been a victim of some or other type of Violance, based on their gender, \n",
        "          starting from rape to importing them for buisness. \n",
        "          2) We concluded from the series of bar graphs that 2014 was the year, when crimes were reported the highest \n",
        "          under each category.\n",
        "          3) We tried finding out the top 10 highest cases reported ever, along with year in which has been reported \n",
        "          and in which state.Where, Madhya Pradesh having highest number of cases of rape in 2014, Uttar Pradesh having \n",
        "          highest cases in Dowry death in 2014, West Bengal having highest cases in Domestic Violance in 2014 and Bihar \n",
        "          having the highest cases in importaion of girls in 2011.\n",
        "          4)We summarised the TOTAL number of cases happening, in 2001-2014, by each state.\n",
        "          5)We also found out the top 5 states where maximum number of TOTAL cases has been reported from 2001-2014, \n",
        "          state   wise.\n",
        "          6)We also merged the data in two different cases, first one being \"Maximum number of rape cases\" and \n",
        "          \"Maximum number of Importation cases\", where we found out it is Madhya Pradesh and in second one being \n",
        "          \"Maximum number of rape cases\" and \"Maximum number of Deaths due to Dowry cases\", which we concluded to \n",
        "          be Uttar Pradesh."
      ],
      "metadata": {
        "id": "FDxa2FJvykNx"
      }
    }
  ]
}