Unemployment_Analysis.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Navjotkhatri/OIBSIP/blob/main/Unemployment_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gRVNempdSpia"
      },
      "source": [
        "**Task-2**\n",
        "# **Project Name-Unemployment Analysis**\n",
        "**Contributor-Navjot Khatri**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CNzCRdpbRjEP"
      },
      "source": [
        "Unemployment is measured by the unemployment rate which is the number of people\n",
        "who are unemployed as a percentage of the total labour force. We have seen a sharp increase in the unemployment rate during Covid-19\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y6n3N1U_VfHG"
      },
      "source": [
        "**Context**\n",
        "\n",
        "The story behind this datasets is how lock-down affects employment opportunities and how the unemployment rate increases during the Covid-19."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kpa_XraLVwey"
      },
      "source": [
        "**Content**\n",
        "\n",
        "This dataset contains the unemployment rate of all the states in India\n",
        "\n",
        "Region = states in India\n",
        "\n",
        "Date = date which the unemployment rate observed\n",
        "\n",
        "Frequency = measuring frequency (Monthly)\n",
        "\n",
        "Estimated Unemployment Rate (%) = percentage of people unemployed in each States of India\n",
        "\n",
        "Estimated Employed = percentage of people employed\n",
        "\n",
        "Estimated Labour Participation Rate (%) = labour force participation rate by dividing the number of people actively participating in the labour force by the\n",
        "total number of people eligible to participate in the labor force\n",
        "force"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TxVZrSYoWBzc"
      },
      "source": [
        "**questions?**\n",
        "\n",
        "* How Covid-19 affects the employment\n",
        "\n",
        "* how far the unemployment rate will go"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Importing Library** "
      ],
      "metadata": {
        "id": "uhGREEzMZ6uz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "XbPGFsFDQOvy"
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      "outputs": [],
      "source": [
        "#import library\n",
        "from ast import increment_lineno\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "W76Ws_jb3Dc_"
      },
      "outputs": [],
      "source": [
        "import calendar\n",
        "import datetime as dt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aasHG_Lge8mF"
      },
      "outputs": [],
      "source": [
        "#mount google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We Have Two Data sets 2019 and 2020. So we Analyze both Dataset And compare the Unemployment rate during these two years."
      ],
      "metadata": {
        "id": "N1J4GLDXdxYQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "XxbZWHcoe8yn"
      },
      "outputs": [],
      "source": [
        "#reading csv file-1\n",
        "working_dir_path1=\"/content/drive/MyDrive/Colab Notebooks/Oasis Intern projects/Unemployment Analysis With Python/Unemployment in India.csv\"\n",
        "un_df1=pd.read_csv(working_dir_path1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Prepration**"
      ],
      "metadata": {
        "id": "M8YOhfJOaFYM"
      }
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      "execution_count": 5,
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              "           Region         Date  Frequency   Estimated Unemployment Rate (%)  \\\n",
              "0  Andhra Pradesh   31-05-2019    Monthly                              3.65   \n",
              "1  Andhra Pradesh   30-06-2019    Monthly                              3.05   \n",
              "2  Andhra Pradesh   31-07-2019    Monthly                              3.75   \n",
              "3  Andhra Pradesh   31-08-2019    Monthly                              3.32   \n",
              "4  Andhra Pradesh   30-09-2019    Monthly                              5.17   \n",
              "\n",
              "    Estimated Employed   Estimated Labour Participation Rate (%)   Area  \n",
              "0           11999139.0                                     43.24  Rural  \n",
              "1           11755881.0                                     42.05  Rural  \n",
              "2           12086707.0                                     43.50  Rural  \n",
              "3           12285693.0                                     43.97  Rural  \n",
              "4           12256762.0                                     44.68  Rural  "
            ],
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              "      <th>Estimated Unemployment Rate (%)</th>\n",
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              "      <td>Andhra Pradesh</td>\n",
              "      <td>31-05-2019</td>\n",
              "      <td>Monthly</td>\n",
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              "      <td>43.24</td>\n",
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              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>30-06-2019</td>\n",
              "      <td>Monthly</td>\n",
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              "      <td>42.05</td>\n",
              "      <td>Rural</td>\n",
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              "      <td>Andhra Pradesh</td>\n",
              "      <td>31-07-2019</td>\n",
              "      <td>Monthly</td>\n",
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              "      <td>43.50</td>\n",
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              "      <td>Andhra Pradesh</td>\n",
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              "      <td>Monthly</td>\n",
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              "      <td>43.97</td>\n",
              "      <td>Rural</td>\n",
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              "      <th>4</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>30-09-2019</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>5.17</td>\n",
              "      <td>12256762.0</td>\n",
              "      <td>44.68</td>\n",
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        "#reading csv file-2\n",
        "working_dir_path2=\"/content/drive/MyDrive/Colab Notebooks/Oasis Intern projects/Unemployment Analysis With Python/Unemployment_Rate_upto_11_2020.csv\"\n",
        "un_df2=pd.read_csv(working_dir_path2)"
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              "           Region         Date  Frequency   Estimated Unemployment Rate (%)  \\\n",
              "0  Andhra Pradesh   31-01-2020          M                              5.48   \n",
              "1  Andhra Pradesh   29-02-2020          M                              5.83   \n",
              "2  Andhra Pradesh   31-03-2020          M                              5.79   \n",
              "3  Andhra Pradesh   30-04-2020          M                             20.51   \n",
              "4  Andhra Pradesh   31-05-2020          M                             17.43   \n",
              "\n",
              "    Estimated Employed   Estimated Labour Participation Rate (%) Region.1  \\\n",
              "0             16635535                                     41.02    South   \n",
              "1             16545652                                     40.90    South   \n",
              "2             15881197                                     39.18    South   \n",
              "3             11336911                                     33.10    South   \n",
              "4             12988845                                     36.46    South   \n",
              "\n",
              "   longitude  latitude  \n",
              "0    15.9129     79.74  \n",
              "1    15.9129     79.74  \n",
              "2    15.9129     79.74  \n",
              "3    15.9129     79.74  \n",
              "4    15.9129     79.74  "
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              "      <th>Estimated Unemployment Rate (%)</th>\n",
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              "      <td>Andhra Pradesh</td>\n",
              "      <td>31-01-2020</td>\n",
              "      <td>M</td>\n",
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              "      <td>Andhra Pradesh</td>\n",
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              "      <td>M</td>\n",
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              "      <td>South</td>\n",
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              "      <td>Andhra Pradesh</td>\n",
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              "      <td>Andhra Pradesh</td>\n",
              "      <td>30-04-2020</td>\n",
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              "      <td>33.10</td>\n",
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              "      <td>79.74</td>\n",
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              "      <td>Andhra Pradesh</td>\n",
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              "          Region         Date  Frequency   Estimated Unemployment Rate (%)  \\\n",
              "262  West Bengal   30-06-2020          M                              7.29   \n",
              "263  West Bengal   31-07-2020          M                              6.83   \n",
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              "\n",
              "      Estimated Employed   Estimated Labour Participation Rate (%) Region.1  \\\n",
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              "263             35372506                                     46.17     East   \n",
              "264             33298644                                     47.48     East   \n",
              "265             35707239                                     47.73     East   \n",
              "266             33962549                                     45.63     East   \n",
              "\n",
              "     longitude  latitude  \n",
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              "        Estimated Unemployment Rate (%)   Estimated Employed  \\\n",
              "count                        740.000000         7.400000e+02   \n",
              "mean                          11.787946         7.204460e+06   \n",
              "std                           10.721298         8.087988e+06   \n",
              "min                            0.000000         4.942000e+04   \n",
              "25%                            4.657500         1.190404e+06   \n",
              "50%                            8.350000         4.744178e+06   \n",
              "75%                           15.887500         1.127549e+07   \n",
              "max                           76.740000         4.577751e+07   \n",
              "\n",
              "        Estimated Labour Participation Rate (%)  \n",
              "count                                740.000000  \n",
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              "std                                    8.111094  \n",
              "min                                   13.330000  \n",
              "25%                                   38.062500  \n",
              "50%                                   41.160000  \n",
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              "      <div>\n",
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              "    .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>Estimated Unemployment Rate (%)</th>\n",
              "      <th>Estimated Employed</th>\n",
              "      <th>Estimated Labour Participation Rate (%)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>740.000000</td>\n",
              "      <td>7.400000e+02</td>\n",
              "      <td>740.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>11.787946</td>\n",
              "      <td>7.204460e+06</td>\n",
              "      <td>42.630122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.721298</td>\n",
              "      <td>8.087988e+06</td>\n",
              "      <td>8.111094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.942000e+04</td>\n",
              "      <td>13.330000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.657500</td>\n",
              "      <td>1.190404e+06</td>\n",
              "      <td>38.062500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.350000</td>\n",
              "      <td>4.744178e+06</td>\n",
              "      <td>41.160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.887500</td>\n",
              "      <td>1.127549e+07</td>\n",
              "      <td>45.505000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>76.740000</td>\n",
              "      <td>4.577751e+07</td>\n",
              "      <td>72.570000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43615b63-59ca-4ac2-829e-e7c93cc536cb')\"\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-43615b63-59ca-4ac2-829e-e7c93cc536cb 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-43615b63-59ca-4ac2-829e-e7c93cc536cb');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "un_df1.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1FPACjFif7KB",
        "outputId": "1eb9f5ed-be73-44bd-b614-c688e29993cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Estimated Unemployment Rate (%)   Estimated Employed  \\\n",
              "count                        267.000000         2.670000e+02   \n",
              "mean                          12.236929         1.396211e+07   \n",
              "std                           10.803283         1.336632e+07   \n",
              "min                            0.500000         1.175420e+05   \n",
              "25%                            4.845000         2.838930e+06   \n",
              "50%                            9.650000         9.732417e+06   \n",
              "75%                           16.755000         2.187869e+07   \n",
              "max                           75.850000         5.943376e+07   \n",
              "\n",
              "        Estimated Labour Participation Rate (%)   longitude    latitude  \n",
              "count                                267.000000  267.000000  267.000000  \n",
              "mean                                  41.681573   22.826048   80.532425  \n",
              "std                                    7.845419    6.270731    5.831738  \n",
              "min                                   16.770000   10.850500   71.192400  \n",
              "25%                                   37.265000   18.112400   76.085600  \n",
              "50%                                   40.390000   23.610200   79.019300  \n",
              "75%                                   44.055000   27.278400   85.279900  \n",
              "max                                   69.690000   33.778200   92.937600  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fa54d46e-de5e-4bd2-b052-7b2eb2263558\">\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>Estimated Unemployment Rate (%)</th>\n",
              "      <th>Estimated Employed</th>\n",
              "      <th>Estimated Labour Participation Rate (%)</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>267.000000</td>\n",
              "      <td>2.670000e+02</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>267.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12.236929</td>\n",
              "      <td>1.396211e+07</td>\n",
              "      <td>41.681573</td>\n",
              "      <td>22.826048</td>\n",
              "      <td>80.532425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.803283</td>\n",
              "      <td>1.336632e+07</td>\n",
              "      <td>7.845419</td>\n",
              "      <td>6.270731</td>\n",
              "      <td>5.831738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.175420e+05</td>\n",
              "      <td>16.770000</td>\n",
              "      <td>10.850500</td>\n",
              "      <td>71.192400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.845000</td>\n",
              "      <td>2.838930e+06</td>\n",
              "      <td>37.265000</td>\n",
              "      <td>18.112400</td>\n",
              "      <td>76.085600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.650000</td>\n",
              "      <td>9.732417e+06</td>\n",
              "      <td>40.390000</td>\n",
              "      <td>23.610200</td>\n",
              "      <td>79.019300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.755000</td>\n",
              "      <td>2.187869e+07</td>\n",
              "      <td>44.055000</td>\n",
              "      <td>27.278400</td>\n",
              "      <td>85.279900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>75.850000</td>\n",
              "      <td>5.943376e+07</td>\n",
              "      <td>69.690000</td>\n",
              "      <td>33.778200</td>\n",
              "      <td>92.937600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa54d46e-de5e-4bd2-b052-7b2eb2263558')\"\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-fa54d46e-de5e-4bd2-b052-7b2eb2263558 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-fa54d46e-de5e-4bd2-b052-7b2eb2263558');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "un_df2.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X79X1DMZM6X2",
        "outputId": "d3418801-7bfc-419d-8789-ee1ad97deb2e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 7 columns):\n",
            " #   Column                                    Non-Null Count  Dtype  \n",
            "---  ------                                    --------------  -----  \n",
            " 0   Region                                    740 non-null    object \n",
            " 1    Date                                     740 non-null    object \n",
            " 2    Frequency                                740 non-null    object \n",
            " 3    Estimated Unemployment Rate (%)          740 non-null    float64\n",
            " 4    Estimated Employed                       740 non-null    float64\n",
            " 5    Estimated Labour Participation Rate (%)  740 non-null    float64\n",
            " 6   Area                                      740 non-null    object \n",
            "dtypes: float64(3), object(4)\n",
            "memory usage: 42.1+ KB\n"
          ]
        }
      ],
      "source": [
        "un_df1.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fPGCuhgxNohB",
        "outputId": "4a4c4971-e8bc-44e4-e76e-7c53897d078e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 267 entries, 0 to 266\n",
            "Data columns (total 9 columns):\n",
            " #   Column                                    Non-Null Count  Dtype  \n",
            "---  ------                                    --------------  -----  \n",
            " 0   Region                                    267 non-null    object \n",
            " 1    Date                                     267 non-null    object \n",
            " 2    Frequency                                267 non-null    object \n",
            " 3    Estimated Unemployment Rate (%)          267 non-null    float64\n",
            " 4    Estimated Employed                       267 non-null    int64  \n",
            " 5    Estimated Labour Participation Rate (%)  267 non-null    float64\n",
            " 6   Region.1                                  267 non-null    object \n",
            " 7   longitude                                 267 non-null    float64\n",
            " 8   latitude                                  267 non-null    float64\n",
            "dtypes: float64(4), int64(1), object(4)\n",
            "memory usage: 18.9+ KB\n"
          ]
        }
      ],
      "source": [
        "un_df2.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "myQm1yy0NvW1",
        "outputId": "9b70ff22-02f4-4a41-cdf9-9aa17e7f6dfb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Region                                      28\n",
              " Date                                       28\n",
              " Frequency                                  28\n",
              " Estimated Unemployment Rate (%)            28\n",
              " Estimated Employed                         28\n",
              " Estimated Labour Participation Rate (%)    28\n",
              "Area                                        28\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "un_df1.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1-9mxBouQm-j",
        "outputId": "34b22d8b-e820-4bf0-fc98-4a59824c3a88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Region                                      0\n",
              " Date                                       0\n",
              " Frequency                                  0\n",
              " Estimated Unemployment Rate (%)            0\n",
              " Estimated Employed                         0\n",
              " Estimated Labour Participation Rate (%)    0\n",
              "Region.1                                    0\n",
              "longitude                                   0\n",
              "latitude                                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "un_df2.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Cleaning**"
      ],
      "metadata": {
        "id": "OkNTjxVef57q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "un_df1=un_df1.dropna()"
      ],
      "metadata": {
        "id": "JrrnwUh2g2UN"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "un_df1.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmC6yy5GhSS-",
        "outputId": "a7e4fcf6-8abe-4b27-cdb1-1ecd32cf50db"
      },
      "execution_count": 19,
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              "Region                                      0\n",
              " Date                                       0\n",
              " Frequency                                  0\n",
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              " Estimated Employed                         0\n",
              " Estimated Labour Participation Rate (%)    0\n",
              "Area                                        0\n",
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      "execution_count": null,
      "outputs": []
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      "execution_count": 25,
      "metadata": {
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              "           States       Date Frequency  Estimated Unemployment Rate  \\\n",
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              "1          11755881.0                                42.05  Rural  \n",
              "2          12086707.0                                43.50  Rural  \n",
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              "           States       Date Frequency  Estimated Unemployment Rate  \\\n",
              "0  Andhra Pradesh 2020-01-31         M                         5.48   \n",
              "1  Andhra Pradesh 2020-02-29         M                         5.83   \n",
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              "4  Andhra Pradesh 2020-05-31         M                        17.43   \n",
              "\n",
              "   Estimated Employed  Estimated Labour Participation Rate Region  longitude  \\\n",
              "0            16635535                                41.02  South    15.9129   \n",
              "1            16545652                                40.90  South    15.9129   \n",
              "2            15881197                                39.18  South    15.9129   \n",
              "3            11336911                                33.10  South    15.9129   \n",
              "4            12988845                                36.46  South    15.9129   \n",
              "\n",
              "   latitude  \n",
              "0     79.74  \n",
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              "2     79.74  \n",
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        "un_df1['Month'] = un_df1['Date'].dt.month"
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      "metadata": {
        "id": "YiOk7qVrQ700"
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      "source": [
        "un_df2['Month'] = un_df2['Date'].dt.month"
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    {
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      "execution_count": 30,
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        "id": "kbMalSMZ5q4x",
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            "text/plain": [
              "           States       Date Frequency  Estimated Unemployment Rate  \\\n",
              "0  Andhra Pradesh 2020-01-31         M                         5.48   \n",
              "1  Andhra Pradesh 2020-02-29         M                         5.83   \n",
              "2  Andhra Pradesh 2020-03-31         M                         5.79   \n",
              "3  Andhra Pradesh 2020-04-30         M                        20.51   \n",
              "4  Andhra Pradesh 2020-05-31         M                        17.43   \n",
              "\n",
              "   Estimated Employed  Estimated Labour Participation Rate Region  longitude  \\\n",
              "0            16635535                                41.02  South    15.9129   \n",
              "1            16545652                                40.90  South    15.9129   \n",
              "2            15881197                                39.18  South    15.9129   \n",
              "3            11336911                                33.10  South    15.9129   \n",
              "4            12988845                                36.46  South    15.9129   \n",
              "\n",
              "   latitude  Month  \n",
              "0     79.74      1  \n",
              "1     79.74      2  \n",
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    {
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      "execution_count": 31,
      "metadata": {
        "id": "aDSFfmn2Q75H"
      },
      "outputs": [],
      "source": [
        "un_df1['Month_int'] =un_df1['Month'].apply(lambda x : int(x))\n",
        "un_df2['Month_int'] =un_df2['Month'].apply(lambda x : int(x))"
      ]
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    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "6Po3FljjQ78f"
      },
      "outputs": [],
      "source": [
        "un_df1['Month_name'] =un_df1['Month_int'].apply(lambda x: calendar.month_abbr[x])\n",
        "un_df2['Month_name'] =un_df2['Month_int'].apply(lambda x: calendar.month_abbr[x])"
      ]
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    {
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      "execution_count": 33,
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        "id": "BQXRHTX-5snE",
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            "text/plain": [
              "           States       Date Frequency  Estimated Unemployment Rate  \\\n",
              "0  Andhra Pradesh 2020-01-31         M                         5.48   \n",
              "1  Andhra Pradesh 2020-02-29         M                         5.83   \n",
              "2  Andhra Pradesh 2020-03-31         M                         5.79   \n",
              "3  Andhra Pradesh 2020-04-30         M                        20.51   \n",
              "4  Andhra Pradesh 2020-05-31         M                        17.43   \n",
              "\n",
              "   Estimated Employed  Estimated Labour Participation Rate Region  longitude  \\\n",
              "0            16635535                                41.02  South    15.9129   \n",
              "1            16545652                                40.90  South    15.9129   \n",
              "2            15881197                                39.18  South    15.9129   \n",
              "3            11336911                                33.10  South    15.9129   \n",
              "4            12988845                                36.46  South    15.9129   \n",
              "\n",
              "   latitude  Month  Month_int Month_name  \n",
              "0     79.74      1          1        Jan  \n",
              "1     79.74      2          2        Feb  \n",
              "2     79.74      3          3        Mar  \n",
              "3     79.74      4          4        Apr  \n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "3Mx__TTw4mCr"
      },
      "outputs": [],
      "source": [
        "un_df1['Region'] = un_df1['Region'].astype('category')\n",
        "un_df2['Region'] = un_df2['Region'].astype('category')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "jW59q21p4l_O"
      },
      "outputs": [],
      "source": [
        "un_df1.drop(columns='Month',inplace=True)\n",
        "un_df2.drop(columns='Month',inplace=True)"
      ]
    },
    {
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        "un_df1.head()"
      ],
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      },
      "execution_count": 36,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           States       Date Frequency  Estimated Unemployment Rate  \\\n",
              "0  Andhra Pradesh 2019-05-31   Monthly                         3.65   \n",
              "1  Andhra Pradesh 2019-06-30   Monthly                         3.05   \n",
              "2  Andhra Pradesh 2019-07-31   Monthly                         3.75   \n",
              "3  Andhra Pradesh 2019-08-31   Monthly                         3.32   \n",
              "4  Andhra Pradesh 2019-09-30   Monthly                         5.17   \n",
              "\n",
              "   Estimated Employed  Estimated Labour Participation Rate Region  Month_int  \\\n",
              "0          11999139.0                                43.24  Rural          5   \n",
              "1          11755881.0                                42.05  Rural          6   \n",
              "2          12086707.0                                43.50  Rural          7   \n",
              "3          12285693.0                                43.97  Rural          8   \n",
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    {
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        "un_df1.tail()"
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      "execution_count": 37,
      "outputs": [
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            "text/plain": [
              "          States       Date Frequency  Estimated Unemployment Rate  \\\n",
              "749  West Bengal 2020-02-29   Monthly                         7.55   \n",
              "750  West Bengal 2020-03-31   Monthly                         6.67   \n",
              "751  West Bengal 2020-04-30   Monthly                        15.63   \n",
              "752  West Bengal 2020-05-31   Monthly                        15.22   \n",
              "753  West Bengal 2020-06-30   Monthly                         9.86   \n",
              "\n",
              "     Estimated Employed  Estimated Labour Participation Rate Region  \\\n",
              "749          10871168.0                                44.09  Urban   \n",
              "750          10806105.0                                43.34  Urban   \n",
              "751           9299466.0                                41.20  Urban   \n",
              "752           9240903.0                                40.67  Urban   \n",
              "753           9088931.0                                37.57  Urban   \n",
              "\n",
              "     Month_int Month_name  \n",
              "749          2        Feb  \n",
              "750          3        Mar  \n",
              "751          4        Apr  \n",
              "752          5        May  \n",
              "753          6        Jun  "
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              "           States       Date Frequency  Estimated Unemployment Rate  \\\n",
              "0  Andhra Pradesh 2020-01-31         M                         5.48   \n",
              "1  Andhra Pradesh 2020-02-29         M                         5.83   \n",
              "2  Andhra Pradesh 2020-03-31         M                         5.79   \n",
              "3  Andhra Pradesh 2020-04-30         M                        20.51   \n",
              "4  Andhra Pradesh 2020-05-31         M                        17.43   \n",
              "\n",
              "   Estimated Employed  Estimated Labour Participation Rate Region  longitude  \\\n",
              "0            16635535                                41.02  South    15.9129   \n",
              "1            16545652                                40.90  South    15.9129   \n",
              "2            15881197                                39.18  South    15.9129   \n",
              "3            11336911                                33.10  South    15.9129   \n",
              "4            12988845                                36.46  South    15.9129   \n",
              "\n",
              "   latitude  Month_int Month_name  \n",
              "0     79.74          1        Jan  \n",
              "1     79.74          2        Feb  \n",
              "2     79.74          3        Mar  \n",
              "3     79.74          4        Apr  \n",
              "4     79.74          5        May  "
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              "      <td>5.79</td>\n",
              "      <td>15881197</td>\n",
              "      <td>39.18</td>\n",
              "      <td>South</td>\n",
              "      <td>15.9129</td>\n",
              "      <td>79.74</td>\n",
              "      <td>3</td>\n",
              "      <td>Mar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>2020-04-30</td>\n",
              "      <td>M</td>\n",
              "      <td>20.51</td>\n",
              "      <td>11336911</td>\n",
              "      <td>33.10</td>\n",
              "      <td>South</td>\n",
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              "      <td>Apr</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>2020-05-31</td>\n",
              "      <td>M</td>\n",
              "      <td>17.43</td>\n",
              "      <td>12988845</td>\n",
              "      <td>36.46</td>\n",
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              "      <td>79.74</td>\n",
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          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "un_df2.head()"
      ]
    },
    {
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        "un_df2.tail()"
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      },
      "execution_count": 39,
      "outputs": [
        {
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            "text/plain": [
              "          States       Date Frequency  Estimated Unemployment Rate  \\\n",
              "262  West Bengal 2020-06-30         M                         7.29   \n",
              "263  West Bengal 2020-07-31         M                         6.83   \n",
              "264  West Bengal 2020-08-31         M                        14.87   \n",
              "265  West Bengal 2020-09-30         M                         9.35   \n",
              "266  West Bengal 2020-10-31         M                         9.98   \n",
              "\n",
              "     Estimated Employed  Estimated Labour Participation Rate Region  \\\n",
              "262            30726310                                40.39   East   \n",
              "263            35372506                                46.17   East   \n",
              "264            33298644                                47.48   East   \n",
              "265            35707239                                47.73   East   \n",
              "266            33962549                                45.63   East   \n",
              "\n",
              "     longitude  latitude  Month_int Month_name  \n",
              "262    22.9868    87.855          6        Jun  \n",
              "263    22.9868    87.855          7        Jul  \n",
              "264    22.9868    87.855          8        Aug  \n",
              "265    22.9868    87.855          9        Sep  \n",
              "266    22.9868    87.855         10        Oct  "
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    {
      "cell_type": "markdown",
      "source": [
        "* We Split our Dataset into two parts one of them before the lockdown[2019-5 to 2020-3] And the Second One during Lockdown[2020-4 to 2020-10]."
      ],
      "metadata": {
        "id": "8g_ACNgeoGuM"
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    {
      "cell_type": "code",
      "source": [
        "mask = (un_df1['Date'] >= '2019-05-31') & (un_df1['Date'] <= '2020-03-31')\n",
        "df1 = un_df1.loc[mask]"
      ],
      "metadata": {
        "id": "NXmVoToslBVj"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
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        "df1"
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        "id": "fJRAD_uul4mA",
        "outputId": "b6740004-6874-4d3f-df9b-8e13d733919e"
      },
      "execution_count": 41,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "             States       Date Frequency  Estimated Unemployment Rate  \\\n",
              "0    Andhra Pradesh 2019-05-31   Monthly                         3.65   \n",
              "1    Andhra Pradesh 2019-06-30   Monthly                         3.05   \n",
              "2    Andhra Pradesh 2019-07-31   Monthly                         3.75   \n",
              "3    Andhra Pradesh 2019-08-31   Monthly                         3.32   \n",
              "4    Andhra Pradesh 2019-09-30   Monthly                         5.17   \n",
              "..              ...        ...       ...                          ...   \n",
              "746     West Bengal 2019-11-30   Monthly                         6.61   \n",
              "747     West Bengal 2019-12-31   Monthly                         7.24   \n",
              "748     West Bengal 2020-01-31   Monthly                         7.27   \n",
              "749     West Bengal 2020-02-29   Monthly                         7.55   \n",
              "750     West Bengal 2020-03-31   Monthly                         6.67   \n",
              "\n",
              "     Estimated Employed  Estimated Labour Participation Rate Region  \\\n",
              "0            11999139.0                                43.24  Rural   \n",
              "1            11755881.0                                42.05  Rural   \n",
              "2            12086707.0                                43.50  Rural   \n",
              "3            12285693.0                                43.97  Rural   \n",
              "4            12256762.0                                44.68  Rural   \n",
              "..                  ...                                  ...    ...   \n",
              "746          10768462.0                                43.44  Urban   \n",
              "747          11335696.0                                45.97  Urban   \n",
              "748          11208617.0                                45.39  Urban   \n",
              "749          10871168.0                                44.09  Urban   \n",
              "750          10806105.0                                43.34  Urban   \n",
              "\n",
              "     Month_int Month_name  \n",
              "0            5        May  \n",
              "1            6        Jun  \n",
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              "3            8        Aug  \n",
              "4            9        Sep  \n",
              "..         ...        ...  \n",
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              "747         12        Dec  \n",
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              "750          3        Mar  \n",
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              "[588 rows x 9 columns]"
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              "      <td>Andhra Pradesh</td>\n",
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              "      <td>2019-08-31</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>3.32</td>\n",
              "      <td>12285693.0</td>\n",
              "      <td>43.97</td>\n",
              "      <td>Rural</td>\n",
              "      <td>8</td>\n",
              "      <td>Aug</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>2019-09-30</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>5.17</td>\n",
              "      <td>12256762.0</td>\n",
              "      <td>44.68</td>\n",
              "      <td>Rural</td>\n",
              "      <td>9</td>\n",
              "      <td>Sep</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>746</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>2019-11-30</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>6.61</td>\n",
              "      <td>10768462.0</td>\n",
              "      <td>43.44</td>\n",
              "      <td>Urban</td>\n",
              "      <td>11</td>\n",
              "      <td>Nov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>747</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>2019-12-31</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>7.24</td>\n",
              "      <td>11335696.0</td>\n",
              "      <td>45.97</td>\n",
              "      <td>Urban</td>\n",
              "      <td>12</td>\n",
              "      <td>Dec</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>748</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>2020-01-31</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>7.27</td>\n",
              "      <td>11208617.0</td>\n",
              "      <td>45.39</td>\n",
              "      <td>Urban</td>\n",
              "      <td>1</td>\n",
              "      <td>Jan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>749</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>2020-02-29</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>7.55</td>\n",
              "      <td>10871168.0</td>\n",
              "      <td>44.09</td>\n",
              "      <td>Urban</td>\n",
              "      <td>2</td>\n",
              "      <td>Feb</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>750</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>2020-03-31</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>6.67</td>\n",
              "      <td>10806105.0</td>\n",
              "      <td>43.34</td>\n",
              "      <td>Urban</td>\n",
              "      <td>3</td>\n",
              "      <td>Mar</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>588 rows × 9 columns</p>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-6dd0949d-b30a-49c2-98e3-2737608093c5 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-6dd0949d-b30a-49c2-98e3-2737608093c5');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mask = (un_df2['Date'] >= '2020-04-30')\n",
        "df2 = un_df2.loc[mask]"
      ],
      "metadata": {
        "id": "r0wyiPtAmZhh"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "4lkLfNvLmZd7",
        "outputId": "51ed87de-4371-4b48-ca05-9de545d030a8"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             States       Date Frequency  Estimated Unemployment Rate  \\\n",
              "3    Andhra Pradesh 2020-04-30         M                        20.51   \n",
              "4    Andhra Pradesh 2020-05-31         M                        17.43   \n",
              "5    Andhra Pradesh 2020-06-30         M                         3.31   \n",
              "6    Andhra Pradesh 2020-07-31         M                         8.34   \n",
              "7    Andhra Pradesh 2020-08-31         M                         6.96   \n",
              "..              ...        ...       ...                          ...   \n",
              "262     West Bengal 2020-06-30         M                         7.29   \n",
              "263     West Bengal 2020-07-31         M                         6.83   \n",
              "264     West Bengal 2020-08-31         M                        14.87   \n",
              "265     West Bengal 2020-09-30         M                         9.35   \n",
              "266     West Bengal 2020-10-31         M                         9.98   \n",
              "\n",
              "     Estimated Employed  Estimated Labour Participation Rate Region  \\\n",
              "3              11336911                                33.10  South   \n",
              "4              12988845                                36.46  South   \n",
              "5              19805400                                47.41  South   \n",
              "6              15431615                                38.91  South   \n",
              "7              15251776                                37.83  South   \n",
              "..                  ...                                  ...    ...   \n",
              "262            30726310                                40.39   East   \n",
              "263            35372506                                46.17   East   \n",
              "264            33298644                                47.48   East   \n",
              "265            35707239                                47.73   East   \n",
              "266            33962549                                45.63   East   \n",
              "\n",
              "     longitude  latitude  Month_int Month_name  \n",
              "3      15.9129    79.740          4        Apr  \n",
              "4      15.9129    79.740          5        May  \n",
              "5      15.9129    79.740          6        Jun  \n",
              "6      15.9129    79.740          7        Jul  \n",
              "7      15.9129    79.740          8        Aug  \n",
              "..         ...       ...        ...        ...  \n",
              "262    22.9868    87.855          6        Jun  \n",
              "263    22.9868    87.855          7        Jul  \n",
              "264    22.9868    87.855          8        Aug  \n",
              "265    22.9868    87.855          9        Sep  \n",
              "266    22.9868    87.855         10        Oct  \n",
              "\n",
              "[188 rows x 11 columns]"
            ],
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              "      <td>Andhra Pradesh</td>\n",
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              "      <td>2020-06-30</td>\n",
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              "      <td>2020-08-31</td>\n",
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              "      <td>10</td>\n",
              "      <td>Oct</td>\n",
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              "                                                     [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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aX5bPPKUqJLX",
        "outputId": "175a1ed0-a709-4bec-ef99-83e000f73f92"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Andhra Pradesh', 'Assam', 'Bihar', 'Chhattisgarh', 'Delhi', 'Goa',\n",
              "       'Gujarat', 'Haryana', 'Himachal Pradesh', 'Jammu & Kashmir',\n",
              "       'Jharkhand', 'Karnataka', 'Kerala', 'Madhya Pradesh',\n",
              "       'Maharashtra', 'Meghalaya', 'Odisha', 'Puducherry', 'Punjab',\n",
              "       'Rajasthan', 'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura',\n",
              "       'Uttar Pradesh', 'Uttarakhand', 'West Bengal', 'Chandigarh'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "df1[\"States\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[\"States\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8Nm4k9fmZbf",
        "outputId": "9627f7d9-2814-4dc2-cecf-b9e8a8d3079b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Andhra Pradesh', 'Assam', 'Bihar', 'Chhattisgarh', 'Delhi', 'Goa',\n",
              "       'Gujarat', 'Haryana', 'Himachal Pradesh', 'Jammu & Kashmir',\n",
              "       'Jharkhand', 'Karnataka', 'Kerala', 'Madhya Pradesh',\n",
              "       'Maharashtra', 'Meghalaya', 'Odisha', 'Puducherry', 'Punjab',\n",
              "       'Rajasthan', 'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura',\n",
              "       'Uttar Pradesh', 'Uttarakhand', 'West Bengal'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "AW80emB54l6C"
      },
      "outputs": [],
      "source": [
        "undf_stats1 = df1[['Estimated Unemployment Rate','Estimated Employed', 'Estimated Labour Participation Rate']]\n",
        "\n",
        "undf_stats2 = df2[['Estimated Unemployment Rate','Estimated Employed', 'Estimated Labour Participation Rate']]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "undf_stats1.head()\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-vlndf4CmZZK",
        "outputId": "c3ef71f8-5510-42a2-a001-ec16d763d6ec"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Estimated Unemployment Rate  Estimated Employed  \\\n",
              "0                         3.65          11999139.0   \n",
              "1                         3.05          11755881.0   \n",
              "2                         3.75          12086707.0   \n",
              "3                         3.32          12285693.0   \n",
              "4                         5.17          12256762.0   \n",
              "\n",
              "   Estimated Labour Participation Rate  \n",
              "0                                43.24  \n",
              "1                                42.05  \n",
              "2                                43.50  \n",
              "3                                43.97  \n",
              "4                                44.68  "
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        "undf_stats2.head()"
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          "height": 206
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        "id": "pwzFUrmArkCU",
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      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Estimated Unemployment Rate  Estimated Employed  \\\n",
              "3                        20.51            11336911   \n",
              "4                        17.43            12988845   \n",
              "5                         3.31            19805400   \n",
              "6                         8.34            15431615   \n",
              "7                         6.96            15251776   \n",
              "\n",
              "   Estimated Labour Participation Rate  \n",
              "3                                33.10  \n",
              "4                                36.46  \n",
              "5                                47.41  \n",
              "6                                38.91  \n",
              "7                                37.83  "
            ],
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              "      <td>11336911</td>\n",
              "      <td>33.10</td>\n",
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              "    <tr>\n",
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              "      <td>47.41</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<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",
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          "metadata": {},
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      "execution_count": 49,
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          "data": {
            "text/plain": [
              "       Estimated Unemployment Rate  Estimated Employed  \\\n",
              "count                   588.000000        5.880000e+02   \n",
              "mean                      9.614864        7.470499e+06   \n",
              "std                       7.372715        8.276125e+06   \n",
              "min                       0.000000        8.618600e+04   \n",
              "25%                       4.332500        1.190404e+06   \n",
              "50%                       7.225000        5.064048e+06   \n",
              "75%                      13.300000        1.164705e+07   \n",
              "max                      34.690000        4.577751e+07   \n",
              "\n",
              "       Estimated Labour Participation Rate  \n",
              "count                           588.000000  \n",
              "mean                             43.815187  \n",
              "std                               7.679054  \n",
              "min                              26.330000  \n",
              "25%                              39.047500  \n",
              "50%                              42.330000  \n",
              "75%                              45.897500  \n",
              "max                              72.570000  "
            ],
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              "      <td>588.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>9.614864</td>\n",
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              "      <td>43.815187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
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              "      <td>8.276125e+06</td>\n",
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              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.332500</td>\n",
              "      <td>1.190404e+06</td>\n",
              "      <td>39.047500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.225000</td>\n",
              "      <td>5.064048e+06</td>\n",
              "      <td>42.330000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.300000</td>\n",
              "      <td>1.164705e+07</td>\n",
              "      <td>45.897500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>34.690000</td>\n",
              "      <td>4.577751e+07</td>\n",
              "      <td>72.570000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a984241c-dafb-46dd-9738-d1701a8fbe8b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
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              "          const element = document.querySelector('#df-a984241c-dafb-46dd-9738-d1701a8fbe8b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "undf_stats1.describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "undf_stats2.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "0KDJsOg6qUfm",
        "outputId": "3cb94d52-be57-4b33-c079-9b7e783fa02c"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Estimated Unemployment Rate  Estimated Employed  \\\n",
              "count                   188.000000        1.880000e+02   \n",
              "mean                     13.277128        1.343658e+07   \n",
              "std                      11.805614        1.307267e+07   \n",
              "min                       0.500000        1.175420e+05   \n",
              "25%                       5.037500        2.719468e+06   \n",
              "50%                      10.525000        9.358904e+06   \n",
              "75%                      17.415000        2.004234e+07   \n",
              "max                      75.850000        5.653952e+07   \n",
              "\n",
              "       Estimated Labour Participation Rate  \n",
              "count                           188.000000  \n",
              "mean                             40.632074  \n",
              "std                               7.395797  \n",
              "min                              16.770000  \n",
              "25%                              36.317500  \n",
              "50%                              39.625000  \n",
              "75%                              43.092500  \n",
              "max                              64.600000  "
            ],
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              "      <th></th>\n",
              "      <th>Estimated Unemployment Rate</th>\n",
              "      <th>Estimated Employed</th>\n",
              "      <th>Estimated Labour Participation Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>188.000000</td>\n",
              "      <td>1.880000e+02</td>\n",
              "      <td>188.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13.277128</td>\n",
              "      <td>1.343658e+07</td>\n",
              "      <td>40.632074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.805614</td>\n",
              "      <td>1.307267e+07</td>\n",
              "      <td>7.395797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.175420e+05</td>\n",
              "      <td>16.770000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.037500</td>\n",
              "      <td>2.719468e+06</td>\n",
              "      <td>36.317500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10.525000</td>\n",
              "      <td>9.358904e+06</td>\n",
              "      <td>39.625000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>17.415000</td>\n",
              "      <td>2.004234e+07</td>\n",
              "      <td>43.092500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>75.850000</td>\n",
              "      <td>5.653952e+07</td>\n",
              "      <td>64.600000</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      "execution_count": 51,
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          "height": 143
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        "id": "3q9Ww_Lt4l3x",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     count        mean         std       min  \\\n",
              "Estimated Unemployment Rate          588.0        9.61        7.37      0.00   \n",
              "Estimated Employed                   588.0  7470498.57  8276124.71  86186.00   \n",
              "Estimated Labour Participation Rate  588.0       43.82        7.68     26.33   \n",
              "\n",
              "                                            25%         50%          75%  \\\n",
              "Estimated Unemployment Rate                4.33        7.22        13.30   \n",
              "Estimated Employed                   1190404.50  5064048.50  11647054.75   \n",
              "Estimated Labour Participation Rate       39.05       42.33        45.90   \n",
              "\n",
              "                                             max  \n",
              "Estimated Unemployment Rate                34.69  \n",
              "Estimated Employed                   45777509.00  \n",
              "Estimated Labour Participation Rate        72.57  "
            ],
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              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Estimated Unemployment Rate</th>\n",
              "      <td>588.0</td>\n",
              "      <td>9.61</td>\n",
              "      <td>7.37</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4.33</td>\n",
              "      <td>7.22</td>\n",
              "      <td>13.30</td>\n",
              "      <td>34.69</td>\n",
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              "    <tr>\n",
              "      <th>Estimated Employed</th>\n",
              "      <td>588.0</td>\n",
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              "      <td>11647054.75</td>\n",
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              "      <th>Estimated Labour Participation Rate</th>\n",
              "      <td>588.0</td>\n",
              "      <td>43.82</td>\n",
              "      <td>7.68</td>\n",
              "      <td>26.33</td>\n",
              "      <td>39.05</td>\n",
              "      <td>42.33</td>\n",
              "      <td>45.90</td>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-b2f5c8ba-929c-4c42-97f2-f1e95f9b7b69');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "round(undf_stats1.describe().T,2)"
      ]
    },
    {
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        "round(undf_stats2.describe().T,2)"
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        "id": "yU49ECNymZWf",
        "outputId": "7424677f-04e1-48a9-fb29-4abfdb91cd3d"
      },
      "execution_count": 52,
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        {
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            "text/plain": [
              "                                     count         mean          std  \\\n",
              "Estimated Unemployment Rate          188.0        13.28        11.81   \n",
              "Estimated Employed                   188.0  13436576.06  13072671.59   \n",
              "Estimated Labour Participation Rate  188.0        40.63         7.40   \n",
              "\n",
              "                                           min         25%         50%  \\\n",
              "Estimated Unemployment Rate               0.50        5.04       10.52   \n",
              "Estimated Employed                   117542.00  2719468.25  9358904.50   \n",
              "Estimated Labour Participation Rate      16.77       36.32       39.62   \n",
              "\n",
              "                                             75%          max  \n",
              "Estimated Unemployment Rate                17.42        75.85  \n",
              "Estimated Employed                   20042339.00  56539521.00  \n",
              "Estimated Labour Participation Rate        43.09        64.60  "
            ],
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              "      <th>Estimated Unemployment Rate</th>\n",
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              "      <th>Estimated Employed</th>\n",
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              "      <td>56539521.00</td>\n",
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              "    <tr>\n",
              "      <th>Estimated Labour Participation Rate</th>\n",
              "      <td>188.0</td>\n",
              "      <td>40.63</td>\n",
              "      <td>7.40</td>\n",
              "      <td>16.77</td>\n",
              "      <td>36.32</td>\n",
              "      <td>39.62</td>\n",
              "      <td>43.09</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "4LgUpqNp4l1n"
      },
      "outputs": [],
      "source": [
        "region_stats1 = df1.groupby(['Region'])[['Estimated Unemployment Rate','Estimated Employed','Estimated Labour Participation Rate']].mean().reset_index()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "region_stats1 = round(region_stats1,2)"
      ],
      "metadata": {
        "id": "ti2eYfNdrykb"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
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        {
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            "text/plain": [
              "  Region  Estimated Unemployment Rate  Estimated Employed  \\\n",
              "0  Rural                         8.23         10580891.91   \n",
              "1  Urban                        10.91          4564736.36   \n",
              "\n",
              "   Estimated Labour Participation Rate  \n",
              "0                                45.52  \n",
              "1                                42.23  "
            ],
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              "          const element = document.querySelector('#df-ca6e5282-3fc9-4475-9b17-b03432d2a233');\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",
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          "metadata": {},
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        }
      ],
      "source": [
        "region_stats1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "region_stats2 = df2.groupby(['Region'])[['Estimated Unemployment Rate','Estimated Employed','Estimated Labour Participation Rate']].mean().reset_index()\n"
      ],
      "metadata": {
        "id": "qL-8lZjomZT8"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "region_stats2 = round(region_stats2,2)"
      ],
      "metadata": {
        "id": "4T948HiVru4L"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "region_stats2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "FbUKA9plmZRQ",
        "outputId": "3e62d24e-eee4-4a3f-b543-2071505a2526"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Region  Estimated Unemployment Rate  Estimated Employed  \\\n",
              "0       East                        16.17         18801824.50   \n",
              "1      North                        16.23         12817156.05   \n",
              "2  Northeast                        10.03          3342133.89   \n",
              "3      South                        12.98         13441439.90   \n",
              "4       West                         9.27         18187468.74   \n",
              "\n",
              "   Estimated Labour Participation Rate  \n",
              "0                                39.32  \n",
              "1                                37.67  \n",
              "2                                49.33  \n",
              "3                                39.45  \n",
              "4                                40.79  "
            ],
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              "      <td>East</td>\n",
              "      <td>16.17</td>\n",
              "      <td>18801824.50</td>\n",
              "      <td>39.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>North</td>\n",
              "      <td>16.23</td>\n",
              "      <td>12817156.05</td>\n",
              "      <td>37.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Northeast</td>\n",
              "      <td>10.03</td>\n",
              "      <td>3342133.89</td>\n",
              "      <td>49.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>South</td>\n",
              "      <td>12.98</td>\n",
              "      <td>13441439.90</td>\n",
              "      <td>39.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>West</td>\n",
              "      <td>9.27</td>\n",
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              "    }\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-303937b7-0eed-48ac-85b2-efd15d45bc35 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-303937b7-0eed-48ac-85b2-efd15d45bc35');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Visualizations**"
      ],
      "metadata": {
        "id": "EbNs6nmnag1X"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "PZZj9e04-RGJ",
        "outputId": "4c4ba3b5-00c7-4e71-9aec-4ca78e33a2fb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(25,8))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.title(\"co-relations of variables Before Lockdown\", fontsize=16)\n",
        "sns.heatmap(undf_stats1.corr(), annot=True, linewidths=0.1, fmt='.2f', square=True)\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.title(\"co-relations of variables During Lockdown\", fontsize=16)\n",
        "sns.heatmap(undf_stats2.corr(), annot=True, linewidths=0.1, fmt='.2f', square=True)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "gQLap-xREktA",
        "outputId": "cbdff5d4-037a-4be6-9908-580112e71478"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(25, 8))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "sns.barplot(x='Region', y='Estimated Unemployment Rate', data=df1)\n",
        "plt.xlabel(\"Region\",fontsize=16)\n",
        "plt.ylabel('Estimated Unemployment Rate',fontsize=16)\n",
        "plt.title('Estimated Unemployment Rate Before Lockdown',fontsize=16)\n",
        "\n",
        "\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "sns.barplot(x='Region', y='Estimated Unemployment Rate', data=df2)\n",
        "plt.xlabel(\"Region\",fontsize=16)\n",
        "plt.ylabel('Estimated Unemployment Rate',fontsize=16)\n",
        "plt.title('Estimated Unemployment Rate During Lockdown',fontsize=16)\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "dhX9SaEN_TEo",
        "outputId": "e1de3229-dcdf-4db8-8d60-2a488edfbc7e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(25,8))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.title(\"State Wise Estimated Unemployment Rate Before Lockdown\",fontsize=16)\n",
        "sns.boxplot(y=\"States\",x ='Estimated Unemployment Rate',data =df1)\n",
        "plt.xlabel(\"Estimated Unemployment Rate\",fontsize=16)\n",
        "plt.ylabel(\"States\",fontsize=16)\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.title(\"State Wise Estimated Unemployment Rate During Lockdown\",fontsize=16)\n",
        "sns.boxplot(y=\"States\",x ='Estimated Unemployment Rate',data =df2)\n",
        "plt.xlabel(\"Estimated Unemployment Rate\",fontsize=16)\n",
        "plt.ylabel(\"States\",fontsize=16)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5SLnRDt0gbd4",
        "outputId": "44dc2431-78f2-4bae-ecec-406dd0333bbb"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           States       Date Frequency  Estimated Unemployment Rate  \\\n",
              "0  Andhra Pradesh 2019-05-31   Monthly                         3.65   \n",
              "1  Andhra Pradesh 2019-06-30   Monthly                         3.05   \n",
              "2  Andhra Pradesh 2019-07-31   Monthly                         3.75   \n",
              "3  Andhra Pradesh 2019-08-31   Monthly                         3.32   \n",
              "4  Andhra Pradesh 2019-09-30   Monthly                         5.17   \n",
              "\n",
              "   Estimated Employed  Estimated Labour Participation Rate Region  Month_int  \\\n",
              "0          11999139.0                                43.24  Rural          5   \n",
              "1          11755881.0                                42.05  Rural          6   \n",
              "2          12086707.0                                43.50  Rural          7   \n",
              "3          12285693.0                                43.97  Rural          8   \n",
              "4          12256762.0                                44.68  Rural          9   \n",
              "\n",
              "  Month_name  \n",
              "0        May  \n",
              "1        Jun  \n",
              "2        Jul  \n",
              "3        Aug  \n",
              "4        Sep  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9e49b06e-fbb9-4f52-8d91-02c99a72908e\">\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>States</th>\n",
              "      <th>Date</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Estimated Unemployment Rate</th>\n",
              "      <th>Estimated Employed</th>\n",
              "      <th>Estimated Labour Participation Rate</th>\n",
              "      <th>Region</th>\n",
              "      <th>Month_int</th>\n",
              "      <th>Month_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>2019-05-31</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>3.65</td>\n",
              "      <td>11999139.0</td>\n",
              "      <td>43.24</td>\n",
              "      <td>Rural</td>\n",
              "      <td>5</td>\n",
              "      <td>May</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>2019-06-30</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>3.05</td>\n",
              "      <td>11755881.0</td>\n",
              "      <td>42.05</td>\n",
              "      <td>Rural</td>\n",
              "      <td>6</td>\n",
              "      <td>Jun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>2019-07-31</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>3.75</td>\n",
              "      <td>12086707.0</td>\n",
              "      <td>43.50</td>\n",
              "      <td>Rural</td>\n",
              "      <td>7</td>\n",
              "      <td>Jul</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>2019-08-31</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>3.32</td>\n",
              "      <td>12285693.0</td>\n",
              "      <td>43.97</td>\n",
              "      <td>Rural</td>\n",
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              "      <th>4</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>2019-09-30</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>5.17</td>\n",
              "      <td>12256762.0</td>\n",
              "      <td>44.68</td>\n",
              "      <td>Rural</td>\n",
              "      <td>9</td>\n",
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              "           States       Date Frequency  Estimated Unemployment Rate  \\\n",
              "3  Andhra Pradesh 2020-04-30         M                        20.51   \n",
              "4  Andhra Pradesh 2020-05-31         M                        17.43   \n",
              "5  Andhra Pradesh 2020-06-30         M                         3.31   \n",
              "6  Andhra Pradesh 2020-07-31         M                         8.34   \n",
              "7  Andhra Pradesh 2020-08-31         M                         6.96   \n",
              "\n",
              "   Estimated Employed  Estimated Labour Participation Rate Region  longitude  \\\n",
              "3            11336911                                33.10  South    15.9129   \n",
              "4            12988845                                36.46  South    15.9129   \n",
              "5            19805400                                47.41  South    15.9129   \n",
              "6            15431615                                38.91  South    15.9129   \n",
              "7            15251776                                37.83  South    15.9129   \n",
              "\n",
              "   latitude  Month_int Month_name  \n",
              "3     79.74          4        Apr  \n",
              "4     79.74          5        May  \n",
              "5     79.74          6        Jun  \n",
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      "source": [
        "States_stats1 = df1.groupby(['States'])[['Estimated Unemployment Rate','Estimated Employed','Estimated Labour Participation Rate']].mean().reset_index()"
      ],
      "metadata": {
        "id": "DxHpdA9XmZOu"
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      "execution_count": 65,
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            "text/plain": [
              "           States  Estimated Unemployment Rate  Estimated Employed  \\\n",
              "0  Andhra Pradesh                     5.213182        8.340685e+06   \n",
              "1           Assam                     6.437619        5.585966e+06   \n",
              "2           Bihar                    13.990000        1.319102e+07   \n",
              "3      Chandigarh                    16.789091        3.177700e+05   \n",
              "4    Chhattisgarh                     7.711364        4.490173e+06   \n",
              "\n",
              "   Estimated Labour Participation Rate  \n",
              "0                            39.877273  \n",
              "1                            45.999048  \n",
              "2                            38.447727  \n",
              "3                            39.840000  \n",
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            "text/plain": [
              "           States  Estimated Unemployment Rate  Estimated Employed  \\\n",
              "0  Andhra Pradesh                     5.213182        8.340685e+06   \n",
              "1           Assam                     6.437619        5.585966e+06   \n",
              "2           Bihar                    13.990000        1.319102e+07   \n",
              "3      Chandigarh                    16.789091        3.177700e+05   \n",
              "4    Chhattisgarh                     7.711364        4.490173e+06   \n",
              "\n",
              "   Estimated Labour Participation Rate  \n",
              "0                            39.877273  \n",
              "1                            45.999048  \n",
              "2                            38.447727  \n",
              "3                            39.840000  \n",
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            "text/plain": [
              "           States  Estimated Unemployment Rate  Estimated Employed  \\\n",
              "0  Andhra Pradesh                     5.213182        8.340685e+06   \n",
              "1           Assam                     6.437619        5.585966e+06   \n",
              "2           Bihar                    13.990000        1.319102e+07   \n",
              "3      Chandigarh                    16.789091        3.177700e+05   \n",
              "4    Chhattisgarh                     7.711364        4.490173e+06   \n",
              "\n",
              "   Estimated Labour Participation Rate  \n",
              "0                            39.877273  \n",
              "1                            45.999048  \n",
              "2                            38.447727  \n",
              "3                            39.840000  \n",
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      "source": [
        "States_stats2 = df2.groupby(['States'])[['Estimated Unemployment Rate','Estimated Employed','Estimated Labour Participation Rate','longitude','latitude']].mean().reset_index()"
      ],
      "metadata": {
        "id": "ziup694emZEV"
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      "execution_count": 69,
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            "text/plain": [
              "           States  Estimated Unemployment Rate  Estimated Employed  \\\n",
              "0  Andhra Pradesh                     9.934286        1.502749e+07   \n",
              "1           Assam                     4.960000        1.048853e+07   \n",
              "2           Bihar                    22.625714        2.252449e+07   \n",
              "3    Chhattisgarh                     7.517143        8.166270e+06   \n",
              "4           Delhi                    18.575714        4.158975e+06   \n",
              "\n",
              "   Estimated Labour Participation Rate  longitude  latitude  \n",
              "0                            38.360000    15.9129   79.7400  \n",
              "1                            42.071429    26.2006   92.9376  \n",
              "2                            36.852857    25.0961   85.3131  \n",
              "3                            39.622857    21.2787   81.8661  \n",
              "4                            32.030000    28.7041   77.1025  "
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              "      <td>8.166270e+06</td>\n",
              "      <td>39.622857</td>\n",
              "      <td>21.2787</td>\n",
              "      <td>81.8661</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Delhi</td>\n",
              "      <td>18.575714</td>\n",
              "      <td>4.158975e+06</td>\n",
              "      <td>32.030000</td>\n",
              "      <td>28.7041</td>\n",
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      "source": [
        "States_stats2=round(States_stats2,2)"
      ],
      "metadata": {
        "id": "XWSywcAqmZB3"
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      "execution_count": 70,
      "outputs": []
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      "execution_count": 71,
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              "           States  Estimated Unemployment Rate  Estimated Employed  \\\n",
              "0  Andhra Pradesh                         9.93         15027488.00   \n",
              "1           Assam                         4.96         10488527.86   \n",
              "2           Bihar                        22.63         22524490.71   \n",
              "3    Chhattisgarh                         7.52          8166269.86   \n",
              "4           Delhi                        18.58          4158974.71   \n",
              "\n",
              "   Estimated Labour Participation Rate  longitude  latitude  \n",
              "0                                38.36      15.91     79.74  \n",
              "1                                42.07      26.20     92.94  \n",
              "2                                36.85      25.10     85.31  \n",
              "3                                39.62      21.28     81.87  \n",
              "4                                32.03      28.70     77.10  "
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              "        States  Estimated Unemployment Rate  Estimated Employed  \\\n",
              "7      Haryana                        29.09          6683266.00   \n",
              "17  Puducherry                        25.13           325670.00   \n",
              "10   Jharkhand                        23.53          8206407.86   \n",
              "23     Tripura                        22.79          1338411.86   \n",
              "2        Bihar                        22.63         22524490.71   \n",
              "\n",
              "    Estimated Labour Participation Rate  longitude  latitude  \n",
              "7                                 41.97      29.06     76.09  \n",
              "17                                35.20      11.94     79.81  \n",
              "10                                39.56      23.61     85.28  \n",
              "23                                53.31      23.94     91.99  \n",
              "2                                 36.85      25.10     85.31  "
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bde62d82-a395-4c57-86ee-efa5ec27c407');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(25,8))\n",
        "\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.title(\"Estimated Unemployment Rate Before Lockdown\",fontsize=16)\n",
        "sns.barplot(data=States_stats1,x=\"States\",y=\"Estimated Unemployment Rate\")\n",
        "plt.xticks(rotation= 90)\n",
        "plt.xlabel('States',fontsize=16)\n",
        "plt.ylabel('Estimated Unemployment Rate',fontsize=16)\n",
        "\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.title(\"Estimated Unemployment Rate During Lockdown\",fontsize=16)\n",
        "sns.barplot(data=States_stats2,x=\"States\",y=\"Estimated Unemployment Rate\")\n",
        "plt.xticks(rotation= 90)\n",
        "plt.xlabel('States',fontsize=16)\n",
        "plt.ylabel('Estimated Unemployment Rate',fontsize=16)\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "2fR42D49lHdM",
        "outputId": "5682c9de-f5fd-4b47-9e87-1e7d7913dc85"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Top Five State Were Estimated Unemployment Rate\tHighest Before Lockdown**\n",
        "\n",
        "1. Tripura\t28.98\t\n",
        "\n",
        "2. Haryana\t23.17\t\n",
        "\n",
        "3. Himachal Pradesh\t19.40\t\n",
        "\n",
        "4. Jammu & Kashmir\t16.99\n",
        "\n",
        "5. Chandigarh\t16.79"
      ],
      "metadata": {
        "id": "oLCPq8OeodSF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Top Five State Were Estimated Unemployment Rate Highest During Lockdown**\n",
        "\n",
        "1.\tHaryana\t29.09\t\n",
        "\n",
        "2.\tPuducherry\t25.13\t\n",
        "\n",
        "3.\tJharkhand\t23.53\t\n",
        "\n",
        "4.\tTripura\t22.79\t\n",
        "\n",
        "5.\tBihar\t22.63"
      ],
      "metadata": {
        "id": "ExjjBJ-Bp3x2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "estimated_employed1=States_stats1.sort_values(by='Estimated Employed', ascending=False)\n",
        "\n",
        "estimated_employed2=States_stats2.sort_values(by='Estimated Employed', ascending=False)"
      ],
      "metadata": {
        "id": "LwuFr4nGlHsp"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(25,8))\n",
        "\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.title(\"Estimated Employed workforce Before Lockdown\",fontsize=16)\n",
        "sns.barplot(data=estimated_employed1,x=\"States\",y=\"Estimated Employed\")\n",
        "plt.xticks(rotation= 90)\n",
        "plt.xlabel('States',fontsize=16)\n",
        "plt.ylabel('Estimated Employed',fontsize=16)\n",
        "\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.title(\"Estimated Employed Workforce During Lockdown\",fontsize=16)\n",
        "sns.barplot(data=estimated_employed2,x=\"States\",y=\"Estimated Employed\")\n",
        "plt.xticks(rotation= 90)\n",
        "plt.xlabel('States',fontsize=16)\n",
        "plt.ylabel('Estimated Employed',fontsize=16)\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "OHvauYvglHz6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "3e0c4fb9-0c7f-46f2-d041-6d4138dfa84f"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "estimated_employed1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5GBF4yE1C-Mz",
        "outputId": "6164302c-2ea4-4c5b-d503-f56887857d79"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           States  Estimated Unemployment Rate  Estimated Employed  \\\n",
              "25  Uttar Pradesh                        10.64         28675914.86   \n",
              "15    Maharashtra                         5.41         20976060.18   \n",
              "27    West Bengal                         6.60         17997669.77   \n",
              "22     Tamil Nadu                         3.16         13837079.95   \n",
              "2           Bihar                        13.99         13191022.05   \n",
              "\n",
              "    Estimated Labour Participation Rate  \n",
              "25                                39.65  \n",
              "15                                43.60  \n",
              "27                                46.82  \n",
              "22                                44.08  \n",
              "2                                 38.45  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5eeb0e0f-487c-494b-9eec-1c331378d8e4\">\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",
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              "      <th></th>\n",
              "      <th>States</th>\n",
              "      <th>Estimated Unemployment Rate</th>\n",
              "      <th>Estimated Employed</th>\n",
              "      <th>Estimated Labour Participation Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>10.64</td>\n",
              "      <td>28675914.86</td>\n",
              "      <td>39.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>5.41</td>\n",
              "      <td>20976060.18</td>\n",
              "      <td>43.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>6.60</td>\n",
              "      <td>17997669.77</td>\n",
              "      <td>46.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>3.16</td>\n",
              "      <td>13837079.95</td>\n",
              "      <td>44.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>13.99</td>\n",
              "      <td>13191022.05</td>\n",
              "      <td>38.45</td>\n",
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          "metadata": {},
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      "execution_count": 80,
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        "id": "8U0Mp0zrq7vq",
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        {
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            "text/plain": [
              "           States  Estimated Unemployment Rate  Estimated Employed  \\\n",
              "24  Uttar Pradesh                        10.10         54396766.00   \n",
              "14    Maharashtra                         9.19         38119655.86   \n",
              "26    West Bengal                        11.88         32051822.71   \n",
              "2           Bihar                        22.63         22524490.71   \n",
              "6         Gujarat                         6.45         22122382.71   \n",
              "\n",
              "    Estimated Labour Participation Rate  longitude  latitude  \n",
              "24                                36.90      26.85     80.95  \n",
              "14                                40.68      19.75     75.71  \n",
              "26                                44.17      22.99     87.86  \n",
              "2                                 36.85      25.10     85.31  \n",
              "6                                 44.08      22.26     71.19  "
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              "      <th>24</th>\n",
              "      <td>Uttar Pradesh</td>\n",
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              "      <td>54396766.00</td>\n",
              "      <td>36.90</td>\n",
              "      <td>26.85</td>\n",
              "      <td>80.95</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>9.19</td>\n",
              "      <td>38119655.86</td>\n",
              "      <td>40.68</td>\n",
              "      <td>19.75</td>\n",
              "      <td>75.71</td>\n",
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              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>11.88</td>\n",
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              "      <td>22.99</td>\n",
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              "      <td>Bihar</td>\n",
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              "      <td>85.31</td>\n",
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              "      <th>6</th>\n",
              "      <td>Gujarat</td>\n",
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              "      <td>22122382.71</td>\n",
              "      <td>44.08</td>\n",
              "      <td>22.26</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "estimated_employed2.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Top Five State Were Maximum Estimated Employed Workforce Before Lockdown**\n",
        "\n",
        "1.\tUttar Pradesh\t28675914.86\t\n",
        "2.\tMaharashtra\t20976060.18\t\n",
        "3.\tWest Bengal\t17997669.77\t\n",
        "4.\tTamil Nadu\t13837079.95\t\n",
        "5.  Bihar\t\t13191022.05\t"
      ],
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        "id": "YFgrs4QJBUuM"
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    {
      "cell_type": "markdown",
      "source": [
        "**Top Five State Were Maximum Estimated Employed Workforce During Lockdown**\n",
        "\n",
        "1.\tUttar Pradesh\t\t54396766.00\t\n",
        "2.\tMaharashtra\t\t38119655.86\t\n",
        "3.\tWest Bengal\t\t32051822.71\n",
        "4.  Bihar\t\t22524490.71\t\n",
        "5.  Gujarat\t\t22122382.71"
      ],
      "metadata": {
        "id": "vIp7cvyUCcMs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Estimated_Labour_Participation_Rate1=States_stats1.sort_values(by='Estimated Labour Participation Rate', ascending=False)\n",
        "\n",
        "Estimated_Labour_Participation_Rate2=States_stats2.sort_values(by='Estimated Labour Participation Rate', ascending=False)"
      ],
      "metadata": {
        "id": "EWx2-y7kDCqc"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Estimated_Labour_Participation_Rate1.head()"
      ],
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        "id": "eI3SVxdhDC_Y",
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          "height": 206
        },
        "outputId": "0f8b73f5-3bb0-465d-ac7d-9cb91ee1eb61"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       States  Estimated Unemployment Rate  Estimated Employed  \\\n",
              "24    Tripura                        28.98           741353.59   \n",
              "16  Meghalaya                         3.82           695116.95   \n",
              "23  Telangana                         4.77          8456889.73   \n",
              "21     Sikkim                         7.44           109052.50   \n",
              "7     Gujarat                         5.30         11952497.59   \n",
              "\n",
              "    Estimated Labour Participation Rate  \n",
              "24                                64.53  \n",
              "16                                58.43  \n",
              "23                                55.01  \n",
              "21                                48.64  \n",
              "7                                 47.91  "
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              "                                                     [key], {});\n",
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              "\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Estimated_Labour_Participation_Rate2.head()"
      ],
      "metadata": {
        "id": "hU4gGl9fDDDR",
        "colab": {
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          "height": 206
        },
        "outputId": "00f91d90-6841-4158-8000-7a4300d21a48"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         States  Estimated Unemployment Rate  Estimated Employed  \\\n",
              "15    Meghalaya                         4.55          1311125.71   \n",
              "22    Telangana                         6.97         15809135.29   \n",
              "23      Tripura                        22.79          1338411.86   \n",
              "26  West Bengal                        11.88         32051822.71   \n",
              "6       Gujarat                         6.45         22122382.71   \n",
              "\n",
              "    Estimated Labour Participation Rate  longitude  latitude  \n",
              "15                                58.31      25.47     91.37  \n",
              "22                                53.42      18.11     79.02  \n",
              "23                                53.31      23.94     91.99  \n",
              "26                                44.17      22.99     87.86  \n",
              "6                                 44.08      22.26     71.19  "
            ],
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              "      <th>States</th>\n",
              "      <th>Estimated Unemployment Rate</th>\n",
              "      <th>Estimated Employed</th>\n",
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              "      <th>15</th>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>4.55</td>\n",
              "      <td>1311125.71</td>\n",
              "      <td>58.31</td>\n",
              "      <td>25.47</td>\n",
              "      <td>91.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Telangana</td>\n",
              "      <td>6.97</td>\n",
              "      <td>15809135.29</td>\n",
              "      <td>53.42</td>\n",
              "      <td>18.11</td>\n",
              "      <td>79.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Tripura</td>\n",
              "      <td>22.79</td>\n",
              "      <td>1338411.86</td>\n",
              "      <td>53.31</td>\n",
              "      <td>23.94</td>\n",
              "      <td>91.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>11.88</td>\n",
              "      <td>32051822.71</td>\n",
              "      <td>44.17</td>\n",
              "      <td>22.99</td>\n",
              "      <td>87.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>6.45</td>\n",
              "      <td>22122382.71</td>\n",
              "      <td>44.08</td>\n",
              "      <td>22.26</td>\n",
              "      <td>71.19</td>\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(25,8))\n",
        "\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.title(\"Estimated_Labour_Participation_Rate Before Lockdown\",fontsize=16)\n",
        "sns.barplot(data=Estimated_Labour_Participation_Rate1,x=\"States\",y=\"Estimated Labour Participation Rate\")\n",
        "plt.xticks(rotation= 90)\n",
        "plt.xlabel('States',fontsize=16)\n",
        "plt.ylabel('Estimated_Labour_Participation_Rate2',fontsize=16)\n",
        "\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.title(\"Estimated_Labour_Participation_Rate During Lockdown\",fontsize=16)\n",
        "sns.barplot(data=Estimated_Labour_Participation_Rate2,x=\"States\",y=\"Estimated Labour Participation Rate\")\n",
        "plt.xticks(rotation= 90)\n",
        "plt.xlabel('States',fontsize=16)\n",
        "plt.ylabel('Estimated_Labour_Participation_Rate',fontsize=16)\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "aqIFySssDDF8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "outputId": "d46cb881-7559-4197-e395-6e202090b03c"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Top Five State Were Maximum Estimated Labour Participation Rate Before Lockdown**\n",
        "\n",
        "1.\tTripura\t\t64.53\n",
        "2.\tMeghalaya\t\t58.43\n",
        "3.\tTelangana\t\t55.01\n",
        "4.\tSikkim\t\t48.64\n",
        "5.\tGujarat\t\t47.91"
      ],
      "metadata": {
        "id": "J2b7nfI-Ttde"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Top Five State Were Maximum Estimated Labour Participation Rate During Lockdown**\n",
        "\n",
        "1.\tMeghalaya\t\t58.31\t\n",
        "2.\tTelangana\t\t53.42\t\n",
        "3.\tTripura\t\t53.31\t\n",
        "4.\tWest Bengal\t\t44.17\t\n",
        "5.\tGujarat\t\t44.08\t"
      ],
      "metadata": {
        "id": "q9JXla7cUHlv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Thank You**\n",
        "\n",
        "            -NAVJOT KHATRI"
      ],
      "metadata": {
        "id": "46IbjHfTY7P1"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM7HyT9ukRCKK4rQiPZarOC",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}