Task1_Unemployment_in_India.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
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      "metadata": {
        "id": "XlydM9a2r46U"
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      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Data analysis/Unemployment in India/Unemployment in India.csv')"
      ]
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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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            "<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",
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            "dtypes: float64(3), object(4)\n",
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              "Region                                      28\n",
              " Date                                       28\n",
              " Frequency                                  28\n",
              " Estimated Unemployment Rate (%)            28\n",
              " Estimated Employed                         28\n",
              " Estimated Labour Participation Rate (%)    28\n",
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              "(740, 7)"
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          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fBSqnm_KbuC",
        "outputId": "f678d1fa-6742-44b4-e3e2-da4664416e54"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Region', ' Date', ' Frequency', ' Estimated Unemployment Rate (%)',\n",
              "       ' Estimated Employed', ' Estimated Labour Participation Rate (%)',\n",
              "       'Area'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lpjiZfGXKfg7"
      },
      "outputs": [],
      "source": [
        "df.columns = df.columns.str.strip()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kIAa-wrdMukl",
        "outputId": "bbe7f8e2-df82-4029-c57f-911c1002ed05"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Region', 'Date', 'Frequency', 'Estimated Unemployment Rate (%)',\n",
              "       'Estimated Employed', 'Estimated Labour Participation Rate (%)',\n",
              "       'Area'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VV6HbAFbKgET"
      },
      "outputs": [],
      "source": [
        "df['Date'] = pd.to_datetime(df['Date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "MpVZ_QiBKM9f",
        "outputId": "17c34206-0a1c-4bf0-ece7-d5307f83639b"
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      "outputs": [
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          "data": {
            "text/plain": [
              "                                Date  Estimated Unemployment Rate (%)  \\\n",
              "count                            740                       740.000000   \n",
              "mean   2019-12-12 18:36:58.378378496                        11.787946   \n",
              "min              2019-05-31 00:00:00                         0.000000   \n",
              "25%              2019-08-31 00:00:00                         4.657500   \n",
              "50%              2019-11-30 00:00:00                         8.350000   \n",
              "75%              2020-03-31 00:00:00                        15.887500   \n",
              "max              2020-06-30 00:00:00                        76.740000   \n",
              "std                              NaN                        10.721298   \n",
              "\n",
              "       Estimated Employed  Estimated Labour Participation Rate (%)  \n",
              "count        7.400000e+02                               740.000000  \n",
              "mean         7.204460e+06                                42.630122  \n",
              "min          4.942000e+04                                13.330000  \n",
              "25%          1.190404e+06                                38.062500  \n",
              "50%          4.744178e+06                                41.160000  \n",
              "75%          1.127549e+07                                45.505000  \n",
              "max          4.577751e+07                                72.570000  \n",
              "std          8.087988e+06                                 8.111094  "
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000740\",\n        \"max\": \"2020-06-30 00:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"740\",\n          \"2019-12-12 18:36:58.378378496\",\n          \"2020-03-31 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Unemployment Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 256.313829221936,\n        \"min\": 0.0,\n        \"max\": 740.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          11.787945945945946,\n          15.887500000000001,\n          740.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15104451.557182284,\n        \"min\": 740.0,\n        \"max\": 45777509.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7204460.025675676,\n          11275489.5,\n          740.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Labour Participation Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 249.226919631411,\n        \"min\": 8.111094365865874,\n        \"max\": 740.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          42.630121621621626,\n          45.505,\n          740.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 47
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      "source": [
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          "height": 175
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        "id": "ZmsTemqzKO9X",
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Region Frequency   Area\n",
              "count              740       740    740\n",
              "unique              28         2      2\n",
              "top     Andhra Pradesh   Monthly  Urban\n",
              "freq                28       381    381"
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"740\",\n          28,\n          \"Andhra Pradesh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"381\",\n          \"740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"381\",\n          \"740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "df.describe(include='object')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FMgThxeztBHY",
        "outputId": "382ec6e5-b8d2-4f2e-9593-49d7e0a097e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Region\n",
            "['Andhra Pradesh' 'Assam' 'Bihar' 'Chhattisgarh' 'Delhi' 'Goa' 'Gujarat'\n",
            " 'Haryana' 'Himachal Pradesh' 'Jammu & Kashmir' 'Jharkhand' 'Karnataka'\n",
            " 'Kerala' 'Madhya Pradesh' 'Maharashtra' 'Meghalaya' 'Odisha' 'Puducherry'\n",
            " 'Punjab' 'Rajasthan' 'Sikkim' 'Tamil Nadu' 'Telangana' 'Tripura'\n",
            " 'Uttar Pradesh' 'Uttarakhand' 'West Bengal' 'Chandigarh']\n",
            "*********************\n",
            "Frequency\n",
            "[' Monthly' 'Monthly']\n",
            "*********************\n",
            "Area\n",
            "['Rural' 'Urban']\n",
            "*********************\n"
          ]
        }
      ],
      "source": [
        "object_columns = df.select_dtypes(include='object').columns\n",
        "for i in object_columns:\n",
        "  print(i)\n",
        "  print(df[i].unique())\n",
        "  print('*********************')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v8FLi7GlK_Mx"
      },
      "outputs": [],
      "source": [
        "df['Frequency'] = df['Frequency'].str.strip()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSYxbD4EvsMK",
        "outputId": "1b4ad476-d5bf-4755-fff5-5c66a9b42698"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Monthly'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "df['Frequency'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rsC7xPwJ8KCb"
      },
      "outputs": [],
      "source": [
        "df_mean_by_region = df.groupby('Region').mean(numeric_only=True)[\n",
        "    ['Estimated Unemployment Rate (%)', 'Estimated Employed']\n",
        "].sort_values(by='Estimated Unemployment Rate (%)', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "rCwxtuZL8Qjo",
        "outputId": "3d3a2a99-cc72-4a1d-c85a-d5413d89072a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Estimated Unemployment Rate (%)  Estimated Employed\n",
              "Region                                                               \n",
              "Tripura                                 28.350357        7.170026e+05\n",
              "Haryana                                 26.283214        3.557072e+06\n",
              "Jharkhand                               20.585000        4.469240e+06\n",
              "Bihar                                   18.918214        1.236619e+07\n",
              "Himachal Pradesh                        18.540357        1.059824e+06\n",
              "Delhi                                   16.495357        2.627513e+06\n",
              "Jammu & Kashmir                         16.188571        1.799932e+06\n",
              "Chandigarh                              15.991667        3.168312e+05\n",
              "Rajasthan                               14.058214        1.004106e+07\n",
              "Uttar Pradesh                           12.551429        2.809483e+07\n",
              "Punjab                                  12.031071        4.539362e+06\n",
              "Puducherry                              10.215000        2.122781e+05\n",
              "Kerala                                  10.123929        4.425900e+06\n",
              "Tamil Nadu                               9.284286        1.226955e+07\n",
              "Goa                                      9.274167        2.263083e+05\n",
              "Chhattisgarh                             9.240357        4.303499e+06\n",
              "West Bengal                              8.124643        1.719854e+07\n",
              "Telangana                                7.737857        7.939663e+06\n",
              "Maharashtra                              7.557500        1.999020e+07\n",
              "Andhra Pradesh                           7.477143        8.154093e+06\n",
              "Madhya Pradesh                           7.406429        1.111548e+07\n",
              "Sikkim                                   7.249412        1.068807e+05\n",
              "Karnataka                                6.676071        1.066712e+07\n",
              "Gujarat                                  6.663929        1.140201e+07\n",
              "Uttarakhand                              6.582963        1.390228e+06\n",
              "Assam                                    6.428077        5.354772e+06\n",
              "Odisha                                   5.657857        6.545747e+06\n",
              "Meghalaya                                4.798889        6.897368e+05"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Estimated Unemployment Rate (%)</th>\n",
              "      <th>Estimated Employed</th>\n",
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              "      <th>Region</th>\n",
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              "      <th>Tripura</th>\n",
              "      <td>28.350357</td>\n",
              "      <td>7.170026e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Haryana</th>\n",
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              "      <td>3.557072e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jharkhand</th>\n",
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              "      <td>4.469240e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bihar</th>\n",
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              "      <td>1.236619e+07</td>\n",
              "    </tr>\n",
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              "      <td>1.059824e+06</td>\n",
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              "      <td>2.627513e+06</td>\n",
              "    </tr>\n",
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              "      <th>Jammu &amp; Kashmir</th>\n",
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              "      <td>1.799932e+06</td>\n",
              "    </tr>\n",
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              "      <th>Chandigarh</th>\n",
              "      <td>15.991667</td>\n",
              "      <td>3.168312e+05</td>\n",
              "    </tr>\n",
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              "      <th>Rajasthan</th>\n",
              "      <td>14.058214</td>\n",
              "      <td>1.004106e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uttar Pradesh</th>\n",
              "      <td>12.551429</td>\n",
              "      <td>2.809483e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Punjab</th>\n",
              "      <td>12.031071</td>\n",
              "      <td>4.539362e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Puducherry</th>\n",
              "      <td>10.215000</td>\n",
              "      <td>2.122781e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kerala</th>\n",
              "      <td>10.123929</td>\n",
              "      <td>4.425900e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tamil Nadu</th>\n",
              "      <td>9.284286</td>\n",
              "      <td>1.226955e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Goa</th>\n",
              "      <td>9.274167</td>\n",
              "      <td>2.263083e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chhattisgarh</th>\n",
              "      <td>9.240357</td>\n",
              "      <td>4.303499e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West Bengal</th>\n",
              "      <td>8.124643</td>\n",
              "      <td>1.719854e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Telangana</th>\n",
              "      <td>7.737857</td>\n",
              "      <td>7.939663e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maharashtra</th>\n",
              "      <td>7.557500</td>\n",
              "      <td>1.999020e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andhra Pradesh</th>\n",
              "      <td>7.477143</td>\n",
              "      <td>8.154093e+06</td>\n",
              "    </tr>\n",
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              "      <th>Madhya Pradesh</th>\n",
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              "      <td>1.111548e+07</td>\n",
              "    </tr>\n",
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              "      <th>Sikkim</th>\n",
              "      <td>7.249412</td>\n",
              "      <td>1.068807e+05</td>\n",
              "    </tr>\n",
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              "      <th>Karnataka</th>\n",
              "      <td>6.676071</td>\n",
              "      <td>1.066712e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gujarat</th>\n",
              "      <td>6.663929</td>\n",
              "      <td>1.140201e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uttarakhand</th>\n",
              "      <td>6.582963</td>\n",
              "      <td>1.390228e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Assam</th>\n",
              "      <td>6.428077</td>\n",
              "      <td>5.354772e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Odisha</th>\n",
              "      <td>5.657857</td>\n",
              "      <td>6.545747e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meghalaya</th>\n",
              "      <td>4.798889</td>\n",
              "      <td>6.897368e+05</td>\n",
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              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-1a22b40e-7454-4a39-a476-3f492e8595a0 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-1a22b40e-7454-4a39-a476-3f492e8595a0');\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",
              "\n",
              "\n",
              "<div id=\"df-6a3a05eb-b739-4659-9c56-9400ca170501\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6a3a05eb-b739-4659-9c56-9400ca170501')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6a3a05eb-b739-4659-9c56-9400ca170501 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_925ff213-e91d-4b7d-a0fc-709de13d61f7\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_mean_by_region')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_925ff213-e91d-4b7d-a0fc-709de13d61f7 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_mean_by_region');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_mean_by_region",
              "summary": "{\n  \"name\": \"df_mean_by_region\",\n  \"rows\": 28,\n  \"fields\": [\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 28,\n        \"samples\": [\n          \"Uttar Pradesh\",\n          \"Assam\",\n          \"Rajasthan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Unemployment Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.231007981004381,\n        \"min\": 4.7988888888888885,\n        \"max\": 28.350357142857142,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          12.551428571428572,\n          6.428076923076923,\n          14.058214285714286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6810419.759634869,\n        \"min\": 106880.70588235294,\n        \"max\": 28094832.17857143,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          28094832.17857143,\n          5354772.153846154,\n          10041064.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "df_mean_by_region"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "DgV-Nm-1NPje",
        "outputId": "0b6ee95d-396b-4764-9f51-59164a4f0eb4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Mean Estimated Unemployment Rate (%) by Region'}, xlabel='Region'>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df_mean_by_region['Estimated Unemployment Rate (%)'].plot(kind='bar',title='Mean Estimated Unemployment Rate (%) by Region',ylabel='')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "-jqEe8DiJjsi",
        "outputId": "ed664635-c5ec-4526-b158-8003637d944e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "correlation_matrix = df[['Estimated Unemployment Rate (%)', 'Estimated Employed', 'Estimated Labour Participation Rate (%)','Date']].corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
        "plt.title('Correlation Matrix Heatmap')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_max_unmployment_trend = df[df['Region']==df_mean_by_region[df_mean_by_region['Estimated Unemployment Rate (%)'] == df_mean_by_region['Estimated Unemployment Rate (%)'].max()].index[0]].sort_values(by='Date')"
      ],
      "metadata": {
        "id": "PWCcreLSR3Zy"
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      "execution_count": null,
      "outputs": []
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "      Region       Date Frequency  Estimated Unemployment Rate (%)  \\\n",
              "304  Tripura 2019-05-31   Monthly                            29.25   \n",
              "698  Tripura 2019-05-31   Monthly                            34.69   \n",
              "305  Tripura 2019-06-30   Monthly                            26.64   \n",
              "699  Tripura 2019-06-30   Monthly                            25.59   \n",
              "306  Tripura 2019-07-31   Monthly                            22.47   \n",
              "\n",
              "     Estimated Employed  Estimated Labour Participation Rate (%)   Area  \n",
              "304           1019549.0                                    64.47  Rural  \n",
              "698            423127.0                                    69.50  Urban  \n",
              "305           1107013.0                                    67.40  Rural  \n",
              "699            408738.0                                    58.80  Urban  \n",
              "306           1052597.0                                    60.54  Rural  "
            ],
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              "type": "dataframe",
              "variable_name": "df_max_unmployment_trend",
              "summary": "{\n  \"name\": \"df_max_unmployment_trend\",\n  \"rows\": 28,\n  \"fields\": [\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Tripura\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-05-31 00:00:00\",\n        \"max\": \"2020-06-30 00:00:00\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"2020-02-29 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Monthly\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Unemployment Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.05833523285488,\n        \"min\": 14.71,\n        \"max\": 43.64,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          33.57\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330238.9785061064,\n        \"min\": 296431.0,\n        \"max\": 1169347.0,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          418043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Labour Participation Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.780526221950686,\n        \"min\": 46.9,\n        \"max\": 72.26,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          66.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Urban\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_max_unmployment_trend_Rural = df_max_unmployment_trend[df_max_unmployment_trend['Area']=='Rural']\n",
        "df_max_unmployment_trend_Urban = df_max_unmployment_trend[df_max_unmployment_trend['Area']=='Urban']"
      ],
      "metadata": {
        "id": "LYXpbIp5jtLk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_mean_by_Date = df.groupby('Date').mean(numeric_only=True)[\n",
        "    ['Estimated Unemployment Rate (%)', 'Estimated Employed']\n",
        "]"
      ],
      "metadata": {
        "id": "aJuWBgXqgBKg"
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    {
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        "df_mean_by_Date"
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            "text/plain": [
              "            Estimated Unemployment Rate (%)  Estimated Employed\n",
              "Date                                                           \n",
              "2019-05-31                         8.874259        7.410148e+06\n",
              "2019-06-30                         9.303333        7.358642e+06\n",
              "2019-07-31                         9.033889        7.404425e+06\n",
              "2019-08-31                         9.637925        7.539815e+06\n",
              "2019-09-30                         9.051731        7.739464e+06\n",
              "2019-10-31                         9.900909        7.298382e+06\n",
              "2019-11-30                         9.868364        7.273661e+06\n",
              "2019-12-31                         9.497358        7.377388e+06\n",
              "2020-01-31                         9.950755        7.677344e+06\n",
              "2020-02-29                         9.964717        7.603996e+06\n",
              "2020-03-31                        10.700577        7.516581e+06\n",
              "2020-04-30                        23.641569        5.283320e+06\n",
              "2020-05-31                        24.875294        5.879363e+06\n",
              "2020-06-30                        11.903600        7.387009e+06"
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              "      <th>2020-03-31</th>\n",
              "      <td>10.700577</td>\n",
              "      <td>7.516581e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-30</th>\n",
              "      <td>23.641569</td>\n",
              "      <td>5.283320e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-31</th>\n",
              "      <td>24.875294</td>\n",
              "      <td>5.879363e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-30</th>\n",
              "      <td>11.903600</td>\n",
              "      <td>7.387009e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_mean_by_Date')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "        document.querySelector('#id_5222f74b-8f6e-4dd6-9801-21841bd60718 button.colab-df-generate');\n",
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              "  </div>\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_mean_by_Date",
              "summary": "{\n  \"name\": \"df_mean_by_Date\",\n  \"rows\": 14,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-05-31 00:00:00\",\n        \"max\": \"2020-06-30 00:00:00\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"2020-02-29 00:00:00\",\n          \"2020-04-30 00:00:00\",\n          \"2019-05-31 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Unemployment Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.309161964157834,\n        \"min\": 8.87425925925926,\n        \"max\": 24.875294117647062,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          9.964716981132076,\n          23.64156862745098,\n          8.87425925925926\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 707542.6421320371,\n        \"min\": 5283319.901960785,\n        \"max\": 7739463.961538462,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          7603996.2830188675,\n          5283319.901960785,\n          7410148.444444444\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_unemployment_rate_region = df_mean_by_region[\n",
        "    df_mean_by_region['Estimated Unemployment Rate (%)'] == df_mean_by_region['Estimated Unemployment Rate (%)'].max()\n",
        "].index[0]\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(df_max_unmployment_trend_Rural['Date'], df_max_unmployment_trend_Rural['Estimated Unemployment Rate (%)'], label='The Region with the most Unemployment Rate (Rural)', marker='x', color='red')\n",
        "plt.plot(df_max_unmployment_trend_Urban['Date'], df_max_unmployment_trend_Urban['Estimated Unemployment Rate (%)'], label='The Region with the most Unemployment Rate (Urban)', marker='x', color='orange')\n",
        "plt.plot(df_mean_by_Date.index, df_mean_by_Date['Estimated Unemployment Rate (%)'], label='The Mean of Unemployment over India', marker='x', color='blue')\n",
        "plt.title('Estimated Unemployment Rate (%) Trends')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Estimated Unemployment Rate (%)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "blGNx1LZWfwM",
        "outputId": "b36f7f78-780d-4c75-956e-2360c7d65f44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **During Covid Analysis**"
      ],
      "metadata": {
        "id": "Sx8DQS31ksFC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_during_pandemic = df[(df['Date'] >= '2020-02-01') & (df['Date'] <= '2022-06-30')]"
      ],
      "metadata": {
        "id": "FbQdLMTWkZRm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_during_pandemic"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "SYN0YY8hkn8m",
        "outputId": "29135736-d1fb-4e0c-d501-9b26b7ad5a16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Region       Date Frequency  Estimated Unemployment Rate (%)  \\\n",
              "9    Andhra Pradesh 2020-02-29   Monthly                             5.91   \n",
              "10   Andhra Pradesh 2020-03-31   Monthly                             4.06   \n",
              "11   Andhra Pradesh 2020-04-30   Monthly                            16.29   \n",
              "12   Andhra Pradesh 2020-05-31   Monthly                            14.46   \n",
              "13   Andhra Pradesh 2020-06-30   Monthly                             0.85   \n",
              "..              ...        ...       ...                              ...   \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 (%)   Area  \n",
              "9            11723617.0                                    42.83  Rural  \n",
              "10           11359660.0                                    40.66  Rural  \n",
              "11            8792827.0                                    36.03  Rural  \n",
              "12            9526902.0                                    38.16  Rural  \n",
              "13           15572975.0                                    53.76  Rural  \n",
              "..                  ...                                      ...    ...  \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",
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              "      <td>38.16</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>15.63</td>\n",
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              "      <th>752</th>\n",
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              "      <td>9240903.0</td>\n",
              "      <td>40.67</td>\n",
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              "    <tr>\n",
              "      <th>753</th>\n",
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              "      <td>9.86</td>\n",
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              "      <td>37.57</td>\n",
              "      <td>Urban</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_during_pandemic",
              "summary": "{\n  \"name\": \"df_during_pandemic\",\n  \"rows\": 257,\n  \"fields\": [\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 28,\n        \"samples\": [\n          \"Jammu & Kashmir\",\n          \"Uttarakhand\",\n          \"Himachal Pradesh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-02-29 00:00:00\",\n        \"max\": \"2020-06-30 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2020-03-31 00:00:00\",\n          \"2020-06-30 00:00:00\",\n          \"2020-04-30 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Monthly\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Unemployment Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.193661336005961,\n        \"min\": 0.0,\n        \"max\": 76.74,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          34.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7710382.949229395,\n        \"min\": 49420.0,\n        \"max\": 44257432.0,\n        \"num_unique_values\": 257,\n        \"samples\": [\n          2304138.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Labour Participation Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.58776795706045,\n        \"min\": 13.33,\n        \"max\": 69.88,\n        \"num_unique_values\": 240,\n        \"samples\": [\n          36.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Urban\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_during_pandemic.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "qlsE77s9sUWI",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                Date  Estimated Unemployment Rate (%)  \\\n",
              "count                            257                       257.000000   \n",
              "mean   2020-04-29 08:52:17.743190784                        16.163813   \n",
              "min              2020-02-29 00:00:00                         0.000000   \n",
              "25%              2020-03-31 00:00:00                         5.910000   \n",
              "50%              2020-04-30 00:00:00                        12.250000   \n",
              "75%              2020-05-31 00:00:00                        21.230000   \n",
              "max              2020-06-30 00:00:00                        76.740000   \n",
              "std                              NaN                        14.193661   \n",
              "\n",
              "       Estimated Employed  Estimated Labour Participation Rate (%)  \n",
              "count        2.570000e+02                               257.000000  \n",
              "mean         6.741328e+06                                40.235992  \n",
              "min          4.942000e+04                                13.330000  \n",
              "25%          1.276291e+06                                36.030000  \n",
              "50%          4.280434e+06                                39.060000  \n",
              "75%          1.008303e+07                                43.900000  \n",
              "max          4.425743e+07                                69.880000  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_during_pandemic\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000257\",\n        \"max\": \"2020-06-30 00:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"257\",\n          \"2020-04-29 08:52:17.743190784\",\n          \"2020-05-31 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Unemployment Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 86.75597321043517,\n        \"min\": 0.0,\n        \"max\": 257.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          16.163813229571986,\n          21.23,\n          257.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14605983.439546386,\n        \"min\": 257.0,\n        \"max\": 44257432.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6741327.953307393,\n          10083026.0,\n          257.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Labour Participation Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.43864122551963,\n        \"min\": 8.58776795706045,\n        \"max\": 257.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          40.23599221789884,\n          43.9,\n          257.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 64
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    {
      "cell_type": "code",
      "source": [
        "df_during_pandemic.describe(include='object')"
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          "data": {
            "text/plain": [
              "                Region Frequency   Area\n",
              "count              257       257    257\n",
              "unique              28         1      2\n",
              "top     Andhra Pradesh   Monthly  Urban\n",
              "freq                10       257    132"
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              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-28c97a31-2995-4a14-abae-849045b2c6e7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_during_pandemic\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          28,\n          \"10\",\n          \"257\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"257\",\n          1,\n          \"Monthly\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"132\",\n          \"257\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_during_pandemic_mean_by_region = df_during_pandemic.groupby(['Region','Area']).mean(numeric_only=True)[\n",
        "    ['Estimated Unemployment Rate (%)', 'Estimated Employed']\n",
        "].sort_values(by='Estimated Unemployment Rate (%)', ascending=False)\n",
        "df_during_pandemic_mean_by_region"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GERq4AUPkxEJ",
        "outputId": "d35d0d1d-3c6d-46be-cafe-772b45a864b7",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Estimated Unemployment Rate (%)  Estimated Employed\n",
              "Region           Area                                                      \n",
              "Jharkhand        Urban                        37.420000        1.820664e+06\n",
              "Haryana          Urban                        33.038000        2.190408e+06\n",
              "                 Rural                        32.502000        4.249867e+06\n",
              "Puducherry       Urban                        31.738000        1.913558e+05\n",
              "Bihar            Urban                        27.986000        2.579150e+06\n",
              "Tripura          Urban                        27.740000        3.876110e+05\n",
              "Bihar            Rural                        27.568000        1.903085e+07\n",
              "Tripura          Rural                        27.004000        9.848286e+05\n",
              "Jharkhand        Rural                        26.484000        5.812088e+06\n",
              "Puducherry       Rural                        26.186667        1.250717e+05\n",
              "Himachal Pradesh Urban                        23.452000        1.911926e+05\n",
              "Delhi            Urban                        22.470000        4.152915e+06\n",
              "Rajasthan        Urban                        21.896000        4.537489e+06\n",
              "Tamil Nadu       Urban                        21.240000        8.166434e+06\n",
              "                 Rural                        20.302000        1.054015e+07\n",
              "Uttar Pradesh    Urban                        19.204000        1.259146e+07\n",
              "Delhi            Rural                        18.716000        1.314236e+05\n",
              "Chhattisgarh     Urban                        17.860000        1.740020e+06\n",
              "Punjab           Rural                        17.040000        5.239303e+06\n",
              "Kerala           Rural                        15.902000        3.770342e+06\n",
              "                 Urban                        15.820000        3.662147e+06\n",
              "Andhra Pradesh   Urban                        15.722000        4.054080e+06\n",
              "Jammu & Kashmir  Urban                        15.703333        9.985513e+05\n",
              "Chandigarh       Urban                        15.106667        3.010770e+05\n",
              "Madhya Pradesh   Urban                        14.982000        5.564905e+06\n",
              "Karnataka        Rural                        14.862000        1.219208e+07\n",
              "Jammu & Kashmir  Rural                        14.605000        2.322746e+06\n",
              "Telangana        Rural                        14.494000        9.705316e+06\n",
              "                 Urban                        13.366000        4.379998e+06\n",
              "Uttar Pradesh    Rural                        12.562000        4.157294e+07\n",
              "Himachal Pradesh Rural                        12.404000        1.739837e+06\n",
              "Maharashtra      Rural                        12.238000        2.109276e+07\n",
              "Sikkim           Urban                        12.150000        7.995400e+04\n",
              "Rajasthan        Rural                        12.102000        1.496307e+07\n",
              "Goa              Rural                        11.970000        1.606223e+05\n",
              "Odisha           Rural                        11.384000        9.418025e+06\n",
              "West Bengal      Urban                        10.986000        9.861315e+06\n",
              "Karnataka        Urban                        10.984000        8.605912e+06\n",
              "Punjab           Urban                        10.836000        2.970081e+06\n",
              "Gujarat          Urban                        10.706000        8.843310e+06\n",
              "Meghalaya        Urban                        10.535000        2.271388e+05\n",
              "Maharashtra      Urban                        10.520000        1.508030e+07\n",
              "West Bengal      Rural                        10.498000        2.183437e+07\n",
              "Uttarakhand      Urban                         9.810000        8.737508e+05\n",
              "Madhya Pradesh   Rural                         9.406000        1.534920e+07\n",
              "Assam            Urban                         8.576000        1.547457e+06\n",
              "Gujarat          Rural                         8.444000        1.208186e+07\n",
              "Andhra Pradesh   Rural                         8.314000        1.139520e+07\n",
              "Odisha           Urban                         7.766000        2.079165e+06\n",
              "Goa              Urban                         6.943333        3.008153e+05\n",
              "Chhattisgarh     Rural                         6.596000        6.302298e+06\n",
              "Uttarakhand      Rural                         5.697500        1.905130e+06\n",
              "Assam            Rural                         4.102500        8.953167e+06\n",
              "Meghalaya        Rural                         3.500000        1.050189e+06\n",
              "Sikkim           Rural                         2.905000        1.373560e+05"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c22b6e2b-a9bd-4424-ab4b-fc490d3b7862\" 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></th>\n",
              "      <th>Estimated Unemployment Rate (%)</th>\n",
              "      <th>Estimated Employed</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Region</th>\n",
              "      <th>Area</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jharkhand</th>\n",
              "      <th>Urban</th>\n",
              "      <td>37.420000</td>\n",
              "      <td>1.820664e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Haryana</th>\n",
              "      <th>Urban</th>\n",
              "      <td>33.038000</td>\n",
              "      <td>2.190408e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rural</th>\n",
              "      <td>32.502000</td>\n",
              "      <td>4.249867e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Puducherry</th>\n",
              "      <th>Urban</th>\n",
              "      <td>31.738000</td>\n",
              "      <td>1.913558e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bihar</th>\n",
              "      <th>Urban</th>\n",
              "      <td>27.986000</td>\n",
              "      <td>2.579150e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tripura</th>\n",
              "      <th>Urban</th>\n",
              "      <td>27.740000</td>\n",
              "      <td>3.876110e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bihar</th>\n",
              "      <th>Rural</th>\n",
              "      <td>27.568000</td>\n",
              "      <td>1.903085e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tripura</th>\n",
              "      <th>Rural</th>\n",
              "      <td>27.004000</td>\n",
              "      <td>9.848286e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jharkhand</th>\n",
              "      <th>Rural</th>\n",
              "      <td>26.484000</td>\n",
              "      <td>5.812088e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Puducherry</th>\n",
              "      <th>Rural</th>\n",
              "      <td>26.186667</td>\n",
              "      <td>1.250717e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Himachal Pradesh</th>\n",
              "      <th>Urban</th>\n",
              "      <td>23.452000</td>\n",
              "      <td>1.911926e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Delhi</th>\n",
              "      <th>Urban</th>\n",
              "      <td>22.470000</td>\n",
              "      <td>4.152915e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rajasthan</th>\n",
              "      <th>Urban</th>\n",
              "      <td>21.896000</td>\n",
              "      <td>4.537489e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Tamil Nadu</th>\n",
              "      <th>Urban</th>\n",
              "      <td>21.240000</td>\n",
              "      <td>8.166434e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rural</th>\n",
              "      <td>20.302000</td>\n",
              "      <td>1.054015e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uttar Pradesh</th>\n",
              "      <th>Urban</th>\n",
              "      <td>19.204000</td>\n",
              "      <td>1.259146e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Delhi</th>\n",
              "      <th>Rural</th>\n",
              "      <td>18.716000</td>\n",
              "      <td>1.314236e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chhattisgarh</th>\n",
              "      <th>Urban</th>\n",
              "      <td>17.860000</td>\n",
              "      <td>1.740020e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Punjab</th>\n",
              "      <th>Rural</th>\n",
              "      <td>17.040000</td>\n",
              "      <td>5.239303e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Kerala</th>\n",
              "      <th>Rural</th>\n",
              "      <td>15.902000</td>\n",
              "      <td>3.770342e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Urban</th>\n",
              "      <td>15.820000</td>\n",
              "      <td>3.662147e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andhra Pradesh</th>\n",
              "      <th>Urban</th>\n",
              "      <td>15.722000</td>\n",
              "      <td>4.054080e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jammu &amp; Kashmir</th>\n",
              "      <th>Urban</th>\n",
              "      <td>15.703333</td>\n",
              "      <td>9.985513e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chandigarh</th>\n",
              "      <th>Urban</th>\n",
              "      <td>15.106667</td>\n",
              "      <td>3.010770e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Madhya Pradesh</th>\n",
              "      <th>Urban</th>\n",
              "      <td>14.982000</td>\n",
              "      <td>5.564905e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Karnataka</th>\n",
              "      <th>Rural</th>\n",
              "      <td>14.862000</td>\n",
              "      <td>1.219208e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jammu &amp; Kashmir</th>\n",
              "      <th>Rural</th>\n",
              "      <td>14.605000</td>\n",
              "      <td>2.322746e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Telangana</th>\n",
              "      <th>Rural</th>\n",
              "      <td>14.494000</td>\n",
              "      <td>9.705316e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Urban</th>\n",
              "      <td>13.366000</td>\n",
              "      <td>4.379998e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uttar Pradesh</th>\n",
              "      <th>Rural</th>\n",
              "      <td>12.562000</td>\n",
              "      <td>4.157294e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Himachal Pradesh</th>\n",
              "      <th>Rural</th>\n",
              "      <td>12.404000</td>\n",
              "      <td>1.739837e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maharashtra</th>\n",
              "      <th>Rural</th>\n",
              "      <td>12.238000</td>\n",
              "      <td>2.109276e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sikkim</th>\n",
              "      <th>Urban</th>\n",
              "      <td>12.150000</td>\n",
              "      <td>7.995400e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rajasthan</th>\n",
              "      <th>Rural</th>\n",
              "      <td>12.102000</td>\n",
              "      <td>1.496307e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Goa</th>\n",
              "      <th>Rural</th>\n",
              "      <td>11.970000</td>\n",
              "      <td>1.606223e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Odisha</th>\n",
              "      <th>Rural</th>\n",
              "      <td>11.384000</td>\n",
              "      <td>9.418025e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West Bengal</th>\n",
              "      <th>Urban</th>\n",
              "      <td>10.986000</td>\n",
              "      <td>9.861315e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Karnataka</th>\n",
              "      <th>Urban</th>\n",
              "      <td>10.984000</td>\n",
              "      <td>8.605912e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Punjab</th>\n",
              "      <th>Urban</th>\n",
              "      <td>10.836000</td>\n",
              "      <td>2.970081e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gujarat</th>\n",
              "      <th>Urban</th>\n",
              "      <td>10.706000</td>\n",
              "      <td>8.843310e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meghalaya</th>\n",
              "      <th>Urban</th>\n",
              "      <td>10.535000</td>\n",
              "      <td>2.271388e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maharashtra</th>\n",
              "      <th>Urban</th>\n",
              "      <td>10.520000</td>\n",
              "      <td>1.508030e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West Bengal</th>\n",
              "      <th>Rural</th>\n",
              "      <td>10.498000</td>\n",
              "      <td>2.183437e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uttarakhand</th>\n",
              "      <th>Urban</th>\n",
              "      <td>9.810000</td>\n",
              "      <td>8.737508e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Madhya Pradesh</th>\n",
              "      <th>Rural</th>\n",
              "      <td>9.406000</td>\n",
              "      <td>1.534920e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Assam</th>\n",
              "      <th>Urban</th>\n",
              "      <td>8.576000</td>\n",
              "      <td>1.547457e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gujarat</th>\n",
              "      <th>Rural</th>\n",
              "      <td>8.444000</td>\n",
              "      <td>1.208186e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andhra Pradesh</th>\n",
              "      <th>Rural</th>\n",
              "      <td>8.314000</td>\n",
              "      <td>1.139520e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Odisha</th>\n",
              "      <th>Urban</th>\n",
              "      <td>7.766000</td>\n",
              "      <td>2.079165e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Goa</th>\n",
              "      <th>Urban</th>\n",
              "      <td>6.943333</td>\n",
              "      <td>3.008153e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chhattisgarh</th>\n",
              "      <th>Rural</th>\n",
              "      <td>6.596000</td>\n",
              "      <td>6.302298e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uttarakhand</th>\n",
              "      <th>Rural</th>\n",
              "      <td>5.697500</td>\n",
              "      <td>1.905130e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Assam</th>\n",
              "      <th>Rural</th>\n",
              "      <td>4.102500</td>\n",
              "      <td>8.953167e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meghalaya</th>\n",
              "      <th>Rural</th>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.050189e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sikkim</th>\n",
              "      <th>Rural</th>\n",
              "      <td>2.905000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_during_pandemic_mean_by_region",
              "summary": "{\n  \"name\": \"df_during_pandemic_mean_by_region\",\n  \"rows\": 55,\n  \"fields\": [\n    {\n      \"column\": \"Estimated Unemployment Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.166450100446273,\n        \"min\": 2.905,\n        \"max\": 37.42,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          12.238000000000001,\n          27.74,\n          12.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7509805.792342415,\n        \"min\": 79954.0,\n        \"max\": 41572944.6,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          21092755.2,\n          387611.0,\n          79954.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_during_pandemic_mean_by_region['Estimated Unemployment Rate (%)'].plot(kind='bar',title='Mean Estimated Unemployment Rate (%) by Region',ylabel='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "collapsed": true,
        "id": "Kw_kLM8-g1Ug",
        "outputId": "50da6f71-84bd-4dbf-9f34-8a772adf05b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Mean Estimated Unemployment Rate (%) by Region'}, xlabel='Region,Area'>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_during_pandemic.groupby('Area').mean(numeric_only=True)['Estimated Unemployment Rate (%)'].plot(kind='bar',title='Mean Estimated Unemployment Rate (%) by Area',ylabel='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "xskjkHl2h0Um",
        "outputId": "549ea298-e44f-4972-85f4-a6f89b6ef89e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Mean Estimated Unemployment Rate (%) by Area'}, xlabel='Area'>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
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