Wind_Data_mod.ipynb

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      "name": "Wind_Data.ipynb",
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        ""
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      "outputs": []
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        ""
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        "outputId": "e5e6d58e-b809-4fbc-86c7-13d15f629121"
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      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "\n",
        "url = \"http://www.inwea.org/wind-energy-in-india/wind-power-potential/\"\n",
        "html = requests.get(url).content\n",
        "df_list = pd.read_html(html)\n",
        "print(len(df_list))\n",
        "df1,df2 = df_list\n",
        "#Estimation of installable wind power potential at 80 m level,Estimation of installable wind power potential at 100 m level\n",
        "df1.to_csv('small_data.csv')\n",
        "df2.to_csv('big_data.csv')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "cell_type": "code",
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        "id": "In6FWk49R_Yh",
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        },
        "outputId": "10a5b549-232d-475b-d9f9-318e2dfdea2a"
      },
      "source": [
        "df1.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sl.No</td>\n",
              "      <td>States / UTs</td>\n",
              "      <td>Estimated potential (MW)</td>\n",
              "      <td>Estimated potential (MW)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sl.No</td>\n",
              "      <td>States / UTs</td>\n",
              "      <td>@ 50 m</td>\n",
              "      <td>@ 80 m (* #)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394</td>\n",
              "      <td>14497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Arunachal Pradesh*</td>\n",
              "      <td>201</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Assam*</td>\n",
              "      <td>53</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Bihar</td>\n",
              "      <td>-</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6</td>\n",
              "      <td>Chhattisgarh*</td>\n",
              "      <td>23</td>\n",
              "      <td>314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7</td>\n",
              "      <td>Dieu Damn</td>\n",
              "      <td>-</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609</td>\n",
              "      <td>35071</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "       0  ...                         3\n",
              "0  Sl.No  ...  Estimated potential (MW)\n",
              "1  Sl.No  ...              @ 80 m (* #)\n",
              "2      1  ...                       365\n",
              "3      2  ...                     14497\n",
              "4      3  ...                       236\n",
              "5      4  ...                       112\n",
              "6      5  ...                       144\n",
              "7      6  ...                       314\n",
              "8      7  ...                         4\n",
              "9      8  ...                     35071\n",
              "\n",
              "[10 rows x 4 columns]"
            ]
          },
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            "tags": []
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      "source": [
        "df2.head(10)"
      ],
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      "outputs": [
        {
          "output_type": "execute_result",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>State</td>\n",
              "      <td>Rank I*</td>\n",
              "      <td>Rank II*</td>\n",
              "      <td>Rank III*</td>\n",
              "      <td>Total</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>4.12</td>\n",
              "      <td>3.43</td>\n",
              "      <td>0.88</td>\n",
              "      <td>8.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>22525.50</td>\n",
              "      <td>20538.10</td>\n",
              "      <td>1165.00</td>\n",
              "      <td>44228.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>3.24</td>\n",
              "      <td>57.03</td>\n",
              "      <td>16.31</td>\n",
              "      <td>76.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Goa</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>52287.59</td>\n",
              "      <td>32037.83</td>\n",
              "      <td>105.09</td>\n",
              "      <td>84431.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>15202.36</td>\n",
              "      <td>39802.59</td>\n",
              "      <td>852.40</td>\n",
              "      <td>55857.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>332.63</td>\n",
              "      <td>1102.56</td>\n",
              "      <td>264.38</td>\n",
              "      <td>1699.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.77</td>\n",
              "      <td>7.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>2216.39</td>\n",
              "      <td>8258.55</td>\n",
              "      <td>8.93</td>\n",
              "      <td>10483.88</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   0         1         2          3         4\n",
              "0              State   Rank I*  Rank II*  Rank III*     Total\n",
              "1  Andaman & Nicobar      4.12      3.43       0.88      8.43\n",
              "2     Andhra Pradesh  22525.50  20538.10    1165.00  44228.60\n",
              "3       Chhattisgarh      3.24     57.03      16.31     76.59\n",
              "4                Goa      0.00      0.08       0.76      0.84\n",
              "5            Gujarat  52287.59  32037.83     105.09  84431.33\n",
              "6          Karnataka  15202.36  39802.59     852.40  55857.36\n",
              "7             Kerala    332.63   1102.56     264.38   1699.56\n",
              "8        Lakshadweep      3.50      3.40       0.77      7.67\n",
              "9     Madhya Pradesh   2216.39   8258.55       8.93  10483.88"
            ]
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      "source": [
        "df1.describe()"
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        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>27</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Sl.No</td>\n",
              "      <td>States / UTs</td>\n",
              "      <td>-</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
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            "text/plain": [
              "            0             1   2   3\n",
              "count      31            31  31  31\n",
              "unique     30            30  27  30\n",
              "top     Sl.No  States / UTs   -  16\n",
              "freq        2             2   5   2"
            ]
          },
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      ]
    },
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      "metadata": {
        "id": "RP2wA5KHS-cg",
        "colab_type": "code",
        "colab": {
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          "height": 217
        },
        "outputId": "48ae1a9e-21cb-41d1-f1e7-f361553c3e05"
      },
      "source": [
        "df1.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 31 entries, 0 to 30\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   0       31 non-null     object\n",
            " 1   1       31 non-null     object\n",
            " 2   2       31 non-null     object\n",
            " 3   3       31 non-null     object\n",
            "dtypes: object(4)\n",
            "memory usage: 1.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CAiN513gaxME",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df1.iloc[2:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1Q7sndea5ym",
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        },
        "outputId": "f12d7ffb-818e-4055-b152-aefad0b5cf07"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
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              "      <th>3</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394</td>\n",
              "      <td>14497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Arunachal Pradesh*</td>\n",
              "      <td>201</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Assam*</td>\n",
              "      <td>53</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Bihar</td>\n",
              "      <td>-</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6</td>\n",
              "      <td>Chhattisgarh*</td>\n",
              "      <td>23</td>\n",
              "      <td>314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7</td>\n",
              "      <td>Dieu Damn</td>\n",
              "      <td>-</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609</td>\n",
              "      <td>35071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9</td>\n",
              "      <td>Haryana</td>\n",
              "      <td>-</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>Himachal Pradesh *</td>\n",
              "      <td>20</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     0                   1      2      3\n",
              "2    1   Andaman & Nicobar      2    365\n",
              "3    2      Andhra Pradesh   5394  14497\n",
              "4    3  Arunachal Pradesh*    201    236\n",
              "5    4              Assam*     53    112\n",
              "6    5               Bihar      -    144\n",
              "7    6       Chhattisgarh*     23    314\n",
              "8    7           Dieu Damn      -      4\n",
              "9    8             Gujarat  10609  35071\n",
              "10   9             Haryana      -     93\n",
              "11  10  Himachal Pradesh *     20     64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
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          "height": 35
        },
        "outputId": "5ef12c3e-02d7-4fcd-c3bd-7c256e6bcf35"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([0, 1, 2, 3], dtype='int64')"
            ]
          },
          "metadata": {
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          "execution_count": 8
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "C6lV6E1tbNhI",
        "colab_type": "code",
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      "source": [
        "cols=[\"Sl\",\"State\",\"50m\",\"80m\"]\n",
        "def merge_list_to_dict(test_keys,test_values):\n",
        "  # using dictionary comprehension\n",
        "  # to convert lists to dictionary\n",
        "  merged_dict = {test_keys[i]: test_values[i] for i in range(len(test_keys))}\n",
        "  return merged_dict\n",
        "filt=merge_list_to_dict(df.columns,cols)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwAJ4HDBTJLT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "a7855457-7e99-4c82-aabd-cd6aa423c4d5"
      },
      "source": [
        "df.rename(columns = filt, inplace = True) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4133: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9wX4VCt-bsIu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b5ad6fcd-63aa-4f12-afb9-5bee633d0efa"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sl</th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394</td>\n",
              "      <td>14497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Arunachal Pradesh*</td>\n",
              "      <td>201</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Assam*</td>\n",
              "      <td>53</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Bihar</td>\n",
              "      <td>-</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Sl               State   50m    80m\n",
              "2  1   Andaman & Nicobar     2    365\n",
              "3  2      Andhra Pradesh  5394  14497\n",
              "4  3  Arunachal Pradesh*   201    236\n",
              "5  4              Assam*    53    112\n",
              "6  5               Bihar     -    144"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IzVMnsqjbudX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "3b0a8f4d-2923-4f9e-db63-dfb7a0ef5814"
      },
      "source": [
        "# this will replace \"-\" with 0\n",
        "df.replace(to_replace =\"-\", \n",
        "                 value =0,inplace=True) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4172: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  method=method,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "czus-0UKdxGJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6e725319-8759-49d1-917d-f607ede6c72b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sl</th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394</td>\n",
              "      <td>14497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Arunachal Pradesh*</td>\n",
              "      <td>201</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Assam*</td>\n",
              "      <td>53</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Bihar</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Sl               State   50m    80m\n",
              "2  1   Andaman & Nicobar     2    365\n",
              "3  2      Andhra Pradesh  5394  14497\n",
              "4  3  Arunachal Pradesh*   201    236\n",
              "5  4              Assam*    53    112\n",
              "6  5               Bihar     0    144"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRdIjhbAdy06",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv('50-80.csv', index = False, header=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EO8vD5uiWEpl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "outputId": "9b11d9cb-31e5-40e8-8363-977c49c0b741"
      },
      "source": [
        "df[[\"50m\", \"80m\"]] = df[[\"50m\", \"80m\"]].apply(pd.to_numeric)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:2963: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[k1] = value[k2]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FPSFJBE3ZlN3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data1=df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPkJERJ2eNWk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "0a2d617f-bba4-41a4-82f6-708c439e5b91"
      },
      "source": [
        "# ten largest values in column Salary \n",
        "df.nlargest(11, ['80m']) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sl</th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Total</td>\n",
              "      <td>Total</td>\n",
              "      <td>49130</td>\n",
              "      <td>102788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609</td>\n",
              "      <td>35071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394</td>\n",
              "      <td>14497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>25</td>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>5374</td>\n",
              "      <td>14152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>13</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>8591</td>\n",
              "      <td>13593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>17</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>5439</td>\n",
              "      <td>5961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12</td>\n",
              "      <td>Jammu &amp; Kashmir *</td>\n",
              "      <td>5311</td>\n",
              "      <td>5685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>23</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>5005</td>\n",
              "      <td>5050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>16</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>920</td>\n",
              "      <td>2931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>21</td>\n",
              "      <td>Orissa</td>\n",
              "      <td>910</td>\n",
              "      <td>1384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>27</td>\n",
              "      <td>Uttar Pradesh *</td>\n",
              "      <td>137</td>\n",
              "      <td>1260</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Sl              State    50m     80m\n",
              "30  Total              Total  49130  102788\n",
              "9       8            Gujarat  10609   35071\n",
              "3       2     Andhra Pradesh   5394   14497\n",
              "26     25         Tamil Nadu   5374   14152\n",
              "14     13          Karnataka   8591   13593\n",
              "18     17        Maharashtra   5439    5961\n",
              "13     12  Jammu & Kashmir *   5311    5685\n",
              "24     23          Rajasthan   5005    5050\n",
              "17     16     Madhya Pradesh    920    2931\n",
              "22     21             Orissa    910    1384\n",
              "28     27    Uttar Pradesh *    137    1260"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0EaJ2-KNe5sg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "a5f5cccf-c8ac-4027-a66a-8399aab4d8f7"
      },
      "source": [
        "# ten largest values in column Salary \n",
        "df.nlargest(11, ['50m']) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sl</th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Total</td>\n",
              "      <td>Total</td>\n",
              "      <td>49130</td>\n",
              "      <td>102788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609</td>\n",
              "      <td>35071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>13</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>8591</td>\n",
              "      <td>13593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>17</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>5439</td>\n",
              "      <td>5961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394</td>\n",
              "      <td>14497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>25</td>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>5374</td>\n",
              "      <td>14152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12</td>\n",
              "      <td>Jammu &amp; Kashmir *</td>\n",
              "      <td>5311</td>\n",
              "      <td>5685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>23</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>5005</td>\n",
              "      <td>5050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>16</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>920</td>\n",
              "      <td>2931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>21</td>\n",
              "      <td>Orissa</td>\n",
              "      <td>910</td>\n",
              "      <td>1384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>14</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>790</td>\n",
              "      <td>837</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Sl              State    50m     80m\n",
              "30  Total              Total  49130  102788\n",
              "9       8            Gujarat  10609   35071\n",
              "14     13          Karnataka   8591   13593\n",
              "18     17        Maharashtra   5439    5961\n",
              "3       2     Andhra Pradesh   5394   14497\n",
              "26     25         Tamil Nadu   5374   14152\n",
              "13     12  Jammu & Kashmir *   5311    5685\n",
              "24     23          Rajasthan   5005    5050\n",
              "17     16     Madhya Pradesh    920    2931\n",
              "22     21             Orissa    910    1384\n",
              "15     14             Kerala    790     837"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjsWokuze5vp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "27c2d2cf-cd1a-4b9d-885b-47068c1e5dd1"
      },
      "source": [
        "df2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>State</td>\n",
              "      <td>Rank I*</td>\n",
              "      <td>Rank II*</td>\n",
              "      <td>Rank III*</td>\n",
              "      <td>Total</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>4.12</td>\n",
              "      <td>3.43</td>\n",
              "      <td>0.88</td>\n",
              "      <td>8.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>22525.50</td>\n",
              "      <td>20538.10</td>\n",
              "      <td>1165.00</td>\n",
              "      <td>44228.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>3.24</td>\n",
              "      <td>57.03</td>\n",
              "      <td>16.31</td>\n",
              "      <td>76.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Goa</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   0         1         2          3         4\n",
              "0              State   Rank I*  Rank II*  Rank III*     Total\n",
              "1  Andaman & Nicobar      4.12      3.43       0.88      8.43\n",
              "2     Andhra Pradesh  22525.50  20538.10    1165.00  44228.60\n",
              "3       Chhattisgarh      3.24     57.03      16.31     76.59\n",
              "4                Goa      0.00      0.08       0.76      0.84"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_mnHPKPe5yr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df2.iloc[1:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3N_UTsLxYH1W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "e1322325-3279-48b0-abb0-1561fd292b34"
      },
      "source": [
        "cols=[\"State\",\"r1\",\"r2\",\"r3\",\"100m\"]\n",
        "def merge_list_to_dict(test_keys,test_values):\n",
        "  # using dictionary comprehension\n",
        "  # to convert lists to dictionary\n",
        "  merged_dict = {test_keys[i]: test_values[i] for i in range(len(test_keys))}\n",
        "  return merged_dict\n",
        "filt=merge_list_to_dict(df.columns,cols)\n",
        "df.rename(columns = filt, inplace = True) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4133: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmdcQYR9Y4d6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "5ed14d74-9c4f-44bd-9fb8-e9334b0d3fb8"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>r1</th>\n",
              "      <th>r2</th>\n",
              "      <th>r3</th>\n",
              "      <th>100m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>4.12</td>\n",
              "      <td>3.43</td>\n",
              "      <td>0.88</td>\n",
              "      <td>8.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>22525.50</td>\n",
              "      <td>20538.10</td>\n",
              "      <td>1165.00</td>\n",
              "      <td>44228.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>3.24</td>\n",
              "      <td>57.03</td>\n",
              "      <td>16.31</td>\n",
              "      <td>76.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Goa</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>52287.59</td>\n",
              "      <td>32037.83</td>\n",
              "      <td>105.09</td>\n",
              "      <td>84431.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               State        r1        r2       r3      100m\n",
              "1  Andaman & Nicobar      4.12      3.43     0.88      8.43\n",
              "2     Andhra Pradesh  22525.50  20538.10  1165.00  44228.60\n",
              "3       Chhattisgarh      3.24     57.03    16.31     76.59\n",
              "4                Goa      0.00      0.08     0.76      0.84\n",
              "5            Gujarat  52287.59  32037.83   105.09  84431.33"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9ta5GT5YqKu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "ff72dbd6-9f04-40c9-f0fd-eb5e381ae5be"
      },
      "source": [
        "# this will replace \"-\" with 0\n",
        "df.replace(to_replace =\"-\", \n",
        "                 value =0,inplace=True) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4172: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  method=method,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cvvsJsqAZBq1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "aa13f132-fdfb-4943-f110-65bb23e871e1"
      },
      "source": [
        "pd.set_option('display.max_rows', None)\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.width', None)\n",
        "pd.set_option('display.max_colwidth', -1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JRS3wIUWZY86",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "outputId": "833b7c75-9b13-4acd-c3c4-abd7a85dd284"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>r1</th>\n",
              "      <th>r2</th>\n",
              "      <th>r3</th>\n",
              "      <th>100m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>4.12</td>\n",
              "      <td>3.43</td>\n",
              "      <td>0.88</td>\n",
              "      <td>8.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>22525.50</td>\n",
              "      <td>20538.10</td>\n",
              "      <td>1165.00</td>\n",
              "      <td>44228.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>3.24</td>\n",
              "      <td>57.03</td>\n",
              "      <td>16.31</td>\n",
              "      <td>76.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Goa</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>52287.59</td>\n",
              "      <td>32037.83</td>\n",
              "      <td>105.09</td>\n",
              "      <td>84431.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>15202.36</td>\n",
              "      <td>39802.59</td>\n",
              "      <td>852.40</td>\n",
              "      <td>55857.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>332.63</td>\n",
              "      <td>1102.56</td>\n",
              "      <td>264.38</td>\n",
              "      <td>1699.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.77</td>\n",
              "      <td>7.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>2216.39</td>\n",
              "      <td>8258.55</td>\n",
              "      <td>8.93</td>\n",
              "      <td>10483.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>31154.76</td>\n",
              "      <td>13747.43</td>\n",
              "      <td>492.15</td>\n",
              "      <td>45394.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>1666.20</td>\n",
              "      <td>1267.06</td>\n",
              "      <td>160.22</td>\n",
              "      <td>3093.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>69.43</td>\n",
              "      <td>79.00</td>\n",
              "      <td>4.40</td>\n",
              "      <td>152.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>15414.91</td>\n",
              "      <td>3342.62</td>\n",
              "      <td>12.96</td>\n",
              "      <td>18770.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>11251.48</td>\n",
              "      <td>22153.34</td>\n",
              "      <td>394.82</td>\n",
              "      <td>33799.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Telangana</td>\n",
              "      <td>887.43</td>\n",
              "      <td>3347.52</td>\n",
              "      <td>9.34</td>\n",
              "      <td>4244.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>0.03</td>\n",
              "      <td>2.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Total in MW</td>\n",
              "      <td>153019.59</td>\n",
              "      <td>145742.59</td>\n",
              "      <td>3489.31</td>\n",
              "      <td>302251.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Total in GW</td>\n",
              "      <td>153</td>\n",
              "      <td>146</td>\n",
              "      <td>3</td>\n",
              "      <td>302</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                State         r1         r2       r3       100m\n",
              "1   Andaman & Nicobar  4.12       3.43       0.88     8.43     \n",
              "2   Andhra Pradesh     22525.50   20538.10   1165.00  44228.60 \n",
              "3   Chhattisgarh       3.24       57.03      16.31    76.59    \n",
              "4   Goa                0.00       0.08       0.76     0.84     \n",
              "5   Gujarat            52287.59   32037.83   105.09   84431.33 \n",
              "6   Karnataka          15202.36   39802.59   852.40   55857.36 \n",
              "7   Kerala             332.63     1102.56    264.38   1699.56  \n",
              "8   Lakshadweep        3.50       3.40       0.77     7.67     \n",
              "9   Madhya Pradesh     2216.39    8258.55    8.93     10483.88 \n",
              "10  Maharashtra        31154.76   13747.43   492.15   45394.34 \n",
              "11  Odisha             1666.20    1267.06    160.22   3093.47  \n",
              "12  Puducherry         69.43      79.00      4.40     152.83   \n",
              "13  Rajasthan          15414.91   3342.62    12.96    18770.49 \n",
              "14  Tamil Nadu         11251.48   22153.34   394.82   33799.65 \n",
              "15  Telangana          887.43     3347.52    9.34     4244.29  \n",
              "16  West Bengal        0.03       2.04       0.01     2.08     \n",
              "17  Total in MW        153019.59  145742.59  3489.31  302251.49\n",
              "18  Total in GW        153        146        3        302      "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UbaJgYYRZ3-I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "205d49fb-af95-476b-a9c4-a3fffe357f98"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iF6GTZhYbRuh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "outputId": "d0a59b1d-31c0-4577-cafc-14232c5b1c05"
      },
      "source": [
        "df[[\"r1\", \"r2\", \"r3\",\"100m\"]] = df[[\"r1\", \"r2\", \"r3\",\"100m\"]].apply(pd.to_numeric)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:2963: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[k1] = value[k2]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KjCV2l32axaQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "outputId": "7a45b71c-860e-4e36-9094-df9be52730e4"
      },
      "source": [
        "# this will replace \"*\" with blank\n",
        "data1['State'] = data1['State'].str.rstrip('*')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNv_kQOPbZJL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hk_Mo0yZZb84",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "outputId": "1d04d386-ffb0-4627-8781-bd396d1af342"
      },
      "source": [
        "data1"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .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>Sl</th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394</td>\n",
              "      <td>14497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>201</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Assam</td>\n",
              "      <td>53</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Bihar</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6</td>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>23</td>\n",
              "      <td>314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7</td>\n",
              "      <td>Dieu Damn</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609</td>\n",
              "      <td>35071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9</td>\n",
              "      <td>Haryana</td>\n",
              "      <td>0</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>20</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>11</td>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>0</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12</td>\n",
              "      <td>Jammu &amp; Kashmir</td>\n",
              "      <td>5311</td>\n",
              "      <td>5685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>13</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>8591</td>\n",
              "      <td>13593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>14</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>790</td>\n",
              "      <td>837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>15</td>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>16</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>920</td>\n",
              "      <td>2931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>17</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>5439</td>\n",
              "      <td>5961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>18</td>\n",
              "      <td>Manipur</td>\n",
              "      <td>7</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>19</td>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>44</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>20</td>\n",
              "      <td>Nagaland</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>21</td>\n",
              "      <td>Orissa</td>\n",
              "      <td>910</td>\n",
              "      <td>1384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>22</td>\n",
              "      <td>Pondicherry</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>23</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>5005</td>\n",
              "      <td>5050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>24</td>\n",
              "      <td>Sikkim</td>\n",
              "      <td>98</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>25</td>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>5374</td>\n",
              "      <td>14152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>26</td>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>161</td>\n",
              "      <td>534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>27</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>137</td>\n",
              "      <td>1260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>28</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Total</td>\n",
              "      <td>Total</td>\n",
              "      <td>49130</td>\n",
              "      <td>102788</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Sl              State    50m     80m\n",
              "2   1      Andaman & Nicobar  2      365   \n",
              "3   2      Andhra Pradesh     5394   14497 \n",
              "4   3      Arunachal Pradesh  201    236   \n",
              "5   4      Assam              53     112   \n",
              "6   5      Bihar              0      144   \n",
              "7   6      Chhattisgarh       23     314   \n",
              "8   7      Dieu Damn          0      4     \n",
              "9   8      Gujarat            10609  35071 \n",
              "10  9      Haryana            0      93    \n",
              "11  10     Himachal Pradesh   20     64    \n",
              "12  11     Jharkhand          0      91    \n",
              "13  12     Jammu & Kashmir    5311   5685  \n",
              "14  13     Karnataka          8591   13593 \n",
              "15  14     Kerala             790    837   \n",
              "16  15     Lakshadweep        16     16    \n",
              "17  16     Madhya Pradesh     920    2931  \n",
              "18  17     Maharashtra        5439   5961  \n",
              "19  18     Manipur            7      56    \n",
              "20  19     Meghalaya          44     82    \n",
              "21  20     Nagaland           3      16    \n",
              "22  21     Orissa             910    1384  \n",
              "23  22     Pondicherry        0      120   \n",
              "24  23     Rajasthan          5005   5050  \n",
              "25  24     Sikkim             98     98    \n",
              "26  25     Tamil Nadu         5374   14152 \n",
              "27  26     Uttarakhand        161    534   \n",
              "28  27     Uttar Pradesh      137    1260  \n",
              "29  28     West Bengal        22     22    \n",
              "30  Total  Total              49130  102788"
            ]
          },
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        "colab_type": "code",
        "colab": {}
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        ""
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "metadata": {
        "id": "XHXAHdLIbOKA",
        "colab_type": "code",
        "colab": {}
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      "outputs": []
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        "\"\"\"cols_to_check = ['State']\n",
        "\n",
        "data1[cols_to_check] = data1[cols_to_check].replace({'*':''},inplace=True)\n",
        "\n",
        "data1\"\"\""
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
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            "text/plain": [
              "\"cols_to_check = ['State']\\n\\ndata1[cols_to_check] = data1[cols_to_check].replace({'*':''},inplace=True)\\n\\ndata1\""
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        "colab_type": "code",
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      "outputs": []
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          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sl</th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394</td>\n",
              "      <td>14497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>201</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Assam</td>\n",
              "      <td>53</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Bihar</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6</td>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>23</td>\n",
              "      <td>314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7</td>\n",
              "      <td>Dieu Damn</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609</td>\n",
              "      <td>35071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9</td>\n",
              "      <td>Haryana</td>\n",
              "      <td>0</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>20</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>11</td>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>0</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12</td>\n",
              "      <td>Jammu &amp; Kashmir</td>\n",
              "      <td>5311</td>\n",
              "      <td>5685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>13</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>8591</td>\n",
              "      <td>13593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>14</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>790</td>\n",
              "      <td>837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>15</td>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>16</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>920</td>\n",
              "      <td>2931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>17</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>5439</td>\n",
              "      <td>5961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>18</td>\n",
              "      <td>Manipur</td>\n",
              "      <td>7</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>19</td>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>44</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>20</td>\n",
              "      <td>Nagaland</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>21</td>\n",
              "      <td>Orissa</td>\n",
              "      <td>910</td>\n",
              "      <td>1384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>22</td>\n",
              "      <td>Pondicherry</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>23</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>5005</td>\n",
              "      <td>5050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>24</td>\n",
              "      <td>Sikkim</td>\n",
              "      <td>98</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>25</td>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>5374</td>\n",
              "      <td>14152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>26</td>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>161</td>\n",
              "      <td>534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>27</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>137</td>\n",
              "      <td>1260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>28</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Total</td>\n",
              "      <td>Total</td>\n",
              "      <td>49130</td>\n",
              "      <td>102788</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Sl              State    50m     80m\n",
              "2   1      Andaman & Nicobar  2      365   \n",
              "3   2      Andhra Pradesh     5394   14497 \n",
              "4   3      Arunachal Pradesh  201    236   \n",
              "5   4      Assam              53     112   \n",
              "6   5      Bihar              0      144   \n",
              "7   6      Chhattisgarh       23     314   \n",
              "8   7      Dieu Damn          0      4     \n",
              "9   8      Gujarat            10609  35071 \n",
              "10  9      Haryana            0      93    \n",
              "11  10     Himachal Pradesh   20     64    \n",
              "12  11     Jharkhand          0      91    \n",
              "13  12     Jammu & Kashmir    5311   5685  \n",
              "14  13     Karnataka          8591   13593 \n",
              "15  14     Kerala             790    837   \n",
              "16  15     Lakshadweep        16     16    \n",
              "17  16     Madhya Pradesh     920    2931  \n",
              "18  17     Maharashtra        5439   5961  \n",
              "19  18     Manipur            7      56    \n",
              "20  19     Meghalaya          44     82    \n",
              "21  20     Nagaland           3      16    \n",
              "22  21     Orissa             910    1384  \n",
              "23  22     Pondicherry        0      120   \n",
              "24  23     Rajasthan          5005   5050  \n",
              "25  24     Sikkim             98     98    \n",
              "26  25     Tamil Nadu         5374   14152 \n",
              "27  26     Uttarakhand        161    534   \n",
              "28  27     Uttar Pradesh      137    1260  \n",
              "29  28     West Bengal        22     22    \n",
              "30  Total  Total              49130  102788"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j18TPhdubuyB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "outputId": "a02bc69a-35bd-4025-d158-7c99a6b381bc"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>r1</th>\n",
              "      <th>r2</th>\n",
              "      <th>r3</th>\n",
              "      <th>100m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>4.12</td>\n",
              "      <td>3.43</td>\n",
              "      <td>0.88</td>\n",
              "      <td>8.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>22525.50</td>\n",
              "      <td>20538.10</td>\n",
              "      <td>1165.00</td>\n",
              "      <td>44228.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>3.24</td>\n",
              "      <td>57.03</td>\n",
              "      <td>16.31</td>\n",
              "      <td>76.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Goa</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>52287.59</td>\n",
              "      <td>32037.83</td>\n",
              "      <td>105.09</td>\n",
              "      <td>84431.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>15202.36</td>\n",
              "      <td>39802.59</td>\n",
              "      <td>852.40</td>\n",
              "      <td>55857.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>332.63</td>\n",
              "      <td>1102.56</td>\n",
              "      <td>264.38</td>\n",
              "      <td>1699.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.77</td>\n",
              "      <td>7.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>2216.39</td>\n",
              "      <td>8258.55</td>\n",
              "      <td>8.93</td>\n",
              "      <td>10483.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>31154.76</td>\n",
              "      <td>13747.43</td>\n",
              "      <td>492.15</td>\n",
              "      <td>45394.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>1666.20</td>\n",
              "      <td>1267.06</td>\n",
              "      <td>160.22</td>\n",
              "      <td>3093.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>69.43</td>\n",
              "      <td>79.00</td>\n",
              "      <td>4.40</td>\n",
              "      <td>152.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>15414.91</td>\n",
              "      <td>3342.62</td>\n",
              "      <td>12.96</td>\n",
              "      <td>18770.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>11251.48</td>\n",
              "      <td>22153.34</td>\n",
              "      <td>394.82</td>\n",
              "      <td>33799.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Telangana</td>\n",
              "      <td>887.43</td>\n",
              "      <td>3347.52</td>\n",
              "      <td>9.34</td>\n",
              "      <td>4244.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>0.03</td>\n",
              "      <td>2.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Total in MW</td>\n",
              "      <td>153019.59</td>\n",
              "      <td>145742.59</td>\n",
              "      <td>3489.31</td>\n",
              "      <td>302251.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Total in GW</td>\n",
              "      <td>153.00</td>\n",
              "      <td>146.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>302.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                State         r1         r2       r3       100m\n",
              "1   Andaman & Nicobar  4.12       3.43       0.88     8.43     \n",
              "2   Andhra Pradesh     22525.50   20538.10   1165.00  44228.60 \n",
              "3   Chhattisgarh       3.24       57.03      16.31    76.59    \n",
              "4   Goa                0.00       0.08       0.76     0.84     \n",
              "5   Gujarat            52287.59   32037.83   105.09   84431.33 \n",
              "6   Karnataka          15202.36   39802.59   852.40   55857.36 \n",
              "7   Kerala             332.63     1102.56    264.38   1699.56  \n",
              "8   Lakshadweep        3.50       3.40       0.77     7.67     \n",
              "9   Madhya Pradesh     2216.39    8258.55    8.93     10483.88 \n",
              "10  Maharashtra        31154.76   13747.43   492.15   45394.34 \n",
              "11  Odisha             1666.20    1267.06    160.22   3093.47  \n",
              "12  Puducherry         69.43      79.00      4.40     152.83   \n",
              "13  Rajasthan          15414.91   3342.62    12.96    18770.49 \n",
              "14  Tamil Nadu         11251.48   22153.34   394.82   33799.65 \n",
              "15  Telangana          887.43     3347.52    9.34     4244.29  \n",
              "16  West Bengal        0.03       2.04       0.01     2.08     \n",
              "17  Total in MW        153019.59  145742.59  3489.31  302251.49\n",
              "18  Total in GW        153.00     146.00     3.00     302.00   "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J-Kxgh3NbzIa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "229dc374-cbea-4ff7-c532-adc4a755f3ab"
      },
      "source": [
        "pd.merge(data1,df,on=['State'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sl</th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "      <th>r1</th>\n",
              "      <th>r2</th>\n",
              "      <th>r3</th>\n",
              "      <th>100m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2</td>\n",
              "      <td>365</td>\n",
              "      <td>4.12</td>\n",
              "      <td>3.43</td>\n",
              "      <td>0.88</td>\n",
              "      <td>8.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394</td>\n",
              "      <td>14497</td>\n",
              "      <td>22525.50</td>\n",
              "      <td>20538.10</td>\n",
              "      <td>1165.00</td>\n",
              "      <td>44228.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>23</td>\n",
              "      <td>314</td>\n",
              "      <td>3.24</td>\n",
              "      <td>57.03</td>\n",
              "      <td>16.31</td>\n",
              "      <td>76.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609</td>\n",
              "      <td>35071</td>\n",
              "      <td>52287.59</td>\n",
              "      <td>32037.83</td>\n",
              "      <td>105.09</td>\n",
              "      <td>84431.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>8591</td>\n",
              "      <td>13593</td>\n",
              "      <td>15202.36</td>\n",
              "      <td>39802.59</td>\n",
              "      <td>852.40</td>\n",
              "      <td>55857.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>14</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>790</td>\n",
              "      <td>837</td>\n",
              "      <td>332.63</td>\n",
              "      <td>1102.56</td>\n",
              "      <td>264.38</td>\n",
              "      <td>1699.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>15</td>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.77</td>\n",
              "      <td>7.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>16</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>920</td>\n",
              "      <td>2931</td>\n",
              "      <td>2216.39</td>\n",
              "      <td>8258.55</td>\n",
              "      <td>8.93</td>\n",
              "      <td>10483.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>17</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>5439</td>\n",
              "      <td>5961</td>\n",
              "      <td>31154.76</td>\n",
              "      <td>13747.43</td>\n",
              "      <td>492.15</td>\n",
              "      <td>45394.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>23</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>5005</td>\n",
              "      <td>5050</td>\n",
              "      <td>15414.91</td>\n",
              "      <td>3342.62</td>\n",
              "      <td>12.96</td>\n",
              "      <td>18770.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>25</td>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>5374</td>\n",
              "      <td>14152</td>\n",
              "      <td>11251.48</td>\n",
              "      <td>22153.34</td>\n",
              "      <td>394.82</td>\n",
              "      <td>33799.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>28</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>0.03</td>\n",
              "      <td>2.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Sl              State    50m    80m        r1        r2       r3      100m\n",
              "0   1   Andaman & Nicobar  2      365    4.12      3.43      0.88     8.43    \n",
              "1   2   Andhra Pradesh     5394   14497  22525.50  20538.10  1165.00  44228.60\n",
              "2   6   Chhattisgarh       23     314    3.24      57.03     16.31    76.59   \n",
              "3   8   Gujarat            10609  35071  52287.59  32037.83  105.09   84431.33\n",
              "4   13  Karnataka          8591   13593  15202.36  39802.59  852.40   55857.36\n",
              "5   14  Kerala             790    837    332.63    1102.56   264.38   1699.56 \n",
              "6   15  Lakshadweep        16     16     3.50      3.40      0.77     7.67    \n",
              "7   16  Madhya Pradesh     920    2931   2216.39   8258.55   8.93     10483.88\n",
              "8   17  Maharashtra        5439   5961   31154.76  13747.43  492.15   45394.34\n",
              "9   23  Rajasthan          5005   5050   15414.91  3342.62   12.96    18770.49\n",
              "10  25  Tamil Nadu         5374   14152  11251.48  22153.34  394.82   33799.65\n",
              "11  28  West Bengal        22     22     0.03      2.04      0.01     2.08    "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWhSVv24cNbP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#puducherry pondicherry\n",
        "data=pd.merge(data1,df,on=['State'],how=\"outer\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9yJjwJGgjYP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del data['Sl']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbYZlJ8_gUXr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d2d8067e-72e8-490a-8da5-4c68529f61c6"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "      <th>r1</th>\n",
              "      <th>r2</th>\n",
              "      <th>r3</th>\n",
              "      <th>100m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2.0</td>\n",
              "      <td>365.0</td>\n",
              "      <td>4.12</td>\n",
              "      <td>3.43</td>\n",
              "      <td>0.88</td>\n",
              "      <td>8.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394.0</td>\n",
              "      <td>14497.0</td>\n",
              "      <td>22525.50</td>\n",
              "      <td>20538.10</td>\n",
              "      <td>1165.00</td>\n",
              "      <td>44228.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>201.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Assam</td>\n",
              "      <td>53.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>0.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>23.0</td>\n",
              "      <td>314.0</td>\n",
              "      <td>3.24</td>\n",
              "      <td>57.03</td>\n",
              "      <td>16.31</td>\n",
              "      <td>76.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dieu Damn</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609.0</td>\n",
              "      <td>35071.0</td>\n",
              "      <td>52287.59</td>\n",
              "      <td>32037.83</td>\n",
              "      <td>105.09</td>\n",
              "      <td>84431.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Haryana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>20.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Jammu &amp; Kashmir</td>\n",
              "      <td>5311.0</td>\n",
              "      <td>5685.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>8591.0</td>\n",
              "      <td>13593.0</td>\n",
              "      <td>15202.36</td>\n",
              "      <td>39802.59</td>\n",
              "      <td>852.40</td>\n",
              "      <td>55857.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>790.0</td>\n",
              "      <td>837.0</td>\n",
              "      <td>332.63</td>\n",
              "      <td>1102.56</td>\n",
              "      <td>264.38</td>\n",
              "      <td>1699.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.77</td>\n",
              "      <td>7.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>920.0</td>\n",
              "      <td>2931.0</td>\n",
              "      <td>2216.39</td>\n",
              "      <td>8258.55</td>\n",
              "      <td>8.93</td>\n",
              "      <td>10483.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>5439.0</td>\n",
              "      <td>5961.0</td>\n",
              "      <td>31154.76</td>\n",
              "      <td>13747.43</td>\n",
              "      <td>492.15</td>\n",
              "      <td>45394.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Manipur</td>\n",
              "      <td>7.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>44.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Nagaland</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Orissa</td>\n",
              "      <td>910.0</td>\n",
              "      <td>1384.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Pondicherry</td>\n",
              "      <td>0.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>5005.0</td>\n",
              "      <td>5050.0</td>\n",
              "      <td>15414.91</td>\n",
              "      <td>3342.62</td>\n",
              "      <td>12.96</td>\n",
              "      <td>18770.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Sikkim</td>\n",
              "      <td>98.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>5374.0</td>\n",
              "      <td>14152.0</td>\n",
              "      <td>11251.48</td>\n",
              "      <td>22153.34</td>\n",
              "      <td>394.82</td>\n",
              "      <td>33799.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>161.0</td>\n",
              "      <td>534.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>137.0</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>2.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Total</td>\n",
              "      <td>49130.0</td>\n",
              "      <td>102788.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Goa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1666.20</td>\n",
              "      <td>1267.06</td>\n",
              "      <td>160.22</td>\n",
              "      <td>3093.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>69.43</td>\n",
              "      <td>79.00</td>\n",
              "      <td>4.40</td>\n",
              "      <td>152.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Telangana</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>887.43</td>\n",
              "      <td>3347.52</td>\n",
              "      <td>9.34</td>\n",
              "      <td>4244.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Total in MW</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>153019.59</td>\n",
              "      <td>145742.59</td>\n",
              "      <td>3489.31</td>\n",
              "      <td>302251.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Total in GW</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>153.00</td>\n",
              "      <td>146.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>302.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                State      50m       80m         r1         r2       r3  \\\n",
              "0   Andaman & Nicobar  2.0      365.0     4.12       3.43       0.88      \n",
              "1   Andhra Pradesh     5394.0   14497.0   22525.50   20538.10   1165.00   \n",
              "2   Arunachal Pradesh  201.0    236.0    NaN        NaN        NaN        \n",
              "3   Assam              53.0     112.0    NaN        NaN        NaN        \n",
              "4   Bihar              0.0      144.0    NaN        NaN        NaN        \n",
              "5   Chhattisgarh       23.0     314.0     3.24       57.03      16.31     \n",
              "6   Dieu Damn          0.0      4.0      NaN        NaN        NaN        \n",
              "7   Gujarat            10609.0  35071.0   52287.59   32037.83   105.09    \n",
              "8   Haryana            0.0      93.0     NaN        NaN        NaN        \n",
              "9   Himachal Pradesh   20.0     64.0     NaN        NaN        NaN        \n",
              "10  Jharkhand          0.0      91.0     NaN        NaN        NaN        \n",
              "11  Jammu & Kashmir    5311.0   5685.0   NaN        NaN        NaN        \n",
              "12  Karnataka          8591.0   13593.0   15202.36   39802.59   852.40    \n",
              "13  Kerala             790.0    837.0     332.63     1102.56    264.38    \n",
              "14  Lakshadweep        16.0     16.0      3.50       3.40       0.77      \n",
              "15  Madhya Pradesh     920.0    2931.0    2216.39    8258.55    8.93      \n",
              "16  Maharashtra        5439.0   5961.0    31154.76   13747.43   492.15    \n",
              "17  Manipur            7.0      56.0     NaN        NaN        NaN        \n",
              "18  Meghalaya          44.0     82.0     NaN        NaN        NaN        \n",
              "19  Nagaland           3.0      16.0     NaN        NaN        NaN        \n",
              "20  Orissa             910.0    1384.0   NaN        NaN        NaN        \n",
              "21  Pondicherry        0.0      120.0    NaN        NaN        NaN        \n",
              "22  Rajasthan          5005.0   5050.0    15414.91   3342.62    12.96     \n",
              "23  Sikkim             98.0     98.0     NaN        NaN        NaN        \n",
              "24  Tamil Nadu         5374.0   14152.0   11251.48   22153.34   394.82    \n",
              "25  Uttarakhand        161.0    534.0    NaN        NaN        NaN        \n",
              "26  Uttar Pradesh      137.0    1260.0   NaN        NaN        NaN        \n",
              "27  West Bengal        22.0     22.0      0.03       2.04       0.01      \n",
              "28  Total              49130.0  102788.0 NaN        NaN        NaN        \n",
              "29  Goa               NaN      NaN        0.00       0.08       0.76      \n",
              "30  Odisha            NaN      NaN        1666.20    1267.06    160.22    \n",
              "31  Puducherry        NaN      NaN        69.43      79.00      4.40      \n",
              "32  Telangana         NaN      NaN        887.43     3347.52    9.34      \n",
              "33  Total in MW       NaN      NaN        153019.59  145742.59  3489.31   \n",
              "34  Total in GW       NaN      NaN        153.00     146.00     3.00      \n",
              "\n",
              "         100m  \n",
              "0   8.43       \n",
              "1   44228.60   \n",
              "2  NaN         \n",
              "3  NaN         \n",
              "4  NaN         \n",
              "5   76.59      \n",
              "6  NaN         \n",
              "7   84431.33   \n",
              "8  NaN         \n",
              "9  NaN         \n",
              "10 NaN         \n",
              "11 NaN         \n",
              "12  55857.36   \n",
              "13  1699.56    \n",
              "14  7.67       \n",
              "15  10483.88   \n",
              "16  45394.34   \n",
              "17 NaN         \n",
              "18 NaN         \n",
              "19 NaN         \n",
              "20 NaN         \n",
              "21 NaN         \n",
              "22  18770.49   \n",
              "23 NaN         \n",
              "24  33799.65   \n",
              "25 NaN         \n",
              "26 NaN         \n",
              "27  2.08       \n",
              "28 NaN         \n",
              "29  0.84       \n",
              "30  3093.47    \n",
              "31  152.83     \n",
              "32  4244.29    \n",
              "33  302251.49  \n",
              "34  302.00     "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBuzuWA4dbO0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# this will replace NaN with 0\n",
        "data=data.fillna(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HhrCJ3RfnUQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data[\"Total\"]=data[\"50m\"]+data[\"80m\"]+data[\"100m\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWfvbc-yfr9u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "88c68574-93d6-4a39-d56e-402ec4180ffb"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "      <th>r1</th>\n",
              "      <th>r2</th>\n",
              "      <th>r3</th>\n",
              "      <th>100m</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2.0</td>\n",
              "      <td>365.0</td>\n",
              "      <td>4.12</td>\n",
              "      <td>3.43</td>\n",
              "      <td>0.88</td>\n",
              "      <td>8.43</td>\n",
              "      <td>375.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394.0</td>\n",
              "      <td>14497.0</td>\n",
              "      <td>22525.50</td>\n",
              "      <td>20538.10</td>\n",
              "      <td>1165.00</td>\n",
              "      <td>44228.60</td>\n",
              "      <td>64119.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>201.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>437.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Assam</td>\n",
              "      <td>53.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>165.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>0.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>144.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>23.0</td>\n",
              "      <td>314.0</td>\n",
              "      <td>3.24</td>\n",
              "      <td>57.03</td>\n",
              "      <td>16.31</td>\n",
              "      <td>76.59</td>\n",
              "      <td>413.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dieu Damn</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609.0</td>\n",
              "      <td>35071.0</td>\n",
              "      <td>52287.59</td>\n",
              "      <td>32037.83</td>\n",
              "      <td>105.09</td>\n",
              "      <td>84431.33</td>\n",
              "      <td>130111.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Haryana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>93.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>20.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>84.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>91.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Jammu &amp; Kashmir</td>\n",
              "      <td>5311.0</td>\n",
              "      <td>5685.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10996.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>8591.0</td>\n",
              "      <td>13593.0</td>\n",
              "      <td>15202.36</td>\n",
              "      <td>39802.59</td>\n",
              "      <td>852.40</td>\n",
              "      <td>55857.36</td>\n",
              "      <td>78041.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>790.0</td>\n",
              "      <td>837.0</td>\n",
              "      <td>332.63</td>\n",
              "      <td>1102.56</td>\n",
              "      <td>264.38</td>\n",
              "      <td>1699.56</td>\n",
              "      <td>3326.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.77</td>\n",
              "      <td>7.67</td>\n",
              "      <td>39.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>920.0</td>\n",
              "      <td>2931.0</td>\n",
              "      <td>2216.39</td>\n",
              "      <td>8258.55</td>\n",
              "      <td>8.93</td>\n",
              "      <td>10483.88</td>\n",
              "      <td>14334.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>5439.0</td>\n",
              "      <td>5961.0</td>\n",
              "      <td>31154.76</td>\n",
              "      <td>13747.43</td>\n",
              "      <td>492.15</td>\n",
              "      <td>45394.34</td>\n",
              "      <td>56794.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Manipur</td>\n",
              "      <td>7.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>63.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>44.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>126.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Nagaland</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>19.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Orissa</td>\n",
              "      <td>910.0</td>\n",
              "      <td>1384.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2294.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Pondicherry</td>\n",
              "      <td>0.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>120.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>5005.0</td>\n",
              "      <td>5050.0</td>\n",
              "      <td>15414.91</td>\n",
              "      <td>3342.62</td>\n",
              "      <td>12.96</td>\n",
              "      <td>18770.49</td>\n",
              "      <td>28825.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Sikkim</td>\n",
              "      <td>98.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>196.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>5374.0</td>\n",
              "      <td>14152.0</td>\n",
              "      <td>11251.48</td>\n",
              "      <td>22153.34</td>\n",
              "      <td>394.82</td>\n",
              "      <td>33799.65</td>\n",
              "      <td>53325.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>161.0</td>\n",
              "      <td>534.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>695.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>137.0</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1397.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>2.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.08</td>\n",
              "      <td>46.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Total</td>\n",
              "      <td>49130.0</td>\n",
              "      <td>102788.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>151918.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Goa</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1666.20</td>\n",
              "      <td>1267.06</td>\n",
              "      <td>160.22</td>\n",
              "      <td>3093.47</td>\n",
              "      <td>3093.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.43</td>\n",
              "      <td>79.00</td>\n",
              "      <td>4.40</td>\n",
              "      <td>152.83</td>\n",
              "      <td>152.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Telangana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>887.43</td>\n",
              "      <td>3347.52</td>\n",
              "      <td>9.34</td>\n",
              "      <td>4244.29</td>\n",
              "      <td>4244.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Total in MW</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>153019.59</td>\n",
              "      <td>145742.59</td>\n",
              "      <td>3489.31</td>\n",
              "      <td>302251.49</td>\n",
              "      <td>302251.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Total in GW</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>153.00</td>\n",
              "      <td>146.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>302.00</td>\n",
              "      <td>302.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                State      50m       80m         r1         r2       r3  \\\n",
              "0   Andaman & Nicobar  2.0      365.0     4.12       3.43       0.88      \n",
              "1   Andhra Pradesh     5394.0   14497.0   22525.50   20538.10   1165.00   \n",
              "2   Arunachal Pradesh  201.0    236.0     0.00       0.00       0.00      \n",
              "3   Assam              53.0     112.0     0.00       0.00       0.00      \n",
              "4   Bihar              0.0      144.0     0.00       0.00       0.00      \n",
              "5   Chhattisgarh       23.0     314.0     3.24       57.03      16.31     \n",
              "6   Dieu Damn          0.0      4.0       0.00       0.00       0.00      \n",
              "7   Gujarat            10609.0  35071.0   52287.59   32037.83   105.09    \n",
              "8   Haryana            0.0      93.0      0.00       0.00       0.00      \n",
              "9   Himachal Pradesh   20.0     64.0      0.00       0.00       0.00      \n",
              "10  Jharkhand          0.0      91.0      0.00       0.00       0.00      \n",
              "11  Jammu & Kashmir    5311.0   5685.0    0.00       0.00       0.00      \n",
              "12  Karnataka          8591.0   13593.0   15202.36   39802.59   852.40    \n",
              "13  Kerala             790.0    837.0     332.63     1102.56    264.38    \n",
              "14  Lakshadweep        16.0     16.0      3.50       3.40       0.77      \n",
              "15  Madhya Pradesh     920.0    2931.0    2216.39    8258.55    8.93      \n",
              "16  Maharashtra        5439.0   5961.0    31154.76   13747.43   492.15    \n",
              "17  Manipur            7.0      56.0      0.00       0.00       0.00      \n",
              "18  Meghalaya          44.0     82.0      0.00       0.00       0.00      \n",
              "19  Nagaland           3.0      16.0      0.00       0.00       0.00      \n",
              "20  Orissa             910.0    1384.0    0.00       0.00       0.00      \n",
              "21  Pondicherry        0.0      120.0     0.00       0.00       0.00      \n",
              "22  Rajasthan          5005.0   5050.0    15414.91   3342.62    12.96     \n",
              "23  Sikkim             98.0     98.0      0.00       0.00       0.00      \n",
              "24  Tamil Nadu         5374.0   14152.0   11251.48   22153.34   394.82    \n",
              "25  Uttarakhand        161.0    534.0     0.00       0.00       0.00      \n",
              "26  Uttar Pradesh      137.0    1260.0    0.00       0.00       0.00      \n",
              "27  West Bengal        22.0     22.0      0.03       2.04       0.01      \n",
              "28  Total              49130.0  102788.0  0.00       0.00       0.00      \n",
              "29  Goa                0.0      0.0       0.00       0.08       0.76      \n",
              "30  Odisha             0.0      0.0       1666.20    1267.06    160.22    \n",
              "31  Puducherry         0.0      0.0       69.43      79.00      4.40      \n",
              "32  Telangana          0.0      0.0       887.43     3347.52    9.34      \n",
              "33  Total in MW        0.0      0.0       153019.59  145742.59  3489.31   \n",
              "34  Total in GW        0.0      0.0       153.00     146.00     3.00      \n",
              "\n",
              "         100m      Total  \n",
              "0   8.43       375.43     \n",
              "1   44228.60   64119.60   \n",
              "2   0.00       437.00     \n",
              "3   0.00       165.00     \n",
              "4   0.00       144.00     \n",
              "5   76.59      413.59     \n",
              "6   0.00       4.00       \n",
              "7   84431.33   130111.33  \n",
              "8   0.00       93.00      \n",
              "9   0.00       84.00      \n",
              "10  0.00       91.00      \n",
              "11  0.00       10996.00   \n",
              "12  55857.36   78041.36   \n",
              "13  1699.56    3326.56    \n",
              "14  7.67       39.67      \n",
              "15  10483.88   14334.88   \n",
              "16  45394.34   56794.34   \n",
              "17  0.00       63.00      \n",
              "18  0.00       126.00     \n",
              "19  0.00       19.00      \n",
              "20  0.00       2294.00    \n",
              "21  0.00       120.00     \n",
              "22  18770.49   28825.49   \n",
              "23  0.00       196.00     \n",
              "24  33799.65   53325.65   \n",
              "25  0.00       695.00     \n",
              "26  0.00       1397.00    \n",
              "27  2.08       46.08      \n",
              "28  0.00       151918.00  \n",
              "29  0.84       0.84       \n",
              "30  3093.47    3093.47    \n",
              "31  152.83     152.83     \n",
              "32  4244.29    4244.29    \n",
              "33  302251.49  302251.49  \n",
              "34  302.00     302.00     "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pi0OJfnQgDGZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.to_csv('wind_data.csv', index = False, header=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZGOWwxPhU08",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c5060a12-3bc8-4374-b110-957d9e279e35"
      },
      "source": [
        "data=data.sort_values('Total',ascending=False)\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "      <th>r1</th>\n",
              "      <th>r2</th>\n",
              "      <th>r3</th>\n",
              "      <th>100m</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Total in MW</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>153019.59</td>\n",
              "      <td>145742.59</td>\n",
              "      <td>3489.31</td>\n",
              "      <td>302251.49</td>\n",
              "      <td>302251.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Total</td>\n",
              "      <td>49130.0</td>\n",
              "      <td>102788.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>151918.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609.0</td>\n",
              "      <td>35071.0</td>\n",
              "      <td>52287.59</td>\n",
              "      <td>32037.83</td>\n",
              "      <td>105.09</td>\n",
              "      <td>84431.33</td>\n",
              "      <td>130111.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>8591.0</td>\n",
              "      <td>13593.0</td>\n",
              "      <td>15202.36</td>\n",
              "      <td>39802.59</td>\n",
              "      <td>852.40</td>\n",
              "      <td>55857.36</td>\n",
              "      <td>78041.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394.0</td>\n",
              "      <td>14497.0</td>\n",
              "      <td>22525.50</td>\n",
              "      <td>20538.10</td>\n",
              "      <td>1165.00</td>\n",
              "      <td>44228.60</td>\n",
              "      <td>64119.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>5439.0</td>\n",
              "      <td>5961.0</td>\n",
              "      <td>31154.76</td>\n",
              "      <td>13747.43</td>\n",
              "      <td>492.15</td>\n",
              "      <td>45394.34</td>\n",
              "      <td>56794.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>5374.0</td>\n",
              "      <td>14152.0</td>\n",
              "      <td>11251.48</td>\n",
              "      <td>22153.34</td>\n",
              "      <td>394.82</td>\n",
              "      <td>33799.65</td>\n",
              "      <td>53325.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>5005.0</td>\n",
              "      <td>5050.0</td>\n",
              "      <td>15414.91</td>\n",
              "      <td>3342.62</td>\n",
              "      <td>12.96</td>\n",
              "      <td>18770.49</td>\n",
              "      <td>28825.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>920.0</td>\n",
              "      <td>2931.0</td>\n",
              "      <td>2216.39</td>\n",
              "      <td>8258.55</td>\n",
              "      <td>8.93</td>\n",
              "      <td>10483.88</td>\n",
              "      <td>14334.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Jammu &amp; Kashmir</td>\n",
              "      <td>5311.0</td>\n",
              "      <td>5685.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10996.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Telangana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>887.43</td>\n",
              "      <td>3347.52</td>\n",
              "      <td>9.34</td>\n",
              "      <td>4244.29</td>\n",
              "      <td>4244.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>790.0</td>\n",
              "      <td>837.0</td>\n",
              "      <td>332.63</td>\n",
              "      <td>1102.56</td>\n",
              "      <td>264.38</td>\n",
              "      <td>1699.56</td>\n",
              "      <td>3326.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1666.20</td>\n",
              "      <td>1267.06</td>\n",
              "      <td>160.22</td>\n",
              "      <td>3093.47</td>\n",
              "      <td>3093.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Orissa</td>\n",
              "      <td>910.0</td>\n",
              "      <td>1384.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2294.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>137.0</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1397.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>161.0</td>\n",
              "      <td>534.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>695.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>201.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>437.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>23.0</td>\n",
              "      <td>314.0</td>\n",
              "      <td>3.24</td>\n",
              "      <td>57.03</td>\n",
              "      <td>16.31</td>\n",
              "      <td>76.59</td>\n",
              "      <td>413.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2.0</td>\n",
              "      <td>365.0</td>\n",
              "      <td>4.12</td>\n",
              "      <td>3.43</td>\n",
              "      <td>0.88</td>\n",
              "      <td>8.43</td>\n",
              "      <td>375.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Total in GW</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>153.00</td>\n",
              "      <td>146.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>302.00</td>\n",
              "      <td>302.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Sikkim</td>\n",
              "      <td>98.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>196.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Assam</td>\n",
              "      <td>53.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>165.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.43</td>\n",
              "      <td>79.00</td>\n",
              "      <td>4.40</td>\n",
              "      <td>152.83</td>\n",
              "      <td>152.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>0.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>144.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>44.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>126.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Pondicherry</td>\n",
              "      <td>0.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>120.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Haryana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>93.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>91.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>20.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>84.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Manipur</td>\n",
              "      <td>7.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>63.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>2.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.08</td>\n",
              "      <td>46.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.77</td>\n",
              "      <td>7.67</td>\n",
              "      <td>39.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Nagaland</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>19.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dieu Damn</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Goa</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                State      50m       80m         r1         r2       r3  \\\n",
              "33  Total in MW        0.0      0.0       153019.59  145742.59  3489.31   \n",
              "28  Total              49130.0  102788.0  0.00       0.00       0.00      \n",
              "7   Gujarat            10609.0  35071.0   52287.59   32037.83   105.09    \n",
              "12  Karnataka          8591.0   13593.0   15202.36   39802.59   852.40    \n",
              "1   Andhra Pradesh     5394.0   14497.0   22525.50   20538.10   1165.00   \n",
              "16  Maharashtra        5439.0   5961.0    31154.76   13747.43   492.15    \n",
              "24  Tamil Nadu         5374.0   14152.0   11251.48   22153.34   394.82    \n",
              "22  Rajasthan          5005.0   5050.0    15414.91   3342.62    12.96     \n",
              "15  Madhya Pradesh     920.0    2931.0    2216.39    8258.55    8.93      \n",
              "11  Jammu & Kashmir    5311.0   5685.0    0.00       0.00       0.00      \n",
              "32  Telangana          0.0      0.0       887.43     3347.52    9.34      \n",
              "13  Kerala             790.0    837.0     332.63     1102.56    264.38    \n",
              "30  Odisha             0.0      0.0       1666.20    1267.06    160.22    \n",
              "20  Orissa             910.0    1384.0    0.00       0.00       0.00      \n",
              "26  Uttar Pradesh      137.0    1260.0    0.00       0.00       0.00      \n",
              "25  Uttarakhand        161.0    534.0     0.00       0.00       0.00      \n",
              "2   Arunachal Pradesh  201.0    236.0     0.00       0.00       0.00      \n",
              "5   Chhattisgarh       23.0     314.0     3.24       57.03      16.31     \n",
              "0   Andaman & Nicobar  2.0      365.0     4.12       3.43       0.88      \n",
              "34  Total in GW        0.0      0.0       153.00     146.00     3.00      \n",
              "23  Sikkim             98.0     98.0      0.00       0.00       0.00      \n",
              "3   Assam              53.0     112.0     0.00       0.00       0.00      \n",
              "31  Puducherry         0.0      0.0       69.43      79.00      4.40      \n",
              "4   Bihar              0.0      144.0     0.00       0.00       0.00      \n",
              "18  Meghalaya          44.0     82.0      0.00       0.00       0.00      \n",
              "21  Pondicherry        0.0      120.0     0.00       0.00       0.00      \n",
              "8   Haryana            0.0      93.0      0.00       0.00       0.00      \n",
              "10  Jharkhand          0.0      91.0      0.00       0.00       0.00      \n",
              "9   Himachal Pradesh   20.0     64.0      0.00       0.00       0.00      \n",
              "17  Manipur            7.0      56.0      0.00       0.00       0.00      \n",
              "27  West Bengal        22.0     22.0      0.03       2.04       0.01      \n",
              "14  Lakshadweep        16.0     16.0      3.50       3.40       0.77      \n",
              "19  Nagaland           3.0      16.0      0.00       0.00       0.00      \n",
              "6   Dieu Damn          0.0      4.0       0.00       0.00       0.00      \n",
              "29  Goa                0.0      0.0       0.00       0.08       0.76      \n",
              "\n",
              "         100m      Total  \n",
              "33  302251.49  302251.49  \n",
              "28  0.00       151918.00  \n",
              "7   84431.33   130111.33  \n",
              "12  55857.36   78041.36   \n",
              "1   44228.60   64119.60   \n",
              "16  45394.34   56794.34   \n",
              "24  33799.65   53325.65   \n",
              "22  18770.49   28825.49   \n",
              "15  10483.88   14334.88   \n",
              "11  0.00       10996.00   \n",
              "32  4244.29    4244.29    \n",
              "13  1699.56    3326.56    \n",
              "30  3093.47    3093.47    \n",
              "20  0.00       2294.00    \n",
              "26  0.00       1397.00    \n",
              "25  0.00       695.00     \n",
              "2   0.00       437.00     \n",
              "5   76.59      413.59     \n",
              "0   8.43       375.43     \n",
              "34  302.00     302.00     \n",
              "23  0.00       196.00     \n",
              "3   0.00       165.00     \n",
              "31  152.83     152.83     \n",
              "4   0.00       144.00     \n",
              "18  0.00       126.00     \n",
              "21  0.00       120.00     \n",
              "8   0.00       93.00      \n",
              "10  0.00       91.00      \n",
              "9   0.00       84.00      \n",
              "17  0.00       63.00      \n",
              "27  2.08       46.08      \n",
              "14  7.67       39.67      \n",
              "19  0.00       19.00      \n",
              "6   0.00       4.00       \n",
              "29  0.84       0.84       "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ujlVD_NIhnAg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=data.iloc[2:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwNqxfbTh5-w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0a4337b1-af92-4cea-9388-a8b58d8ce0ce"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "      <th>r1</th>\n",
              "      <th>r2</th>\n",
              "      <th>r3</th>\n",
              "      <th>100m</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609.0</td>\n",
              "      <td>35071.0</td>\n",
              "      <td>52287.59</td>\n",
              "      <td>32037.83</td>\n",
              "      <td>105.09</td>\n",
              "      <td>84431.33</td>\n",
              "      <td>130111.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>8591.0</td>\n",
              "      <td>13593.0</td>\n",
              "      <td>15202.36</td>\n",
              "      <td>39802.59</td>\n",
              "      <td>852.40</td>\n",
              "      <td>55857.36</td>\n",
              "      <td>78041.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394.0</td>\n",
              "      <td>14497.0</td>\n",
              "      <td>22525.50</td>\n",
              "      <td>20538.10</td>\n",
              "      <td>1165.00</td>\n",
              "      <td>44228.60</td>\n",
              "      <td>64119.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>5439.0</td>\n",
              "      <td>5961.0</td>\n",
              "      <td>31154.76</td>\n",
              "      <td>13747.43</td>\n",
              "      <td>492.15</td>\n",
              "      <td>45394.34</td>\n",
              "      <td>56794.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>5374.0</td>\n",
              "      <td>14152.0</td>\n",
              "      <td>11251.48</td>\n",
              "      <td>22153.34</td>\n",
              "      <td>394.82</td>\n",
              "      <td>33799.65</td>\n",
              "      <td>53325.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>5005.0</td>\n",
              "      <td>5050.0</td>\n",
              "      <td>15414.91</td>\n",
              "      <td>3342.62</td>\n",
              "      <td>12.96</td>\n",
              "      <td>18770.49</td>\n",
              "      <td>28825.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>920.0</td>\n",
              "      <td>2931.0</td>\n",
              "      <td>2216.39</td>\n",
              "      <td>8258.55</td>\n",
              "      <td>8.93</td>\n",
              "      <td>10483.88</td>\n",
              "      <td>14334.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Jammu &amp; Kashmir</td>\n",
              "      <td>5311.0</td>\n",
              "      <td>5685.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10996.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Telangana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>887.43</td>\n",
              "      <td>3347.52</td>\n",
              "      <td>9.34</td>\n",
              "      <td>4244.29</td>\n",
              "      <td>4244.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>790.0</td>\n",
              "      <td>837.0</td>\n",
              "      <td>332.63</td>\n",
              "      <td>1102.56</td>\n",
              "      <td>264.38</td>\n",
              "      <td>1699.56</td>\n",
              "      <td>3326.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1666.20</td>\n",
              "      <td>1267.06</td>\n",
              "      <td>160.22</td>\n",
              "      <td>3093.47</td>\n",
              "      <td>3093.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Orissa</td>\n",
              "      <td>910.0</td>\n",
              "      <td>1384.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2294.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>137.0</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1397.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>161.0</td>\n",
              "      <td>534.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>695.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>201.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>437.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>23.0</td>\n",
              "      <td>314.0</td>\n",
              "      <td>3.24</td>\n",
              "      <td>57.03</td>\n",
              "      <td>16.31</td>\n",
              "      <td>76.59</td>\n",
              "      <td>413.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2.0</td>\n",
              "      <td>365.0</td>\n",
              "      <td>4.12</td>\n",
              "      <td>3.43</td>\n",
              "      <td>0.88</td>\n",
              "      <td>8.43</td>\n",
              "      <td>375.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Total in GW</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>153.00</td>\n",
              "      <td>146.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>302.00</td>\n",
              "      <td>302.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Sikkim</td>\n",
              "      <td>98.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>196.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Assam</td>\n",
              "      <td>53.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>165.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.43</td>\n",
              "      <td>79.00</td>\n",
              "      <td>4.40</td>\n",
              "      <td>152.83</td>\n",
              "      <td>152.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>0.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>144.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>44.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>126.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Pondicherry</td>\n",
              "      <td>0.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>120.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Haryana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>93.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>91.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>20.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>84.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Manipur</td>\n",
              "      <td>7.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>63.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>2.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.08</td>\n",
              "      <td>46.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.77</td>\n",
              "      <td>7.67</td>\n",
              "      <td>39.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Nagaland</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>19.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dieu Damn</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Goa</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                State      50m      80m        r1        r2       r3  \\\n",
              "7   Gujarat            10609.0  35071.0  52287.59  32037.83  105.09    \n",
              "12  Karnataka          8591.0   13593.0  15202.36  39802.59  852.40    \n",
              "1   Andhra Pradesh     5394.0   14497.0  22525.50  20538.10  1165.00   \n",
              "16  Maharashtra        5439.0   5961.0   31154.76  13747.43  492.15    \n",
              "24  Tamil Nadu         5374.0   14152.0  11251.48  22153.34  394.82    \n",
              "22  Rajasthan          5005.0   5050.0   15414.91  3342.62   12.96     \n",
              "15  Madhya Pradesh     920.0    2931.0   2216.39   8258.55   8.93      \n",
              "11  Jammu & Kashmir    5311.0   5685.0   0.00      0.00      0.00      \n",
              "32  Telangana          0.0      0.0      887.43    3347.52   9.34      \n",
              "13  Kerala             790.0    837.0    332.63    1102.56   264.38    \n",
              "30  Odisha             0.0      0.0      1666.20   1267.06   160.22    \n",
              "20  Orissa             910.0    1384.0   0.00      0.00      0.00      \n",
              "26  Uttar Pradesh      137.0    1260.0   0.00      0.00      0.00      \n",
              "25  Uttarakhand        161.0    534.0    0.00      0.00      0.00      \n",
              "2   Arunachal Pradesh  201.0    236.0    0.00      0.00      0.00      \n",
              "5   Chhattisgarh       23.0     314.0    3.24      57.03     16.31     \n",
              "0   Andaman & Nicobar  2.0      365.0    4.12      3.43      0.88      \n",
              "34  Total in GW        0.0      0.0      153.00    146.00    3.00      \n",
              "23  Sikkim             98.0     98.0     0.00      0.00      0.00      \n",
              "3   Assam              53.0     112.0    0.00      0.00      0.00      \n",
              "31  Puducherry         0.0      0.0      69.43     79.00     4.40      \n",
              "4   Bihar              0.0      144.0    0.00      0.00      0.00      \n",
              "18  Meghalaya          44.0     82.0     0.00      0.00      0.00      \n",
              "21  Pondicherry        0.0      120.0    0.00      0.00      0.00      \n",
              "8   Haryana            0.0      93.0     0.00      0.00      0.00      \n",
              "10  Jharkhand          0.0      91.0     0.00      0.00      0.00      \n",
              "9   Himachal Pradesh   20.0     64.0     0.00      0.00      0.00      \n",
              "17  Manipur            7.0      56.0     0.00      0.00      0.00      \n",
              "27  West Bengal        22.0     22.0     0.03      2.04      0.01      \n",
              "14  Lakshadweep        16.0     16.0     3.50      3.40      0.77      \n",
              "19  Nagaland           3.0      16.0     0.00      0.00      0.00      \n",
              "6   Dieu Damn          0.0      4.0      0.00      0.00      0.00      \n",
              "29  Goa                0.0      0.0      0.00      0.08      0.76      \n",
              "\n",
              "        100m      Total  \n",
              "7   84431.33  130111.33  \n",
              "12  55857.36  78041.36   \n",
              "1   44228.60  64119.60   \n",
              "16  45394.34  56794.34   \n",
              "24  33799.65  53325.65   \n",
              "22  18770.49  28825.49   \n",
              "15  10483.88  14334.88   \n",
              "11  0.00      10996.00   \n",
              "32  4244.29   4244.29    \n",
              "13  1699.56   3326.56    \n",
              "30  3093.47   3093.47    \n",
              "20  0.00      2294.00    \n",
              "26  0.00      1397.00    \n",
              "25  0.00      695.00     \n",
              "2   0.00      437.00     \n",
              "5   76.59     413.59     \n",
              "0   8.43      375.43     \n",
              "34  302.00    302.00     \n",
              "23  0.00      196.00     \n",
              "3   0.00      165.00     \n",
              "31  152.83    152.83     \n",
              "4   0.00      144.00     \n",
              "18  0.00      126.00     \n",
              "21  0.00      120.00     \n",
              "8   0.00      93.00      \n",
              "10  0.00      91.00      \n",
              "9   0.00      84.00      \n",
              "17  0.00      63.00      \n",
              "27  2.08      46.08      \n",
              "14  7.67      39.67      \n",
              "19  0.00      19.00      \n",
              "6   0.00      4.00       \n",
              "29  0.84      0.84       "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vnJkYy6Oh6ze",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.to_csv('wind_data_sorted.csv', index = False, header=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEyx3QJsiAv7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "outputId": "b731224d-e260-46ae-80f7-b4c32d292c85"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# Load the example Titanic dataset\n",
        "titanic = sns.load_dataset(\"titanic\")\n",
        "\n",
        "# Draw a nested barplot to show survival for class and sex\n",
        "g = sns.catplot(x=\"class\", y=\"survived\", hue=\"sex\", data=titanic,\n",
        "                height=6, kind=\"bar\", palette=\"muted\")\n",
        "g.despine(left=True)\n",
        "g.set_ylabels(\"survival probability\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f09039eebe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 509.975x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CzvZ_Dj3iaaD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "4620452b-19ff-4a5e-9430-82b402d21d2b"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.set(style=\"darkgrid\")\n",
        "\n",
        "# Load the example Titanic dataset\n",
        "titanic = sns.load_dataset(\"titanic\")\n",
        "\n",
        "# Draw a nested barplot to show survival for class and sex\n",
        "g = sns.catplot(x=\"class\", y=\"survived\", hue=\"sex\", data=titanic,\n",
        "                height=6, kind=\"bar\", palette=\"muted\")\n",
        "g.despine(left=True)\n",
        "g.set_ylabels(\"survival probability\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0903992cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 509.975x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQWCqbK8qvut",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "5ddbcbe2-b381-4b34-98bc-87fa30bfc934"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "N=3\n",
        "Coal=[909064,0.43,2168]\n",
        "Oil=[162135,0.58,2041]\n",
        "Gas=[161577,0.37,2071]\n",
        "\n",
        "#plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
        "plt.rcParams[\"font.size\"] = \"16\"\n",
        "\n",
        "Coal=[0.909064,1.65*0.43,4.2*0.2168]\n",
        "Oil=[0.162135,1.65*0.58,4.2*0.2041]\n",
        "Gas=[0.161577,1.65*0.37,4.2*0.2071]\n",
        "\n",
        "\n",
        "\n",
        "Coal=[0.909064,1.60*0.43,10*5.5*(0.2168-0.2004)]\n",
        "Oil=[0.162135,1.60*0.58,10*5.5*(0.2041-0.2004)]\n",
        "Gas=[0.161577,1.60*0.37,10*5.5*(0.2071-0.2004)]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "plt.style.use('dark_background')\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "\n",
        "ind = np.arange(N) \n",
        "width1 = 0.25\n",
        "width2= 0.25  \n",
        "plt.bar(ind, Coal, width1, label='Coal')\n",
        "plt.bar(ind + width1, Oil, width2,label='Oil')\n",
        "plt.bar(ind + 2*width1, Gas, width2,label='Gas')\n",
        "#plt.ylabel('Info')\n",
        "plt.title('World reserve of Basic Fossil Fuels', fontsize=30)\n",
        "plt.grid(False)\n",
        "\n",
        "plt.xticks(ind + width1 , ('Total World Reserve', 'Annual consumption per capita 2004', 'Last Until'))\n",
        "plt.yticks([])\n",
        "plt.legend(loc='best')\n",
        "#plt.legend(loc='center left', bbox_to_anchor=(1, 1),fancybox = True, shadow = True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7eGWpsb8q-8t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "13763c26-ddc6-402b-d0ea-962ef0008481"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "df=data.iloc[:10]\n",
        "N=len(df)\n",
        "row1=df[\"50m\"]\n",
        "row2=df[\"80m\"]\n",
        "row3=df[\"100m\"]\n",
        "\n",
        "#plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
        "plt.rcParams[\"font.size\"] = \"16\"\n",
        "\n",
        "\n",
        "plt.style.use('dark_background')\n",
        "\n",
        "plt.figure(figsize=(25,12.5))\n",
        "\n",
        "ind = np.arange(N) \n",
        "width1 = 0.25\n",
        "width2= 0.25  \n",
        "plt.bar(ind, row1, width1, label='50m')\n",
        "plt.bar(ind + width1, row2, width2,label='80m')\n",
        "plt.bar(ind + 2*width1, row3, width2,label='100m')\n",
        "#plt.ylabel('Info')\n",
        "plt.title('Estimation of Installable Wind Power Potential \\nat Top 10 States in India', fontsize=30)\n",
        "plt.grid(False)\n",
        "\n",
        "plt.xticks(ind + width1 , df[\"State\"])\n",
        "plt.rc('xtick',labelsize=16)\n",
        "#plt.yticks([])\n",
        "plt.legend(loc='best')\n",
        "#plt.legend(loc='center left', bbox_to_anchor=(1, 1),fancybox = True, shadow = True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x900 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2BTLcdZOq_Fu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a2ef54d9-cc2e-463d-fb45-7b05f8a52103"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "      <th>r1</th>\n",
              "      <th>r2</th>\n",
              "      <th>r3</th>\n",
              "      <th>100m</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609.0</td>\n",
              "      <td>35071.0</td>\n",
              "      <td>52287.59</td>\n",
              "      <td>32037.83</td>\n",
              "      <td>105.09</td>\n",
              "      <td>84431.33</td>\n",
              "      <td>130111.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>8591.0</td>\n",
              "      <td>13593.0</td>\n",
              "      <td>15202.36</td>\n",
              "      <td>39802.59</td>\n",
              "      <td>852.40</td>\n",
              "      <td>55857.36</td>\n",
              "      <td>78041.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394.0</td>\n",
              "      <td>14497.0</td>\n",
              "      <td>22525.50</td>\n",
              "      <td>20538.10</td>\n",
              "      <td>1165.00</td>\n",
              "      <td>44228.60</td>\n",
              "      <td>64119.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>5439.0</td>\n",
              "      <td>5961.0</td>\n",
              "      <td>31154.76</td>\n",
              "      <td>13747.43</td>\n",
              "      <td>492.15</td>\n",
              "      <td>45394.34</td>\n",
              "      <td>56794.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>5374.0</td>\n",
              "      <td>14152.0</td>\n",
              "      <td>11251.48</td>\n",
              "      <td>22153.34</td>\n",
              "      <td>394.82</td>\n",
              "      <td>33799.65</td>\n",
              "      <td>53325.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>5005.0</td>\n",
              "      <td>5050.0</td>\n",
              "      <td>15414.91</td>\n",
              "      <td>3342.62</td>\n",
              "      <td>12.96</td>\n",
              "      <td>18770.49</td>\n",
              "      <td>28825.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>920.0</td>\n",
              "      <td>2931.0</td>\n",
              "      <td>2216.39</td>\n",
              "      <td>8258.55</td>\n",
              "      <td>8.93</td>\n",
              "      <td>10483.88</td>\n",
              "      <td>14334.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Jammu &amp; Kashmir</td>\n",
              "      <td>5311.0</td>\n",
              "      <td>5685.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10996.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Telangana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>887.43</td>\n",
              "      <td>3347.52</td>\n",
              "      <td>9.34</td>\n",
              "      <td>4244.29</td>\n",
              "      <td>4244.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>790.0</td>\n",
              "      <td>837.0</td>\n",
              "      <td>332.63</td>\n",
              "      <td>1102.56</td>\n",
              "      <td>264.38</td>\n",
              "      <td>1699.56</td>\n",
              "      <td>3326.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1666.20</td>\n",
              "      <td>1267.06</td>\n",
              "      <td>160.22</td>\n",
              "      <td>3093.47</td>\n",
              "      <td>3093.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Orissa</td>\n",
              "      <td>910.0</td>\n",
              "      <td>1384.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2294.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>137.0</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1397.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>161.0</td>\n",
              "      <td>534.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>695.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>201.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>437.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>23.0</td>\n",
              "      <td>314.0</td>\n",
              "      <td>3.24</td>\n",
              "      <td>57.03</td>\n",
              "      <td>16.31</td>\n",
              "      <td>76.59</td>\n",
              "      <td>413.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2.0</td>\n",
              "      <td>365.0</td>\n",
              "      <td>4.12</td>\n",
              "      <td>3.43</td>\n",
              "      <td>0.88</td>\n",
              "      <td>8.43</td>\n",
              "      <td>375.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Total in GW</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>153.00</td>\n",
              "      <td>146.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>302.00</td>\n",
              "      <td>302.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Sikkim</td>\n",
              "      <td>98.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>196.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Assam</td>\n",
              "      <td>53.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>165.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.43</td>\n",
              "      <td>79.00</td>\n",
              "      <td>4.40</td>\n",
              "      <td>152.83</td>\n",
              "      <td>152.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>0.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>144.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>44.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>126.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Pondicherry</td>\n",
              "      <td>0.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>120.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Haryana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>93.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>91.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>20.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>84.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Manipur</td>\n",
              "      <td>7.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>63.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>2.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.08</td>\n",
              "      <td>46.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.77</td>\n",
              "      <td>7.67</td>\n",
              "      <td>39.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Nagaland</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>19.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dieu Damn</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Goa</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                State      50m      80m        r1        r2       r3  \\\n",
              "7   Gujarat            10609.0  35071.0  52287.59  32037.83  105.09    \n",
              "12  Karnataka          8591.0   13593.0  15202.36  39802.59  852.40    \n",
              "1   Andhra Pradesh     5394.0   14497.0  22525.50  20538.10  1165.00   \n",
              "16  Maharashtra        5439.0   5961.0   31154.76  13747.43  492.15    \n",
              "24  Tamil Nadu         5374.0   14152.0  11251.48  22153.34  394.82    \n",
              "22  Rajasthan          5005.0   5050.0   15414.91  3342.62   12.96     \n",
              "15  Madhya Pradesh     920.0    2931.0   2216.39   8258.55   8.93      \n",
              "11  Jammu & Kashmir    5311.0   5685.0   0.00      0.00      0.00      \n",
              "32  Telangana          0.0      0.0      887.43    3347.52   9.34      \n",
              "13  Kerala             790.0    837.0    332.63    1102.56   264.38    \n",
              "30  Odisha             0.0      0.0      1666.20   1267.06   160.22    \n",
              "20  Orissa             910.0    1384.0   0.00      0.00      0.00      \n",
              "26  Uttar Pradesh      137.0    1260.0   0.00      0.00      0.00      \n",
              "25  Uttarakhand        161.0    534.0    0.00      0.00      0.00      \n",
              "2   Arunachal Pradesh  201.0    236.0    0.00      0.00      0.00      \n",
              "5   Chhattisgarh       23.0     314.0    3.24      57.03     16.31     \n",
              "0   Andaman & Nicobar  2.0      365.0    4.12      3.43      0.88      \n",
              "34  Total in GW        0.0      0.0      153.00    146.00    3.00      \n",
              "23  Sikkim             98.0     98.0     0.00      0.00      0.00      \n",
              "3   Assam              53.0     112.0    0.00      0.00      0.00      \n",
              "31  Puducherry         0.0      0.0      69.43     79.00     4.40      \n",
              "4   Bihar              0.0      144.0    0.00      0.00      0.00      \n",
              "18  Meghalaya          44.0     82.0     0.00      0.00      0.00      \n",
              "21  Pondicherry        0.0      120.0    0.00      0.00      0.00      \n",
              "8   Haryana            0.0      93.0     0.00      0.00      0.00      \n",
              "10  Jharkhand          0.0      91.0     0.00      0.00      0.00      \n",
              "9   Himachal Pradesh   20.0     64.0     0.00      0.00      0.00      \n",
              "17  Manipur            7.0      56.0     0.00      0.00      0.00      \n",
              "27  West Bengal        22.0     22.0     0.03      2.04      0.01      \n",
              "14  Lakshadweep        16.0     16.0     3.50      3.40      0.77      \n",
              "19  Nagaland           3.0      16.0     0.00      0.00      0.00      \n",
              "6   Dieu Damn          0.0      4.0      0.00      0.00      0.00      \n",
              "29  Goa                0.0      0.0      0.00      0.08      0.76      \n",
              "\n",
              "        100m      Total  \n",
              "7   84431.33  130111.33  \n",
              "12  55857.36  78041.36   \n",
              "1   44228.60  64119.60   \n",
              "16  45394.34  56794.34   \n",
              "24  33799.65  53325.65   \n",
              "22  18770.49  28825.49   \n",
              "15  10483.88  14334.88   \n",
              "11  0.00      10996.00   \n",
              "32  4244.29   4244.29    \n",
              "13  1699.56   3326.56    \n",
              "30  3093.47   3093.47    \n",
              "20  0.00      2294.00    \n",
              "26  0.00      1397.00    \n",
              "25  0.00      695.00     \n",
              "2   0.00      437.00     \n",
              "5   76.59     413.59     \n",
              "0   8.43      375.43     \n",
              "34  302.00    302.00     \n",
              "23  0.00      196.00     \n",
              "3   0.00      165.00     \n",
              "31  152.83    152.83     \n",
              "4   0.00      144.00     \n",
              "18  0.00      126.00     \n",
              "21  0.00      120.00     \n",
              "8   0.00      93.00      \n",
              "10  0.00      91.00      \n",
              "9   0.00      84.00      \n",
              "17  0.00      63.00      \n",
              "27  2.08      46.08      \n",
              "14  7.67      39.67      \n",
              "19  0.00      19.00      \n",
              "6   0.00      4.00       \n",
              "29  0.84      0.84       "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7cJAxqNq_Qi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "3a1c1b66-27e3-40fb-ce72-5e995f2166c4"
      },
      "source": [
        "df=data.iloc[:10]\n",
        "N=len(df)\n",
        "row1_=list(df[\"50m\"]/1000)\n",
        "row2_=list(df[\"80m\"]/1000)\n",
        "row3_=list(df[\"100m\"]/1000)\n",
        "\n",
        "\n",
        "#plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
        "plt.rcParams[\"font.size\"] = \"16\"\n",
        "\n",
        "\n",
        "row1=np.array(row1_)\n",
        "row2=np.array(row2_)\n",
        "row3=np.array(row3_)\n",
        "\n",
        "\n",
        "#print(y)\n",
        "\n",
        "plt.style.use('dark_background')\n",
        "\n",
        "plt.figure(figsize=(30,15))\n",
        "\n",
        "ind = np.arange(N) \n",
        "width1 = 0.25\n",
        "width2 = 0.25\n",
        "\n",
        "#colouring\n",
        "#RGB is a way of making colors. You have to to provide an amount of red, green and blue + the transparency and it returns a color.\n",
        "#color1=(0, 0, 1, 1)\n",
        "#color2=(0, 1, 0, 1)\n",
        "#color3=(1, 0, 0, 1)\n",
        "\"\"\"\n",
        "'b' \tblue\n",
        "'g' \tgreen\n",
        "'r' \tred\n",
        "'c' \tcyan\n",
        "'m' \tmagenta\n",
        "'y' \tyellow\n",
        "'k' \tblack\n",
        "'w' \twhite\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "color1=\"c\"\n",
        "color2=\"m\"\n",
        "color3=\"w\"\n",
        "\n",
        "bar1=plt.bar(ind, row1, width1, label='50m',color=color1)\n",
        "bar2=plt.bar(ind + width1, row2, width2,label='80m',color=color2)\n",
        "bar3=plt.bar(ind + 2*width1, row3, width2,label='100m',color=color3)\n",
        "#plt.ylabel('Info')\n",
        "plt.title('Estimation of Installable Wind Power Potential \\nat Top 10 States in India', fontsize=30)\n",
        "plt.grid(False)\n",
        "\n",
        "\n",
        "\n",
        "#plt.text(0, 0.8, str(1), color='white', fontweight='bold')\n",
        "\n",
        "#for i, v in enumerate(y):\n",
        "#plt.text(v + .25, i+ 3, str(v), color='blue', fontweight='bold')\n",
        "\n",
        "for rect,val in zip(bar1+bar2+bar3,row1_+row2_+row3_):\n",
        "    height = rect.get_height()\n",
        "    #print(val)\n",
        "    if(val==0):\n",
        "      plt.text(rect.get_x() + rect.get_width()/2.0, height+0.01, str(\"-\"), ha='center', va='bottom',fontsize=20)\n",
        "    else:\n",
        "      plt.text(rect.get_x() + rect.get_width()/2.0, height+0.01, str(round(val,1)), ha='center', va='bottom',fontsize=20)\n",
        "\n",
        "\n",
        "#plt.ylim(0,1)\n",
        "\n",
        "#plt.text(v + .25, i+ 3, str(v), color='blue', fontweight='bold')\n",
        "\n",
        "plt.xticks(ind + width1 , df[\"State\"])\n",
        "#plt.rc('xtick',labelsize=16)\n",
        "#plt.rc('ytick',labelsize=16)\n",
        "\"\"\"plt.xticks(ind + width , ('Total World Reserve \\n (Solids-$10^6$ t) \\n (Fluids-Mtoe)',\n",
        "                          'Annual consumption per capita 2004 \\n (Unit-toe)',\n",
        "                          ' Will Last Until \\n (Year) \\n (Ref. by 2004)'))\n",
        "                          \"\"\"\n",
        "#plt.yticks([])\n",
        "plt.rcParams['xtick.labelsize']=16\n",
        "plt.rcParams['ytick.labelsize']=16\n",
        "\n",
        "plt.xlabel('State/UT', fontsize=18)\n",
        "plt.ylabel('Installable Wind Power Potential (in GigaWatts)', fontsize=20)\n",
        "\n",
        "lg=plt.legend(loc='best',title='           Levels of \\n  Wind Power Potential', prop={'size': 20})\n",
        "lg.get_title().set_fontsize(20)\n",
        "lg._legend_box.align = \"center\"\n",
        "#plt.legend(loc='center left', bbox_to_anchor=(1, 1),fancybox = True, shadow = True)\n",
        "plt.savefig(\"wind_power_potential_final.png\" ,dpi=100)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "#print(bar1+bar2+bar3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RjD7EX6vhkj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "30713b74-3bb2-44fa-8dc5-44cafa9fb576"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "      <th>r1</th>\n",
              "      <th>r2</th>\n",
              "      <th>r3</th>\n",
              "      <th>100m</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>10609.0</td>\n",
              "      <td>35071.0</td>\n",
              "      <td>52287.59</td>\n",
              "      <td>32037.83</td>\n",
              "      <td>105.09</td>\n",
              "      <td>84431.33</td>\n",
              "      <td>130111.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>8591.0</td>\n",
              "      <td>13593.0</td>\n",
              "      <td>15202.36</td>\n",
              "      <td>39802.59</td>\n",
              "      <td>852.40</td>\n",
              "      <td>55857.36</td>\n",
              "      <td>78041.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>5394.0</td>\n",
              "      <td>14497.0</td>\n",
              "      <td>22525.50</td>\n",
              "      <td>20538.10</td>\n",
              "      <td>1165.00</td>\n",
              "      <td>44228.60</td>\n",
              "      <td>64119.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>5439.0</td>\n",
              "      <td>5961.0</td>\n",
              "      <td>31154.76</td>\n",
              "      <td>13747.43</td>\n",
              "      <td>492.15</td>\n",
              "      <td>45394.34</td>\n",
              "      <td>56794.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>5374.0</td>\n",
              "      <td>14152.0</td>\n",
              "      <td>11251.48</td>\n",
              "      <td>22153.34</td>\n",
              "      <td>394.82</td>\n",
              "      <td>33799.65</td>\n",
              "      <td>53325.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>5005.0</td>\n",
              "      <td>5050.0</td>\n",
              "      <td>15414.91</td>\n",
              "      <td>3342.62</td>\n",
              "      <td>12.96</td>\n",
              "      <td>18770.49</td>\n",
              "      <td>28825.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>920.0</td>\n",
              "      <td>2931.0</td>\n",
              "      <td>2216.39</td>\n",
              "      <td>8258.55</td>\n",
              "      <td>8.93</td>\n",
              "      <td>10483.88</td>\n",
              "      <td>14334.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Jammu &amp; Kashmir</td>\n",
              "      <td>5311.0</td>\n",
              "      <td>5685.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10996.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Telangana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>887.43</td>\n",
              "      <td>3347.52</td>\n",
              "      <td>9.34</td>\n",
              "      <td>4244.29</td>\n",
              "      <td>4244.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>790.0</td>\n",
              "      <td>837.0</td>\n",
              "      <td>332.63</td>\n",
              "      <td>1102.56</td>\n",
              "      <td>264.38</td>\n",
              "      <td>1699.56</td>\n",
              "      <td>3326.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1666.20</td>\n",
              "      <td>1267.06</td>\n",
              "      <td>160.22</td>\n",
              "      <td>3093.47</td>\n",
              "      <td>3093.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Orissa</td>\n",
              "      <td>910.0</td>\n",
              "      <td>1384.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2294.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>137.0</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1397.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>161.0</td>\n",
              "      <td>534.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>695.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>201.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>437.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>23.0</td>\n",
              "      <td>314.0</td>\n",
              "      <td>3.24</td>\n",
              "      <td>57.03</td>\n",
              "      <td>16.31</td>\n",
              "      <td>76.59</td>\n",
              "      <td>413.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman &amp; Nicobar</td>\n",
              "      <td>2.0</td>\n",
              "      <td>365.0</td>\n",
              "      <td>4.12</td>\n",
              "      <td>3.43</td>\n",
              "      <td>0.88</td>\n",
              "      <td>8.43</td>\n",
              "      <td>375.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Total in GW</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>153.00</td>\n",
              "      <td>146.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>302.00</td>\n",
              "      <td>302.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Sikkim</td>\n",
              "      <td>98.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>196.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Assam</td>\n",
              "      <td>53.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>165.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.43</td>\n",
              "      <td>79.00</td>\n",
              "      <td>4.40</td>\n",
              "      <td>152.83</td>\n",
              "      <td>152.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>0.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>144.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>44.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>126.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Pondicherry</td>\n",
              "      <td>0.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>120.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Haryana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>93.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>91.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>20.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>84.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Manipur</td>\n",
              "      <td>7.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>63.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>2.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.08</td>\n",
              "      <td>46.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.77</td>\n",
              "      <td>7.67</td>\n",
              "      <td>39.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Nagaland</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>19.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dieu Damn</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Goa</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                State      50m      80m        r1        r2       r3  \\\n",
              "7   Gujarat            10609.0  35071.0  52287.59  32037.83  105.09    \n",
              "12  Karnataka          8591.0   13593.0  15202.36  39802.59  852.40    \n",
              "1   Andhra Pradesh     5394.0   14497.0  22525.50  20538.10  1165.00   \n",
              "16  Maharashtra        5439.0   5961.0   31154.76  13747.43  492.15    \n",
              "24  Tamil Nadu         5374.0   14152.0  11251.48  22153.34  394.82    \n",
              "22  Rajasthan          5005.0   5050.0   15414.91  3342.62   12.96     \n",
              "15  Madhya Pradesh     920.0    2931.0   2216.39   8258.55   8.93      \n",
              "11  Jammu & Kashmir    5311.0   5685.0   0.00      0.00      0.00      \n",
              "32  Telangana          0.0      0.0      887.43    3347.52   9.34      \n",
              "13  Kerala             790.0    837.0    332.63    1102.56   264.38    \n",
              "30  Odisha             0.0      0.0      1666.20   1267.06   160.22    \n",
              "20  Orissa             910.0    1384.0   0.00      0.00      0.00      \n",
              "26  Uttar Pradesh      137.0    1260.0   0.00      0.00      0.00      \n",
              "25  Uttarakhand        161.0    534.0    0.00      0.00      0.00      \n",
              "2   Arunachal Pradesh  201.0    236.0    0.00      0.00      0.00      \n",
              "5   Chhattisgarh       23.0     314.0    3.24      57.03     16.31     \n",
              "0   Andaman & Nicobar  2.0      365.0    4.12      3.43      0.88      \n",
              "34  Total in GW        0.0      0.0      153.00    146.00    3.00      \n",
              "23  Sikkim             98.0     98.0     0.00      0.00      0.00      \n",
              "3   Assam              53.0     112.0    0.00      0.00      0.00      \n",
              "31  Puducherry         0.0      0.0      69.43     79.00     4.40      \n",
              "4   Bihar              0.0      144.0    0.00      0.00      0.00      \n",
              "18  Meghalaya          44.0     82.0     0.00      0.00      0.00      \n",
              "21  Pondicherry        0.0      120.0    0.00      0.00      0.00      \n",
              "8   Haryana            0.0      93.0     0.00      0.00      0.00      \n",
              "10  Jharkhand          0.0      91.0     0.00      0.00      0.00      \n",
              "9   Himachal Pradesh   20.0     64.0     0.00      0.00      0.00      \n",
              "17  Manipur            7.0      56.0     0.00      0.00      0.00      \n",
              "27  West Bengal        22.0     22.0     0.03      2.04      0.01      \n",
              "14  Lakshadweep        16.0     16.0     3.50      3.40      0.77      \n",
              "19  Nagaland           3.0      16.0     0.00      0.00      0.00      \n",
              "6   Dieu Damn          0.0      4.0      0.00      0.00      0.00      \n",
              "29  Goa                0.0      0.0      0.00      0.08      0.76      \n",
              "\n",
              "        100m      Total  \n",
              "7   84431.33  130111.33  \n",
              "12  55857.36  78041.36   \n",
              "1   44228.60  64119.60   \n",
              "16  45394.34  56794.34   \n",
              "24  33799.65  53325.65   \n",
              "22  18770.49  28825.49   \n",
              "15  10483.88  14334.88   \n",
              "11  0.00      10996.00   \n",
              "32  4244.29   4244.29    \n",
              "13  1699.56   3326.56    \n",
              "30  3093.47   3093.47    \n",
              "20  0.00      2294.00    \n",
              "26  0.00      1397.00    \n",
              "25  0.00      695.00     \n",
              "2   0.00      437.00     \n",
              "5   76.59     413.59     \n",
              "0   8.43      375.43     \n",
              "34  302.00    302.00     \n",
              "23  0.00      196.00     \n",
              "3   0.00      165.00     \n",
              "31  152.83    152.83     \n",
              "4   0.00      144.00     \n",
              "18  0.00      126.00     \n",
              "21  0.00      120.00     \n",
              "8   0.00      93.00      \n",
              "10  0.00      91.00      \n",
              "9   0.00      84.00      \n",
              "17  0.00      63.00      \n",
              "27  2.08      46.08      \n",
              "14  7.67      39.67      \n",
              "19  0.00      19.00      \n",
              "6   0.00      4.00       \n",
              "29  0.84      0.84       "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0LkKW17VSXE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ad677cff-0b75-4cbb-d8ed-4046991543cd"
      },
      "source": [
        "df=data[[\"50m\",\"80m\",\"100m\"]]\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "      <th>100m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10609.0</td>\n",
              "      <td>35071.0</td>\n",
              "      <td>84431.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>8591.0</td>\n",
              "      <td>13593.0</td>\n",
              "      <td>55857.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5394.0</td>\n",
              "      <td>14497.0</td>\n",
              "      <td>44228.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5439.0</td>\n",
              "      <td>5961.0</td>\n",
              "      <td>45394.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>5374.0</td>\n",
              "      <td>14152.0</td>\n",
              "      <td>33799.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>5005.0</td>\n",
              "      <td>5050.0</td>\n",
              "      <td>18770.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>920.0</td>\n",
              "      <td>2931.0</td>\n",
              "      <td>10483.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>5311.0</td>\n",
              "      <td>5685.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4244.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>790.0</td>\n",
              "      <td>837.0</td>\n",
              "      <td>1699.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3093.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>910.0</td>\n",
              "      <td>1384.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>137.0</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>161.0</td>\n",
              "      <td>534.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>201.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>23.0</td>\n",
              "      <td>314.0</td>\n",
              "      <td>76.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>365.0</td>\n",
              "      <td>8.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>302.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>98.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>152.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>44.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>20.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>7.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>2.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>7.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>3.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        50m      80m      100m\n",
              "7   10609.0  35071.0  84431.33\n",
              "12  8591.0   13593.0  55857.36\n",
              "1   5394.0   14497.0  44228.60\n",
              "16  5439.0   5961.0   45394.34\n",
              "24  5374.0   14152.0  33799.65\n",
              "22  5005.0   5050.0   18770.49\n",
              "15  920.0    2931.0   10483.88\n",
              "11  5311.0   5685.0   0.00    \n",
              "32  0.0      0.0      4244.29 \n",
              "13  790.0    837.0    1699.56 \n",
              "30  0.0      0.0      3093.47 \n",
              "20  910.0    1384.0   0.00    \n",
              "26  137.0    1260.0   0.00    \n",
              "25  161.0    534.0    0.00    \n",
              "2   201.0    236.0    0.00    \n",
              "5   23.0     314.0    76.59   \n",
              "0   2.0      365.0    8.43    \n",
              "34  0.0      0.0      302.00  \n",
              "23  98.0     98.0     0.00    \n",
              "3   53.0     112.0    0.00    \n",
              "31  0.0      0.0      152.83  \n",
              "4   0.0      144.0    0.00    \n",
              "18  44.0     82.0     0.00    \n",
              "21  0.0      120.0    0.00    \n",
              "8   0.0      93.0     0.00    \n",
              "10  0.0      91.0     0.00    \n",
              "9   20.0     64.0     0.00    \n",
              "17  7.0      56.0     0.00    \n",
              "27  22.0     22.0     2.08    \n",
              "14  16.0     16.0     7.67    \n",
              "19  3.0      16.0     0.00    \n",
              "6   0.0      4.0      0.00    \n",
              "29  0.0      0.0      0.84    "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CN8wdkCbWYTY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "535f899b-5e97-46bd-8ed3-0b87d0113278"
      },
      "source": [
        "df = df[~(df == 0).any(axis=1)]\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>50m</th>\n",
              "      <th>80m</th>\n",
              "      <th>100m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10609.0</td>\n",
              "      <td>35071.0</td>\n",
              "      <td>84431.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>8591.0</td>\n",
              "      <td>13593.0</td>\n",
              "      <td>55857.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5394.0</td>\n",
              "      <td>14497.0</td>\n",
              "      <td>44228.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5439.0</td>\n",
              "      <td>5961.0</td>\n",
              "      <td>45394.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>5374.0</td>\n",
              "      <td>14152.0</td>\n",
              "      <td>33799.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>5005.0</td>\n",
              "      <td>5050.0</td>\n",
              "      <td>18770.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>920.0</td>\n",
              "      <td>2931.0</td>\n",
              "      <td>10483.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>790.0</td>\n",
              "      <td>837.0</td>\n",
              "      <td>1699.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>23.0</td>\n",
              "      <td>314.0</td>\n",
              "      <td>76.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>365.0</td>\n",
              "      <td>8.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>2.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>7.67</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        50m      80m      100m\n",
              "7   10609.0  35071.0  84431.33\n",
              "12  8591.0   13593.0  55857.36\n",
              "1   5394.0   14497.0  44228.60\n",
              "16  5439.0   5961.0   45394.34\n",
              "24  5374.0   14152.0  33799.65\n",
              "22  5005.0   5050.0   18770.49\n",
              "15  920.0    2931.0   10483.88\n",
              "13  790.0    837.0    1699.56 \n",
              "5   23.0     314.0    76.59   \n",
              "0   2.0      365.0    8.43    \n",
              "27  22.0     22.0     2.08    \n",
              "14  16.0     16.0     7.67    "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzkqUEBSWp8h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "28c3b0c9-2480-428e-cccc-6a15d3270476"
      },
      "source": [
        "df.values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.060900e+04, 3.507100e+04, 8.443133e+04],\n",
              "       [8.591000e+03, 1.359300e+04, 5.585736e+04],\n",
              "       [5.394000e+03, 1.449700e+04, 4.422860e+04],\n",
              "       [5.439000e+03, 5.961000e+03, 4.539434e+04],\n",
              "       [5.374000e+03, 1.415200e+04, 3.379965e+04],\n",
              "       [5.005000e+03, 5.050000e+03, 1.877049e+04],\n",
              "       [9.200000e+02, 2.931000e+03, 1.048388e+04],\n",
              "       [7.900000e+02, 8.370000e+02, 1.699560e+03],\n",
              "       [2.300000e+01, 3.140000e+02, 7.659000e+01],\n",
              "       [2.000000e+00, 3.650000e+02, 8.430000e+00],\n",
              "       [2.200000e+01, 2.200000e+01, 2.080000e+00],\n",
              "       [1.600000e+01, 1.600000e+01, 7.670000e+00]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3386vsZYWvEW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}