Reading_HTML_tables_as_Pandas_Dataframe.ipynb

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    "colab": {
      "name": "Reading HTML tables as Pandas Dataframe",
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      "name": "python3",
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        "id": "q9opDsMqX8Oz",
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      "source": [
        "import pandas as pd\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sW6_wWdbX_5t",
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      "source": [
        "india_states = pd.read_html(\"https://en.wikipedia.org/wiki/List_of_states_in_India_by_past_population\")"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
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      "source": [
        "india_states[0].dtypes"
      ],
      "execution_count": 15,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rank                             object\n",
              "State or union territory         object\n",
              "Population (1951 Census)[11]    float64\n",
              "Population (1961 Census)[11]    float64\n",
              "Population (1971 Census)[11]    float64\n",
              "Population (1981 Census)[11]    float64\n",
              "Population (1991 Census)[11]    float64\n",
              "Population (2001 Census)[11]    float64\n",
              "Population (2011 Census)[11]      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "cV-mCeuTZiWi",
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      "source": [
        "world_pop = pd.read_html(\"https://en.wikipedia.org/wiki/World_population\")"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
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        },
        "outputId": "93473998-1126-4041-e3b8-6f76904751ff"
      },
      "source": [
        "len(world_pop)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 22
        }
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        "outputId": "5fe8a2ec-33d5-43d2-94ce-d4390bc1df5e"
      },
      "source": [
        "world_pop[1]"
      ],
      "execution_count": 24,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"5\" halign=\"left\">World population (millions, UN estimates)[15]</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>#</th>\n",
              "      <th>Top ten most populous countries</th>\n",
              "      <th>2000</th>\n",
              "      <th>2015</th>\n",
              "      <th>2030[A]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>China[B]</td>\n",
              "      <td>1270</td>\n",
              "      <td>1376</td>\n",
              "      <td>1416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>India</td>\n",
              "      <td>1053</td>\n",
              "      <td>1311</td>\n",
              "      <td>1528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>United States</td>\n",
              "      <td>283</td>\n",
              "      <td>322</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>212</td>\n",
              "      <td>258</td>\n",
              "      <td>295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>136</td>\n",
              "      <td>208</td>\n",
              "      <td>245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>176</td>\n",
              "      <td>206</td>\n",
              "      <td>228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>123</td>\n",
              "      <td>182</td>\n",
              "      <td>263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Bangladesh</td>\n",
              "      <td>131</td>\n",
              "      <td>161</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Russia</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>103</td>\n",
              "      <td>127</td>\n",
              "      <td>148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>NaN</td>\n",
              "      <td>World total</td>\n",
              "      <td>6127</td>\n",
              "      <td>7349</td>\n",
              "      <td>8501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Notes: ^ 2030 = Medium variant. ^ China exclud...</td>\n",
              "      <td>Notes: ^ 2030 = Medium variant. ^ China exclud...</td>\n",
              "      <td>Notes: ^ 2030 = Medium variant. ^ China exclud...</td>\n",
              "      <td>Notes: ^ 2030 = Medium variant. ^ China exclud...</td>\n",
              "      <td>Notes: ^ 2030 = Medium variant. ^ China exclud...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        World population (millions, UN estimates)[15]  ...                                                   \n",
              "                                                    #  ...                                            2030[A]\n",
              "0                                                   1  ...                                               1416\n",
              "1                                                   2  ...                                               1528\n",
              "2                                                   3  ...                                                356\n",
              "3                                                   4  ...                                                295\n",
              "4                                                   5  ...                                                245\n",
              "5                                                   6  ...                                                228\n",
              "6                                                   7  ...                                                263\n",
              "7                                                   8  ...                                                186\n",
              "8                                                   9  ...                                                149\n",
              "9                                                  10  ...                                                148\n",
              "10                                                NaN  ...                                               8501\n",
              "11  Notes: ^ 2030 = Medium variant. ^ China exclud...  ...  Notes: ^ 2030 = Medium variant. ^ China exclud...\n",
              "\n",
              "[12 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
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        "id": "hK4VfRIeaARg",
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      "source": [
        ""
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      "execution_count": null,
      "outputs": []
    }
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}