McKinsey_LifeExpentancy_Insights2.ipynb

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        "<a href=\"https://colab.research.google.com/github/shivendra1-cyber/The-McKinsey-Life-Expectancy-Insights-/blob/main/McKinsey_LifeExpentancy_Insights2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "gENDopSiyOKy",
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        },
        "outputId": "0ba9bd6c-a069-4a83-fcf9-1058e276e6bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1E3bwvYGf1ig32RmcYiWc0IXPN-mD_bI_\n",
            "To: /content/mckinsey.csv\n",
            "\r  0% 0.00/83.8k [00:00<?, ?B/s]\r100% 83.8k/83.8k [00:00<00:00, 46.4MB/s]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "!gdown 1E3bwvYGf1ig32RmcYiWc0IXPN-mD_bI_"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"mckinsey.csv\") # dataframe\n",
        "df"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "0uuSdOGfUHdz",
        "outputId": "64a8341e-60ee-421e-f91a-ec00ea49322d"
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      "execution_count": 7,
      "outputs": [
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              "          country  year  population continent  life_exp     gdp_cap\n",
              "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
              "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
              "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...           ...   ...         ...       ...       ...         ...\n",
              "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
              "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
              "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
              "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
              "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
              "\n",
              "[1704 rows x 6 columns]"
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              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1957</td>\n",
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              "      <th>1699</th>\n",
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              "      <td>60.377</td>\n",
              "      <td>693.420786</td>\n",
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              "      <th>1701</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>1997</td>\n",
              "      <td>11404948</td>\n",
              "      <td>Africa</td>\n",
              "      <td>46.809</td>\n",
              "      <td>792.449960</td>\n",
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              "    <tr>\n",
              "      <th>1702</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2002</td>\n",
              "      <td>11926563</td>\n",
              "      <td>Africa</td>\n",
              "      <td>39.989</td>\n",
              "      <td>672.038623</td>\n",
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              "      <td>2007</td>\n",
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              "      <td>Africa</td>\n",
              "      <td>43.487</td>\n",
              "      <td>469.709298</td>\n",
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            "text/plain": [
              "array([   0,    1,    2, ..., 1701, 1702, 1703])"
            ]
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          "execution_count": 8
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      ]
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        "df.index = list(range(1, 1705)) #changing explicit indexes\n",
        "df\n"
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        "id": "mTPJALRAYBi_",
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            "text/plain": [
              "          country  year  population continent  life_exp     gdp_cap\n",
              "1     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "2     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "3     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
              "4     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
              "5     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...           ...   ...         ...       ...       ...         ...\n",
              "1700     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
              "1701     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
              "1702     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
              "1703     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
              "1704     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
              "\n",
              "[1704 rows x 6 columns]"
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              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1952</td>\n",
              "      <td>8425333</td>\n",
              "      <td>Asia</td>\n",
              "      <td>28.801</td>\n",
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              "      <td>Afghanistan</td>\n",
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              "      <td>Afghanistan</td>\n",
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              "      <td>Asia</td>\n",
              "      <td>31.997</td>\n",
              "      <td>853.100710</td>\n",
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              "      <td>Afghanistan</td>\n",
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              "      <td>62.351</td>\n",
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              "      <th>1701</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>1992</td>\n",
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              "      <td>Africa</td>\n",
              "      <td>60.377</td>\n",
              "      <td>693.420786</td>\n",
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              "      <th>1702</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>1997</td>\n",
              "      <td>11404948</td>\n",
              "      <td>Africa</td>\n",
              "      <td>46.809</td>\n",
              "      <td>792.449960</td>\n",
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              "      <th>1703</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2002</td>\n",
              "      <td>11926563</td>\n",
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              "      <td>39.989</td>\n",
              "      <td>672.038623</td>\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q75p1jBWY-Zv",
        "outputId": "edbbe1bc-f000-4f45-d83d-caa03b5ab44e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index[-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5ME2LUJbBZP",
        "outputId": "2f7f9649-a806-4bfd-c712-820a3fb9a9c9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1704"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index = np.arange(1, 1705, dtype='float') #convert all explicit indexes to floating point numbers"
      ],
      "metadata": {
        "id": "suWIX4zsbQYg"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "df"
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        "outputId": "def64b6a-dba3-45a3-feb4-9530772269bf"
      },
      "execution_count": 13,
      "outputs": [
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              "            country  year  population continent  life_exp     gdp_cap\n",
              "1.0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "2.0     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "3.0     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
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              "5.0     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...             ...   ...         ...       ...       ...         ...\n",
              "1700.0     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
              "1701.0     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
              "1702.0     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
              "1703.0     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
              "1704.0     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
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              "         country  year  population continent  life_exp     gdp_cap\n",
              "1.0  Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "2.0  Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
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              "4.0  Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
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              "          const element = document.querySelector('#df-e454bd4b-8134-4168-83a8-eace7d82b482');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample.index.values"
      ],
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        "id": "qPVXlWz0c0NP",
        "outputId": "7cfd7752-345b-48aa-d5a9-781118216325"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 2., 3., 4., 5.])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
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    {
      "cell_type": "code",
      "source": [
        "sample.index=['a','b','c','d','e']"
      ],
      "metadata": {
        "id": "CiSxbESEc-AP"
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      "execution_count": null,
      "outputs": []
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          "height": 206
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      "execution_count": 15,
      "outputs": [
        {
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              "         country  year  population continent  life_exp     gdp_cap\n",
              "1.0  Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "2.0  Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
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              "4.0  Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
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        "df.index=list(range(1,1705))"
      ],
      "metadata": {
        "id": "VBJFCw9MzIJL"
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      "execution_count": 16,
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        "df\n"
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      "execution_count": 17,
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            "text/plain": [
              "          country  year  population continent  life_exp     gdp_cap\n",
              "1     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "2     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "3     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
              "4     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
              "5     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...           ...   ...         ...       ...       ...         ...\n",
              "1700     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
              "1701     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
              "1702     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
              "1703     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
              "1704     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
              "\n",
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              "<p>1704 rows × 6 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[1] #both columns and rows are 1d objects and can be saved in series "
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "WfgsNHif0OI4",
        "outputId": "631aedab-0c74-4e2d-c12f-4639d775cac1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "country       Afghanistan\n",
              "year                 1957\n",
              "population        9240934\n",
              "continent            Asia\n",
              "life_exp           30.332\n",
              "gdp_cap         820.85303\n",
              "Name: 2, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
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    {
      "cell_type": "code",
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        "df.iloc[2] # name is basically a explicit index of row"
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        "id": "8gGh1Tga1bvW",
        "outputId": "b59a5e31-7558-4459-f1b4-27f331fdf670"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "country       Afghanistan\n",
              "year                 1962\n",
              "population       10267083\n",
              "continent            Asia\n",
              "life_exp           31.997\n",
              "gdp_cap         853.10071\n",
              "Name: 3, dtype: object"
            ]
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          "metadata": {},
          "execution_count": 20
        }
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        "id": "k6H0HGcp1ACo",
        "outputId": "6ce64779-7c34-47f5-c71b-a6933b4f0221"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "country       Afghanistan\n",
              "year                 1952\n",
              "population        8425333\n",
              "continent            Asia\n",
              "life_exp           28.801\n",
              "gdp_cap        779.445314\n",
              "Name: 1, dtype: object"
            ]
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          "execution_count": 19
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        "outputId": "d857a5a7-72e1-4155-af9d-920993a48cde"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       country  year  population continent  life_exp    gdp_cap\n",
              "2  Afghanistan  1957     9240934      Asia    30.332  820.85303\n",
              "3  Afghanistan  1962    10267083      Asia    31.997  853.10071"
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    {
      "cell_type": "code",
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        "df.loc[1 :3] #loc while slicing end points are consisdered "
      ],
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          "height": 143
        },
        "id": "UCj9Uk7K6FhI",
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      },
      "execution_count": 24,
      "outputs": [
        {
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              "       country  year  population continent  life_exp     gdp_cap\n",
              "1  Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "2  Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
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      "execution_count": 30,
      "outputs": [
        {
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              "         country  year  population continent  life_exp     gdp_cap\n",
              "2    Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "11   Afghanistan  2002    25268405      Asia    42.129  726.734055\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d05b08fc-9df9-4619-ba50-d378d26f5af6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQWvXm357px4",
        "outputId": "3eaafb71-6464-48b1-f0e5-df179c1c1895"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "country         Zimbabwe\n",
              "year                2007\n",
              "population      12311143\n",
              "continent         Africa\n",
              "life_exp          43.487\n",
              "gdp_cap       469.709298\n",
              "Name: 1704, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[-1] #its error becaues there is no any -1 index in explicit index table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "YbtVoIZ776JJ",
        "outputId": "a30f3484-e097-4401-87d6-37271c2c7bd9"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3628\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3629\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3630\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: -1",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-c8e3ddd1947a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 967\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    968\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    969\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1203\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1204\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1207\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_slice_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m   1151\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m         \u001b[0;31m# GH#5667 this will fail if the label is not present in the axis.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1153\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1155\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_handle_lowerdim_multi_index_axis0\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mxs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   3862\u001b[0m                     \u001b[0mnew_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3863\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3864\u001b[0;31m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3866\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3629\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3630\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3631\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3632\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3633\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: -1"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert index use any other column \n",
        "temp=df.set_index('country')"
      ],
      "metadata": {
        "id": "khnay5aF9viK"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "gapm4nek-qd6",
        "outputId": "6aab8668-bf40-4512-cc8a-775f5532a28e"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             year  population continent  life_exp     gdp_cap\n",
              "country                                                      \n",
              "Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
              "Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
              "Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...           ...         ...       ...       ...         ...\n",
              "Zimbabwe     1987     9216418    Africa    62.351  706.157306\n",
              "Zimbabwe     1992    10704340    Africa    60.377  693.420786\n",
              "Zimbabwe     1997    11404948    Africa    46.809  792.449960\n",
              "Zimbabwe     2002    11926563    Africa    39.989  672.038623\n",
              "Zimbabwe     2007    12311143    Africa    43.487  469.709298\n",
              "\n",
              "[1704 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ffc41ad2-44b7-49f4-9a55-8dd46c44e111\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>population</th>\n",
              "      <th>continent</th>\n",
              "      <th>life_exp</th>\n",
              "      <th>gdp_cap</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>1952</td>\n",
              "      <td>8425333</td>\n",
              "      <td>Asia</td>\n",
              "      <td>28.801</td>\n",
              "      <td>779.445314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>1957</td>\n",
              "      <td>9240934</td>\n",
              "      <td>Asia</td>\n",
              "      <td>30.332</td>\n",
              "      <td>820.853030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>1962</td>\n",
              "      <td>10267083</td>\n",
              "      <td>Asia</td>\n",
              "      <td>31.997</td>\n",
              "      <td>853.100710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>1967</td>\n",
              "      <td>11537966</td>\n",
              "      <td>Asia</td>\n",
              "      <td>34.020</td>\n",
              "      <td>836.197138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>1972</td>\n",
              "      <td>13079460</td>\n",
              "      <td>Asia</td>\n",
              "      <td>36.088</td>\n",
              "      <td>739.981106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zimbabwe</th>\n",
              "      <td>1987</td>\n",
              "      <td>9216418</td>\n",
              "      <td>Africa</td>\n",
              "      <td>62.351</td>\n",
              "      <td>706.157306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zimbabwe</th>\n",
              "      <td>1992</td>\n",
              "      <td>10704340</td>\n",
              "      <td>Africa</td>\n",
              "      <td>60.377</td>\n",
              "      <td>693.420786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zimbabwe</th>\n",
              "      <td>1997</td>\n",
              "      <td>11404948</td>\n",
              "      <td>Africa</td>\n",
              "      <td>46.809</td>\n",
              "      <td>792.449960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zimbabwe</th>\n",
              "      <td>2002</td>\n",
              "      <td>11926563</td>\n",
              "      <td>Africa</td>\n",
              "      <td>39.989</td>\n",
              "      <td>672.038623</td>\n",
              "    </tr>\n",
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              "      <th>Zimbabwe</th>\n",
              "      <td>2007</td>\n",
              "      <td>12311143</td>\n",
              "      <td>Africa</td>\n",
              "      <td>43.487</td>\n",
              "      <td>469.709298</td>\n",
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              "<p>1704 rows × 5 columns</p>\n",
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              "          const element = document.querySelector('#df-ffc41ad2-44b7-49f4-9a55-8dd46c44e111');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp.reset_index() #country column is back now"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "UjhhqxhFCxxN",
        "outputId": "7fe55894-9bf7-4d7b-cc97-2f38d59d4e58"
      },
      "execution_count": 42,
      "outputs": [
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              "          country  year  population continent  life_exp     gdp_cap\n",
              "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
              "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
              "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...           ...   ...         ...       ...       ...         ...\n",
              "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
              "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
              "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
              "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
              "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
              "\n",
              "[1704 rows x 6 columns]"
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              "          const element = document.querySelector('#df-cf5b8164-cf79-4bdd-bcc4-876641d68abd');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "6l7tnQgHDOY8"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " temp"
      ],
      "metadata": {
        "colab": {
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          "height": 424
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        "id": "0ILNMj_pDvXL",
        "outputId": "4cde2c15-e21f-431d-830d-d0f72e1fb32d"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          country  year  population continent  life_exp     gdp_cap\n",
              "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
              "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
              "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...           ...   ...         ...       ...       ...         ...\n",
              "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
              "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
              "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
              "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
              "1703        India  2000    13500000      Asia    37.080  900.200000\n",
              "\n",
              "[1704 rows x 6 columns]"
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              "      <td>853.100710</td>\n",
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              "      <td>Afghanistan</td>\n",
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              "      <td>Afghanistan</td>\n",
              "      <td>1972</td>\n",
              "      <td>13079460</td>\n",
              "      <td>Asia</td>\n",
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              "      <td>739.981106</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1699</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>1987</td>\n",
              "      <td>9216418</td>\n",
              "      <td>Africa</td>\n",
              "      <td>62.351</td>\n",
              "      <td>706.157306</td>\n",
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              "    <tr>\n",
              "      <th>1700</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>1992</td>\n",
              "      <td>10704340</td>\n",
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              "      <td>60.377</td>\n",
              "      <td>693.420786</td>\n",
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              "    <tr>\n",
              "      <th>1701</th>\n",
              "      <td>Zimbabwe</td>\n",
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              "      <td>46.809</td>\n",
              "      <td>792.449960</td>\n",
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              "    <tr>\n",
              "      <th>1702</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2002</td>\n",
              "      <td>11926563</td>\n",
              "      <td>Africa</td>\n",
              "      <td>39.989</td>\n",
              "      <td>672.038623</td>\n",
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              "      <td>India</td>\n",
              "      <td>2000</td>\n",
              "      <td>13500000</td>\n",
              "      <td>Asia</td>\n",
              "      <td>37.080</td>\n",
              "      <td>900.200000</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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    {
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        "df.reset_index()"
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      "execution_count": 46,
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            "text/plain": [
              "      index      country  year  population continent  life_exp     gdp_cap\n",
              "0         1  Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "1         2  Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "2         3  Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
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              "4         5  Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...     ...          ...   ...         ...       ...       ...         ...\n",
              "1699   1700     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
              "1700   1701     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
              "1701   1702     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
              "1702   1703     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
              "1703   1704     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
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              "          const element = document.querySelector('#df-ea8f3b34-861e-435c-9214-4ca4a64d3bbe');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "code",
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        "id": "sQxBHc3AEzst",
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      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          country  year  population continent  life_exp     gdp_cap\n",
              "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
              "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
              "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...           ...   ...         ...       ...       ...         ...\n",
              "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
              "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
              "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
              "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
              "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
              "\n",
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              "      <td>13079460</td>\n",
              "      <td>Asia</td>\n",
              "      <td>36.088</td>\n",
              "      <td>739.981106</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1699</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>1987</td>\n",
              "      <td>9216418</td>\n",
              "      <td>Africa</td>\n",
              "      <td>62.351</td>\n",
              "      <td>706.157306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1700</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>1992</td>\n",
              "      <td>10704340</td>\n",
              "      <td>Africa</td>\n",
              "      <td>60.377</td>\n",
              "      <td>693.420786</td>\n",
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              "      <th>1701</th>\n",
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              "      <td>46.809</td>\n",
              "      <td>792.449960</td>\n",
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              "    <tr>\n",
              "      <th>1702</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2002</td>\n",
              "      <td>11926563</td>\n",
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              "      <td>39.989</td>\n",
              "      <td>672.038623</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          "metadata": {},
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    {
      "cell_type": "code",
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        "temp.reset_index(drop=True,inplace=True)\n",
        "temp\n"
      ],
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          "height": 424
        },
        "id": "smeZLctdFDq8",
        "outputId": "dbf338ce-cb13-4693-f47f-4fea452b2412"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "          country  year  population continent  life_exp     gdp_cap\n",
              "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
              "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
              "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...           ...   ...         ...       ...       ...         ...\n",
              "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
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              "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
              "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
              "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
              "\n",
              "[1704 rows x 6 columns]"
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    {
      "cell_type": "code",
      "source": [
        "new_row={'country':'India',\t'year' : 2000,\t'population':13500000,\t'continent':'Asia',\t'life_exp':37.08,\t'gdp_cap': 900.230}\n",
        "df.append(new_row,ignore_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "00yxRH22HXMd",
        "outputId": "ae8560ec-4bc9-4824-8a72-b43dedd11bc7"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-52-3240cc541629>:2: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df.append(new_row,ignore_index=True)\n"
          ]
        },
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              "          country  year  population continent  life_exp     gdp_cap\n",
              "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
              "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
              "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...           ...   ...         ...       ...       ...         ...\n",
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              "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
              "1704        India  2000    13500000      Asia    37.080  900.230000\n",
              "\n",
              "[1705 rows x 6 columns]"
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              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1972</td>\n",
              "      <td>13079460</td>\n",
              "      <td>Asia</td>\n",
              "      <td>36.088</td>\n",
              "      <td>739.981106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1700</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>1992</td>\n",
              "      <td>10704340</td>\n",
              "      <td>Africa</td>\n",
              "      <td>60.377</td>\n",
              "      <td>693.420786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1701</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>1997</td>\n",
              "      <td>11404948</td>\n",
              "      <td>Africa</td>\n",
              "      <td>46.809</td>\n",
              "      <td>792.449960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1702</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2002</td>\n",
              "      <td>11926563</td>\n",
              "      <td>Africa</td>\n",
              "      <td>39.989</td>\n",
              "      <td>672.038623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1703</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2007</td>\n",
              "      <td>12311143</td>\n",
              "      <td>Africa</td>\n",
              "      <td>43.487</td>\n",
              "      <td>469.709298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1704</th>\n",
              "      <td>India</td>\n",
              "      <td>2000</td>\n",
              "      <td>13500000</td>\n",
              "      <td>Asia</td>\n",
              "      <td>37.080</td>\n",
              "      <td>900.230000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1705 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7da577eb-4e82-49f1-8463-c73908a740f5')\"\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7da577eb-4e82-49f1-8463-c73908a740f5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[1704]=['India',2000,13500000,'Asia',37.08,900.2]"
      ],
      "metadata": {
        "id": "Mjo_vXjBK8sU"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "WihMXRj6LiE_",
        "outputId": "482c1ce7-8c32-4b52-ee78-d8a5b76b929b"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          country  year  population continent  life_exp     gdp_cap\n",
              "1     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "2     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "3     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
              "4     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
              "5     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...           ...   ...         ...       ...       ...         ...\n",
              "1700     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
              "1701     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
              "1702     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
              "1703     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
              "1704        India  2000    13500000      Asia    37.080  900.200000\n",
              "\n",
              "[1704 rows x 6 columns]"
            ],
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              "      <td>28.801</td>\n",
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              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
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              "      <td>30.332</td>\n",
              "      <td>820.853030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1962</td>\n",
              "      <td>10267083</td>\n",
              "      <td>Asia</td>\n",
              "      <td>31.997</td>\n",
              "      <td>853.100710</td>\n",
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              "      <td>Afghanistan</td>\n",
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              "      <td>739.981106</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1700</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>1987</td>\n",
              "      <td>9216418</td>\n",
              "      <td>Africa</td>\n",
              "      <td>62.351</td>\n",
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              "    <tr>\n",
              "      <th>1701</th>\n",
              "      <td>Zimbabwe</td>\n",
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              "      <th>1702</th>\n",
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              "      <td>11404948</td>\n",
              "      <td>Africa</td>\n",
              "      <td>46.809</td>\n",
              "      <td>792.449960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1703</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2002</td>\n",
              "      <td>11926563</td>\n",
              "      <td>Africa</td>\n",
              "      <td>39.989</td>\n",
              "      <td>672.038623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1704</th>\n",
              "      <td>India</td>\n",
              "      <td>2000</td>\n",
              "      <td>13500000</td>\n",
              "      <td>Asia</td>\n",
              "      <td>37.080</td>\n",
              "      <td>900.200000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1704 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab11788b-427c-498f-b52e-43eabc6bdca2')\"\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ab11788b-427c-498f-b52e-43eabc6bdca2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
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          "metadata": {},
          "execution_count": 55
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    },
    {
      "cell_type": "code",
      "source": [
        "df[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "SIXCUatCL1KN",
        "outputId": "c125f883-f14a-4aaa-d449-3f0d80c9f1ab"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3628\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3629\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3630\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 1",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-56-78a33f69c558>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3503\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3504\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3505\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3506\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3507\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3629\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3630\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3631\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3632\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3633\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 1"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(3,axis=0) "
      ],
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          "height": 424
        },
        "id": "56aRHZGSMXjv",
        "outputId": "b191cea2-6ba0-4dd3-93ac-87cbcd188021"
      },
      "execution_count": 61,
      "outputs": [
        {
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            "text/plain": [
              "          country  year  population continent  life_exp     gdp_cap\n",
              "1     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "2     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "4     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
              "5     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "6     Afghanistan  1977    14880372      Asia    38.438  786.113360\n",
              "...           ...   ...         ...       ...       ...         ...\n",
              "1700     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
              "1701     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
              "1702     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
              "1703     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
              "1704        India  2000    13500000      Asia    37.080  900.200000\n",
              "\n",
              "[1703 rows x 6 columns]"
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              "      <td>Afghanistan</td>\n",
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              "      <td>28.801</td>\n",
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              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
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              "      <td>820.853030</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
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              "      <td>Afghanistan</td>\n",
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              "      <td>Zimbabwe</td>\n",
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              "      <td>46.809</td>\n",
              "      <td>792.449960</td>\n",
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              "      <td>Zimbabwe</td>\n",
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              "      <td>11926563</td>\n",
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              "      <td>39.989</td>\n",
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              "      <td>India</td>\n",
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              "<p>1703 rows × 6 columns</p>\n",
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      },
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            "text/plain": [
              "1       False\n",
              "2       False\n",
              "3       False\n",
              "4       False\n",
              "5       False\n",
              "        ...  \n",
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              "1704    False\n",
              "Length: 1704, dtype: bool"
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      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "Empty DataFrame\n",
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              "          document.querySelector('#df-8ce3da1d-09e6-438d-8fde-05aa79d8ae6c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8ce3da1d-09e6-438d-8fde-05aa79d8ae6c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp=df.set_index('country')"
      ],
      "metadata": {
        "id": "l_59DKd1Pv2Q"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " temp\n",
        " "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "MVbhUfN9QITQ",
        "outputId": "0f3701cf-d52e-4350-9f30-5cd8610924a8"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             year  population continent  life_exp     gdp_cap\n",
              "country                                                      \n",
              "Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
              "Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
              "Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
              "Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
              "Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
              "...           ...         ...       ...       ...         ...\n",
              "Zimbabwe     1987     9216418    Africa    62.351  706.157306\n",
              "Zimbabwe     1992    10704340    Africa    60.377  693.420786\n",
              "Zimbabwe     1997    11404948    Africa    46.809  792.449960\n",
              "Zimbabwe     2002    11926563    Africa    39.989  672.038623\n",
              "India        2000    13500000      Asia    37.080  900.200000\n",
              "\n",
              "[1704 rows x 5 columns]"
            ],
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              "      <td>28.801</td>\n",
              "      <td>779.445314</td>\n",
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              "      <td>30.332</td>\n",
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              "      <th>Afghanistan</th>\n",
              "      <td>1962</td>\n",
              "      <td>10267083</td>\n",
              "      <td>Asia</td>\n",
              "      <td>31.997</td>\n",
              "      <td>853.100710</td>\n",
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              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>1967</td>\n",
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              "      <th>Afghanistan</th>\n",
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              "      <th>Zimbabwe</th>\n",
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              "      <td>Africa</td>\n",
              "      <td>62.351</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zimbabwe</th>\n",
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              "    </tr>\n",
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              "      <th>Zimbabwe</th>\n",
              "      <td>2002</td>\n",
              "      <td>11926563</td>\n",
              "      <td>Africa</td>\n",
              "      <td>39.989</td>\n",
              "      <td>672.038623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>2000</td>\n",
              "      <td>13500000</td>\n",
              "      <td>Asia</td>\n",
              "      <td>37.080</td>\n",
              "      <td>900.200000</td>\n",
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              "<p>1704 rows × 5 columns</p>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c6cfd275-6d76-4448-9a49-3dfe909122db');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    }
  ]
}