Population_distribution_visualization.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "978f8bab-c712-4ca3-9887-7c62aef8869c",
      "metadata": {
        "id": "978f8bab-c712-4ca3-9887-7c62aef8869c"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "812b1d17-767c-4bca-b9f4-b2c7ce77c015",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "812b1d17-767c-4bca-b9f4-b2c7ce77c015",
        "outputId": "7955fc52-7e25-4d23-8123-1f15da1b3480"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Country Name Country Code      Region IncomeGroup  Year  \\\n",
              "0  Afghanistan          AFG  South Asia  Low income  2018   \n",
              "1  Afghanistan          AFG  South Asia  Low income  2017   \n",
              "2  Afghanistan          AFG  South Asia  Low income  2016   \n",
              "3  Afghanistan          AFG  South Asia  Low income  2015   \n",
              "4  Afghanistan          AFG  South Asia  Low income  2014   \n",
              "\n",
              "   Birth rate, crude (per 1,000 people)  Death rate, crude (per 1,000 people)  \\\n",
              "0                                   NaN                                   NaN   \n",
              "1                                33.211                                 6.575   \n",
              "2                                33.981                                 6.742   \n",
              "3                                34.809                                 6.929   \n",
              "4                                35.706                                 7.141   \n",
              "\n",
              "   Electric power consumption (kWh per capita)     GDP (USD)  \\\n",
              "0                                          NaN  1.936300e+10   \n",
              "1                                          NaN  2.019180e+10   \n",
              "2                                          NaN  1.936260e+10   \n",
              "3                                          NaN  1.990710e+10   \n",
              "4                                          NaN  2.048490e+10   \n",
              "\n",
              "   GDP per capita (USD)  Individuals using the Internet (% of population)  \\\n",
              "0               520.897                                               NaN   \n",
              "1               556.302                                             13.50   \n",
              "2               547.228                                             11.20   \n",
              "3               578.466                                              8.26   \n",
              "4               613.856                                              7.00   \n",
              "\n",
              "   Infant mortality rate (per 1,000 live births)  \\\n",
              "0                                           47.9   \n",
              "1                                           49.5   \n",
              "2                                           51.2   \n",
              "3                                           53.1   \n",
              "4                                           55.1   \n",
              "\n",
              "   Life expectancy at birth (years)  \\\n",
              "0                               NaN   \n",
              "1                            64.130   \n",
              "2                            63.763   \n",
              "3                            63.377   \n",
              "4                            62.966   \n",
              "\n",
              "   Population density (people per sq. km of land area)  \\\n",
              "0                                            56.9378     \n",
              "1                                            55.5960     \n",
              "2                                            54.1971     \n",
              "3                                            52.7121     \n",
              "4                                            51.1148     \n",
              "\n",
              "   Unemployment (% of total labor force) (modeled ILO estimate)  \n",
              "0                                              1.542             \n",
              "1                                              1.559             \n",
              "2                                              1.634             \n",
              "3                                              1.679             \n",
              "4                                              1.735             "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-41753144-afbe-4300-89f7-a0f7a86d7a81\" 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>Country Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>IncomeGroup</th>\n",
              "      <th>Year</th>\n",
              "      <th>Birth rate, crude (per 1,000 people)</th>\n",
              "      <th>Death rate, crude (per 1,000 people)</th>\n",
              "      <th>Electric power consumption (kWh per capita)</th>\n",
              "      <th>GDP (USD)</th>\n",
              "      <th>GDP per capita (USD)</th>\n",
              "      <th>Individuals using the Internet (% of population)</th>\n",
              "      <th>Infant mortality rate (per 1,000 live births)</th>\n",
              "      <th>Life expectancy at birth (years)</th>\n",
              "      <th>Population density (people per sq. km of land area)</th>\n",
              "      <th>Unemployment (% of total labor force) (modeled ILO estimate)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>Low income</td>\n",
              "      <td>2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.936300e+10</td>\n",
              "      <td>520.897</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>56.9378</td>\n",
              "      <td>1.542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>Low income</td>\n",
              "      <td>2017</td>\n",
              "      <td>33.211</td>\n",
              "      <td>6.575</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.019180e+10</td>\n",
              "      <td>556.302</td>\n",
              "      <td>13.50</td>\n",
              "      <td>49.5</td>\n",
              "      <td>64.130</td>\n",
              "      <td>55.5960</td>\n",
              "      <td>1.559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>Low income</td>\n",
              "      <td>2016</td>\n",
              "      <td>33.981</td>\n",
              "      <td>6.742</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.936260e+10</td>\n",
              "      <td>547.228</td>\n",
              "      <td>11.20</td>\n",
              "      <td>51.2</td>\n",
              "      <td>63.763</td>\n",
              "      <td>54.1971</td>\n",
              "      <td>1.634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>Low income</td>\n",
              "      <td>2015</td>\n",
              "      <td>34.809</td>\n",
              "      <td>6.929</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.990710e+10</td>\n",
              "      <td>578.466</td>\n",
              "      <td>8.26</td>\n",
              "      <td>53.1</td>\n",
              "      <td>63.377</td>\n",
              "      <td>52.7121</td>\n",
              "      <td>1.679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>Low income</td>\n",
              "      <td>2014</td>\n",
              "      <td>35.706</td>\n",
              "      <td>7.141</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.048490e+10</td>\n",
              "      <td>613.856</td>\n",
              "      <td>7.00</td>\n",
              "      <td>55.1</td>\n",
              "      <td>62.966</td>\n",
              "      <td>51.1148</td>\n",
              "      <td>1.735</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41753144-afbe-4300-89f7-a0f7a86d7a81')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-41753144-afbe-4300-89f7-a0f7a86d7a81 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-41753144-afbe-4300-89f7-a0f7a86d7a81');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6f028a91-a075-40e4-954e-639b1d365a25\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f028a91-a075-40e4-954e-639b1d365a25')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6f028a91-a075-40e4-954e-639b1d365a25 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 12449,\n  \"fields\": [\n    {\n      \"column\": \"Country Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 211,\n        \"samples\": [\n          \"Burundi\",\n          \"Spain\",\n          \"Northern Mariana Islands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country Code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 211,\n        \"samples\": [\n          \"BDI\",\n          \"ESP\",\n          \"MNP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"South Asia\",\n          \"Europe & Central Asia\",\n          \"Latin America & Caribbean\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IncomeGroup\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Upper middle income\",\n          \"High income: OECD\",\n          \"High income: nonOECD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 1960,\n        \"max\": 2018,\n        \"num_unique_values\": 59,\n        \"samples\": [\n          2018,\n          2013,\n          1984\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Birth rate, crude (per 1,000 people)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.13189341980669,\n        \"min\": 6.9,\n        \"max\": 58.227,\n        \"num_unique_values\": 7732,\n        \"samples\": [\n          48.192,\n          48.364,\n          21.401\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Death rate, crude (per 1,000 people)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.48938189379907,\n        \"min\": 1.127,\n        \"max\": 54.444,\n        \"num_unique_values\": 6731,\n        \"samples\": [\n          5.615,\n          5.257,\n          5.484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Electric power consumption (kWh per capita)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4467.139297660007,\n        \"min\": 0.0,\n        \"max\": 54799.2,\n        \"num_unique_values\": 5823,\n        \"samples\": [\n          4533.54,\n          576.302,\n          4366.91\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 897986611233.9719,\n        \"min\": 8824450.0,\n        \"max\": 20500000000000.0,\n        \"num_unique_values\": 8600,\n        \"samples\": [\n          2053460000.0,\n          44856600000.0,\n          536000000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16173.539954330705,\n        \"min\": 34.7906,\n        \"max\": 189171.0,\n        \"num_unique_values\": 9551,\n        \"samples\": [\n          1214.56,\n          2361.39,\n          38454.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Individuals using the Internet (% of population)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.319388122491763,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 4036,\n        \"samples\": [\n          50.2087,\n          0.0862771,\n          0.0232339\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Infant mortality rate (per 1,000 live births)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46.13103854198913,\n        \"min\": 1.4,\n        \"max\": 279.4,\n        \"num_unique_values\": 1726,\n        \"samples\": [\n          108.9,\n          74.8,\n          61.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life expectancy at birth (years)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.491086538596551,\n        \"min\": 18.907,\n        \"max\": 85.4171,\n        \"num_unique_values\": 10080,\n        \"samples\": [\n          72.168,\n          46.04,\n          61.243\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population density (people per sq. km of land area)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1593.4060408523953,\n        \"min\": 0.0986245,\n        \"max\": 21389.1,\n        \"num_unique_values\": 11789,\n        \"samples\": [\n          12.8932,\n          6.12178,\n          105.537\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unemployment (% of total labor force) (modeled ILO estimate)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.2907026744412375,\n        \"min\": 0.14,\n        \"max\": 37.94,\n        \"num_unique_values\": 4215,\n        \"samples\": [\n          3.317,\n          13.184,\n          17.723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "data = pd.read_excel(\"/content/WorldBank Data.xlsx\")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "4184d7ba-0369-45f2-92d9-f07831882655",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "4184d7ba-0369-45f2-92d9-f07831882655",
        "outputId": "a2443b51-70d0-423d-c30a-3bd7cc75ef63"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Country Name Country Code              Region IncomeGroup  Year  \\\n",
              "12444     Zimbabwe          ZWE  Sub-Saharan Africa  Low income  1964   \n",
              "12445     Zimbabwe          ZWE  Sub-Saharan Africa  Low income  1963   \n",
              "12446     Zimbabwe          ZWE  Sub-Saharan Africa  Low income  1962   \n",
              "12447     Zimbabwe          ZWE  Sub-Saharan Africa  Low income  1961   \n",
              "12448     Zimbabwe          ZWE  Sub-Saharan Africa  Low income  1960   \n",
              "\n",
              "       Birth rate, crude (per 1,000 people)  \\\n",
              "12444                                47.770   \n",
              "12445                                47.876   \n",
              "12446                                47.950   \n",
              "12447                                47.988   \n",
              "12448                                47.996   \n",
              "\n",
              "       Death rate, crude (per 1,000 people)  \\\n",
              "12444                                13.083   \n",
              "12445                                13.419   \n",
              "12446                                13.762   \n",
              "12447                                14.104   \n",
              "12448                                14.441   \n",
              "\n",
              "       Electric power consumption (kWh per capita)     GDP (USD)  \\\n",
              "12444                                          NaN  1.217138e+09   \n",
              "12445                                          NaN  1.159512e+09   \n",
              "12446                                          NaN  1.117602e+09   \n",
              "12447                                          NaN  1.096647e+09   \n",
              "12448                                          NaN  1.052990e+09   \n",
              "\n",
              "       GDP per capita (USD)  Individuals using the Internet (% of population)  \\\n",
              "12444               281.558                                               NaN   \n",
              "12445               277.480                                               NaN   \n",
              "12446               276.689                                               NaN   \n",
              "12447               280.829                                               NaN   \n",
              "12448               278.814                                               NaN   \n",
              "\n",
              "       Infant mortality rate (per 1,000 live births)  \\\n",
              "12444                                           83.2   \n",
              "12445                                           85.7   \n",
              "12446                                           88.1   \n",
              "12447                                           90.5   \n",
              "12448                                           92.8   \n",
              "\n",
              "       Life expectancy at birth (years)  \\\n",
              "12444                            54.849   \n",
              "12445                            54.403   \n",
              "12446                            53.946   \n",
              "12447                            53.483   \n",
              "12448                            53.019   \n",
              "\n",
              "       Population density (people per sq. km of land area)  \\\n",
              "12444                                            11.1745     \n",
              "12445                                            10.8019     \n",
              "12446                                            10.4413     \n",
              "12447                                            10.0944     \n",
              "12448                                                NaN     \n",
              "\n",
              "       Unemployment (% of total labor force) (modeled ILO estimate)  \n",
              "12444                                                NaN             \n",
              "12445                                                NaN             \n",
              "12446                                                NaN             \n",
              "12447                                                NaN             \n",
              "12448                                                NaN             "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a2bc9bbb-2b8a-4fe8-a427-27ae85537d4b\" 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>Country Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>IncomeGroup</th>\n",
              "      <th>Year</th>\n",
              "      <th>Birth rate, crude (per 1,000 people)</th>\n",
              "      <th>Death rate, crude (per 1,000 people)</th>\n",
              "      <th>Electric power consumption (kWh per capita)</th>\n",
              "      <th>GDP (USD)</th>\n",
              "      <th>GDP per capita (USD)</th>\n",
              "      <th>Individuals using the Internet (% of population)</th>\n",
              "      <th>Infant mortality rate (per 1,000 live births)</th>\n",
              "      <th>Life expectancy at birth (years)</th>\n",
              "      <th>Population density (people per sq. km of land area)</th>\n",
              "      <th>Unemployment (% of total labor force) (modeled ILO estimate)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12444</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>Low income</td>\n",
              "      <td>1964</td>\n",
              "      <td>47.770</td>\n",
              "      <td>13.083</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.217138e+09</td>\n",
              "      <td>281.558</td>\n",
              "      <td>NaN</td>\n",
              "      <td>83.2</td>\n",
              "      <td>54.849</td>\n",
              "      <td>11.1745</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12445</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>Low income</td>\n",
              "      <td>1963</td>\n",
              "      <td>47.876</td>\n",
              "      <td>13.419</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.159512e+09</td>\n",
              "      <td>277.480</td>\n",
              "      <td>NaN</td>\n",
              "      <td>85.7</td>\n",
              "      <td>54.403</td>\n",
              "      <td>10.8019</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12446</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>Low income</td>\n",
              "      <td>1962</td>\n",
              "      <td>47.950</td>\n",
              "      <td>13.762</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.117602e+09</td>\n",
              "      <td>276.689</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.1</td>\n",
              "      <td>53.946</td>\n",
              "      <td>10.4413</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12447</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>Low income</td>\n",
              "      <td>1961</td>\n",
              "      <td>47.988</td>\n",
              "      <td>14.104</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.096647e+09</td>\n",
              "      <td>280.829</td>\n",
              "      <td>NaN</td>\n",
              "      <td>90.5</td>\n",
              "      <td>53.483</td>\n",
              "      <td>10.0944</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12448</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>Low income</td>\n",
              "      <td>1960</td>\n",
              "      <td>47.996</td>\n",
              "      <td>14.441</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.052990e+09</td>\n",
              "      <td>278.814</td>\n",
              "      <td>NaN</td>\n",
              "      <td>92.8</td>\n",
              "      <td>53.019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a2bc9bbb-2b8a-4fe8-a427-27ae85537d4b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a2bc9bbb-2b8a-4fe8-a427-27ae85537d4b 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-a2bc9bbb-2b8a-4fe8-a427-27ae85537d4b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-49ed7f4b-ad19-4ef6-bd01-a8ed7bb7221d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-49ed7f4b-ad19-4ef6-bd01-a8ed7bb7221d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-49ed7f4b-ad19-4ef6-bd01-a8ed7bb7221d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Country Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Zimbabwe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country Code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"ZWE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Sub-Saharan Africa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IncomeGroup\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Low income\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1960,\n        \"max\": 1964,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1963\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Birth rate, crude (per 1,000 people)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09441398201537704,\n        \"min\": 47.77,\n        \"max\": 47.996,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          47.876\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Death rate, crude (per 1,000 people)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.537748733145881,\n        \"min\": 13.083,\n        \"max\": 14.441,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          13.419\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Electric power consumption (kWh per capita)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62556272.690658286,\n        \"min\": 1052990400.0,\n        \"max\": 1217138000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.094453508674746,\n        \"min\": 276.689,\n        \"max\": 281.558,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Individuals using the Internet (% of population)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Infant mortality rate (per 1,000 live births)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.7951284563239733,\n        \"min\": 83.2,\n        \"max\": 92.8,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life expectancy at birth (years)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7241850592217431,\n        \"min\": 53.019,\n        \"max\": 54.849,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population density (people per sq. km of land area)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.464933438784521,\n        \"min\": 10.0944,\n        \"max\": 11.1745,\n        \"num_unique_values\": 4,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unemployment (% of total labor force) (modeled ILO estimate)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "data.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "835d99f1-4aa1-4d74-be4f-14c16c66a237",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "835d99f1-4aa1-4d74-be4f-14c16c66a237",
        "outputId": "a0eb2bbc-73e8-4f83-cce2-01c741ae59dc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Country Name', 'Country Code', 'Region', 'IncomeGroup', 'Year',\n",
              "       'Birth rate, crude (per 1,000 people)',\n",
              "       'Death rate, crude (per 1,000 people)',\n",
              "       'Electric power consumption (kWh per capita)', 'GDP (USD)',\n",
              "       'GDP per capita (USD)',\n",
              "       'Individuals using the Internet (% of population)',\n",
              "       'Infant mortality rate (per 1,000 live births)',\n",
              "       'Life expectancy at birth (years)',\n",
              "       'Population density (people per sq. km of land area)',\n",
              "       'Unemployment (% of total labor force) (modeled ILO estimate)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "c613ce68-40fe-4f76-9806-abcf303e14f5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c613ce68-40fe-4f76-9806-abcf303e14f5",
        "outputId": "f9f86924-d6e3-460e-d8dc-e52decf7bfba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12449, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "12b17e15-5d23-481c-9b62-8afb9ff6eecf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12b17e15-5d23-481c-9b62-8afb9ff6eecf",
        "outputId": "491829c5-0f36-44f5-94a3-614366c60638"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "186735"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "data.size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "3386bb9b-7421-49cb-8f74-84941904ccea",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3386bb9b-7421-49cb-8f74-84941904ccea",
        "outputId": "35a6bdd7-73bc-409e-9c8a-995d5c37340a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=12449, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "data.index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "1f3b1c7a-584e-4467-9657-ac9d816442cf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "1f3b1c7a-584e-4467-9657-ac9d816442cf",
        "outputId": "91ec3777-9a58-42b4-bbbf-cd0a686c14d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Year  Birth rate, crude (per 1,000 people)  \\\n",
              "count  12449.00000                          11440.000000   \n",
              "mean    1989.00000                             28.643276   \n",
              "std       17.03007                             13.131893   \n",
              "min     1960.00000                              6.900000   \n",
              "25%     1974.00000                             16.600000   \n",
              "50%     1989.00000                             27.545500   \n",
              "75%     2004.00000                             40.881250   \n",
              "max     2018.00000                             58.227000   \n",
              "\n",
              "       Death rate, crude (per 1,000 people)  \\\n",
              "count                          11416.000000   \n",
              "mean                              10.588539   \n",
              "std                                5.489382   \n",
              "min                                1.127000   \n",
              "25%                                6.863750   \n",
              "50%                                9.200000   \n",
              "75%                               12.687000   \n",
              "max                               54.444000   \n",
              "\n",
              "       Electric power consumption (kWh per capita)     GDP (USD)  \\\n",
              "count                                  5848.000000  9.578000e+03   \n",
              "mean                                   3175.294686  1.700740e+11   \n",
              "std                                    4467.139298  8.979866e+11   \n",
              "min                                       0.000000  8.824450e+06   \n",
              "25%                                     390.385750  1.393010e+09   \n",
              "50%                                    1541.895000  7.275305e+09   \n",
              "75%                                    4313.767500  4.857782e+10   \n",
              "max                                   54799.200000  2.050000e+13   \n",
              "\n",
              "       GDP per capita (USD)  Individuals using the Internet (% of population)  \\\n",
              "count           9575.000000                                       5064.000000   \n",
              "mean            8231.812259                                         23.334471   \n",
              "std            16173.539954                                         28.319388   \n",
              "min               34.790600                                          0.000000   \n",
              "25%              513.145500                                          0.594949   \n",
              "50%             1852.810000                                          8.406225   \n",
              "75%             7774.565000                                         41.295950   \n",
              "max           189171.000000                                        100.000000   \n",
              "\n",
              "       Infant mortality rate (per 1,000 live births)  \\\n",
              "count                                    9984.000000   \n",
              "mean                                       51.704437   \n",
              "std                                        46.131039   \n",
              "min                                         1.400000   \n",
              "25%                                        14.475000   \n",
              "50%                                        37.000000   \n",
              "75%                                        78.200000   \n",
              "max                                       279.400000   \n",
              "\n",
              "       Life expectancy at birth (years)  \\\n",
              "count                      11176.000000   \n",
              "mean                          64.044692   \n",
              "std                           11.491087   \n",
              "min                           18.907000   \n",
              "25%                           55.917750   \n",
              "50%                           67.276000   \n",
              "75%                           72.692250   \n",
              "max                           85.417100   \n",
              "\n",
              "       Population density (people per sq. km of land area)  \\\n",
              "count                                       11845.000000     \n",
              "mean                                          318.861370     \n",
              "std                                          1593.406041     \n",
              "min                                             0.098625     \n",
              "25%                                            19.783400     \n",
              "50%                                            64.007500     \n",
              "75%                                           144.823000     \n",
              "max                                         21389.100000     \n",
              "\n",
              "       Unemployment (% of total labor force) (modeled ILO estimate)  \n",
              "count                                        5208.000000             \n",
              "mean                                            8.295079             \n",
              "std                                             6.290703             \n",
              "min                                             0.140000             \n",
              "25%                                             3.687000             \n",
              "50%                                             6.775000             \n",
              "75%                                            11.212250             \n",
              "max                                            37.940000             "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5355ecc6-8e11-48b4-bf64-67e868513a26\" 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>Birth rate, crude (per 1,000 people)</th>\n",
              "      <th>Death rate, crude (per 1,000 people)</th>\n",
              "      <th>Electric power consumption (kWh per capita)</th>\n",
              "      <th>GDP (USD)</th>\n",
              "      <th>GDP per capita (USD)</th>\n",
              "      <th>Individuals using the Internet (% of population)</th>\n",
              "      <th>Infant mortality rate (per 1,000 live births)</th>\n",
              "      <th>Life expectancy at birth (years)</th>\n",
              "      <th>Population density (people per sq. km of land area)</th>\n",
              "      <th>Unemployment (% of total labor force) (modeled ILO estimate)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12449.00000</td>\n",
              "      <td>11440.000000</td>\n",
              "      <td>11416.000000</td>\n",
              "      <td>5848.000000</td>\n",
              "      <td>9.578000e+03</td>\n",
              "      <td>9575.000000</td>\n",
              "      <td>5064.000000</td>\n",
              "      <td>9984.000000</td>\n",
              "      <td>11176.000000</td>\n",
              "      <td>11845.000000</td>\n",
              "      <td>5208.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1989.00000</td>\n",
              "      <td>28.643276</td>\n",
              "      <td>10.588539</td>\n",
              "      <td>3175.294686</td>\n",
              "      <td>1.700740e+11</td>\n",
              "      <td>8231.812259</td>\n",
              "      <td>23.334471</td>\n",
              "      <td>51.704437</td>\n",
              "      <td>64.044692</td>\n",
              "      <td>318.861370</td>\n",
              "      <td>8.295079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>17.03007</td>\n",
              "      <td>13.131893</td>\n",
              "      <td>5.489382</td>\n",
              "      <td>4467.139298</td>\n",
              "      <td>8.979866e+11</td>\n",
              "      <td>16173.539954</td>\n",
              "      <td>28.319388</td>\n",
              "      <td>46.131039</td>\n",
              "      <td>11.491087</td>\n",
              "      <td>1593.406041</td>\n",
              "      <td>6.290703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1960.00000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>1.127000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.824450e+06</td>\n",
              "      <td>34.790600</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>18.907000</td>\n",
              "      <td>0.098625</td>\n",
              "      <td>0.140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1974.00000</td>\n",
              "      <td>16.600000</td>\n",
              "      <td>6.863750</td>\n",
              "      <td>390.385750</td>\n",
              "      <td>1.393010e+09</td>\n",
              "      <td>513.145500</td>\n",
              "      <td>0.594949</td>\n",
              "      <td>14.475000</td>\n",
              "      <td>55.917750</td>\n",
              "      <td>19.783400</td>\n",
              "      <td>3.687000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1989.00000</td>\n",
              "      <td>27.545500</td>\n",
              "      <td>9.200000</td>\n",
              "      <td>1541.895000</td>\n",
              "      <td>7.275305e+09</td>\n",
              "      <td>1852.810000</td>\n",
              "      <td>8.406225</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>67.276000</td>\n",
              "      <td>64.007500</td>\n",
              "      <td>6.775000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2004.00000</td>\n",
              "      <td>40.881250</td>\n",
              "      <td>12.687000</td>\n",
              "      <td>4313.767500</td>\n",
              "      <td>4.857782e+10</td>\n",
              "      <td>7774.565000</td>\n",
              "      <td>41.295950</td>\n",
              "      <td>78.200000</td>\n",
              "      <td>72.692250</td>\n",
              "      <td>144.823000</td>\n",
              "      <td>11.212250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2018.00000</td>\n",
              "      <td>58.227000</td>\n",
              "      <td>54.444000</td>\n",
              "      <td>54799.200000</td>\n",
              "      <td>2.050000e+13</td>\n",
              "      <td>189171.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>279.400000</td>\n",
              "      <td>85.417100</td>\n",
              "      <td>21389.100000</td>\n",
              "      <td>37.940000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5355ecc6-8e11-48b4-bf64-67e868513a26')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5355ecc6-8e11-48b4-bf64-67e868513a26 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-5355ecc6-8e11-48b4-bf64-67e868513a26');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-61e80159-e905-449c-b573-aa11f7d3c1d9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-61e80159-e905-449c-b573-aa11f7d3c1d9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-61e80159-e905-449c-b573-aa11f7d3c1d9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3859.9882604171503,\n        \"min\": 17.03007037317131,\n        \"max\": 12449.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          12449.0,\n          1989.0,\n          2004.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Birth rate, crude (per 1,000 people)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4034.99008972199,\n        \"min\": 6.9,\n        \"max\": 11440.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.643276420454548,\n          27.5455,\n          11440.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Death rate, crude (per 1,000 people)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4031.129294241572,\n        \"min\": 1.127,\n        \"max\": 11416.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.58853901804485,\n          9.2,\n          11416.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Electric power consumption (kWh per capita)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18492.785060274076,\n        \"min\": 0.0,\n        \"max\": 54799.2,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3175.29468627736,\n          1541.895,\n          5848.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7197528699525.497,\n        \"min\": 9578.0,\n        \"max\": 20500000000000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          170073972643.42523,\n          7275305000.0,\n          9578.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64879.7445957139,\n        \"min\": 34.7906,\n        \"max\": 189171.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8231.812258882506,\n          1852.81,\n          9575.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Individuals using the Internet (% of population)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1780.4870477374986,\n        \"min\": 0.0,\n        \"max\": 5064.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          23.334470976944115,\n          8.406225,\n          5064.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Infant mortality rate (per 1,000 live births)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3505.296912582056,\n        \"min\": 1.4,\n        \"max\": 9984.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          51.70443709935897,\n          37.0,\n          9984.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life expectancy at birth (years)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3932.419216040437,\n        \"min\": 11.491086538596551,\n        \"max\": 11176.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          64.04469248389405,\n          67.27600000000001,\n          11176.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population density (people per sq. km of land area)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7964.5568990757065,\n        \"min\": 0.0986245,\n        \"max\": 21389.1,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          318.8613704823554,\n          64.0075,\n          11845.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unemployment (% of total labor force) (modeled ILO estimate)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1837.5879970260537,\n        \"min\": 0.14,\n        \"max\": 5208.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.295078725038401,\n          6.775,\n          5208.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "3b7abc10-3e77-4f49-8c0c-1536444302a4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "3b7abc10-3e77-4f49-8c0c-1536444302a4",
        "outputId": "ca9fbbbb-be3e-4a16-d66c-edc782928e31"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Country Name Country Code                 Region          IncomeGroup  \\\n",
              "63      Albania          ALB  Europe & Central Asia  Upper middle income   \n",
              "64      Albania          ALB  Europe & Central Asia  Upper middle income   \n",
              "65      Albania          ALB  Europe & Central Asia  Upper middle income   \n",
              "66      Albania          ALB  Europe & Central Asia  Upper middle income   \n",
              "67      Albania          ALB  Europe & Central Asia  Upper middle income   \n",
              "\n",
              "    Year  Birth rate, crude (per 1,000 people)  \\\n",
              "63  2014                                12.259   \n",
              "64  2013                                12.257   \n",
              "65  2012                                12.197   \n",
              "66  2011                                12.100   \n",
              "67  2010                                12.001   \n",
              "\n",
              "    Death rate, crude (per 1,000 people)  \\\n",
              "63                                 7.219   \n",
              "64                                 7.096   \n",
              "65                                 6.996   \n",
              "66                                 6.915   \n",
              "67                                 6.841   \n",
              "\n",
              "    Electric power consumption (kWh per capita)     GDP (USD)  \\\n",
              "63                                      2309.37  1.322820e+10   \n",
              "64                                      2533.25  1.277630e+10   \n",
              "65                                      2118.33  1.231980e+10   \n",
              "66                                      2205.70  1.289090e+10   \n",
              "67                                      1943.34  1.192700e+10   \n",
              "\n",
              "    GDP per capita (USD)  Individuals using the Internet (% of population)  \\\n",
              "63               4578.67                                            60.100   \n",
              "64               4413.08                                            57.200   \n",
              "65               4247.61                                            54.656   \n",
              "66               4437.18                                            49.000   \n",
              "67               4094.36                                            45.000   \n",
              "\n",
              "    Infant mortality rate (per 1,000 live births)  \\\n",
              "63                                            8.9   \n",
              "64                                            9.5   \n",
              "65                                           10.2   \n",
              "66                                           11.0   \n",
              "67                                           11.9   \n",
              "\n",
              "    Life expectancy at birth (years)  \\\n",
              "63                            77.813   \n",
              "64                            77.554   \n",
              "65                            77.252   \n",
              "66                            76.914   \n",
              "67                            76.562   \n",
              "\n",
              "    Population density (people per sq. km of land area)  \\\n",
              "63                                            105.442     \n",
              "64                                            105.660     \n",
              "65                                            105.854     \n",
              "66                                            106.029     \n",
              "67                                            106.315     \n",
              "\n",
              "    Unemployment (% of total labor force) (modeled ILO estimate)  \n",
              "63                                             17.490             \n",
              "64                                             15.866             \n",
              "65                                             13.376             \n",
              "66                                             13.481             \n",
              "67                                             14.086             "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-73e66d83-ed1d-4ecb-ac5e-b2163d4b7eab\" 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>Country Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>IncomeGroup</th>\n",
              "      <th>Year</th>\n",
              "      <th>Birth rate, crude (per 1,000 people)</th>\n",
              "      <th>Death rate, crude (per 1,000 people)</th>\n",
              "      <th>Electric power consumption (kWh per capita)</th>\n",
              "      <th>GDP (USD)</th>\n",
              "      <th>GDP per capita (USD)</th>\n",
              "      <th>Individuals using the Internet (% of population)</th>\n",
              "      <th>Infant mortality rate (per 1,000 live births)</th>\n",
              "      <th>Life expectancy at birth (years)</th>\n",
              "      <th>Population density (people per sq. km of land area)</th>\n",
              "      <th>Unemployment (% of total labor force) (modeled ILO estimate)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Albania</td>\n",
              "      <td>ALB</td>\n",
              "      <td>Europe &amp; Central Asia</td>\n",
              "      <td>Upper middle income</td>\n",
              "      <td>2014</td>\n",
              "      <td>12.259</td>\n",
              "      <td>7.219</td>\n",
              "      <td>2309.37</td>\n",
              "      <td>1.322820e+10</td>\n",
              "      <td>4578.67</td>\n",
              "      <td>60.100</td>\n",
              "      <td>8.9</td>\n",
              "      <td>77.813</td>\n",
              "      <td>105.442</td>\n",
              "      <td>17.490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Albania</td>\n",
              "      <td>ALB</td>\n",
              "      <td>Europe &amp; Central Asia</td>\n",
              "      <td>Upper middle income</td>\n",
              "      <td>2013</td>\n",
              "      <td>12.257</td>\n",
              "      <td>7.096</td>\n",
              "      <td>2533.25</td>\n",
              "      <td>1.277630e+10</td>\n",
              "      <td>4413.08</td>\n",
              "      <td>57.200</td>\n",
              "      <td>9.5</td>\n",
              "      <td>77.554</td>\n",
              "      <td>105.660</td>\n",
              "      <td>15.866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Albania</td>\n",
              "      <td>ALB</td>\n",
              "      <td>Europe &amp; Central Asia</td>\n",
              "      <td>Upper middle income</td>\n",
              "      <td>2012</td>\n",
              "      <td>12.197</td>\n",
              "      <td>6.996</td>\n",
              "      <td>2118.33</td>\n",
              "      <td>1.231980e+10</td>\n",
              "      <td>4247.61</td>\n",
              "      <td>54.656</td>\n",
              "      <td>10.2</td>\n",
              "      <td>77.252</td>\n",
              "      <td>105.854</td>\n",
              "      <td>13.376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Albania</td>\n",
              "      <td>ALB</td>\n",
              "      <td>Europe &amp; Central Asia</td>\n",
              "      <td>Upper middle income</td>\n",
              "      <td>2011</td>\n",
              "      <td>12.100</td>\n",
              "      <td>6.915</td>\n",
              "      <td>2205.70</td>\n",
              "      <td>1.289090e+10</td>\n",
              "      <td>4437.18</td>\n",
              "      <td>49.000</td>\n",
              "      <td>11.0</td>\n",
              "      <td>76.914</td>\n",
              "      <td>106.029</td>\n",
              "      <td>13.481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Albania</td>\n",
              "      <td>ALB</td>\n",
              "      <td>Europe &amp; Central Asia</td>\n",
              "      <td>Upper middle income</td>\n",
              "      <td>2010</td>\n",
              "      <td>12.001</td>\n",
              "      <td>6.841</td>\n",
              "      <td>1943.34</td>\n",
              "      <td>1.192700e+10</td>\n",
              "      <td>4094.36</td>\n",
              "      <td>45.000</td>\n",
              "      <td>11.9</td>\n",
              "      <td>76.562</td>\n",
              "      <td>106.315</td>\n",
              "      <td>14.086</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73e66d83-ed1d-4ecb-ac5e-b2163d4b7eab')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-73e66d83-ed1d-4ecb-ac5e-b2163d4b7eab 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-73e66d83-ed1d-4ecb-ac5e-b2163d4b7eab');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e5c22426-c10e-46fb-bb4e-3df5da74a0f8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e5c22426-c10e-46fb-bb4e-3df5da74a0f8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e5c22426-c10e-46fb-bb4e-3df5da74a0f8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data1",
              "summary": "{\n  \"name\": \"data1\",\n  \"rows\": 2775,\n  \"fields\": [\n    {\n      \"column\": \"Country Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 135,\n        \"samples\": [\n          \"Poland\",\n          \"Kyrgyz Republic\",\n          \"Serbia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country Code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 135,\n        \"samples\": [\n          \"POL\",\n          \"KGZ\",\n          \"SRB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Europe & Central Asia\",\n          \"Middle East & North Africa\",\n          \"South Asia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IncomeGroup\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"High income: nonOECD\",\n          \"Low income\",\n          \"Lower middle income\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 1991,\n        \"max\": 2014,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          2006,\n          1998,\n          2014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Birth rate, crude (per 1,000 people)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.241780136068842,\n        \"min\": 7.6,\n        \"max\": 53.541,\n        \"num_unique_values\": 1875,\n        \"samples\": [\n          10.832,\n          15.206,\n          36.991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Death rate, crude (per 1,000 people)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.3305417671726736,\n        \"min\": 1.127,\n        \"max\": 19.146,\n        \"num_unique_values\": 1780,\n        \"samples\": [\n          1.605,\n          5.408,\n          2.504\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Electric power consumption (kWh per capita)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5320.200582950454,\n        \"min\": 22.7558,\n        \"max\": 54799.2,\n        \"num_unique_values\": 2772,\n        \"samples\": [\n          561.248,\n          4261.21,\n          13480.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1321400300103.0134,\n        \"min\": 467873000.0,\n        \"max\": 17500000000000.0,\n        \"num_unique_values\": 2315,\n        \"samples\": [\n          22593500000.0,\n          49170400000.0,\n          1315160000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16684.086326219516,\n        \"min\": 102.598,\n        \"max\": 118824.0,\n        \"num_unique_values\": 2773,\n        \"samples\": [\n          24974.3,\n          957.19,\n          9814.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Individuals using the Internet (% of population)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.505454032526828,\n        \"min\": 0.0,\n        \"max\": 98.16,\n        \"num_unique_values\": 2488,\n        \"samples\": [\n          79.12,\n          4.75622,\n          2.53757\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Infant mortality rate (per 1,000 live births)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.435820116959302,\n        \"min\": 1.8,\n        \"max\": 138.7,\n        \"num_unique_values\": 764,\n        \"samples\": [\n          49.6,\n          45.8,\n          84.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life expectancy at birth (years)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.511527783397346,\n        \"min\": 43.065,\n        \"max\": 83.5878,\n        \"num_unique_values\": 2627,\n        \"samples\": [\n          80.5878,\n          73.3,\n          78.3805\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population density (people per sq. km of land area)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 600.4703853016283,\n        \"min\": 1.4792,\n        \"max\": 7714.7,\n        \"num_unique_values\": 2774,\n        \"samples\": [\n          109.916,\n          278.539,\n          6.7904\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unemployment (% of total labor force) (modeled ILO estimate)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.999432636549038,\n        \"min\": 0.19,\n        \"max\": 37.25,\n        \"num_unique_values\": 2433,\n        \"samples\": [\n          8.424,\n          11.649,\n          5.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "data1=data.dropna()\n",
        "data1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "b5e518f8-9099-4603-9c6b-3cfcbc504456",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b5e518f8-9099-4603-9c6b-3cfcbc504456",
        "outputId": "a5a74de5-b104-4003-a498-6c8c6de16d12"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country Name                                                    0\n",
              "Country Code                                                    0\n",
              "Region                                                          0\n",
              "IncomeGroup                                                     0\n",
              "Year                                                            0\n",
              "Birth rate, crude (per 1,000 people)                            0\n",
              "Death rate, crude (per 1,000 people)                            0\n",
              "Electric power consumption (kWh per capita)                     0\n",
              "GDP (USD)                                                       0\n",
              "GDP per capita (USD)                                            0\n",
              "Individuals using the Internet (% of population)                0\n",
              "Infant mortality rate (per 1,000 live births)                   0\n",
              "Life expectancy at birth (years)                                0\n",
              "Population density (people per sq. km of land area)             0\n",
              "Unemployment (% of total labor force) (modeled ILO estimate)    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "data1.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "6b60ffc0-ce13-4240-80f5-0a4b633c5ed1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6b60ffc0-ce13-4240-80f5-0a4b633c5ed1",
        "outputId": "ff93bb94-8caa-406d-ecaf-5a7f7de95f7e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2775 entries, 63 to 12414\n",
            "Data columns (total 15 columns):\n",
            " #   Column                                                        Non-Null Count  Dtype  \n",
            "---  ------                                                        --------------  -----  \n",
            " 0   Country Name                                                  2775 non-null   object \n",
            " 1   Country Code                                                  2775 non-null   object \n",
            " 2   Region                                                        2775 non-null   object \n",
            " 3   IncomeGroup                                                   2775 non-null   object \n",
            " 4   Year                                                          2775 non-null   int64  \n",
            " 5   Birth rate, crude (per 1,000 people)                          2775 non-null   float64\n",
            " 6   Death rate, crude (per 1,000 people)                          2775 non-null   float64\n",
            " 7   Electric power consumption (kWh per capita)                   2775 non-null   float64\n",
            " 8   GDP (USD)                                                     2775 non-null   float64\n",
            " 9   GDP per capita (USD)                                          2775 non-null   float64\n",
            " 10  Individuals using the Internet (% of population)              2775 non-null   float64\n",
            " 11  Infant mortality rate (per 1,000 live births)                 2775 non-null   float64\n",
            " 12  Life expectancy at birth (years)                              2775 non-null   float64\n",
            " 13  Population density (people per sq. km of land area)           2775 non-null   float64\n",
            " 14  Unemployment (% of total labor force) (modeled ILO estimate)  2775 non-null   float64\n",
            "dtypes: float64(10), int64(1), object(4)\n",
            "memory usage: 346.9+ KB\n"
          ]
        }
      ],
      "source": [
        "data1.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "b6be689e-64ff-4ed0-a8b0-45eb49b7e038",
      "metadata": {
        "id": "b6be689e-64ff-4ed0-a8b0-45eb49b7e038"
      },
      "outputs": [],
      "source": [
        "year = 2012\n",
        "variable = 'Life expectancy at birth (years)'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "5a0b1930-6b60-40fd-a090-e480bcf64a0d",
      "metadata": {
        "id": "5a0b1930-6b60-40fd-a090-e480bcf64a0d"
      },
      "outputs": [],
      "source": [
        "data_year = data[data['Year'] == year]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "26ff6c5b-a3d1-4858-b35a-7e8d4aab3b33",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "26ff6c5b-a3d1-4858-b35a-7e8d4aab3b33",
        "outputId": "2ac7bbe5-4ade-49b0-ca98-a54f118af7be"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Create A Histogram\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(data_year[variable], bins=20, edgecolor='k', alpha=0.7)\n",
        "plt.title(f'Distribution of {variable} in {year}')\n",
        "plt.xlabel(variable)\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "fa05bf17-d051-41ad-b474-7f8513fccaa6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "fa05bf17-d051-41ad-b474-7f8513fccaa6",
        "outputId": "fca659c5-4ab5-4cf9-ae0c-af75d9ae318f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Customize the plot further\n",
        "plt.title(f'Distribution of {variable} in {year}')\n",
        "plt.xlabel(variable)\n",
        "plt.ylabel('Frequency')\n",
        "plt.grid(True, linestyle='--', alpha=0.7)\n",
        "\n",
        "# Add labels for mean and median\n",
        "mean_value = data_year[variable].mean()\n",
        "median_value = data_year[variable].median()\n",
        "plt.axvline(mean_value, color='red', linestyle='dashed', linewidth=2, label=f'Mean: {mean_value:.2f}')\n",
        "plt.axvline(median_value, color='green', linestyle='dashed', linewidth=2, label=f'Median: {median_value:.2f}')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "5e24ba4e-4809-458a-bde0-c09b9df658bf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "id": "5e24ba4e-4809-458a-bde0-c09b9df658bf",
        "outputId": "abe5e02d-bc11-4588-c491-f950f82e182a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "region_counts = data['Region'].value_counts()\n",
        "\n",
        "# Create a bar chart\n",
        "plt.figure(figsize=(10, 6))\n",
        "region_counts.plot(kind='bar', color='skyblue')\n",
        "plt.xlabel('Region')\n",
        "plt.ylabel('Number of Countries')\n",
        "plt.title('Distribution of Countries by Region')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "19f435e5-d83c-4bc1-a062-fc3188dc1a92",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "19f435e5-d83c-4bc1-a062-fc3188dc1a92",
        "outputId": "932203fb-ce32-44fe-df5d-4650888ccc4c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15, 6))\n",
        "plt.subplot(1, 2, 2)\n",
        "region_percentage = (region_counts / region_counts.sum()) * 100\n",
        "colors = ['gold', 'lightcoral', 'lightgreen', 'lightskyblue']\n",
        "region_percentage.plot(kind='pie', autopct='%1.1f%%', colors=colors)\n",
        "plt.axis('equal')\n",
        "plt.title('Distribution of Countries by Region (Percentage)')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.0"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}