Amaliyot (not full).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AbdullahTheAnalyst/Data-Science-and-AI-mohir_dev-/blob/NumPy-and-Pandas/Amaliyot%20(not%20full).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aI1hQBhZ6zBE"
      },
      "source": [
        "# Data Science va Sun'iy Intellekt Praktikum\n",
        "\n",
        "# 3-MODUL: MA’LUMOTLARGA ISHLOV BERISH. UYG’UNLASHTIRISH, ULASH VA QAYTA SHAKLLASH\n",
        "\n",
        "## AMALIYOT\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hdr6cBxy7Vx4"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LwcsDaLZ67sa"
      },
      "source": [
        "### Ma'lumotlar to'plamini yuklab olish"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FnqTd1Ot6kjH",
        "outputId": "c3afc349-f669-44fa-a3c5-27beb577694d"
      },
      "source": [
        "!git clone https://github.com/MansurCompAI/Practicum_Datasets.git"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Practicum_Datasets'...\n",
            "remote: Enumerating objects: 60, done.\u001b[K\n",
            "remote: Counting objects: 100% (60/60), done.\u001b[K\n",
            "remote: Compressing objects: 100% (55/55), done.\u001b[K\n",
            "remote: Total 60 (delta 17), reused 16 (delta 1), pack-reused 0 (from 0)\u001b[K\n",
            "Receiving objects: 100% (60/60), 20.24 MiB | 14.55 MiB/s, done.\n",
            "Resolving deltas: 100% (17/17), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3w0cugnuCHaT"
      },
      "source": [
        "## Ma'lumolar to'plami 5 ta csv fayllardan iborat. Har bir fayl nomidagi yillar uchun ma'lumotlarni o'zida saqlaydi. Masalan `2015.csv` faylining ichida 2015 yilda dunyo mamlakatlarining baxtlilik darajalari keltirilgan ma'lumotlarni o'z ichiga oladi va h.k."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMYPtdDNBsWc"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yuokd0Bl8hl-"
      },
      "source": [
        "## Barcha ma'lumotlar to'plamini o'zgaruvchilarga biriktirib olish\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CbmJmehkAa51"
      },
      "source": [
        "# Ma'lumotlarni (data15, data16, data17, data18, data19) o'zgaruvchilariga biriktirib o'qib oling\n",
        "# yordam: pd.read_csv dan foydalaning\n",
        "# Javobni shu yerga yozing\n",
        "data15 = pd.read_csv(\"/content/Practicum_Datasets/Happiness/2015.csv\")\n",
        "data16 = pd.read_csv(\"/content/Practicum_Datasets/Happiness/2016.csv\")\n",
        "data17 = pd.read_csv(\"/content/Practicum_Datasets/Happiness/2017.csv\")\n",
        "data18 = pd.read_csv(\"/content/Practicum_Datasets/Happiness/2018.csv\")\n",
        "data19 = pd.read_csv(\"/content/Practicum_Datasets/Happiness/2019.csv\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h86s6jZFDM72"
      },
      "source": [
        "## Ma'lumotlar to'plamini kuzatish"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VyMuUpV7DRGf"
      },
      "source": [
        "## `data15`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "Cn_nwSNb8VAs",
        "outputId": "85918aeb-401f-4ac0-af9b-e677a6a5ab34"
      },
      "source": [
        "data15.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Country          Region  Happiness Rank  Happiness Score  \\\n",
              "0  Switzerland  Western Europe               1            7.587   \n",
              "1      Iceland  Western Europe               2            7.561   \n",
              "2      Denmark  Western Europe               3            7.527   \n",
              "3       Norway  Western Europe               4            7.522   \n",
              "4       Canada   North America               5            7.427   \n",
              "\n",
              "   Standard Error  Economy (GDP per Capita)   Family  \\\n",
              "0         0.03411                   1.39651  1.34951   \n",
              "1         0.04884                   1.30232  1.40223   \n",
              "2         0.03328                   1.32548  1.36058   \n",
              "3         0.03880                   1.45900  1.33095   \n",
              "4         0.03553                   1.32629  1.32261   \n",
              "\n",
              "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
              "0                   0.94143  0.66557                        0.41978   \n",
              "1                   0.94784  0.62877                        0.14145   \n",
              "2                   0.87464  0.64938                        0.48357   \n",
              "3                   0.88521  0.66973                        0.36503   \n",
              "4                   0.90563  0.63297                        0.32957   \n",
              "\n",
              "   Generosity  Dystopia Residual  \n",
              "0     0.29678            2.51738  \n",
              "1     0.43630            2.70201  \n",
              "2     0.34139            2.49204  \n",
              "3     0.34699            2.46531  \n",
              "4     0.45811            2.45176  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9a8105e2-62ef-44ee-87f2-a40e8bd3c675\" 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</th>\n",
              "      <th>Region</th>\n",
              "      <th>Happiness Rank</th>\n",
              "      <th>Happiness Score</th>\n",
              "      <th>Standard Error</th>\n",
              "      <th>Economy (GDP per Capita)</th>\n",
              "      <th>Family</th>\n",
              "      <th>Health (Life Expectancy)</th>\n",
              "      <th>Freedom</th>\n",
              "      <th>Trust (Government Corruption)</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Dystopia Residual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>1</td>\n",
              "      <td>7.587</td>\n",
              "      <td>0.03411</td>\n",
              "      <td>1.39651</td>\n",
              "      <td>1.34951</td>\n",
              "      <td>0.94143</td>\n",
              "      <td>0.66557</td>\n",
              "      <td>0.41978</td>\n",
              "      <td>0.29678</td>\n",
              "      <td>2.51738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>2</td>\n",
              "      <td>7.561</td>\n",
              "      <td>0.04884</td>\n",
              "      <td>1.30232</td>\n",
              "      <td>1.40223</td>\n",
              "      <td>0.94784</td>\n",
              "      <td>0.62877</td>\n",
              "      <td>0.14145</td>\n",
              "      <td>0.43630</td>\n",
              "      <td>2.70201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>3</td>\n",
              "      <td>7.527</td>\n",
              "      <td>0.03328</td>\n",
              "      <td>1.32548</td>\n",
              "      <td>1.36058</td>\n",
              "      <td>0.87464</td>\n",
              "      <td>0.64938</td>\n",
              "      <td>0.48357</td>\n",
              "      <td>0.34139</td>\n",
              "      <td>2.49204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Norway</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>4</td>\n",
              "      <td>7.522</td>\n",
              "      <td>0.03880</td>\n",
              "      <td>1.45900</td>\n",
              "      <td>1.33095</td>\n",
              "      <td>0.88521</td>\n",
              "      <td>0.66973</td>\n",
              "      <td>0.36503</td>\n",
              "      <td>0.34699</td>\n",
              "      <td>2.46531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Canada</td>\n",
              "      <td>North America</td>\n",
              "      <td>5</td>\n",
              "      <td>7.427</td>\n",
              "      <td>0.03553</td>\n",
              "      <td>1.32629</td>\n",
              "      <td>1.32261</td>\n",
              "      <td>0.90563</td>\n",
              "      <td>0.63297</td>\n",
              "      <td>0.32957</td>\n",
              "      <td>0.45811</td>\n",
              "      <td>2.45176</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "            'suggestCharts', [key], {});\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data15",
              "summary": "{\n  \"name\": \"data15\",\n  \"rows\": 158,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 158,\n        \"samples\": [\n          \"Myanmar\",\n          \"Japan\",\n          \"Egypt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Sub-Saharan Africa\",\n          \"North America\",\n          \"Southeastern Asia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Happiness Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45,\n        \"min\": 1,\n        \"max\": 158,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          128,\n          46,\n          135\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Happiness Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1450101349520665,\n        \"min\": 2.839,\n        \"max\": 7.587,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          4.332,\n          5.987,\n          4.194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Standard Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.017146178556969302,\n        \"min\": 0.01848,\n        \"max\": 0.13693,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          0.06607,\n          0.04802,\n          0.06698\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economy (GDP per Capita)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40312077853791006,\n        \"min\": 0.0,\n        \"max\": 1.69042,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          0.27108,\n          1.27074,\n          0.8818\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27236908600791543,\n        \"min\": 0.0,\n        \"max\": 1.40223,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          0.70905,\n          1.25712,\n          0.747\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Health (Life Expectancy)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24707776630217204,\n        \"min\": 0.0,\n        \"max\": 1.02525,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          0.04776,\n          0.99111,\n          0.61712\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Freedom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15069278393767796,\n        \"min\": 0.0,\n        \"max\": 0.66973,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          0.44017,\n          0.49615,\n          0.17288\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust (Government Corruption)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12003407357455917,\n        \"min\": 0.0,\n        \"max\": 0.55191,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          0.12474,\n          0.07857,\n          0.06324\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Generosity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12668493402020542,\n        \"min\": 0.0,\n        \"max\": 0.79588,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          0.79588,\n          0.10705,\n          0.11291\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dystopia Residual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5535497923037984,\n        \"min\": 0.32858,\n        \"max\": 3.60214,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          1.41805,\n          1.68435,\n          1.59927\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bm8zSuJMDijq"
      },
      "source": [
        "## `data16`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "hEYQl-18Dk3N",
        "outputId": "3a8de93a-ba30-4c20-b022-d60b4054b46b"
      },
      "source": [
        "data16.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Country          Region  Happiness Rank  Happiness Score  \\\n",
              "0      Denmark  Western Europe               1            7.526   \n",
              "1  Switzerland  Western Europe               2            7.509   \n",
              "2      Iceland  Western Europe               3            7.501   \n",
              "3       Norway  Western Europe               4            7.498   \n",
              "4      Finland  Western Europe               5            7.413   \n",
              "\n",
              "   Lower Confidence Interval  Upper Confidence Interval  \\\n",
              "0                      7.460                      7.592   \n",
              "1                      7.428                      7.590   \n",
              "2                      7.333                      7.669   \n",
              "3                      7.421                      7.575   \n",
              "4                      7.351                      7.475   \n",
              "\n",
              "   Economy (GDP per Capita)   Family  Health (Life Expectancy)  Freedom  \\\n",
              "0                   1.44178  1.16374                   0.79504  0.57941   \n",
              "1                   1.52733  1.14524                   0.86303  0.58557   \n",
              "2                   1.42666  1.18326                   0.86733  0.56624   \n",
              "3                   1.57744  1.12690                   0.79579  0.59609   \n",
              "4                   1.40598  1.13464                   0.81091  0.57104   \n",
              "\n",
              "   Trust (Government Corruption)  Generosity  Dystopia Residual  \n",
              "0                        0.44453     0.36171            2.73939  \n",
              "1                        0.41203     0.28083            2.69463  \n",
              "2                        0.14975     0.47678            2.83137  \n",
              "3                        0.35776     0.37895            2.66465  \n",
              "4                        0.41004     0.25492            2.82596  "
            ],
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              "      <th></th>\n",
              "      <th>Country</th>\n",
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              "      <th>Happiness Score</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>1</td>\n",
              "      <td>7.526</td>\n",
              "      <td>7.460</td>\n",
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              "      <th>1</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>Western Europe</td>\n",
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              "      <th>2</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>3</td>\n",
              "      <td>7.501</td>\n",
              "      <td>7.333</td>\n",
              "      <td>7.669</td>\n",
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              "      <td>1.18326</td>\n",
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              "      <td>2.83137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Norway</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>4</td>\n",
              "      <td>7.498</td>\n",
              "      <td>7.421</td>\n",
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              "      <td>1.12690</td>\n",
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              "      <td>0.35776</td>\n",
              "      <td>0.37895</td>\n",
              "      <td>2.66465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Finland</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>5</td>\n",
              "      <td>7.413</td>\n",
              "      <td>7.351</td>\n",
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              "      <td>0.81091</td>\n",
              "      <td>0.57104</td>\n",
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              "            'suggestCharts', [key], {});\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data16",
              "summary": "{\n  \"name\": \"data16\",\n  \"rows\": 157,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 157,\n        \"samples\": [\n          \"Congo (Brazzaville)\",\n          \"El Salvador\",\n          \"Gabon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Sub-Saharan Africa\",\n          \"North America\",\n          \"Southeastern Asia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Happiness Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45,\n        \"min\": 1,\n        \"max\": 157,\n        \"num_unique_values\": 154,\n        \"samples\": [\n          16,\n          97,\n          156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Happiness Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1416735176005712,\n        \"min\": 2.905,\n        \"max\": 7.526,\n        \"num_unique_values\": 154,\n        \"samples\": [\n          6.994,\n          5.057,\n          3.069\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lower Confidence Interval\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1480426137782485,\n        \"min\": 2.732,\n        \"max\": 7.46,\n        \"num_unique_values\": 154,\n        \"samples\": [\n          6.93,\n          5.017,\n          2.936\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Upper Confidence Interval\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1364932228133044,\n        \"min\": 3.078,\n        \"max\": 7.669,\n        \"num_unique_values\": 154,\n        \"samples\": [\n          7.058,\n          5.18,\n          3.202\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economy (GDP per Capita)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4125954051030412,\n        \"min\": 0.0,\n        \"max\": 1.82427,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          0.77109,\n          0.8737,\n          1.15851\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26670574619957355,\n        \"min\": 0.0,\n        \"max\": 1.18326,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          0.47799,\n          0.80975,\n          0.72368\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Health (Life Expectancy)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2293491914878582,\n        \"min\": 0.0,\n        \"max\": 0.95277,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          0.33108,\n          0.58295,\n          0.73561\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Freedom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1455067674417439,\n        \"min\": 0.0,\n        \"max\": 0.60848,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          0.37938,\n          0.37269,\n          0.28098\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust (Government Corruption)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1110379099181551,\n        \"min\": 0.0,\n        \"max\": 0.50521,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          0.36794,\n          0.07396,\n          0.02745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Generosity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13375569129812104,\n        \"min\": 0.0,\n        \"max\": 0.81971,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          0.12077,\n          0.08877,\n          0.06244\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dystopia Residual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5422199984864142,\n        \"min\": 0.81789,\n        \"max\": 3.83772,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          2.10681,\n          3.22134,\n          1.45332\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w2LqJ2EsDnCO"
      },
      "source": [
        "## `data17`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "2Jhll-M2DqzB",
        "outputId": "585ea03b-c822-482b-f78e-71e7d0c995ca"
      },
      "source": [
        "data17.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Country  Happiness.Rank  Happiness.Score  Whisker.high  Whisker.low  \\\n",
              "0       Norway               1            7.537      7.594445     7.479556   \n",
              "1      Denmark               2            7.522      7.581728     7.462272   \n",
              "2      Iceland               3            7.504      7.622030     7.385970   \n",
              "3  Switzerland               4            7.494      7.561772     7.426227   \n",
              "4      Finland               5            7.469      7.527542     7.410458   \n",
              "\n",
              "   Economy..GDP.per.Capita.    Family  Health..Life.Expectancy.   Freedom  \\\n",
              "0                  1.616463  1.533524                  0.796667  0.635423   \n",
              "1                  1.482383  1.551122                  0.792566  0.626007   \n",
              "2                  1.480633  1.610574                  0.833552  0.627163   \n",
              "3                  1.564980  1.516912                  0.858131  0.620071   \n",
              "4                  1.443572  1.540247                  0.809158  0.617951   \n",
              "\n",
              "   Generosity  Trust..Government.Corruption.  Dystopia.Residual  \n",
              "0    0.362012                       0.315964           2.277027  \n",
              "1    0.355280                       0.400770           2.313707  \n",
              "2    0.475540                       0.153527           2.322715  \n",
              "3    0.290549                       0.367007           2.276716  \n",
              "4    0.245483                       0.382612           2.430182  "
            ],
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              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>Happiness.Rank</th>\n",
              "      <th>Happiness.Score</th>\n",
              "      <th>Whisker.high</th>\n",
              "      <th>Whisker.low</th>\n",
              "      <th>Economy..GDP.per.Capita.</th>\n",
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              "      <th>Health..Life.Expectancy.</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Norway</td>\n",
              "      <td>1</td>\n",
              "      <td>7.537</td>\n",
              "      <td>7.594445</td>\n",
              "      <td>7.479556</td>\n",
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              "      <th>1</th>\n",
              "      <td>Denmark</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>3</td>\n",
              "      <td>7.504</td>\n",
              "      <td>7.622030</td>\n",
              "      <td>7.385970</td>\n",
              "      <td>1.480633</td>\n",
              "      <td>1.610574</td>\n",
              "      <td>0.833552</td>\n",
              "      <td>0.627163</td>\n",
              "      <td>0.475540</td>\n",
              "      <td>0.153527</td>\n",
              "      <td>2.322715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>4</td>\n",
              "      <td>7.494</td>\n",
              "      <td>7.561772</td>\n",
              "      <td>7.426227</td>\n",
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              "      <td>0.290549</td>\n",
              "      <td>0.367007</td>\n",
              "      <td>2.276716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Finland</td>\n",
              "      <td>5</td>\n",
              "      <td>7.469</td>\n",
              "      <td>7.527542</td>\n",
              "      <td>7.410458</td>\n",
              "      <td>1.443572</td>\n",
              "      <td>1.540247</td>\n",
              "      <td>0.809158</td>\n",
              "      <td>0.617951</td>\n",
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              "      <td>0.382612</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-23010312-df5d-47d3-8d28-0a889ee70c85');\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",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "  .colab-df-quickchart {\n",
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              "    display: none;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "  }\n",
              "\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
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              "      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",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d3094d4f-92b6-4e9a-ba87-69a1360ac690 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"data17\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 155,\n        \"samples\": [\n          \"Venezuela\",\n          \"Benin\",\n          \"Thailand\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Happiness.Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44,\n        \"min\": 1,\n        \"max\": 155,\n        \"num_unique_values\": 155,\n        \"samples\": [\n          82,\n          143,\n          32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Happiness.Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.131230089914994,\n        \"min\": 2.69300007820129,\n        \"max\": 7.53700017929077,\n       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3.56821657836437,\n          6.33888271704316\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economy..GDP.per.Capita.\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4207927094490493,\n        \"min\": 0.0,\n        \"max\": 1.87076568603516,\n        \"num_unique_values\": 155,\n        \"samples\": [\n          1.12843120098114,\n          0.431085407733917,\n          1.12786877155304\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28726291227731227,\n        \"min\": 0.0,\n        \"max\": 1.6105740070343,\n        \"num_unique_values\": 155,\n        \"samples\": [\n          1.43133759498596,\n          0.435299843549728,\n          1.42579245567322\n        ],\n        \"semantic_type\": \"\",\n        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            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kG48t3AnDxBH"
      },
      "source": [
        "## `data18`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "0-9RqmA-DzkI",
        "outputId": "c7c48d49-7082-4c25-f0e7-6c75fee118a5"
      },
      "source": [
        "data18.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Overall rank Country or region  Score  GDP per capita  Social support  \\\n",
              "0             1           Finland  7.632           1.305           1.592   \n",
              "1             2            Norway  7.594           1.456           1.582   \n",
              "2             3           Denmark  7.555           1.351           1.590   \n",
              "3             4           Iceland  7.495           1.343           1.644   \n",
              "4             5       Switzerland  7.487           1.420           1.549   \n",
              "\n",
              "   Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
              "0                    0.874                         0.681       0.202   \n",
              "1                    0.861                         0.686       0.286   \n",
              "2                    0.868                         0.683       0.284   \n",
              "3                    0.914                         0.677       0.353   \n",
              "4                    0.927                         0.660       0.256   \n",
              "\n",
              "   Perceptions of corruption  \n",
              "0                      0.393  \n",
              "1                      0.340  \n",
              "2                      0.408  \n",
              "3                      0.138  \n",
              "4                      0.357  "
            ],
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Overall rank</th>\n",
              "      <th>Country or region</th>\n",
              "      <th>Score</th>\n",
              "      <th>GDP per capita</th>\n",
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              "      <th>Healthy life expectancy</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Finland</td>\n",
              "      <td>7.632</td>\n",
              "      <td>1.305</td>\n",
              "      <td>1.592</td>\n",
              "      <td>0.874</td>\n",
              "      <td>0.681</td>\n",
              "      <td>0.202</td>\n",
              "      <td>0.393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Norway</td>\n",
              "      <td>7.594</td>\n",
              "      <td>1.456</td>\n",
              "      <td>1.582</td>\n",
              "      <td>0.861</td>\n",
              "      <td>0.686</td>\n",
              "      <td>0.286</td>\n",
              "      <td>0.340</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>7.555</td>\n",
              "      <td>1.351</td>\n",
              "      <td>1.590</td>\n",
              "      <td>0.868</td>\n",
              "      <td>0.683</td>\n",
              "      <td>0.284</td>\n",
              "      <td>0.408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>7.495</td>\n",
              "      <td>1.343</td>\n",
              "      <td>1.644</td>\n",
              "      <td>0.914</td>\n",
              "      <td>0.677</td>\n",
              "      <td>0.353</td>\n",
              "      <td>0.138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>7.487</td>\n",
              "      <td>1.420</td>\n",
              "      <td>1.549</td>\n",
              "      <td>0.927</td>\n",
              "      <td>0.660</td>\n",
              "      <td>0.256</td>\n",
              "      <td>0.357</td>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\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",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "        const element = document.querySelector('#df-a3aca4a0-00a8-41a9-a94c-273c86502904');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  }\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",
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              "      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-d892b5da-658d-43a7-92c8-0b9493544788 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": "data18",
              "summary": "{\n  \"name\": \"data18\",\n  \"rows\": 156,\n  \"fields\": [\n    {\n      \"column\": \"Overall rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45,\n        \"min\": 1,\n        \"max\": 156,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          97,\n          70,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country or region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 156,\n        \"samples\": [\n          \"Bhutan\",\n          \"Libya\",\n          \"Dominican Republic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.119505852731943,\n        \"min\": 2.905,\n        \"max\": 7.632,\n        \"num_unique_values\": 154,\n        \"samples\": [\n          6.927,\n          5.082,\n          3.083\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3919209481658884,\n        \"min\": 0.0,\n        \"max\": 2.096,\n        \"num_unique_values\": 147,\n        \"samples\": [\n          0.069,\n          1.148,\n          0.332\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Social support\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3023718734184349,\n        \"min\": 0.0,\n        \"max\": 1.644,\n        \"num_unique_values\": 146,\n        \"samples\": [\n          1.527,\n          1.41,\n          1.468\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Healthy life expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24757876957930375,\n        \"min\": 0.0,\n        \"max\": 1.03,\n        \"num_unique_values\": 143,\n        \"samples\": [\n          0.666,\n          0.908,\n          0.594\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Freedom to make life choices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16242431475354802,\n        \"min\": 0.0,\n        \"max\": 0.724,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          0.562,\n          0.281,\n          0.541\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Generosity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09847059471314082,\n        \"min\": 0.0,\n        \"max\": 0.598,\n        \"num_unique_values\": 122,\n        \"samples\": [\n          0.354,\n          0.114,\n          0.026\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Perceptions of corruption\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09649177341961508,\n        \"min\": 0.0,\n        \"max\": 0.457,\n        \"num_unique_values\": 110,\n        \"samples\": [\n          0.018,\n          0.082,\n          0.357\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76L4b4tPD2DF"
      },
      "source": [
        "## `data19`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "uzjv8sZuD5PX",
        "outputId": "40e36c56-3f14-4d8c-f435-925512a1a442"
      },
      "source": [
        "data19.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Overall rank Country or region  Score  GDP per capita  Social support  \\\n",
              "0             1           Finland  7.769           1.340           1.587   \n",
              "1             2           Denmark  7.600           1.383           1.573   \n",
              "2             3            Norway  7.554           1.488           1.582   \n",
              "3             4           Iceland  7.494           1.380           1.624   \n",
              "4             5       Netherlands  7.488           1.396           1.522   \n",
              "\n",
              "   Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
              "0                    0.986                         0.596       0.153   \n",
              "1                    0.996                         0.592       0.252   \n",
              "2                    1.028                         0.603       0.271   \n",
              "3                    1.026                         0.591       0.354   \n",
              "4                    0.999                         0.557       0.322   \n",
              "\n",
              "   Perceptions of corruption  \n",
              "0                      0.393  \n",
              "1                      0.410  \n",
              "2                      0.341  \n",
              "3                      0.118  \n",
              "4                      0.298  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Overall rank</th>\n",
              "      <th>Country or region</th>\n",
              "      <th>Score</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Perceptions of corruption</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Finland</td>\n",
              "      <td>7.769</td>\n",
              "      <td>1.340</td>\n",
              "      <td>1.587</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.596</td>\n",
              "      <td>0.153</td>\n",
              "      <td>0.393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>7.600</td>\n",
              "      <td>1.383</td>\n",
              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
              "      <td>0.592</td>\n",
              "      <td>0.252</td>\n",
              "      <td>0.410</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Norway</td>\n",
              "      <td>7.554</td>\n",
              "      <td>1.488</td>\n",
              "      <td>1.582</td>\n",
              "      <td>1.028</td>\n",
              "      <td>0.603</td>\n",
              "      <td>0.271</td>\n",
              "      <td>0.341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>7.494</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.624</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.591</td>\n",
              "      <td>0.354</td>\n",
              "      <td>0.118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>7.488</td>\n",
              "      <td>1.396</td>\n",
              "      <td>1.522</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.557</td>\n",
              "      <td>0.322</td>\n",
              "      <td>0.298</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      display: none;\n",
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              "      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-4a09593e-4d0c-4c49-9d7d-621bb9880ff3 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-4a09593e-4d0c-4c49-9d7d-621bb9880ff3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </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",
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              "      --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",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    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",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    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-832ef77b-aad3-4225-ac2f-f8cd8fc7e728 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": "data19",
              "summary": "{\n  \"name\": \"data19\",\n  \"rows\": 156,\n  \"fields\": [\n    {\n      \"column\": \"Overall rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45,\n        \"min\": 1,\n        \"max\": 156,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          97,\n          70,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country or region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 156,\n        \"samples\": [\n          \"Bulgaria\",\n          \"Serbia\",\n          \"Mongolia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1131198687956712,\n        \"min\": 2.853,\n        \"max\": 7.769,\n        \"num_unique_values\": 155,\n        \"samples\": [\n          5.287,\n          3.802,\n          6.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3983894642422028,\n        \"min\": 0.0,\n        \"max\": 1.684,\n        \"num_unique_values\": 146,\n        \"samples\": [\n          0.882,\n          0.673,\n          1.403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Social support\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2991914006976929,\n        \"min\": 0.0,\n        \"max\": 1.624,\n        \"num_unique_values\": 145,\n        \"samples\": [\n          1.277,\n          0.711,\n          1.313\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Healthy life expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2421239984053725,\n        \"min\": 0.0,\n        \"max\": 1.141,\n        \"num_unique_values\": 119,\n        \"samples\": [\n          1.122,\n          0.428,\n          0.999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Freedom to make life choices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1432894707060473,\n        \"min\": 0.0,\n        \"max\": 0.631,\n        \"num_unique_values\": 130,\n        \"samples\": [\n          0.514,\n          0.47,\n          0.598\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Generosity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0952544405092202,\n        \"min\": 0.0,\n        \"max\": 0.566,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          0.047,\n          0.177,\n          0.322\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Perceptions of corruption\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09453783536745278,\n        \"min\": 0.0,\n        \"max\": 0.453,\n        \"num_unique_values\": 113,\n        \"samples\": [\n          0.04,\n          0.298,\n          0.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BbXJvfXPYdFb"
      },
      "source": [
        "## 1- Topshiriq\n",
        "## Irarxik indeks: 2015 va 2016 yillarga tegishli ma'lumotlar to'plamlarini `Region` va `Country` ustunlarini irarxik ustun ko'rinishiga keltiring va ularning indekslarini tekshiring."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFlhRiCCaaGt",
        "outputId": "6f0a6b1a-05d2-4a32-ab47-a101de597eb3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Javobni shu yerga yozing (o'zgaruvchilarni data15_mod va data16_mod deb nomlab oling)\n",
        "# yordam: .set_index() dan foydlanishingiz mumkin\n",
        "data15_mod = data15.set_index(['Region', 'Country'])\n",
        "data16_mod = data16.set_index(['Region', 'Country'])\n",
        "print(f\"2015 yil ma'lumolar to'plami\\n {data15_mod.index}\\n\")\n",
        "print(f\"2016 yil ma'lumolar to'plami\\n {data16_mod.index}\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2015 yil ma'lumolar to'plami\n",
            " MultiIndex([(                 'Western Europe',  'Switzerland'),\n",
            "            (                 'Western Europe',      'Iceland'),\n",
            "            (                 'Western Europe',      'Denmark'),\n",
            "            (                 'Western Europe',       'Norway'),\n",
            "            (                  'North America',       'Canada'),\n",
            "            (                 'Western Europe',      'Finland'),\n",
            "            (                 'Western Europe',  'Netherlands'),\n",
            "            (                 'Western Europe',       'Sweden'),\n",
            "            (      'Australia and New Zealand',  'New Zealand'),\n",
            "            (      'Australia and New Zealand',    'Australia'),\n",
            "            ...\n",
            "            (             'Sub-Saharan Africa',         'Chad'),\n",
            "            (             'Sub-Saharan Africa',       'Guinea'),\n",
            "            (             'Sub-Saharan Africa',  'Ivory Coast'),\n",
            "            (             'Sub-Saharan Africa', 'Burkina Faso'),\n",
            "            (                  'Southern Asia',  'Afghanistan'),\n",
            "            (             'Sub-Saharan Africa',       'Rwanda'),\n",
            "            (             'Sub-Saharan Africa',        'Benin'),\n",
            "            ('Middle East and Northern Africa',        'Syria'),\n",
            "            (             'Sub-Saharan Africa',      'Burundi'),\n",
            "            (             'Sub-Saharan Africa',         'Togo')],\n",
            "           names=['Region', 'Country'], length=158)\n",
            "\n",
            "2016 yil ma'lumolar to'plami\n",
            " MultiIndex([(                 'Western Europe',     'Denmark'),\n",
            "            (                 'Western Europe', 'Switzerland'),\n",
            "            (                 'Western Europe',     'Iceland'),\n",
            "            (                 'Western Europe',      'Norway'),\n",
            "            (                 'Western Europe',     'Finland'),\n",
            "            (                  'North America',      'Canada'),\n",
            "            (                 'Western Europe', 'Netherlands'),\n",
            "            (      'Australia and New Zealand', 'New Zealand'),\n",
            "            (      'Australia and New Zealand',   'Australia'),\n",
            "            (                 'Western Europe',      'Sweden'),\n",
            "            ...\n",
            "            (             'Sub-Saharan Africa',  'Madagascar'),\n",
            "            (             'Sub-Saharan Africa',    'Tanzania'),\n",
            "            (             'Sub-Saharan Africa',     'Liberia'),\n",
            "            (             'Sub-Saharan Africa',      'Guinea'),\n",
            "            (             'Sub-Saharan Africa',      'Rwanda'),\n",
            "            (             'Sub-Saharan Africa',       'Benin'),\n",
            "            (                  'Southern Asia', 'Afghanistan'),\n",
            "            (             'Sub-Saharan Africa',        'Togo'),\n",
            "            ('Middle East and Northern Africa',       'Syria'),\n",
            "            (             'Sub-Saharan Africa',     'Burundi')],\n",
            "           names=['Region', 'Country'], length=157)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "rSUqeS4XYb35",
        "outputId": "6433a559-facb-4a16-89e2-af6fd95eba81"
      },
      "source": [
        "data15_mod.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Happiness Rank  Happiness Score  Standard Error  \\\n",
              "Region         Country                                                        \n",
              "Western Europe Switzerland               1            7.587         0.03411   \n",
              "               Iceland                   2            7.561         0.04884   \n",
              "               Denmark                   3            7.527         0.03328   \n",
              "               Norway                    4            7.522         0.03880   \n",
              "North America  Canada                    5            7.427         0.03553   \n",
              "\n",
              "                            Economy (GDP per Capita)   Family  \\\n",
              "Region         Country                                          \n",
              "Western Europe Switzerland                   1.39651  1.34951   \n",
              "               Iceland                       1.30232  1.40223   \n",
              "               Denmark                       1.32548  1.36058   \n",
              "               Norway                        1.45900  1.33095   \n",
              "North America  Canada                        1.32629  1.32261   \n",
              "\n",
              "                            Health (Life Expectancy)  Freedom  \\\n",
              "Region         Country                                          \n",
              "Western Europe Switzerland                   0.94143  0.66557   \n",
              "               Iceland                       0.94784  0.62877   \n",
              "               Denmark                       0.87464  0.64938   \n",
              "               Norway                        0.88521  0.66973   \n",
              "North America  Canada                        0.90563  0.63297   \n",
              "\n",
              "                            Trust (Government Corruption)  Generosity  \\\n",
              "Region         Country                                                  \n",
              "Western Europe Switzerland                        0.41978     0.29678   \n",
              "               Iceland                            0.14145     0.43630   \n",
              "               Denmark                            0.48357     0.34139   \n",
              "               Norway                             0.36503     0.34699   \n",
              "North America  Canada                             0.32957     0.45811   \n",
              "\n",
              "                            Dystopia Residual  \n",
              "Region         Country                         \n",
              "Western Europe Switzerland            2.51738  \n",
              "               Iceland                2.70201  \n",
              "               Denmark                2.49204  \n",
              "               Norway                 2.46531  \n",
              "North America  Canada                 2.45176  "
            ],
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              "      <th></th>\n",
              "      <th>Happiness Rank</th>\n",
              "      <th>Happiness Score</th>\n",
              "      <th>Standard Error</th>\n",
              "      <th>Economy (GDP per Capita)</th>\n",
              "      <th>Family</th>\n",
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              "      <th>Freedom</th>\n",
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              "      <th>Generosity</th>\n",
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              "      <th>Country</th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Western Europe</th>\n",
              "      <th>Switzerland</th>\n",
              "      <td>1</td>\n",
              "      <td>7.587</td>\n",
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              "    </tr>\n",
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              "      <th>Iceland</th>\n",
              "      <td>2</td>\n",
              "      <td>7.561</td>\n",
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              "      <td>0.94784</td>\n",
              "      <td>0.62877</td>\n",
              "      <td>0.14145</td>\n",
              "      <td>0.43630</td>\n",
              "      <td>2.70201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Denmark</th>\n",
              "      <td>3</td>\n",
              "      <td>7.527</td>\n",
              "      <td>0.03328</td>\n",
              "      <td>1.32548</td>\n",
              "      <td>1.36058</td>\n",
              "      <td>0.87464</td>\n",
              "      <td>0.64938</td>\n",
              "      <td>0.48357</td>\n",
              "      <td>0.34139</td>\n",
              "      <td>2.49204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>4</td>\n",
              "      <td>7.522</td>\n",
              "      <td>0.03880</td>\n",
              "      <td>1.45900</td>\n",
              "      <td>1.33095</td>\n",
              "      <td>0.88521</td>\n",
              "      <td>0.66973</td>\n",
              "      <td>0.36503</td>\n",
              "      <td>0.34699</td>\n",
              "      <td>2.46531</td>\n",
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              "    <tr>\n",
              "      <th>North America</th>\n",
              "      <th>Canada</th>\n",
              "      <td>5</td>\n",
              "      <td>7.427</td>\n",
              "      <td>0.03553</td>\n",
              "      <td>1.32629</td>\n",
              "      <td>1.32261</td>\n",
              "      <td>0.90563</td>\n",
              "      <td>0.63297</td>\n",
              "      <td>0.32957</td>\n",
              "      <td>0.45811</td>\n",
              "      <td>2.45176</td>\n",
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              "      display:flex;\n",
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              "      border-radius: 50%;\n",
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              "      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-54428fe1-693f-4fae-9c6f-20fdc2f7ba47 button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data15_mod",
              "summary": "{\n  \"name\": \"data15_mod\",\n  \"rows\": 158,\n  \"fields\": [\n    {\n      \"column\": \"Happiness Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45,\n        \"min\": 1,\n        \"max\": 158,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          128,\n          46,\n          135\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Happiness Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1450101349520665,\n        \"min\": 2.839,\n        \"max\": 7.587,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          4.332,\n          5.987,\n          4.194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Standard Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.017146178556969302,\n        \"min\": 0.01848,\n        \"max\": 0.13693,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          0.06607,\n          0.04802,\n          0.06698\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economy (GDP per Capita)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40312077853791006,\n        \"min\": 0.0,\n        \"max\": 1.69042,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          0.27108,\n          1.27074,\n          0.8818\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27236908600791543,\n        \"min\": 0.0,\n        \"max\": 1.40223,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          0.70905,\n          1.25712,\n          0.747\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Health (Life Expectancy)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24707776630217204,\n        \"min\": 0.0,\n        \"max\": 1.02525,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          0.04776,\n          0.99111,\n          0.61712\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Freedom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15069278393767796,\n        \"min\": 0.0,\n        \"max\": 0.66973,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          0.44017,\n          0.49615,\n          0.17288\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust (Government Corruption)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12003407357455917,\n        \"min\": 0.0,\n        \"max\": 0.55191,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          0.12474,\n          0.07857,\n          0.06324\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Generosity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12668493402020542,\n        \"min\": 0.0,\n        \"max\": 0.79588,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          0.79588,\n          0.10705,\n          0.11291\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dystopia Residual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5535497923037984,\n        \"min\": 0.32858,\n        \"max\": 3.60214,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          1.41805,\n          1.68435,\n          1.59927\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "ynBUyrGrYbvx",
        "outputId": "a3edde4d-69cd-4cc3-bf41-a145a54c6504"
      },
      "source": [
        "data16_mod.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Happiness Rank  Happiness Score  \\\n",
              "Region         Country                                        \n",
              "Western Europe Denmark                   1            7.526   \n",
              "               Switzerland               2            7.509   \n",
              "               Iceland                   3            7.501   \n",
              "               Norway                    4            7.498   \n",
              "               Finland                   5            7.413   \n",
              "\n",
              "                            Lower Confidence Interval  \\\n",
              "Region         Country                                  \n",
              "Western Europe Denmark                          7.460   \n",
              "               Switzerland                      7.428   \n",
              "               Iceland                          7.333   \n",
              "               Norway                           7.421   \n",
              "               Finland                          7.351   \n",
              "\n",
              "                            Upper Confidence Interval  \\\n",
              "Region         Country                                  \n",
              "Western Europe Denmark                          7.592   \n",
              "               Switzerland                      7.590   \n",
              "               Iceland                          7.669   \n",
              "               Norway                           7.575   \n",
              "               Finland                          7.475   \n",
              "\n",
              "                            Economy (GDP per Capita)   Family  \\\n",
              "Region         Country                                          \n",
              "Western Europe Denmark                       1.44178  1.16374   \n",
              "               Switzerland                   1.52733  1.14524   \n",
              "               Iceland                       1.42666  1.18326   \n",
              "               Norway                        1.57744  1.12690   \n",
              "               Finland                       1.40598  1.13464   \n",
              "\n",
              "                            Health (Life Expectancy)  Freedom  \\\n",
              "Region         Country                                          \n",
              "Western Europe Denmark                       0.79504  0.57941   \n",
              "               Switzerland                   0.86303  0.58557   \n",
              "               Iceland                       0.86733  0.56624   \n",
              "               Norway                        0.79579  0.59609   \n",
              "               Finland                       0.81091  0.57104   \n",
              "\n",
              "                            Trust (Government Corruption)  Generosity  \\\n",
              "Region         Country                                                  \n",
              "Western Europe Denmark                            0.44453     0.36171   \n",
              "               Switzerland                        0.41203     0.28083   \n",
              "               Iceland                            0.14975     0.47678   \n",
              "               Norway                             0.35776     0.37895   \n",
              "               Finland                            0.41004     0.25492   \n",
              "\n",
              "                            Dystopia Residual  \n",
              "Region         Country                         \n",
              "Western Europe Denmark                2.73939  \n",
              "               Switzerland            2.69463  \n",
              "               Iceland                2.83137  \n",
              "               Norway                 2.66465  \n",
              "               Finland                2.82596  "
            ],
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              "      <th>Happiness Rank</th>\n",
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              "      <th>Economy (GDP per Capita)</th>\n",
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              "    <tr>\n",
              "      <th>Region</th>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th rowspan=\"5\" valign=\"top\">Western Europe</th>\n",
              "      <th>Denmark</th>\n",
              "      <td>1</td>\n",
              "      <td>7.526</td>\n",
              "      <td>7.460</td>\n",
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              "      <td>2.73939</td>\n",
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              "      <th>Switzerland</th>\n",
              "      <td>2</td>\n",
              "      <td>7.509</td>\n",
              "      <td>7.428</td>\n",
              "      <td>7.590</td>\n",
              "      <td>1.52733</td>\n",
              "      <td>1.14524</td>\n",
              "      <td>0.86303</td>\n",
              "      <td>0.58557</td>\n",
              "      <td>0.41203</td>\n",
              "      <td>0.28083</td>\n",
              "      <td>2.69463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iceland</th>\n",
              "      <td>3</td>\n",
              "      <td>7.501</td>\n",
              "      <td>7.333</td>\n",
              "      <td>7.669</td>\n",
              "      <td>1.42666</td>\n",
              "      <td>1.18326</td>\n",
              "      <td>0.86733</td>\n",
              "      <td>0.56624</td>\n",
              "      <td>0.14975</td>\n",
              "      <td>0.47678</td>\n",
              "      <td>2.83137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>4</td>\n",
              "      <td>7.498</td>\n",
              "      <td>7.421</td>\n",
              "      <td>7.575</td>\n",
              "      <td>1.57744</td>\n",
              "      <td>1.12690</td>\n",
              "      <td>0.79579</td>\n",
              "      <td>0.59609</td>\n",
              "      <td>0.35776</td>\n",
              "      <td>0.37895</td>\n",
              "      <td>2.66465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Finland</th>\n",
              "      <td>5</td>\n",
              "      <td>7.413</td>\n",
              "      <td>7.351</td>\n",
              "      <td>7.475</td>\n",
              "      <td>1.40598</td>\n",
              "      <td>1.13464</td>\n",
              "      <td>0.81091</td>\n",
              "      <td>0.57104</td>\n",
              "      <td>0.41004</td>\n",
              "      <td>0.25492</td>\n",
              "      <td>2.82596</td>\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-38caa537-2cf4-4cdf-8ec5-3706a91c8acc 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": "data16_mod",
              "summary": "{\n  \"name\": \"data16_mod\",\n  \"rows\": 157,\n  \"fields\": [\n    {\n      \"column\": \"Happiness Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45,\n        \"min\": 1,\n        \"max\": 157,\n        \"num_unique_values\": 154,\n        \"samples\": [\n          16,\n          97,\n          156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Happiness Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1416735176005712,\n        \"min\": 2.905,\n        \"max\": 7.526,\n        \"num_unique_values\": 154,\n        \"samples\": [\n          6.994,\n          5.057,\n          3.069\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lower Confidence Interval\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1480426137782485,\n        \"min\": 2.732,\n        \"max\": 7.46,\n        \"num_unique_values\": 154,\n        \"samples\": [\n          6.93,\n          5.017,\n          2.936\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Upper Confidence Interval\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1364932228133044,\n        \"min\": 3.078,\n        \"max\": 7.669,\n        \"num_unique_values\": 154,\n        \"samples\": [\n          7.058,\n          5.18,\n          3.202\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economy (GDP per Capita)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4125954051030412,\n        \"min\": 0.0,\n        \"max\": 1.82427,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          0.77109,\n          0.8737,\n          1.15851\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26670574619957355,\n        \"min\": 0.0,\n        \"max\": 1.18326,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          0.47799,\n          0.80975,\n          0.72368\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Health (Life Expectancy)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2293491914878582,\n        \"min\": 0.0,\n        \"max\": 0.95277,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          0.33108,\n          0.58295,\n          0.73561\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Freedom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1455067674417439,\n        \"min\": 0.0,\n        \"max\": 0.60848,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          0.37938,\n          0.37269,\n          0.28098\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust (Government Corruption)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1110379099181551,\n        \"min\": 0.0,\n        \"max\": 0.50521,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          0.36794,\n          0.07396,\n          0.02745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Generosity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13375569129812104,\n        \"min\": 0.0,\n        \"max\": 0.81971,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          0.12077,\n          0.08877,\n          0.06244\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dystopia Residual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5422199984864142,\n        \"min\": 0.81789,\n        \"max\": 3.83772,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          2.10681,\n          3.22134,\n          1.45332\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHVUVCFGk6Lj"
      },
      "source": [
        "## 2- Topshiriq\n",
        "## **Vazifa**: Yuqorida ajratib olingan ma'lumotlar to'plamidan `Western Europe` hududiga kiruvchi mamlakatlarning har ikkala yillar uchun ma'lumotlarini ajratib oling.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEAuThfSk5sU"
      },
      "source": [
        "# Javobni shu yerga yozing (we_2015, we_2016 o'zgaruvchilaridan foydalaning)\n",
        "we_2015 = data15_mod.loc[\"Western Europe\"]\n",
        "we_2016 = data16_mod.loc[\"Western Europe\"]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "we_2015"
      ],
      "metadata": {
        "id": "YdAVVNyESVIM",
        "outputId": "4746e5c1-3517-4867-be9d-8c0cead565d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Happiness Rank  Happiness Score  Standard Error  \\\n",
              "Country                                                           \n",
              "Switzerland                  1            7.587         0.03411   \n",
              "Iceland                      2            7.561         0.04884   \n",
              "Denmark                      3            7.527         0.03328   \n",
              "Norway                       4            7.522         0.03880   \n",
              "Finland                      6            7.406         0.03140   \n",
              "Netherlands                  7            7.378         0.02799   \n",
              "Sweden                       8            7.364         0.03157   \n",
              "Austria                     13            7.200         0.03751   \n",
              "Luxembourg                  17            6.946         0.03499   \n",
              "Ireland                     18            6.940         0.03676   \n",
              "Belgium                     19            6.937         0.03595   \n",
              "United Kingdom              21            6.867         0.01866   \n",
              "Germany                     26            6.750         0.01848   \n",
              "France                      29            6.575         0.03512   \n",
              "Spain                       36            6.329         0.03468   \n",
              "Malta                       37            6.302         0.04206   \n",
              "Italy                       50            5.948         0.03914   \n",
              "North Cyprus                66            5.695         0.05635   \n",
              "Cyprus                      67            5.689         0.05580   \n",
              "Portugal                    88            5.102         0.04802   \n",
              "Greece                     102            4.857         0.05062   \n",
              "\n",
              "                Economy (GDP per Capita)   Family  Health (Life Expectancy)  \\\n",
              "Country                                                                       \n",
              "Switzerland                      1.39651  1.34951                   0.94143   \n",
              "Iceland                          1.30232  1.40223                   0.94784   \n",
              "Denmark                          1.32548  1.36058                   0.87464   \n",
              "Norway                           1.45900  1.33095                   0.88521   \n",
              "Finland                          1.29025  1.31826                   0.88911   \n",
              "Netherlands                      1.32944  1.28017                   0.89284   \n",
              "Sweden                           1.33171  1.28907                   0.91087   \n",
              "Austria                          1.33723  1.29704                   0.89042   \n",
              "Luxembourg                       1.56391  1.21963                   0.91894   \n",
              "Ireland                          1.33596  1.36948                   0.89533   \n",
              "Belgium                          1.30782  1.28566                   0.89667   \n",
              "United Kingdom                   1.26637  1.28548                   0.90943   \n",
              "Germany                          1.32792  1.29937                   0.89186   \n",
              "France                           1.27778  1.26038                   0.94579   \n",
              "Spain                            1.23011  1.31379                   0.95562   \n",
              "Malta                            1.20740  1.30203                   0.88721   \n",
              "Italy                            1.25114  1.19777                   0.95446   \n",
              "North Cyprus                     1.20806  1.07008                   0.92356   \n",
              "Cyprus                           1.20813  0.89318                   0.92356   \n",
              "Portugal                         1.15991  1.13935                   0.87519   \n",
              "Greece                           1.15406  0.92933                   0.88213   \n",
              "\n",
              "                Freedom  Trust (Government Corruption)  Generosity  \\\n",
              "Country                                                              \n",
              "Switzerland     0.66557                        0.41978     0.29678   \n",
              "Iceland         0.62877                        0.14145     0.43630   \n",
              "Denmark         0.64938                        0.48357     0.34139   \n",
              "Norway          0.66973                        0.36503     0.34699   \n",
              "Finland         0.64169                        0.41372     0.23351   \n",
              "Netherlands     0.61576                        0.31814     0.47610   \n",
              "Sweden          0.65980                        0.43844     0.36262   \n",
              "Austria         0.62433                        0.18676     0.33088   \n",
              "Luxembourg      0.61583                        0.37798     0.28034   \n",
              "Ireland         0.61777                        0.28703     0.45901   \n",
              "Belgium         0.58450                        0.22540     0.22250   \n",
              "United Kingdom  0.59625                        0.32067     0.51912   \n",
              "Germany         0.61477                        0.21843     0.28214   \n",
              "France          0.55011                        0.20646     0.12332   \n",
              "Spain           0.45951                        0.06398     0.18227   \n",
              "Malta           0.60365                        0.13586     0.51752   \n",
              "Italy           0.26236                        0.02901     0.22823   \n",
              "North Cyprus    0.49027                        0.14280     0.26169   \n",
              "Cyprus          0.40672                        0.06146     0.30638   \n",
              "Portugal        0.51469                        0.01078     0.13719   \n",
              "Greece          0.07699                        0.01397     0.00000   \n",
              "\n",
              "                Dystopia Residual  \n",
              "Country                            \n",
              "Switzerland               2.51738  \n",
              "Iceland                   2.70201  \n",
              "Denmark                   2.49204  \n",
              "Norway                    2.46531  \n",
              "Finland                   2.61955  \n",
              "Netherlands               2.46570  \n",
              "Sweden                    2.37119  \n",
              "Austria                   2.53320  \n",
              "Luxembourg                1.96961  \n",
              "Ireland                   1.97570  \n",
              "Belgium                   2.41484  \n",
              "United Kingdom            1.96994  \n",
              "Germany                   2.11569  \n",
              "France                    2.21126  \n",
              "Spain                     2.12367  \n",
              "Malta                     1.64880  \n",
              "Italy                     2.02518  \n",
              "North Cyprus              1.59888  \n",
              "Cyprus                    1.88931  \n",
              "Portugal                  1.26462  \n",
              "Greece                    1.80101  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8896c32c-3396-4dff-9b3f-614d2ca7fc43\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Happiness Rank</th>\n",
              "      <th>Happiness Score</th>\n",
              "      <th>Standard Error</th>\n",
              "      <th>Economy (GDP per Capita)</th>\n",
              "      <th>Family</th>\n",
              "      <th>Health (Life Expectancy)</th>\n",
              "      <th>Freedom</th>\n",
              "      <th>Trust (Government Corruption)</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Dystopia Residual</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Switzerland</th>\n",
              "      <td>1</td>\n",
              "      <td>7.587</td>\n",
              "      <td>0.03411</td>\n",
              "      <td>1.39651</td>\n",
              "      <td>1.34951</td>\n",
              "      <td>0.94143</td>\n",
              "      <td>0.66557</td>\n",
              "      <td>0.41978</td>\n",
              "      <td>0.29678</td>\n",
              "      <td>2.51738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iceland</th>\n",
              "      <td>2</td>\n",
              "      <td>7.561</td>\n",
              "      <td>0.04884</td>\n",
              "      <td>1.30232</td>\n",
              "      <td>1.40223</td>\n",
              "      <td>0.94784</td>\n",
              "      <td>0.62877</td>\n",
              "      <td>0.14145</td>\n",
              "      <td>0.43630</td>\n",
              "      <td>2.70201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Denmark</th>\n",
              "      <td>3</td>\n",
              "      <td>7.527</td>\n",
              "      <td>0.03328</td>\n",
              "      <td>1.32548</td>\n",
              "      <td>1.36058</td>\n",
              "      <td>0.87464</td>\n",
              "      <td>0.64938</td>\n",
              "      <td>0.48357</td>\n",
              "      <td>0.34139</td>\n",
              "      <td>2.49204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>4</td>\n",
              "      <td>7.522</td>\n",
              "      <td>0.03880</td>\n",
              "      <td>1.45900</td>\n",
              "      <td>1.33095</td>\n",
              "      <td>0.88521</td>\n",
              "      <td>0.66973</td>\n",
              "      <td>0.36503</td>\n",
              "      <td>0.34699</td>\n",
              "      <td>2.46531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Finland</th>\n",
              "      <td>6</td>\n",
              "      <td>7.406</td>\n",
              "      <td>0.03140</td>\n",
              "      <td>1.29025</td>\n",
              "      <td>1.31826</td>\n",
              "      <td>0.88911</td>\n",
              "      <td>0.64169</td>\n",
              "      <td>0.41372</td>\n",
              "      <td>0.23351</td>\n",
              "      <td>2.61955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Netherlands</th>\n",
              "      <td>7</td>\n",
              "      <td>7.378</td>\n",
              "      <td>0.02799</td>\n",
              "      <td>1.32944</td>\n",
              "      <td>1.28017</td>\n",
              "      <td>0.89284</td>\n",
              "      <td>0.61576</td>\n",
              "      <td>0.31814</td>\n",
              "      <td>0.47610</td>\n",
              "      <td>2.46570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sweden</th>\n",
              "      <td>8</td>\n",
              "      <td>7.364</td>\n",
              "      <td>0.03157</td>\n",
              "      <td>1.33171</td>\n",
              "      <td>1.28907</td>\n",
              "      <td>0.91087</td>\n",
              "      <td>0.65980</td>\n",
              "      <td>0.43844</td>\n",
              "      <td>0.36262</td>\n",
              "      <td>2.37119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austria</th>\n",
              "      <td>13</td>\n",
              "      <td>7.200</td>\n",
              "      <td>0.03751</td>\n",
              "      <td>1.33723</td>\n",
              "      <td>1.29704</td>\n",
              "      <td>0.89042</td>\n",
              "      <td>0.62433</td>\n",
              "      <td>0.18676</td>\n",
              "      <td>0.33088</td>\n",
              "      <td>2.53320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Luxembourg</th>\n",
              "      <td>17</td>\n",
              "      <td>6.946</td>\n",
              "      <td>0.03499</td>\n",
              "      <td>1.56391</td>\n",
              "      <td>1.21963</td>\n",
              "      <td>0.91894</td>\n",
              "      <td>0.61583</td>\n",
              "      <td>0.37798</td>\n",
              "      <td>0.28034</td>\n",
              "      <td>1.96961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ireland</th>\n",
              "      <td>18</td>\n",
              "      <td>6.940</td>\n",
              "      <td>0.03676</td>\n",
              "      <td>1.33596</td>\n",
              "      <td>1.36948</td>\n",
              "      <td>0.89533</td>\n",
              "      <td>0.61777</td>\n",
              "      <td>0.28703</td>\n",
              "      <td>0.45901</td>\n",
              "      <td>1.97570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Belgium</th>\n",
              "      <td>19</td>\n",
              "      <td>6.937</td>\n",
              "      <td>0.03595</td>\n",
              "      <td>1.30782</td>\n",
              "      <td>1.28566</td>\n",
              "      <td>0.89667</td>\n",
              "      <td>0.58450</td>\n",
              "      <td>0.22540</td>\n",
              "      <td>0.22250</td>\n",
              "      <td>2.41484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United Kingdom</th>\n",
              "      <td>21</td>\n",
              "      <td>6.867</td>\n",
              "      <td>0.01866</td>\n",
              "      <td>1.26637</td>\n",
              "      <td>1.28548</td>\n",
              "      <td>0.90943</td>\n",
              "      <td>0.59625</td>\n",
              "      <td>0.32067</td>\n",
              "      <td>0.51912</td>\n",
              "      <td>1.96994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>26</td>\n",
              "      <td>6.750</td>\n",
              "      <td>0.01848</td>\n",
              "      <td>1.32792</td>\n",
              "      <td>1.29937</td>\n",
              "      <td>0.89186</td>\n",
              "      <td>0.61477</td>\n",
              "      <td>0.21843</td>\n",
              "      <td>0.28214</td>\n",
              "      <td>2.11569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>29</td>\n",
              "      <td>6.575</td>\n",
              "      <td>0.03512</td>\n",
              "      <td>1.27778</td>\n",
              "      <td>1.26038</td>\n",
              "      <td>0.94579</td>\n",
              "      <td>0.55011</td>\n",
              "      <td>0.20646</td>\n",
              "      <td>0.12332</td>\n",
              "      <td>2.21126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spain</th>\n",
              "      <td>36</td>\n",
              "      <td>6.329</td>\n",
              "      <td>0.03468</td>\n",
              "      <td>1.23011</td>\n",
              "      <td>1.31379</td>\n",
              "      <td>0.95562</td>\n",
              "      <td>0.45951</td>\n",
              "      <td>0.06398</td>\n",
              "      <td>0.18227</td>\n",
              "      <td>2.12367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Malta</th>\n",
              "      <td>37</td>\n",
              "      <td>6.302</td>\n",
              "      <td>0.04206</td>\n",
              "      <td>1.20740</td>\n",
              "      <td>1.30203</td>\n",
              "      <td>0.88721</td>\n",
              "      <td>0.60365</td>\n",
              "      <td>0.13586</td>\n",
              "      <td>0.51752</td>\n",
              "      <td>1.64880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>50</td>\n",
              "      <td>5.948</td>\n",
              "      <td>0.03914</td>\n",
              "      <td>1.25114</td>\n",
              "      <td>1.19777</td>\n",
              "      <td>0.95446</td>\n",
              "      <td>0.26236</td>\n",
              "      <td>0.02901</td>\n",
              "      <td>0.22823</td>\n",
              "      <td>2.02518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>North Cyprus</th>\n",
              "      <td>66</td>\n",
              "      <td>5.695</td>\n",
              "      <td>0.05635</td>\n",
              "      <td>1.20806</td>\n",
              "      <td>1.07008</td>\n",
              "      <td>0.92356</td>\n",
              "      <td>0.49027</td>\n",
              "      <td>0.14280</td>\n",
              "      <td>0.26169</td>\n",
              "      <td>1.59888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cyprus</th>\n",
              "      <td>67</td>\n",
              "      <td>5.689</td>\n",
              "      <td>0.05580</td>\n",
              "      <td>1.20813</td>\n",
              "      <td>0.89318</td>\n",
              "      <td>0.92356</td>\n",
              "      <td>0.40672</td>\n",
              "      <td>0.06146</td>\n",
              "      <td>0.30638</td>\n",
              "      <td>1.88931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portugal</th>\n",
              "      <td>88</td>\n",
              "      <td>5.102</td>\n",
              "      <td>0.04802</td>\n",
              "      <td>1.15991</td>\n",
              "      <td>1.13935</td>\n",
              "      <td>0.87519</td>\n",
              "      <td>0.51469</td>\n",
              "      <td>0.01078</td>\n",
              "      <td>0.13719</td>\n",
              "      <td>1.26462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greece</th>\n",
              "      <td>102</td>\n",
              "      <td>4.857</td>\n",
              "      <td>0.05062</td>\n",
              "      <td>1.15406</td>\n",
              "      <td>0.92933</td>\n",
              "      <td>0.88213</td>\n",
              "      <td>0.07699</td>\n",
              "      <td>0.01397</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.80101</td>\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('we_2015')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "we_2015",
              "summary": "{\n  \"name\": \"we_2015\",\n  \"rows\": 21,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"Switzerland\",\n          \"North Cyprus\",\n          \"Malta\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Happiness Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 1,\n        \"max\": 102,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          1,\n          66,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Happiness Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8245818016540551,\n        \"min\": 4.857,\n        \"max\": 7.587,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          7.587,\n          5.695,\n          6.302\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Standard Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010144321869424107,\n        \"min\": 0.01848,\n        \"max\": 0.05635,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.03411,\n          0.05635,\n          0.04206\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economy (GDP per Capita)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09634110527554832,\n        \"min\": 1.15406,\n        \"max\": 1.56391,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          1.39651,\n          1.20806,\n          1.2074\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13519525204751268,\n        \"min\": 0.89318,\n        \"max\": 1.40223,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          1.34951,\n          1.07008,\n          1.30203\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Health (Life Expectancy)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026833696096335236,\n        \"min\": 0.87464,\n        \"max\": 0.95562,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.94143,\n          0.92356,\n          0.88721\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Freedom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14721099862021828,\n        \"min\": 0.07699,\n        \"max\": 0.66973,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.66557,\n          0.49027,\n          0.60365\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust (Government Corruption)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15080130878552936,\n        \"min\": 0.01078,\n        \"max\": 0.48357,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.41978,\n          0.1428,\n          0.13586\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Generosity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13384503480838258,\n        \"min\": 0.0,\n        \"max\": 0.51912,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.29678,\n          0.26169,\n          0.51752\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dystopia Residual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3799505350386948,\n        \"min\": 1.26462,\n        \"max\": 2.70201,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          2.51738,\n          1.59888,\n          1.6488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "we_2016"
      ],
      "metadata": {
        "id": "0Mlp4jqdSXPH",
        "outputId": "63882b33-5962-45d6-8d65-38efeeb829ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Happiness Rank  Happiness Score  Lower Confidence Interval  \\\n",
              "Country                                                                      \n",
              "Denmark                      1            7.526                      7.460   \n",
              "Switzerland                  2            7.509                      7.428   \n",
              "Iceland                      3            7.501                      7.333   \n",
              "Norway                       4            7.498                      7.421   \n",
              "Finland                      5            7.413                      7.351   \n",
              "Netherlands                  7            7.339                      7.284   \n",
              "Sweden                      10            7.291                      7.227   \n",
              "Austria                     12            7.119                      7.045   \n",
              "Germany                     16            6.994                      6.930   \n",
              "Belgium                     18            6.929                      6.861   \n",
              "Ireland                     19            6.907                      6.836   \n",
              "Luxembourg                  20            6.871                      6.804   \n",
              "United Kingdom              23            6.725                      6.647   \n",
              "Malta                       30            6.488                      6.409   \n",
              "France                      32            6.478                      6.397   \n",
              "Spain                       37            6.361                      6.288   \n",
              "Italy                       50            5.977                      5.898   \n",
              "North Cyprus                62            5.771                      5.670   \n",
              "Cyprus                      69            5.546                      5.442   \n",
              "Portugal                    94            5.123                      5.030   \n",
              "Greece                      99            5.033                      4.935   \n",
              "\n",
              "                Upper Confidence Interval  Economy (GDP per Capita)   Family  \\\n",
              "Country                                                                        \n",
              "Denmark                             7.592                   1.44178  1.16374   \n",
              "Switzerland                         7.590                   1.52733  1.14524   \n",
              "Iceland                             7.669                   1.42666  1.18326   \n",
              "Norway                              7.575                   1.57744  1.12690   \n",
              "Finland                             7.475                   1.40598  1.13464   \n",
              "Netherlands                         7.394                   1.46468  1.02912   \n",
              "Sweden                              7.355                   1.45181  1.08764   \n",
              "Austria                             7.193                   1.45038  1.08383   \n",
              "Germany                             7.058                   1.44787  1.09774   \n",
              "Belgium                             6.997                   1.42539  1.05249   \n",
              "Ireland                             6.978                   1.48341  1.16157   \n",
              "Luxembourg                          6.938                   1.69752  1.03999   \n",
              "United Kingdom                      6.803                   1.40283  1.08672   \n",
              "Malta                               6.567                   1.30782  1.09879   \n",
              "France                              6.559                   1.39488  1.00508   \n",
              "Spain                               6.434                   1.34253  1.12945   \n",
              "Italy                               6.056                   1.35495  1.04167   \n",
              "North Cyprus                        5.872                   1.31141  0.81826   \n",
              "Cyprus                              5.650                   1.31857  0.70697   \n",
              "Portugal                            5.216                   1.27607  0.94367   \n",
              "Greece                              5.131                   1.24886  0.75473   \n",
              "\n",
              "                Health (Life Expectancy)  Freedom  \\\n",
              "Country                                             \n",
              "Denmark                          0.79504  0.57941   \n",
              "Switzerland                      0.86303  0.58557   \n",
              "Iceland                          0.86733  0.56624   \n",
              "Norway                           0.79579  0.59609   \n",
              "Finland                          0.81091  0.57104   \n",
              "Netherlands                      0.81231  0.55211   \n",
              "Sweden                           0.83121  0.58218   \n",
              "Austria                          0.80565  0.54355   \n",
              "Germany                          0.81487  0.53466   \n",
              "Belgium                          0.81959  0.51354   \n",
              "Ireland                          0.81455  0.54008   \n",
              "Luxembourg                       0.84542  0.54870   \n",
              "United Kingdom                   0.80991  0.50036   \n",
              "Malta                            0.80315  0.54994   \n",
              "France                           0.83795  0.46562   \n",
              "Spain                            0.87896  0.37545   \n",
              "Italy                            0.85102  0.18827   \n",
              "North Cyprus                     0.84142  0.43596   \n",
              "Cyprus                           0.84880  0.29507   \n",
              "Portugal                         0.79363  0.44727   \n",
              "Greece                           0.80029  0.05822   \n",
              "\n",
              "                Trust (Government Corruption)  Generosity  Dystopia Residual  \n",
              "Country                                                                       \n",
              "Denmark                               0.44453     0.36171            2.73939  \n",
              "Switzerland                           0.41203     0.28083            2.69463  \n",
              "Iceland                               0.14975     0.47678            2.83137  \n",
              "Norway                                0.35776     0.37895            2.66465  \n",
              "Finland                               0.41004     0.25492            2.82596  \n",
              "Netherlands                           0.29927     0.47416            2.70749  \n",
              "Sweden                                0.40867     0.38254            2.54734  \n",
              "Austria                               0.21348     0.32865            2.69343  \n",
              "Germany                               0.28551     0.30452            2.50931  \n",
              "Belgium                               0.26248     0.24240            2.61355  \n",
              "Ireland                               0.29754     0.44963            2.15988  \n",
              "Luxembourg                            0.35329     0.27571            2.11055  \n",
              "United Kingdom                        0.27399     0.50156            2.14999  \n",
              "Malta                                 0.17554     0.56237            1.99032  \n",
              "France                                0.17808     0.12160            2.47440  \n",
              "Spain                                 0.06137     0.17665            2.39663  \n",
              "Italy                                 0.02556     0.16684            2.34918  \n",
              "North Cyprus                          0.16578     0.26322            1.93447  \n",
              "Cyprus                                0.05228     0.27906            2.04497  \n",
              "Portugal                              0.01521     0.11691            1.53015  \n",
              "Greece                                0.04127     0.00000            2.12944  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Happiness Rank</th>\n",
              "      <th>Happiness Score</th>\n",
              "      <th>Lower Confidence Interval</th>\n",
              "      <th>Upper Confidence Interval</th>\n",
              "      <th>Economy (GDP per Capita)</th>\n",
              "      <th>Family</th>\n",
              "      <th>Health (Life Expectancy)</th>\n",
              "      <th>Freedom</th>\n",
              "      <th>Trust (Government Corruption)</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Dystopia Residual</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Denmark</th>\n",
              "      <td>1</td>\n",
              "      <td>7.526</td>\n",
              "      <td>7.460</td>\n",
              "      <td>7.592</td>\n",
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              "      <td>0.36171</td>\n",
              "      <td>2.73939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Switzerland</th>\n",
              "      <td>2</td>\n",
              "      <td>7.509</td>\n",
              "      <td>7.428</td>\n",
              "      <td>7.590</td>\n",
              "      <td>1.52733</td>\n",
              "      <td>1.14524</td>\n",
              "      <td>0.86303</td>\n",
              "      <td>0.58557</td>\n",
              "      <td>0.41203</td>\n",
              "      <td>0.28083</td>\n",
              "      <td>2.69463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iceland</th>\n",
              "      <td>3</td>\n",
              "      <td>7.501</td>\n",
              "      <td>7.333</td>\n",
              "      <td>7.669</td>\n",
              "      <td>1.42666</td>\n",
              "      <td>1.18326</td>\n",
              "      <td>0.86733</td>\n",
              "      <td>0.56624</td>\n",
              "      <td>0.14975</td>\n",
              "      <td>0.47678</td>\n",
              "      <td>2.83137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>4</td>\n",
              "      <td>7.498</td>\n",
              "      <td>7.421</td>\n",
              "      <td>7.575</td>\n",
              "      <td>1.57744</td>\n",
              "      <td>1.12690</td>\n",
              "      <td>0.79579</td>\n",
              "      <td>0.59609</td>\n",
              "      <td>0.35776</td>\n",
              "      <td>0.37895</td>\n",
              "      <td>2.66465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Finland</th>\n",
              "      <td>5</td>\n",
              "      <td>7.413</td>\n",
              "      <td>7.351</td>\n",
              "      <td>7.475</td>\n",
              "      <td>1.40598</td>\n",
              "      <td>1.13464</td>\n",
              "      <td>0.81091</td>\n",
              "      <td>0.57104</td>\n",
              "      <td>0.41004</td>\n",
              "      <td>0.25492</td>\n",
              "      <td>2.82596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Netherlands</th>\n",
              "      <td>7</td>\n",
              "      <td>7.339</td>\n",
              "      <td>7.284</td>\n",
              "      <td>7.394</td>\n",
              "      <td>1.46468</td>\n",
              "      <td>1.02912</td>\n",
              "      <td>0.81231</td>\n",
              "      <td>0.55211</td>\n",
              "      <td>0.29927</td>\n",
              "      <td>0.47416</td>\n",
              "      <td>2.70749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sweden</th>\n",
              "      <td>10</td>\n",
              "      <td>7.291</td>\n",
              "      <td>7.227</td>\n",
              "      <td>7.355</td>\n",
              "      <td>1.45181</td>\n",
              "      <td>1.08764</td>\n",
              "      <td>0.83121</td>\n",
              "      <td>0.58218</td>\n",
              "      <td>0.40867</td>\n",
              "      <td>0.38254</td>\n",
              "      <td>2.54734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austria</th>\n",
              "      <td>12</td>\n",
              "      <td>7.119</td>\n",
              "      <td>7.045</td>\n",
              "      <td>7.193</td>\n",
              "      <td>1.45038</td>\n",
              "      <td>1.08383</td>\n",
              "      <td>0.80565</td>\n",
              "      <td>0.54355</td>\n",
              "      <td>0.21348</td>\n",
              "      <td>0.32865</td>\n",
              "      <td>2.69343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>16</td>\n",
              "      <td>6.994</td>\n",
              "      <td>6.930</td>\n",
              "      <td>7.058</td>\n",
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              "      <td>0.30452</td>\n",
              "      <td>2.50931</td>\n",
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              "    <tr>\n",
              "      <th>Belgium</th>\n",
              "      <td>18</td>\n",
              "      <td>6.929</td>\n",
              "      <td>6.861</td>\n",
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              "      <td>0.24240</td>\n",
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              "      <th>Ireland</th>\n",
              "      <td>19</td>\n",
              "      <td>6.907</td>\n",
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              "      <td>6.978</td>\n",
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              "      <td>0.44963</td>\n",
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              "      <th>Luxembourg</th>\n",
              "      <td>20</td>\n",
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              "      <td>6.938</td>\n",
              "      <td>1.69752</td>\n",
              "      <td>1.03999</td>\n",
              "      <td>0.84542</td>\n",
              "      <td>0.54870</td>\n",
              "      <td>0.35329</td>\n",
              "      <td>0.27571</td>\n",
              "      <td>2.11055</td>\n",
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              "    <tr>\n",
              "      <th>United Kingdom</th>\n",
              "      <td>23</td>\n",
              "      <td>6.725</td>\n",
              "      <td>6.647</td>\n",
              "      <td>6.803</td>\n",
              "      <td>1.40283</td>\n",
              "      <td>1.08672</td>\n",
              "      <td>0.80991</td>\n",
              "      <td>0.50036</td>\n",
              "      <td>0.27399</td>\n",
              "      <td>0.50156</td>\n",
              "      <td>2.14999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Malta</th>\n",
              "      <td>30</td>\n",
              "      <td>6.488</td>\n",
              "      <td>6.409</td>\n",
              "      <td>6.567</td>\n",
              "      <td>1.30782</td>\n",
              "      <td>1.09879</td>\n",
              "      <td>0.80315</td>\n",
              "      <td>0.54994</td>\n",
              "      <td>0.17554</td>\n",
              "      <td>0.56237</td>\n",
              "      <td>1.99032</td>\n",
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              "      <th>France</th>\n",
              "      <td>32</td>\n",
              "      <td>6.478</td>\n",
              "      <td>6.397</td>\n",
              "      <td>6.559</td>\n",
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              "      <td>1.00508</td>\n",
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              "      <td>0.46562</td>\n",
              "      <td>0.17808</td>\n",
              "      <td>0.12160</td>\n",
              "      <td>2.47440</td>\n",
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              "      <th>Spain</th>\n",
              "      <td>37</td>\n",
              "      <td>6.361</td>\n",
              "      <td>6.288</td>\n",
              "      <td>6.434</td>\n",
              "      <td>1.34253</td>\n",
              "      <td>1.12945</td>\n",
              "      <td>0.87896</td>\n",
              "      <td>0.37545</td>\n",
              "      <td>0.06137</td>\n",
              "      <td>0.17665</td>\n",
              "      <td>2.39663</td>\n",
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              "      <th>Italy</th>\n",
              "      <td>50</td>\n",
              "      <td>5.977</td>\n",
              "      <td>5.898</td>\n",
              "      <td>6.056</td>\n",
              "      <td>1.35495</td>\n",
              "      <td>1.04167</td>\n",
              "      <td>0.85102</td>\n",
              "      <td>0.18827</td>\n",
              "      <td>0.02556</td>\n",
              "      <td>0.16684</td>\n",
              "      <td>2.34918</td>\n",
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              "      <th>North Cyprus</th>\n",
              "      <td>62</td>\n",
              "      <td>5.771</td>\n",
              "      <td>5.670</td>\n",
              "      <td>5.872</td>\n",
              "      <td>1.31141</td>\n",
              "      <td>0.81826</td>\n",
              "      <td>0.84142</td>\n",
              "      <td>0.43596</td>\n",
              "      <td>0.16578</td>\n",
              "      <td>0.26322</td>\n",
              "      <td>1.93447</td>\n",
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              "    <tr>\n",
              "      <th>Cyprus</th>\n",
              "      <td>69</td>\n",
              "      <td>5.546</td>\n",
              "      <td>5.442</td>\n",
              "      <td>5.650</td>\n",
              "      <td>1.31857</td>\n",
              "      <td>0.70697</td>\n",
              "      <td>0.84880</td>\n",
              "      <td>0.29507</td>\n",
              "      <td>0.05228</td>\n",
              "      <td>0.27906</td>\n",
              "      <td>2.04497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portugal</th>\n",
              "      <td>94</td>\n",
              "      <td>5.123</td>\n",
              "      <td>5.030</td>\n",
              "      <td>5.216</td>\n",
              "      <td>1.27607</td>\n",
              "      <td>0.94367</td>\n",
              "      <td>0.79363</td>\n",
              "      <td>0.44727</td>\n",
              "      <td>0.01521</td>\n",
              "      <td>0.11691</td>\n",
              "      <td>1.53015</td>\n",
              "    </tr>\n",
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              "      <th>Greece</th>\n",
              "      <td>99</td>\n",
              "      <td>5.033</td>\n",
              "      <td>4.935</td>\n",
              "      <td>5.131</td>\n",
              "      <td>1.24886</td>\n",
              "      <td>0.75473</td>\n",
              "      <td>0.80029</td>\n",
              "      <td>0.05822</td>\n",
              "      <td>0.04127</td>\n",
              "      <td>0.00000</td>\n",
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              "      --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-40fad858-1804-4abd-bbd4-2742ae0c38ea button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_92a68ec5-5959-4738-b0b7-2785a45764fe\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('we_2016')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_92a68ec5-5959-4738-b0b7-2785a45764fe button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('we_2016');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "we_2016",
              "summary": "{\n  \"name\": \"we_2016\",\n  \"rows\": 21,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"Denmark\",\n          \"North Cyprus\",\n          \"Spain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Happiness Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          1,\n          62,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Happiness Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.789190239000289,\n        \"min\": 5.033,\n        \"max\": 7.526,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          7.526,\n          5.771,\n          6.361\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lower Confidence Interval\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7951872465921829,\n        \"min\": 4.935,\n        \"max\": 7.46,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          7.46,\n          5.67,\n          6.288\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Upper Confidence Interval\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7838736444582063,\n        \"min\": 5.131,\n        \"max\": 7.669,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          7.592,\n          5.872,\n          6.434\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economy (GDP per Capita)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1045091547937992,\n        \"min\": 1.24886,\n        \"max\": 1.69752,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          1.44178,\n          1.31141,\n          1.34253\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1325649408744545,\n        \"min\": 0.70697,\n        \"max\": 1.18326,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          1.16374,\n          0.81826,\n          1.12945\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Health (Life Expectancy)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.025787487174246053,\n        \"min\": 0.79363,\n        \"max\": 0.87896,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.79504,\n          0.84142,\n          0.87896\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Freedom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14122447242396968,\n        \"min\": 0.05822,\n        \"max\": 0.59609,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.57941,\n          0.43596,\n          0.37545\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust (Government Corruption)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1397108859599505,\n        \"min\": 0.01521,\n        \"max\": 0.44453,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.44453,\n          0.16578,\n          0.06137\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Generosity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14266376756622712,\n        \"min\": 0.0,\n        \"max\": 0.56237,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.36171,\n          0.26322,\n          0.17665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dystopia Residual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34982959450961537,\n        \"min\": 1.53015,\n        \"max\": 2.83137,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          2.73939,\n          1.93447,\n          2.39663\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MGugVbn7EFOW"
      },
      "source": [
        "## 3-Topshiriq\n",
        "\n",
        "### Barcha ma'lumotlar to'plamini kuzatgan bo'lsangiz, ba'zilarining ustunlari o'zgarib kelmoqda. Lekin bu bizga qiziq emas. Biz asosiy maqsadni ushbu ma'lumotlardan faqat mamlakatlarning baxtlilik darajalarinigina ajratib olishga qaratamiz.\n",
        "\n",
        "### **Vazifa**: Barcha ma'lumotlar to'plamlaridan faqat mamalakatlar (`Country` yoki `Country or region`), ularning baxtlilik darajasini (`Happiness Rank`, `Happiness.Rank` yoki `Overall rank`) va baholarini (`Happiness Score`, `Happiness.Score` yoki `Score`)  ajratib oling."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndOABDOuEIZA"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "def extractor(dataframe):\n",
        "  \"\"\"Ma'lumotlar to'plamidan faqat bizga kerakli ustunlarga tegishli\n",
        "     ma'lumotlarni ajratib olguvchi funksiya\n",
        "  \"\"\"\n",
        "  columns = ['Country', 'Country or region', 'Happiness Rank', 'Happiness.Rank','Overall rank', 'Happiness Score','Happiness.Score', 'Score']\n",
        "  mask = [col for col in dataframe.columns if col in columns]\n",
        "  return dataframe[mask]\n",
        "\n",
        "dataframes = [data15, data16, data17, data18, data19]\n",
        "data15, data16, data17, data18, data19 = [extractor(dataframe) for dataframe in dataframes]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I6Zkf_CSEMA-"
      },
      "source": [
        "## Vazifa: Ma'lumotlarni ajratib olgan bo'lsangiz, unda e'tibor bering ba'zi yillardagi ma'lumotlarning ustunlari bir biridan farq qiladi. `Ana endi bularning barchasini bir xil formatga keltirib olishimiz kerak.` (`data_15, data_16` larning ustunlaridek formatiga keltirib oling: Ya'ni `Country`, `Happiness Rank` va `Happiness Score` ketma-ketligida)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4xsYxkjGwmZ"
      },
      "source": [
        "# Javobni shu yerga yozing\n",
        "columns = ['Country', 'Happiness Rank', 'Happiness Score']\n",
        "\n",
        "data17.columns = columns # data17 ning tartibi mos keladi\n",
        "\n",
        "data18 = data18[['Country or region', 'Overall rank', 'Score']] # data18 ning tartibini moslab olish\n",
        "data18.columns = columns\n",
        "\n",
        "data19 = data19[['Country or region', 'Overall rank', 'Score']] # data19 ning tartibini moslab olish\n",
        "data19.columns = columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbUGYuHQJmrm"
      },
      "source": [
        "## 4-Topshiriq\n",
        "\n",
        "## Navbatdagi mashqimiz irarxik ustunlar haqida bo'ladi.\n",
        "\n",
        "## **Vazifa**: Barcha ma'lumotlar to'plamini ustunlarini irarxik ko'rinishga keltirish kerak. Quyidagi rasmda `2015` yil uchun qanday ko'rinishda bo'lishi ko'rsatilgan. Qolganlarini mustaqil bajaring."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivjQfLxtK9UP"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsj6DjwpHo-t"
      },
      "source": [
        "# Javobni shu yerga yozing.\n",
        "# Yordam: Irarxik ustunlar haqidagi video darslikga murojat qiling\n",
        "# Fayllarni o‘qish va qayta ishlash\n",
        "years = [2015, 2016, 2017, 2018, 2019]\n",
        "dfs = []\n",
        "\n",
        "for year in years:\n",
        "    df = pd.read_csv(f\"/content/Practicum_Datasets/Happiness/{year}.csv\")\n",
        "    df = df[[\"Country\", \"Happiness Rank\", \"Happiness Score\"]]\n",
        "    df[\"Year\"] = year\n",
        "    dfs.append(df)\n",
        "\n",
        "# Barcha yillardagi ma'lumotlarni birlashtirish\n",
        "df_all = pd.concat(dfs)\n",
        "\n",
        "# Irarxik ko‘rinishni yaratish\n",
        "df_pivot = df_all.pivot(index=\"Country\", columns=\"Year\", values=[\"Happiness Rank\", \"Happiness Score\"])"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5zLDJVUM4rm"
      },
      "source": [
        "## 5-Topshiriq\n",
        "\n",
        "## Barcha ma'lumotlarni irarxik ustunlar ko'rinishiga keltirib olgan bo'lsangiz, unda navbatdagi mashqimizda ularni birlashtiramiz.\n",
        "\n",
        "## **Vazifa:** Gorizantal o'q ko'rinishida barcha ma'lumotlar to'plamlarini birlashtiring (`axis=1`). Va natijaviy dataframe ustunlariga `Years` va `Info` deb nom bering.\n",
        "\n",
        "## `Natijangiz quyidagi rasm ko'rinishida bo'lsin.`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrD3SsIlM7fh"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qmdXBsjMV_v"
      },
      "source": [
        "# Javobni shu yerga yozing.\n",
        "# Yordam: .concat() usulida foydalaning.\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}