{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In Search Of Happiness: What If Health?\n",
    "\n",
    "Let's continue our happiness challenge! Previously, we found out the happiest places on the planet. You can read about this and much more [here](https://nbviewer.jupyter.org/github/chupstee/data.sugar/blob/master/00002_world_happiness/world_happiness.map.ipynb).  \n",
    "And now we would like to know whether the level of happiness is associated with health.  \n",
    "We'll take [the same report from Kaggle](https://www.kaggle.com/unsdsn/world-happiness?select=2019.csv), which ranks 156 countries by their level of happiness on a 10-point scale.\n",
    "\n",
    "\n",
    "## The World Happiness Report\n",
    "\n",
    "Recall quoting Kaggle:\n",
    "\n",
    "The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.\n",
    "\n",
    "You can read more [here](https://www.kaggle.com/unsdsn/world-happiness).\n",
    "\n",
    "We are most interested in the following columns:\n",
    "\n",
    "- `Country or region` - country name\n",
    "- `Overall rank` - country's place in the rating\n",
    "- `Score` - happiness score\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "So, what is happiness? Or, better yet, are there any patterns in people's happiness?\n",
    "\n",
    "[The World Factbook](https://www.cia.gov/library/publications/the-world-factbook) by CIA provides information on the history, people and society, government, economy, energy, geography, communications, transportation, military, and transnational issues for 267 world entities.  \n",
    "What is interesting for us in this project is to compare the level of happiness of the country and such health indicators as:\n",
    "\n",
    "- `Obesity` - adult prevalence rate gives the percent of a country's population considered to be obese.\n",
    "- `Maternal mortality rate` (`MMR`) - the annual number of female deaths per 100,000 live births from any cause related to or aggravated by pregnancy or its management (excluding accidental or incidental causes).\n",
    "- `Infant mortality rate` - the number of deaths of infants under one year old in a given year per 1,000 live births in the same year. This rate is often used as an indicator of the level of health in a country.\n",
    "- `Total fertility rate` (`TFR`) - the average number of children that would be born per woman if all women lived to the end of their childbearing years and bore children according to a given fertility rate at each age. TFR is a more direct measure of the level of fertility than the crude birth rate, since it refers to births per woman.\n",
    "- `HIV/AIDS - adult prevalence rate` - an estimate of the percentage of adults (aged 15-49) living with HIV/AIDS. The adult prevalence rate is calculated by dividing the estimated number of adults living with HIV/AIDS at yearend by the total adult population at yearend.\n",
    "- `HIV/AIDS - deaths` - the number of adults and children who died of AIDS during a given calendar year.\n",
    "\n",
    "We will compare the happiness scores with the CIA rates for 2016, as this is the year when the data is presented in the most complete way.  \n",
    "\n",
    "In the search for an answer to the question ***what*** *is happiness?*, let's try to establish *the relationship between happiness and health indicators*.\n",
    "\n",
    "\n",
    "## Reading The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libs\n",
    "from glob import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Happiness Reports\n",
    "\n",
    "As we mentioned above, we'll read the happiness report, published in 2016.  \n",
    "We'll also rename the columns according to the snake_case format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['country', 'region', 'rank', 'score', 'whisker_low', 'whisker_high', 'gdp_per_capita', 'family',\n",
      "       'life_expectancy', 'freedom', 'trust_corruption', 'generosity', 'dystopia_residual'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
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       "<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>rank</th>\n",
       "      <th>score</th>\n",
       "      <th>whisker_low</th>\n",
       "      <th>whisker_high</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>family</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>freedom</th>\n",
       "      <th>trust_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>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.526</td>\n",
       "      <td>7.460</td>\n",
       "      <td>7.592</td>\n",
       "      <td>1.44178</td>\n",
       "      <td>1.16374</td>\n",
       "      <td>0.79504</td>\n",
       "      <td>0.57941</td>\n",
       "      <td>0.44453</td>\n",
       "      <td>0.36171</td>\n",
       "      <td>2.73939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\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>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",
       "      <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>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",
       "      <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>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",
       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country          region  rank  score  whisker_low  whisker_high  gdp_per_capita   family  life_expectancy  \\\n",
       "0      Denmark  Western Europe     1  7.526        7.460         7.592         1.44178  1.16374          0.79504   \n",
       "1  Switzerland  Western Europe     2  7.509        7.428         7.590         1.52733  1.14524          0.86303   \n",
       "2      Iceland  Western Europe     3  7.501        7.333         7.669         1.42666  1.18326          0.86733   \n",
       "3       Norway  Western Europe     4  7.498        7.421         7.575         1.57744  1.12690          0.79579   \n",
       "4      Finland  Western Europe     5  7.413        7.351         7.475         1.40598  1.13464          0.81091   \n",
       "\n",
       "   freedom  trust_corruption  generosity  dystopia_residual  \n",
       "0  0.57941           0.44453     0.36171            2.73939  \n",
       "1  0.58557           0.41203     0.28083            2.69463  \n",
       "2  0.56624           0.14975     0.47678            2.83137  \n",
       "3  0.59609           0.35776     0.37895            2.66465  \n",
       "4  0.57104           0.41004     0.25492            2.82596  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the width to display\n",
    "pd.set_option('display.width', 120)\n",
    "# Increase the number of rows to display\n",
    "pd.set_option('display.max_rows', 60) \n",
    "\n",
    "# Get the data\n",
    "happiness = pd.read_csv('data/happiness_2016.csv')\n",
    "\n",
    "# Column map to rename\n",
    "cols_dict = {'Country':'country',\n",
    "             'Country or region':'country',\n",
    "             'Region':'region',\n",
    "             'Happiness Rank':'rank',\n",
    "             'Happiness.Rank':'rank',\n",
    "             'Overall rank':'rank',\n",
    "             'Happiness Score':'score',\n",
    "             'Happiness.Score':'score',\n",
    "             'Score':'score',\n",
    "             'Economy (GDP per Capita)':'gdp_per_capita',\n",
    "             'Economy..GDP.per.Capita.':'gdp_per_capita',\n",
    "             'GDP per capita':'gdp_per_capita',\n",
    "             'Family':'family',\n",
    "             'Freedom':'freedom',\n",
    "             'Freedom to make life choices':'freedom',\n",
    "             'Generosity':'generosity',\n",
    "             'Health (Life Expectancy)':'life_expectancy',\n",
    "             'Health..Life.Expectancy.':'life_expectancy',\n",
    "             'Healthy life expectancy':'life_expectancy',\n",
    "             'Perceptions of corruption':'trust_corruption',\n",
    "             'Trust (Government Corruption)':'trust_corruption',\n",
    "             'Trust..Government.Corruption.':'trust_corruption',\n",
    "             'Social support':'social_support',\n",
    "             'Dystopia Residual':'dystopia_residual',\n",
    "             'Dystopia.Residual':'dystopia_residual',\n",
    "             'Standard Error':'standard_error',\n",
    "             'Upper Confidence Interval':'whisker_high',\n",
    "             'Whisker.high':'whisker_high',\n",
    "             'Lower Confidence Interval':'whisker_low',\n",
    "             'Whisker.low':'whisker_low'\n",
    "            }\n",
    "\n",
    "# Rename the columns\n",
    "happiness.rename(columns=cols_dict, inplace=True)\n",
    "\n",
    "print(happiness.columns) # check the new column names\n",
    "happiness.head() # check the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 157 entries, 0 to 156\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   country            157 non-null    object \n",
      " 1   region             157 non-null    object \n",
      " 2   rank               157 non-null    int64  \n",
      " 3   score              157 non-null    float64\n",
      " 4   whisker_low        157 non-null    float64\n",
      " 5   whisker_high       157 non-null    float64\n",
      " 6   gdp_per_capita     157 non-null    float64\n",
      " 7   family             157 non-null    float64\n",
      " 8   life_expectancy    157 non-null    float64\n",
      " 9   freedom            157 non-null    float64\n",
      " 10  trust_corruption   157 non-null    float64\n",
      " 11  generosity         157 non-null    float64\n",
      " 12  dystopia_residual  157 non-null    float64\n",
      "dtypes: float64(10), int64(1), object(2)\n",
      "memory usage: 16.1+ KB\n"
     ]
    }
   ],
   "source": [
    "happiness.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see 157 countries in the report of 2016. There are no missing values for the `country`, `rank`, `score` columns.\n",
    "\n",
    "Let's check for duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated: 0\n"
     ]
    }
   ],
   "source": [
    "# Duplicated\n",
    "print('Duplicated: {}'.format(happiness.duplicated(subset='country').sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's OK. Let's get the CIA data.\n",
    "\n",
    "### CIA Reports\n",
    "\n",
    "We have downloaded The World Factbook archive for different years and saved the data that was collected in 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialize: hiv_aids\n",
      "Merge hiv_aids_death: 110\n",
      "Merge infant_mortality: 226\n",
      "Merge mmr: 226\n",
      "Merge obesity: 227\n",
      "Merge tfr: 227\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 227 entries, 0 to 226\n",
      "Data columns (total 7 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   country_cia       227 non-null    object \n",
      " 1   hiv_aids          109 non-null    float64\n",
      " 2   hiv_aids_death    63 non-null     float64\n",
      " 3   infant_mortality  225 non-null    float64\n",
      " 4   mmr               184 non-null    float64\n",
      " 5   obesity           192 non-null    float64\n",
      " 6   tfr               224 non-null    float64\n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 14.2+ KB\n"
     ]
    },
    {
     "data": {
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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>country_cia</th>\n",
       "      <th>hiv_aids</th>\n",
       "      <th>hiv_aids_death</th>\n",
       "      <th>infant_mortality</th>\n",
       "      <th>mmr</th>\n",
       "      <th>obesity</th>\n",
       "      <th>tfr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Swaziland</td>\n",
       "      <td>27.2</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>50.4</td>\n",
       "      <td>389.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>47.6</td>\n",
       "      <td>487.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>21.9</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>129.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>18.9</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>28.3</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Namibia</td>\n",
       "      <td>13.8</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>36.4</td>\n",
       "      <td>265.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.9</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Japan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Monaco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Niue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>227 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      country_cia  hiv_aids  hiv_aids_death  infant_mortality    mmr  obesity   tfr\n",
       "0       Swaziland      27.2          3900.0              50.4  389.0     16.5  2.74\n",
       "1         Lesotho      25.0          9900.0              47.6  487.0     16.6  2.68\n",
       "2        Botswana      21.9          3900.0               8.6  129.0     18.9  2.30\n",
       "3    South Africa      18.9        110000.0              32.0  138.0     28.3  2.31\n",
       "4         Namibia      13.8          4300.0              36.4  265.0     17.2  3.36\n",
       "..            ...       ...             ...               ...    ...      ...   ...\n",
       "222     Singapore       NaN             NaN               2.4   10.0      6.1  0.82\n",
       "223       Iceland       NaN             NaN               2.1    3.0     21.9  2.01\n",
       "224         Japan       NaN             NaN               2.0    5.0      4.3  1.41\n",
       "225        Monaco       NaN             NaN               1.8    NaN      NaN  1.53\n",
       "226          Niue       NaN             NaN               NaN    NaN     50.0   NaN\n",
       "\n",
       "[227 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cia_files = glob('data/cia.health.*.201?.txt')\n",
    "cia = pd.DataFrame()\n",
    "\n",
    "for file in cia_files:\n",
    "    c = pd.read_csv(file,\n",
    "                    engine='python', sep=r'\\s{3,}', header=None, names=['country_cia', file.split('.')[2]],\n",
    "                    squeeze=False, skiprows=0, index_col=[0],\n",
    "                    thousands=',', dtype={file.split('.')[2]:'float64'}\n",
    "                   ) # read the file\n",
    "    if cia.size == 0:\n",
    "        cia = cia.append(c)\n",
    "        print('Initialize: {}'.format(file.split('.')[2], cia.shape[0])) # for the first file\n",
    "    else:\n",
    "        cia = cia.merge(c, on='country_cia', how='outer')\n",
    "        print('Merge {}: {}'.format(file.split('.')[2], cia.shape[0]))\n",
    "\n",
    "cia.reset_index()\n",
    "\n",
    "cia.info()\n",
    "cia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing The Data Sets\n",
    "\n",
    "Now we should combine the `happiness` and `cia` datasets.\n",
    "\n",
    "First, we need to check the columns that will be used for merging.\n",
    "Country names may differ in data sets, for instance, `eSwatini` and `Swaziland`, `Trinidad and Tobago` and `Trinidad & Tobago`. In this case, the rows will not match.\n",
    "\n",
    "Before, we store the `country_cia` column of the `cia` in a new column `country`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cia['country'] = cia['country_cia']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the `country` columns of the `happiness` data set and the `cia` data set.\n",
    "To do this, we'll combine two data sets using `outer` join."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>score</th>\n",
       "      <th>infant_mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>3.916</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Congo (Brazzaville)</td>\n",
       "      <td>4.236</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Congo (Kinshasa)</td>\n",
       "      <td>4.272</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>4.395</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Palestinian Territories</td>\n",
       "      <td>4.754</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Somaliland Region</td>\n",
       "      <td>5.057</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>5.161</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Kosovo</td>\n",
       "      <td>5.401</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>North Cyprus</td>\n",
       "      <td>5.771</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>5.835</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>6.596</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Bahamas, The</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>British Virgin Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Brunei</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Burma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Cayman Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Congo, Democratic Republic of the</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Congo, Republic of the</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Cook Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Curacao</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>Czechia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Dominica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>European Union</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Faroe Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>French Polynesia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Gambia, The</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Gaza Strip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Gibraltar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Greenland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>Grenada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Guam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>Guernsey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Guyana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Isle of Man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Jersey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Kiribati</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Korea, North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Korea, South</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>Macau</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Marshall Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Micronesia, Federated States of</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Monaco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Montserrat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Nauru</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>New Caledonia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>Niue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>Northern Mariana Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Oman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>Palau</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Saint Helena, Ascension, and Tristan da Cunha</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Saint Lucia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Saint Pierre and Miquelon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Saint Vincent and the Grenadines</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>San Marino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Sint Maarten</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Swaziland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Turks and Caicos Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>West Bank</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           country  score  infant_mortality\n",
       "138                                    Ivory Coast  3.916               NaN\n",
       "126                            Congo (Brazzaville)  4.236               NaN\n",
       "124                               Congo (Kinshasa)  4.272               NaN\n",
       "118                                        Myanmar  4.395               NaN\n",
       "107                        Palestinian Territories  4.754               NaN\n",
       "96                               Somaliland Region  5.057               NaN\n",
       "87                                      Montenegro  5.161               NaN\n",
       "76                                          Kosovo  5.401               NaN\n",
       "61                                    North Cyprus  5.771               NaN\n",
       "57                                     South Korea  5.835               NaN\n",
       "26                                  Czech Republic  6.596               NaN\n",
       "209                                 American Samoa    NaN               8.5\n",
       "228                                        Andorra    NaN               3.6\n",
       "230                                       Anguilla    NaN               3.4\n",
       "196                            Antigua and Barbuda    NaN              12.5\n",
       "200                                          Aruba    NaN              11.0\n",
       "162                                   Bahamas, The    NaN              11.7\n",
       "168                                       Barbados    NaN              10.5\n",
       "234                                        Bermuda    NaN               2.5\n",
       "195                         British Virgin Islands    NaN              12.6\n",
       "206                                         Brunei    NaN               9.9\n",
       "172                                          Burma    NaN              42.2\n",
       "171                                     Cabo Verde    NaN              22.7\n",
       "215                                 Cayman Islands    NaN               6.0\n",
       "161                       Central African Republic    NaN              88.4\n",
       "173              Congo, Democratic Republic of the    NaN              69.8\n",
       "163                         Congo, Republic of the    NaN              56.4\n",
       "192                                   Cook Islands    NaN              13.5\n",
       "165                                  Cote d'Ivoire    NaN              57.2\n",
       "175                                           Cuba    NaN               4.5\n",
       "212                                        Curacao    NaN               7.7\n",
       "233                                        Czechia    NaN               2.6\n",
       "169                                       Djibouti    NaN              47.2\n",
       "201                                       Dominica    NaN              10.9\n",
       "160                              Equatorial Guinea    NaN              67.2\n",
       "174                                        Eritrea    NaN              45.6\n",
       "226                                 European Union    NaN               4.0\n",
       "217                                  Faroe Islands    NaN               5.5\n",
       "176                                           Fiji    NaN               9.7\n",
       "221                               French Polynesia    NaN               4.7\n",
       "166                                    Gambia, The    NaN              62.0\n",
       "187                                     Gaza Strip    NaN              17.1\n",
       "216                                      Gibraltar    NaN               6.0\n",
       "207                                      Greenland    NaN               9.0\n",
       "205                                        Grenada    NaN               9.9\n",
       "218                                           Guam    NaN               5.3\n",
       "229                                       Guernsey    NaN               3.4\n",
       "164                                  Guinea-Bissau    NaN              87.5\n",
       "167                                         Guyana    NaN              31.5\n",
       "225                                    Isle of Man    NaN               4.1\n",
       "227                                         Jersey    NaN               3.8\n",
       "180                                       Kiribati    NaN              33.2\n",
       "182                                   Korea, North    NaN              22.9\n",
       "232                                   Korea, South    NaN               3.0\n",
       "158                                        Lesotho    NaN              47.6\n",
       "224                                  Liechtenstein    NaN               4.3\n",
       "231                                          Macau    NaN               3.1\n",
       "183                                       Maldives    NaN              22.9\n",
       "185                               Marshall Islands    NaN              20.0\n",
       "184                Micronesia, Federated States of    NaN              20.5\n",
       "235                                         Monaco    NaN               1.8\n",
       "194                                     Montserrat    NaN              12.7\n",
       "159                                     Mozambique    NaN              67.9\n",
       "211                                          Nauru    NaN               7.9\n",
       "219                                  New Caledonia    NaN               5.3\n",
       "236                                           Niue    NaN               NaN\n",
       "220                       Northern Mariana Islands    NaN               5.3\n",
       "193                                           Oman    NaN              13.2\n",
       "202                                          Palau    NaN              10.9\n",
       "170                               Papua New Guinea    NaN              37.4\n",
       "191  Saint Helena, Ascension, and Tristan da Cunha    NaN              13.7\n",
       "208                          Saint Kitts and Nevis    NaN               8.6\n",
       "199                                    Saint Lucia    NaN              11.2\n",
       "213                      Saint Pierre and Miquelon    NaN               6.6\n",
       "197               Saint Vincent and the Grenadines    NaN              12.3\n",
       "186                                          Samoa    NaN              19.0\n",
       "223                                     San Marino    NaN               4.4\n",
       "178                          Sao Tome and Principe    NaN              46.6\n",
       "204                                     Seychelles    NaN              10.2\n",
       "210                                   Sint Maarten    NaN               8.3\n",
       "188                                Solomon Islands    NaN              15.2\n",
       "157                                      Swaziland    NaN              50.4\n",
       "179                                    Timor-Leste    NaN              36.3\n",
       "198                                          Tonga    NaN              11.6\n",
       "203                       Turks and Caicos Islands    NaN              10.4\n",
       "181                                         Tuvalu    NaN              29.9\n",
       "189                                        Vanuatu    NaN              15.1\n",
       "214                                 Virgin Islands    NaN               6.5\n",
       "222                              Wallis and Futuna    NaN               4.4\n",
       "190                                      West Bank    NaN              14.6\n",
       "177                                 Western Sahara    NaN              53.3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_cia = happiness.merge(cia, on='country', how='outer')[['country', 'score', 'infant_mortality']]\n",
    "\n",
    "pd.set_option('display.max_rows', 100) # increase the number of rows to display\n",
    "happiness_cia[happiness_cia.isnull().any(axis=1)].sort_values(by=['score', 'country']) # the countries don't match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To provide the same country names:\n",
    "\n",
    "- Create a dictionary mapping all names to the values in the `happiness` dataset since we explore the happiness data.\n",
    "- Rename the countries in the `cia` dataset by replacing the values according to the map dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_cia</th>\n",
       "      <th>hiv_aids</th>\n",
       "      <th>hiv_aids_death</th>\n",
       "      <th>infant_mortality</th>\n",
       "      <th>mmr</th>\n",
       "      <th>obesity</th>\n",
       "      <th>tfr</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.64</td>\n",
       "      <td>Puerto Rico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>1.61</td>\n",
       "      <td>Luxembourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>37.4</td>\n",
       "      <td>215.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>3.10</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Nauru</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2.84</td>\n",
       "      <td>Nauru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>2.7</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>57.2</td>\n",
       "      <td>645.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>3.46</td>\n",
       "      <td>Ivory Coast</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country_cia  hiv_aids  hiv_aids_death  infant_mortality    mmr  obesity   tfr           country\n",
       "172       Puerto Rico       NaN             NaN               7.4   14.0      NaN  1.64       Puerto Rico\n",
       "214        Luxembourg       NaN             NaN               3.4   10.0     22.6  1.61        Luxembourg\n",
       "46   Papua New Guinea       0.9          1100.0              37.4  215.0     21.3  3.10  Papua New Guinea\n",
       "168             Nauru       NaN             NaN               7.9    NaN     61.0  2.84             Nauru\n",
       "21      Cote d'Ivoire       2.7         25000.0              57.2  645.0     10.3  3.46       Ivory Coast"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Countries map to rename\n",
    "country_to_rename = {'Cote d\\'Ivoire':'Ivory Coast',\n",
    "                     'Congo, Republic of the':'Congo (Brazzaville)',\n",
    "                     'Congo, Democratic Republic of the':'Congo (Kinshasa)',\n",
    "                     'Burma':'Myanmar',\n",
    "                     'Korea, South':'South Korea',\n",
    "                     'Czechia':'Czech Republic'\n",
    "                    }\n",
    "# Rename the countries\n",
    "cia['country'].replace(country_to_rename, inplace=True)\n",
    "\n",
    "cia.sample(5, random_state=5) # check the values randomly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use the `country` column to merge the two datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 157 entries, 0 to 156\n",
      "Data columns (total 20 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   country            157 non-null    object \n",
      " 1   region             157 non-null    object \n",
      " 2   rank               157 non-null    int64  \n",
      " 3   score              157 non-null    float64\n",
      " 4   whisker_low        157 non-null    float64\n",
      " 5   whisker_high       157 non-null    float64\n",
      " 6   gdp_per_capita     157 non-null    float64\n",
      " 7   family             157 non-null    float64\n",
      " 8   life_expectancy    157 non-null    float64\n",
      " 9   freedom            157 non-null    float64\n",
      " 10  trust_corruption   157 non-null    float64\n",
      " 11  generosity         157 non-null    float64\n",
      " 12  dystopia_residual  157 non-null    float64\n",
      " 13  country_cia        153 non-null    object \n",
      " 14  hiv_aids           93 non-null     float64\n",
      " 15  hiv_aids_death     56 non-null     float64\n",
      " 16  infant_mortality   152 non-null    float64\n",
      " 17  mmr                151 non-null    float64\n",
      " 18  obesity            150 non-null    float64\n",
      " 19  tfr                152 non-null    float64\n",
      "dtypes: float64(16), int64(1), object(3)\n",
      "memory usage: 25.8+ KB\n"
     ]
    }
   ],
   "source": [
    "happiness_cia = happiness.merge(cia, on='country', how='left').copy()\n",
    "\n",
    "happiness_cia.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build a correlation matrix by columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>region</th>\n",
       "      <th>score</th>\n",
       "      <th>obesity</th>\n",
       "      <th>tfr</th>\n",
       "      <th>mmr</th>\n",
       "      <th>infant_mortality</th>\n",
       "      <th>hiv_aids</th>\n",
       "      <th>hiv_aids_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.526</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1.73</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.509</td>\n",
       "      <td>19.5</td>\n",
       "      <td>1.55</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.501</td>\n",
       "      <td>21.9</td>\n",
       "      <td>2.01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.498</td>\n",
       "      <td>23.1</td>\n",
       "      <td>1.86</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.413</td>\n",
       "      <td>22.2</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.484</td>\n",
       "      <td>9.6</td>\n",
       "      <td>4.86</td>\n",
       "      <td>405.0</td>\n",
       "      <td>54.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>3.360</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.22</td>\n",
       "      <td>396.0</td>\n",
       "      <td>112.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Togo</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.303</td>\n",
       "      <td>8.4</td>\n",
       "      <td>4.43</td>\n",
       "      <td>368.0</td>\n",
       "      <td>43.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>5100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Syria</td>\n",
       "      <td>Middle East and Northern Africa</td>\n",
       "      <td>3.069</td>\n",
       "      <td>27.8</td>\n",
       "      <td>2.55</td>\n",
       "      <td>68.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2.905</td>\n",
       "      <td>5.4</td>\n",
       "      <td>6.04</td>\n",
       "      <td>712.0</td>\n",
       "      <td>60.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2900.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country                           region  score  obesity   tfr    mmr  infant_mortality  hiv_aids  \\\n",
       "0        Denmark                   Western Europe  7.526     19.7  1.73    6.0               4.0       NaN   \n",
       "1    Switzerland                   Western Europe  7.509     19.5  1.55    5.0               3.6       NaN   \n",
       "2        Iceland                   Western Europe  7.501     21.9  2.01    3.0               2.1       NaN   \n",
       "3         Norway                   Western Europe  7.498     23.1  1.86    5.0               2.5       NaN   \n",
       "4        Finland                   Western Europe  7.413     22.2  1.75    3.0               2.5       NaN   \n",
       "..           ...                              ...    ...      ...   ...    ...               ...       ...   \n",
       "152        Benin               Sub-Saharan Africa  3.484      9.6  4.86  405.0              54.2       1.0   \n",
       "153  Afghanistan                    Southern Asia  3.360      5.5  5.22  396.0             112.8       NaN   \n",
       "154         Togo               Sub-Saharan Africa  3.303      8.4  4.43  368.0              43.7       2.1   \n",
       "155        Syria  Middle East and Northern Africa  3.069     27.8  2.55   68.0              15.2       NaN   \n",
       "156      Burundi               Sub-Saharan Africa  2.905      5.4  6.04  712.0              60.4       1.1   \n",
       "\n",
       "     hiv_aids_death  \n",
       "0               NaN  \n",
       "1               NaN  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4               NaN  \n",
       "..              ...  \n",
       "152          2400.0  \n",
       "153             NaN  \n",
       "154          5100.0  \n",
       "155             NaN  \n",
       "156          2900.0  \n",
       "\n",
       "[157 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the columns of interest\n",
    "cols_corr = ['country', 'region', 'score',\n",
    "             'obesity', 'tfr',\n",
    "             'mmr', 'infant_mortality',\n",
    "             'hiv_aids', 'hiv_aids_death'\n",
    "            ]\n",
    "happiness_cia = happiness_cia[cols_corr]\n",
    "happiness_cia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>obesity</th>\n",
       "      <th>tfr</th>\n",
       "      <th>mmr</th>\n",
       "      <th>infant_mortality</th>\n",
       "      <th>hiv_aids</th>\n",
       "      <th>hiv_aids_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.558845</td>\n",
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       "      <th>obesity</th>\n",
       "      <td>0.558845</td>\n",
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       "      <td>0.053922</td>\n",
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       "      <th>mmr</th>\n",
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       "      <td>0.164997</td>\n",
       "      <td>0.141207</td>\n",
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       "    <tr>\n",
       "      <th>infant_mortality</th>\n",
       "      <td>-0.703726</td>\n",
       "      <td>-0.638373</td>\n",
       "      <td>0.882613</td>\n",
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       "    <tr>\n",
       "      <th>hiv_aids</th>\n",
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       "      <td>0.314998</td>\n",
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      "text/plain": [
       "                     score   obesity       tfr       mmr  infant_mortality  hiv_aids  hiv_aids_death\n",
       "score             1.000000  0.558845 -0.649370 -0.615258         -0.703726 -0.303608       -0.017830\n",
       "obesity           0.558845  1.000000 -0.540814 -0.589450         -0.638373 -0.068440       -0.021306\n",
       "tfr              -0.649370 -0.540814  1.000000  0.822510          0.882613  0.215809        0.053922\n",
       "mmr              -0.615258 -0.589450  0.822510  1.000000          0.836917  0.164997        0.141207\n",
       "infant_mortality -0.703726 -0.638373  0.882613  0.836917          1.000000  0.106235        0.074404\n",
       "hiv_aids         -0.303608 -0.068440  0.215809  0.164997          0.106235  1.000000        0.314998\n",
       "hiv_aids_death   -0.017830 -0.021306  0.053922  0.141207          0.074404  0.314998        1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get correlation matrix\n",
    "happiness_cia_corr = happiness_cia.corr()\n",
    "happiness_cia_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems, there are explicitely relationships between some parameters. We'll look at them further.  \n",
    "Let's continue with the plots.\n",
    "\n",
    "## Correlation Map\n",
    "\n",
    "We'll plot the correlation matrix below, but first, we'll prepare the data to make the plot easier to read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A triangular mask to avoid repeated values\n",
    "happiness_cia_corr = happiness_cia_corr.iloc[1:, :-1]\n",
    "mask = np.triu(np.ones_like(happiness_cia_corr), k=1)\n",
    "\n",
    "# Readable names for the plot\n",
    "cols_dict = {'score':'Happiness',\n",
    "             'hiv_aids':'HIV/AIDS',\n",
    "             'hiv_aids_death':'HIV/AIDS\\ndeath',\n",
    "             'infant_mortality':'Infant\\nmortality',\n",
    "             'mmr':'Maternal\\nmortality',\n",
    "             'tfr':'Total\\nfertility',\n",
    "             'obesity':'Obesity'\n",
    "            }\n",
    "# Rename columns in the correlation matrix\n",
    "happiness_cia_corr.rename(columns=cols_dict, index=cols_dict, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Turn on svg rendering\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "# Color palette for the blog\n",
    "snark_palette = ['#e0675a', # red\n",
    "                 '#5ca0af', # green\n",
    "                 '#edde7e', # yellow\n",
    "                 '#211c47' # dark blue\n",
    "                ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_2\">\r\n",
       "     <g id=\"text_2\">\r\n",
       "      <!-- Obesity -->\r\n",
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       "       <defs>\r\n",
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       "Q 14.59375 50.875 14.59375 34.71875 \r\n",
       "z\r\n",
       "\" id=\"ArialMT-79\"/>\r\n",
       "        <path d=\"M 14.703125 0 \r\n",
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       "Q 14.59375 35.796875 14.59375 26.3125 \r\n",
       "z\r\n",
       "\" id=\"ArialMT-98\"/>\r\n",
       "        <path d=\"M 25.78125 7.859375 \r\n",
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       "\" id=\"ArialMT-121\"/>\r\n",
       "       </defs>\r\n",
       "       <use xlink:href=\"#ArialMT-79\"/>\r\n",
       "       <use x=\"77.783203\" xlink:href=\"#ArialMT-98\"/>\r\n",
       "       <use x=\"133.398438\" xlink:href=\"#ArialMT-101\"/>\r\n",
       "       <use x=\"189.013672\" xlink:href=\"#ArialMT-115\"/>\r\n",
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       "       <use x=\"289.013672\" xlink:href=\"#ArialMT-121\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_3\">\r\n",
       "     <g id=\"text_3\">\r\n",
       "      <!-- Total -->\r\n",
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       "       <defs>\r\n",
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       "        <path d=\"M 3.328125 25.921875 \r\n",
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       "z\r\n",
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       "        <path d=\"M 6.390625 0 \r\n",
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       "       </defs>\r\n",
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       "      </g>\r\n",
       "      <!-- fertility -->\r\n",
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       "\" id=\"ArialMT-114\"/>\r\n",
       "       </defs>\r\n",
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       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_4\">\r\n",
       "     <g id=\"text_4\">\r\n",
       "      <!-- Maternal -->\r\n",
       "      <g style=\"fill:#211c47;\" transform=\"translate(245.718875 260.691375)scale(0.088 -0.088)\">\r\n",
       "       <defs>\r\n",
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       "       </defs>\r\n",
       "       <use xlink:href=\"#ArialMT-77\"/>\r\n",
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       "      </g>\r\n",
       "      <!-- mortality -->\r\n",
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       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
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    "# Color palette for the data\n",
    "palette = [snark_palette[0], # red\n",
    "           'lightgrey',\n",
    "           snark_palette[1] # green\n",
    "          ]\n",
    "\n",
    "# Inscriptions\n",
    "title = \"\"\"Relationship Between Health Indicators And The Happiness score\"\"\"\n",
    "description = \"\"\"\n",
    "Сorrelation of health indicators with the happiness score by country based on 2016 data.\n",
    "Data: Gallup World Poll - www.kaggle.com/unsdsn/world-happiness & CIA - www.cia.gov/library/publications/the-world-factbook | Author: @data.sugar\n",
    "\"\"\"\n",
    "\n",
    "# Plot size\n",
    "figsize = (6,4)\n",
    "\n",
    "# Set the figure\n",
    "sns.set(context='paper', style='ticks', palette=palette,\n",
    "        rc={'xtick.bottom':False, 'ytick.left':False, \n",
    "            'axes.spines.left': False, 'axes.spines.bottom': False,\n",
    "            'axes.spines.right': False, 'axes.spines.top': False\n",
    "           }\n",
    "       )\n",
    "\n",
    "# Create the plot\n",
    "fig, ax = plt.subplots(1, 1, figsize=figsize, facecolor='w')\n",
    "sns.heatmap(happiness_cia_corr, mask=mask, cmap=palette,\n",
    "            vmin=-1, vmax=1, center=0,\n",
    "            square=False, linewidths=.5, annot=True, fmt='.2g',\n",
    "            cbar_kws={'shrink': 1, 'ticks':[], 'label':'-1 negative <- correlation -> positive +1'},\n",
    "            ax=ax)\n",
    "\n",
    "# Set some aesthetic params for the plot\n",
    "ax.set_title(title, fontdict={'fontsize': 16}, loc='center', pad=10, c=snark_palette[-1]) # set a title of the plot\n",
    "ax.annotate(description, xy=(20, -4), size=6, xycoords='figure points', c=snark_palette[-1])\n",
    "ax.tick_params(axis='both', colors=snark_palette[-1]) # set x/y ticks\n",
    "\n",
    "# Save and plot\n",
    "plt.savefig('plot.pic/plot.happiness.health.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We expected some correlation between the HIV/AIDS rates and the happiness score, but there is no such correlation.  \n",
    "Indeed, the higher the level of obesity, the higher the happiness index, but this relationship is not very strict.  \n",
    "Let's plot these correlations.\n",
    "\n",
    "## Obesity And Happiness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Inscriptions\n",
    "title = \"\"\"The Relationship Between Obesity And Happiness\"\"\"\n",
    "description = \"\"\"\n",
    "Correlation of the obesity rate with the happiness score by country based on 2016 data.\n",
    "Data: Gallup World Poll - www.kaggle.com/unsdsn/world-happiness & CIA - www.cia.gov/library/publications/the-world-factbook | Author: @data.sugar\n",
    "\"\"\"\n",
    "\n",
    "# Plot size\n",
    "figsize = (6,4)\n",
    "\n",
    "# Set the figure\n",
    "sns.set(context='paper', style='ticks', palette=snark_palette,\n",
    "        rc={'xtick.major.size': 4, 'ytick.major.size':4,\n",
    "            'axes.spines.left': False, 'axes.spines.bottom': False,\n",
    "            'axes.spines.right': False, 'axes.spines.top': False\n",
    "           }\n",
    "       )\n",
    "\n",
    "# Create the plot\n",
    "fig = plt.figure(figsize=figsize, facecolor='w')\n",
    "g = sns.jointplot(x='score', y='obesity', data=happiness_cia,\n",
    "                  kind='reg', truncate=False, dropna=True,\n",
    "                  xlim=(2, 8), ylim=(0, 40),\n",
    "                  marginal_kws=dict(hist=True, bins=10),\n",
    "                  color=snark_palette[1]\n",
    "                 )\n",
    "\n",
    "# Set some aesthetic params for the plot\n",
    "g.ax_marg_x.set_title(title, fontdict={'fontsize': 16}, loc='center', pad=10, c=snark_palette[-1]) # set a title of the plot\n",
    "g.ax_marg_x.annotate(description, xy=(0.015, -0.01), size=6, xycoords='figure fraction', c=snark_palette[-1])\n",
    "g.ax_joint.set_xlabel('Happiness score', loc='center', size='x-large', c=snark_palette[-1]) # set label of x axis\n",
    "g.ax_joint.set_ylabel('Obesity rate', loc='center', size='x-large', c=snark_palette[-1]) # set label of x axis\n",
    "g.ax_joint.tick_params(axis='both', labelsize='large', colors=snark_palette[-1]) # set x/y ticks\n",
    "g.ax_joint.spines['bottom'].set_color(snark_palette[-1]) # color x axis\n",
    "g.ax_joint.spines['left'].set_color(snark_palette[-1]) # color y axis\n",
    "g.ax_marg_x.tick_params(axis='x', bottom=False) # disable x margin ticks\n",
    "g.ax_marg_x.spines['bottom'].set_color(snark_palette[1])\n",
    "g.ax_marg_y.tick_params(axis='y', left=False) # disable y margin ticks\n",
    "g.ax_marg_y.spines['left'].set_color(snark_palette[1])\n",
    "\n",
    "# Save and plot\n",
    "plt.savefig('plot.pic/plot.happiness.health.obesity.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total Fertility Rate And Happiness Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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    "# Inscriptions\n",
    "title = \"\"\"The Relationship Between Total Fertility And Happiness\"\"\"\n",
    "description = \"\"\"\n",
    "Correlation of the total fertility rate with the happiness score by country based on 2016 data.\n",
    "Data: Gallup World Poll - www.kaggle.com/unsdsn/world-happiness & CIA - www.cia.gov/library/publications/the-world-factbook | Author: @data.sugar\n",
    "\"\"\"\n",
    "\n",
    "# Plot size\n",
    "figsize = (6,4)\n",
    "\n",
    "# Set the figure\n",
    "sns.set(context='paper', style='ticks', palette=snark_palette,\n",
    "        rc={'xtick.major.size': 4, 'ytick.major.size':4,\n",
    "            'axes.spines.left': False, 'axes.spines.bottom': False,\n",
    "            'axes.spines.right': False, 'axes.spines.top': False\n",
    "           }\n",
    "       )\n",
    "\n",
    "# Create the plot\n",
    "fig = plt.figure(figsize=figsize, facecolor='w')\n",
    "g = sns.jointplot(x='score', y='tfr', data=happiness_cia,\n",
    "                  kind='reg', truncate=False, dropna=True,\n",
    "                  xlim=(2, 8), ylim=(0, 7),\n",
    "                  marginal_kws=dict(hist=True, bins=8),\n",
    "                  color=snark_palette[0]\n",
    "                 )\n",
    "\n",
    "# Set some aesthetic params for the plot\n",
    "g.ax_marg_x.set_title(title, fontdict={'fontsize': 16}, loc='center', pad=10, c=snark_palette[-1]) # set a title of the plot\n",
    "g.ax_marg_x.annotate(description, xy=(0.015, -0.01), size=6, xycoords='figure fraction', c=snark_palette[-1])\n",
    "g.ax_joint.set_xlabel('Happiness score', loc='center', size='x-large', c=snark_palette[-1]) # set label of x axis\n",
    "g.ax_joint.set_ylabel('Total fertility rate', loc='center', size='x-large', c=snark_palette[-1]) # set label of x axis\n",
    "g.ax_joint.tick_params(axis='both', labelsize='large', colors=snark_palette[-1]) # set x/y ticks\n",
    "g.ax_joint.spines['bottom'].set_color(snark_palette[-1]) # color x axis\n",
    "g.ax_joint.spines['left'].set_color(snark_palette[-1]) # color y axis\n",
    "g.ax_marg_x.tick_params(axis='x', bottom=False) # disable x margin ticks\n",
    "g.ax_marg_x.spines['bottom'].set_color(snark_palette[0])\n",
    "g.ax_marg_y.tick_params(axis='y', left=False) # disable y margin ticks\n",
    "g.ax_marg_y.spines['left'].set_color(snark_palette[0])\n",
    "\n",
    "# Save and plot\n",
    "plt.savefig('plot.pic/plot.happiness.health.tfr.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Infant Mortality Rate And Happiness Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Inscriptions\n",
    "title = \"\"\"The Relationship Between Infant Mortality And Happiness\"\"\"\n",
    "description = \"\"\"\n",
    "Correlation of the infant mortality rate with the happiness score by country based on 2016 data.\n",
    "Data: Gallup World Poll - www.kaggle.com/unsdsn/world-happiness & CIA - www.cia.gov/library/publications/the-world-factbook | Author: @data.sugar\n",
    "\"\"\"\n",
    "\n",
    "# Plot size\n",
    "figsize = (6,4)\n",
    "\n",
    "# Set the figure\n",
    "sns.set(context='paper', style='ticks', palette=snark_palette,\n",
    "        rc={'xtick.major.size': 4, 'ytick.major.size':4,\n",
    "            'axes.spines.left': False, 'axes.spines.bottom': False,\n",
    "            'axes.spines.right': False, 'axes.spines.top': False\n",
    "           }\n",
    "       )\n",
    "\n",
    "# Create the plot\n",
    "fig = plt.figure(figsize=figsize, facecolor='w')\n",
    "g = sns.jointplot(x='score', y='infant_mortality', data=happiness_cia,\n",
    "                  kind='reg', truncate=False, dropna=True,\n",
    "                  xlim=(2, 8), ylim=(0, 125),\n",
    "                  marginal_kws=dict(hist=True, bins=10),\n",
    "                  color=snark_palette[0]\n",
    "                 )\n",
    "\n",
    "# Set some aesthetic params for the plot\n",
    "g.ax_marg_x.set_title(title, fontdict={'fontsize': 16}, loc='center', pad=10, c=snark_palette[-1]) # set a title of the plot\n",
    "g.ax_marg_x.annotate(description, xy=(0.015, -0.01), size=6, xycoords='figure fraction', c=snark_palette[-1])\n",
    "g.ax_joint.set_xlabel('Happiness score', loc='center', size='x-large', c=snark_palette[-1]) # set label of x axis\n",
    "g.ax_joint.set_ylabel('Infant mortality rate', loc='center', size='x-large', c=snark_palette[-1]) # set label of x axis\n",
    "g.ax_joint.tick_params(axis='both', labelsize='large', colors=snark_palette[-1]) # set x/y ticks\n",
    "g.ax_joint.spines['bottom'].set_color(snark_palette[-1]) # color x axis\n",
    "g.ax_joint.spines['left'].set_color(snark_palette[-1]) # color y axis\n",
    "g.ax_marg_x.tick_params(axis='x', bottom=False) # disable x margin ticks\n",
    "g.ax_marg_x.spines['bottom'].set_color(snark_palette[0])\n",
    "g.ax_marg_y.tick_params(axis='y', left=False) # disable y margin ticks\n",
    "g.ax_marg_y.spines['left'].set_color(snark_palette[0])\n",
    "\n",
    "# Save and plot\n",
    "plt.savefig('plot.pic/plot.happiness.health.infant_mortality.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total Fertility Rate And Obesity Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     },
     "metadata": {},
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   ],
   "source": [
    "# Inscriptions\n",
    "title = \"\"\"The Relationship Between Total Fertility And Obesity\"\"\"\n",
    "description = \"\"\"\n",
    "Correlation of the total fertility rate with the obesity rate by country based on 2016 data.\n",
    "Data: Gallup World Poll - www.kaggle.com/unsdsn/world-happiness & CIA - www.cia.gov/library/publications/the-world-factbook | Author: @data.sugar\n",
    "\"\"\"\n",
    "\n",
    "# Plot size\n",
    "figsize = (6,4)\n",
    "\n",
    "# Set the figure\n",
    "sns.set(context='paper', style='ticks', palette=snark_palette,\n",
    "        rc={'xtick.major.size': 4, 'ytick.major.size':4,\n",
    "            'axes.spines.left': False, 'axes.spines.bottom': False,\n",
    "            'axes.spines.right': False, 'axes.spines.top': False\n",
    "           }\n",
    "       )\n",
    "\n",
    "# Create the plot\n",
    "fig = plt.figure(figsize=figsize, facecolor='w')\n",
    "g = sns.jointplot(x='obesity', y='tfr', data=happiness_cia,\n",
    "                  kind='reg', truncate=False, dropna=True,\n",
    "                  xlim=(0, 40), ylim=(0, 7),\n",
    "                  marginal_kws=dict(hist=True, bins=10),\n",
    "                  color=snark_palette[0]\n",
    "                 )\n",
    "\n",
    "# Set some aesthetic params for the plot\n",
    "g.ax_marg_x.set_title(title, fontdict={'fontsize': 16}, loc='center', pad=10, c=snark_palette[-1]) # set a title of the plot\n",
    "g.ax_marg_x.annotate(description, xy=(0.015, -0.01), size=6, xycoords='figure fraction', c=snark_palette[-1])\n",
    "g.ax_joint.set_xlabel('Obesity rate', loc='center', size='x-large', c=snark_palette[-1]) # set label of x axis\n",
    "g.ax_joint.set_ylabel('Total fertility rate', loc='center', size='x-large', c=snark_palette[-1]) # set label of x axis\n",
    "g.ax_joint.tick_params(axis='both', labelsize='large', colors=snark_palette[-1]) # set x/y ticks\n",
    "g.ax_joint.spines['bottom'].set_color(snark_palette[-1]) # color x axis\n",
    "g.ax_joint.spines['left'].set_color(snark_palette[-1]) # color y axis\n",
    "g.ax_marg_x.tick_params(axis='x', bottom=False) # disable x margin ticks\n",
    "g.ax_marg_x.spines['bottom'].set_color(snark_palette[0])\n",
    "g.ax_marg_y.tick_params(axis='y', left=False) # disable y margin ticks\n",
    "g.ax_marg_y.spines['left'].set_color(snark_palette[0])\n",
    "\n",
    "# Save and plot\n",
    "plt.savefig('plot.pic/plot.happiness.health.tfr_obesity.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "As we mentioned above, there is no significant correlation between the HIV/AIDS rates and the happiness score.\n",
    "\n",
    "And these correlations are quite high (above 0.8):\n",
    "\n",
    "- total fertility rate and infant mortality rate;\n",
    "- maternal mortality rate and infant mortality rate;\n",
    "- maternal mortality rate and total fertility rate.\n",
    "\n",
    "And it seems natural. If the birth rate per woman is higher, the risk of maternal and infant mortality is higher, given the fact that total fertility, maternal mortality, and infant mortality are inversely proportional to the level of happiness.  In other words, the birth rate, maternal and infant mortality are higher in the least happy countries.  \n",
    "\n",
    "Statistics do not give the root cause: people are happy, and therefore infant mortality is lower, or mortality is lower, and therefore people are happier (because they are generally healthier). Which came first - the chicken or the egg.  \n",
    "\n",
    "There is a low negative correlation between the total fertility rate and the obesity rate.  \n",
    "What does it mean? Does this mean that satisfied people don't want to think about procreation?\n",
    "\n",
    "What about happiness? \n",
    "The higher a country is on the happiness stairs, the higher the obesity rate, but the lower the total fertility, as well as maternal and infant mortality.\n",
    "\n",
    "\n",
    "# Blog Post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1935"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blog_post = r\"\"\"\n",
    "## IN SEARCH OF 🎈HAPPINESS: 🍔WHAT if HEALTH?  \n",
    "\n",
    "\n",
    "💪We continue our happiness #challenge!  \n",
    "\n",
    "👀And now we wonder whether high \\#health indicators contribute to \\#happiness.\n",
    "To compare health indicators with happiness score, we'll take the World Happiness Report from \\#Kaggle, which ranks 156 countries for happiness on a 10-point scale, and use \\#CIA data from The World Factbook.\n",
    "\n",
    "📌Today we would like to know how health makes us happy!  \n",
    "\n",
    "✔The correlation map we built says that the more food, the more happiness (see the relationship between obesity and happiness).  \n",
    "Oh, we already knew that, a hungry man is an angry man.\n",
    "\n",
    "✔Surprisingly, there is no correlation between HIV/AIDS rates and the level of happiness we expected.\n",
    "\n",
    "✔There is a low negative correlation between the total fertility rate and the obesity rate.\n",
    "What does it mean? Does this mean that satisfied people don't want to think about procreation?\n",
    "\n",
    "✔If the birth rate per woman is higher, the risk of maternal and infant mortality is higher, given the fact that total fertility, maternal mortality and infant mortality are inversely proportional to the level of happiness.  \n",
    "In other words, the birth rate, maternal and infant mortality are higher in the least happy countries.\n",
    "\n",
    "Statistics don't give the root cause: people are happy, and therefore infant mortality is lower, or mortality is lower, and therefore people are happier (because they are generally healthier).  \n",
    "Which came first - the chicken or the egg.\n",
    "\n",
    "📝What about happiness?  \n",
    "The higher a country is on the happiness stairs, the higher the obesity rate, but the lower the total fertility, as well as maternal and infant mortality.\n",
    "\n",
    "#tobecontinued\n",
    "\n",
    "(Interested in more details? Follow the link in bio for the entire research project!)  \n",
    ".  \n",
    ".  \n",
    ".  \n",
    "\\#funtime \\#probably \\#datascience \\#datapower \\#data_sugar_happiness \\#happy\n",
    "\\#data_know_everything_and_nothing \\#linkinbio \\#datajournalism \\#python\n",
    "\"\"\"\n",
    "\n",
    "# Check post text length for Instagram\n",
    "len(blog_post)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "## IN SEARCH OF 🎈HAPPINESS: 🍔WHAT if HEALTH?  \n",
       "\n",
       "\n",
       "💪We continue our happiness #challenge!  \n",
       "\n",
       "👀And now we wonder whether high \\#health indicators contribute to \\#happiness.\n",
       "To compare health indicators with happiness score, we'll take the World Happiness Report from \\#Kaggle, which ranks 156 countries for happiness on a 10-point scale, and use \\#CIA data from The World Factbook.\n",
       "\n",
       "📌Today we would like to know how health makes us happy!  \n",
       "\n",
       "✔The correlation map we built says that the more food, the more happiness (see the relationship between obesity and happiness).  \n",
       "Oh, we already knew that, a hungry man is an angry man.\n",
       "\n",
       "✔Surprisingly, there is no correlation between HIV/AIDS rates and the level of happiness we expected.\n",
       "\n",
       "✔There is a low negative correlation between the total fertility rate and the obesity rate.\n",
       "What does it mean? Does this mean that satisfied people don't want to think about procreation?\n",
       "\n",
       "✔If the birth rate per woman is higher, the risk of maternal and infant mortality is higher, given the fact that total fertility, maternal mortality and infant mortality are inversely proportional to the level of happiness.  \n",
       "In other words, the birth rate, maternal and infant mortality are higher in the least happy countries.\n",
       "\n",
       "Statistics don't give the root cause: people are happy, and therefore infant mortality is lower, or mortality is lower, and therefore people are happier (because they are generally healthier).  \n",
       "Which came first - the chicken or the egg.\n",
       "\n",
       "📝What about happiness?  \n",
       "The higher a country is on the happiness stairs, the higher the obesity rate, but the lower the total fertility, as well as maternal and infant mortality.\n",
       "\n",
       "#tobecontinued\n",
       "\n",
       "(Interested in more details? Follow the link in bio for the entire research project!)  \n",
       ".  \n",
       ".  \n",
       ".  \n",
       "\\#funtime \\#probably \\#datascience \\#datapower \\#data_sugar_happiness \\#happy\n",
       "\\#data_know_everything_and_nothing \\#linkinbio \\#datajournalism \\#python\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Markdown as md\n",
    "md(blog_post)"
   ]
  }
 ],
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