ecological-footprint-countries/ecological_footprint.ipynb

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    "%%html\n",
    "<style>\n",
    "p {\n",
    "    max-width: 45em;\n",
    "    word-wrap: break-word;\n",
    "    }\n",
    "li{\n",
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   "source": [
    "<br>\n",
    "<br>\n",
    "<p style=\"text-align:center; font-family: Arial; max-width: 65em\">\n",
    "<font size=6>Ecological Footprint and Human Development Index 2016</font>\n",
    "<br>\n",
    "<br>\n",
    "<br></p>"
   ]
  },
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      "PACKAGE              VERSION\n",
      "Python               3.8.5\n",
      "requests             2.24.0\n",
      "json                 2.0.9\n",
      "re                   2.2.1\n",
      "numpy                1.19.2\n",
      "pandas               1.1.3\n",
      "seaborn              0.11.0\n",
      "altair               4.1.0\n"
     ]
    }
   ],
   "source": [
    "# Import packages and set package parameters\n",
    "import os\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import json\n",
    "import re\n",
    "from math import sqrt, floor, ceil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('precision',2) # Limit number of significant figures displayed in outputs\n",
    "# pd.set_option('display.max_rows', None) # Display full pd.DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (10,6) # Set default figure size in inches (width, height)\n",
    "import seaborn as sns\n",
    "import altair as alt\n",
    "\n",
    "pyv = !python --version\n",
    "print(f'{\"PACKAGE\":<20} VERSION')\n",
    "print(f'{\"Python\":<20} {str(pyv[0]).split()[1]}')\n",
    "print('\\n'.join(f'{m.__name__:<20} {m.__version__}' for m in globals().values() if getattr(m, '__version__', None)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#1.-Introduction\" data-toc-modified-id=\"1.-Introduction-1\">1. Introduction</a></span></li><li><span><a href=\"#2.-Data-extraction-and-preparation\" data-toc-modified-id=\"2.-Data-extraction-and-preparation-2\">2. Data extraction and preparation</a></span><ul class=\"toc-item\"><li><span><a href=\"#2.1-Import-data-through-the-GFN-API\" data-toc-modified-id=\"2.1-Import-data-through-the-GFN-API-2.1\">2.1 Import data through the GFN API</a></span></li><li><span><a href=\"#2.2-Import-list-of-independent-States-by-scraping-online-HTML-table\" data-toc-modified-id=\"2.2-Import-list-of-independent-States-by-scraping-online-HTML-table-2.2\">2.2 Import list of independent States by scraping online HTML table</a></span></li><li><span><a href=\"#2.3-Import-list-of-countries-with-regions-from-GitHub-repository\" data-toc-modified-id=\"2.3-Import-list-of-countries-with-regions-from-GitHub-repository-2.3\">2.3 Import list of countries with regions from GitHub repository</a></span></li></ul></li><li><span><a href=\"#3.-Data-analysis\" data-toc-modified-id=\"3.-Data-analysis-3\">3. Data analysis</a></span><ul class=\"toc-item\"><li><span><a href=\"#3.1-Ecological-Footprint-by-continent\" data-toc-modified-id=\"3.1-Ecological-Footprint-by-continent-3.1\">3.1 Ecological Footprint by continent</a></span></li><li><span><a href=\"#3.2-Human-Development-Index-by-continent\" data-toc-modified-id=\"3.2-Human-Development-Index-by-continent-3.2\">3.2 Human Development Index by continent</a></span></li><li><span><a href=\"#3.3-Ecological-Footprint-vs-Human-Development-Index\" data-toc-modified-id=\"3.3-Ecological-Footprint-vs-Human-Development-Index-3.3\">3.3 Ecological Footprint vs Human Development Index</a></span></li><li><span><a href=\"#3.4-Development-efficiency\" data-toc-modified-id=\"3.4-Development-efficiency-3.4\">3.4 Development efficiency</a></span></li></ul></li><li><span><a href=\"#4.-Conclusion\" data-toc-modified-id=\"4.-Conclusion-4\">4. Conclusion</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this data analysis is to study the relationship between the ecological footprint (EF) and the Human Development Index (HDI) of countries worldwide using the latest data available which dates from 2016 (as of October 2020).\n",
    "\n",
    "The EF measures the surface area needed to provide the resources necessary to meet consumer demand in any given country. This includes the surface area used for food crops, fiber production, timber regeneration, absorption of  carbon dioxide emissions from fossil fuel burning, and built infrastructure. Imports and exports are taken into account. Further details can be found [here](https://www.footprintnetwork.org/resources/data/). This indicator is provided by the *Global Footprint Network* (GFN), a non-profit organization whose mission is to help end ecological overshoot by making ecological limits central to decision-making.\n",
    "\n",
    "The HDI measures the level of development of a country by combining indices that assess three key dimensions: health, education and standard of living. Health is assessed by life expectancy at birth, education is measured by mean of years of schooling for adults aged 25 years and more and expected years of schooling for children of school entering age. Standard of living is measured by gross national income per capita. Further details can be found [here](http://hdr.undp.org/en/content/human-development-index-hdi). The HDI is calculated by the *United Nations Development Programme* using data from various sources.\n",
    "\n",
    "This notebook presents the entire data analysis process starting with the extraction of the raw data from online sources, followed by data preparation operations and finishing with a few descriptive graphs, the one of key interest being the scatterplot showing the relationship betweenship ecological footprint and the HDI for countries worldwide."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data extraction and preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Import data through the GFN API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data concerning both the EF and the HDI can be accessed on the GFN website as an [excel file containing all the data](https://www.footprintnetwork.org/licenses/public-data-package-free/), as individual datasets through the [Ecological Footprint Explorer open data platform](https://data.footprintnetwork.org/#/exploreData) or through the [website's API](https://data.footprintnetwork.org/#/api) for which an API key must be requested using their *API Key Request Form* to be able to access the data. The data is provided by the GFN under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/).\n",
    "  \n",
    "For this data analysis, the data will be imported through the API as it makes the whole process easier to reproduce and requires fewer files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set API key and username needed for authentication to access the GFN API\n",
    "import config # Script file containing confidential authentication details\n",
    "api_key = config.api_key # API key received from the GFN by email\n",
    "user_agent = config.user_agent # User name which can be any string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Different datasets can be accessed through the API, the *types* dataset lists all the indicators that are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API HTTP response code: 200\n"
     ]
    }
   ],
   "source": [
    "# Extract list of available types of indicators and check that API\n",
    "# access is successful\n",
    "url = 'http://api.footprintnetwork.org/v1/types'\n",
    "request_types = requests.get(url, auth=(user_agent, api_key))\n",
    "print(f'API HTTP response code: {request_types.status_code}') # Check that request is successful: HTTP code = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>version</th>\n",
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       "      <th>name</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>BCpc</td>\n",
       "      <td>BiocapPerCap</td>\n",
       "      <td>Biocapacity divided by population in global he...</td>\n",
       "      <td>1</td>\n",
       "      <td>Biocapacity per person</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>BCtot</td>\n",
       "      <td>BiocapTotGHA</td>\n",
       "      <td>Total biocapacity in global hectares (gha)</td>\n",
       "      <td>1</td>\n",
       "      <td>Biocapacity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>EFCpc</td>\n",
       "      <td>EFConsPerCap</td>\n",
       "      <td>Ecological Footprint of consumption in global ...</td>\n",
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       "      <td>Ecological Footprint per person</td>\n",
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       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>EFCtot</td>\n",
       "      <td>EFConsTotGHA</td>\n",
       "      <td>Total Ecological Footprint of consumption in g...</td>\n",
       "      <td>1</td>\n",
       "      <td>Ecological Footprint</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>None</td>\n",
       "      <td>earth</td>\n",
       "      <td>BiocapTotGHA</td>\n",
       "      <td>EFConsPerCap of Country / EFConsPerCap of World</td>\n",
       "      <td>1</td>\n",
       "      <td>Earths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>None</td>\n",
       "      <td>pop</td>\n",
       "      <td>Population</td>\n",
       "      <td>Population</td>\n",
       "      <td>1</td>\n",
       "      <td>Population</td>\n",
       "    </tr>\n",
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       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>None</td>\n",
       "      <td>hdi</td>\n",
       "      <td>HDI</td>\n",
       "      <td>Human Development Index; Source: Trends in the...</td>\n",
       "      <td>1</td>\n",
       "      <td>Human Development Index</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>None</td>\n",
       "      <td>gdp</td>\n",
       "      <td>GDP</td>\n",
       "      <td>GDP per capita (constant 2010 US$); Source: Wo...</td>\n",
       "      <td>1</td>\n",
       "      <td>Gross Domestic Product</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   id version    code        record  \\\n",
       "0   3    None    BCpc  BiocapPerCap   \n",
       "1   4    None   BCtot  BiocapTotGHA   \n",
       "2   5    None   EFCpc  EFConsPerCap   \n",
       "3   6    None  EFCtot  EFConsTotGHA   \n",
       "4  13    None   earth  BiocapTotGHA   \n",
       "5  14    None     pop    Population   \n",
       "6  15    None     hdi           HDI   \n",
       "7  16    None     gdp           GDP   \n",
       "\n",
       "                                                note  enabled  \\\n",
       "0  Biocapacity divided by population in global he...        1   \n",
       "1         Total biocapacity in global hectares (gha)        1   \n",
       "2  Ecological Footprint of consumption in global ...        1   \n",
       "3  Total Ecological Footprint of consumption in g...        1   \n",
       "4    EFConsPerCap of Country / EFConsPerCap of World        1   \n",
       "5                                         Population        1   \n",
       "6  Human Development Index; Source: Trends in the...        1   \n",
       "7  GDP per capita (constant 2010 US$); Source: Wo...        1   \n",
       "\n",
       "                              name  \n",
       "0           Biocapacity per person  \n",
       "1                      Biocapacity  \n",
       "2  Ecological Footprint per person  \n",
       "3             Ecological Footprint  \n",
       "4                           Earths  \n",
       "5                       Population  \n",
       "6          Human Development Index  \n",
       "7           Gross Domestic Product  "
      ]
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   ],
   "source": [
    "# Display json file contents listing available indicators\n",
    "types_json = request_types.json()\n",
    "df_types = pd.DataFrame(types_json)\n",
    "df_types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this data analysis, these three variables will be extracted: *Earths, Population and Human Development Index*. *Earths* measures how many Earths would be required to provide the surface required to meet consumption needs if everyone on the planet were to consume at the same level as the given country.\n",
    "\n",
    "The HDI can also be accessed through the [API of the United Nations Human Development Report Office](http://hdr.undp.org/en/content/human-development-report-office-statistical-data-api) where the data may be slightly more up to date as it is recalculated retroactively each year with the integration of new methodologies and updated data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API HTTP response code: 200\n"
     ]
    }
   ],
   "source": [
    "# Extract data on population, ecological footprint (in terms of number\n",
    "# of Earths), and the Human Development Index\n",
    "url = 'http://api.footprintnetwork.org/v1/data/all/2016/pop,earth,hdi'\n",
    "request_ef2016 = requests.get(url, auth=(user_agent, api_key))\n",
    "print(f'API HTTP response code: {request_ef2016.status_code}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
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       "      <th>year</th>\n",
       "      <th>countryCode</th>\n",
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       "      <th>shortName</th>\n",
       "      <th>isoa2</th>\n",
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       "      <th>cropLand</th>\n",
       "      <th>grazingLand</th>\n",
       "      <th>forestLand</th>\n",
       "      <th>fishingGround</th>\n",
       "      <th>builtupLand</th>\n",
       "      <th>carbon</th>\n",
       "      <th>value</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>221204</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
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       "      <td>Armenia</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>AM</td>\n",
       "      <td>Earths</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.15</td>\n",
       "      <td>4.62e-03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.17e+00</td>\n",
       "      <td>3A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121257</td>\n",
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       "      <td>2016</td>\n",
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       "      <td>Armenia</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>AM</td>\n",
       "      <td>HDI</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.51e-01</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>125727</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>AM</td>\n",
       "      <td>Population</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.92e+06</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>221261</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>Earths</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.41e-04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.09</td>\n",
       "      <td>4.39e-01</td>\n",
       "      <td>3A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>121285</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>HDI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.91e-01</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
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       "      <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>596</th>\n",
       "      <td>231913</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>2005</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>º Oceania</td>\n",
       "      <td>None</td>\n",
       "      <td>Earths</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.78e-02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2.82</td>\n",
       "      <td>3.94e+00</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>216927</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>2005</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>º Oceania</td>\n",
       "      <td>None</td>\n",
       "      <td>HDI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.38e-01</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>231970</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>5001</td>\n",
       "      <td>World</td>\n",
       "      <td>º World</td>\n",
       "      <td>None</td>\n",
       "      <td>Earths</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.17</td>\n",
       "      <td>5.58e-02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.70e+00</td>\n",
       "      <td>3A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>125701</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>5001</td>\n",
       "      <td>World</td>\n",
       "      <td>º World</td>\n",
       "      <td>None</td>\n",
       "      <td>HDI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.27e-01</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>135252</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>5001</td>\n",
       "      <td>World</td>\n",
       "      <td>º World</td>\n",
       "      <td>None</td>\n",
       "      <td>Population</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.47e+09</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>601 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id version  year  countryCode  countryName    shortName isoa2  \\\n",
       "0    221204    None  2016            1      Armenia      Armenia    AM   \n",
       "1    121257    None  2016            1      Armenia      Armenia    AM   \n",
       "2    125727    None  2016            1      Armenia      Armenia    AM   \n",
       "3    221261    None  2016            2  Afghanistan  Afghanistan    AF   \n",
       "4    121285    None  2016            2  Afghanistan  Afghanistan    AF   \n",
       "..      ...     ...   ...          ...          ...          ...   ...   \n",
       "596  231913    None  2016         2005      Oceania    º Oceania  None   \n",
       "597  216927    None  2016         2005      Oceania    º Oceania  None   \n",
       "598  231970    None  2016         5001        World      º World  None   \n",
       "599  125701    None  2016         5001        World      º World  None   \n",
       "600  135252    None  2016         5001        World      º World  None   \n",
       "\n",
       "         record  cropLand  grazingLand  forestLand  fishingGround  \\\n",
       "0        Earths      0.33         0.14        0.15       4.62e-03   \n",
       "1           HDI       NaN          NaN         NaN            NaN   \n",
       "2    Population       NaN          NaN         NaN            NaN   \n",
       "3        Earths      0.18         0.11        0.04       1.41e-04   \n",
       "4           HDI       NaN          NaN         NaN            NaN   \n",
       "..          ...       ...          ...         ...            ...   \n",
       "596      Earths      0.34         0.10        0.57       8.78e-02   \n",
       "597         HDI       NaN          NaN         NaN            NaN   \n",
       "598      Earths      0.32         0.09        0.17       5.58e-02   \n",
       "599         HDI       NaN          NaN         NaN            NaN   \n",
       "600  Population       NaN          NaN         NaN            NaN   \n",
       "\n",
       "     builtupLand  carbon     value score  \n",
       "0           0.03    0.51  1.17e+00    3A  \n",
       "1            NaN     NaN  7.51e-01  None  \n",
       "2            NaN     NaN  2.92e+06  None  \n",
       "3           0.02    0.09  4.39e-01    3A  \n",
       "4            NaN     NaN  4.91e-01  None  \n",
       "..           ...     ...       ...   ...  \n",
       "596         0.03    2.82  3.94e+00        \n",
       "597          NaN     NaN  8.38e-01  None  \n",
       "598         0.04    1.03  1.70e+00    3A  \n",
       "599          NaN     NaN  7.27e-01  None  \n",
       "600          NaN     NaN  7.47e+09  None  \n",
       "\n",
       "[601 rows x 16 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ef2016_raw = pd.DataFrame(request_ef2016.json())\n",
    "df_ef2016_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *record* column shows that the values of three variables of interest are contained on different lines of the *value* column. Country names and the ISO 3166-1 alpha-2 country codes are also needed for merging with other datasets later on."
   ]
  },
  {
   "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>shortName</th>\n",
       "      <th>isoa2</th>\n",
       "      <th>record</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>AM</td>\n",
       "      <td>Earths</td>\n",
       "      <td>1.17e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>AM</td>\n",
       "      <td>HDI</td>\n",
       "      <td>7.51e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>AM</td>\n",
       "      <td>Population</td>\n",
       "      <td>2.92e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>Earths</td>\n",
       "      <td>4.39e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>HDI</td>\n",
       "      <td>4.91e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>º Oceania</td>\n",
       "      <td>None</td>\n",
       "      <td>Earths</td>\n",
       "      <td>3.94e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>º Oceania</td>\n",
       "      <td>None</td>\n",
       "      <td>HDI</td>\n",
       "      <td>8.38e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>º World</td>\n",
       "      <td>None</td>\n",
       "      <td>Earths</td>\n",
       "      <td>1.70e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>º World</td>\n",
       "      <td>None</td>\n",
       "      <td>HDI</td>\n",
       "      <td>7.27e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>º World</td>\n",
       "      <td>None</td>\n",
       "      <td>Population</td>\n",
       "      <td>7.47e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>601 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       shortName isoa2      record     value\n",
       "0        Armenia    AM      Earths  1.17e+00\n",
       "1        Armenia    AM         HDI  7.51e-01\n",
       "2        Armenia    AM  Population  2.92e+06\n",
       "3    Afghanistan    AF      Earths  4.39e-01\n",
       "4    Afghanistan    AF         HDI  4.91e-01\n",
       "..           ...   ...         ...       ...\n",
       "596    º Oceania  None      Earths  3.94e+00\n",
       "597    º Oceania  None         HDI  8.38e-01\n",
       "598      º World  None      Earths  1.70e+00\n",
       "599      º World  None         HDI  7.27e-01\n",
       "600      º World  None  Population  7.47e+09\n",
       "\n",
       "[601 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select columns of interest\n",
    "df_ef2016_select = df_ef2016_raw[['shortName', 'isoa2', 'record', 'value']]\n",
    "df_ef2016_select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 601 entries, 0 to 600\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   shortName  601 non-null    object \n",
      " 1   isoa2      547 non-null    object \n",
      " 2   record     601 non-null    object \n",
      " 3   value      601 non-null    float64\n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 18.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_ef2016_select.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *record* column needs to be split into separate columns containing the variables of interest and the columns can be renamed for better readability."
   ]
  },
  {
   "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>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.49</td>\n",
       "      <td>3.47e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2.93e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZ</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4.06e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AO</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2.88e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>AG</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.01e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>VE</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.16e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>VN</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.69</td>\n",
       "      <td>9.46e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>YE</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2.76e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZM</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.66e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.62e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>188 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country code  Earths   HDI  population\n",
       "0            Afghanistan   AF    0.44  0.49    3.47e+07\n",
       "1                Albania   AL    1.23  0.79    2.93e+06\n",
       "2                Algeria   DZ    1.48  0.75    4.06e+07\n",
       "3                 Angola   AO    0.57  0.57    2.88e+07\n",
       "4    Antigua and Barbuda   AG    2.69  0.77    1.01e+05\n",
       "..                   ...  ...     ...   ...         ...\n",
       "183            Venezuela   VE    1.55  0.75    3.16e+07\n",
       "184             Viet Nam   VN    1.37  0.69    9.46e+07\n",
       "185                Yemen   YE    0.40  0.48    2.76e+07\n",
       "186               Zambia   ZM    0.60  0.58    1.66e+07\n",
       "187             Zimbabwe   ZW    0.65  0.55    1.62e+07\n",
       "\n",
       "[188 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pivot table, reset index and rename columns\n",
    "df_ef2016_pivot = df_ef2016_select.pivot_table(index=['shortName','isoa2'], columns=['record'], values='value').reset_index()\n",
    "df_ef2016_pivot.columns = ['country', 'code', 'Earths', 'HDI', 'population']\n",
    "df_ef2016_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 188 entries, 0 to 187\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   country     188 non-null    object \n",
      " 1   code        188 non-null    object \n",
      " 2   Earths      184 non-null    float64\n",
      " 3   HDI         175 non-null    float64\n",
      " 4   population  188 non-null    float64\n",
      "dtypes: float64(3), object(2)\n",
      "memory usage: 7.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_ef2016_pivot.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains data on 187 countries, of which 12 are missing a value for the HDI (shown in a later section).\n",
    "\n",
    "We can now look at how *Earths* and *HDI* are distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create histogram function\n",
    "def hist_align_bins(variable, xlabel, title, bin_width):\n",
    "    \"\"\"\n",
    "    Create a histogram with bins of a selected width and overlaid with a\n",
    "    kernel density estimator.\n",
    "    \n",
    "    A list of bins for the histogram 'bins' argument is created based on \n",
    "    the range of the variable scores and on the selected width of bins.\n",
    "    The bins are automatically aligned with round units making the\n",
    "    histogram more readable.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    variable: pandas Series with dtype integer or float\n",
    "        Numerical variable to plot in histogram.\n",
    "    xlabel: str\n",
    "        Label x axis.\n",
    "    title: str\n",
    "        Figure title.\n",
    "    bin_width: integer, float\n",
    "        Width of histogram bins in the units of the variable.\n",
    "    \"\"\"\n",
    "    \n",
    "    leftmost_bin_edge = variable.min() - variable.min()%bin_width\n",
    "    rightmost_bin_edge = variable.max() - variable.max()%bin_width + 2*bin_width\n",
    "    bins_list = np.arange(leftmost_bin_edge, rightmost_bin_edge, bin_width)\n",
    "    \n",
    "    ax_hist = sns.histplot(data=variable, bins=bins_list, kde=True, alpha= 0.9,\n",
    "                           edgecolor='white', linewidth=0.5,\n",
    "                           line_kws=dict(alpha=0.5, linewidth=1.5,\n",
    "                           label='kernel density estimator'))\n",
    "    ax_hist.get_lines()[0].set_color('black') # manually edit line color due to bug with line_kws\n",
    "\n",
    "    # Additional formatting\n",
    "    ax_hist.set_xlabel(xlabel, size=12, labelpad=15)\n",
    "    ax_hist.set_ylabel('Count', size=12, labelpad=15)\n",
    "    \n",
    "    plt.title(title, size=14, pad=30)\n",
    "    plt.legend(frameon=False)\n",
    "    sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw Earths histogram\n",
    "hist_align_bins(variable=df_ef2016_pivot['Earths'],\n",
    "                xlabel='Earths',\n",
    "                title='Distribution of Ecological Footprint scores',\n",
    "                bin_width=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>QA</td>\n",
       "      <td>8.91</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.57e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>LU</td>\n",
       "      <td>7.99</td>\n",
       "      <td>0.90</td>\n",
       "      <td>5.76e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>AE</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0.86</td>\n",
       "      <td>9.27e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>BH</td>\n",
       "      <td>5.47</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.43e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>KW</td>\n",
       "      <td>5.29</td>\n",
       "      <td>0.81</td>\n",
       "      <td>4.05e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>5.11</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.22e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "      <td>5.10</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.63e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>TT</td>\n",
       "      <td>4.90</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.36e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>MN</td>\n",
       "      <td>4.77</td>\n",
       "      <td>0.73</td>\n",
       "      <td>3.03e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>BM</td>\n",
       "      <td>4.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.17e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country code  Earths   HDI  population\n",
       "139                 Qatar   QA    8.91  0.85    2.57e+06\n",
       "102            Luxembourg   LU    7.99  0.90    5.76e+05\n",
       "179  United Arab Emirates   AE    5.52  0.86    9.27e+06\n",
       "12                Bahrain   BH    5.47  0.84    1.43e+06\n",
       "93                 Kuwait   KW    5.29  0.81    4.05e+06\n",
       "181         United States   US    5.11  0.92    3.22e+08\n",
       "33                 Canada   CA    5.10  0.92    3.63e+07\n",
       "173   Trinidad and Tobago   TT    4.90  0.80    1.36e+06\n",
       "114              Mongolia   MN    4.77  0.73    3.03e+06\n",
       "19                Bermuda   BM    4.72   NaN    6.17e+04"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Earths top 10 countries\n",
    "df_ef2016_pivot.nlargest(10, 'Earths')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list contains mainly fossil fuel producing countries with the exceptions of Luxembourg and Bermuda. As a wealthy country, Luxembourg has a high per capita consumption level and it also experiences a [high level of tank tourism](https://web.archive.org/web/20200204044407/https://today.rtl.lu/news/luxembourg/a/1412471.html). Bermuda also is a wealthy country with a high per capita consumption level and it additionaly relies heavily on fossil fuels for all its [energy needs](https://www.worlddata.info/america/bermuda/energy-consumption.php)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>TL</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.27e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>ER</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.43</td>\n",
       "      <td>4.95e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>YE</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2.76e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>HT</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.08e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>BI</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.05e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>RW</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.19e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.49</td>\n",
       "      <td>3.47e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Palestine</td>\n",
       "      <td>PS</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.69</td>\n",
       "      <td>4.79e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>MZ</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2.88e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Congo DR</td>\n",
       "      <td>CD</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.45</td>\n",
       "      <td>7.87e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country code  Earths   HDI  population\n",
       "170  Timor-Leste   TL    0.31  0.63    1.27e+06\n",
       "58       Eritrea   ER    0.32  0.43    4.95e+06\n",
       "185        Yemen   YE    0.40  0.48    2.76e+07\n",
       "78         Haiti   HT    0.41  0.50    1.08e+07\n",
       "29       Burundi   BI    0.41  0.43    1.05e+07\n",
       "143       Rwanda   RW    0.43  0.52    1.19e+07\n",
       "0    Afghanistan   AF    0.44  0.49    3.47e+07\n",
       "131    Palestine   PS    0.47  0.69    4.79e+06\n",
       "118   Mozambique   MZ    0.48  0.44    2.88e+07\n",
       "42      Congo DR   CD    0.48  0.45    7.87e+07"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Earths bottom 10 countries\n",
    "df_ef2016_pivot.nsmallest(10, 'Earths')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw HDI histogram\n",
    "hist_align_bins(variable=df_ef2016_pivot['HDI'],\n",
    "                xlabel='HDI',\n",
    "                title='Distribution of HDI scores',\n",
    "                bin_width=0.025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Norway</td>\n",
       "      <td>NO</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.95</td>\n",
       "      <td>5.25e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CH</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.94</td>\n",
       "      <td>8.40e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0.94</td>\n",
       "      <td>8.19e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>IE</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.94</td>\n",
       "      <td>4.73e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AU</td>\n",
       "      <td>4.02</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2.41e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>SE</td>\n",
       "      <td>3.84</td>\n",
       "      <td>0.93</td>\n",
       "      <td>9.84e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>SG</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.93</td>\n",
       "      <td>5.62e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NL</td>\n",
       "      <td>3.12</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.70e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>DK</td>\n",
       "      <td>4.33</td>\n",
       "      <td>0.93</td>\n",
       "      <td>5.71e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Finland</td>\n",
       "      <td>FI</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.92</td>\n",
       "      <td>5.50e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country code  Earths   HDI  population\n",
       "128       Norway   NO    3.42  0.95    5.25e+06\n",
       "165  Switzerland   CH    2.88  0.94    8.40e+06\n",
       "69       Germany   DE    2.98  0.94    8.19e+07\n",
       "85       Ireland   IE    3.16  0.94    4.73e+06\n",
       "8      Australia   AU    4.02  0.94    2.41e+07\n",
       "164       Sweden   SE    3.84  0.93    9.84e+06\n",
       "150    Singapore   SG    3.96  0.93    5.62e+06\n",
       "122  Netherlands   NL    3.12  0.93    1.70e+07\n",
       "50       Denmark   DK    4.33  0.93    5.71e+06\n",
       "62       Finland   FI    3.85  0.92    5.50e+06"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HDI top 10\n",
    "df_ef2016_pivot.nlargest(10, 'HDI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Niger</td>\n",
       "      <td>NE</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.37</td>\n",
       "      <td>2.07e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>CF</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.37</td>\n",
       "      <td>4.59e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Chad</td>\n",
       "      <td>TD</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.45e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>BF</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.86e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Mali</td>\n",
       "      <td>ML</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.80e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>SL</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.42</td>\n",
       "      <td>7.40e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>BI</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.05e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>ER</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.43</td>\n",
       "      <td>4.95e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>MZ</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2.88e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Congo DR</td>\n",
       "      <td>CD</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.45</td>\n",
       "      <td>7.87e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country code  Earths   HDI  population\n",
       "125                     Niger   NE    1.00  0.37    2.07e+07\n",
       "35   Central African Republic   CF    0.73  0.37    4.59e+06\n",
       "36                       Chad   TD    1.10  0.40    1.45e+07\n",
       "28               Burkina Faso   BF    0.71  0.42    1.86e+07\n",
       "107                      Mali   ML    0.92  0.42    1.80e+07\n",
       "149              Sierra Leone   SL    0.62  0.42    7.40e+06\n",
       "29                    Burundi   BI    0.41  0.43    1.05e+07\n",
       "58                    Eritrea   ER    0.32  0.43    4.95e+06\n",
       "118                Mozambique   MZ    0.48  0.44    2.88e+07\n",
       "42                   Congo DR   CD    0.48  0.45    7.87e+07"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HDI bottom 10\n",
    "df_ef2016_pivot.nsmallest(10, 'HDI')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a better understanding of how much of the world is covered in the dataset, we can compare the countries listed in the dataset to a comprehensive list of countries considered to be independent States."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Import list of independent States by scraping online HTML table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list of independent States will be used as reference to better understand the coverage of the available data, with the assumption that countries or territories that are not independent are small in size and therefore have less impact on the global consumption of resources.\n",
    "  \n",
    "The list of [Independent States in the World](https://www.state.gov/independent-states-in-the-world/) published by the United States Department of State (USDS) will be used as reference. It includes GENC country codes which are based on the ISO 3166 country code standard and will be used to merge with the other dataset. This data is in the [public domain](https://www.state.gov/copyright-information/) and may be copied and distributed without permission.\n",
    "  \n",
    "The USDS [allows scraping of its website](https://www.state.gov/robots.txt) but for some reason the pandas function *pd.read_html(url)* does not work on this page so the BeautifulSoup package is used here instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API HTTP response code: 200\n"
     ]
    }
   ],
   "source": [
    "# Check that webpage access is successful\n",
    "url = 'https://www.state.gov/independent-states-in-the-world/'\n",
    "response = requests.get(url)\n",
    "print(f'API HTTP response code: {response.status_code}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Short-form name</th>\n",
       "      <th>Long-form name</th>\n",
       "      <th>GENC 2A Code (see Note 2)</th>\n",
       "      <th>GENC 3A Code (see Note 2)</th>\n",
       "      <th>Capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan *+</td>\n",
       "      <td>Islamic Republic of Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Kabul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania *+</td>\n",
       "      <td>Republic of Albania</td>\n",
       "      <td>AL</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Tirana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria *+</td>\n",
       "      <td>People’s Democratic Republic of Algeria</td>\n",
       "      <td>DZ</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algiers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra *+</td>\n",
       "      <td>Principality of Andorra</td>\n",
       "      <td>AD</td>\n",
       "      <td>AND</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola *+</td>\n",
       "      <td>Republic of Angola</td>\n",
       "      <td>AO</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Luanda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Venezuela *+</td>\n",
       "      <td>Bolivarian Republic of Venezuela</td>\n",
       "      <td>VE</td>\n",
       "      <td>VEN</td>\n",
       "      <td>Caracas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Vietnam *+</td>\n",
       "      <td>Socialist Republic of Vietnam</td>\n",
       "      <td>VN</td>\n",
       "      <td>VNM</td>\n",
       "      <td>Hanoi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Yemen *+</td>\n",
       "      <td>Republic of Yemen</td>\n",
       "      <td>YE</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Sanaa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia *+</td>\n",
       "      <td>Republic of Zambia</td>\n",
       "      <td>ZM</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>Lusaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe *+</td>\n",
       "      <td>Republic of Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Harare</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Short-form name                           Long-form name  \\\n",
       "0    Afghanistan *+          Islamic Republic of Afghanistan   \n",
       "1        Albania *+                      Republic of Albania   \n",
       "2        Algeria *+  People’s Democratic Republic of Algeria   \n",
       "3        Andorra *+                  Principality of Andorra   \n",
       "4         Angola *+                       Republic of Angola   \n",
       "..              ...                                      ...   \n",
       "190    Venezuela *+         Bolivarian Republic of Venezuela   \n",
       "191      Vietnam *+            Socialist Republic of Vietnam   \n",
       "192        Yemen *+                        Republic of Yemen   \n",
       "193       Zambia *+                       Republic of Zambia   \n",
       "194     Zimbabwe *+                     Republic of Zimbabwe   \n",
       "\n",
       "    GENC 2A Code (see Note 2) GENC 3A Code (see Note 2)           Capital  \n",
       "0                          AF                       AFG             Kabul  \n",
       "1                          AL                       ALB            Tirana  \n",
       "2                          DZ                       DZA           Algiers  \n",
       "3                          AD                       AND  Andorra la Vella  \n",
       "4                          AO                       AGO            Luanda  \n",
       "..                        ...                       ...               ...  \n",
       "190                        VE                       VEN           Caracas  \n",
       "191                        VN                       VNM             Hanoi  \n",
       "192                        YE                       YEM             Sanaa  \n",
       "193                        ZM                       ZMB            Lusaka  \n",
       "194                        ZW                       ZWE            Harare  \n",
       "\n",
       "[195 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scrape table, code based on pluralsight.com/guides/extracting-data-html-beautifulsoup\n",
    "# and kite.com/python/examples/4420/beautifulsoup-parse-an-html-table-and-write-to-a-csv\n",
    "\n",
    "html_content = requests.get(url).text\n",
    "soup = BeautifulSoup(html_content, 'lxml')\n",
    "table = soup.find('table')\n",
    "\n",
    "output_rows = []\n",
    "for table_row in table.findAll('tr'):\n",
    "    columns = table_row.findAll('td')\n",
    "    output_row = []\n",
    "    for column in columns:\n",
    "        output_row.append(column.text)\n",
    "    output_rows.append(output_row)\n",
    "\n",
    "df_USDS_raw = pd.DataFrame(data=output_rows[1:], columns=output_rows[0])\n",
    "df_USDS_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 195 independent States according to the USDS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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_USDS</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Cambodia *+</td>\n",
       "      <td>KH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Cameroon *+</td>\n",
       "      <td>CM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Canada *+</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Central\\nAfrican Republic *+</td>\n",
       "      <td>CF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Chad *+</td>\n",
       "      <td>TD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Chile *+</td>\n",
       "      <td>CL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>China *+ (see note 3)</td>\n",
       "      <td>CN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Colombia *+</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Comoros *+</td>\n",
       "      <td>KM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Congo (Brazzaville) *+\\n(see note 4)</td>\n",
       "      <td>CG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Congo (Kinshasa) *+\\n(see note 4)</td>\n",
       "      <td>CD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Costa Rica *+</td>\n",
       "      <td>CR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Côte d’Ivoire *+</td>\n",
       "      <td>CI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Croatia *+</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Cuba *+</td>\n",
       "      <td>CU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Cyprus *+</td>\n",
       "      <td>CY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Czechia *+</td>\n",
       "      <td>CZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Denmark *+</td>\n",
       "      <td>DK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Djibouti *+</td>\n",
       "      <td>DJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Dominica *+</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Dominican Republic *+</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Ecuador *+</td>\n",
       "      <td>EC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Egypt *+</td>\n",
       "      <td>EG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>El Salvador *+</td>\n",
       "      <td>SV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Equatorial Guinea *+</td>\n",
       "      <td>GQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Eritrea *+</td>\n",
       "      <td>ER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Estonia *+</td>\n",
       "      <td>EE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>\\n \\nEswatini *+</td>\n",
       "      <td>SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Ethiopia *+</td>\n",
       "      <td>ET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Fiji *+</td>\n",
       "      <td>FJ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            country_USDS  code\n",
       "30                           Cambodia *+    KH\n",
       "31                           Cameroon *+    CM\n",
       "32                             Canada *+    CA\n",
       "33          Central\\nAfrican Republic *+    CF\n",
       "34                               Chad *+    TD\n",
       "35                              Chile *+    CL\n",
       "36                 China *+ (see note 3)    CN\n",
       "37                           Colombia *+    CO\n",
       "38                            Comoros *+    KM\n",
       "39  Congo (Brazzaville) *+\\n(see note 4)    CG\n",
       "40     Congo (Kinshasa) *+\\n(see note 4)    CD\n",
       "41                         Costa Rica *+    CR\n",
       "42                      Côte d’Ivoire *+    CI\n",
       "43                            Croatia *+    HR\n",
       "44                               Cuba *+    CU\n",
       "45                             Cyprus *+    CY\n",
       "46                            Czechia *+    CZ\n",
       "47                            Denmark *+    DK\n",
       "48                           Djibouti *+    DJ\n",
       "49                           Dominica *+    DM\n",
       "50                 Dominican Republic *+    DO\n",
       "51                            Ecuador *+    EC\n",
       "52                              Egypt *+    EG\n",
       "53                        El Salvador *+    SV\n",
       "54                  Equatorial Guinea *+    GQ\n",
       "55                            Eritrea *+    ER\n",
       "56                            Estonia *+    EE\n",
       "57                      \\n \\nEswatini *+    SZ\n",
       "58                           Ethiopia *+    ET\n",
       "59                               Fiji *+    FJ"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_USDS = df_USDS_raw[['Short-form name', 'GENC 2A Code (see Note 2)']].copy()\n",
    "df_USDS.columns = ['country_USDS', 'code']\n",
    "df_USDS[30:60]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This sample of the table shows that country names contain extra characters and notes which can be removed for better readability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        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_USDS</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>KH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>CM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CentralAfrican Republic</td>\n",
       "      <td>CF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Chad</td>\n",
       "      <td>TD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Chile</td>\n",
       "      <td>CL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>China</td>\n",
       "      <td>CN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Comoros</td>\n",
       "      <td>KM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Congo (Brazzaville)</td>\n",
       "      <td>CG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Congo (Kinshasa)</td>\n",
       "      <td>CD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>CR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Côte d’Ivoire</td>\n",
       "      <td>CI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>CU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>CY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Czechia</td>\n",
       "      <td>CZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>DK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>DJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Dominica</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>EC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>EG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>El Salvador</td>\n",
       "      <td>SV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>GQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>ER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>EE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Eswatini</td>\n",
       "      <td>SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>ET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>FJ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country_USDS code\n",
       "30                 Cambodia   KH\n",
       "31                 Cameroon   CM\n",
       "32                   Canada   CA\n",
       "33  CentralAfrican Republic   CF\n",
       "34                     Chad   TD\n",
       "35                    Chile   CL\n",
       "36                    China   CN\n",
       "37                 Colombia   CO\n",
       "38                  Comoros   KM\n",
       "39      Congo (Brazzaville)   CG\n",
       "40         Congo (Kinshasa)   CD\n",
       "41               Costa Rica   CR\n",
       "42            Côte d’Ivoire   CI\n",
       "43                  Croatia   HR\n",
       "44                     Cuba   CU\n",
       "45                   Cyprus   CY\n",
       "46                  Czechia   CZ\n",
       "47                  Denmark   DK\n",
       "48                 Djibouti   DJ\n",
       "49                 Dominica   DM\n",
       "50       Dominican Republic   DO\n",
       "51                  Ecuador   EC\n",
       "52                    Egypt   EG\n",
       "53              El Salvador   SV\n",
       "54        Equatorial Guinea   GQ\n",
       "55                  Eritrea   ER\n",
       "56                  Estonia   EE\n",
       "57                 Eswatini   SZ\n",
       "58                 Ethiopia   ET\n",
       "59                     Fiji   FJ"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove extra characters and notes from country names\n",
    "\n",
    "# Remove '(see note [digit])'\n",
    "for name in df_USDS.loc[:,'country_USDS']:\n",
    "    if re.findall(r'[0-9]', name):\n",
    "        df_USDS.loc[:,'country_USDS'][df_USDS.loc[:,'country_USDS'] == name] = name[:-13]\n",
    "\n",
    "# Remove '\\n' '*+' white spaces and no-break space left of Eswatini\n",
    "df_USDS.loc[:,'country_USDS'] = df_USDS.loc[:,'country_USDS']\\\n",
    "                                       .str.replace('\\n','')\\\n",
    "                                       .str.lstrip('\\xa0')\\\n",
    "                                       .str.rstrip(' *+')\n",
    "\n",
    "# Remove no-break space left of Eswatini country code\n",
    "df_USDS.loc[:,'code'] = df_USDS.loc[:,'code'].str.lstrip('\\xa0 ')\n",
    "\n",
    "# Print subset of column to check that strings have been formatted correctly\n",
    "df_USDS[30:60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>country_USDS</th>\n",
       "      <th>code</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>AL</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZ</td>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>AD</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AO</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GP</td>\n",
       "      <td>Guadeloupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>NaN</td>\n",
       "      <td>MQ</td>\n",
       "      <td>Martinique</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>NaN</td>\n",
       "      <td>MS</td>\n",
       "      <td>Montserrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>NaN</td>\n",
       "      <td>PS</td>\n",
       "      <td>Palestine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>NaN</td>\n",
       "      <td>RE</td>\n",
       "      <td>Reunion</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    country_USDS code      country\n",
       "0    Afghanistan   AF  Afghanistan\n",
       "1        Albania   AL      Albania\n",
       "2        Algeria   DZ      Algeria\n",
       "3        Andorra   AD          NaN\n",
       "4         Angola   AO       Angola\n",
       "..           ...  ...          ...\n",
       "202          NaN   GP   Guadeloupe\n",
       "203          NaN   MQ   Martinique\n",
       "204          NaN   MS   Montserrat\n",
       "205          NaN   PS    Palestine\n",
       "206          NaN   RE      Reunion\n",
       "\n",
       "[207 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge list of independent States with list of countries in EF dataset for comparison\n",
    "df_ef2016_merge_outer = pd.merge(df_USDS, df_ef2016_pivot[['country', 'code']], how='outer', on='code')\n",
    "df_ef2016_merge_outer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 207 entries, 0 to 206\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   country_USDS  195 non-null    object\n",
      " 1   code          207 non-null    object\n",
      " 2   country       188 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 6.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check that merge is successful\n",
    "df_ef2016_merge_outer.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell displays the independent States not included in the EF dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       country_USDS code country\n",
      "3                           Andorra   AD     NaN\n",
      "74                         Holy See   VA     NaN\n",
      "77                          Iceland   IS     NaN\n",
      "90                         Kiribati   KI     NaN\n",
      "93                           Kosovo   XK     NaN\n",
      "102                   Liechtenstein   LI     NaN\n",
      "108                        Maldives   MV     NaN\n",
      "111                Marshall Islands   MH     NaN\n",
      "115  Micronesia,Federated States of   FM     NaN\n",
      "117                          Monaco   MC     NaN\n",
      "123                           Nauru   NR     NaN\n",
      "134                           Palau   PW     NaN\n",
      "146           Saint Kitts and Nevis   KN     NaN\n",
      "150                      San Marino   SM     NaN\n",
      "155                      Seychelles   SC     NaN\n",
      "163                     South Sudan   SS     NaN\n",
      "181                          Tuvalu   TV     NaN\n",
      "187                         Uruguay   UY     NaN\n",
      "189                         Vanuatu   VU     NaN\n",
      "\n",
      "Number of independent countries not included in EF dataset: 19\n"
     ]
    }
   ],
   "source": [
    "missing_states = df_ef2016_merge_outer[df_ef2016_merge_outer['country'].isna()]\n",
    "print(missing_states)\n",
    "print()\n",
    "print(f'Number of independent countries not included in EF dataset:\\\n",
    " {len(missing_states)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also interesting to see which are the 10 out of the 187 countries listed in the EF dataset that are not independent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    country_USDS code                 country\n",
      "195          NaN   AW                   Aruba\n",
      "196          NaN   BM                 Bermuda\n",
      "197          NaN   VG  British Virgin Islands\n",
      "198          NaN   KY          Cayman Islands\n",
      "199          NaN   CK            Cook Islands\n",
      "200          NaN   GF           French Guiana\n",
      "201          NaN   PF        French Polynesia\n",
      "202          NaN   GP              Guadeloupe\n",
      "203          NaN   MQ              Martinique\n",
      "204          NaN   MS              Montserrat\n",
      "205          NaN   PS               Palestine\n",
      "206          NaN   RE                 Reunion\n",
      "\n",
      "Number of dependent territories included in EF dataset: 12\n"
     ]
    }
   ],
   "source": [
    "additional_territories = df_ef2016_merge_outer[df_ef2016_merge_outer['country_USDS'].isna()]\n",
    "print(additional_territories)\n",
    "print()\n",
    "print(f'Number of dependent territories included in EF dataset:\\\n",
    " {len(additional_territories)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "10 dependent territories are included in the EF dataset with a computed ecological footprint. As they are not independent states they have not been included in the HDI, along with North Korea and Somalia, as shown in the table below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>country</th>\n",
       "      <th>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>AW</td>\n",
       "      <td>4.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.05e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>BM</td>\n",
       "      <td>4.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.17e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>British Virgin Islands</td>\n",
       "      <td>VG</td>\n",
       "      <td>2.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.07e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Cayman Islands</td>\n",
       "      <td>KY</td>\n",
       "      <td>3.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.08e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Cook Islands</td>\n",
       "      <td>CK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.74e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>French Guiana</td>\n",
       "      <td>GF</td>\n",
       "      <td>1.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>French Polynesia</td>\n",
       "      <td>PF</td>\n",
       "      <td>2.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.80e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Guadeloupe</td>\n",
       "      <td>GP</td>\n",
       "      <td>2.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.50e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Martinique</td>\n",
       "      <td>MQ</td>\n",
       "      <td>2.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.85e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Montserrat</td>\n",
       "      <td>MS</td>\n",
       "      <td>2.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.15e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>North Korea</td>\n",
       "      <td>KP</td>\n",
       "      <td>0.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.54e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Reunion</td>\n",
       "      <td>RE</td>\n",
       "      <td>2.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.70e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>SO</td>\n",
       "      <td>0.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.43e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    country code  Earths  HDI  population\n",
       "7                     Aruba   AW    4.11  NaN    1.05e+05\n",
       "19                  Bermuda   BM    4.72  NaN    6.17e+04\n",
       "25   British Virgin Islands   VG    2.07  NaN    3.07e+04\n",
       "34           Cayman Islands   KY    3.65  NaN    6.08e+04\n",
       "43             Cook Islands   CK     NaN  NaN    1.74e+04\n",
       "64            French Guiana   GF    1.50  NaN    2.76e+05\n",
       "65         French Polynesia   PF    2.47  NaN    2.80e+05\n",
       "73               Guadeloupe   GP    2.56  NaN    4.50e+05\n",
       "109              Martinique   MQ    2.80  NaN    3.85e+05\n",
       "116              Montserrat   MS    2.82  NaN    5.15e+03\n",
       "127             North Korea   KP    0.54  NaN    2.54e+07\n",
       "140                 Reunion   RE    2.98  NaN    8.70e+05\n",
       "154                 Somalia   SO    0.60  NaN    1.43e+07"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display EF dataset countries/territories with no HDI\n",
    "df_ef2016_pivot[df_ef2016_pivot['HDI'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we better understand the coverage of the dataset, it would be interesting to include information about the region to which each country belongs as it would provide a better overview of the geographical distribution of the scores of both indicators."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Import list of countries with regions from GitHub repository"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This information can be obtained from different places. A list built by [GitHub user *lukes*](https://github.com/lukes/ISO-3166-Countries-with-Regional-Codes) last updated on 19 March 2019 will be imported here. This list has been obtained by merging two sources, the [Wikipedia ISO 3166-1 article](https://en.wikipedia.org/wiki/ISO_3166-1#Officially_assigned_code_elements) table containing the alpha and numeric country codes, and the [United Nations Statistics Division table](https://unstats.un.org/unsd/methodology/m49/overview) containing regional, and sub-regional names and codes. The information on regions may also be obtained by downloading a CSV or Excel file from the [United Nations Statistics Division page](https://unstats.un.org/unsd/methodology/m49/overview)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>name</th>\n",
       "      <th>alpha-2</th>\n",
       "      <th>alpha-3</th>\n",
       "      <th>country-code</th>\n",
       "      <th>iso_3166-2</th>\n",
       "      <th>region</th>\n",
       "      <th>sub-region</th>\n",
       "      <th>intermediate-region</th>\n",
       "      <th>region-code</th>\n",
       "      <th>sub-region-code</th>\n",
       "      <th>intermediate-region-code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>4</td>\n",
       "      <td>ISO 3166-2:AF</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td></td>\n",
       "      <td>142</td>\n",
       "      <td>034</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Åland Islands</td>\n",
       "      <td>AX</td>\n",
       "      <td>ALA</td>\n",
       "      <td>248</td>\n",
       "      <td>ISO 3166-2:AX</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td></td>\n",
       "      <td>150</td>\n",
       "      <td>154</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>AL</td>\n",
       "      <td>ALB</td>\n",
       "      <td>8</td>\n",
       "      <td>ISO 3166-2:AL</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td></td>\n",
       "      <td>150</td>\n",
       "      <td>039</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZ</td>\n",
       "      <td>DZA</td>\n",
       "      <td>12</td>\n",
       "      <td>ISO 3166-2:DZ</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td></td>\n",
       "      <td>002</td>\n",
       "      <td>015</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>AS</td>\n",
       "      <td>ASM</td>\n",
       "      <td>16</td>\n",
       "      <td>ISO 3166-2:AS</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td></td>\n",
       "      <td>009</td>\n",
       "      <td>061</td>\n",
       "      <td></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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>WF</td>\n",
       "      <td>WLF</td>\n",
       "      <td>876</td>\n",
       "      <td>ISO 3166-2:WF</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td></td>\n",
       "      <td>009</td>\n",
       "      <td>061</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>EH</td>\n",
       "      <td>ESH</td>\n",
       "      <td>732</td>\n",
       "      <td>ISO 3166-2:EH</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td></td>\n",
       "      <td>002</td>\n",
       "      <td>015</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>YE</td>\n",
       "      <td>YEM</td>\n",
       "      <td>887</td>\n",
       "      <td>ISO 3166-2:YE</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td></td>\n",
       "      <td>142</td>\n",
       "      <td>145</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZM</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>894</td>\n",
       "      <td>ISO 3166-2:ZM</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>002</td>\n",
       "      <td>202</td>\n",
       "      <td>014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>716</td>\n",
       "      <td>ISO 3166-2:ZW</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>002</td>\n",
       "      <td>202</td>\n",
       "      <td>014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name alpha-2 alpha-3  country-code     iso_3166-2   region  \\\n",
       "0          Afghanistan      AF     AFG             4  ISO 3166-2:AF     Asia   \n",
       "1        Åland Islands      AX     ALA           248  ISO 3166-2:AX   Europe   \n",
       "2              Albania      AL     ALB             8  ISO 3166-2:AL   Europe   \n",
       "3              Algeria      DZ     DZA            12  ISO 3166-2:DZ   Africa   \n",
       "4       American Samoa      AS     ASM            16  ISO 3166-2:AS  Oceania   \n",
       "..                 ...     ...     ...           ...            ...      ...   \n",
       "244  Wallis and Futuna      WF     WLF           876  ISO 3166-2:WF  Oceania   \n",
       "245     Western Sahara      EH     ESH           732  ISO 3166-2:EH   Africa   \n",
       "246              Yemen      YE     YEM           887  ISO 3166-2:YE     Asia   \n",
       "247             Zambia      ZM     ZMB           894  ISO 3166-2:ZM   Africa   \n",
       "248           Zimbabwe      ZW     ZWE           716  ISO 3166-2:ZW   Africa   \n",
       "\n",
       "             sub-region intermediate-region region-code sub-region-code  \\\n",
       "0         Southern Asia                             142             034   \n",
       "1       Northern Europe                             150             154   \n",
       "2       Southern Europe                             150             039   \n",
       "3       Northern Africa                             002             015   \n",
       "4             Polynesia                             009             061   \n",
       "..                  ...                 ...         ...             ...   \n",
       "244           Polynesia                             009             061   \n",
       "245     Northern Africa                             002             015   \n",
       "246        Western Asia                             142             145   \n",
       "247  Sub-Saharan Africa      Eastern Africa         002             202   \n",
       "248  Sub-Saharan Africa      Eastern Africa         002             202   \n",
       "\n",
       "    intermediate-region-code  \n",
       "0                             \n",
       "1                             \n",
       "2                             \n",
       "3                             \n",
       "4                             \n",
       "..                       ...  \n",
       "244                           \n",
       "245                           \n",
       "246                           \n",
       "247                      014  \n",
       "248                      014  \n",
       "\n",
       "[249 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: set keep_default_na to False to avoid Namibia code NA from being\n",
    "# interpreted as NaN when merging dataframes later on\n",
    "url = 'https://raw.githubusercontent.com/lukes/ISO-3166-Countries-with-Regional-Codes/master/all/all.csv'\n",
    "df_regions_raw = pd.read_csv(url, keep_default_na=False)\n",
    "df_regions_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AF</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AX</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DZ</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AS</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>WF</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>EH</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>YE</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>ZM</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code continent\n",
       "0     AF      Asia\n",
       "1     AX    Europe\n",
       "2     AL    Europe\n",
       "3     DZ    Africa\n",
       "4     AS   Oceania\n",
       "..   ...       ...\n",
       "244   WF   Oceania\n",
       "245   EH    Africa\n",
       "246   YE      Asia\n",
       "247   ZM    Africa\n",
       "248   ZW    Africa\n",
       "\n",
       "[249 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_regions = df_regions_raw[['alpha-2', 'region']]\n",
    "df_regions.columns = ['code', 'continent']\n",
    "df_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.49</td>\n",
       "      <td>3.47e+07</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2.93e+06</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZ</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4.06e+07</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AO</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2.88e+07</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>AG</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.01e+05</td>\n",
       "      <td>Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>VE</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.16e+07</td>\n",
       "      <td>Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>VN</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.69</td>\n",
       "      <td>9.46e+07</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>YE</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2.76e+07</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZM</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.66e+07</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.62e+07</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>188 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country code  Earths   HDI  population continent\n",
       "0            Afghanistan   AF    0.44  0.49    3.47e+07      Asia\n",
       "1                Albania   AL    1.23  0.79    2.93e+06    Europe\n",
       "2                Algeria   DZ    1.48  0.75    4.06e+07    Africa\n",
       "3                 Angola   AO    0.57  0.57    2.88e+07    Africa\n",
       "4    Antigua and Barbuda   AG    2.69  0.77    1.01e+05  Americas\n",
       "..                   ...  ...     ...   ...         ...       ...\n",
       "183            Venezuela   VE    1.55  0.75    3.16e+07  Americas\n",
       "184             Viet Nam   VN    1.37  0.69    9.46e+07      Asia\n",
       "185                Yemen   YE    0.40  0.48    2.76e+07      Asia\n",
       "186               Zambia   ZM    0.60  0.58    1.66e+07    Africa\n",
       "187             Zimbabwe   ZW    0.65  0.55    1.62e+07    Africa\n",
       "\n",
       "[188 rows x 6 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge df_ef2016_pivot with df_regions to get continents for all\n",
    "# countries and territories\n",
    "df_ef2016 = pd.merge(df_ef2016_pivot, df_regions, how='left', on='code')\n",
    "df_ef2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 188 entries, 0 to 187\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   country     188 non-null    object \n",
      " 1   code        188 non-null    object \n",
      " 2   Earths      184 non-null    float64\n",
      " 3   HDI         175 non-null    float64\n",
      " 4   population  188 non-null    float64\n",
      " 5   continent   188 non-null    object \n",
      "dtypes: float64(3), object(3)\n",
      "memory usage: 10.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check that merge is successful\n",
    "df_ef2016.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The merging appears to be successful as all countries and territories have an attributed continent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the dataset is ready for further analysis, it is time to look at the distribution of the country scores for the EF and HDI grouped by continent and to visualize the relationship between these two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Ecological Footprint by continent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=df_ef2016\n",
    "var_cat='continent'\n",
    "var_num='Earths'\n",
    "\n",
    "# Draw boxplots overlaid with stripplots of EF scores grouped by continent\n",
    "# Note: the boxplots are drawn with whiskers that reach out to farthest\n",
    "# data point within the interval contained by the 5th and 95th percentiles\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "sns.boxplot(data=data, x=var_cat, y=var_num, whis=(5, 95), linewidth=1,\n",
    "            width=0.3, fliersize=False, saturation=1)\n",
    "sns.stripplot(data=data, x=var_cat, y=var_num, color=\"k\", alpha=0.6, size=3)\n",
    "\n",
    "# Add horizontal line for one planet limit and additional formatting\n",
    "ax.axhline(1, color='grey', ls='dashed', alpha=0.5)\n",
    "ax.set_xlabel('Continent', size=12, labelpad=15)\n",
    "ax.set_ylabel('Earths', size=12, labelpad=15)\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dashed line represents the one planet limit. The boxplots are drawn with whiskers that reach out to farthest data point within the interval contained by the 5th and 95th percentiles. Countries from both Europe and Oceania are all consuming in excess of Earth's resources, as well as nearly all of North and South American countries with a few exceptions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>HT</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.08e+07</td>\n",
       "      <td>Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Honduras</td>\n",
       "      <td>HN</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.62</td>\n",
       "      <td>9.11e+06</td>\n",
       "      <td>Americas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country code  Earths   HDI  population continent\n",
       "78     Haiti   HT    0.41  0.50    1.08e+07  Americas\n",
       "79  Honduras   HN    0.96  0.62    9.11e+06  Americas"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ef2016[(df_ef2016['Earths']<=1) & (df_ef2016['continent']=='Americas')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many Asian and African countries appear to be within the planet's limits. These can be identified in the scatterplot further below.\n",
    "\n",
    "African countries are very frugal in their consumption, very few exceed the 2 Earths mark:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Libya</td>\n",
       "      <td>LY</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.69</td>\n",
       "      <td>6.29e+06</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Mauritius</td>\n",
       "      <td>MU</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.26e+06</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Reunion</td>\n",
       "      <td>RE</td>\n",
       "      <td>2.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.70e+05</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country code  Earths   HDI  population continent\n",
       "100      Libya   LY    2.24  0.69    6.29e+06    Africa\n",
       "111  Mauritius   MU    2.18  0.79    1.26e+06    Africa\n",
       "140    Reunion   RE    2.98   NaN    8.70e+05    Africa"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ef2016[(df_ef2016['Earths']>=2) & (df_ef2016['continent']=='Africa')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Asian countries on the other hand show a very wide distribution in resource consumption. Their EF ranges from 0.3 Earth equivalents up to 8.8 with a median of 1.3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    47.00\n",
       "mean      2.12\n",
       "std       1.78\n",
       "min       0.31\n",
       "25%       0.83\n",
       "50%       1.30\n",
       "75%       2.91\n",
       "max       8.91\n",
       "Name: Earths, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ef2016[df_ef2016['continent']=='Asia']['Earths'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Human Development Index by continent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nnZfmdJIuKsg0WbBgAWvXrmXt2rXMmjWL8ePH7zU/FAoxbtw4b8INYu1djhte6GR3u+OsSSHmT9GmLhl4U6ZM4Vvf+pbXMWSA6SCdNJk0aRIAQ4cOpaKiwuM0maOhw+3Z57i+Pu5xGhHJJBpBpslpp53G0UcfTVFREXl5vd/Yd8uWLfz+97+noqKCf/mXf9lzFKykNqoowNmHhVi3O855h2v0KCL9Rz9906gvI8enn36aTZs2sWnTJo4//nimTZuWhmT+tqomxpKtMeaMDTKlfN99jOdMVjGKSP/TJlafmTp1KmbG0KFDGTt2rNdxfOHXr3expDrGb17XOWcikj4aQfrMcccdx7Rp08jNzSUnJ8frOL5QMcSobnJUFOj0F/HO9u3baW9v9zqGpJEK0oeKioq8juArX5+Tw4b6OBOHaYOHeGPTpk3cfPPNVFdX8/LLL3PCCSd4HUnSQAUpvjckbEwfrvMbxTt1dXV7bna+c+dOj9NIuuhXchGRD3DMMcdw5plnUlJSwumnn+51HEkTFaSIyAcIBAKce+65DB8+nIKCAq/jSJqoIEVERFJQQYqIiKSgghRfW7Ezxo0vdvLkWp0DKSLppYIUX3vorS42NcR59O3onvs9iqTDe0etSvZSQYqvTS5LnN4xtiTAEF1RTtLkD3/4A1deeSV/+MMfvI4iHlJBim8t3hJlza4YHx0f5Jsn5hAwIxZ33P1mhB+80MlG3b1DBsjixYsBdCPkLKcLBXjMOUcsFvM6hm+MLB/K3L/NAGDjxo1Eo1EAfrnzUAKBAG1tbWzduhXnHL/bUnhAN5geWT50ICJLBjrjjDN44YUX+OhHP+p1FPGQCjJNnHN7fiudM2cOZoZzjp///Ods2LCBBx54gAsvvNDjlN67/6HH9jz+/e9/z4svvsjhhx/OtddeC0Braytf//rXeemll9i2bRs33HADp512GuXl5R4llkx0zjnncM4553gdQzymgkyTxYsXc++99wJgZsyZM4fOzk5WrVoFwBtvvKGC7OEzn/kMn/zkJ/e6Nm1BQQGnnHIKnZ2d3H///dx3332sXbuW733vex4mFZFMpIIcIPPP+yfqd9Xued7U1LTnGo5LliyhuLgYSFzjMRwO8+STT/Lyyy/36b1Lyyp49OE/939oH3pvPXX30Y9+lLfeeotQKER5eTmBgHali0j/U0EOkPpdtbw75xt7njvnYNvbgFE/ejIN9v6tmwJAY/JPnyy+uR+TDj7Dhw/nu9/9Lk888QQXX3wxH/nIR7yOJCIZyJe/epvZGWa2xszWmdk3U8wvMbPHzWy5ma0ys895kfNAmBm5Yw4nd8wULFmO8a4OOrevJd7Z6nG6wSk3N5fTTjuNsrIyr6OISAby3QjSzILA7cCpQDXwmpk95pxb3W2xq4DVzrlzzKwCWGNmf3DORTyI3Ged29bQWb2K3Mpp5I6eTPMbC4jW7yCQX0Tx7AvAxQnm616QIl559dVXefHFFznxxBOZPXu213HEY74rSGAWsM45twHAzO4D5gPdC9IBRZYYihUCu4FouoMeqNbVz+OiXUQbd5I7ejLxzjYAYq31NLz4vxCPUnTUmeSMONTjpCLZ6Q9/+AMdHR1s2rRJBSm+3MQ6BtjS7Xl1clp3vwAOB7YB/wC+6pzb56xxM7vczJaa2dLa2tqes9MuNHTUXn8XzTiD3LHTyJ9wDMSi4KCrfruXEUWy2qGHHrrn73fffZfFixfT1aXrAGcrP44gLcW0nhfhPB1YBpwMHAo8bWYvOuea9nqRc3cCdwLMnDnT8wt5Fh3zCWIt9QQLSwEIlQynsGQ4LtaF6+ogHukgb9wMj1OKZK8vf/nL7Nixg9zcXK6//nq6urpYt24dl156qdfRxAN+LMhqYGy355UkRordfQ64yTnngHVmthGYAryanogHxwJBQsX7ntBuwTCFR57mQSIR6S4UClFZWUlNTc2eqzi1tbV5nEq84sdNrK8Bk8xsgpnlABcDj/VY5l3gFAAzGwFMBjakNaV4qrOzk4aGBt555x2vo0gGGj58OFdccQVnnnkmn/70p72OIx7xXUE656LA1cBC4C3gfufcKjP7kpl9KbnYD4A5ZvYP4FngG865Om8Sixfuv/9+amtr+clPfrLnAgyyfx0dHfzsZz/jRz/6ETt27PA6jm9Fo1HWrFnD5MmTOffccykqKmLt2rU0NDR4HU3SzI+bWHHOPQk82WPaHd0ebwO0TTKLvXeBd+cc8Xic5uZmHnroIYqLi5k/f76urpPC8uXL91zacNGiRVx00UUeJ/Kn3/zmNyxbtozhw4dz/fXX88gjj7Bw4UKGDBmyZ7OrZAdfFqTIB7nooou49dZbueqqqxg1ahT333//novBjx8/nqOPPtrjhP5z6KGHUlhYSEdHB9OmTfM6ji9cMH8+NfX1e03btGnTniNXn3vuOXbs2EFra+JiHlVVVcydO7dP7z28tJQHH320fwNLWqkgZVAaMmQIw4YN44gjjgBg1KjkKTShEBUVFV5G863y8nJuuukmYrEYeXl5XsfxhZr6en5ds/cpYJtDYf4eiXB4bh5H1u2iDuM5B6PDYU5obOrlnfZ1RX+HlbRTQUpGOOmkk6isrKSgoIDhw4d7Hce3wuEw4XDY6xi+Ni4nh3E5OXuel4dCXDh0qHeBxDMqSMkYEyZM8DqCiGQQHckgIiKSggpSREQkBRWkj3XVbaF19QtEm7y/jqyISLZRQfqIi3bRUb2KaGMNLh6j+c0FdLz7D1qWP+V1NBGRrKODdHykdfVzdG57BwsGKTnxEiycj4u1EMjN9zqaSFbb0dVFfiBASTDodRRJIxWkj7ho4uRkF09cJab4uAuI1m8jXF7lZSyRrLa0rY0HGhqojUW5ZGgppxTppubZQgXpIwXTPkawqIxQyQiC+cUABEcd5nEqkey2PdrFlq4umuIxHmtqZEZ+PuUh/ejMBvpf9pFA7hCGTDrO6xgi0s3cgkKWtLayLWqMDIXJD6S6Za1kIhWkDGqtra3k5uYS0m/0MkBKgkFuGDmK9ZEIFaEQBQHth8wWOopVBq2mpia+/vWv893vfpeWlhav40gG2tHVxXMtzeyKxZiUm8tQHaSTVVSQMmi1trbinGPXrl1UV1d7HUcy0L319Tzf0sofetzxQ7KDtkvJoFVaWkplZSUjR45k4sSJXseRDNEYi5FnRm4gQCi5uzGo3Y5ZSQUpg1ZeXh7f+c53vI4hGWR5ezsPNzaSHzCuKCvnstJhrO3sZEqK24O9G4nwVmcHR+XlM0J3SMlIKkjJCCtWrCAnJ4cpU6Z4HUUGsY2RCA5oizt2dnUxOS+PsuQBYO90dhDEODQ3l7hz/L6+nk7nWNPZyTXlugdpJlJByqD38ssvc++99wJwzTXXMG3aNI8TDR7xeJxt27YxfPhwcrrdAzFbnVBQwO5YlKHBIIfm5u6Zvry9nYcaGwH41NChHJqTw9auCLXRKNPzdKWrTKWC9AnnHGba0dGbuXPn9jq9vr6euro6AJYsWUJxcTGLFi1KZ7xB65577mHJkiWMHTuWb3/721n/GawIhfj8sLJ9prfG43set7s4f2ioZ2Oki5Bp/2QmU0F6LN7ZRuOSP+MibRQdfRbhsrFeR/Kl/RVeV1cXCxcuJCcnh1NPPTXrf8gfiPXr1wNQXV1NJBIht9uoKVtcMbwC5xzNzc0Eg0EKCgr2Wca5curr6zEztg8dyvr162nJyyUWi7GkvJwrhmsTayZSQXossmMtHZuXAUa4YrUK8iCEw2HOPvtsr2MMSp/61Kd46qmnOOaYY7KyHAF+XVPLy62tPNXcDMBlpaUcsr91UVvHinAOS/PymJSTywmBIIGafW9Jp9Ic/FSQHot1tIJzuFiEaEs9zcufIm/ckZgZ7ZuWkzPiEHJH6hQGGRjTp09n+vTpXsfwXJdzex5HcftZMuHI/HyOzNe+x0yngvRYzvAJ5I6ahANiu7cSb64j1rgTAgFiLfVEdqwlp7wKC+kACpGBckJBASGDIRbgsNx9T+mQ7KSC9Fi4dBRD530OgMZX7ife1kSgYChmRqylnkBeIejaj3ssX76c+++/n8MOO4xLL71U+xulX4TNOLGg0OsY4jMqSB8IhBO/sZbMvoBoUw3hYWMAo2t3NaHi4ZgKco+FCxdSV1dHXV0dp59+OiNHjvQ6kmSwmHM81dxMm4tzRlGRLlSeZXQtVh8J5A4hp2I8FgxjwRA5FeMJ5A7xOpavHHPMMQCMGzeO8vJyj9NIplvV0cHf29pY0d7By62tXseRNNMIUgaVj3/845x44onk5uZq86oMuPJQiJBB1MGIkC4nl21UkDLo5KW4LqbIhxWJx2mJxxnW7d6io8Nhri4rp8M5Rul6q1lHBSkiWa8jHudXu3bREItxcmEhHy18/4CdUt2MO2tpH6RIlnnuuee4+uqr+fWvf41zH3zOXzZoiMVoiMUA2ByJeJxG/EIFKZJlXnjhBbq6unjjjTdoamryOo4vjAyHmVMwhKpwmI8V6nQPSVBBimSZefPmkZOTw8yZMykuLvY6jm+cXlTMF8rKGKu7mkiSNq77SLSphmj9DnJGH7bn3EiR/jZv3jzmzZvndQwR31NB+kQ80kHTkodwsShdu96l6BhdfFvEz5xzPNbUxNauLs4sLmJCTnZe7D2TaROrX7j4ngMmXDzmcRgRiThHdD8HMW2PRnmjvZ2d0SgvtugiAplII0gPxLs66NyyilDxcMLlidtbBXKHUPyRc+iq30be2GkeJxTJbpsiEX5fv5uwGV8YVkZ5ilM9yoJBykNB6qIxDsvSW4VlOhXkAKpafHPK6Tt27KCruZk4MGr8eMI9T0CueW7gw4lIr9Z1dtLlErfB2hSJpCzI3ECAL5eV0+HiukZrhlJBDqB353wj5fSWFU/RvuF1oi31xKvOIn/CMQf0vr0Vr4j0j6Pz81kf6STHjMO7XblpQ2cnr7S1MTUvl6PzhxA0o8BUjplK+yA9UDB1HliAUFEZbe+8on2OIj5TFgpxRVk5nxtWRkHg/R+TC5qbeKezk0cbm4jE4x4mlHRQQXrAQjnkVk4lkFtAsKhMt7MSzz3yyCNce+21PPLII15H8bURyU2tBQFjTWenSvIg7Kh/lx3173odo0+0idUjRUefRbSpllBhmddRRHjmmWfo6urimWee4dxzz/U6TtoMLy3ligNY3lWU09bWxo4dO/hLpJPCnDCjho/o9b1lb1tq3+GltxYAcOLUcxhbPsnjRPungvSIBYKEh+pmvzKw5s6d26flampqaGxs5L//+78HOJG/PPjoowe0/Ny5c3nhhRf493//d+LxOIcccgjf+EbqYw1kX7tbaqht2kZ+TgHtEf+fGqOCFMlgixYtSjl97ty5+8xzzukem31QWFjIFVdcwVtvvcXJJ5/sdRzP/N/q7x/wa7Zs2UJDawPWZrxaG+XNhkf6PVd/UkGKCIDK8QAcddRRHHXUUV7H8NQ/T/3+Ab/mJRawJf8dckJ5nDP18+SkuKTmwRTvQFFBiohIWhw3+XTGlk9kWNGIlOXoNypIERFJi1AwzLjhU7yO0Wc6zcMjsfZmunZv9TqGiIj0QgXpgVhHCw2L7qXp1YdoW/eq13F8bdWqVfz6179mxYoVXkcRkRTiLk5nV7vXMQaENrF6oGXF03S8+w8C+UXkjPT3eUBeu+uuu2htbWXVqlX87Gc/8zqOiADlZcP5v9XfxzlHdXU1HR0dDBs2jLKyD39ed3nZ8H5I2D98WZBmdgZwGxAEfuucu6nH/H8HLkk+DQGHAxXOud1pDXqQ4q31hEpG4Lo6yZ80e695sbZGYi27CVeMw0wD/JEjR7J+/XpGjtQ5ox9GQ0MDt99+O9FolCuvvNLrODLIPfTwgwDU19fzzW9+E4DKykqefvrpA36v3k5F8gPfFaSZBYHbgVOBauA1M3vMObf6vWWcc7cAtySXPwf4f4OlHAHyJ85KXG5u9GRCBe9fbSPe2Ubj4vtw0S7yqo4gVDqazq1vkVs5ldyREz1M7J2vfvWrbNy4kfHjx3sdZVB7/fXXeffdxOW9Xn1Vm/Wlf5SWlnLKKaewcuVKPvGJT/Cd73zH60j9yncFCcwC1jnnNgCY2X3AfGB1L8t/CvhTmrL1i7yx08kbO32f6a6rExftAiDe0UzrymdxsSjRhu1ZW5C5ublMmTJ4jnrzq6lTp1JYWEgsFuOII47wOo5kkIsuuoiLLrrI6xgDwo8FOQbY0u15NTA71YJmNgQ4A7i6l/mXA5cDVFVV9W/KARAsLKVg2jyijTXkH/IRWv7xLNH6bYSK/bNNXgafbdu2sXXrVm688UZCoRCBgDbdi/SFHwsy1eU8XC/LngO83NvmVefcncCdADNnzuztPXwlb+x0GJt4XDzzk8RadhHUBc3lIDU2NnLjjTcSiUQ44YQTuPTSS4HEZeX+9Kc/sX37di6++GJGjx7tcVIR//Hjr5LV7KkIACqBbb0sezGDbPPqgbBgiFDJCCzox99jZDDo7Oykqyux2b6lpWXP9Pb2dp5//nnWrFnDE0884VW8Qcm5QfG7tvQDP/7kfQ2YZGYTgK0kSvDTPRcysxLgo8Bn0htPZPAYPnw4X/jCF9i4cSOnnnrqnuk5OTkUFRXR3NzMpEk61aivmpqauPrqq6mqquJrX/sa4XDY60gygHxXkM65qJldDSwkcZrH3c65VWb2peT8O5KLngc85Zzz/z1TRDzw4osv8vzzz3PSSSftcxBFKBTihhtuoKWlheHDtY+7r5qbm4lGo2zYsIGtW7fq6OoM57uCBHDOPQk82WPaHT2e3wPck75U6eNiXVhQv5nKh/PAAw/Q2dnJAw88wLx58/aZP2TIEIYMGZL+YINYSUkJhYWFjBs3jsrKSq/jyADzZUFms/b1r9G2dgnhYaMpOvZcXSxADtr06dN5/fXXmTZtmtdRMkZhYSE//vGPvY4haaKC9FhH9Vu0b3iN3BETGTJ5Dp071gHQtXsb8c42gnmFHieUweqLX/wiF154IUOHDvU6isigpOGJx9rXv0q8rYn2jW/gohHyJxxDIK+Q3LHTVI7yoZgZpaWluhGyyEHSCNJjOSMOpWPTMsJlYyEYJnf0ZHJHT/Y6lohI1lNBeqxgyonkHzoTC+XqN30RER9RQfpAIJzndQQREelB+yBFRERSUEGKiIikoIL0qVhrA/GOlg9eUEREBoT2QfpQ5451tCz/KxYIUjz7QkLF5V5HEhHJOhpB+ky8q4O2da8S72zHxWLEmuu8jiQZzjnHkiVLWLFihddRRHxFBekzrf94lljjTmItu8kZeSg5Iyd6HUky3LPPPsvdd9/N7bffzqpVq7yOI+IbH7iJ1cxu+KBlnHPf7Z844uIxLBgmXD6WgsM/qntByoB7736RPR9LYn08/PDDAJx77rnehpG068tP37EfMF93D+1HhdNPpqN6FeGhownk6k4LMvBOPfVUgsEgQ4YM4aijjvI6jq+8+OKLPPvsswCUlZV5nEbS7QML0jn3uXQEkYRAXiFDJs72OoZkkVAoxGmnneZ1DF+qqKjAzIjFYjQ0NBCNRr2OJGnU532QZjbVzK4ws28l/546kMFERLx2xBFHcN111zFhwgSeeuoptmzZopLMIn3ZB2nAXcBlQDWwDRgDjDaz/wU+75zTZlYRyUhVVVXE43EAotEonZ2dhEI6NiAb9GUEeTkwDzjOOTfOOXe8c64KOB44CbhiAPOJiHju0ksvZfbs2YwcOZKCggKv40ia9KUgPwtc45x7rfvE5PNrk/NFRDJWVVUVn//85ykqKvI6iqRRXwpyKvBCL/NeSM4XERHJKH0pyKBzrjnVjOR0XWxAREQyTl/2NIfN7GNAb3fz1d5qERHJOH0ptxrg7g+YLyKSseLxOIkD+hPWr1/Pyy+/zLHHHsvhhx/uYTIZSH25UMD4NOTIGs45ItvWQDBErq6zKuJ7b7/9NrfffjuFhYV7zoH8zW9+Q319PUuXLuW2227bqzwlc/TlPMgX+YDLyTnn5vZbogzX+e4/aH1rEQB2zFnkDD/E40SS6Xbt2kVjYyOHHKLPWl/Nnfv+j7SamhoaGxsBGDlyJHPnzmXLli10dHQQDodZtmwZixYt8iqqDKC+bGL9bbfHBtwOfHlg4mQ+5+LvP47FPEwi2aCmpoYf/OAHRCIRLrjgAk499VSvIw0K3Qvv3Xff5c4776S4uJirrrqKgoIC2traWLNmDRMnTtSpHxmsL5tYf9f9uZn9d89p0nd5444EMywY1q2sZMDt2rWLSCQCwLZt2zxOMzhVVVXxwx/+cK9pQ4YM4eijj/YokaSLjkBNM7MA+eNmeB1DssSUKVM466yzqK2t5ZOf/KTXcUQGFRWkSAYzM+bPn/+Byy1atIjly5dzxhlnMGnSpDQkE/G/vhykc3LP1/Q8L9I597f+DjbYlZZVwOKb+7Rsc3MzkUiEoUOHEgwG+/beIv2ktbWVP/7xjzjn2L17N9/73ve8jiTiC30ZQd7V4/ku9j4v0gE6PK6HRx/+c5+W27JlC7NmzeKTn/wkc+bM4bLLLhvgZCJ7y8vLo6KigpqaGqqqqryOI+IbfTlIZ0I6gmSrnJycPY/z8vI8TCLZKhgMct1117F9+3bGjx/vdRwR39A+SI+NGDGCyspKLrvsMmbNmuV1HMlS+fn5Ok9SpAcVpA/k5+czZ84cr2OIiEg3uhOHD8RiMTZu3LjnruUiIuI9FaTHotEoW7Zs4aabbuLee+/1Oo6IiCSpID3W2dlJV1cXAJs3b/Y4jYiIvEf7ID1WUFBARUUF06dP54wzzvA6joiIJKkgfWDo0KF85Stf8TqGiIh0o02sIiIiKaggRURSaGxsZNmyZXR2dnodRTyiTawiIj3E43FuvPFG6uvrmTZtGtdcc43XkcQDGkGKiPTQ1dVFU1MTALt37/Y4jXhFI0iRLLFy5UpCoRBTpkzxOorv5ebmcsUVV7Bs2TLmzZvndRzxiApSJAssXryY3/3udwBcffXVHqcZHGbMmMGMGbq5eTbTJtY0eOONN/jjH/9ITU2N11EkS7W0tKR8LCK90whygDU1NXHnnXfinGPbtm3827/9m9eRJAudfPLJRCIRwuEws2fP9jqOyKCgghxgOTk5DBkyhNbWVoqLi72OI1kqFApx9tlnex1DZFBRQQ6wvLw8rrvuOjZt2qT9GSIig4gKMg3Ky8spLy/3OoaIiBwAXx6kY2ZnmNkaM1tnZt/sZZl5ZrbMzFaZ2QvpzigiIpnNdyNIMwsCtwOnAtXAa2b2mHNudbdlhgK/BM5wzr1rZsM9CTuAnHM0NTVRXFyMmXkdR0Qk6/hxBDkLWOec2+CciwD3AfN7LPNp4CHn3LsAzrlBe/7EkiVLqK+v33NPyPf88pe/5D/+4z+45557vAkmIpLl/FiQY4At3Z5XJ6d1dxhQambPm9nrZnZpqjcys8vNbKmZLa2trR2guAfv7bff5u6776auro4nnnhiz3TnHCtXrgRgxYoVXsWTDOWco7W1lS1btnzwwiJZzI8FmWp7ouvxPAR8BPgEcDrwHTM7bJ8XOXenc26mc25mRUVF/yf9kMLh8J7HodD7W7vNjPPOO49Ro0Zx3nnneRFNMtjTTz/Ntm3b+NGPfqSSFNkP3+2DJDFiHNvteSWwLcUydc65VqDVzBYBM4B30hOxfxx66KFcc801LFmyhLPOOmuveaeddhqnnXaaR8kkk9XX1wNQW1vLgw8+yGWXXcawYcM8TiXiP34syNeASWY2AdgKXExin2N3jwK/MLMQkAPMBn6S1pT9ZNq0aRQXFxMI+HEwL4PF/H+aT31tfZ+WjcVi5OTksGDBAhYuXMjPf/5zKisre12+tKKUR//8aH9FFRk0fFeQzrmomV0NLASCwN3OuVVm9qXk/Ducc2+Z2V+BFUAc+K1zbqV3qUW8VV9bT/0X+laQAJGnI/A8ROIRWo5qoX7+fl5714fPJzIY+a4gAZxzTwJP9ph2R4/ntwC3pDOXSKYoPLyQzi2dEIBhp2jzqkgq2q4nkoXyKvMYccEIgnlB6h6rI1IX8TqSiO+oIEWyVMfWDmIdMeKROB3vdngdR8R3VJAiWSq/Kp9waZhQcYj8Q/K9jiPiO77cBykiAy9YEGT4/Iy7SqNIv1FBimSgjuoOGv/eSLgiTOlJpVhA1/MVOVDaxCqSgVpWtRBtidK+sZ2u+q4PfoGI7EMFKZKB8scn9imGh4UJlWhDkcjB0HeOSAYqmFxA/iH5WMh0uzSRg6QRpMcaGhqIRqMp5zU3N++5bqbIgQqEAypHkQ9BBemh++67j4suuogNGzawefPmveZVV1dz3XXXcd111+mWVyIiHlBBemTbtm3cc889bNmyhfb2djZt2rTX/M2bNxOJRIjH46xfv96bkJKRmpY1se3ebdS/qK0TIvujfZAeyc3NpaqqipaWFurr65k9e/Ze82fOnMnq1avp6OjgYx/7mEcpJRO1vdOGizva1rcRyA/Qub2T4qOLyavM8zqaiK+oID1SVlbGd7/7XbZv387XvvY18vL2/uGUm5vLF7/4RY/SSSYrmFJA84pm8sbk0bKyBYCmN5pUkCI9qCA9VFlZSWVlpQ6kkLQqOrKIoiOLcDFHdEGUrvouckfleh1LxHdUkCJZyoJGxdkVxNpihIr0o0CkJx2kI5LFLGgqR5FeqCBFRERSUEGKiIikoIL0WCQSYefOndx99920trZ6HUcynIs6GhY3sPuF3cQ7417HEfE1FaTHXnnlFZqamliyZAnPP/+813Ekw7VtaKP1nVbaN7bTsrrF6zgivqa982k0d+7cfaa1t7djZjz22GMsXbqUm2++ea/5ixYtSlc8yQLhoWEsYLi4Izws7HUcEV9TQaZRb2VXV1dHLBZjxIgRaU4k2SZneA7DzxuOiznCQ1WQIvujgvSB8vJyryNIFgkVhYi1xmhc0ki4PMyQQ4d4HUnEl1SQIlmo4e8NdGzpACCnPEc3VRZJQQfpiGShYH4QSFwowEK61KFIKvq1USQLlcwuIXdULqHSEMGCoNdxRHxJBSmShSxo5E/I9zqGiK9pE6uIiAy4RYsW8Z//+Z8sWLDA6yh9poL0oRUrVnDHHXewYsUKr6OIiPSLxx57jNraWh5//HFisZjXcfpEBelDd911F2+++SZ3332311Ekw7m4o+WtFlrXtOKc8zqOZLCjjz4agCOPPJJgcHDs99Y+SB8aPXo0GzZsYPTo0V5HkQzXuqaVxiWNQGK/5JCJOidSBsYll1zCueeey5Ahg+czpoL0oWuvvZZNmzYxfvx4r6NIhrOgpXwsMhAKCgq8jnBAVJA+lJuby+TJk72OIVlgyKQhiXMhAzqqVaQnFaRIFjMzXWpOpBc6SEdEAIh1xOhq6PI6hohvaAQpIsRaY9Q8VkO8M07J7BIKDy/0OpKI5zSCFMlQ8c44rW+30lX/waPCaGOUeGccgEhNZKCjiQwKGkGKZKj6RfV0bO0gEA4w4sIRBHJ6/304Z1QOhYcXEm2KUjSjKI0pRfxLBSmSoVw0ceK/izn4gGsAmBkls0vSkEpk8FBBimSo0rmlNC1rwkUdsbYYgVztURE5EPqOEclQwYIgkboIDa808O7P3qV9c/ueeS7u6NjWQax1cFwTU8QLKkiRDBbviCcOwOmK07yyec/0xlcb2fXUrj1HrorIvrSJVSRDlN5Vus+0IZ1D2BTbhIs6hr81nNIdiWUaNzXimh3BnCBFDUXk5OSkO66I76kgRTJE/RfqU06vcBW4Lgc5UE898Uic5nubidRGsNFG62WttNLa6/umKl6RbKCCFMlwZoblGLG2GA1/b8CCRiAvQO6oXHJH5nodT8S3VJAiWaJldQsd73YAUDSjiEBeQBcoF9kPFaRIlsgpT+xntKCRPy6f8LAwkDiitX1TO6GiEDkV2hcp8h4VpEgGijZFsaARLHj/zu354/MZcf6IPdNb3mqhdXVi32O0OYqZMfzc4YRK9GNBBFSQIhmnfXM7u5/bjety5I7NpfDwQvLHJTalhooT3/KRmgg77ttBPBInkBcgrzIPghDv0ikfIu9RQYpkmK66xMXJO7Z2EGuL0bWzi9xP5e51Ldau3V0E84PEI3FyR+RSNKOI8NDwns2wIuLTgjSzM4DbgCDwW+fcTT3mzwMeBTYmJz3knLshnRlF/KpgagHRpsTdOSycOGLVgrbXMvmH5lMyp4R4W5zSuaUEhwR7eTeR7OW7gjSzIHA7cCpQDbxmZo8551b3WPRF59zZaQ8o4nPB/CDDPjaMoScOpXNbJznlOfsUZCAcYNjcYR4lFBkc/HipuVnAOufcBudcBLgPmO9xJpFBJxAOkD8uf68DdUSk7/xYkGOALd2eVyen9XS8mS03s7+Y2bRUb2Rml5vZUjNbWltbOxBZRSSLtba2csstt3D99dezfft2r+NIP/NjQVqKaT3vZvcGMM45NwP4OfBIqjdyzt3pnJvpnJtZUVHRvylFJOstX76cdevWsW3bNl566SWv40g/82NBVgNjuz2vBLZ1X8A51+Sca0k+fhIIm1l5+iKKiMBhhx1GcXExOTk5zJgxw+s40s98d5AO8BowycwmAFuBi4FPd1/AzEYCO51zzsxmkSj6XWlP2o/+8Y9/UFRUxPjx472OIiJ9VF5ezs0330wsFiMcDnsdR/qZ7wrSORc1s6uBhSRO87jbObfKzL6UnH8HcAFwpZlFgXbgYudcz82wg8YzzzzDAw88gJnxzW9+UyUpaeVijmhzlFBxCAuk2sMh+xMIBAgE/LgxTj4s3xUk7Nls+mSPaXd0e/wL4BfpzjVQGhsbAXDO0dTU5HEayTZ1f60jUhshf0I+wz6qUz9E3uPLgsw2n/jEJwAoLi7miCOO8DiNZBMXc0RqIwBEdkY8TiPiLypIH8jLy+Of/umfvI4hWciCRsnsEto3tlM4tdDrOCK+ooIUyXKFhxdSeLjKUaQn7VkWERFJQQUpIiKSggrSI845WlpavI4hWap9Y+KekZ07Or2OIuJbKkiP3HXXXXz961/n7rvv9jqKZBkXddQvqqd9czsNLzV4HUfEt1SQHnnzzTf3+lskbYIQLErc4SM0VMfpifRG3x0eOffcc1m0aBHz5s3zOopkGTOj4hMVdO3qImd4jtdxRHxLBemRU089lVNPPdXrGJKlArkBckfneh1DxNe0iVVERCQFFaSIiEgKKkgREZEUVJAiIiIp6CAdkQznnKNxSSORmgglx5aQO0oH54j0hUaQIhku2hClZXULXbu7aF7W7HUckUFDBSmSwVzc0fBKAx2bO+iq6yJ3jEaPIn2lTawiGSzeGSdSEyFvXB7BwiBFRxYBEGuNEW2MkjMyBwuYxylF/EkFKZIBSitK4a7U8+I1cVpbWykrK6P4rmJisRibN2+mq6sLMyMvL49wOExZWRk5OfteWae0onSA04v4kwpSJAM8+udH+7xsXV0dRxxxBIcddhitra20t7dz9NFHc8IJJ3DVVVcNYEqRwUUFKZKBdu7cyS9/+UvC4TBXX301Q4cO3TOvvLyc4cOHM2PGDGpra1m7di25ubmMGTPGu8AiPqSCFMlAS5YsYceOHUDijjEf+9jH9ppfUlLCL37xC9avX08wGCQajXLooYd6EVXEt1SQIhloxowZPPfcc4RCIaZOndrrcipFkd6pIEUy0Lhx4/jxj3+MmWGmo1RFDoYKUiRDBQI6zVnkw9B3kIiISAoqSBERkRRUkCIiIimoIEVERFJQQYqIiKSgghTJUGvXrmX79u1exxAZtHSah0gGev755/nTn/5EMBjkuuuuo7Kycq/5kUiEX//614wePZpzzjnHo5Qi/qYRpEgGqqmpASAWi7Fr16595u/evZs33niDBQsWsHbt2nTHExkUNIIUyUBnnXUWkUiEkpISjjzyyH3mv3dbq7y8PMrLy9MdT2RQUEGKZKDCwkI+85nP9Dp/2LBhfOtb32Lo0KF73elDRN6nghTJItFolL/+9a/U19dTVVWly9GJ7Ie+O0SyyN/+9jcef/xx6urqePXVV72OI+JrKkiRLFJQUJDycSQSYfPmzcRiMS9iifiSNrGKZJETTjiBoqIiXn/9dY444og902+99VY2b97MjBkz+PKXv+xhQhH/0AhSJAts3bqVv/3tbzQ1NTFx4sQ9I0aAeDzOli1bANi0aZOHKUX8RSNIkQzX1dXFrbfeSltbG2+88QYFBQXU1tZy6623cuONN1JYWMhnP/tZXn31VU4++WSv44r4hjnnvM6QFjNnznRLly71OoZIWs2dO5d4PM6GDRtwzpGXl0cgEKCtrQ0zY/z48YRC7/+evGjRIg/TinjCepuhEaRIBnuv8NavX89bb73F8ccfTzgcZtGiRUycOJEpU6Z4nFDEvzSCFBGRbNbrCFIH6YiIiKSgghQREUlBBSkiIpKCClJERCQFFaSIiEgKKkgREZEUfFmQZnaGma0xs3Vm9s39LHesmcXM7IJ05hMRkcznu4I0syBwO3AmMBX4lJlN7WW5m4GF6U0oIiLZwHcFCcwC1jnnNjjnIsB9wPwUy30F+DNQk85wIoOVc45XX32VJUuWkC0XCBH5MPx4qbkxwJZuz6uB2d0XMLMxwHnAycCxvb2RmV0OXA5QVVXV70FFBpMlS5bwP//zP0DiDh7HH3+8x4lE/M2PI8hUl/3p+evuT4FvOOf2e3dX59ydzrmZzrmZFRUV/ZVPZFDqfjNk3RhZ5IP5cQRZDYzt9rwS2NZjmZnAfWYGUA6cZWZR59wjaUkoMgjNmTOHeDyOc44TTjjB6zgivufHgnwNmGRmE4CtwMXAp7sv4Jyb8N5jM7sHWKByFNk/M+Okk07yOobIoOG7gnTORc3sahJHpwaBu51zq8zsS8n5d3gaUEREsoJudyUiItlMt7sSERE5ECpIERGRFFSQIiIiKaggRUREUlBBioiIpKCCFBERSUEFKSIikoIKUkREJAUVpEiWa25uJhqNeh1DxHd8d6k5EUmf5557jvvuu48RI0Zw3XXXkZeX53UkEd/QCFIki61YsQKAnTt3snPnTo/TiPiLRpAiWezMM8+ksbGR8ePH66biIj3oYuUiIpLNdLFyEUlt6dKlfPWrX+UnP/mJDtYR6UYFKZLlXnrpJTo6Onj77bfZunWr13FEfEMFKZLlTjzxRHJzc5k8eTJjxozxOo6Ib+ggHZEsN3PmTGbOnOl1DBHf0QhSREQkBRWkiIhICipIERGRFFSQIiIiKaggRUREUlBBioiIpKCCFBERSUEFKSIikoIKUkREJAUVpIiISAoqSBERkRRUkCIiIimoIEVERFJQQYqIiKSgghQREUnBnHNeZ0gLM6sFNnudoxflQJ3XIQYhrbeDp3V3cLTeDo6f11udc+6MVDOypiD9zMyWOud0x9oDpPV28LTuDo7W28EZrOtNm1hFRERSUEGKiIikoIL0hzu9DjBIab0dPK27g6P1dnAG5XrTPkgREZEUNIIUERFJQQUpIiKSggoyDczsPDNzZjblA5Z70syGpimW58wsZmbLuv35pteZBqOeny8zqzCzJWb2ppmdlGL535rZ1PQnTZ++fs99iPefaWY/G4j39gszqzSzR81srZmtN7PbzCwnDV93tJk9ONBfpy+0DzINzOx+YBTwrHPu+x7H8Q0za3HOFR7ka0POuWh/ZxqMen6+zOxi4Ezn3GUplg0652JpD5lmA/k9lw2fPTMzYAnwK+fc/5hZkMSBNrudc//ubbo0cs7pzwD+AQqBrcBhwNvJaaOARcAyYCVwUnL6JqA8+fgR4HVgFXC51/+OAVo3Lb1M774eZgLPJx9/n8Q36VPAH4FxwLPAiuTfVcnl7gHuAF4E3gHOTk4PArcAryVfc4XX66C/P1/AUcC7QG3y85UPtAA3kPiBdyLwPDAz+fozgDeA5STKBGAWsBh4M/n3ZK//nR9mnSSnzQNeAO5PfiZuAi4BXgX+ARyaXK4C+HPyM/IacEIvn715wIJuX+9/ku+zAvin5PRfAUuT38PXd8t3E7A6ueytXq+vXtbhKcCiHtOKgV1AAXBrt3/vV5LzP5Jcx68DC4FRyelfTK7L5cl1OyQ5/R7gZ8nP2AbgguT08cDKbo9fTH5G3wDmpHU9eP0fkel/gM8AdyUfLwaOAb4OfDs5LQgUJR9v4v1iGJb8O59EiZZ5/W8ZgHUTI/FD/L0//5xiPfQsyNeB/OTzx4HLko8/DzySfHwP8FcSuxAmAdVAHnA58J/JZXKTP7wmeL0eBuDz9S/AL7ot44CLuj1/PrleK4At762Dbp+5YiCUfPxx4M9e/zv7YZ3MAxpI/HKaS6JAr08u81Xgp8nHfwROTD6uAt7q5bM3j/cL8ub3Xp98XtpjfQaT6/xIYBiwhve33g31en31sg6vAX6SYvqbyfX1526fkWFAOLmuK5LT/hm4O/m4rNvrf8j7hXoP8EDy+3QqsC45fTzvF+QQIC/5eBKwNJ3rIYQMtE8BP00+vi/5/HHgbjMLk/ihvizF664xs/OSj8eS+HDsGtioadfunDvqAF/zmHOuPfn4eOD85OP/Bf6r23L3O+fiwFoz2wBMAU4DjjSzC5LLlJBYrxsPJrxPpPp8reqxTIzED7SejiMxStgI4JzbnZxeAvzOzCaRKNdwP2ceaKnWyRPAa8657QBmtp7EaBASI6GPJR9/HJia2MIIQLGZFSUfd//sdfdx4OL3njjn6pMPLzKzy4EQiWKeSmLk2AH81syeABYc/D9zQBmJ//tU0+cCd7jkZmbn3G4zmw5MB55OrrsgsD35mulm9kNgKInR9sJu7/dI8vt0tZmNSPH1wsAvzOwoEp/jwz7kv+uAqCAHkJmVASeT+IA4Eh8aB/wHiQ/ZJ4D/NbNbnHP3dnvdPBLfdMc759rM7HkSI6BsEeX9A8h6/rtb9/M618vj954bid9eF5IB9vP5+l6PRTtc6v2Ovf0Q/AHwnHPuPDMbT2L0MyjsZ508CXR2WzTe7Xmc938WBkh83+1VhMkf+r199vZZj2Y2Afg34FjnXL2Z3UNiJBQ1s1kkNmFeDFydzOs3q4B/6j7BzIpJ/LK+gX0/Nwascs4dn+K97gHOdc4tN7N/ITH6fk/3/xNjX/8P2AnMIPF/09Hnf0E/0FGsA+sC4F7n3Djn3Hjn3FgSo5W5QI1z7jfAXSQ2AXVXAtQny3EKid/0s8kmEvszoMc3aQ+Lef8390uAl7rNu9DMAmZ2KHAIic1aC4ErkyN3zOwwMyvoz+Bp1tvnq7KPr38F+GjyhzlmNiw5vYTEJkhIbK4dTHpbJyf28fVPkSgtAJIjlwN9TSmJzdStQGNyZHRmcl4hUOKcexK4lsQ+Yz96FhhiZpdC4uAu4Mckyu4p4EtmFkrOe2+zcYWZHZ+cFjazacn3KgK2J7/vLjnAHCXA9uQo87MkfuFJGxXkwPoU8HCPaX8m8SFbZmZvkiiA23os81cgZGYrSPw2//cBzumV/B6nedyUnH49cJuZvUhis0pvrgE+l1xPnyWxb+Q9a0gcMPAX4EvOuQ7gtyQ2cb1hZiuBXzO4t6L09vm6ri8vds7Vktgv+5CZLQf+Lznrv4Abzexl0vwDqR/0tk4+3cfXXwPMNLMVZrYa+FIfXvNDoNTMVibX48ecc8tJ7K9bBdwNvJxctghYkPzMvkBihOQ7LrHT7zwSv2iuJXFgUweJz9ZvSRwItiL57/20cy5C4peTm5PTlgFzkm/3HRIHiD1N4kCyA/FL4DIz+zuJzav724LU73Sah2Sc5OasBc45X5xLJSKDk0aQIiIiKWgEKSIikoJGkCIiIimoIEVERFJQQYqIiKSgghQREUlBBSkiIpKCClJERCQFFaSIiEgKKkgREZEUVJAiIiIpqCBFRERSUEGKiIikoIIUERFJQQUpIiKSggpSREQkBRWkSJYxs1VmNs/rHCJ+p4IU8Qkz+7SZLTWzFjPbbmZ/MbMTP+R73mNmP+w+zTk3zTn3/IcK27evvcnMPj7QX0dkoKggRXzAzL4G/BT4ETACqAJ+Ccz3MJZIVlNBinjMzEqAG4CrnHMPOedanXNdzrnHnXP/bma5ZvZTM9uW/PNTM8tNvnaemVWb2dfNrCY58vxcct7lwCXAfyRHpY8np+8Z2ZnZ983sfjO718yak5tfZ3bLNtrM/mxmtWa20cyu6Tav19ea2f+SKPnHk1/7P9KzNkX6jwpSxHvHA3nAw73M/zZwHHAUMAOYBfxnt/kjgRJgDPAF4HYzK3XO3Qn8Afgv51yhc+6cXt7/k8B9wFDgMeAXAGYWAB4Hliff+xTgWjM7/YNe65z7LPAucE7ya/9XH9aDiK+oIEW8VwbUOeeivcy/BLjBOVfjnKsFrgc+221+V3J+l3PuSaAFmHwAX/8l59yTzrkY8L8kShjgWKDCOXeDcy7inNsA/Aa4uA+vFRn0Ql4HEBF2AeVmFuqlJEcDm7s935yctuf1PV7XBhQewNff0eO1eWYWAsYBo82sodv8IPDiB712P2UvMmhoBCnivVeADuDcXuZvI1FW76lKTusLd/Cx2AJsdM4N7fanyDl3Vhq+tojnVJAiHnPONQLfJbHv8FwzG2JmYTM708z+C/gT8J9mVmFm5cllf9/Ht98JHHKQ0V4FmszsG2aWb2ZBM5tuZsem4WuLeE4FKeIDzrn/Br5G4uCbWhKjt6uBR4AfAkuBFcA/gDeS0/riLmCqmTWY2SMHmCkGnEPi4KCNQB3wWxIHBPXFjSSKvcHM/u1AvraIH5hz2goiIiLSk0aQIiIiKaggRUREUlBBioiIpKCCFBERSUEFKSIikoIKUkREJAUVpIiISAoqSBERkRT+fy/bI/jG1egYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=df_ef2016.dropna(axis=0, how='any') # Drop rows with NaN (present only in HDI column)\n",
    "var_cat='continent'\n",
    "var_num='HDI'\n",
    "\n",
    "# Draw boxplots overlaid with stripplots of HDI scores grouped by continent\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "sns.boxplot(data=data, x=var_cat, y=var_num, whis=(5, 95), linewidth=1,\n",
    "            width=0.3, fliersize=False, saturation=1)\n",
    "sns.stripplot(data=data, x=var_cat, y=var_num, color=\"k\", alpha=0.6, size=3)\n",
    "\n",
    "# Additional formatting\n",
    "ax.set_xlabel('Continent', size=12, labelpad=15)\n",
    "ax.set_ylabel('HDI', size=12, labelpad=15)\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The HDI scores are widely spread within each continent and a few countries stand out as high-performers while some others lag behind the rest. These outliers are the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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></th>\n",
       "      <th>country</th>\n",
       "      <th>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</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 rowspan=\"3\" valign=\"top\">Africa</th>\n",
       "      <th>35</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>CF</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.37</td>\n",
       "      <td>4.59e+06</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Chad</td>\n",
       "      <td>TD</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.45e+07</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Niger</td>\n",
       "      <td>NE</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.37</td>\n",
       "      <td>2.07e+07</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Americas</th>\n",
       "      <th>78</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>HT</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.08e+07</td>\n",
       "      <td>Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Honduras</td>\n",
       "      <td>HN</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.62</td>\n",
       "      <td>9.11e+06</td>\n",
       "      <td>Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Asia</th>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.49</td>\n",
       "      <td>3.47e+07</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Syria</td>\n",
       "      <td>SY</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.84e+07</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>YE</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2.76e+07</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Europe</th>\n",
       "      <th>113</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>MD</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.70</td>\n",
       "      <td>4.06e+06</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>UA</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4.44e+07</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <th>133</th>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>PG</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.54</td>\n",
       "      <td>8.08e+06</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                country code  Earths   HDI  population  \\\n",
       "continent                                                                \n",
       "Africa    35   Central African Republic   CF    0.73  0.37    4.59e+06   \n",
       "          36                       Chad   TD    1.10  0.40    1.45e+07   \n",
       "          125                     Niger   NE    1.00  0.37    2.07e+07   \n",
       "Americas  78                      Haiti   HT    0.41  0.50    1.08e+07   \n",
       "          79                   Honduras   HN    0.96  0.62    9.11e+06   \n",
       "Asia      0                 Afghanistan   AF    0.44  0.49    3.47e+07   \n",
       "          166                     Syria   SY    0.72  0.54    1.84e+07   \n",
       "          185                     Yemen   YE    0.40  0.48    2.76e+07   \n",
       "Europe    113                   Moldova   MD    1.07  0.70    4.06e+06   \n",
       "          178                   Ukraine   UA    1.74  0.75    4.44e+07   \n",
       "Oceania   133          Papua New Guinea   PG    1.13  0.54    8.08e+06   \n",
       "\n",
       "              continent  \n",
       "continent                \n",
       "Africa    35     Africa  \n",
       "          36     Africa  \n",
       "          125    Africa  \n",
       "Americas  78   Americas  \n",
       "          79   Americas  \n",
       "Asia      0        Asia  \n",
       "          166      Asia  \n",
       "          185      Asia  \n",
       "Europe    113    Europe  \n",
       "          178    Europe  \n",
       "Oceania   133   Oceania  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HDI scores bottom 5% of each continent\n",
    "df_ef2016.groupby('continent')\\\n",
    "         .apply(lambda df: df[df['HDI'] <= df['HDI'].quantile(0.05)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</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 rowspan=\"3\" valign=\"top\">Africa</th>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZ</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4.06e+07</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Mauritius</td>\n",
       "      <td>MU</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.26e+06</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>TN</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.14e+07</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Americas</th>\n",
       "      <th>33</th>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "      <td>5.10</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.63e+07</td>\n",
       "      <td>Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>5.11</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.22e+08</td>\n",
       "      <td>Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Asia</th>\n",
       "      <th>86</th>\n",
       "      <td>Israel</td>\n",
       "      <td>IL</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.90</td>\n",
       "      <td>8.19e+06</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Japan</td>\n",
       "      <td>JP</td>\n",
       "      <td>2.80</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.28e+08</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>SG</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.93</td>\n",
       "      <td>5.62e+06</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Europe</th>\n",
       "      <th>128</th>\n",
       "      <td>Norway</td>\n",
       "      <td>NO</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.95</td>\n",
       "      <td>5.25e+06</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CH</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.94</td>\n",
       "      <td>8.40e+06</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AU</td>\n",
       "      <td>4.02</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2.41e+07</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                     country code  Earths   HDI  population continent\n",
       "continent                                                            \n",
       "Africa    2          Algeria   DZ    1.48  0.75    4.06e+07    Africa\n",
       "          111      Mauritius   MU    2.18  0.79    1.26e+06    Africa\n",
       "          174        Tunisia   TN    1.35  0.74    1.14e+07    Africa\n",
       "Americas  33          Canada   CA    5.10  0.92    3.63e+07  Americas\n",
       "          181  United States   US    5.11  0.92    3.22e+08  Americas\n",
       "Asia      86          Israel   IL    2.99  0.90    8.19e+06      Asia\n",
       "          89           Japan   JP    2.80  0.91    1.28e+08      Asia\n",
       "          150      Singapore   SG    3.96  0.93    5.62e+06      Asia\n",
       "Europe    128         Norway   NO    3.42  0.95    5.25e+06    Europe\n",
       "          165    Switzerland   CH    2.88  0.94    8.40e+06    Europe\n",
       "Oceania   8        Australia   AU    4.02  0.94    2.41e+07   Oceania"
      ]
     },
     "execution_count": 38,
     "metadata": {},
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    }
   ],
   "source": [
    "# HDI scores top 5% of each continent\n",
    "df_ef2016.groupby('continent')\\\n",
    "         .apply(lambda df: df[df['HDI'] >= df['HDI'].quantile(0.95)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Ecological Footprint vs Human Development Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
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    "scrolled": false
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0.5960000157356262, \"population\": 1343100.0, \"continent\": \"Africa\"}, {\"country\": \"Sweden\", \"code\": \"SE\", \"Earths\": 3.84050562148903, \"HDI\": 0.9340000152587891, \"population\": 9837530.0, \"continent\": \"Europe\"}, {\"country\": \"Switzerland\", \"code\": \"CH\", \"Earths\": 2.881584315926277, \"HDI\": 0.9430000185966492, \"population\": 8401740.0, \"continent\": \"Europe\"}, {\"country\": \"Syria\", \"code\": \"SY\", \"Earths\": 0.7162779586105473, \"HDI\": 0.5389999747276306, \"population\": 18430500.0, \"continent\": \"Asia\"}, {\"country\": \"Tajikistan\", \"code\": \"TJ\", \"Earths\": 0.5875684862839179, \"HDI\": 0.6470000147819519, \"population\": 8734950.0, \"continent\": \"Asia\"}, {\"country\": \"Tanzania\", \"code\": \"TZ\", \"Earths\": 0.7282955439933159, \"HDI\": 0.5180000066757202, \"population\": 55572200.0, \"continent\": \"Africa\"}, {\"country\": \"Thailand\", \"code\": \"TH\", \"Earths\": 1.5557232015616893, \"HDI\": 0.753000020980835, \"population\": 68863504.0, \"continent\": \"Asia\"}, {\"country\": \"Timor-Leste\", \"code\": \"TL\", \"Earths\": 0.30824853357008913, \"HDI\": 0.628000020980835, \"population\": 1268670.0, \"continent\": \"Asia\"}, {\"country\": \"Togo\", \"code\": \"TG\", \"Earths\": 0.6600564408611579, \"HDI\": 0.5059999823570251, \"population\": 7606370.0, \"continent\": \"Africa\"}, {\"country\": \"Tonga\", \"code\": \"TO\", \"Earths\": 2.0345989225184384, \"HDI\": 0.7149999737739563, \"population\": 107122.0, \"continent\": \"Oceania\"}, {\"country\": \"Trinidad and Tobago\", \"code\": \"TT\", \"Earths\": 4.898554582202762, \"HDI\": 0.7960000038146973, \"population\": 1364960.0, \"continent\": \"Americas\"}, {\"country\": \"Tunisia\", \"code\": \"TN\", \"Earths\": 1.3490155101506622, \"HDI\": 0.7360000014305115, \"population\": 11403200.0, \"continent\": \"Africa\"}, {\"country\": \"Turkey\", \"code\": \"TR\", \"Earths\": 2.067772687926637, \"HDI\": 0.800000011920929, \"population\": 79512400.0, \"continent\": \"Asia\"}, {\"country\": \"Turkmenistan\", \"code\": \"TM\", \"Earths\": 3.295997655401025, \"HDI\": 0.7059999704360962, \"population\": 5662540.0, \"continent\": \"Asia\"}, {\"country\": \"Uganda\", \"code\": \"UG\", \"Earths\": 0.6634422064383623, \"HDI\": 0.5199999809265137, \"population\": 41488000.0, \"continent\": \"Africa\"}, {\"country\": \"Ukraine\", \"code\": \"UA\", \"Earths\": 1.744444564603162, \"HDI\": 0.7459999918937683, \"population\": 44438600.0, \"continent\": \"Europe\"}, {\"country\": \"United Arab Emirates\", \"code\": \"AE\", \"Earths\": 5.521888556726889, \"HDI\": 0.8629999756813049, \"population\": 9269610.0, \"continent\": \"Asia\"}, {\"country\": \"United Kingdom\", \"code\": \"GB\", \"Earths\": 2.7111862787930887, \"HDI\": 0.9179999828338623, \"population\": 65788600.0, \"continent\": \"Europe\"}, {\"country\": \"United States\", \"code\": \"US\", \"Earths\": 5.114673752280755, \"HDI\": 0.9190000295639038, \"population\": 322180000.0, \"continent\": \"Americas\"}, {\"country\": \"Uzbekistan\", \"code\": \"UZ\", \"Earths\": 1.2953519193551128, \"HDI\": 0.7009999752044678, \"population\": 31446800.0, \"continent\": \"Asia\"}, {\"country\": \"Venezuela\", \"code\": \"VE\", \"Earths\": 1.5499005437168403, \"HDI\": 0.7519999742507935, \"population\": 31568200.0, \"continent\": \"Americas\"}, {\"country\": \"Viet Nam\", \"code\": \"VN\", \"Earths\": 1.3738994742956272, \"HDI\": 0.6850000023841858, \"population\": 94569104.0, \"continent\": \"Asia\"}, {\"country\": \"Yemen\", \"code\": \"YE\", \"Earths\": 0.40173798754041706, \"HDI\": 0.47699999809265137, \"population\": 27584200.0, \"continent\": \"Asia\"}, {\"country\": \"Zambia\", \"code\": \"ZM\", \"Earths\": 0.602938380118099, \"HDI\": 0.5799999833106995, \"population\": 16591400.0, \"continent\": \"Africa\"}, {\"country\": \"Zimbabwe\", \"code\": \"ZW\", \"Earths\": 0.6472136986761878, \"HDI\": 0.5490000247955322, \"population\": 16150400.0, \"continent\": \"Africa\"}], \"data-d764767e7dd3b10b2c76c1355ce10ef3\": [{\"y\": 1}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Draw interactive scatterplot of EF vs HDI using Altair library\n",
    "\n",
    "base = alt.Chart(df_ef2016)\n",
    "\n",
    "# Draw circles\n",
    "circle = base.mark_circle(clip=True).encode(\n",
    "    x=alt.X('HDI', title='Human Development Index 2016'),\n",
    "    y=alt.Y('Earths', title='Ecological Footprint (Earths equivalent)'),\n",
    "    color='continent',\n",
    "    tooltip=['country',\n",
    "             alt.Tooltip('HDI:Q', format='.2f'),\n",
    "             alt.Tooltip('Earths:Q', format='.2f'),\n",
    "             alt.Tooltip('population:Q', format=',')]\n",
    ").properties(\n",
    "    width=600,\n",
    "    height=400\n",
    ").interactive()\n",
    "\n",
    "# Draw horizontal dashed line\n",
    "Earth_limit = alt.Chart(pd.DataFrame({'y': [1]})).mark_rule(\n",
    "    color=\"#808080\",\n",
    "    strokeDash=[4,4]\n",
    ").encode(\n",
    "    y='y')\n",
    "\n",
    "# Draw vertical dashed line\n",
    "HDI_median = base.mark_rule(\n",
    "    color=\"#808080\",\n",
    "    strokeDash=[4,4]\n",
    ").encode(\n",
    "    x='median(HDI)'\n",
    ")\n",
    "\n",
    "circle + Earth_limit + HDI_median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The horizontal dashed line marks the one planet limit. The vertical dashed line marks the median HDI. Nearly all countries consuming resources within Earth's total biocapacity score low on the HDI. The only exception is Sri Lanka which has an HDI score of 0.77 while maintaining consumption at a sustainable level. It is followed by Jamaica with an HDI score of 0.73 just below the median HDI. All other countries in the the top half of HDI scores are consuming resources at an unsustainable level.\n",
    "\n",
    "The duty of policy-makers and other development stakeholders is to shift countries to the bottom-right quadrant. This means that those in high-consumption countries must put in place policies to decrease resource use, while those in low-consumption countries must find alternative ways to increase health, education and standard of living, as they cannot follow the development path of high-consumption countries.\n",
    "\n",
    "In facing these challenges, it can be interesting to look more closely at which countries are the most efficient in resource consumption relative to their HDI score. Knowing which countries are most resource-efficient per HDI point may bring to light successful policies that may serve as examples for others. The development efficiency indicator is computed in the next section to identify those countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Development efficiency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    172.00\n",
       "mean       0.52\n",
       "std        0.29\n",
       "min        0.10\n",
       "25%        0.33\n",
       "50%        0.45\n",
       "75%        0.65\n",
       "max        2.04\n",
       "Name: development_efficiency, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ef2016['development_efficiency'] = df_ef2016['HDI']/df_ef2016['Earths']\n",
    "df_ef2016['development_efficiency'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw development efficiency histogram\n",
    "hist_align_bins(variable=df_ef2016['development_efficiency'],\n",
    "                xlabel='Development Efficiency (HDI/Earths)',\n",
    "                title='Distribution of Development Efficiency scores',\n",
    "                bin_width=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>development_efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>TL</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.27e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Palestine</td>\n",
       "      <td>PS</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.69</td>\n",
       "      <td>4.79e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>ER</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.43</td>\n",
       "      <td>4.95e+06</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>HT</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.08e+07</td>\n",
       "      <td>Americas</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>RW</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.19e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>YE</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2.76e+07</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>BD</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.63e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.49</td>\n",
       "      <td>3.47e+07</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>TJ</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.65</td>\n",
       "      <td>8.73e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>PK</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.93e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country code  Earths   HDI  population continent  \\\n",
       "170  Timor-Leste   TL    0.31  0.63    1.27e+06      Asia   \n",
       "131    Palestine   PS    0.47  0.69    4.79e+06      Asia   \n",
       "58       Eritrea   ER    0.32  0.43    4.95e+06    Africa   \n",
       "78         Haiti   HT    0.41  0.50    1.08e+07  Americas   \n",
       "143       Rwanda   RW    0.43  0.52    1.19e+07    Africa   \n",
       "185        Yemen   YE    0.40  0.48    2.76e+07      Asia   \n",
       "13    Bangladesh   BD    0.51  0.60    1.63e+08      Asia   \n",
       "0    Afghanistan   AF    0.44  0.49    3.47e+07      Asia   \n",
       "167   Tajikistan   TJ    0.59  0.65    8.73e+06      Asia   \n",
       "130     Pakistan   PK    0.52  0.56    1.93e+08      Asia   \n",
       "\n",
       "     development_efficiency  \n",
       "170                    2.04  \n",
       "131                    1.45  \n",
       "58                     1.35  \n",
       "78                     1.22  \n",
       "143                    1.22  \n",
       "185                    1.19  \n",
       "13                     1.18  \n",
       "0                      1.12  \n",
       "167                    1.10  \n",
       "130                    1.08  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Development efficienty top 10\n",
    "df_ef2016.nlargest(10, 'development_efficiency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among the top 10 countries in terms of development efficiency, none are even close to the median HDI, only Timor-Leste and Tajikistan make it above the 0.6 mark. To get a list of potential standard-setters, we can further refine the list by setting a minimum HDI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>development_efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>PH</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.03e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>LK</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.08e+07</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Jamaica</td>\n",
       "      <td>JM</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.72</td>\n",
       "      <td>2.88e+06</td>\n",
       "      <td>Americas</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>EC</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.64e+07</td>\n",
       "      <td>Americas</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>DO</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.06e+07</td>\n",
       "      <td>Americas</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>MD</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.70</td>\n",
       "      <td>4.06e+06</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>CU</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.15e+07</td>\n",
       "      <td>Americas</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>AM</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.92e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>AL</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2.93e+06</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>AZ</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.75</td>\n",
       "      <td>9.73e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                country code  Earths   HDI  population continent  \\\n",
       "136         Philippines   PH    0.82  0.70    1.03e+08      Asia   \n",
       "158           Sri Lanka   LK    0.95  0.77    2.08e+07      Asia   \n",
       "88              Jamaica   JM    1.01  0.72    2.88e+06  Americas   \n",
       "54              Ecuador   EC    1.06  0.76    1.64e+07  Americas   \n",
       "53   Dominican Republic   DO    1.05  0.74    1.06e+07  Americas   \n",
       "113             Moldova   MD    1.07  0.70    4.06e+06    Europe   \n",
       "47                 Cuba   CU    1.18  0.77    1.15e+07  Americas   \n",
       "6               Armenia   AM    1.17  0.75    2.92e+06      Asia   \n",
       "1               Albania   AL    1.23  0.79    2.93e+06    Europe   \n",
       "10           Azerbaijan   AZ    1.23  0.75    9.73e+06      Asia   \n",
       "\n",
       "     development_efficiency  \n",
       "136                    0.86  \n",
       "158                    0.82  \n",
       "88                     0.72  \n",
       "54                     0.71  \n",
       "53                     0.70  \n",
       "113                    0.66  \n",
       "47                     0.65  \n",
       "6                      0.64  \n",
       "1                      0.64  \n",
       "10                     0.61  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select top 10 countries with near and above median HDI\n",
    "df_ef2016[df_ef2016['HDI'] >= 0.7].nlargest(10, 'development_efficiency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As observed in the scatterplot, only Sri Lanka and Jamaica are within the one planet limit, with a near-median HDI. Uruguay stands out as the only country reaching the 0.8 HDI mark. Albania makes it in the list as the only European country and, along with the last three countries in the list, it is considered as an economy in transition by the United Nations country classification in the [World Economic Situation and Prospects report from 2019](https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/WESP2019_BOOK-ANNEX-en.pdf). The other 6 countries are considered to be developing economies.\n",
    "\n",
    "Each of these countries would have to be studied further to better understand how they score so high on the HDI in such a resource-efficient manner. Some of them may have policies in place that could serve as examples of good practice for other countries facing similar challenges."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can have a look at what countries are the least resource efficient relative to the HDI:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\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</th>\n",
       "      <th>code</th>\n",
       "      <th>Earths</th>\n",
       "      <th>HDI</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>development_efficiency</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>QA</td>\n",
       "      <td>8.91</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.57e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>LU</td>\n",
       "      <td>7.99</td>\n",
       "      <td>0.90</td>\n",
       "      <td>5.76e+05</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>MN</td>\n",
       "      <td>4.77</td>\n",
       "      <td>0.73</td>\n",
       "      <td>3.03e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>KW</td>\n",
       "      <td>5.29</td>\n",
       "      <td>0.81</td>\n",
       "      <td>4.05e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>BH</td>\n",
       "      <td>5.47</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.43e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>AE</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0.86</td>\n",
       "      <td>9.27e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>TT</td>\n",
       "      <td>4.90</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.36e+06</td>\n",
       "      <td>Americas</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>5.11</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.22e+08</td>\n",
       "      <td>Americas</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "      <td>5.10</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.63e+07</td>\n",
       "      <td>Americas</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>EE</td>\n",
       "      <td>4.40</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.31e+06</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country code  Earths   HDI  population continent  \\\n",
       "139                 Qatar   QA    8.91  0.85    2.57e+06      Asia   \n",
       "102            Luxembourg   LU    7.99  0.90    5.76e+05    Europe   \n",
       "114              Mongolia   MN    4.77  0.73    3.03e+06      Asia   \n",
       "93                 Kuwait   KW    5.29  0.81    4.05e+06      Asia   \n",
       "12                Bahrain   BH    5.47  0.84    1.43e+06      Asia   \n",
       "179  United Arab Emirates   AE    5.52  0.86    9.27e+06      Asia   \n",
       "173   Trinidad and Tobago   TT    4.90  0.80    1.36e+06  Americas   \n",
       "181         United States   US    5.11  0.92    3.22e+08  Americas   \n",
       "33                 Canada   CA    5.10  0.92    3.63e+07  Americas   \n",
       "59                Estonia   EE    4.40  0.88    1.31e+06    Europe   \n",
       "\n",
       "     development_efficiency  \n",
       "139                    0.10  \n",
       "102                    0.11  \n",
       "114                    0.15  \n",
       "93                     0.15  \n",
       "12                     0.15  \n",
       "179                    0.16  \n",
       "173                    0.16  \n",
       "181                    0.18  \n",
       "33                     0.18  \n",
       "59                     0.20  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Development efficiency bottom 10\n",
    "df_ef2016.nsmallest(10, 'development_efficiency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are mainly fossil fuel producing countries with the exception of Luxembourg. These countries would have to be investigated further to better understand why they make it into this list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This analysis has shed some light on the situation of countries regarding their ecological footprint and their level of development. Sri Lanka stands out as being the only country with a sustainable level resource consumption while achieving an above-median score on the HDI. None of the countries among what are considered as developed economies according to the United Nations are even close to the one planet limit, most are well above the 2 Earths mark, with the exception of Romania.\n",
    "\n",
    "This goes to show that all countries still have a lot of work to do in order to achieve a sustainable level of development on both social and environmental issues. As the UN Sustainable Development Goals are becoming the new benchmark for assessing the level of development of countries, one could question the use of the classification 'developed', 'in transition', and 'developing', as the scores on the ecological footprint clearly show that the so-called developed countries still have a way to go in order to achieve a sustainable level of consumption while maintaining a high standard of living."
   ]
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