exercise-2.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Best practices in data visualization - ASPP 2022, Bilbao\n",
    "## Exercise 2 - Which visualization should I use? \n",
    "\n",
    "In this exercise you get a dataset of the human development index (HDI) for different countries around the world over the years. The HDI is a measure of 'how developed' a country is. It depends on how rich the country is, but also on other factors such as life expectancy and education. This dataset contains several other variables that are interesting and could relate somehow to the HDI. **Your job is to visualize possible relationships and in this way answer some questions (see below)**. \n",
    "\n",
    "Here a short description of each column in the dataset\n",
    "\n",
    "- *Country, Year*, Region are self-explanatory\n",
    "- *GDP per capita*: gross domestic product of a country per inhabitant. It is considered a measure of how rich a country is.\n",
    "- *Expenditure on health/education/social protection*: percentage of public spending destined to those sectors\n",
    "- *HDI*: human development index\n",
    "- *Code*: three letter country code\n",
    "- *Global*: Whether the country belongs to the so called 'Global North' or 'Global South'\n",
    "\n",
    "Note that this dataset is incomplete as it does not contain data of *all* the countries in the world.\n",
    "\n",
    "Sources: [ourworldindata.org](https://ourworldindata.org/human-development-index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3fd7dc1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# matplotlib comes alread with a set of stylesheets. \n",
    "# here we use one of the default sheets from *seaborn*, another python visualization library based on matplotlib.\n",
    "# for more styles see: https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html\n",
    "plt.style.use('seaborn-darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12845b76",
   "metadata": {},
   "outputs": [
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Expenditure on health</th>\n",
       "      <th>Expenditure on education</th>\n",
       "      <th>Expenditure on social protection</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Code</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Region</th>\n",
       "      <th>Global</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2006</td>\n",
       "      <td>1.387150</td>\n",
       "      <td>4.684761</td>\n",
       "      <td>0.887695</td>\n",
       "      <td>0.429</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1315.789063</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2007</td>\n",
       "      <td>2.376418</td>\n",
       "      <td>4.174895</td>\n",
       "      <td>1.357564</td>\n",
       "      <td>0.447</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1460.825806</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>2.313319</td>\n",
       "      <td>4.383672</td>\n",
       "      <td>1.787371</td>\n",
       "      <td>0.447</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1484.114502</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2009</td>\n",
       "      <td>2.222690</td>\n",
       "      <td>4.810640</td>\n",
       "      <td>1.211468</td>\n",
       "      <td>0.460</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1758.904419</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>2.562634</td>\n",
       "      <td>5.383646</td>\n",
       "      <td>5.380668</td>\n",
       "      <td>0.472</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1957.029053</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>2.365495</td>\n",
       "      <td>5.660089</td>\n",
       "      <td>6.987072</td>\n",
       "      <td>0.701</td>\n",
       "      <td>UZB</td>\n",
       "      <td>6401.115234</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.284768</td>\n",
       "      <td>5.567202</td>\n",
       "      <td>6.612210</td>\n",
       "      <td>0.705</td>\n",
       "      <td>UZB</td>\n",
       "      <td>6663.949707</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2017</td>\n",
       "      <td>2.137274</td>\n",
       "      <td>5.079615</td>\n",
       "      <td>6.158300</td>\n",
       "      <td>0.713</td>\n",
       "      <td>UZB</td>\n",
       "      <td>6840.709961</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.215873</td>\n",
       "      <td>5.162037</td>\n",
       "      <td>6.040904</td>\n",
       "      <td>0.717</td>\n",
       "      <td>UZB</td>\n",
       "      <td>7082.925293</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2019</td>\n",
       "      <td>2.435022</td>\n",
       "      <td>6.769696</td>\n",
       "      <td>6.267907</td>\n",
       "      <td>0.720</td>\n",
       "      <td>UZB</td>\n",
       "      <td>7348.146973</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1075 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Year  Expenditure on health  Expenditure on education  \\\n",
       "0     Afghanistan  2006               1.387150                  4.684761   \n",
       "1     Afghanistan  2007               2.376418                  4.174895   \n",
       "2     Afghanistan  2008               2.313319                  4.383672   \n",
       "3     Afghanistan  2009               2.222690                  4.810640   \n",
       "4     Afghanistan  2010               2.562634                  5.383646   \n",
       "...           ...   ...                    ...                       ...   \n",
       "1070   Uzbekistan  2015               2.365495                  5.660089   \n",
       "1071   Uzbekistan  2016               2.284768                  5.567202   \n",
       "1072   Uzbekistan  2017               2.137274                  5.079615   \n",
       "1073   Uzbekistan  2018               2.215873                  5.162037   \n",
       "1074   Uzbekistan  2019               2.435022                  6.769696   \n",
       "\n",
       "      Expenditure on social protection    HDI Code  GDP per capita  \\\n",
       "0                             0.887695  0.429  AFG     1315.789063   \n",
       "1                             1.357564  0.447  AFG     1460.825806   \n",
       "2                             1.787371  0.447  AFG     1484.114502   \n",
       "3                             1.211468  0.460  AFG     1758.904419   \n",
       "4                             5.380668  0.472  AFG     1957.029053   \n",
       "...                                ...    ...  ...             ...   \n",
       "1070                          6.987072  0.701  UZB     6401.115234   \n",
       "1071                          6.612210  0.705  UZB     6663.949707   \n",
       "1072                          6.158300  0.713  UZB     6840.709961   \n",
       "1073                          6.040904  0.717  UZB     7082.925293   \n",
       "1074                          6.267907  0.720  UZB     7348.146973   \n",
       "\n",
       "              Region        Global  \n",
       "0     Asia & Pacific  Global South  \n",
       "1     Asia & Pacific  Global South  \n",
       "2     Asia & Pacific  Global South  \n",
       "3     Asia & Pacific  Global South  \n",
       "4     Asia & Pacific  Global South  \n",
       "...              ...           ...  \n",
       "1070  Asia & Pacific  Global South  \n",
       "1071  Asia & Pacific  Global South  \n",
       "1072  Asia & Pacific  Global South  \n",
       "1073  Asia & Pacific  Global South  \n",
       "1074  Asia & Pacific  Global South  \n",
       "\n",
       "[1075 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we use pandas to read the dataset on a table\n",
    "df = pd.read_csv('data/hdi_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f196d54c",
   "metadata": {},
   "source": [
    "#### How to get data from one column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "82ded147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1315.789063, 1460.825806, 1484.114502, ..., 6840.709961,\n",
       "       7082.925293, 7348.146973])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can get the values of one column in this way, returning a numpy array\n",
    "df['GDP per capita'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c46d9025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Afghanistan', 'Afghanistan', ..., 'Uzbekistan',\n",
       "       'Uzbekistan', 'Uzbekistan'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for the categorical variables you can get it also as a numpy array, of type 'object'\n",
    "df['Country'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fa81e00",
   "metadata": {},
   "source": [
    "#### How to filter data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c9155a7",
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>Year</th>\n",
       "      <th>Expenditure on health</th>\n",
       "      <th>Expenditure on education</th>\n",
       "      <th>Expenditure on social protection</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Code</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Region</th>\n",
       "      <th>Global</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.822734</td>\n",
       "      <td>4.336241</td>\n",
       "      <td>16.782265</td>\n",
       "      <td>0.838</td>\n",
       "      <td>ITA</td>\n",
       "      <td>43053.93359</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2001</td>\n",
       "      <td>6.131799</td>\n",
       "      <td>4.417285</td>\n",
       "      <td>16.585233</td>\n",
       "      <td>0.845</td>\n",
       "      <td>ITA</td>\n",
       "      <td>43869.42969</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2002</td>\n",
       "      <td>6.247251</td>\n",
       "      <td>4.438759</td>\n",
       "      <td>16.243340</td>\n",
       "      <td>0.850</td>\n",
       "      <td>ITA</td>\n",
       "      <td>43915.38672</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2003</td>\n",
       "      <td>6.252702</td>\n",
       "      <td>4.544670</td>\n",
       "      <td>17.057517</td>\n",
       "      <td>0.855</td>\n",
       "      <td>ITA</td>\n",
       "      <td>43781.21875</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2004</td>\n",
       "      <td>6.583200</td>\n",
       "      <td>4.320473</td>\n",
       "      <td>17.114984</td>\n",
       "      <td>0.860</td>\n",
       "      <td>ITA</td>\n",
       "      <td>44118.03516</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2005</td>\n",
       "      <td>6.822216</td>\n",
       "      <td>4.443763</td>\n",
       "      <td>17.178628</td>\n",
       "      <td>0.865</td>\n",
       "      <td>ITA</td>\n",
       "      <td>44260.82813</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2006</td>\n",
       "      <td>6.900167</td>\n",
       "      <td>4.372923</td>\n",
       "      <td>17.253767</td>\n",
       "      <td>0.870</td>\n",
       "      <td>ITA</td>\n",
       "      <td>44918.17188</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2007</td>\n",
       "      <td>6.680224</td>\n",
       "      <td>4.420460</td>\n",
       "      <td>17.399422</td>\n",
       "      <td>0.874</td>\n",
       "      <td>ITA</td>\n",
       "      <td>45356.53516</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2008</td>\n",
       "      <td>7.009526</td>\n",
       "      <td>4.286164</td>\n",
       "      <td>17.974354</td>\n",
       "      <td>0.876</td>\n",
       "      <td>ITA</td>\n",
       "      <td>44623.60156</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2009</td>\n",
       "      <td>7.420609</td>\n",
       "      <td>4.529398</td>\n",
       "      <td>19.722607</td>\n",
       "      <td>0.876</td>\n",
       "      <td>ITA</td>\n",
       "      <td>42074.92188</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2010</td>\n",
       "      <td>7.386240</td>\n",
       "      <td>4.346594</td>\n",
       "      <td>19.688695</td>\n",
       "      <td>0.879</td>\n",
       "      <td>ITA</td>\n",
       "      <td>42664.35547</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2011</td>\n",
       "      <td>7.112150</td>\n",
       "      <td>4.060637</td>\n",
       "      <td>19.542249</td>\n",
       "      <td>0.883</td>\n",
       "      <td>ITA</td>\n",
       "      <td>42892.30469</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2012</td>\n",
       "      <td>7.112164</td>\n",
       "      <td>4.056273</td>\n",
       "      <td>20.253971</td>\n",
       "      <td>0.882</td>\n",
       "      <td>ITA</td>\n",
       "      <td>41501.71094</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2013</td>\n",
       "      <td>7.118028</td>\n",
       "      <td>4.086932</td>\n",
       "      <td>20.908262</td>\n",
       "      <td>0.881</td>\n",
       "      <td>ITA</td>\n",
       "      <td>40268.11328</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2014</td>\n",
       "      <td>7.124713</td>\n",
       "      <td>4.039164</td>\n",
       "      <td>21.118885</td>\n",
       "      <td>0.882</td>\n",
       "      <td>ITA</td>\n",
       "      <td>39898.52734</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2015</td>\n",
       "      <td>7.031785</td>\n",
       "      <td>3.997868</td>\n",
       "      <td>21.279786</td>\n",
       "      <td>0.882</td>\n",
       "      <td>ITA</td>\n",
       "      <td>40247.82813</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2016</td>\n",
       "      <td>6.910715</td>\n",
       "      <td>3.858462</td>\n",
       "      <td>20.877091</td>\n",
       "      <td>0.884</td>\n",
       "      <td>ITA</td>\n",
       "      <td>40837.73828</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.817257</td>\n",
       "      <td>3.860553</td>\n",
       "      <td>20.689073</td>\n",
       "      <td>0.886</td>\n",
       "      <td>ITA</td>\n",
       "      <td>41581.12109</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2018</td>\n",
       "      <td>6.811713</td>\n",
       "      <td>3.924991</td>\n",
       "      <td>20.654946</td>\n",
       "      <td>0.890</td>\n",
       "      <td>ITA</td>\n",
       "      <td>42045.92188</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2019</td>\n",
       "      <td>6.830260</td>\n",
       "      <td>3.913343</td>\n",
       "      <td>21.118216</td>\n",
       "      <td>0.892</td>\n",
       "      <td>ITA</td>\n",
       "      <td>42708.03516</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country  Year  Expenditure on health  Expenditure on education  \\\n",
       "494   Italy  2000               5.822734                  4.336241   \n",
       "495   Italy  2001               6.131799                  4.417285   \n",
       "496   Italy  2002               6.247251                  4.438759   \n",
       "497   Italy  2003               6.252702                  4.544670   \n",
       "498   Italy  2004               6.583200                  4.320473   \n",
       "499   Italy  2005               6.822216                  4.443763   \n",
       "500   Italy  2006               6.900167                  4.372923   \n",
       "501   Italy  2007               6.680224                  4.420460   \n",
       "502   Italy  2008               7.009526                  4.286164   \n",
       "503   Italy  2009               7.420609                  4.529398   \n",
       "504   Italy  2010               7.386240                  4.346594   \n",
       "505   Italy  2011               7.112150                  4.060637   \n",
       "506   Italy  2012               7.112164                  4.056273   \n",
       "507   Italy  2013               7.118028                  4.086932   \n",
       "508   Italy  2014               7.124713                  4.039164   \n",
       "509   Italy  2015               7.031785                  3.997868   \n",
       "510   Italy  2016               6.910715                  3.858462   \n",
       "511   Italy  2017               6.817257                  3.860553   \n",
       "512   Italy  2018               6.811713                  3.924991   \n",
       "513   Italy  2019               6.830260                  3.913343   \n",
       "\n",
       "     Expenditure on social protection    HDI Code  GDP per capita  Region  \\\n",
       "494                         16.782265  0.838  ITA     43053.93359  Europe   \n",
       "495                         16.585233  0.845  ITA     43869.42969  Europe   \n",
       "496                         16.243340  0.850  ITA     43915.38672  Europe   \n",
       "497                         17.057517  0.855  ITA     43781.21875  Europe   \n",
       "498                         17.114984  0.860  ITA     44118.03516  Europe   \n",
       "499                         17.178628  0.865  ITA     44260.82813  Europe   \n",
       "500                         17.253767  0.870  ITA     44918.17188  Europe   \n",
       "501                         17.399422  0.874  ITA     45356.53516  Europe   \n",
       "502                         17.974354  0.876  ITA     44623.60156  Europe   \n",
       "503                         19.722607  0.876  ITA     42074.92188  Europe   \n",
       "504                         19.688695  0.879  ITA     42664.35547  Europe   \n",
       "505                         19.542249  0.883  ITA     42892.30469  Europe   \n",
       "506                         20.253971  0.882  ITA     41501.71094  Europe   \n",
       "507                         20.908262  0.881  ITA     40268.11328  Europe   \n",
       "508                         21.118885  0.882  ITA     39898.52734  Europe   \n",
       "509                         21.279786  0.882  ITA     40247.82813  Europe   \n",
       "510                         20.877091  0.884  ITA     40837.73828  Europe   \n",
       "511                         20.689073  0.886  ITA     41581.12109  Europe   \n",
       "512                         20.654946  0.890  ITA     42045.92188  Europe   \n",
       "513                         21.118216  0.892  ITA     42708.03516  Europe   \n",
       "\n",
       "           Global  \n",
       "494  Global North  \n",
       "495  Global North  \n",
       "496  Global North  \n",
       "497  Global North  \n",
       "498  Global North  \n",
       "499  Global North  \n",
       "500  Global North  \n",
       "501  Global North  \n",
       "502  Global North  \n",
       "503  Global North  \n",
       "504  Global North  \n",
       "505  Global North  \n",
       "506  Global North  \n",
       "507  Global North  \n",
       "508  Global North  \n",
       "509  Global North  \n",
       "510  Global North  \n",
       "511  Global North  \n",
       "512  Global North  \n",
       "513  Global North  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can filter the data in this way. Say you are only interested in country 'Germany'. Then you select \n",
    "# only the rows where that is true with df[df['colname'] == value]\n",
    "df[df['Country']=='Italy']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56f94c59",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff3325d3",
   "metadata": {},
   "source": [
    "### A: How is the HDI distributed? normally distributed, or skewed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae06139f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5badd06a",
   "metadata": {},
   "source": [
    "### B: Is there a relationship between GDP per capita and HDI? is this relationship different for different regions of the world? \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4df91d53",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384c3053",
   "metadata": {},
   "source": [
    "### C: Is expenditure on education or health correlated with the HDI *and* GDP?\n",
    "\n",
    "Can you visualize the three variables at once? (HDI, GDP and % expenditure in health/education).\n",
    "\n",
    "First think (even better, make a sketch) of the different possibilities you have. Then implement the one you think its clearer here below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50d958e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4467089d",
   "metadata": {},
   "source": [
    "### D: are countries getting richer, i.e. is their GDP growing over time? \n",
    "\n",
    "You can visualize some countries relevant for you, or randomly pick some, say ten of them.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "480843d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "eb90e9ce",
   "metadata": {},
   "source": [
    "### E: According to this dataset, which is - on average - the less developed region? Visualize the fact that the Global North is more developed than the Global South.\n",
    "\n",
    "Consider that your message is simple, comparing regions. Which kind of plot is better at comparing categorial entities?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c3e5ef4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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