solutions/Solution_21_the_gap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below you can find the data from the world inequality database on the income in percent ranked by percent from 2000 to 2020.\n",
    "The first line can be read as the top 10% earners in 2020 earned 31% of the total income in germany, while the top 10% in india earned 39% of the total income of india.\n",
    "The task today is to plot the data to get a meaningful interpretation of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = \"\"\"Percentile\tYear\tsptinc_z_DE\tsptinc_z_IN\n",
    "p90p100\t2000\t0,3176\t0,3987\n",
    "p90p100\t2001\t0,3184\t0,4095\n",
    "p90p100\t2002\t0,3197\t0,4206\n",
    "p90p100\t2003\t0,3128\t0,4318\n",
    "p90p100\t2004\t0,3239\t0,4432\n",
    "p90p100\t2005\t0,3442\t0,4548\n",
    "p90p100\t2006\t0,3487\t0,4676\n",
    "p90p100\t2007\t0,3661\t0,4807\n",
    "p90p100\t2008\t0,3634\t0,4941\n",
    "p90p100\t2009\t0,369\t0,5078\n",
    "p90p100\t2010\t0,3643\t0,5217\n",
    "p90p100\t2011\t0,3653\t0,5414\n",
    "p90p100\t2012\t0,3605\t0,55\n",
    "p90p100\t2013\t0,375\t0,5523\n",
    "p90p100\t2014\t0,3788\t0,561\n",
    "p90p100\t2015\t0,3794\t0,561\n",
    "p90p100\t2016\t0,3781\t0,5613\n",
    "p90p100\t2017\t0,3752\t0,5613\n",
    "p90p100\t2018\t0,3728\t0,5613\n",
    "p90p100\t2019\t0,3732\t0,5613\n",
    "p50p90\t2000\t0,4598\t0,3953\n",
    "p50p90\t2001\t0,4561\t0,3888\n",
    "p50p90\t2002\t0,4615\t0,3821\n",
    "p50p90\t2003\t0,4655\t0,3753\n",
    "p50p90\t2004\t0,4583\t0,3683\n",
    "p50p90\t2005\t0,4515\t0,3612\n",
    "p50p90\t2006\t0,4446\t0,3531\n",
    "p50p90\t2007\t0,4356\t0,3447\n",
    "p50p90\t2008\t0,4373\t0,3361\n",
    "p50p90\t2009\t0,4339\t0,3272\n",
    "p50p90\t2010\t0,4369\t0,3182\n",
    "p50p90\t2011\t0,4385\t0,3051\n",
    "p50p90\t2012\t0,4441\t0,2994\n",
    "p50p90\t2013\t0,4395\t0,2979\n",
    "p50p90\t2014\t0,4353\t0,2921\n",
    "p50p90\t2015\t0,4345\t0,2921\n",
    "p50p90\t2016\t0,4328\t0,2921\n",
    "p50p90\t2017\t0,4344\t0,2921\n",
    "p50p90\t2018\t0,4358\t0,2921\n",
    "p50p90\t2019\t0,4356\t0,2921\n",
    "p0p50\t2000\t0,2226\t0,206\n",
    "p0p50\t2001\t0,2255\t0,2017\n",
    "p0p50\t2002\t0,2188\t0,1973\n",
    "p0p50\t2003\t0,2217\t0,1929\n",
    "p0p50\t2004\t0,2178\t0,1884\n",
    "p0p50\t2005\t0,2043\t0,1839\n",
    "p0p50\t2006\t0,2067\t0,1793\n",
    "p0p50\t2007\t0,1982\t0,1746\n",
    "p0p50\t2008\t0,1993\t0,1699\n",
    "p0p50\t2009\t0,1971\t0,165\n",
    "p0p50\t2010\t0,1987\t0,1601\n",
    "p0p50\t2011\t0,1962\t0,1535\n",
    "p0p50\t2012\t0,1954\t0,1506\n",
    "p0p50\t2013\t0,1856\t0,1498\n",
    "p0p50\t2014\t0,1859\t0,1469\n",
    "p0p50\t2015\t0,1861\t0,1469\n",
    "p0p50\t2016\t0,1891\t0,1468\n",
    "p0p50\t2017\t0,1904\t0,1468\n",
    "p0p50\t2018\t0,1914\t0,1468\n",
    "p0p50\t2019\t0,1912\t0,1468\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import io\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(io.StringIO(data), sep=\"\\t\", decimal=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "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>Percentile</th>\n",
       "      <th>Year</th>\n",
       "      <th>sptinc_z_DE</th>\n",
       "      <th>sptinc_z_IN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.3176</td>\n",
       "      <td>0.3987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.3184</td>\n",
       "      <td>0.4095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.3197</td>\n",
       "      <td>0.4206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.3128</td>\n",
       "      <td>0.4318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.3239</td>\n",
       "      <td>0.4432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.3442</td>\n",
       "      <td>0.4548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.3487</td>\n",
       "      <td>0.4676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.3661</td>\n",
       "      <td>0.4807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.3634</td>\n",
       "      <td>0.4941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.3690</td>\n",
       "      <td>0.5078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.3643</td>\n",
       "      <td>0.5217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.3653</td>\n",
       "      <td>0.5414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.3605</td>\n",
       "      <td>0.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.5523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.3788</td>\n",
       "      <td>0.5610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.3794</td>\n",
       "      <td>0.5610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.3781</td>\n",
       "      <td>0.5613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.3752</td>\n",
       "      <td>0.5613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.3728</td>\n",
       "      <td>0.5613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>p90p100</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.3732</td>\n",
       "      <td>0.5613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.4598</td>\n",
       "      <td>0.3953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.4561</td>\n",
       "      <td>0.3888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.4615</td>\n",
       "      <td>0.3821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.4655</td>\n",
       "      <td>0.3753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.4583</td>\n",
       "      <td>0.3683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.4515</td>\n",
       "      <td>0.3612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.4446</td>\n",
       "      <td>0.3531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.4356</td>\n",
       "      <td>0.3447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.4373</td>\n",
       "      <td>0.3361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.4339</td>\n",
       "      <td>0.3272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.4369</td>\n",
       "      <td>0.3182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.4385</td>\n",
       "      <td>0.3051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.4441</td>\n",
       "      <td>0.2994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.4395</td>\n",
       "      <td>0.2979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.4353</td>\n",
       "      <td>0.2921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.4345</td>\n",
       "      <td>0.2921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.4328</td>\n",
       "      <td>0.2921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.4344</td>\n",
       "      <td>0.2921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.4358</td>\n",
       "      <td>0.2921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>p50p90</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.4356</td>\n",
       "      <td>0.2921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.2226</td>\n",
       "      <td>0.2060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.2188</td>\n",
       "      <td>0.1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.2217</td>\n",
       "      <td>0.1929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.2178</td>\n",
       "      <td>0.1884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.2043</td>\n",
       "      <td>0.1839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.2067</td>\n",
       "      <td>0.1793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.1982</td>\n",
       "      <td>0.1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.1993</td>\n",
       "      <td>0.1699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.1971</td>\n",
       "      <td>0.1650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.1987</td>\n",
       "      <td>0.1601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.1962</td>\n",
       "      <td>0.1535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.1954</td>\n",
       "      <td>0.1506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.1856</td>\n",
       "      <td>0.1498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.1859</td>\n",
       "      <td>0.1469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.1861</td>\n",
       "      <td>0.1469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.1891</td>\n",
       "      <td>0.1468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.1904</td>\n",
       "      <td>0.1468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.1914</td>\n",
       "      <td>0.1468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>p0p50</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.1912</td>\n",
       "      <td>0.1468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Percentile  Year  sptinc_z_DE  sptinc_z_IN\n",
       "0     p90p100  2000       0.3176       0.3987\n",
       "1     p90p100  2001       0.3184       0.4095\n",
       "2     p90p100  2002       0.3197       0.4206\n",
       "3     p90p100  2003       0.3128       0.4318\n",
       "4     p90p100  2004       0.3239       0.4432\n",
       "5     p90p100  2005       0.3442       0.4548\n",
       "6     p90p100  2006       0.3487       0.4676\n",
       "7     p90p100  2007       0.3661       0.4807\n",
       "8     p90p100  2008       0.3634       0.4941\n",
       "9     p90p100  2009       0.3690       0.5078\n",
       "10    p90p100  2010       0.3643       0.5217\n",
       "11    p90p100  2011       0.3653       0.5414\n",
       "12    p90p100  2012       0.3605       0.5500\n",
       "13    p90p100  2013       0.3750       0.5523\n",
       "14    p90p100  2014       0.3788       0.5610\n",
       "15    p90p100  2015       0.3794       0.5610\n",
       "16    p90p100  2016       0.3781       0.5613\n",
       "17    p90p100  2017       0.3752       0.5613\n",
       "18    p90p100  2018       0.3728       0.5613\n",
       "19    p90p100  2019       0.3732       0.5613\n",
       "20     p50p90  2000       0.4598       0.3953\n",
       "21     p50p90  2001       0.4561       0.3888\n",
       "22     p50p90  2002       0.4615       0.3821\n",
       "23     p50p90  2003       0.4655       0.3753\n",
       "24     p50p90  2004       0.4583       0.3683\n",
       "25     p50p90  2005       0.4515       0.3612\n",
       "26     p50p90  2006       0.4446       0.3531\n",
       "27     p50p90  2007       0.4356       0.3447\n",
       "28     p50p90  2008       0.4373       0.3361\n",
       "29     p50p90  2009       0.4339       0.3272\n",
       "30     p50p90  2010       0.4369       0.3182\n",
       "31     p50p90  2011       0.4385       0.3051\n",
       "32     p50p90  2012       0.4441       0.2994\n",
       "33     p50p90  2013       0.4395       0.2979\n",
       "34     p50p90  2014       0.4353       0.2921\n",
       "35     p50p90  2015       0.4345       0.2921\n",
       "36     p50p90  2016       0.4328       0.2921\n",
       "37     p50p90  2017       0.4344       0.2921\n",
       "38     p50p90  2018       0.4358       0.2921\n",
       "39     p50p90  2019       0.4356       0.2921\n",
       "40      p0p50  2000       0.2226       0.2060\n",
       "41      p0p50  2001       0.2255       0.2017\n",
       "42      p0p50  2002       0.2188       0.1973\n",
       "43      p0p50  2003       0.2217       0.1929\n",
       "44      p0p50  2004       0.2178       0.1884\n",
       "45      p0p50  2005       0.2043       0.1839\n",
       "46      p0p50  2006       0.2067       0.1793\n",
       "47      p0p50  2007       0.1982       0.1746\n",
       "48      p0p50  2008       0.1993       0.1699\n",
       "49      p0p50  2009       0.1971       0.1650\n",
       "50      p0p50  2010       0.1987       0.1601\n",
       "51      p0p50  2011       0.1962       0.1535\n",
       "52      p0p50  2012       0.1954       0.1506\n",
       "53      p0p50  2013       0.1856       0.1498\n",
       "54      p0p50  2014       0.1859       0.1469\n",
       "55      p0p50  2015       0.1861       0.1469\n",
       "56      p0p50  2016       0.1891       0.1468\n",
       "57      p0p50  2017       0.1904       0.1468\n",
       "58      p0p50  2018       0.1914       0.1468\n",
       "59      p0p50  2019       0.1912       0.1468"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "pivotedDf = df.pivot(index=\"Year\", columns=\"Percentile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">sptinc_z_DE</th>\n",
       "      <th colspan=\"3\" halign=\"left\">sptinc_z_IN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percentile</th>\n",
       "      <th>p0p50</th>\n",
       "      <th>p50p90</th>\n",
       "      <th>p90p100</th>\n",
       "      <th>p0p50</th>\n",
       "      <th>p50p90</th>\n",
       "      <th>p90p100</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</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>2000</th>\n",
       "      <td>0.2226</td>\n",
       "      <td>0.4598</td>\n",
       "      <td>0.3176</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.3953</td>\n",
       "      <td>0.3987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.4561</td>\n",
       "      <td>0.3184</td>\n",
       "      <td>0.2017</td>\n",
       "      <td>0.3888</td>\n",
       "      <td>0.4095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>0.2188</td>\n",
       "      <td>0.4615</td>\n",
       "      <td>0.3197</td>\n",
       "      <td>0.1973</td>\n",
       "      <td>0.3821</td>\n",
       "      <td>0.4206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>0.2217</td>\n",
       "      <td>0.4655</td>\n",
       "      <td>0.3128</td>\n",
       "      <td>0.1929</td>\n",
       "      <td>0.3753</td>\n",
       "      <td>0.4318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.2178</td>\n",
       "      <td>0.4583</td>\n",
       "      <td>0.3239</td>\n",
       "      <td>0.1884</td>\n",
       "      <td>0.3683</td>\n",
       "      <td>0.4432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>0.2043</td>\n",
       "      <td>0.4515</td>\n",
       "      <td>0.3442</td>\n",
       "      <td>0.1839</td>\n",
       "      <td>0.3612</td>\n",
       "      <td>0.4548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>0.2067</td>\n",
       "      <td>0.4446</td>\n",
       "      <td>0.3487</td>\n",
       "      <td>0.1793</td>\n",
       "      <td>0.3531</td>\n",
       "      <td>0.4676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>0.1982</td>\n",
       "      <td>0.4356</td>\n",
       "      <td>0.3661</td>\n",
       "      <td>0.1746</td>\n",
       "      <td>0.3447</td>\n",
       "      <td>0.4807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.1993</td>\n",
       "      <td>0.4373</td>\n",
       "      <td>0.3634</td>\n",
       "      <td>0.1699</td>\n",
       "      <td>0.3361</td>\n",
       "      <td>0.4941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>0.1971</td>\n",
       "      <td>0.4339</td>\n",
       "      <td>0.3690</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>0.3272</td>\n",
       "      <td>0.5078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.1987</td>\n",
       "      <td>0.4369</td>\n",
       "      <td>0.3643</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>0.3182</td>\n",
       "      <td>0.5217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.1962</td>\n",
       "      <td>0.4385</td>\n",
       "      <td>0.3653</td>\n",
       "      <td>0.1535</td>\n",
       "      <td>0.3051</td>\n",
       "      <td>0.5414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.1954</td>\n",
       "      <td>0.4441</td>\n",
       "      <td>0.3605</td>\n",
       "      <td>0.1506</td>\n",
       "      <td>0.2994</td>\n",
       "      <td>0.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.1856</td>\n",
       "      <td>0.4395</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.1498</td>\n",
       "      <td>0.2979</td>\n",
       "      <td>0.5523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.1859</td>\n",
       "      <td>0.4353</td>\n",
       "      <td>0.3788</td>\n",
       "      <td>0.1469</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.5610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.1861</td>\n",
       "      <td>0.4345</td>\n",
       "      <td>0.3794</td>\n",
       "      <td>0.1469</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.5610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.1891</td>\n",
       "      <td>0.4328</td>\n",
       "      <td>0.3781</td>\n",
       "      <td>0.1468</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.5613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.1904</td>\n",
       "      <td>0.4344</td>\n",
       "      <td>0.3752</td>\n",
       "      <td>0.1468</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.5613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.1914</td>\n",
       "      <td>0.4358</td>\n",
       "      <td>0.3728</td>\n",
       "      <td>0.1468</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.5613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.1912</td>\n",
       "      <td>0.4356</td>\n",
       "      <td>0.3732</td>\n",
       "      <td>0.1468</td>\n",
       "      <td>0.2921</td>\n",
       "      <td>0.5613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sptinc_z_DE                 sptinc_z_IN                \n",
       "Percentile       p0p50  p50p90 p90p100       p0p50  p50p90 p90p100\n",
       "Year                                                              \n",
       "2000            0.2226  0.4598  0.3176      0.2060  0.3953  0.3987\n",
       "2001            0.2255  0.4561  0.3184      0.2017  0.3888  0.4095\n",
       "2002            0.2188  0.4615  0.3197      0.1973  0.3821  0.4206\n",
       "2003            0.2217  0.4655  0.3128      0.1929  0.3753  0.4318\n",
       "2004            0.2178  0.4583  0.3239      0.1884  0.3683  0.4432\n",
       "2005            0.2043  0.4515  0.3442      0.1839  0.3612  0.4548\n",
       "2006            0.2067  0.4446  0.3487      0.1793  0.3531  0.4676\n",
       "2007            0.1982  0.4356  0.3661      0.1746  0.3447  0.4807\n",
       "2008            0.1993  0.4373  0.3634      0.1699  0.3361  0.4941\n",
       "2009            0.1971  0.4339  0.3690      0.1650  0.3272  0.5078\n",
       "2010            0.1987  0.4369  0.3643      0.1601  0.3182  0.5217\n",
       "2011            0.1962  0.4385  0.3653      0.1535  0.3051  0.5414\n",
       "2012            0.1954  0.4441  0.3605      0.1506  0.2994  0.5500\n",
       "2013            0.1856  0.4395  0.3750      0.1498  0.2979  0.5523\n",
       "2014            0.1859  0.4353  0.3788      0.1469  0.2921  0.5610\n",
       "2015            0.1861  0.4345  0.3794      0.1469  0.2921  0.5610\n",
       "2016            0.1891  0.4328  0.3781      0.1468  0.2921  0.5613\n",
       "2017            0.1904  0.4344  0.3752      0.1468  0.2921  0.5613\n",
       "2018            0.1914  0.4358  0.3728      0.1468  0.2921  0.5613\n",
       "2019            0.1912  0.4356  0.3732      0.1468  0.2921  0.5613"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivotedDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18cc39445c8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivotedDf.plot(style=['.', '.', '.'], figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "source": [
    "This plot shows that in Germany the gap between mid income (green) and top income(orange) decreased over the last 20 years. In India this gap increased drastically (brown and purple). Unfortunately the gap of the lower income to the other mid and top income increased in both countries (blue and red)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "source": [
    "If you are interested in more such data and want to find out some of our misconceptions about the world, I highly recommend checking this page: https://www.gapminder.org/ or reading the book factfulness."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}