05-pair-agrupacion_de_datos_metodo_apply.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejercicios apply\n",
    "1. Las columnas land_area, armed_forces_size, co2-emissions, gdp, population, urban_population, latitude, longitude, density, cpi son de tipo object pero en realidad deberían ser numéricas. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('display.max_columns', None) # para poder visualizar todas las columnas de los DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>density</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>agriculturalland</th>\n",
       "      <th>landarea</th>\n",
       "      <th>armedforcessize</th>\n",
       "      <th>birthrate</th>\n",
       "      <th>callingcode</th>\n",
       "      <th>capital/majorcity</th>\n",
       "      <th>co2-emissions</th>\n",
       "      <th>cpi</th>\n",
       "      <th>cpichange</th>\n",
       "      <th>currency-code</th>\n",
       "      <th>fertilityrate</th>\n",
       "      <th>forestedarea</th>\n",
       "      <th>gasolineprice</th>\n",
       "      <th>gdp</th>\n",
       "      <th>grossprimaryeducationenrollment</th>\n",
       "      <th>grosstertiaryeducationenrollment</th>\n",
       "      <th>infantmortality</th>\n",
       "      <th>largestcity</th>\n",
       "      <th>lifeexpectancy</th>\n",
       "      <th>maternalmortalityratio</th>\n",
       "      <th>minimumwage</th>\n",
       "      <th>officiallanguage</th>\n",
       "      <th>outofpockethealthexpenditure</th>\n",
       "      <th>physiciansperthousand</th>\n",
       "      <th>population</th>\n",
       "      <th>populationlaborforceparticipation</th>\n",
       "      <th>taxrevenue</th>\n",
       "      <th>totaltaxrate</th>\n",
       "      <th>unemploymentrate</th>\n",
       "      <th>urban_population</th>\n",
       "      <th>longitud</th>\n",
       "      <th>latitud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>60</td>\n",
       "      <td>AF</td>\n",
       "      <td>58.1</td>\n",
       "      <td>652,230</td>\n",
       "      <td>323,000</td>\n",
       "      <td>32.49</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>8,672</td>\n",
       "      <td>149.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>AFN</td>\n",
       "      <td>4.47</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.70</td>\n",
       "      <td>19,101,353,833</td>\n",
       "      <td>104.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>47.9</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>64.5</td>\n",
       "      <td>638.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>Pashto</td>\n",
       "      <td>78.4</td>\n",
       "      <td>0.28</td>\n",
       "      <td>38,041,754</td>\n",
       "      <td>48.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>71.4</td>\n",
       "      <td>11.12</td>\n",
       "      <td>9,797,273</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>105</td>\n",
       "      <td>AL</td>\n",
       "      <td>43.1</td>\n",
       "      <td>28,748</td>\n",
       "      <td>9,000</td>\n",
       "      <td>11.78</td>\n",
       "      <td>355.0</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>4,536</td>\n",
       "      <td>119.05</td>\n",
       "      <td>1.4</td>\n",
       "      <td>ALL</td>\n",
       "      <td>1.62</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.36</td>\n",
       "      <td>15,278,077,447</td>\n",
       "      <td>107.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>78.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.12</td>\n",
       "      <td>Albanian</td>\n",
       "      <td>56.9</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2,854,191</td>\n",
       "      <td>55.7</td>\n",
       "      <td>18.6</td>\n",
       "      <td>36.6</td>\n",
       "      <td>12.33</td>\n",
       "      <td>1,747,593</td>\n",
       "      <td>41.153332</td>\n",
       "      <td>20.168331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>18</td>\n",
       "      <td>DZ</td>\n",
       "      <td>17.4</td>\n",
       "      <td>2,381,741</td>\n",
       "      <td>317,000</td>\n",
       "      <td>24.28</td>\n",
       "      <td>213.0</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>150,006</td>\n",
       "      <td>151.36</td>\n",
       "      <td>2.0</td>\n",
       "      <td>DZD</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.28</td>\n",
       "      <td>169,988,236,398</td>\n",
       "      <td>109.9</td>\n",
       "      <td>51.4</td>\n",
       "      <td>20.1</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>76.7</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Arabic</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.72</td>\n",
       "      <td>43,053,054</td>\n",
       "      <td>41.2</td>\n",
       "      <td>37.2</td>\n",
       "      <td>66.1</td>\n",
       "      <td>11.70</td>\n",
       "      <td>31,510,100</td>\n",
       "      <td>28.033886</td>\n",
       "      <td>1.659626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>164</td>\n",
       "      <td>AD</td>\n",
       "      <td>40.0</td>\n",
       "      <td>468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.20</td>\n",
       "      <td>376.0</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "      <td>469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EUR</td>\n",
       "      <td>1.27</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>3,154,057,987</td>\n",
       "      <td>106.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.7</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.63</td>\n",
       "      <td>Catalan</td>\n",
       "      <td>36.4</td>\n",
       "      <td>3.33</td>\n",
       "      <td>77,142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67,873</td>\n",
       "      <td>42.506285</td>\n",
       "      <td>1.521801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>26</td>\n",
       "      <td>AO</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1,246,700</td>\n",
       "      <td>117,000</td>\n",
       "      <td>40.73</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>34,693</td>\n",
       "      <td>261.73</td>\n",
       "      <td>17.1</td>\n",
       "      <td>AOA</td>\n",
       "      <td>5.52</td>\n",
       "      <td>46.3</td>\n",
       "      <td>0.97</td>\n",
       "      <td>94,635,415,870</td>\n",
       "      <td>113.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>51.6</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>60.8</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>33.4</td>\n",
       "      <td>0.21</td>\n",
       "      <td>31,825,295</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>49.1</td>\n",
       "      <td>6.89</td>\n",
       "      <td>21,061,025</td>\n",
       "      <td>-11.202692</td>\n",
       "      <td>17.873887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country density abbreviation  agriculturalland   landarea  \\\n",
       "0  Afghanistan      60           AF              58.1    652,230   \n",
       "1      Albania     105           AL              43.1     28,748   \n",
       "2      Algeria      18           DZ              17.4  2,381,741   \n",
       "3      Andorra     164           AD              40.0        468   \n",
       "4       Angola      26           AO              47.5  1,246,700   \n",
       "\n",
       "  armedforcessize  birthrate  callingcode capital/majorcity co2-emissions  \\\n",
       "0         323,000      32.49         93.0             Kabul         8,672   \n",
       "1           9,000      11.78        355.0            Tirana         4,536   \n",
       "2         317,000      24.28        213.0           Algiers       150,006   \n",
       "3             NaN       7.20        376.0  Andorra la Vella           469   \n",
       "4         117,000      40.73        244.0            Luanda        34,693   \n",
       "\n",
       "      cpi  cpichange currency-code  fertilityrate  forestedarea  \\\n",
       "0   149.9        2.3           AFN           4.47           2.1   \n",
       "1  119.05        1.4           ALL           1.62          28.1   \n",
       "2  151.36        2.0           DZD           3.02           0.8   \n",
       "3     NaN        NaN           EUR           1.27          34.0   \n",
       "4  261.73       17.1           AOA           5.52          46.3   \n",
       "\n",
       "   gasolineprice               gdp  grossprimaryeducationenrollment  \\\n",
       "0           0.70   19,101,353,833                             104.0   \n",
       "1           1.36   15,278,077,447                             107.0   \n",
       "2           0.28  169,988,236,398                             109.9   \n",
       "3           1.51    3,154,057,987                             106.4   \n",
       "4           0.97   94,635,415,870                             113.5   \n",
       "\n",
       "   grosstertiaryeducationenrollment  infantmortality       largestcity  \\\n",
       "0                               9.7             47.9             Kabul   \n",
       "1                              55.0              7.8            Tirana   \n",
       "2                              51.4             20.1           Algiers   \n",
       "3                               NaN              2.7  Andorra la Vella   \n",
       "4                               9.3             51.6            Luanda   \n",
       "\n",
       "   lifeexpectancy  maternalmortalityratio  minimumwage officiallanguage  \\\n",
       "0            64.5                   638.0         0.43           Pashto   \n",
       "1            78.5                    15.0         1.12         Albanian   \n",
       "2            76.7                   112.0         0.95           Arabic   \n",
       "3             NaN                     NaN         6.63          Catalan   \n",
       "4            60.8                   241.0         0.71       Portuguese   \n",
       "\n",
       "   outofpockethealthexpenditure  physiciansperthousand  population  \\\n",
       "0                          78.4                   0.28  38,041,754   \n",
       "1                          56.9                   1.20   2,854,191   \n",
       "2                          28.1                   1.72  43,053,054   \n",
       "3                          36.4                   3.33      77,142   \n",
       "4                          33.4                   0.21  31,825,295   \n",
       "\n",
       "   populationlaborforceparticipation  taxrevenue  totaltaxrate  \\\n",
       "0                               48.9         9.3          71.4   \n",
       "1                               55.7        18.6          36.6   \n",
       "2                               41.2        37.2          66.1   \n",
       "3                                NaN         NaN           NaN   \n",
       "4                               77.5         9.2          49.1   \n",
       "\n",
       "   unemploymentrate urban_population     longitud     latitud  \n",
       "0             11.12        9,797,273    33.93911    67.709953  \n",
       "1             12.33        1,747,593   41.153332    20.168331  \n",
       "2             11.70       31,510,100   28.033886     1.659626  \n",
       "3               NaN           67,873   42.506285     1.521801  \n",
       "4              6.89       21,061,025  -11.202692    17.873887  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mergeworld = pd.read_csv(\"merge_world.csv\")\n",
    "df_mergeworld.head(5\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 35 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   country                            195 non-null    object \n",
      " 1   density                            195 non-null    object \n",
      " 2   abbreviation                       188 non-null    object \n",
      " 3   agriculturalland                   188 non-null    float64\n",
      " 4   landarea                           194 non-null    object \n",
      " 5   armedforcessize                    171 non-null    object \n",
      " 6   birthrate                          189 non-null    float64\n",
      " 7   callingcode                        194 non-null    float64\n",
      " 8   capital/majorcity                  192 non-null    object \n",
      " 9   co2-emissions                      188 non-null    object \n",
      " 10  cpi                                178 non-null    object \n",
      " 11  cpichange                          179 non-null    float64\n",
      " 12  currency-code                      180 non-null    object \n",
      " 13  fertilityrate                      188 non-null    float64\n",
      " 14  forestedarea                       188 non-null    float64\n",
      " 15  gasolineprice                      175 non-null    float64\n",
      " 16  gdp                                193 non-null    object \n",
      " 17  grossprimaryeducationenrollment    188 non-null    float64\n",
      " 18  grosstertiaryeducationenrollment   183 non-null    float64\n",
      " 19  infantmortality                    189 non-null    float64\n",
      " 20  largestcity                        189 non-null    object \n",
      " 21  lifeexpectancy                     187 non-null    float64\n",
      " 22  maternalmortalityratio             181 non-null    float64\n",
      " 23  minimumwage                        150 non-null    float64\n",
      " 24  officiallanguage                   190 non-null    object \n",
      " 25  outofpockethealthexpenditure       188 non-null    float64\n",
      " 26  physiciansperthousand              188 non-null    float64\n",
      " 27  population                         194 non-null    object \n",
      " 28  populationlaborforceparticipation  176 non-null    float64\n",
      " 29  taxrevenue                         169 non-null    float64\n",
      " 30  totaltaxrate                       183 non-null    float64\n",
      " 31  unemploymentrate                   176 non-null    float64\n",
      " 32  urban_population                   190 non-null    object \n",
      " 33  longitud                           195 non-null    object \n",
      " 34  latitud                            195 non-null    object \n",
      "dtypes: float64(19), object(16)\n",
      "memory usage: 53.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_mergeworld.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Crea una función que nos permita cambiar el tipo de dato de estas columnas al que le corresponde."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_columnas = [\"landarea\", \"armedforcessize\", \"co2-emissions\", \"gdp\", \"population\", \"urban_population\", \"latitud\", \"longitud\", \"cpi\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hacemos una función que convierta de string a numérico\n",
    "\n",
    "def convertir_columna_numerica(nombre_columna):\n",
    "    return pd.to_numeric(str(nombre_columna).replace(',', ''), errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Usamos Apply para aplicar la función \n",
    "\n",
    "df_mergeworld[lista_columnas] = df_mergeworld[lista_columnas].apply(lambda x: x.apply(convertir_columna_numerica))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>density</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>agriculturalland</th>\n",
       "      <th>landarea</th>\n",
       "      <th>armedforcessize</th>\n",
       "      <th>birthrate</th>\n",
       "      <th>callingcode</th>\n",
       "      <th>capital/majorcity</th>\n",
       "      <th>co2-emissions</th>\n",
       "      <th>cpi</th>\n",
       "      <th>cpichange</th>\n",
       "      <th>currency-code</th>\n",
       "      <th>fertilityrate</th>\n",
       "      <th>forestedarea</th>\n",
       "      <th>gasolineprice</th>\n",
       "      <th>gdp</th>\n",
       "      <th>grossprimaryeducationenrollment</th>\n",
       "      <th>grosstertiaryeducationenrollment</th>\n",
       "      <th>infantmortality</th>\n",
       "      <th>largestcity</th>\n",
       "      <th>lifeexpectancy</th>\n",
       "      <th>maternalmortalityratio</th>\n",
       "      <th>minimumwage</th>\n",
       "      <th>officiallanguage</th>\n",
       "      <th>outofpockethealthexpenditure</th>\n",
       "      <th>physiciansperthousand</th>\n",
       "      <th>population</th>\n",
       "      <th>populationlaborforceparticipation</th>\n",
       "      <th>taxrevenue</th>\n",
       "      <th>totaltaxrate</th>\n",
       "      <th>unemploymentrate</th>\n",
       "      <th>urban_population</th>\n",
       "      <th>longitud</th>\n",
       "      <th>latitud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>60</td>\n",
       "      <td>AF</td>\n",
       "      <td>58.1</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>323000.0</td>\n",
       "      <td>32.49</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>8672.0</td>\n",
       "      <td>149.90</td>\n",
       "      <td>2.3</td>\n",
       "      <td>AFN</td>\n",
       "      <td>4.47</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.910135e+10</td>\n",
       "      <td>104.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>47.9</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>64.5</td>\n",
       "      <td>638.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>Pashto</td>\n",
       "      <td>78.4</td>\n",
       "      <td>0.28</td>\n",
       "      <td>38041754.0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>71.4</td>\n",
       "      <td>11.12</td>\n",
       "      <td>9797273.0</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>105</td>\n",
       "      <td>AL</td>\n",
       "      <td>43.1</td>\n",
       "      <td>28748.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>11.78</td>\n",
       "      <td>355.0</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>4536.0</td>\n",
       "      <td>119.05</td>\n",
       "      <td>1.4</td>\n",
       "      <td>ALL</td>\n",
       "      <td>1.62</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.527808e+10</td>\n",
       "      <td>107.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>78.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.12</td>\n",
       "      <td>Albanian</td>\n",
       "      <td>56.9</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2854191.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>18.6</td>\n",
       "      <td>36.6</td>\n",
       "      <td>12.33</td>\n",
       "      <td>1747593.0</td>\n",
       "      <td>41.153332</td>\n",
       "      <td>20.168331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>18</td>\n",
       "      <td>DZ</td>\n",
       "      <td>17.4</td>\n",
       "      <td>2381741.0</td>\n",
       "      <td>317000.0</td>\n",
       "      <td>24.28</td>\n",
       "      <td>213.0</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>150006.0</td>\n",
       "      <td>151.36</td>\n",
       "      <td>2.0</td>\n",
       "      <td>DZD</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.699882e+11</td>\n",
       "      <td>109.9</td>\n",
       "      <td>51.4</td>\n",
       "      <td>20.1</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>76.7</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Arabic</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.72</td>\n",
       "      <td>43053054.0</td>\n",
       "      <td>41.2</td>\n",
       "      <td>37.2</td>\n",
       "      <td>66.1</td>\n",
       "      <td>11.70</td>\n",
       "      <td>31510100.0</td>\n",
       "      <td>28.033886</td>\n",
       "      <td>1.659626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>164</td>\n",
       "      <td>AD</td>\n",
       "      <td>40.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.20</td>\n",
       "      <td>376.0</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "      <td>469.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EUR</td>\n",
       "      <td>1.27</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>3.154058e+09</td>\n",
       "      <td>106.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.7</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.63</td>\n",
       "      <td>Catalan</td>\n",
       "      <td>36.4</td>\n",
       "      <td>3.33</td>\n",
       "      <td>77142.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67873.0</td>\n",
       "      <td>42.506285</td>\n",
       "      <td>1.521801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>26</td>\n",
       "      <td>AO</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1246700.0</td>\n",
       "      <td>117000.0</td>\n",
       "      <td>40.73</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>34693.0</td>\n",
       "      <td>261.73</td>\n",
       "      <td>17.1</td>\n",
       "      <td>AOA</td>\n",
       "      <td>5.52</td>\n",
       "      <td>46.3</td>\n",
       "      <td>0.97</td>\n",
       "      <td>9.463542e+10</td>\n",
       "      <td>113.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>51.6</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>60.8</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>33.4</td>\n",
       "      <td>0.21</td>\n",
       "      <td>31825295.0</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>49.1</td>\n",
       "      <td>6.89</td>\n",
       "      <td>21061025.0</td>\n",
       "      <td>-11.202692</td>\n",
       "      <td>17.873887</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",
       "      <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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32</td>\n",
       "      <td>VE</td>\n",
       "      <td>24.5</td>\n",
       "      <td>912050.0</td>\n",
       "      <td>343000.0</td>\n",
       "      <td>17.88</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Caracas</td>\n",
       "      <td>164175.0</td>\n",
       "      <td>2740.27</td>\n",
       "      <td>254.9</td>\n",
       "      <td>VED</td>\n",
       "      <td>2.27</td>\n",
       "      <td>52.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.823593e+11</td>\n",
       "      <td>97.2</td>\n",
       "      <td>79.3</td>\n",
       "      <td>21.4</td>\n",
       "      <td>Caracas</td>\n",
       "      <td>72.1</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>45.8</td>\n",
       "      <td>1.92</td>\n",
       "      <td>28515829.0</td>\n",
       "      <td>59.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.3</td>\n",
       "      <td>8.80</td>\n",
       "      <td>25162368.0</td>\n",
       "      <td>6.423750</td>\n",
       "      <td>-66.589730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>314</td>\n",
       "      <td>VN</td>\n",
       "      <td>39.3</td>\n",
       "      <td>331210.0</td>\n",
       "      <td>522000.0</td>\n",
       "      <td>16.75</td>\n",
       "      <td>84.0</td>\n",
       "      <td>Hanoi</td>\n",
       "      <td>192668.0</td>\n",
       "      <td>163.52</td>\n",
       "      <td>2.8</td>\n",
       "      <td>VND</td>\n",
       "      <td>2.05</td>\n",
       "      <td>48.1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.619212e+11</td>\n",
       "      <td>110.6</td>\n",
       "      <td>28.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Ho Chi Minh City</td>\n",
       "      <td>75.3</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Vietnamese</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.82</td>\n",
       "      <td>96462106.0</td>\n",
       "      <td>77.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>37.6</td>\n",
       "      <td>2.01</td>\n",
       "      <td>35332140.0</td>\n",
       "      <td>14.058324</td>\n",
       "      <td>108.277199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>56</td>\n",
       "      <td>YE</td>\n",
       "      <td>44.6</td>\n",
       "      <td>527968.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>30.45</td>\n",
       "      <td>967.0</td>\n",
       "      <td>Sanaa</td>\n",
       "      <td>10609.0</td>\n",
       "      <td>157.58</td>\n",
       "      <td>8.1</td>\n",
       "      <td>YER</td>\n",
       "      <td>3.79</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2.691440e+10</td>\n",
       "      <td>93.6</td>\n",
       "      <td>10.2</td>\n",
       "      <td>42.9</td>\n",
       "      <td>Sanaa</td>\n",
       "      <td>66.1</td>\n",
       "      <td>164.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arabic</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>29161922.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.6</td>\n",
       "      <td>12.91</td>\n",
       "      <td>10869523.0</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>25</td>\n",
       "      <td>ZM</td>\n",
       "      <td>32.1</td>\n",
       "      <td>752618.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>36.19</td>\n",
       "      <td>260.0</td>\n",
       "      <td>Lusaka</td>\n",
       "      <td>5141.0</td>\n",
       "      <td>212.31</td>\n",
       "      <td>9.2</td>\n",
       "      <td>ZMW</td>\n",
       "      <td>4.63</td>\n",
       "      <td>65.2</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.306472e+10</td>\n",
       "      <td>98.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>40.4</td>\n",
       "      <td>Lusaka</td>\n",
       "      <td>63.5</td>\n",
       "      <td>213.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>English</td>\n",
       "      <td>27.5</td>\n",
       "      <td>1.19</td>\n",
       "      <td>17861030.0</td>\n",
       "      <td>74.6</td>\n",
       "      <td>16.2</td>\n",
       "      <td>15.6</td>\n",
       "      <td>11.43</td>\n",
       "      <td>7871713.0</td>\n",
       "      <td>-13.133897</td>\n",
       "      <td>27.849332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>38</td>\n",
       "      <td>ZW</td>\n",
       "      <td>41.9</td>\n",
       "      <td>390757.0</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>30.68</td>\n",
       "      <td>263.0</td>\n",
       "      <td>Harare</td>\n",
       "      <td>10983.0</td>\n",
       "      <td>105.51</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.62</td>\n",
       "      <td>35.5</td>\n",
       "      <td>1.34</td>\n",
       "      <td>2.144076e+10</td>\n",
       "      <td>109.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33.9</td>\n",
       "      <td>Harare</td>\n",
       "      <td>61.2</td>\n",
       "      <td>458.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shona</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0.21</td>\n",
       "      <td>14645468.0</td>\n",
       "      <td>83.1</td>\n",
       "      <td>20.7</td>\n",
       "      <td>31.6</td>\n",
       "      <td>4.95</td>\n",
       "      <td>4717305.0</td>\n",
       "      <td>-19.015438</td>\n",
       "      <td>29.154857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country density abbreviation  agriculturalland   landarea  \\\n",
       "0    Afghanistan      60           AF              58.1   652230.0   \n",
       "1        Albania     105           AL              43.1    28748.0   \n",
       "2        Algeria      18           DZ              17.4  2381741.0   \n",
       "3        Andorra     164           AD              40.0      468.0   \n",
       "4         Angola      26           AO              47.5  1246700.0   \n",
       "..           ...     ...          ...               ...        ...   \n",
       "190    Venezuela      32           VE              24.5   912050.0   \n",
       "191      Vietnam     314           VN              39.3   331210.0   \n",
       "192        Yemen      56           YE              44.6   527968.0   \n",
       "193       Zambia      25           ZM              32.1   752618.0   \n",
       "194     Zimbabwe      38           ZW              41.9   390757.0   \n",
       "\n",
       "     armedforcessize  birthrate  callingcode capital/majorcity  co2-emissions  \\\n",
       "0           323000.0      32.49         93.0             Kabul         8672.0   \n",
       "1             9000.0      11.78        355.0            Tirana         4536.0   \n",
       "2           317000.0      24.28        213.0           Algiers       150006.0   \n",
       "3                NaN       7.20        376.0  Andorra la Vella          469.0   \n",
       "4           117000.0      40.73        244.0            Luanda        34693.0   \n",
       "..               ...        ...          ...               ...            ...   \n",
       "190         343000.0      17.88         58.0           Caracas       164175.0   \n",
       "191         522000.0      16.75         84.0             Hanoi       192668.0   \n",
       "192          40000.0      30.45        967.0             Sanaa        10609.0   \n",
       "193          16000.0      36.19        260.0            Lusaka         5141.0   \n",
       "194          51000.0      30.68        263.0            Harare        10983.0   \n",
       "\n",
       "         cpi  cpichange currency-code  fertilityrate  forestedarea  \\\n",
       "0     149.90        2.3           AFN           4.47           2.1   \n",
       "1     119.05        1.4           ALL           1.62          28.1   \n",
       "2     151.36        2.0           DZD           3.02           0.8   \n",
       "3        NaN        NaN           EUR           1.27          34.0   \n",
       "4     261.73       17.1           AOA           5.52          46.3   \n",
       "..       ...        ...           ...            ...           ...   \n",
       "190  2740.27      254.9           VED           2.27          52.7   \n",
       "191   163.52        2.8           VND           2.05          48.1   \n",
       "192   157.58        8.1           YER           3.79           1.0   \n",
       "193   212.31        9.2           ZMW           4.63          65.2   \n",
       "194   105.51        0.9           NaN           3.62          35.5   \n",
       "\n",
       "     gasolineprice           gdp  grossprimaryeducationenrollment  \\\n",
       "0             0.70  1.910135e+10                            104.0   \n",
       "1             1.36  1.527808e+10                            107.0   \n",
       "2             0.28  1.699882e+11                            109.9   \n",
       "3             1.51  3.154058e+09                            106.4   \n",
       "4             0.97  9.463542e+10                            113.5   \n",
       "..             ...           ...                              ...   \n",
       "190           0.00  4.823593e+11                             97.2   \n",
       "191           0.80  2.619212e+11                            110.6   \n",
       "192           0.92  2.691440e+10                             93.6   \n",
       "193           1.40  2.306472e+10                             98.7   \n",
       "194           1.34  2.144076e+10                            109.9   \n",
       "\n",
       "     grosstertiaryeducationenrollment  infantmortality       largestcity  \\\n",
       "0                                 9.7             47.9             Kabul   \n",
       "1                                55.0              7.8            Tirana   \n",
       "2                                51.4             20.1           Algiers   \n",
       "3                                 NaN              2.7  Andorra la Vella   \n",
       "4                                 9.3             51.6            Luanda   \n",
       "..                                ...              ...               ...   \n",
       "190                              79.3             21.4           Caracas   \n",
       "191                              28.5             16.5  Ho Chi Minh City   \n",
       "192                              10.2             42.9             Sanaa   \n",
       "193                               4.1             40.4            Lusaka   \n",
       "194                              10.0             33.9            Harare   \n",
       "\n",
       "     lifeexpectancy  maternalmortalityratio  minimumwage officiallanguage  \\\n",
       "0              64.5                   638.0         0.43           Pashto   \n",
       "1              78.5                    15.0         1.12         Albanian   \n",
       "2              76.7                   112.0         0.95           Arabic   \n",
       "3               NaN                     NaN         6.63          Catalan   \n",
       "4              60.8                   241.0         0.71       Portuguese   \n",
       "..              ...                     ...          ...              ...   \n",
       "190            72.1                   125.0         0.01          Spanish   \n",
       "191            75.3                    43.0         0.73       Vietnamese   \n",
       "192            66.1                   164.0          NaN           Arabic   \n",
       "193            63.5                   213.0         0.24          English   \n",
       "194            61.2                   458.0          NaN            Shona   \n",
       "\n",
       "     outofpockethealthexpenditure  physiciansperthousand  population  \\\n",
       "0                            78.4                   0.28  38041754.0   \n",
       "1                            56.9                   1.20   2854191.0   \n",
       "2                            28.1                   1.72  43053054.0   \n",
       "3                            36.4                   3.33     77142.0   \n",
       "4                            33.4                   0.21  31825295.0   \n",
       "..                            ...                    ...         ...   \n",
       "190                          45.8                   1.92  28515829.0   \n",
       "191                          43.5                   0.82  96462106.0   \n",
       "192                          81.0                   0.31  29161922.0   \n",
       "193                          27.5                   1.19  17861030.0   \n",
       "194                          25.8                   0.21  14645468.0   \n",
       "\n",
       "     populationlaborforceparticipation  taxrevenue  totaltaxrate  \\\n",
       "0                                 48.9         9.3          71.4   \n",
       "1                                 55.7        18.6          36.6   \n",
       "2                                 41.2        37.2          66.1   \n",
       "3                                  NaN         NaN           NaN   \n",
       "4                                 77.5         9.2          49.1   \n",
       "..                                 ...         ...           ...   \n",
       "190                               59.7         NaN          73.3   \n",
       "191                               77.4        19.1          37.6   \n",
       "192                               38.0         NaN          26.6   \n",
       "193                               74.6        16.2          15.6   \n",
       "194                               83.1        20.7          31.6   \n",
       "\n",
       "     unemploymentrate  urban_population   longitud     latitud  \n",
       "0               11.12         9797273.0  33.939110   67.709953  \n",
       "1               12.33         1747593.0  41.153332   20.168331  \n",
       "2               11.70        31510100.0  28.033886    1.659626  \n",
       "3                 NaN           67873.0  42.506285    1.521801  \n",
       "4                6.89        21061025.0 -11.202692   17.873887  \n",
       "..                ...               ...        ...         ...  \n",
       "190              8.80        25162368.0   6.423750  -66.589730  \n",
       "191              2.01        35332140.0  14.058324  108.277199  \n",
       "192             12.91        10869523.0  15.552727   48.516388  \n",
       "193             11.43         7871713.0 -13.133897   27.849332  \n",
       "194              4.95         4717305.0 -19.015438   29.154857  \n",
       "\n",
       "[195 rows x 35 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mergeworld"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Crea una función para añadir una nueva columna llamada 'Continent' al DataFrame para indicar a qué continente pertenece cada país."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic_paises_por_continente= {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n",
       "       'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',\n",
       "       'Austria', 'Azerbaijan', 'The Bahamas', 'Bahrain', 'Bangladesh',\n",
       "       'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',\n",
       "       'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Ivory Coast',\n",
       "       'Cape Verde', 'Cambodia', 'Cameroon', 'Canada',\n",
       "       'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia',\n",
       "       'Comoros', 'Republic of the Congo', 'Costa Rica', 'Croatia',\n",
       "       'Cuba', 'Cyprus', 'Czech Republic',\n",
       "       'Democratic Republic of the Congo', 'Denmark', 'Djibouti',\n",
       "       'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',\n",
       "       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',\n",
       "       'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon',\n",
       "       'The Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada',\n",
       "       'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti',\n",
       "       'Vatican City', 'Honduras', 'Hungary', 'Iceland', 'India',\n",
       "       'Indonesia', 'Iran', 'Iraq', 'Republic of Ireland', 'Israel',\n",
       "       'Italy', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya',\n",
       "       'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon',\n",
       "       'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n",
       "       'Luxembourg', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives',\n",
       "       'Mali', 'Malta', 'Marshall Islands', 'Mauritania', 'Mauritius',\n",
       "       'Mexico', 'Federated States of Micronesia', 'Moldova', 'Monaco',\n",
       "       'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar',\n",
       "       'Namibia', 'Nauru', 'Nepal', 'Netherlands', 'New Zealand',\n",
       "       'Nicaragua', 'Niger', 'Nigeria', 'North Korea', 'North Macedonia',\n",
       "       'Norway', 'Oman', 'Pakistan', 'Palau',\n",
       "       'Palestinian National Authority', 'Panama', 'Papua New Guinea',\n",
       "       'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar',\n",
       "       'Romania', 'Russia', 'Rwanda', 'Saint Kitts and Nevis',\n",
       "       'Saint Lucia', 'Saint Vincent and the Grenadines', 'Samoa',\n",
       "       'San Marino', 'S�����������', 'Saudi Arabia', 'Senegal', 'Serbia',\n",
       "       'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',\n",
       "       'Solomon Islands', 'Somalia', 'South Africa', 'South Korea',\n",
       "       'South Sudan', 'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Sweden',\n",
       "       'Switzerland', 'Syria', 'Tajikistan', 'Tanzania', 'Thailand',\n",
       "       'East Timor', 'Togo', 'Tonga', 'Trinidad and Tobago', 'Tunisia',\n",
       "       'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine',\n",
       "       'United Arab Emirates', 'United Kingdom', 'United States',\n",
       "       'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam',\n",
       "       'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Miramos todos los países que se encuentran en la columna \"country\"\n",
    "paises = df_mergeworld[\"country\"].values\n",
    "paises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detectamos que el país \"Sao Tome and Principe\" está escrito en carácteres estraños: 'S�����������'\n",
    "\n",
    "# Buscamos el índice de \"San Marino\", que es el país que està justo antes que el que queremos modificar.\n",
    "# establecemos éste como índice 0\n",
    "indice_san_marino = df_mergeworld[df_mergeworld[\"country\"] == \"San Marino\"].index[0]\n",
    "\n",
    "# AHora podemos buscar donde esta el índice de Encuentra el índice del país después de 'S�����������'\n",
    "indice_pais_siguiente = indice_san_marino + 1\n",
    "\n",
    "\n",
    "# Cambia el país en el DataFrame\n",
    "df_mergeworld.at[indice_pais_siguiente, \"country\"] = \"Sao Tome and Principe\"\n",
    "\n",
    "#  at se utiliza para asignar el nuevo valor a la celda específica en el DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sao Tome and Principe'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mergeworld[\"country\"][150]\n",
    "#Comprobamos que se ha cambiado el nombre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n",
       "       'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',\n",
       "       'Austria', 'Azerbaijan', 'The Bahamas', 'Bahrain', 'Bangladesh',\n",
       "       'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',\n",
       "       'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Ivory Coast',\n",
       "       'Cape Verde', 'Cambodia', 'Cameroon', 'Canada',\n",
       "       'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia',\n",
       "       'Comoros', 'Republic of the Congo', 'Costa Rica', 'Croatia',\n",
       "       'Cuba', 'Cyprus', 'Czech Republic',\n",
       "       'Democratic Republic of the Congo', 'Denmark', 'Djibouti',\n",
       "       'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',\n",
       "       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',\n",
       "       'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon',\n",
       "       'The Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada',\n",
       "       'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti',\n",
       "       'Vatican City', 'Honduras', 'Hungary', 'Iceland', 'India',\n",
       "       'Indonesia', 'Iran', 'Iraq', 'Republic of Ireland', 'Israel',\n",
       "       'Italy', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya',\n",
       "       'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon',\n",
       "       'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n",
       "       'Luxembourg', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives',\n",
       "       'Mali', 'Malta', 'Marshall Islands', 'Mauritania', 'Mauritius',\n",
       "       'Mexico', 'Federated States of Micronesia', 'Moldova', 'Monaco',\n",
       "       'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar',\n",
       "       'Namibia', 'Nauru', 'Nepal', 'Netherlands', 'New Zealand',\n",
       "       'Nicaragua', 'Niger', 'Nigeria', 'North Korea', 'North Macedonia',\n",
       "       'Norway', 'Oman', 'Pakistan', 'Palau',\n",
       "       'Palestinian National Authority', 'Panama', 'Papua New Guinea',\n",
       "       'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar',\n",
       "       'Romania', 'Russia', 'Rwanda', 'Saint Kitts and Nevis',\n",
       "       'Saint Lucia', 'Saint Vincent and the Grenadines', 'Samoa',\n",
       "       'San Marino', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal',\n",
       "       'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia',\n",
       "       'Slovenia', 'Solomon Islands', 'Somalia', 'South Africa',\n",
       "       'South Korea', 'South Sudan', 'Spain', 'Sri Lanka', 'Sudan',\n",
       "       'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Tajikistan',\n",
       "       'Tanzania', 'Thailand', 'East Timor', 'Togo', 'Tonga',\n",
       "       'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Turkmenistan',\n",
       "       'Tuvalu', 'Uganda', 'Ukraine', 'United Arab Emirates',\n",
       "       'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan',\n",
       "       'Vanuatu', 'Venezuela', 'Vietnam', 'Yemen', 'Zambia', 'Zimbabwe'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mergeworld[\"country\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creamos un diccionario donde key es el Continente y value los paises:\n",
    "\n",
    "dic_paises_por_continente = {\n",
    "    'Asia': ['Afghanistan', 'Armenia', 'Azerbaijan', 'Bahrain', 'Bangladesh', 'Bhutan', 'Brunei', 'Cambodia', 'China', 'Georgia', 'India', 'Indonesia', 'Iran', 'Iraq', 'Israel', 'Japan', 'Jordan', 'Kazakhstan', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Lebanon', 'Malaysia', 'Maldives', 'Mongolia', 'Myanmar', 'Nepal', 'North Korea', 'Oman', 'Pakistan', 'Palestinian National Authority', 'Qatar', 'Saudi Arabia', 'Singapore', 'South Korea', 'Sri Lanka', 'Syria', 'Tajikistan', 'Thailand', 'East Timor', 'Turkey', 'Turkmenistan', 'United Arab Emirates', 'Uzbekistan', 'Vietnam', 'Yemen'],\n",
    "    'Africa': ['Algeria', 'Angola', 'Benin', 'Botswana', 'Burkina Faso', 'Burundi', 'Ivory Coast', 'Cape Verde', 'Cameroon', 'Central African Republic', 'Chad', 'Comoros', 'Republic of the Congo', 'Democratic Republic of the Congo', 'Djibouti', 'Egypt', 'Equatorial Guinea', 'Eritrea', 'Eswatini', 'Ethiopia', 'Gabon', 'The Gambia', 'Ghana', 'Guinea', 'Guinea-Bissau', 'Kenya', 'Lesotho', 'Liberia', 'Libya', 'Madagascar', 'Malawi', 'Mali', 'Mauritania', 'Mauritius', 'Morocco', 'Mozambique', 'Namibia', 'Niger', 'Nigeria', 'Rwanda', 'Sao Tome and Principe', 'Senegal', 'Seychelles', 'Sierra Leone', 'Somalia', 'South Africa', 'South Sudan', 'Sudan', 'Tanzania', 'Togo', 'Uganda', 'Zambia', 'Zimbabwe'],\n",
    "    'Europe': ['Albania', 'Andorra', 'Austria', 'Belarus', 'Belgium', 'Bosnia and Herzegovina', 'Bulgaria', 'Croatia', 'Cyprus', 'Czech Republic', 'Denmark', 'Estonia', 'Finland', 'France', 'Georgia', 'Germany', 'Greece', 'Hungary', 'Iceland', 'Republic of Ireland', 'Italy', 'Latvia', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Malta', 'Moldova', 'Monaco', 'Montenegro', 'Netherlands', 'North Macedonia', 'Norway', 'Poland', 'Portugal', 'Romania', 'Russia', 'San Marino', 'Serbia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland', 'Ukraine', 'United Kingdom'],\n",
    "    'North America': ['The Bahamas', 'Canada', 'Cuba', 'Dominica', 'Dominican Republic', 'El Salvador', 'Guatemala', 'Honduras', 'Jamaica', 'Mexico', 'Panama', 'United States'],\n",
    "    'South America': ['Argentina', 'Bolivia', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Guyana', 'Paraguay', 'Peru', 'Suriname', 'Uruguay', 'Venezuela'],\n",
    "    'Oceania': ['Australia', 'Fiji', 'Kiribati', 'Marshall Islands', 'Micronesia', 'Nauru', 'New Zealand', 'Palau', 'Papua New Guinea', 'Samoa', 'Solomon Islands', 'Tonga', 'Tuvalu', 'Vanuatu']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El país Armenia se encuentra en el continente: Asia\n"
     ]
    }
   ],
   "source": [
    "def pais_continente(pais, dic_paises_por_continente):\n",
    "    for continente, paises in dic_paises_por_continente.items():\n",
    "        if pais in paises:\n",
    "            return continente\n",
    "\n",
    "# probamos para uno:\n",
    "pais = 'Armenia'\n",
    "print(f\"El país {pais} se encuentra en el continente: {pais_continente(pais, dic_paises_por_continente)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aplicamos la función \"def pais_continente\" a todo el data frame y\n",
    "# creamos la nueva columna \"continent\":\n",
    "\n",
    "df_mergeworld['continent'] = df_mergeworld['country'].apply(lambda x: pais_continente(x, dic_paises_por_continente))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>density</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>agriculturalland</th>\n",
       "      <th>landarea</th>\n",
       "      <th>armedforcessize</th>\n",
       "      <th>birthrate</th>\n",
       "      <th>callingcode</th>\n",
       "      <th>capital/majorcity</th>\n",
       "      <th>co2-emissions</th>\n",
       "      <th>cpi</th>\n",
       "      <th>cpichange</th>\n",
       "      <th>currency-code</th>\n",
       "      <th>fertilityrate</th>\n",
       "      <th>forestedarea</th>\n",
       "      <th>gasolineprice</th>\n",
       "      <th>gdp</th>\n",
       "      <th>grossprimaryeducationenrollment</th>\n",
       "      <th>grosstertiaryeducationenrollment</th>\n",
       "      <th>infantmortality</th>\n",
       "      <th>largestcity</th>\n",
       "      <th>lifeexpectancy</th>\n",
       "      <th>maternalmortalityratio</th>\n",
       "      <th>minimumwage</th>\n",
       "      <th>officiallanguage</th>\n",
       "      <th>outofpockethealthexpenditure</th>\n",
       "      <th>physiciansperthousand</th>\n",
       "      <th>population</th>\n",
       "      <th>populationlaborforceparticipation</th>\n",
       "      <th>taxrevenue</th>\n",
       "      <th>totaltaxrate</th>\n",
       "      <th>unemploymentrate</th>\n",
       "      <th>urban_population</th>\n",
       "      <th>longitud</th>\n",
       "      <th>latitud</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>60</td>\n",
       "      <td>AF</td>\n",
       "      <td>58.1</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>323000.0</td>\n",
       "      <td>32.49</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>8672.0</td>\n",
       "      <td>149.90</td>\n",
       "      <td>2.3</td>\n",
       "      <td>AFN</td>\n",
       "      <td>4.47</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.910135e+10</td>\n",
       "      <td>104.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>47.9</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>64.5</td>\n",
       "      <td>638.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>Pashto</td>\n",
       "      <td>78.4</td>\n",
       "      <td>0.28</td>\n",
       "      <td>38041754.0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>71.4</td>\n",
       "      <td>11.12</td>\n",
       "      <td>9797273.0</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>105</td>\n",
       "      <td>AL</td>\n",
       "      <td>43.1</td>\n",
       "      <td>28748.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>11.78</td>\n",
       "      <td>355.0</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>4536.0</td>\n",
       "      <td>119.05</td>\n",
       "      <td>1.4</td>\n",
       "      <td>ALL</td>\n",
       "      <td>1.62</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.527808e+10</td>\n",
       "      <td>107.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>78.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.12</td>\n",
       "      <td>Albanian</td>\n",
       "      <td>56.9</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2854191.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>18.6</td>\n",
       "      <td>36.6</td>\n",
       "      <td>12.33</td>\n",
       "      <td>1747593.0</td>\n",
       "      <td>41.153332</td>\n",
       "      <td>20.168331</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>18</td>\n",
       "      <td>DZ</td>\n",
       "      <td>17.4</td>\n",
       "      <td>2381741.0</td>\n",
       "      <td>317000.0</td>\n",
       "      <td>24.28</td>\n",
       "      <td>213.0</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>150006.0</td>\n",
       "      <td>151.36</td>\n",
       "      <td>2.0</td>\n",
       "      <td>DZD</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.699882e+11</td>\n",
       "      <td>109.9</td>\n",
       "      <td>51.4</td>\n",
       "      <td>20.1</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>76.7</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Arabic</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.72</td>\n",
       "      <td>43053054.0</td>\n",
       "      <td>41.2</td>\n",
       "      <td>37.2</td>\n",
       "      <td>66.1</td>\n",
       "      <td>11.70</td>\n",
       "      <td>31510100.0</td>\n",
       "      <td>28.033886</td>\n",
       "      <td>1.659626</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>164</td>\n",
       "      <td>AD</td>\n",
       "      <td>40.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.20</td>\n",
       "      <td>376.0</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "      <td>469.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EUR</td>\n",
       "      <td>1.27</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>3.154058e+09</td>\n",
       "      <td>106.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.7</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.63</td>\n",
       "      <td>Catalan</td>\n",
       "      <td>36.4</td>\n",
       "      <td>3.33</td>\n",
       "      <td>77142.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67873.0</td>\n",
       "      <td>42.506285</td>\n",
       "      <td>1.521801</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>26</td>\n",
       "      <td>AO</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1246700.0</td>\n",
       "      <td>117000.0</td>\n",
       "      <td>40.73</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>34693.0</td>\n",
       "      <td>261.73</td>\n",
       "      <td>17.1</td>\n",
       "      <td>AOA</td>\n",
       "      <td>5.52</td>\n",
       "      <td>46.3</td>\n",
       "      <td>0.97</td>\n",
       "      <td>9.463542e+10</td>\n",
       "      <td>113.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>51.6</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>60.8</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>33.4</td>\n",
       "      <td>0.21</td>\n",
       "      <td>31825295.0</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>49.1</td>\n",
       "      <td>6.89</td>\n",
       "      <td>21061025.0</td>\n",
       "      <td>-11.202692</td>\n",
       "      <td>17.873887</td>\n",
       "      <td>Africa</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",
       "      <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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>32</td>\n",
       "      <td>VE</td>\n",
       "      <td>24.5</td>\n",
       "      <td>912050.0</td>\n",
       "      <td>343000.0</td>\n",
       "      <td>17.88</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Caracas</td>\n",
       "      <td>164175.0</td>\n",
       "      <td>2740.27</td>\n",
       "      <td>254.9</td>\n",
       "      <td>VED</td>\n",
       "      <td>2.27</td>\n",
       "      <td>52.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.823593e+11</td>\n",
       "      <td>97.2</td>\n",
       "      <td>79.3</td>\n",
       "      <td>21.4</td>\n",
       "      <td>Caracas</td>\n",
       "      <td>72.1</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>45.8</td>\n",
       "      <td>1.92</td>\n",
       "      <td>28515829.0</td>\n",
       "      <td>59.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.3</td>\n",
       "      <td>8.80</td>\n",
       "      <td>25162368.0</td>\n",
       "      <td>6.423750</td>\n",
       "      <td>-66.589730</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>314</td>\n",
       "      <td>VN</td>\n",
       "      <td>39.3</td>\n",
       "      <td>331210.0</td>\n",
       "      <td>522000.0</td>\n",
       "      <td>16.75</td>\n",
       "      <td>84.0</td>\n",
       "      <td>Hanoi</td>\n",
       "      <td>192668.0</td>\n",
       "      <td>163.52</td>\n",
       "      <td>2.8</td>\n",
       "      <td>VND</td>\n",
       "      <td>2.05</td>\n",
       "      <td>48.1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.619212e+11</td>\n",
       "      <td>110.6</td>\n",
       "      <td>28.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Ho Chi Minh City</td>\n",
       "      <td>75.3</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>Vietnamese</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.82</td>\n",
       "      <td>96462106.0</td>\n",
       "      <td>77.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>37.6</td>\n",
       "      <td>2.01</td>\n",
       "      <td>35332140.0</td>\n",
       "      <td>14.058324</td>\n",
       "      <td>108.277199</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>56</td>\n",
       "      <td>YE</td>\n",
       "      <td>44.6</td>\n",
       "      <td>527968.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>30.45</td>\n",
       "      <td>967.0</td>\n",
       "      <td>Sanaa</td>\n",
       "      <td>10609.0</td>\n",
       "      <td>157.58</td>\n",
       "      <td>8.1</td>\n",
       "      <td>YER</td>\n",
       "      <td>3.79</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2.691440e+10</td>\n",
       "      <td>93.6</td>\n",
       "      <td>10.2</td>\n",
       "      <td>42.9</td>\n",
       "      <td>Sanaa</td>\n",
       "      <td>66.1</td>\n",
       "      <td>164.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arabic</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>29161922.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.6</td>\n",
       "      <td>12.91</td>\n",
       "      <td>10869523.0</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>25</td>\n",
       "      <td>ZM</td>\n",
       "      <td>32.1</td>\n",
       "      <td>752618.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>36.19</td>\n",
       "      <td>260.0</td>\n",
       "      <td>Lusaka</td>\n",
       "      <td>5141.0</td>\n",
       "      <td>212.31</td>\n",
       "      <td>9.2</td>\n",
       "      <td>ZMW</td>\n",
       "      <td>4.63</td>\n",
       "      <td>65.2</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.306472e+10</td>\n",
       "      <td>98.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>40.4</td>\n",
       "      <td>Lusaka</td>\n",
       "      <td>63.5</td>\n",
       "      <td>213.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>English</td>\n",
       "      <td>27.5</td>\n",
       "      <td>1.19</td>\n",
       "      <td>17861030.0</td>\n",
       "      <td>74.6</td>\n",
       "      <td>16.2</td>\n",
       "      <td>15.6</td>\n",
       "      <td>11.43</td>\n",
       "      <td>7871713.0</td>\n",
       "      <td>-13.133897</td>\n",
       "      <td>27.849332</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>38</td>\n",
       "      <td>ZW</td>\n",
       "      <td>41.9</td>\n",
       "      <td>390757.0</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>30.68</td>\n",
       "      <td>263.0</td>\n",
       "      <td>Harare</td>\n",
       "      <td>10983.0</td>\n",
       "      <td>105.51</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.62</td>\n",
       "      <td>35.5</td>\n",
       "      <td>1.34</td>\n",
       "      <td>2.144076e+10</td>\n",
       "      <td>109.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33.9</td>\n",
       "      <td>Harare</td>\n",
       "      <td>61.2</td>\n",
       "      <td>458.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shona</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0.21</td>\n",
       "      <td>14645468.0</td>\n",
       "      <td>83.1</td>\n",
       "      <td>20.7</td>\n",
       "      <td>31.6</td>\n",
       "      <td>4.95</td>\n",
       "      <td>4717305.0</td>\n",
       "      <td>-19.015438</td>\n",
       "      <td>29.154857</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country density abbreviation  agriculturalland   landarea  \\\n",
       "0    Afghanistan      60           AF              58.1   652230.0   \n",
       "1        Albania     105           AL              43.1    28748.0   \n",
       "2        Algeria      18           DZ              17.4  2381741.0   \n",
       "3        Andorra     164           AD              40.0      468.0   \n",
       "4         Angola      26           AO              47.5  1246700.0   \n",
       "..           ...     ...          ...               ...        ...   \n",
       "190    Venezuela      32           VE              24.5   912050.0   \n",
       "191      Vietnam     314           VN              39.3   331210.0   \n",
       "192        Yemen      56           YE              44.6   527968.0   \n",
       "193       Zambia      25           ZM              32.1   752618.0   \n",
       "194     Zimbabwe      38           ZW              41.9   390757.0   \n",
       "\n",
       "     armedforcessize  birthrate  callingcode capital/majorcity  co2-emissions  \\\n",
       "0           323000.0      32.49         93.0             Kabul         8672.0   \n",
       "1             9000.0      11.78        355.0            Tirana         4536.0   \n",
       "2           317000.0      24.28        213.0           Algiers       150006.0   \n",
       "3                NaN       7.20        376.0  Andorra la Vella          469.0   \n",
       "4           117000.0      40.73        244.0            Luanda        34693.0   \n",
       "..               ...        ...          ...               ...            ...   \n",
       "190         343000.0      17.88         58.0           Caracas       164175.0   \n",
       "191         522000.0      16.75         84.0             Hanoi       192668.0   \n",
       "192          40000.0      30.45        967.0             Sanaa        10609.0   \n",
       "193          16000.0      36.19        260.0            Lusaka         5141.0   \n",
       "194          51000.0      30.68        263.0            Harare        10983.0   \n",
       "\n",
       "         cpi  cpichange currency-code  fertilityrate  forestedarea  \\\n",
       "0     149.90        2.3           AFN           4.47           2.1   \n",
       "1     119.05        1.4           ALL           1.62          28.1   \n",
       "2     151.36        2.0           DZD           3.02           0.8   \n",
       "3        NaN        NaN           EUR           1.27          34.0   \n",
       "4     261.73       17.1           AOA           5.52          46.3   \n",
       "..       ...        ...           ...            ...           ...   \n",
       "190  2740.27      254.9           VED           2.27          52.7   \n",
       "191   163.52        2.8           VND           2.05          48.1   \n",
       "192   157.58        8.1           YER           3.79           1.0   \n",
       "193   212.31        9.2           ZMW           4.63          65.2   \n",
       "194   105.51        0.9           NaN           3.62          35.5   \n",
       "\n",
       "     gasolineprice           gdp  grossprimaryeducationenrollment  \\\n",
       "0             0.70  1.910135e+10                            104.0   \n",
       "1             1.36  1.527808e+10                            107.0   \n",
       "2             0.28  1.699882e+11                            109.9   \n",
       "3             1.51  3.154058e+09                            106.4   \n",
       "4             0.97  9.463542e+10                            113.5   \n",
       "..             ...           ...                              ...   \n",
       "190           0.00  4.823593e+11                             97.2   \n",
       "191           0.80  2.619212e+11                            110.6   \n",
       "192           0.92  2.691440e+10                             93.6   \n",
       "193           1.40  2.306472e+10                             98.7   \n",
       "194           1.34  2.144076e+10                            109.9   \n",
       "\n",
       "     grosstertiaryeducationenrollment  infantmortality       largestcity  \\\n",
       "0                                 9.7             47.9             Kabul   \n",
       "1                                55.0              7.8            Tirana   \n",
       "2                                51.4             20.1           Algiers   \n",
       "3                                 NaN              2.7  Andorra la Vella   \n",
       "4                                 9.3             51.6            Luanda   \n",
       "..                                ...              ...               ...   \n",
       "190                              79.3             21.4           Caracas   \n",
       "191                              28.5             16.5  Ho Chi Minh City   \n",
       "192                              10.2             42.9             Sanaa   \n",
       "193                               4.1             40.4            Lusaka   \n",
       "194                              10.0             33.9            Harare   \n",
       "\n",
       "     lifeexpectancy  maternalmortalityratio  minimumwage officiallanguage  \\\n",
       "0              64.5                   638.0         0.43           Pashto   \n",
       "1              78.5                    15.0         1.12         Albanian   \n",
       "2              76.7                   112.0         0.95           Arabic   \n",
       "3               NaN                     NaN         6.63          Catalan   \n",
       "4              60.8                   241.0         0.71       Portuguese   \n",
       "..              ...                     ...          ...              ...   \n",
       "190            72.1                   125.0         0.01          Spanish   \n",
       "191            75.3                    43.0         0.73       Vietnamese   \n",
       "192            66.1                   164.0          NaN           Arabic   \n",
       "193            63.5                   213.0         0.24          English   \n",
       "194            61.2                   458.0          NaN            Shona   \n",
       "\n",
       "     outofpockethealthexpenditure  physiciansperthousand  population  \\\n",
       "0                            78.4                   0.28  38041754.0   \n",
       "1                            56.9                   1.20   2854191.0   \n",
       "2                            28.1                   1.72  43053054.0   \n",
       "3                            36.4                   3.33     77142.0   \n",
       "4                            33.4                   0.21  31825295.0   \n",
       "..                            ...                    ...         ...   \n",
       "190                          45.8                   1.92  28515829.0   \n",
       "191                          43.5                   0.82  96462106.0   \n",
       "192                          81.0                   0.31  29161922.0   \n",
       "193                          27.5                   1.19  17861030.0   \n",
       "194                          25.8                   0.21  14645468.0   \n",
       "\n",
       "     populationlaborforceparticipation  taxrevenue  totaltaxrate  \\\n",
       "0                                 48.9         9.3          71.4   \n",
       "1                                 55.7        18.6          36.6   \n",
       "2                                 41.2        37.2          66.1   \n",
       "3                                  NaN         NaN           NaN   \n",
       "4                                 77.5         9.2          49.1   \n",
       "..                                 ...         ...           ...   \n",
       "190                               59.7         NaN          73.3   \n",
       "191                               77.4        19.1          37.6   \n",
       "192                               38.0         NaN          26.6   \n",
       "193                               74.6        16.2          15.6   \n",
       "194                               83.1        20.7          31.6   \n",
       "\n",
       "     unemploymentrate  urban_population   longitud     latitud      continent  \n",
       "0               11.12         9797273.0  33.939110   67.709953           Asia  \n",
       "1               12.33         1747593.0  41.153332   20.168331         Europe  \n",
       "2               11.70        31510100.0  28.033886    1.659626         Africa  \n",
       "3                 NaN           67873.0  42.506285    1.521801         Europe  \n",
       "4                6.89        21061025.0 -11.202692   17.873887         Africa  \n",
       "..                ...               ...        ...         ...            ...  \n",
       "190              8.80        25162368.0   6.423750  -66.589730  South America  \n",
       "191              2.01        35332140.0  14.058324  108.277199           Asia  \n",
       "192             12.91        10869523.0  15.552727   48.516388           Asia  \n",
       "193             11.43         7871713.0 -13.133897   27.849332         Africa  \n",
       "194              4.95         4717305.0 -19.015438   29.154857         Africa  \n",
       "\n",
       "[195 rows x 36 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mergeworld"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Guarda el DataFrame después de los cambios creados después de los dos ejercicios anteriores para usarlo en el pairprogramming de mañana.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mergeworld.to_csv(\"merge_world_2.csv\",index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejercicios groupby\n",
    "1- ¿Cuál es la densidad de población promedio por continente?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2- ¿Cuál es la esperanza de vida promedio por idioma oficial? Agrupa los países por idioma oficial y calcula la esperanza de vida promedio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>density</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>agriculturalland</th>\n",
       "      <th>landarea</th>\n",
       "      <th>armedforcessize</th>\n",
       "      <th>birthrate</th>\n",
       "      <th>callingcode</th>\n",
       "      <th>capital/majorcity</th>\n",
       "      <th>co2-emissions</th>\n",
       "      <th>cpi</th>\n",
       "      <th>cpichange</th>\n",
       "      <th>currency-code</th>\n",
       "      <th>fertilityrate</th>\n",
       "      <th>forestedarea</th>\n",
       "      <th>gasolineprice</th>\n",
       "      <th>gdp</th>\n",
       "      <th>grossprimaryeducationenrollment</th>\n",
       "      <th>grosstertiaryeducationenrollment</th>\n",
       "      <th>infantmortality</th>\n",
       "      <th>largestcity</th>\n",
       "      <th>lifeexpectancy</th>\n",
       "      <th>maternalmortalityratio</th>\n",
       "      <th>minimumwage</th>\n",
       "      <th>officiallanguage</th>\n",
       "      <th>outofpockethealthexpenditure</th>\n",
       "      <th>physiciansperthousand</th>\n",
       "      <th>population</th>\n",
       "      <th>populationlaborforceparticipation</th>\n",
       "      <th>taxrevenue</th>\n",
       "      <th>totaltaxrate</th>\n",
       "      <th>unemploymentrate</th>\n",
       "      <th>urban_population</th>\n",
       "      <th>longitud</th>\n",
       "      <th>latitud</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>60</td>\n",
       "      <td>AF</td>\n",
       "      <td>58.1</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>323000.0</td>\n",
       "      <td>32.49</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>8672.0</td>\n",
       "      <td>149.90</td>\n",
       "      <td>2.3</td>\n",
       "      <td>AFN</td>\n",
       "      <td>4.47</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.910135e+10</td>\n",
       "      <td>104.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>47.9</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>64.5</td>\n",
       "      <td>638.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>Pashto</td>\n",
       "      <td>78.4</td>\n",
       "      <td>0.28</td>\n",
       "      <td>38041754.0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>71.4</td>\n",
       "      <td>11.12</td>\n",
       "      <td>9797273.0</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>105</td>\n",
       "      <td>AL</td>\n",
       "      <td>43.1</td>\n",
       "      <td>28748.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>11.78</td>\n",
       "      <td>355.0</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>4536.0</td>\n",
       "      <td>119.05</td>\n",
       "      <td>1.4</td>\n",
       "      <td>ALL</td>\n",
       "      <td>1.62</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.527808e+10</td>\n",
       "      <td>107.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>78.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.12</td>\n",
       "      <td>Albanian</td>\n",
       "      <td>56.9</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2854191.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>18.6</td>\n",
       "      <td>36.6</td>\n",
       "      <td>12.33</td>\n",
       "      <td>1747593.0</td>\n",
       "      <td>41.153332</td>\n",
       "      <td>20.168331</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country density abbreviation  agriculturalland  landarea  \\\n",
       "0  Afghanistan      60           AF              58.1  652230.0   \n",
       "1      Albania     105           AL              43.1   28748.0   \n",
       "\n",
       "   armedforcessize  birthrate  callingcode capital/majorcity  co2-emissions  \\\n",
       "0         323000.0      32.49         93.0             Kabul         8672.0   \n",
       "1           9000.0      11.78        355.0            Tirana         4536.0   \n",
       "\n",
       "      cpi  cpichange currency-code  fertilityrate  forestedarea  \\\n",
       "0  149.90        2.3           AFN           4.47           2.1   \n",
       "1  119.05        1.4           ALL           1.62          28.1   \n",
       "\n",
       "   gasolineprice           gdp  grossprimaryeducationenrollment  \\\n",
       "0           0.70  1.910135e+10                            104.0   \n",
       "1           1.36  1.527808e+10                            107.0   \n",
       "\n",
       "   grosstertiaryeducationenrollment  infantmortality largestcity  \\\n",
       "0                               9.7             47.9       Kabul   \n",
       "1                              55.0              7.8      Tirana   \n",
       "\n",
       "   lifeexpectancy  maternalmortalityratio  minimumwage officiallanguage  \\\n",
       "0            64.5                   638.0         0.43           Pashto   \n",
       "1            78.5                    15.0         1.12         Albanian   \n",
       "\n",
       "   outofpockethealthexpenditure  physiciansperthousand  population  \\\n",
       "0                          78.4                   0.28  38041754.0   \n",
       "1                          56.9                   1.20   2854191.0   \n",
       "\n",
       "   populationlaborforceparticipation  taxrevenue  totaltaxrate  \\\n",
       "0                               48.9         9.3          71.4   \n",
       "1                               55.7        18.6          36.6   \n",
       "\n",
       "   unemploymentrate  urban_population   longitud    latitud continent  \n",
       "0             11.12         9797273.0  33.939110  67.709953      Asia  \n",
       "1             12.33         1747593.0  41.153332  20.168331    Europe  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mergeworld.head(2)\n",
    "\n",
    "# officiallanguage\n",
    "# country\n",
    "#lifeexpectancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>officiallanguage</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afrikaans</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albanian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amharic</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Tuvaluan Language</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Ukrainian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Urdu</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Uzbek</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Vietnamese</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     officiallanguage  country\n",
       "0           Afrikaans        1\n",
       "1            Albanian        1\n",
       "2             Amharic        1\n",
       "3              Arabic       18\n",
       "4            Armenian        1\n",
       "..                ...      ...\n",
       "71  Tuvaluan Language        1\n",
       "72          Ukrainian        1\n",
       "73               Urdu        1\n",
       "74              Uzbek        1\n",
       "75         Vietnamese        1\n",
       "\n",
       "[76 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1: Agrupamos los idiomas oficiales por país\n",
    "idiomas_pais = df_mergeworld.groupby(\"officiallanguage\")[[\"country\"]].count().reset_index()\n",
    "idiomas_pais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        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>officiallanguage</th>\n",
       "      <th>lifeexpectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afrikaans</td>\n",
       "      <td>63.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albanian</td>\n",
       "      <td>78.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amharic</td>\n",
       "      <td>66.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>72.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenian</td>\n",
       "      <td>74.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Tuvaluan Language</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Ukrainian</td>\n",
       "      <td>71.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Urdu</td>\n",
       "      <td>67.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Uzbek</td>\n",
       "      <td>71.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Vietnamese</td>\n",
       "      <td>75.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     officiallanguage  lifeexpectancy\n",
       "0           Afrikaans           63.90\n",
       "1            Albanian           78.50\n",
       "2             Amharic           66.20\n",
       "3              Arabic           72.86\n",
       "4            Armenian           74.90\n",
       "..                ...             ...\n",
       "71  Tuvaluan Language             NaN\n",
       "72          Ukrainian           71.60\n",
       "73               Urdu           67.10\n",
       "74              Uzbek           71.60\n",
       "75         Vietnamese           75.30\n",
       "\n",
       "[76 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2: Calculamos la esperanza de vida de promedio \n",
    "esperanza_vida_por_idioma = df_mergeworld.groupby(\"officiallanguage\")[\"lifeexpectancy\"].mean().reset_index().round(2)\n",
    "esperanza_vida_por_idioma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 - ¿Cuál es la tasa de mortalidad infantil promedio por moneda?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4- ¿Cuál es el salario mínimo por continente?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>continent</th>\n",
       "      <th>minimumwage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>4.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North America</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South America</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       continent  minimumwage\n",
       "0         Africa         0.53\n",
       "1           Asia         1.48\n",
       "2         Europe         4.78\n",
       "3  North America         2.53\n",
       "4        Oceania         3.92\n",
       "5  South America         1.58"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# El  salario mínimo promedio que se cobra agrupado por continente:\n",
    "promedio_salario_minimo_continente = df_mergeworld.groupby('continent')['minimumwage'].mean().reset_index().round(2)\n",
    "promedio_salario_minimo_continente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>minimumwage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North America</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South America</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       continent  minimumwage\n",
       "0         Africa         0.01\n",
       "1           Asia         0.05\n",
       "2         Europe         0.31\n",
       "3  North America         0.05\n",
       "4        Oceania         0.40\n",
       "5  South America         0.01"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# El mínimo salario que se cobra agrupado por continente:\n",
    "\n",
    "minimo_salario_minimo_continente = df_mergeworld.groupby('continent')['minimumwage'].min().reset_index().round(2)\n",
    "minimo_salario_minimo_continente"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5 - ¿Cuál es la tasa de impuestos total máxima por densidad de población por continente? Para solucionar este ejercicio puedes:\n",
    "- Primero, necesitas calcular la tasa de impuestos total para cada continente. La tasa de impuestos total se encuentra en la columna \"total_tax_rate\".\n",
    "- Luego, calcula la densidad de población para cada continente. La densidad de población se encuentra en la columna \"density\".\n",
    "- Ahora, divide la tasa de impuestos total entre la densidad de población para obtener la tasa de impuestos total por persona por kilómetro cuadrado.\n",
    "- Encuentra el país que tiene el valor más alto en esta nueva columna calculada."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6 - ¿Cuál es el porcentaje de población urbana promedio por continente?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mergeworld[\"urban_population\"].isnull().sum()\n",
    "#hay muchos nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "190"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mergeworld[\"urban_population\"].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.900000e+02\n",
       "mean     2.230454e+07\n",
       "std      7.543050e+07\n",
       "min      5.464000e+03\n",
       "25%      1.152961e+06\n",
       "50%      4.678104e+06\n",
       "75%      1.490324e+07\n",
       "max      8.429340e+08\n",
       "Name: urban_population, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mergeworld[\"urban_population\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>continent</th>\n",
       "      <th>urban_population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1.064831e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>4.933036e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>1.293913e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North America</td>\n",
       "      <td>3.710186e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>2.350117e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South America</td>\n",
       "      <td>2.999807e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       continent  urban_population\n",
       "0         Africa      1.064831e+07\n",
       "1           Asia      4.933036e+07\n",
       "2         Europe      1.293913e+07\n",
       "3  North America      3.710186e+07\n",
       "4        Oceania      2.350117e+06\n",
       "5  South America      2.999807e+07"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1: calculamos la media de urban_population para cada continente\n",
    "media_poblacion_ubana = df_mergeworld.groupby('continent')['urban_population'].mean().reset_index()\n",
    "media_poblacion_ubana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>continent</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1.298193e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>4.426883e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>7.453570e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North America</td>\n",
       "      <td>5.537777e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>4.176245e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South America</td>\n",
       "      <td>4.285732e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       continent    population\n",
       "0         Africa  1.298193e+09\n",
       "1           Asia  4.426883e+09\n",
       "2         Europe  7.453570e+08\n",
       "3  North America  5.537777e+08\n",
       "4        Oceania  4.176245e+07\n",
       "5  South America  4.285732e+08"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2 calculamos el total de población por continente\n",
    "total_poblacion_continente = df_mergeworld.groupby('continent')['population'].sum().reset_index()\n",
    "total_poblacion_continente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>continent</th>\n",
       "      <th>media_poblacion_urbana</th>\n",
       "      <th>total_poblacion_continente</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1.064831e+07</td>\n",
       "      <td>1.298193e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>4.933036e+07</td>\n",
       "      <td>4.426883e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>1.293913e+07</td>\n",
       "      <td>7.453570e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North America</td>\n",
       "      <td>3.710186e+07</td>\n",
       "      <td>5.537777e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>2.350117e+06</td>\n",
       "      <td>4.176245e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South America</td>\n",
       "      <td>2.999807e+07</td>\n",
       "      <td>4.285732e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       continent  media_poblacion_urbana  total_poblacion_continente\n",
       "0         Africa            1.064831e+07                1.298193e+09\n",
       "1           Asia            4.933036e+07                4.426883e+09\n",
       "2         Europe            1.293913e+07                7.453570e+08\n",
       "3  North America            3.710186e+07                5.537777e+08\n",
       "4        Oceania            2.350117e+06                4.176245e+07\n",
       "5  South America            2.999807e+07                4.285732e+08"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Si juntamos los pasos 2 y 3:\n",
    "\n",
    "resultados_grupo = df_mergeworld.groupby('continent').agg(\n",
    "    media_poblacion_urbana=('urban_population', 'mean'),\n",
    "    total_poblacion_continente=('population', 'sum')).reset_index()\n",
    "resultados_grupo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.820241\n",
       "1    1.114336\n",
       "2    1.735965\n",
       "3    6.699776\n",
       "4    5.627345\n",
       "5    6.999521\n",
       "Name: porcentaje_poblacion_urbana, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3: Aplicamos el porcentaje\n",
    "resultados_grupo['porcentaje_poblacion_urbana'] = (resultados_grupo['media_poblacion_urbana'] / resultados_grupo['total_poblacion_continente']) * 100\n",
    "resultados_grupo['porcentaje_poblacion_urbana']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>continent</th>\n",
       "      <th>porcentaje_poblacion_urbana</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>1.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North America</td>\n",
       "      <td>6.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>5.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South America</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       continent  porcentaje_poblacion_urbana\n",
       "0         Africa                         0.82\n",
       "1           Asia                         1.11\n",
       "2         Europe                         1.74\n",
       "3  North America                         6.70\n",
       "4        Oceania                         5.63\n",
       "5  South America                         7.00"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4: Creamos un DataFrame solo con las columnas de interés\n",
    "porcentaje_df = (resultados_grupo[['continent', 'porcentaje_poblacion_urbana']]).round(2)\n",
    "\n",
    "porcentaje_df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7 - ¿Cuál es la diferencia entre el PIB promedio y el gasto en salud de bolsillo promedio por idioma oficial? Para contestar esta pregunta dabes seguir los siguientes pasos:\n",
    "- Agrupa el DataFrame por idioma oficial y calcula la media del Gasto de Bolsillo en salud (out_of_pocket_health_expenditure) y del GPD.\n",
    "- Calcula la diferencia entre el PIB promedio y el gasto en salud de bolsillo promedio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8- ¿Cuál es la tasa de maternidad promedio por tasa de impuestos total? Para resolver el ejercicio deberás:\n",
    "- Primero, calcula la tasa de maternidad promedio para cada continente. Puedes usar la columna \"maternal_mortality_ratio\" que representa la tasa de mortalidad materna.\n",
    "Calcula la tasa de impuestos total promedio para cada continente. Puedes usar la columna \"total_tax_rate\" que representa la tasa de impuestos total en cada continente.\n",
    "- Luego, divide la tasa de maternidad promedio por la tasa de impuestos total promedio para obtener la relación entre ambas tasas.\n",
    "- Encuentra el país con la tasa de maternidad más alta por punto porcentual de tasa de impuestos total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      638.0\n",
       "1       15.0\n",
       "2      112.0\n",
       "3        NaN\n",
       "4      241.0\n",
       "       ...  \n",
       "190    125.0\n",
       "191     43.0\n",
       "192    164.0\n",
       "193    213.0\n",
       "194    458.0\n",
       "Name: maternalmortalityratio, Length: 195, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mergeworld['maternalmortalityratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>continent</th>\n",
       "      <th>maternalmortalityratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>419.673077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>76.755556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>7.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North America</td>\n",
       "      <td>54.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>61.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South America</td>\n",
       "      <td>88.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       continent  maternalmortalityratio\n",
       "0         Africa              419.673077\n",
       "1           Asia               76.755556\n",
       "2         Europe                7.692308\n",
       "3  North America               54.636364\n",
       "4        Oceania               61.888889\n",
       "5  South America               88.083333"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1: calculamos la media de urban_population para cada continente\n",
    "media_tasa_maternidad = df_mergeworld.groupby('continent')['maternalmortalityratio'].mean().reset_index()\n",
    "media_tasa_maternidad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>continent</th>\n",
       "      <th>totaltaxrate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>47.305882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>32.002326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>38.251220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North America</td>\n",
       "      <td>37.672727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>37.609091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South America</td>\n",
       "      <td>53.341667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       continent  totaltaxrate\n",
       "0         Africa     47.305882\n",
       "1           Asia     32.002326\n",
       "2         Europe     38.251220\n",
       "3  North America     37.672727\n",
       "4        Oceania     37.609091\n",
       "5  South America     53.341667"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2: tasa de impuestos total promedio para cada continente. \"total_tax_rate\" que representa la tasa de impuestos total en cada continente.\n",
    "\n",
    "media_tasa_impuestos = df_mergeworld.groupby('continent')['totaltaxrate'].mean().reset_index()\n",
    "media_tasa_impuestos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3: Agregamos la columna con la relaciónentre tasa de maternidad e impuestos\n",
    "df_mergeworld['relacion_tasa_maternidad_impuestos'] = media_tasa_maternidad['maternalmortalityratio'] / media_tasa_impuestos['totaltaxrate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Afghanistan'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pais_max_relacion = df_mergeworld.loc[df_mergeworld['relacion_tasa_maternidad_impuestos'].idxmax(), 'country']\n",
    "pais_max_relacion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9 - Ejercicios bonus groupby:\n",
    "- ¿Cuál es la tasa de natalidad máxima por área forestal por continente? Encuentra el país con la tasa de natalidad más alta por cada punto porcentual de área forestal.\n",
    "- ¿Cuál es la diferencia entre la tasa de impuestos total y la tasa de desempleo promedio por continente?"
   ]
  }
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