Pandas II.ipynb

{
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   "source": [
    "# <img src=\"uni-logo.png\" alt=\"Logo UNI\" width=100 hight=200 align=\"right\">\n",
    "\n",
    "\n",
    "<br><br><br>\n",
    "<h1><font color=\"#1D65DD\" size=5>Python desde cero</font></h1>\n",
    "\n",
    "\n",
    "\n",
    "<h1><font color=\"#1D65DD\" size=6>Pandas II</font></h1>\n",
    "\n",
    "<br>\n",
    "<div style=\"text-align: right\">\n",
    "<font color=\"#1D65DD\" size=3>Yuri Coicca, M.Sc.</font><br>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"indice\"></a>\n",
    "<h2><font color=\"#7F000E\" size=5>Índice</font></h2>\n",
    "\n",
    "\n",
    "* [5. Selección y ordenación](#section5)\n",
    "    * [Consulta y selección](#section51)  \n",
    "    * [Ordenación](#section52)\n",
    "* [6. Operaciones sobre elementos](#section6)\n",
    "    * [Operaciones básicas](#section61)\n",
    "    * [Operaciones de transformación sobre elementos](#section62)   \n",
    " \n",
    "* [7. Agregación](#section7)   \n",
    "    * [Estadísticos descriptivos](#section71)\n",
    "    * [Agregación de datos: <font face=\"monospace\">apply() y agg()</font>](#section72)  \n",
    "    * [Agregación de datos mediante ventana deslizante: <font face=\"monospace\">rolling()</font> y <font face=\"monospace\">expanding()</font>](#section73)  \n",
    "* [8. Agrupamiento: <font face=\"monospace\">groupby()</font>](#section8)\n",
    "    * [Agregación: <font face=\"monospace\">GroupBy.agg()</font>](#section81)\n",
    "    * [Transformación: <font face=\"monospace\">GroupBy.transform()</font>](#section82)\n",
    "    * [<font face=\"monospace\">GroupBy.apply()</font>](#section83)\n",
    "    * [Filtrado: <font face=\"monospace\">GroupBy.filter()</font>](#section84)\n",
    "    * [Eficiencia](#section85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container{ width:98%}</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Permite ajustar la anchura de la parte útil de la libreta (reduce los márgenes)\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container{ width:98%}</style>\"))\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id=\"section5\"></a>\n",
    "# <font color=\"#7F000E\"> 5. Selección y ordenación </font>\n",
    "<br>\n",
    "\n",
    "Para ilustrar esta sección, se utilizará uno de los _DataFrames_  usados la libreta anterior. Se utilizará el nombre como índice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L. Messi</th>\n",
       "      <td>158023</td>\n",
       "      <td>31</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>110.5</td>\n",
       "      <td>565.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>Jul 1, 2004</td>\n",
       "      <td>170.18</td>\n",
       "      <td>72.121128</td>\n",
       "      <td>€226.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cristiano Ronaldo</th>\n",
       "      <td>20801</td>\n",
       "      <td>33</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>77.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>Jul 10, 2018</td>\n",
       "      <td>187.96</td>\n",
       "      <td>83.007336</td>\n",
       "      <td>€127.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neymar Jr</th>\n",
       "      <td>190871</td>\n",
       "      <td>26</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>118.5</td>\n",
       "      <td>290.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Aug 3, 2017</td>\n",
       "      <td>175.26</td>\n",
       "      <td>68.038800</td>\n",
       "      <td>€228.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Gea</th>\n",
       "      <td>193080</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>72.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Jul 1, 2011</td>\n",
       "      <td>193.04</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€138.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K. De Bruyne</th>\n",
       "      <td>192985</td>\n",
       "      <td>27</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>102.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>RCM</td>\n",
       "      <td>Aug 30, 2015</td>\n",
       "      <td>154.94</td>\n",
       "      <td>69.853168</td>\n",
       "      <td>€196.4M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ID  Age Nationality  Overall  Potential  \\\n",
       "Name                                                             \n",
       "L. Messi           158023   31   Argentina       94         94   \n",
       "Cristiano Ronaldo   20801   33    Portugal       94         94   \n",
       "Neymar Jr          190871   26      Brazil       92         93   \n",
       "De Gea             193080   27       Spain       91         93   \n",
       "K. De Bruyne       192985   27     Belgium       91         92   \n",
       "\n",
       "                                  Club  Value (M)  Wage (K) Position  \\\n",
       "Name                                                                   \n",
       "L. Messi                  FC Barcelona      110.5     565.0       RF   \n",
       "Cristiano Ronaldo             Juventus       77.0     405.0       ST   \n",
       "Neymar Jr          Paris Saint-Germain      118.5     290.0       LW   \n",
       "De Gea               Manchester United       72.0     260.0       GK   \n",
       "K. De Bruyne           Manchester City      102.0     355.0      RCM   \n",
       "\n",
       "                         Joined  Height     Weight Release Clause  \n",
       "Name                                                               \n",
       "L. Messi            Jul 1, 2004  170.18  72.121128        €226.5M  \n",
       "Cristiano Ronaldo  Jul 10, 2018  187.96  83.007336        €127.1M  \n",
       "Neymar Jr           Aug 3, 2017  175.26  68.038800        €228.1M  \n",
       "De Gea              Jul 1, 2011  193.04  76.203456        €138.6M  \n",
       "K. De Bruyne       Aug 30, 2015  154.94  69.853168        €196.4M  "
      ]
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   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "\n",
    "# Lee el archivo, utiliza el campo Name como índice.\n",
    "df_fifa = pd.read_csv('./data/fifa19.csv', index_col=0).set_index('Name')\n",
    "# Columnas seleccionadas\n",
    "sel_columns = ['ID', 'Age', 'Nationality', 'Overall', 'Potential','Club', 'Value', 'Wage', 'Position', 'Joined', 'Height', 'Weight', 'Release Clause']\n",
    "# Selecciona filas y columnas\n",
    "df_fifa = df_fifa[:200][sel_columns] \n",
    "# Confierte las columnas a un formato adecuado (Cada columna es una serie)\n",
    "df_fifa['Weight'] = df_fifa['Weight'].map(lambda w: float(w[:-3])*0.453592)\n",
    "df_fifa['Height'] = df_fifa['Height'].map(lambda h: float(h[0])*30.48 + float(h[2])*2.54) \n",
    "df_fifa['Value'] = df_fifa['Value'].map(lambda v: float(v[1:-1]))\n",
    "df_fifa['Wage'] = df_fifa['Wage'].map(lambda v: float(v[1:-1]))\n",
    "# Renombra unas columnas\n",
    "df_fifa.rename(columns={'Value':'Value (M)', 'Wage':'Wage (K)'}, inplace=True)\n",
    "# Muestra la cabecera\n",
    "df_fifa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "<i class=\"fa fa-exclamation-triangle\" aria-hidden=\"true\"></i>\n",
    "Verán que, si tratan de utilizar el conjunto de datos completo, se produce un fallo. Esto se debe a que alguno de los valores numéricos codificados no están bien en el conjunto, y al decodificarlo con las funciones _lambda_ no corresponde al formato esperado. Estas situaciones se tienen que manejar en la lectura. Se verán ejemplos tanto en los ejercicios como en un seminario dedicado a limpieza y preparación, pero en este punto es mejor no complicar más la cosa. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section51\"></a> \n",
    "## <font color=\"#7F000E\">Consulta y selección </font>\n",
    "<br>\n",
    "\n",
    "La aplicación de un operador booleano sobre una columna, que es un objeto `Series`, genera como resultado otro objeto de tipo `Series` cuyo índice es el mismo del `DataFrame`, y en el que los elementos correspoden al resultado de la operación.\n",
    "\n",
    "Por ejemplo, el siguiente código comprueba si los jugadores tienen menos de 30 años."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "L. Messi             False\n",
       "Cristiano Ronaldo    False\n",
       "Neymar Jr             True\n",
       "De Gea                True\n",
       "K. De Bruyne          True\n",
       "Name: Age, dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consulta = df_fifa['Age']<30\n",
    "consulta[:5]                           # Muestra los 5 primeros elementos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es posible utilizar indexación lógica en un `DataFrame` para devolver las filas de interés."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Neymar Jr</th>\n",
       "      <td>190871</td>\n",
       "      <td>26</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>118.5</td>\n",
       "      <td>290.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Aug 3, 2017</td>\n",
       "      <td>175.26</td>\n",
       "      <td>68.038800</td>\n",
       "      <td>€228.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Gea</th>\n",
       "      <td>193080</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>72.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Jul 1, 2011</td>\n",
       "      <td>193.04</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€138.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K. De Bruyne</th>\n",
       "      <td>192985</td>\n",
       "      <td>27</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>102.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>RCM</td>\n",
       "      <td>Aug 30, 2015</td>\n",
       "      <td>154.94</td>\n",
       "      <td>69.853168</td>\n",
       "      <td>€196.4M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E. Hazard</th>\n",
       "      <td>183277</td>\n",
       "      <td>27</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>93.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>LF</td>\n",
       "      <td>Jul 1, 2012</td>\n",
       "      <td>172.72</td>\n",
       "      <td>73.935496</td>\n",
       "      <td>€172.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J. Oblak</th>\n",
       "      <td>200389</td>\n",
       "      <td>25</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>90</td>\n",
       "      <td>93</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>68.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Jul 16, 2014</td>\n",
       "      <td>187.96</td>\n",
       "      <td>87.089664</td>\n",
       "      <td>€144.5M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID  Age Nationality  Overall  Potential  \\\n",
       "Name                                                        \n",
       "Neymar Jr     190871   26      Brazil       92         93   \n",
       "De Gea        193080   27       Spain       91         93   \n",
       "K. De Bruyne  192985   27     Belgium       91         92   \n",
       "E. Hazard     183277   27     Belgium       91         91   \n",
       "J. Oblak      200389   25    Slovenia       90         93   \n",
       "\n",
       "                             Club  Value (M)  Wage (K) Position        Joined  \\\n",
       "Name                                                                            \n",
       "Neymar Jr     Paris Saint-Germain      118.5     290.0       LW   Aug 3, 2017   \n",
       "De Gea          Manchester United       72.0     260.0       GK   Jul 1, 2011   \n",
       "K. De Bruyne      Manchester City      102.0     355.0      RCM  Aug 30, 2015   \n",
       "E. Hazard                 Chelsea       93.0     340.0       LF   Jul 1, 2012   \n",
       "J. Oblak          Atlético Madrid       68.0      94.0       GK  Jul 16, 2014   \n",
       "\n",
       "              Height     Weight Release Clause  \n",
       "Name                                            \n",
       "Neymar Jr     175.26  68.038800        €228.1M  \n",
       "De Gea        193.04  76.203456        €138.6M  \n",
       "K. De Bruyne  154.94  69.853168        €196.4M  \n",
       "E. Hazard     172.72  73.935496        €172.1M  \n",
       "J. Oblak      187.96  87.089664        €144.5M  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jovenes = df_fifa.loc[df_fifa['Age']<30]\n",
    "#jovenes = df_fifa[df_fifa['Age']<30]\n",
    "#jovenes = df_fifa.loc[df_fifa['Age']<30,['Nationality','Overall']]\n",
    "\n",
    "jovenes.head()\n",
    "#jovenes = df_fifa[df_fifa['Age']<30,['Nationality','Overall']] # No funciona!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se pueden utilizar las operaciones entre `Series` para generar condiciones más complejas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>De Gea</th>\n",
       "      <td>193080</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>72.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Jul 1, 2011</td>\n",
       "      <td>193.04</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€138.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sergio Busquets</th>\n",
       "      <td>189511</td>\n",
       "      <td>29</td>\n",
       "      <td>Spain</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>51.5</td>\n",
       "      <td>315.0</td>\n",
       "      <td>CDM</td>\n",
       "      <td>Sep 1, 2008</td>\n",
       "      <td>187.96</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€105.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isco</th>\n",
       "      <td>197781</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>73.5</td>\n",
       "      <td>315.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Jul 3, 2013</td>\n",
       "      <td>175.26</td>\n",
       "      <td>78.925008</td>\n",
       "      <td>€156.2M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jordi Alba</th>\n",
       "      <td>189332</td>\n",
       "      <td>29</td>\n",
       "      <td>Spain</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>38.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>LB</td>\n",
       "      <td>Jul 1, 2012</td>\n",
       "      <td>170.18</td>\n",
       "      <td>68.038800</td>\n",
       "      <td>€77.9M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thiago</th>\n",
       "      <td>189509</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>45.5</td>\n",
       "      <td>130.0</td>\n",
       "      <td>CM</td>\n",
       "      <td>Jul 14, 2013</td>\n",
       "      <td>175.26</td>\n",
       "      <td>69.853168</td>\n",
       "      <td>€75.1M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ID  Age Nationality  Overall  Potential  \\\n",
       "Name                                                           \n",
       "De Gea           193080   27       Spain       91         93   \n",
       "Sergio Busquets  189511   29       Spain       89         89   \n",
       "Isco             197781   26       Spain       88         91   \n",
       "Jordi Alba       189332   29       Spain       87         87   \n",
       "Thiago           189509   27       Spain       86         86   \n",
       "\n",
       "                              Club  Value (M)  Wage (K) Position  \\\n",
       "Name                                                               \n",
       "De Gea           Manchester United       72.0     260.0       GK   \n",
       "Sergio Busquets       FC Barcelona       51.5     315.0      CDM   \n",
       "Isco                   Real Madrid       73.5     315.0       LW   \n",
       "Jordi Alba            FC Barcelona       38.0     250.0       LB   \n",
       "Thiago           FC Bayern München       45.5     130.0       CM   \n",
       "\n",
       "                       Joined  Height     Weight Release Clause  \n",
       "Name                                                             \n",
       "De Gea            Jul 1, 2011  193.04  76.203456        €138.6M  \n",
       "Sergio Busquets   Sep 1, 2008  187.96  76.203456        €105.6M  \n",
       "Isco              Jul 3, 2013  175.26  78.925008        €156.2M  \n",
       "Jordi Alba        Jul 1, 2012  170.18  68.038800         €77.9M  \n",
       "Thiago           Jul 14, 2013  175.26  69.853168         €75.1M  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jovenes_españoles = df_fifa[(df_fifa['Age']<30) & (df_fifa['Nationality']=='Spain')]\n",
    "jovenes_españoles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "<i class=\"fa fa-exclamation-triangle\" aria-hidden=\"true\"></i>\n",
    "Debido a que los operadores binarios tienen precedencia sobre las comparaciones, la siguiente expresión, en la que se han quitado los paréntesis de la expresión, devolvería un error.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#jovenes_españoles = df_fifa[df_fifa['Age']<30 & df_fifa['Nationality']=='Spain']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#7F000E\" face=\"monospace\"> where() </font>\n",
    "\n",
    "Las consultas pueden hacerse mediante  el método `where()` (como en las `Series`). En ese caso se devuelven __todas las filas__, pero aquellas que no cumplen la condición se reemplazan (la fila entera) con un valor (por defecto NaN) que puede ser un valor o el contenido de otra columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead th {\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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L. Messi</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cristiano Ronaldo</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neymar Jr</th>\n",
       "      <td>190871</td>\n",
       "      <td>26</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>118.5</td>\n",
       "      <td>290</td>\n",
       "      <td>LW</td>\n",
       "      <td>Aug 3, 2017</td>\n",
       "      <td>175.26</td>\n",
       "      <td>68.0388</td>\n",
       "      <td>€228.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Gea</th>\n",
       "      <td>193080</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>72</td>\n",
       "      <td>260</td>\n",
       "      <td>GK</td>\n",
       "      <td>Jul 1, 2011</td>\n",
       "      <td>193.04</td>\n",
       "      <td>76.2035</td>\n",
       "      <td>€138.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K. De Bruyne</th>\n",
       "      <td>192985</td>\n",
       "      <td>27</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>102</td>\n",
       "      <td>355</td>\n",
       "      <td>RCM</td>\n",
       "      <td>Aug 30, 2015</td>\n",
       "      <td>154.94</td>\n",
       "      <td>69.8532</td>\n",
       "      <td>€196.4M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ID  Age Nationality Overall Potential  \\\n",
       "Name                                                           \n",
       "L. Messi              ---  ---         ---     ---       ---   \n",
       "Cristiano Ronaldo     ---  ---         ---     ---       ---   \n",
       "Neymar Jr          190871   26      Brazil      92        93   \n",
       "De Gea             193080   27       Spain      91        93   \n",
       "K. De Bruyne       192985   27     Belgium      91        92   \n",
       "\n",
       "                                  Club Value (M) Wage (K) Position  \\\n",
       "Name                                                                 \n",
       "L. Messi                           ---       ---      ---      ---   \n",
       "Cristiano Ronaldo                  ---       ---      ---      ---   \n",
       "Neymar Jr          Paris Saint-Germain     118.5      290       LW   \n",
       "De Gea               Manchester United        72      260       GK   \n",
       "K. De Bruyne           Manchester City       102      355      RCM   \n",
       "\n",
       "                         Joined  Height   Weight Release Clause  \n",
       "Name                                                             \n",
       "L. Messi                    ---     ---      ---            ---  \n",
       "Cristiano Ronaldo           ---     ---      ---            ---  \n",
       "Neymar Jr           Aug 3, 2017  175.26  68.0388        €228.1M  \n",
       "De Gea              Jul 1, 2011  193.04  76.2035        €138.6M  \n",
       "K. De Bruyne       Aug 30, 2015  154.94  69.8532        €196.4M  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#jovenes = df_fifa.where(df_fifa['Age']<30)\n",
    "jovenes = df_fifa.where(df_fifa['Age']<30, '---')\n",
    "jovenes.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Del mismo modo que la indexación mediante valores booleanos, `where` admite condiciones obtenidas mediante operaciones booleanas entre series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L. Messi</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cristiano Ronaldo</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neymar Jr</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Gea</th>\n",
       "      <td>193080</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>72</td>\n",
       "      <td>260</td>\n",
       "      <td>GK</td>\n",
       "      <td>Jul 1, 2011</td>\n",
       "      <td>193.04</td>\n",
       "      <td>76.2035</td>\n",
       "      <td>€138.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K. De Bruyne</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ID  Age Nationality Overall Potential  \\\n",
       "Name                                                           \n",
       "L. Messi              ---  ---         ---     ---       ---   \n",
       "Cristiano Ronaldo     ---  ---         ---     ---       ---   \n",
       "Neymar Jr             ---  ---         ---     ---       ---   \n",
       "De Gea             193080   27       Spain      91        93   \n",
       "K. De Bruyne          ---  ---         ---     ---       ---   \n",
       "\n",
       "                                Club Value (M) Wage (K) Position       Joined  \\\n",
       "Name                                                                            \n",
       "L. Messi                         ---       ---      ---      ---          ---   \n",
       "Cristiano Ronaldo                ---       ---      ---      ---          ---   \n",
       "Neymar Jr                        ---       ---      ---      ---          ---   \n",
       "De Gea             Manchester United        72      260       GK  Jul 1, 2011   \n",
       "K. De Bruyne                     ---       ---      ---      ---          ---   \n",
       "\n",
       "                   Height   Weight Release Clause  \n",
       "Name                                               \n",
       "L. Messi              ---      ---            ---  \n",
       "Cristiano Ronaldo     ---      ---            ---  \n",
       "Neymar Jr             ---      ---            ---  \n",
       "De Gea             193.04  76.2035        €138.6M  \n",
       "K. De Bruyne          ---      ---            ---  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jovenes_españoles = df_fifa.where((df_fifa['Age']<30) & (df_fifa['Nationality']=='Spain'),'---')\n",
    "jovenes_españoles.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#7F000E\" face=\"monospace\"> mask() </font>\n",
    "\n",
    "La función `mask` es opuesta a `where`, es decir, sustituye (_enmascara_) los valores que cumplen la condición."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L. Messi</th>\n",
       "      <td>158023</td>\n",
       "      <td>31</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>110.5</td>\n",
       "      <td>565</td>\n",
       "      <td>RF</td>\n",
       "      <td>Jul 1, 2004</td>\n",
       "      <td>170.18</td>\n",
       "      <td>72.1211</td>\n",
       "      <td>€226.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cristiano Ronaldo</th>\n",
       "      <td>20801</td>\n",
       "      <td>33</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>77</td>\n",
       "      <td>405</td>\n",
       "      <td>ST</td>\n",
       "      <td>Jul 10, 2018</td>\n",
       "      <td>187.96</td>\n",
       "      <td>83.0073</td>\n",
       "      <td>€127.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neymar Jr</th>\n",
       "      <td>190871</td>\n",
       "      <td>26</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>118.5</td>\n",
       "      <td>290</td>\n",
       "      <td>LW</td>\n",
       "      <td>Aug 3, 2017</td>\n",
       "      <td>175.26</td>\n",
       "      <td>68.0388</td>\n",
       "      <td>€228.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Gea</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K. De Bruyne</th>\n",
       "      <td>192985</td>\n",
       "      <td>27</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>102</td>\n",
       "      <td>355</td>\n",
       "      <td>RCM</td>\n",
       "      <td>Aug 30, 2015</td>\n",
       "      <td>154.94</td>\n",
       "      <td>69.8532</td>\n",
       "      <td>€196.4M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ID  Age Nationality Overall Potential  \\\n",
       "Name                                                           \n",
       "L. Messi           158023   31   Argentina      94        94   \n",
       "Cristiano Ronaldo   20801   33    Portugal      94        94   \n",
       "Neymar Jr          190871   26      Brazil      92        93   \n",
       "De Gea                ---  ---         ---     ---       ---   \n",
       "K. De Bruyne       192985   27     Belgium      91        92   \n",
       "\n",
       "                                  Club Value (M) Wage (K) Position  \\\n",
       "Name                                                                 \n",
       "L. Messi                  FC Barcelona     110.5      565       RF   \n",
       "Cristiano Ronaldo             Juventus        77      405       ST   \n",
       "Neymar Jr          Paris Saint-Germain     118.5      290       LW   \n",
       "De Gea                             ---       ---      ---      ---   \n",
       "K. De Bruyne           Manchester City       102      355      RCM   \n",
       "\n",
       "                         Joined  Height   Weight Release Clause  \n",
       "Name                                                             \n",
       "L. Messi            Jul 1, 2004  170.18  72.1211        €226.5M  \n",
       "Cristiano Ronaldo  Jul 10, 2018  187.96  83.0073        €127.1M  \n",
       "Neymar Jr           Aug 3, 2017  175.26  68.0388        €228.1M  \n",
       "De Gea                      ---     ---      ---            ---  \n",
       "K. De Bruyne       Aug 30, 2015  154.94  69.8532        €196.4M  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nojovenes_noespañoles = df_fifa.mask((df_fifa['Age']<30) & (df_fifa['Nationality']=='Spain'),'---')\n",
    "nojovenes_noespañoles.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#7F000E\" face=\"monospace\"> isin() </font>\n",
    "\n",
    "La función `isin` también es de __suma utilidad__, ya que permite seleccionar las filas cuyo valor esté dentro de un conjunto. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cristiano Ronaldo</th>\n",
       "      <td>20801</td>\n",
       "      <td>33</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>77.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>Jul 10, 2018</td>\n",
       "      <td>187.96</td>\n",
       "      <td>83.007336</td>\n",
       "      <td>€127.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Gea</th>\n",
       "      <td>193080</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>72.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Jul 1, 2011</td>\n",
       "      <td>193.04</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€138.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sergio Ramos</th>\n",
       "      <td>155862</td>\n",
       "      <td>32</td>\n",
       "      <td>Spain</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>51.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>Aug 1, 2005</td>\n",
       "      <td>182.88</td>\n",
       "      <td>82.100152</td>\n",
       "      <td>€104.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Silva</th>\n",
       "      <td>168542</td>\n",
       "      <td>32</td>\n",
       "      <td>Spain</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>60.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>LCM</td>\n",
       "      <td>Jul 14, 2010</td>\n",
       "      <td>172.72</td>\n",
       "      <td>67.131616</td>\n",
       "      <td>€111M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sergio Busquets</th>\n",
       "      <td>189511</td>\n",
       "      <td>29</td>\n",
       "      <td>Spain</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>51.5</td>\n",
       "      <td>315.0</td>\n",
       "      <td>CDM</td>\n",
       "      <td>Sep 1, 2008</td>\n",
       "      <td>187.96</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€105.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isco</th>\n",
       "      <td>197781</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>73.5</td>\n",
       "      <td>315.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Jul 3, 2013</td>\n",
       "      <td>175.26</td>\n",
       "      <td>78.925008</td>\n",
       "      <td>€156.2M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jordi Alba</th>\n",
       "      <td>189332</td>\n",
       "      <td>29</td>\n",
       "      <td>Spain</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>38.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>LB</td>\n",
       "      <td>Jul 1, 2012</td>\n",
       "      <td>170.18</td>\n",
       "      <td>68.038800</td>\n",
       "      <td>€77.9M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Piqué</th>\n",
       "      <td>152729</td>\n",
       "      <td>31</td>\n",
       "      <td>Spain</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>34.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>Jul 1, 2008</td>\n",
       "      <td>193.04</td>\n",
       "      <td>84.821704</td>\n",
       "      <td>€69.7M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bernardo Silva</th>\n",
       "      <td>218667</td>\n",
       "      <td>23</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>86</td>\n",
       "      <td>91</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>59.5</td>\n",
       "      <td>180.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 1, 2017</td>\n",
       "      <td>172.72</td>\n",
       "      <td>63.956472</td>\n",
       "      <td>€114.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thiago</th>\n",
       "      <td>189509</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>45.5</td>\n",
       "      <td>130.0</td>\n",
       "      <td>CM</td>\n",
       "      <td>Jul 14, 2013</td>\n",
       "      <td>175.26</td>\n",
       "      <td>69.853168</td>\n",
       "      <td>€75.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azpilicueta</th>\n",
       "      <td>184432</td>\n",
       "      <td>28</td>\n",
       "      <td>Spain</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>35.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>RB</td>\n",
       "      <td>Aug 24, 2012</td>\n",
       "      <td>154.94</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€64.8M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iniesta</th>\n",
       "      <td>41</td>\n",
       "      <td>34</td>\n",
       "      <td>Spain</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>Vissel Kobe</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>LF</td>\n",
       "      <td>Jul 16, 2018</td>\n",
       "      <td>170.18</td>\n",
       "      <td>68.038800</td>\n",
       "      <td>€26.9M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>220834</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>54.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 1, 2015</td>\n",
       "      <td>182.88</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€121.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saúl</th>\n",
       "      <td>208421</td>\n",
       "      <td>23</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>50.5</td>\n",
       "      <td>77.0</td>\n",
       "      <td>RCM</td>\n",
       "      <td>Jul 1, 2013</td>\n",
       "      <td>182.88</td>\n",
       "      <td>77.110640</td>\n",
       "      <td>€107.3M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koke</th>\n",
       "      <td>193747</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>45.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>LM</td>\n",
       "      <td>Jan 1, 2011</td>\n",
       "      <td>175.26</td>\n",
       "      <td>73.935496</td>\n",
       "      <td>€95.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parejo</th>\n",
       "      <td>189513</td>\n",
       "      <td>29</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>Valencia CF</td>\n",
       "      <td>37.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>RCM</td>\n",
       "      <td>Jul 1, 2011</td>\n",
       "      <td>182.88</td>\n",
       "      <td>73.935496</td>\n",
       "      <td>€77.7M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diego Costa</th>\n",
       "      <td>179844</td>\n",
       "      <td>29</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>38.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>LS</td>\n",
       "      <td>Jan 1, 2018</td>\n",
       "      <td>187.96</td>\n",
       "      <td>86.182480</td>\n",
       "      <td>€78.9M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pepe</th>\n",
       "      <td>120533</td>\n",
       "      <td>35</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>Beşiktaş JK</td>\n",
       "      <td>9.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>Jul 4, 2017</td>\n",
       "      <td>187.96</td>\n",
       "      <td>81.192968</td>\n",
       "      <td>€17.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bruno Fernandes</th>\n",
       "      <td>212198</td>\n",
       "      <td>23</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>Sporting CP</td>\n",
       "      <td>40.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>LCM</td>\n",
       "      <td>Jul 1, 2017</td>\n",
       "      <td>154.94</td>\n",
       "      <td>68.945984</td>\n",
       "      <td>€84M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>William Carvalho</th>\n",
       "      <td>207566</td>\n",
       "      <td>26</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>84</td>\n",
       "      <td>86</td>\n",
       "      <td>Real Betis</td>\n",
       "      <td>31.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>CDM</td>\n",
       "      <td>Jul 13, 2018</td>\n",
       "      <td>187.96</td>\n",
       "      <td>83.007336</td>\n",
       "      <td>€68.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carvajal</th>\n",
       "      <td>204963</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>84</td>\n",
       "      <td>87</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>31.5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>RB</td>\n",
       "      <td>Jul 5, 2013</td>\n",
       "      <td>172.72</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€66.9M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A. Lopes</th>\n",
       "      <td>199482</td>\n",
       "      <td>27</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>84</td>\n",
       "      <td>86</td>\n",
       "      <td>Olympique Lyonnais</td>\n",
       "      <td>25.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Jul 1, 2011</td>\n",
       "      <td>182.88</td>\n",
       "      <td>81.192968</td>\n",
       "      <td>€49.4M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iago Aspas</th>\n",
       "      <td>192629</td>\n",
       "      <td>30</td>\n",
       "      <td>Spain</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>RC Celta</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>Jul 1, 2015</td>\n",
       "      <td>175.26</td>\n",
       "      <td>67.131616</td>\n",
       "      <td>€63M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illarramendi</th>\n",
       "      <td>190584</td>\n",
       "      <td>28</td>\n",
       "      <td>Spain</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>31.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>RDM</td>\n",
       "      <td>Aug 26, 2015</td>\n",
       "      <td>154.94</td>\n",
       "      <td>74.842680</td>\n",
       "      <td>€65.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>José Callejón</th>\n",
       "      <td>185020</td>\n",
       "      <td>31</td>\n",
       "      <td>Spain</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>27.5</td>\n",
       "      <td>105.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>Jul 11, 2013</td>\n",
       "      <td>154.94</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€46.8M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sergio Asenjo</th>\n",
       "      <td>178750</td>\n",
       "      <td>29</td>\n",
       "      <td>Spain</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "      <td>Villarreal CF</td>\n",
       "      <td>24.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Jun 30, 2014</td>\n",
       "      <td>187.96</td>\n",
       "      <td>87.089664</td>\n",
       "      <td>€52.2M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raúl Albiol</th>\n",
       "      <td>157481</td>\n",
       "      <td>32</td>\n",
       "      <td>Spain</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>18.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>Jul 21, 2013</td>\n",
       "      <td>190.50</td>\n",
       "      <td>82.100152</td>\n",
       "      <td>€30.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quaresma</th>\n",
       "      <td>20775</td>\n",
       "      <td>34</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>Beşiktaş JK</td>\n",
       "      <td>15.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>Jul 22, 2015</td>\n",
       "      <td>175.26</td>\n",
       "      <td>67.131616</td>\n",
       "      <td>€29.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lucas Vázquez</th>\n",
       "      <td>208618</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>27.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 2, 2015</td>\n",
       "      <td>172.72</td>\n",
       "      <td>69.853168</td>\n",
       "      <td>€55.4M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gerard Moreno</th>\n",
       "      <td>208093</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>85</td>\n",
       "      <td>Villarreal CF</td>\n",
       "      <td>32.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>LS</td>\n",
       "      <td>Jul 2, 2018</td>\n",
       "      <td>154.94</td>\n",
       "      <td>77.110640</td>\n",
       "      <td>€69.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kepa</th>\n",
       "      <td>206585</td>\n",
       "      <td>23</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>91</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>28.5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Aug 8, 2018</td>\n",
       "      <td>185.42</td>\n",
       "      <td>84.821704</td>\n",
       "      <td>€58.4M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manu Trigueros</th>\n",
       "      <td>206545</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "      <td>Villarreal CF</td>\n",
       "      <td>30.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>RCM</td>\n",
       "      <td>Jul 1, 2011</td>\n",
       "      <td>154.94</td>\n",
       "      <td>74.842680</td>\n",
       "      <td>€65.3M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suso</th>\n",
       "      <td>202651</td>\n",
       "      <td>24</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>Milan</td>\n",
       "      <td>33.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jan 17, 2015</td>\n",
       "      <td>175.26</td>\n",
       "      <td>69.853168</td>\n",
       "      <td>€58.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Danilo Pereira</th>\n",
       "      <td>200888</td>\n",
       "      <td>26</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>FC Porto</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>CDM</td>\n",
       "      <td>Jul 2, 2015</td>\n",
       "      <td>187.96</td>\n",
       "      <td>83.007336</td>\n",
       "      <td>€56M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nacho Fernández</th>\n",
       "      <td>200724</td>\n",
       "      <td>28</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>85</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>24.5</td>\n",
       "      <td>180.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Aug 1, 2010</td>\n",
       "      <td>154.94</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€52.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sergi Roberto</th>\n",
       "      <td>199564</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>26.5</td>\n",
       "      <td>170.0</td>\n",
       "      <td>RB</td>\n",
       "      <td>Jul 1, 2013</td>\n",
       "      <td>154.94</td>\n",
       "      <td>68.038800</td>\n",
       "      <td>€56.3M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rodrigo</th>\n",
       "      <td>198329</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "      <td>Valencia CF</td>\n",
       "      <td>30.5</td>\n",
       "      <td>57.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>Dec 3, 2014</td>\n",
       "      <td>182.88</td>\n",
       "      <td>77.110640</td>\n",
       "      <td>€66.3M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pizzi</th>\n",
       "      <td>197965</td>\n",
       "      <td>28</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>SL Benfica</td>\n",
       "      <td>25.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>LCM</td>\n",
       "      <td>Jul 30, 2013</td>\n",
       "      <td>154.94</td>\n",
       "      <td>72.121128</td>\n",
       "      <td>€51M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ID  Age Nationality  Overall  Potential  \\\n",
       "Name                                                             \n",
       "Cristiano Ronaldo   20801   33    Portugal       94         94   \n",
       "De Gea             193080   27       Spain       91         93   \n",
       "Sergio Ramos       155862   32       Spain       91         91   \n",
       "David Silva        168542   32       Spain       90         90   \n",
       "Sergio Busquets    189511   29       Spain       89         89   \n",
       "Isco               197781   26       Spain       88         91   \n",
       "Jordi Alba         189332   29       Spain       87         87   \n",
       "Piqué              152729   31       Spain       87         87   \n",
       "Bernardo Silva     218667   23    Portugal       86         91   \n",
       "Thiago             189509   27       Spain       86         86   \n",
       "Azpilicueta        184432   28       Spain       86         86   \n",
       "Iniesta                41   34       Spain       86         86   \n",
       "Marco Asensio      220834   22       Spain       85         92   \n",
       "Saúl               208421   23       Spain       85         90   \n",
       "Koke               193747   26       Spain       85         86   \n",
       "Parejo             189513   29       Spain       85         85   \n",
       "Diego Costa        179844   29       Spain       85         85   \n",
       "Pepe               120533   35    Portugal       85         85   \n",
       "Bruno Fernandes    212198   23    Portugal       84         88   \n",
       "William Carvalho   207566   26    Portugal       84         86   \n",
       "Carvajal           204963   26       Spain       84         87   \n",
       "A. Lopes           199482   27    Portugal       84         86   \n",
       "Iago Aspas         192629   30       Spain       84         84   \n",
       "Illarramendi       190584   28       Spain       84         84   \n",
       "José Callejón      185020   31       Spain       84         84   \n",
       "Sergio Asenjo      178750   29       Spain       84         85   \n",
       "Raúl Albiol        157481   32       Spain       84         84   \n",
       "Quaresma            20775   34    Portugal       84         84   \n",
       "Lucas Vázquez      208618   27       Spain       83         83   \n",
       "Gerard Moreno      208093   26       Spain       83         85   \n",
       "Kepa               206585   23       Spain       83         91   \n",
       "Manu Trigueros     206545   26       Spain       83         84   \n",
       "Suso               202651   24       Spain       83         86   \n",
       "Danilo Pereira     200888   26    Portugal       83         86   \n",
       "Nacho Fernández    200724   28       Spain       83         85   \n",
       "Sergi Roberto      199564   26       Spain       83         86   \n",
       "Rodrigo            198329   27       Spain       83         84   \n",
       "Pizzi              197965   28    Portugal       83         83   \n",
       "\n",
       "                                 Club  Value (M)  Wage (K) Position  \\\n",
       "Name                                                                  \n",
       "Cristiano Ronaldo            Juventus       77.0     405.0       ST   \n",
       "De Gea              Manchester United       72.0     260.0       GK   \n",
       "Sergio Ramos              Real Madrid       51.0     380.0      RCB   \n",
       "David Silva           Manchester City       60.0     285.0      LCM   \n",
       "Sergio Busquets          FC Barcelona       51.5     315.0      CDM   \n",
       "Isco                      Real Madrid       73.5     315.0       LW   \n",
       "Jordi Alba               FC Barcelona       38.0     250.0       LB   \n",
       "Piqué                    FC Barcelona       34.0     240.0      RCB   \n",
       "Bernardo Silva        Manchester City       59.5     180.0       RW   \n",
       "Thiago              FC Bayern München       45.5     130.0       CM   \n",
       "Azpilicueta                   Chelsea       35.0     175.0       RB   \n",
       "Iniesta                   Vissel Kobe       21.5      21.0       LF   \n",
       "Marco Asensio             Real Madrid       54.0     215.0       RW   \n",
       "Saúl                  Atlético Madrid       50.5      77.0      RCM   \n",
       "Koke                  Atlético Madrid       45.0      88.0       LM   \n",
       "Parejo                    Valencia CF       37.0      66.0      RCM   \n",
       "Diego Costa           Atlético Madrid       38.5     100.0       LS   \n",
       "Pepe                      Beşiktaş JK        9.0      57.0      RCB   \n",
       "Bruno Fernandes           Sporting CP       40.5      22.0      LCM   \n",
       "William Carvalho           Real Betis       31.5      38.0      CDM   \n",
       "Carvajal                  Real Madrid       31.5     185.0       RB   \n",
       "A. Lopes           Olympique Lyonnais       25.0      72.0       GK   \n",
       "Iago Aspas                   RC Celta       30.0      45.0       ST   \n",
       "Illarramendi            Real Sociedad       31.0      51.0      RDM   \n",
       "José Callejón                  Napoli       27.5     105.0       RM   \n",
       "Sergio Asenjo           Villarreal CF       24.0      42.0       GK   \n",
       "Raúl Albiol                    Napoli       18.0      88.0      RCB   \n",
       "Quaresma                  Beşiktaş JK       15.5      80.0       RM   \n",
       "Lucas Vázquez             Real Madrid       27.0     205.0       RW   \n",
       "Gerard Moreno           Villarreal CF       32.0      53.0       LS   \n",
       "Kepa                          Chelsea       28.5      84.0       GK   \n",
       "Manu Trigueros          Villarreal CF       30.0      49.0      RCM   \n",
       "Suso                            Milan       33.0     115.0       RW   \n",
       "Danilo Pereira               FC Porto       27.0      21.0      CDM   \n",
       "Nacho Fernández           Real Madrid       24.5     180.0       CB   \n",
       "Sergi Roberto            FC Barcelona       26.5     170.0       RB   \n",
       "Rodrigo                   Valencia CF       30.5      57.0       ST   \n",
       "Pizzi                      SL Benfica       25.5      22.0      LCM   \n",
       "\n",
       "                         Joined  Height     Weight Release Clause  \n",
       "Name                                                               \n",
       "Cristiano Ronaldo  Jul 10, 2018  187.96  83.007336        €127.1M  \n",
       "De Gea              Jul 1, 2011  193.04  76.203456        €138.6M  \n",
       "Sergio Ramos        Aug 1, 2005  182.88  82.100152        €104.6M  \n",
       "David Silva        Jul 14, 2010  172.72  67.131616          €111M  \n",
       "Sergio Busquets     Sep 1, 2008  187.96  76.203456        €105.6M  \n",
       "Isco                Jul 3, 2013  175.26  78.925008        €156.2M  \n",
       "Jordi Alba          Jul 1, 2012  170.18  68.038800         €77.9M  \n",
       "Piqué               Jul 1, 2008  193.04  84.821704         €69.7M  \n",
       "Bernardo Silva      Jul 1, 2017  172.72  63.956472        €114.5M  \n",
       "Thiago             Jul 14, 2013  175.26  69.853168         €75.1M  \n",
       "Azpilicueta        Aug 24, 2012  154.94  76.203456         €64.8M  \n",
       "Iniesta            Jul 16, 2018  170.18  68.038800         €26.9M  \n",
       "Marco Asensio       Jul 1, 2015  182.88  76.203456        €121.5M  \n",
       "Saúl                Jul 1, 2013  182.88  77.110640        €107.3M  \n",
       "Koke                Jan 1, 2011  175.26  73.935496         €95.6M  \n",
       "Parejo              Jul 1, 2011  182.88  73.935496         €77.7M  \n",
       "Diego Costa         Jan 1, 2018  187.96  86.182480         €78.9M  \n",
       "Pepe                Jul 4, 2017  187.96  81.192968         €17.1M  \n",
       "Bruno Fernandes     Jul 1, 2017  154.94  68.945984           €84M  \n",
       "William Carvalho   Jul 13, 2018  187.96  83.007336         €68.5M  \n",
       "Carvajal            Jul 5, 2013  172.72  73.028312         €66.9M  \n",
       "A. Lopes            Jul 1, 2011  182.88  81.192968         €49.4M  \n",
       "Iago Aspas          Jul 1, 2015  175.26  67.131616           €63M  \n",
       "Illarramendi       Aug 26, 2015  154.94  74.842680         €65.1M  \n",
       "José Callejón      Jul 11, 2013  154.94  73.028312         €46.8M  \n",
       "Sergio Asenjo      Jun 30, 2014  187.96  87.089664         €52.2M  \n",
       "Raúl Albiol        Jul 21, 2013  190.50  82.100152         €30.6M  \n",
       "Quaresma           Jul 22, 2015  175.26  67.131616         €29.5M  \n",
       "Lucas Vázquez       Jul 2, 2015  172.72  69.853168         €55.4M  \n",
       "Gerard Moreno       Jul 2, 2018  154.94  77.110640         €69.6M  \n",
       "Kepa                Aug 8, 2018  185.42  84.821704         €58.4M  \n",
       "Manu Trigueros      Jul 1, 2011  154.94  74.842680         €65.3M  \n",
       "Suso               Jan 17, 2015  175.26  69.853168         €58.6M  \n",
       "Danilo Pereira      Jul 2, 2015  187.96  83.007336           €56M  \n",
       "Nacho Fernández     Aug 1, 2010  154.94  76.203456         €52.1M  \n",
       "Sergi Roberto       Jul 1, 2013  154.94  68.038800         €56.3M  \n",
       "Rodrigo             Dec 3, 2014  182.88  77.110640         €66.3M  \n",
       "Pizzi              Jul 30, 2013  154.94  72.121128           €51M  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fifa[df_fifa['Nationality'].isin(['Spain','Portugal'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función `isin()` admite también que se especifiquen valores para cada columna mediante un diccionario. En ese caso devuelve un `DataFrame`, que ha de ser filtrado adecuadamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L. Messi</th>\n",
       "      <td>158023</td>\n",
       "      <td>31</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>110.5</td>\n",
       "      <td>565.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>Jul 1, 2004</td>\n",
       "      <td>170.18</td>\n",
       "      <td>72.121128</td>\n",
       "      <td>€226.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sergio Ramos</th>\n",
       "      <td>155862</td>\n",
       "      <td>32</td>\n",
       "      <td>Spain</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>51.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>Aug 1, 2005</td>\n",
       "      <td>182.88</td>\n",
       "      <td>82.100152</td>\n",
       "      <td>€104.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sergio Busquets</th>\n",
       "      <td>189511</td>\n",
       "      <td>29</td>\n",
       "      <td>Spain</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>51.5</td>\n",
       "      <td>315.0</td>\n",
       "      <td>CDM</td>\n",
       "      <td>Sep 1, 2008</td>\n",
       "      <td>187.96</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€105.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isco</th>\n",
       "      <td>197781</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>73.5</td>\n",
       "      <td>315.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Jul 3, 2013</td>\n",
       "      <td>175.26</td>\n",
       "      <td>78.925008</td>\n",
       "      <td>€156.2M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jordi Alba</th>\n",
       "      <td>189332</td>\n",
       "      <td>29</td>\n",
       "      <td>Spain</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>38.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>LB</td>\n",
       "      <td>Jul 1, 2012</td>\n",
       "      <td>170.18</td>\n",
       "      <td>68.038800</td>\n",
       "      <td>€77.9M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Piqué</th>\n",
       "      <td>152729</td>\n",
       "      <td>31</td>\n",
       "      <td>Spain</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>34.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>Jul 1, 2008</td>\n",
       "      <td>193.04</td>\n",
       "      <td>84.821704</td>\n",
       "      <td>€69.7M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>220834</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>54.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 1, 2015</td>\n",
       "      <td>182.88</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€121.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carvajal</th>\n",
       "      <td>204963</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>84</td>\n",
       "      <td>87</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>31.5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>RB</td>\n",
       "      <td>Jul 5, 2013</td>\n",
       "      <td>172.72</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€66.9M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lucas Vázquez</th>\n",
       "      <td>208618</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>27.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 2, 2015</td>\n",
       "      <td>172.72</td>\n",
       "      <td>69.853168</td>\n",
       "      <td>€55.4M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nacho Fernández</th>\n",
       "      <td>200724</td>\n",
       "      <td>28</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>85</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>24.5</td>\n",
       "      <td>180.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Aug 1, 2010</td>\n",
       "      <td>154.94</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€52.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sergi Roberto</th>\n",
       "      <td>199564</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>26.5</td>\n",
       "      <td>170.0</td>\n",
       "      <td>RB</td>\n",
       "      <td>Jul 1, 2013</td>\n",
       "      <td>154.94</td>\n",
       "      <td>68.038800</td>\n",
       "      <td>€56.3M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ID  Age Nationality  Overall  Potential          Club  \\\n",
       "Name                                                                         \n",
       "L. Messi         158023   31   Argentina       94         94  FC Barcelona   \n",
       "Sergio Ramos     155862   32       Spain       91         91   Real Madrid   \n",
       "Sergio Busquets  189511   29       Spain       89         89  FC Barcelona   \n",
       "Isco             197781   26       Spain       88         91   Real Madrid   \n",
       "Jordi Alba       189332   29       Spain       87         87  FC Barcelona   \n",
       "Piqué            152729   31       Spain       87         87  FC Barcelona   \n",
       "Marco Asensio    220834   22       Spain       85         92   Real Madrid   \n",
       "Carvajal         204963   26       Spain       84         87   Real Madrid   \n",
       "Lucas Vázquez    208618   27       Spain       83         83   Real Madrid   \n",
       "Nacho Fernández  200724   28       Spain       83         85   Real Madrid   \n",
       "Sergi Roberto    199564   26       Spain       83         86  FC Barcelona   \n",
       "\n",
       "                 Value (M)  Wage (K) Position       Joined  Height     Weight  \\\n",
       "Name                                                                            \n",
       "L. Messi             110.5     565.0       RF  Jul 1, 2004  170.18  72.121128   \n",
       "Sergio Ramos          51.0     380.0      RCB  Aug 1, 2005  182.88  82.100152   \n",
       "Sergio Busquets       51.5     315.0      CDM  Sep 1, 2008  187.96  76.203456   \n",
       "Isco                  73.5     315.0       LW  Jul 3, 2013  175.26  78.925008   \n",
       "Jordi Alba            38.0     250.0       LB  Jul 1, 2012  170.18  68.038800   \n",
       "Piqué                 34.0     240.0      RCB  Jul 1, 2008  193.04  84.821704   \n",
       "Marco Asensio         54.0     215.0       RW  Jul 1, 2015  182.88  76.203456   \n",
       "Carvajal              31.5     185.0       RB  Jul 5, 2013  172.72  73.028312   \n",
       "Lucas Vázquez         27.0     205.0       RW  Jul 2, 2015  172.72  69.853168   \n",
       "Nacho Fernández       24.5     180.0       CB  Aug 1, 2010  154.94  76.203456   \n",
       "Sergi Roberto         26.5     170.0       RB  Jul 1, 2013  154.94  68.038800   \n",
       "\n",
       "                Release Clause  \n",
       "Name                            \n",
       "L. Messi               €226.5M  \n",
       "Sergio Ramos           €104.6M  \n",
       "Sergio Busquets        €105.6M  \n",
       "Isco                   €156.2M  \n",
       "Jordi Alba              €77.9M  \n",
       "Piqué                   €69.7M  \n",
       "Marco Asensio          €121.5M  \n",
       "Carvajal                €66.9M  \n",
       "Lucas Vázquez           €55.4M  \n",
       "Nacho Fernández         €52.1M  \n",
       "Sergi Roberto           €56.3M  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = {'Nationality':['Spain','Portugal','Argentina'], 'Club':['FC Barcelona','Real Madrid']} \n",
    "#df_fifa.isin(cond)[['Nationality','Club']]\n",
    "df_fifa[df_fifa.isin(cond)[['Nationality','Club']].all(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#7F000E\" face=\"monospace\"> filter() </font>\n",
    "\n",
    "La función `filter()` permite filtrar elementos del `DataFrame` en función del valor del índice. Admite varios parámetros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_fifa.filter(items=['Age','Nationality'], axis=1) # Columnas\n",
    "#df_fifa.filter(like='De', axis=0)                   # En alguna parte del nombre aparece `De`\n",
    "#df_fifa.filter(regex='\\w* \\w* \\w*', axis=0)          # Expresión regular. Nombre con tres partes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section52\"></a> \n",
    "## <font color=\"#7F000E\">Ordenación </font>\n",
    "<br>\n",
    "\n",
    "La operación `DataFrame.sort_index` ordena el `DataFrame` en función de su índice. Mediante el parámetro `ascending` se establece el orden de la ordenación. Además, por defecto produce un nuevo `DataFrame`, a no ser que se indique lo contrario mediante el parámetro `inplace`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
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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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Z. Ibrahimović</th>\n",
       "      <td>41236</td>\n",
       "      <td>36</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>LA Galaxy</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>RS</td>\n",
       "      <td>Mar 23, 2018</td>\n",
       "      <td>195.58</td>\n",
       "      <td>94.800728</td>\n",
       "      <td>€21M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y. Carrasco</th>\n",
       "      <td>208418</td>\n",
       "      <td>24</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>Dalian YiFang FC</td>\n",
       "      <td>33.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>LM</td>\n",
       "      <td>Feb 26, 2018</td>\n",
       "      <td>154.94</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€73.4M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y. Brahimi</th>\n",
       "      <td>184267</td>\n",
       "      <td>28</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>FC Porto</td>\n",
       "      <td>39.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>LM</td>\n",
       "      <td>Jul 22, 2014</td>\n",
       "      <td>175.26</td>\n",
       "      <td>66.224432</td>\n",
       "      <td>€78M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Willian</th>\n",
       "      <td>180403</td>\n",
       "      <td>29</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>30.5</td>\n",
       "      <td>175.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Aug 28, 2013</td>\n",
       "      <td>175.26</td>\n",
       "      <td>78.017824</td>\n",
       "      <td>€56.4M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>William Carvalho</th>\n",
       "      <td>207566</td>\n",
       "      <td>26</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>84</td>\n",
       "      <td>86</td>\n",
       "      <td>Real Betis</td>\n",
       "      <td>31.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>CDM</td>\n",
       "      <td>Jul 13, 2018</td>\n",
       "      <td>187.96</td>\n",
       "      <td>83.007336</td>\n",
       "      <td>€68.5M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID  Age Nationality  Overall  Potential  \\\n",
       "Name                                                            \n",
       "Z. Ibrahimović     41236   36      Sweden       85         85   \n",
       "Y. Carrasco       208418   24     Belgium       83         86   \n",
       "Y. Brahimi        184267   28     Algeria       85         85   \n",
       "Willian           180403   29      Brazil       84         84   \n",
       "William Carvalho  207566   26    Portugal       84         86   \n",
       "\n",
       "                              Club  Value (M)  Wage (K) Position  \\\n",
       "Name                                                               \n",
       "Z. Ibrahimović           LA Galaxy       14.0      15.0       RS   \n",
       "Y. Carrasco       Dalian YiFang FC       33.0      20.0       LM   \n",
       "Y. Brahimi                FC Porto       39.0      28.0       LM   \n",
       "Willian                    Chelsea       30.5     175.0       RW   \n",
       "William Carvalho        Real Betis       31.5      38.0      CDM   \n",
       "\n",
       "                        Joined  Height     Weight Release Clause  \n",
       "Name                                                              \n",
       "Z. Ibrahimović    Mar 23, 2018  195.58  94.800728           €21M  \n",
       "Y. Carrasco       Feb 26, 2018  154.94  73.028312         €73.4M  \n",
       "Y. Brahimi        Jul 22, 2014  175.26  66.224432           €78M  \n",
       "Willian           Aug 28, 2013  175.26  78.017824         €56.4M  \n",
       "William Carvalho  Jul 13, 2018  187.96  83.007336         €68.5M  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fifa.sort_index(ascending=False, inplace=True)\n",
    "df_fifa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El método `DataFrame.sort_values` ordena el `DataFrame` en función de una o varias columnas. El siguiente código ordena ascendentemente en función del campo _Age_ y, en segunda instancia, descencentemente en función del campo _Overall_ (en realidad se hace al revés)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K. Mbappé</th>\n",
       "      <td>231747</td>\n",
       "      <td>19</td>\n",
       "      <td>France</td>\n",
       "      <td>88</td>\n",
       "      <td>95</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>81.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>Jul 1, 2018</td>\n",
       "      <td>154.94</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€166.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O. Dembélé</th>\n",
       "      <td>231443</td>\n",
       "      <td>21</td>\n",
       "      <td>France</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>40.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Aug 28, 2017</td>\n",
       "      <td>154.94</td>\n",
       "      <td>67.131616</td>\n",
       "      <td>€90M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabriel Jesus</th>\n",
       "      <td>230666</td>\n",
       "      <td>21</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>41.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>Aug 3, 2016</td>\n",
       "      <td>175.26</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€84.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L. Sané</th>\n",
       "      <td>222492</td>\n",
       "      <td>22</td>\n",
       "      <td>Germany</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>61.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Aug 2, 2016</td>\n",
       "      <td>182.88</td>\n",
       "      <td>74.842680</td>\n",
       "      <td>€125.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>220834</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>54.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 1, 2015</td>\n",
       "      <td>182.88</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€121.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N. Süle</th>\n",
       "      <td>212190</td>\n",
       "      <td>22</td>\n",
       "      <td>Germany</td>\n",
       "      <td>84</td>\n",
       "      <td>90</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>36.5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Jul 1, 2017</td>\n",
       "      <td>195.58</td>\n",
       "      <td>97.068688</td>\n",
       "      <td>€67.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D. Sánchez</th>\n",
       "      <td>220793</td>\n",
       "      <td>22</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>Tottenham Hotspur</td>\n",
       "      <td>34.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>Aug 26, 2017</td>\n",
       "      <td>187.96</td>\n",
       "      <td>78.925008</td>\n",
       "      <td>€65.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D. Alli</th>\n",
       "      <td>211117</td>\n",
       "      <td>22</td>\n",
       "      <td>England</td>\n",
       "      <td>84</td>\n",
       "      <td>90</td>\n",
       "      <td>Tottenham Hotspur</td>\n",
       "      <td>42.5</td>\n",
       "      <td>115.0</td>\n",
       "      <td>LCM</td>\n",
       "      <td>Feb 2, 2015</td>\n",
       "      <td>187.96</td>\n",
       "      <td>79.832192</td>\n",
       "      <td>€87.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A. Martial</th>\n",
       "      <td>211300</td>\n",
       "      <td>22</td>\n",
       "      <td>France</td>\n",
       "      <td>84</td>\n",
       "      <td>90</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>42.5</td>\n",
       "      <td>165.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Sep 1, 2015</td>\n",
       "      <td>182.88</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€87.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T. Werner</th>\n",
       "      <td>212188</td>\n",
       "      <td>22</td>\n",
       "      <td>Germany</td>\n",
       "      <td>83</td>\n",
       "      <td>87</td>\n",
       "      <td>RB Leipzig</td>\n",
       "      <td>34.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 1, 2016</td>\n",
       "      <td>154.94</td>\n",
       "      <td>74.842680</td>\n",
       "      <td>€61.2M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ID  Age Nationality  Overall  Potential  \\\n",
       "Name                                                         \n",
       "K. Mbappé      231747   19      France       88         95   \n",
       "O. Dembélé     231443   21      France       83         92   \n",
       "Gabriel Jesus  230666   21      Brazil       83         92   \n",
       "L. Sané        222492   22     Germany       86         92   \n",
       "Marco Asensio  220834   22       Spain       85         92   \n",
       "N. Süle        212190   22     Germany       84         90   \n",
       "D. Sánchez     220793   22    Colombia       84         88   \n",
       "D. Alli        211117   22     England       84         90   \n",
       "A. Martial     211300   22      France       84         90   \n",
       "T. Werner      212188   22     Germany       83         87   \n",
       "\n",
       "                              Club  Value (M)  Wage (K) Position  \\\n",
       "Name                                                               \n",
       "K. Mbappé      Paris Saint-Germain       81.0     100.0       RM   \n",
       "O. Dembélé            FC Barcelona       40.0     155.0       RW   \n",
       "Gabriel Jesus      Manchester City       41.0     130.0       ST   \n",
       "L. Sané            Manchester City       61.0     195.0       LW   \n",
       "Marco Asensio          Real Madrid       54.0     215.0       RW   \n",
       "N. Süle          FC Bayern München       36.5      84.0       CB   \n",
       "D. Sánchez       Tottenham Hotspur       34.0     105.0      RCB   \n",
       "D. Alli          Tottenham Hotspur       42.5     115.0      LCM   \n",
       "A. Martial       Manchester United       42.5     165.0       LW   \n",
       "T. Werner               RB Leipzig       34.5      70.0       RW   \n",
       "\n",
       "                     Joined  Height     Weight Release Clause  \n",
       "Name                                                           \n",
       "K. Mbappé       Jul 1, 2018  154.94  73.028312        €166.1M  \n",
       "O. Dembélé     Aug 28, 2017  154.94  67.131616           €90M  \n",
       "Gabriel Jesus   Aug 3, 2016  175.26  73.028312         €84.1M  \n",
       "L. Sané         Aug 2, 2016  182.88  74.842680        €125.1M  \n",
       "Marco Asensio   Jul 1, 2015  182.88  76.203456        €121.5M  \n",
       "N. Süle         Jul 1, 2017  195.58  97.068688         €67.5M  \n",
       "D. Sánchez     Aug 26, 2017  187.96  78.925008         €65.5M  \n",
       "D. Alli         Feb 2, 2015  187.96  79.832192         €87.1M  \n",
       "A. Martial      Sep 1, 2015  182.88  76.203456         €87.1M  \n",
       "T. Werner       Jul 1, 2016  154.94  74.842680         €61.2M  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fifa.sort_values(['Age', 'Overall'], ascending=[True, False], inplace=True)\n",
    "df_fifa.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "<i class=\"fa fa-exclamation-circle\" aria-hidden=\"true\"></i>\n",
    "__Importante__: El parámetro `kind` permite determinar el algoritmo de ordenación cuando se ordena una sola columna. En este caso, es interesante saber que `mergesort` es un algoritmo de ordenación estable. Es decir, cuando ordena por un campo, para los elementos con un mismo valor de ese campo, preserva el orden relativo de los elementos anterior a la ordenación.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función `rank()` ordena todas las columnas y devuelve un `DataFrame` con el orden correspondiente a cada fila según cada columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K. Mbappé</th>\n",
       "      <td>199.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>167.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>162.5</td>\n",
       "      <td>124.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O. Dembélé</th>\n",
       "      <td>198.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>189.5</td>\n",
       "      <td>44.5</td>\n",
       "      <td>126.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabriel Jesus</th>\n",
       "      <td>197.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>41.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>189.5</td>\n",
       "      <td>116.5</td>\n",
       "      <td>130.0</td>\n",
       "      <td>107.5</td>\n",
       "      <td>191.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>79.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L. Sané</th>\n",
       "      <td>191.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>112.0</td>\n",
       "      <td>134.5</td>\n",
       "      <td>189.5</td>\n",
       "      <td>116.5</td>\n",
       "      <td>174.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>120.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>189.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>180.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>189.5</td>\n",
       "      <td>167.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>103.0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N. Süle</th>\n",
       "      <td>173.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>112.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>165.5</td>\n",
       "      <td>58.0</td>\n",
       "      <td>108.5</td>\n",
       "      <td>54.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>114.0</td>\n",
       "      <td>196.5</td>\n",
       "      <td>199.5</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D. Sánchez</th>\n",
       "      <td>188.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>60.5</td>\n",
       "      <td>68.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>78.5</td>\n",
       "      <td>136.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>116.0</td>\n",
       "      <td>136.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D. Alli</th>\n",
       "      <td>170.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>74.5</td>\n",
       "      <td>68.0</td>\n",
       "      <td>165.5</td>\n",
       "      <td>188.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>90.5</td>\n",
       "      <td>88.5</td>\n",
       "      <td>42.5</td>\n",
       "      <td>148.5</td>\n",
       "      <td>126.0</td>\n",
       "      <td>182.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A. Martial</th>\n",
       "      <td>171.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>68.0</td>\n",
       "      <td>165.5</td>\n",
       "      <td>127.5</td>\n",
       "      <td>133.5</td>\n",
       "      <td>133.0</td>\n",
       "      <td>120.5</td>\n",
       "      <td>190.5</td>\n",
       "      <td>103.0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>182.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T. Werner</th>\n",
       "      <td>172.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>112.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>103.5</td>\n",
       "      <td>156.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T. Lemar</th>\n",
       "      <td>182.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>147.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>104.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>176.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S. Gnabry</th>\n",
       "      <td>150.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>112.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>57.5</td>\n",
       "      <td>191.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P. Kimpembe</th>\n",
       "      <td>193.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>147.5</td>\n",
       "      <td>150.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>78.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>148.5</td>\n",
       "      <td>126.0</td>\n",
       "      <td>125.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K. Coman</th>\n",
       "      <td>181.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>103.5</td>\n",
       "      <td>58.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>57.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44.5</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J. Tah</th>\n",
       "      <td>180.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>112.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>57.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>149.0</td>\n",
       "      <td>196.5</td>\n",
       "      <td>199.5</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R. Sterling</th>\n",
       "      <td>140.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>74.5</td>\n",
       "      <td>134.5</td>\n",
       "      <td>147.5</td>\n",
       "      <td>116.5</td>\n",
       "      <td>164.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bernardo Silva</th>\n",
       "      <td>186.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>134.5</td>\n",
       "      <td>180.0</td>\n",
       "      <td>116.5</td>\n",
       "      <td>169.5</td>\n",
       "      <td>145.5</td>\n",
       "      <td>176.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saúl</th>\n",
       "      <td>159.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>165.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>153.5</td>\n",
       "      <td>46.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S. Milinković-Savić</th>\n",
       "      <td>192.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>159.5</td>\n",
       "      <td>107.0</td>\n",
       "      <td>165.5</td>\n",
       "      <td>99.0</td>\n",
       "      <td>153.5</td>\n",
       "      <td>41.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>174.0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M. Škriniar</th>\n",
       "      <td>200.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>161.5</td>\n",
       "      <td>107.0</td>\n",
       "      <td>189.5</td>\n",
       "      <td>78.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>78.5</td>\n",
       "      <td>180.5</td>\n",
       "      <td>148.5</td>\n",
       "      <td>126.0</td>\n",
       "      <td>186.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        ID   Age  Nationality  Overall  Potential   Club  \\\n",
       "Name                                                                       \n",
       "K. Mbappé            199.0   1.0         89.5    167.0      200.0  150.0   \n",
       "O. Dembélé           198.0   2.5         89.5     23.0      189.5   44.5   \n",
       "Gabriel Jesus        197.0   2.5         41.5     23.0      189.5  116.5   \n",
       "L. Sané              191.0   9.5        112.0    134.5      189.5  116.5   \n",
       "Marco Asensio        189.0   9.5        180.0    107.0      189.5  167.0   \n",
       "N. Süle              173.0   9.5        112.0     68.0      165.5   58.0   \n",
       "D. Sánchez           188.0   9.5         60.5     68.0      127.0  188.0   \n",
       "D. Alli              170.0   9.5         74.5     68.0      165.5  188.0   \n",
       "A. Martial           171.0   9.5         89.5     68.0      165.5  127.5   \n",
       "T. Werner            172.0   9.5        112.0     23.0      103.5  156.5   \n",
       "T. Lemar             182.0   9.5         89.5     23.0      147.5   16.5   \n",
       "S. Gnabry            150.0   9.5        112.0     23.0      127.0   58.0   \n",
       "P. Kimpembe          193.0   9.5         89.5     23.0      147.5  150.0   \n",
       "K. Coman             181.0   9.5         89.5     23.0      103.5   58.0   \n",
       "J. Tah               180.0   9.5        112.0     23.0      127.0   23.5   \n",
       "R. Sterling          140.0  22.0         74.5    134.5      147.5  116.5   \n",
       "Bernardo Silva       186.0  22.0        151.0    134.5      180.0  116.5   \n",
       "Saúl                 159.0  22.0        180.0    107.0      165.5   16.5   \n",
       "S. Milinković-Savić  192.0  22.0        159.5    107.0      165.5   99.0   \n",
       "M. Škriniar          200.0  22.0        161.5    107.0      189.5   78.0   \n",
       "\n",
       "                     Value (M)  Wage (K)  Position  Joined  Height  Weight  \\\n",
       "Name                                                                         \n",
       "K. Mbappé                194.0      75.0     162.5   124.0    22.0    58.0   \n",
       "O. Dembélé               126.0     122.0     176.0    21.0    22.0    15.5   \n",
       "Gabriel Jesus            130.0     107.5     191.0    23.5    79.0    58.0   \n",
       "L. Sané                  174.0     153.0     120.5     8.0   103.0    76.0   \n",
       "Marco Asensio            162.0     168.0     176.0    98.5   103.0    89.5   \n",
       "N. Süle                  108.5      54.5      17.5   114.0   196.5   199.5   \n",
       "D. Sánchez                93.5      78.5     136.5    17.0   148.5   116.0   \n",
       "D. Alli                  133.5      90.5      88.5    42.5   148.5   126.0   \n",
       "A. Martial               133.5     133.0     120.5   190.5   103.0    89.5   \n",
       "T. Werner                 99.5      38.0     176.0   106.0    22.0    76.0   \n",
       "T. Lemar                 104.0      33.5     106.0   124.0    51.0     4.0   \n",
       "S. Gnabry                 99.5      57.5     191.0   114.0    79.0    76.0   \n",
       "P. Kimpembe               69.5      52.5      78.5    98.5   148.5   126.0   \n",
       "K. Coman                  93.5      57.5     106.0    27.5    22.0    44.5   \n",
       "J. Tah                    57.5      36.5      17.5   149.0   196.5   199.5   \n",
       "R. Sterling              164.0     153.0     176.0   144.0    51.0    27.5   \n",
       "Bernardo Silva           169.5     145.5     176.0   114.0    60.0     5.5   \n",
       "Saúl                     153.5      46.0     148.0    86.0   103.0   100.0   \n",
       "S. Milinković-Savić      153.5      41.0      38.0    33.5   174.0    89.5   \n",
       "M. Škriniar              148.5      52.5      78.5   180.5   148.5   126.0   \n",
       "\n",
       "                     Release Clause  \n",
       "Name                                 \n",
       "K. Mbappé                      41.0  \n",
       "O. Dembélé                    192.0  \n",
       "Gabriel Jesus                 180.0  \n",
       "L. Sané                        24.0  \n",
       "Marco Asensio                  20.0  \n",
       "N. Süle                       147.0  \n",
       "D. Sánchez                    136.5  \n",
       "D. Alli                       182.5  \n",
       "A. Martial                    182.5  \n",
       "T. Werner                     123.0  \n",
       "T. Lemar                      176.5  \n",
       "S. Gnabry                     117.0  \n",
       "P. Kimpembe                   125.5  \n",
       "K. Coman                      114.0  \n",
       "J. Tah                         96.0  \n",
       "R. Sterling                     9.0  \n",
       "Bernardo Silva                 14.5  \n",
       "Saúl                            8.0  \n",
       "S. Milinković-Savić           189.0  \n",
       "M. Škriniar                   186.5  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fifa.rank().head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section6\"></a>\n",
    "# <font color=\"#7F000E\" size=5> 6. Operaciones sobre elementos </font>\n",
    "<br>\n",
    "\n",
    "En este caso, se utilizará el _DataFrame_ ejemplo, parecido al utilizado en la libreta anterior. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Nombre</th>\n",
       "      <th>Producto</th>\n",
       "      <th>Apellido</th>\n",
       "      <th>Compra</th>\n",
       "      <th>Venta</th>\n",
       "      <th>Localidad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Álvaro</td>\n",
       "      <td>Queso</td>\n",
       "      <td>Álvarez</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Benito</td>\n",
       "      <td>Vino</td>\n",
       "      <td>Benitez</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 2</th>\n",
       "      <td>Fernando</td>\n",
       "      <td>Jamón</td>\n",
       "      <td>Fernández</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Martín</td>\n",
       "      <td>Aceite</td>\n",
       "      <td>Martínez</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Hernán</td>\n",
       "      <td>Azafrán</td>\n",
       "      <td>Hernández</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nombre Producto   Apellido  Compra  Venta Localidad\n",
       "Tienda 1    Álvaro    Queso    Álvarez    15.0   22.5  Albacete\n",
       "Tienda 1    Benito     Vino    Benitez    10.0   14.5  Albacete\n",
       "Tienda 2  Fernando    Jamón  Fernández    35.0   50.0  Albacete\n",
       "Tienda 3    Martín   Aceite   Martínez    12.0   20.0  Albacete\n",
       "Tienda 3    Hernán  Azafrán  Hernández     3.0    5.0  Albacete"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ventas = [('Álvaro', 'Queso', 'Álvarez', 15.0, 22.5),\n",
    "         ('Benito',  'Vino', 'Benitez', 10.0, 14.5),\n",
    "         ('Fernando', 'Jamón', 'Fernández', 35, 50),\n",
    "         ('Martín',  'Aceite', 'Martínez', 12, 20),\n",
    "         ('Hernán', 'Azafrán', 'Hernández', 3, 5)]\n",
    "\n",
    "columnas = ['Nombre', 'Producto', 'Apellido', 'Compra', 'Venta']\n",
    "indice =['Tienda 1', 'Tienda 1', 'Tienda 2', 'Tienda 3','Tienda 3']\n",
    "\n",
    "df = pd.DataFrame(ventas, index=indice, columns=columnas)\n",
    "df['Localidad']='Albacete'\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section61\"></a> \n",
    "## <font color=\"#7F000E\">Operaciones básicas </font>\n",
    "\n",
    "Es posible operar sobre las columnas del mismo modo que se hace en el caso de los `Series`.\n",
    "\n",
    "El siguiente ejemplo añade una columna con el _IVA_ de los productos, calculada a partir de los valores en la columna _Precio_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Producto</th>\n",
       "      <th>Apellido</th>\n",
       "      <th>Compra</th>\n",
       "      <th>Venta</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>IVA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Álvaro</td>\n",
       "      <td>Queso</td>\n",
       "      <td>Álvarez</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>4.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Benito</td>\n",
       "      <td>Vino</td>\n",
       "      <td>Benitez</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>3.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 2</th>\n",
       "      <td>Fernando</td>\n",
       "      <td>Jamón</td>\n",
       "      <td>Fernández</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>10.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Martín</td>\n",
       "      <td>Aceite</td>\n",
       "      <td>Martínez</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>4.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Hernán</td>\n",
       "      <td>Azafrán</td>\n",
       "      <td>Hernández</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>1.050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nombre Producto   Apellido  Compra  Venta Localidad     IVA\n",
       "Tienda 1    Álvaro    Queso    Álvarez    15.0   22.5  Albacete   4.725\n",
       "Tienda 1    Benito     Vino    Benitez    10.0   14.5  Albacete   3.045\n",
       "Tienda 2  Fernando    Jamón  Fernández    35.0   50.0  Albacete  10.500\n",
       "Tienda 3    Martín   Aceite   Martínez    12.0   20.0  Albacete   4.200\n",
       "Tienda 3    Hernán  Azafrán  Hernández     3.0    5.0  Albacete   1.050"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copia = df.copy()                             # Copia el original (por claridad en los ejemplos))\n",
    "df_copia['IVA']= df_copia['Venta']*0.21          # Calcula y añade el iva\n",
    "df_copia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el siguiente ejemplo, se crea una columna mediante una suma de otras dos (se suman dos `Series`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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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>Nombre</th>\n",
       "      <th>Producto</th>\n",
       "      <th>Apellido</th>\n",
       "      <th>Compra</th>\n",
       "      <th>Venta</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>IVA</th>\n",
       "      <th>P.V.P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Álvaro</td>\n",
       "      <td>Queso</td>\n",
       "      <td>Álvarez</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>4.725</td>\n",
       "      <td>27.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Benito</td>\n",
       "      <td>Vino</td>\n",
       "      <td>Benitez</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>3.045</td>\n",
       "      <td>17.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 2</th>\n",
       "      <td>Fernando</td>\n",
       "      <td>Jamón</td>\n",
       "      <td>Fernández</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>10.500</td>\n",
       "      <td>60.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Martín</td>\n",
       "      <td>Aceite</td>\n",
       "      <td>Martínez</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>4.200</td>\n",
       "      <td>24.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Hernán</td>\n",
       "      <td>Azafrán</td>\n",
       "      <td>Hernández</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>1.050</td>\n",
       "      <td>6.050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nombre Producto   Apellido  Compra  Venta Localidad     IVA  \\\n",
       "Tienda 1    Álvaro    Queso    Álvarez    15.0   22.5  Albacete   4.725   \n",
       "Tienda 1    Benito     Vino    Benitez    10.0   14.5  Albacete   3.045   \n",
       "Tienda 2  Fernando    Jamón  Fernández    35.0   50.0  Albacete  10.500   \n",
       "Tienda 3    Martín   Aceite   Martínez    12.0   20.0  Albacete   4.200   \n",
       "Tienda 3    Hernán  Azafrán  Hernández     3.0    5.0  Albacete   1.050   \n",
       "\n",
       "           P.V.P  \n",
       "Tienda 1  27.225  \n",
       "Tienda 1  17.545  \n",
       "Tienda 2  60.500  \n",
       "Tienda 3  24.200  \n",
       "Tienda 3   6.050  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copia['P.V.P'] = df_copia['Venta']+df_copia['IVA']\n",
    "df_copia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También se pueden modificar las propias columnas, y utilizar otro tipo de operaciones, como por ejemplo, para Strings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Nombre</th>\n",
       "      <th>Producto</th>\n",
       "      <th>Compra</th>\n",
       "      <th>Venta</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>IVA</th>\n",
       "      <th>P.V.P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Álvaro Álvarez</td>\n",
       "      <td>Queso</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>4.725</td>\n",
       "      <td>27.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Benito Benitez</td>\n",
       "      <td>Vino</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>3.045</td>\n",
       "      <td>17.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 2</th>\n",
       "      <td>Fernando Fernández</td>\n",
       "      <td>Jamón</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>10.500</td>\n",
       "      <td>60.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Martín Martínez</td>\n",
       "      <td>Aceite</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>4.200</td>\n",
       "      <td>24.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Hernán Hernández</td>\n",
       "      <td>Azafrán</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>1.050</td>\n",
       "      <td>6.050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Nombre Producto  Compra  Venta Localidad     IVA   P.V.P\n",
       "Tienda 1      Álvaro Álvarez    Queso    15.0   22.5  Albacete   4.725  27.225\n",
       "Tienda 1      Benito Benitez     Vino    10.0   14.5  Albacete   3.045  17.545\n",
       "Tienda 2  Fernando Fernández    Jamón    35.0   50.0  Albacete  10.500  60.500\n",
       "Tienda 3     Martín Martínez   Aceite    12.0   20.0  Albacete   4.200  24.200\n",
       "Tienda 3    Hernán Hernández  Azafrán     3.0    5.0  Albacete   1.050   6.050"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copia['Nombre'] =  df_copia['Nombre']+\" \"+df_copia['Apellido'] # Junta nombre y apellido en 'Nombre'\n",
    "del df_copia['Apellido']                                          # Borra la columna \"apellido\"\n",
    "df_copia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "<i class=\"fa fa-exclamation-circle\" aria-hidden=\"true\"></i>\n",
    "__Importante__: Si es posible, se ha de operar así, ya que es el modo más eficiente. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section62\"></a> \n",
    "## <font color=\"#7F000E\">Operaciones de transformación sobre elementos</font>\n",
    "\n",
    "\n",
    "### <font color=\"#7F000E\" face=\"monospace\">Series.map()</font>\n",
    "\n",
    "La función `Series.map()` se puede aplicar sobre columnas individuales, a cada uno de los elementos, del mismo modo que se aplicaba sobre  _Series_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Producto</th>\n",
       "      <th>Apellido</th>\n",
       "      <th>Compra</th>\n",
       "      <th>Venta</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>IVA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Álvaro</td>\n",
       "      <td>Queso</td>\n",
       "      <td>Álvarez</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>4.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Benito</td>\n",
       "      <td>Vino</td>\n",
       "      <td>Benitez</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>3.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 2</th>\n",
       "      <td>Fernando</td>\n",
       "      <td>Jamón</td>\n",
       "      <td>Fernández</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>10.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Martín</td>\n",
       "      <td>Aceite</td>\n",
       "      <td>Martínez</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>4.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Hernán</td>\n",
       "      <td>Azafrán</td>\n",
       "      <td>Hernández</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>1.050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nombre Producto   Apellido  Compra  Venta Localidad     IVA\n",
       "Tienda 1    Álvaro    Queso    Álvarez    15.0   22.5  Albacete   4.725\n",
       "Tienda 1    Benito     Vino    Benitez    10.0   14.5  Albacete   3.045\n",
       "Tienda 2  Fernando    Jamón  Fernández    35.0   50.0  Albacete  10.500\n",
       "Tienda 3    Martín   Aceite   Martínez    12.0   20.0  Albacete   4.200\n",
       "Tienda 3    Hernán  Azafrán  Hernández     3.0    5.0  Albacete   1.050"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copia = df.copy()            # Copia el original (por claridad en los ejemplos))\n",
    "\n",
    "def IVA(x):                     # Crea la función que aplica el IVA\n",
    "    return x*0.21\n",
    "\n",
    "\n",
    "df_copia['IVA'] = df_copia['Venta'].map(IVA)\n",
    "#df_copia['IVA'] = df_copia['Venta'].map(lambda pr: pr*0.21) \n",
    "df_copia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#7F000E\" face=\"monospace\">Series.apply()</font>\n",
    "\n",
    "Del mismo modo, puede  la función `Series.apply()` permite generar nuevas columnas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Producto</th>\n",
       "      <th>Apellido</th>\n",
       "      <th>Compra</th>\n",
       "      <th>Venta</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>IVA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Álvaro</td>\n",
       "      <td>Queso</td>\n",
       "      <td>Álvarez</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>4.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Benito</td>\n",
       "      <td>Vino</td>\n",
       "      <td>Benitez</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>3.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 2</th>\n",
       "      <td>Fernando</td>\n",
       "      <td>Jamón</td>\n",
       "      <td>Fernández</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>10.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Martín</td>\n",
       "      <td>Aceite</td>\n",
       "      <td>Martínez</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>4.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Hernán</td>\n",
       "      <td>Azafrán</td>\n",
       "      <td>Hernández</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>1.050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nombre Producto   Apellido  Compra  Venta Localidad     IVA\n",
       "Tienda 1    Álvaro    Queso    Álvarez    15.0   22.5  Albacete   4.725\n",
       "Tienda 1    Benito     Vino    Benitez    10.0   14.5  Albacete   3.045\n",
       "Tienda 2  Fernando    Jamón  Fernández    35.0   50.0  Albacete  10.500\n",
       "Tienda 3    Martín   Aceite   Martínez    12.0   20.0  Albacete   4.200\n",
       "Tienda 3    Hernán  Azafrán  Hernández     3.0    5.0  Albacete   1.050"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copia = df.copy()            # Copia el original (por claridad en los ejemplos))\n",
    "\n",
    "\n",
    "df_copia['IVA'] = df_copia['Venta'].apply(IVA) \n",
    "#df_copia['IVA'] = df_copia['Venta'].apply(lambda pr: pr*0.21)  # Hace lo mismo, pero utilizando una función lambda\n",
    "\n",
    "df_copia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#7F000E\" face=\"monospace\">DataFrame.apply()</font>\n",
    "\n",
    "La función `DataFrame.apply()`, acepta como  parámetro `axis`, que puede tomar los valores `index/0` o `columns/1`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "<i class=\"fa fa-exclamation-circle\" aria-hidden=\"true\"></i>\n",
    "__Importante__: El parámetro `axis=0` o `axis='index'` indica que los datos que se pasan a la función _usan el índice_ del `DataFrame`, es decir, se le pasa un `Series` por cada columna.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Compra</th>\n",
       "      <th>Venta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>14.25</td>\n",
       "      <td>21.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>9.50</td>\n",
       "      <td>13.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 2</th>\n",
       "      <td>33.25</td>\n",
       "      <td>47.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>11.40</td>\n",
       "      <td>19.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Compra   Venta\n",
       "Tienda 1   14.25  21.375\n",
       "Tienda 1    9.50  13.775\n",
       "Tienda 2   33.25  47.500\n",
       "Tienda 3   11.40  19.000\n",
       "Tienda 3    2.85   4.750"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def descuento(x):                     # Crea la función que aplica el descuento\n",
    "    return x*0.95\n",
    "\n",
    "df_copia[['Compra','Venta']].apply(descuento, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "<i class=\"fa fa-exclamation-circle\" aria-hidden=\"true\"></i>\n",
    "__Importante__: El parámetro `axis=1` o `axis='columns'` indica que los datos que se pasan a la función usan _las columnas_ del `DataFrame` como índice, es decir, se le pasa un `Series` por cada fila.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esto permite operar con varias columnas y obtener nuevas columnas como resultado. Este ejemplo construye un String a partir de los datos de tres columnas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Producto  Compra  Venta\n",
      "Tienda 1    Queso    15.0   22.5\n",
      "Tienda 1     Vino    10.0   14.5\n",
      "Tienda 2    Jamón    35.0   50.0\n",
      "Tienda 3   Aceite    12.0   20.0\n",
      "Tienda 3  Azafrán     3.0    5.0\n",
      "\n",
      "Tienda 1      ¡Queso a 7.5 euros!\n",
      "Tienda 1       ¡Vino a 4.5 euros!\n",
      "Tienda 2     ¡Jamón a 15.0 euros!\n",
      "Tienda 3     ¡Aceite a 8.0 euros!\n",
      "Tienda 3    ¡Azafrán a 2.0 euros!\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "df_precios = df[['Producto','Compra','Venta']]\n",
    "print(df_precios)\n",
    "print()\n",
    "\n",
    "def anuncio(entrada):\n",
    "    return '¡'+entrada['Producto']+' a '+str(entrada['Venta']-entrada['Compra']) + ' euros!'\n",
    "\n",
    "print(df_precios.apply(anuncio, axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### <font color=\"#7F000E\" face=\"monospace\"> applymap() </font>\n",
    "\n",
    "La función `applymap` aplica la función de transformación sobre todos los elementos del _DataFrame_. En este ejemplo concreto, copiamos todas las columnas numéricas (tipo `np.number`) en el `DataFrame` y se aplica una función que convierte los valores a String con tres decimales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Compra</th>\n",
       "      <th>Venta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>15.000</td>\n",
       "      <td>22.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>10.000</td>\n",
       "      <td>14.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 2</th>\n",
       "      <td>35.000</td>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>12.000</td>\n",
       "      <td>20.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Compra   Venta\n",
       "Tienda 1  15.000  22.500\n",
       "Tienda 1  10.000  14.500\n",
       "Tienda 2  35.000  50.000\n",
       "Tienda 3  12.000  20.000\n",
       "Tienda 3   3.000   5.000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "df_copia = df.select_dtypes(include=np.number)\n",
    "df_copia = df_copia.applymap(lambda x: '{:.3f}'.format(x))\n",
    "df_copia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#7F000E\" face=\"monospace\"> transform() </font>\n",
    "\n",
    "La función `transform()` permite llevar a cabo una transformación de los elementos de un `DataFrame`. Permite especificar qué columnas se transforman, y también aplicar varias transformaciones con una sola llamada. Como resultado, devuelve una estructura que contiene todas las transformaciones llevadas a cabo  ([documentación](http://pandas.pydata.org/pandas-docs/version/0.23/basics.html#transform-api))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Nombre</th>\n",
       "      <th>Producto</th>\n",
       "      <th>Apellido</th>\n",
       "      <th>Compra</th>\n",
       "      <th>Venta</th>\n",
       "      <th>Localidad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Álvaro</td>\n",
       "      <td>Queso</td>\n",
       "      <td>Álvarez</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Benito</td>\n",
       "      <td>Vino</td>\n",
       "      <td>Benitez</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 2</th>\n",
       "      <td>Fernando</td>\n",
       "      <td>Jamón</td>\n",
       "      <td>Fernández</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Martín</td>\n",
       "      <td>Aceite</td>\n",
       "      <td>Martínez</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Hernán</td>\n",
       "      <td>Azafrán</td>\n",
       "      <td>Hernández</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nombre Producto   Apellido  Compra  Venta Localidad\n",
       "Tienda 1    Álvaro    Queso    Álvarez    15.0   22.5  Albacete\n",
       "Tienda 1    Benito     Vino    Benitez    10.0   14.5  Albacete\n",
       "Tienda 2  Fernando    Jamón  Fernández    35.0   50.0  Albacete\n",
       "Tienda 3    Martín   Aceite   Martínez    12.0   20.0  Albacete\n",
       "Tienda 3    Hernán  Azafrán  Hernández     3.0    5.0  Albacete"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Venta</th>\n",
       "      <th>Localidad</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "      <th>upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>27.225</td>\n",
       "      <td>27.2 euros</td>\n",
       "      <td>ALBACETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>17.545</td>\n",
       "      <td>17.5 euros</td>\n",
       "      <td>ALBACETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 2</th>\n",
       "      <td>60.500</td>\n",
       "      <td>60.5 euros</td>\n",
       "      <td>ALBACETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>24.200</td>\n",
       "      <td>24.2 euros</td>\n",
       "      <td>ALBACETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>6.050</td>\n",
       "      <td>6.0 euros</td>\n",
       "      <td>ALBACETE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Venta             Localidad\n",
       "         <lambda>    <lambda>     upper\n",
       "Tienda 1   27.225  27.2 euros  ALBACETE\n",
       "Tienda 1   17.545  17.5 euros  ALBACETE\n",
       "Tienda 2   60.500  60.5 euros  ALBACETE\n",
       "Tienda 3   24.200  24.2 euros  ALBACETE\n",
       "Tienda 3    6.050   6.0 euros  ALBACETE"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(df)\n",
    "df_copia = df.copy()\n",
    "df_copia.transform({'Venta': [lambda p: p*1.21, lambda p: \"{:.1f} euros\".format(p*1.21)], 'Localidad':str.upper})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función `assign`, que permite ___replicar___ un `DataFrame` con nuevas columnas, también permite la operación con funciones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Nombre</th>\n",
       "      <th>Producto</th>\n",
       "      <th>Apellido</th>\n",
       "      <th>Compra</th>\n",
       "      <th>Venta</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>PVP</th>\n",
       "      <th>Margen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Álvaro</td>\n",
       "      <td>Queso</td>\n",
       "      <td>Álvarez</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>4.725</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 1</th>\n",
       "      <td>Benito</td>\n",
       "      <td>Vino</td>\n",
       "      <td>Benitez</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>3.045</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 2</th>\n",
       "      <td>Fernando</td>\n",
       "      <td>Jamón</td>\n",
       "      <td>Fernández</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>10.500</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Martín</td>\n",
       "      <td>Aceite</td>\n",
       "      <td>Martínez</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>4.200</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tienda 3</th>\n",
       "      <td>Hernán</td>\n",
       "      <td>Azafrán</td>\n",
       "      <td>Hernández</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>1.050</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nombre Producto   Apellido  Compra  Venta Localidad     PVP  \\\n",
       "Tienda 1    Álvaro    Queso    Álvarez    15.0   22.5  Albacete   4.725   \n",
       "Tienda 1    Benito     Vino    Benitez    10.0   14.5  Albacete   3.045   \n",
       "Tienda 2  Fernando    Jamón  Fernández    35.0   50.0  Albacete  10.500   \n",
       "Tienda 3    Martín   Aceite   Martínez    12.0   20.0  Albacete   4.200   \n",
       "Tienda 3    Hernán  Azafrán  Hernández     3.0    5.0  Albacete   1.050   \n",
       "\n",
       "          Margen  \n",
       "Tienda 1     7.5  \n",
       "Tienda 1     4.5  \n",
       "Tienda 2    15.0  \n",
       "Tienda 3     8.0  \n",
       "Tienda 3     2.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copia.assign(PVP=lambda p: p['Venta']*0.21, Margen=lambda p: p['Venta']-p['Compra'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función asociada con `aplymap()` se aplica a todos los elementos del DataFrame dado y, por lo tanto, el método applymap() se define solo para **DataFrames**. De manera similar, la función asociada con el método `apply()` se puede aplicar a todos los elementos de **DataFrame** o **Series**, y por lo tanto, el método apply() se define para los objetos Series y DataFrame. El método `map()` solo se puede definir para objetos **Series** en Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section7\"></a>\n",
    "# <font color=\"#7F000E\" size=5> 7. Agregación </font>\n",
    "<br>\n",
    "\n",
    "<a id=\"section71\"></a> \n",
    "## <font color=\"#7F000E\">Estadísticos descriptivos </font>\n",
    "\n",
    "Cuando las funciones para la obtención de medidas resumen datos estadísticos se aplican sobre un objeto `DataFrame`, es necesario especificar sobre qué eje se hacen mediante el parámetro `axis` que  puede tomar los valores `index/0` o `columns/1`. Un resumen de las funciones disponbiles puede encontrarse en la ([documentación](http://pandas.pydata.org/pandas-docs/version/0.23/basics.html#descriptive-statistics)).\n",
    "\n",
    "El siguiente código hace la suma y media de las filas, es decir, calcula para cada columna. Puede especificarse que solamente lo haga con las numéricas, aunque este es el comportamiento por defecto en algunas funciones cuando no se pueden sumar las filas o columnas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compra     75.0\n",
      "Venta     112.0\n",
      "dtype: float64\n",
      "\n",
      "Compra    15.0\n",
      "Venta     22.4\n",
      "dtype: float64\n",
      "\n",
      "Compra    35.0\n",
      "Venta     50.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Obtiene estadísticos por columnas\n",
    "print(df_copia.sum(axis=0, numeric_only=True))\n",
    "print()\n",
    "print(df_copia.mean(axis=0,numeric_only=True))\n",
    "print()\n",
    "print(df_copia.max(axis=0, numeric_only=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section72\"></a> \n",
    "\n",
    "## <font color=\"#7F000E\" > Agregación de datos: <font face=\"monospace\">agg() y apply()</font></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#7F000E\" face=\"monospace\"> agg()  </font>\n",
    "\n",
    "Las funciones `aggregate()` o `agg()` (son la misma) permiten aplicar funciones de agregación a filas o columnas (también admite el parámetro `axis`. A diferencia de `apply()`, descrito anteriormente, permite aplicar varias funciones en una operación. Éstas pueden ser referidas por un nombre (String), identificador, o incluso ser funciones `lambda` ([documentación](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.aggregate.html)).\n",
    "\n",
    "Por ejemplo, el código siguiente aplica las funciones suma, media y rango de dos columnas (en realidad, se crea un `DataFrame` con dos columnas y se aplica la agregación). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "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>Compra</th>\n",
       "      <th>Venta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>75.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.0</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "      <td>32.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Compra  Venta\n",
       "sum         75.0  112.0\n",
       "mean        15.0   22.4\n",
       "<lambda>    32.0   45.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_precios[['Compra','Venta']].agg(['sum', np.mean, lambda col: col.max()-col.min()], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este código aplica dos funciones para cada fila. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Review: https://github.com/pandas-dev/pandas/issues/7186\n",
    "# This has caused me huge frustration and I believe this should be updated to allow passing the same function and then providing the desired name of the output column. \n",
    "# I'm working with a custom aggregation function that takes an additional argument by using functool's partial or simply using multiple lambda functions. \n",
    "# I was hoping to avoid 6 separate named functions, but with the current method I have to do that, even though each function is only slightly different than the other. \n",
    "# The \"workarounds\" here don't save any time compared to just having separately defined functions that are all very similar.\n",
    "\n",
    "#df_precios[['Compra','Venta']].agg(lambda p:p['Venta']- p['Compra'],lambda p:p['Venta']/p['Compra']-1,axis=1)\n",
    "#df_precios.agg({Compra:lambda p:p['Venta']- p['Compra'],Venta:lambda p:p['Venta']/p['Compra']-1},axis=1)                                    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`agg()` permite que se especifique, mediante un diccionario, qué función o funciones se aplican a cada columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Compra</th>\n",
       "      <th>Venta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>amin</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>NaN</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Compra  Venta\n",
       "amin     3.0    NaN\n",
       "mean     NaN   22.4\n",
       "sum      NaN  112.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_precios.agg({'Compra':np.min, 'Venta':[np.mean, np.sum]}, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#7F000E\" face=\"monospace\"> apply()  </font>\n",
    "\n",
    "La función `DataFrame.apply()`, puede utilizarse también para hacer agregaciones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Compra  Venta\n",
      "Tienda 1    15.0   22.5\n",
      "Tienda 1    10.0   14.5\n",
      "Tienda 2    35.0   50.0\n",
      "Tienda 3    12.0   20.0\n",
      "Tienda 3     3.0    5.0\n",
      "\n",
      "   Compra  Venta\n",
      "0     3.0    5.0\n",
      "1    35.0   50.0\n"
     ]
    }
   ],
   "source": [
    "df_precios = df[['Compra','Venta']]\n",
    "print(df_precios)\n",
    "print()\n",
    "\n",
    "def min_max(valores):\n",
    "    return (np.min(valores),np.max(valores))\n",
    "\n",
    "# Valor mínimo y máximo por cada producto\n",
    "# print(df_precios.apply(min_max, axis=0))\n",
    "print(df_precios.apply(min_max))                # Equivalente\n",
    "#print(df_precios.apply(np.mean))                # Calcula la media"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section73\"></a> \n",
    "\n",
    "## <font color=\"#7F000E\" > Agregación de datos mediante ventana: <font face=\"monospace\">rolling()</font> y <font face=\"monospace\">expanding()</font></font>\n",
    "<br>\n",
    "\n",
    "La función `rolling` permite hacer agregaciones mediante una ventana deslizante. El parámetro `window` determina el tamaño de  la ventana, y `min_periods` el número mínimo de observaciones necesario para obtener un valor.  \n",
    "\n",
    "La siguiente celda lee un `DataFrame` con el nivel de `CO` en la ciudad de madrid en el año 2017. Como resultado de la aplicación del operador, en cada entrada, registra la media de las cuatro últimas. Como `min_periods=2`, en la segunda y tercera fila calcula la media con los datos disponibles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station   date               \n",
       "28079004  2017-01-01 01:00:00    0.6\n",
       "          2017-01-01 02:00:00    0.6\n",
       "          2017-01-01 03:00:00    0.5\n",
       "          2017-01-01 04:00:00    0.5\n",
       "          2017-01-01 05:00:00    0.4\n",
       "          2017-01-01 06:00:00    0.4\n",
       "          2017-01-01 07:00:00    0.4\n",
       "          2017-01-01 08:00:00    0.6\n",
       "          2017-01-01 09:00:00    0.4\n",
       "          2017-01-01 10:00:00    0.4\n",
       "          2017-01-01 11:00:00    0.5\n",
       "          2017-01-01 12:00:00    0.6\n",
       "          2017-01-01 13:00:00    0.5\n",
       "          2017-01-01 14:00:00    0.4\n",
       "          2017-01-01 15:00:00    0.3\n",
       "          2017-01-01 16:00:00    0.2\n",
       "          2017-01-01 17:00:00    0.2\n",
       "          2017-01-01 18:00:00    0.3\n",
       "          2017-01-01 19:00:00    0.3\n",
       "          2017-01-01 20:00:00    0.5\n",
       "Name: CO, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "station   date               \n",
       "28079004  2017-01-01 01:00:00         NaN\n",
       "          2017-01-01 02:00:00    0.600000\n",
       "          2017-01-01 03:00:00    0.566667\n",
       "          2017-01-01 04:00:00    0.550000\n",
       "          2017-01-01 05:00:00    0.500000\n",
       "          2017-01-01 06:00:00    0.450000\n",
       "          2017-01-01 07:00:00    0.425000\n",
       "          2017-01-01 08:00:00    0.450000\n",
       "          2017-01-01 09:00:00    0.450000\n",
       "          2017-01-01 10:00:00    0.450000\n",
       "          2017-01-01 11:00:00    0.475000\n",
       "          2017-01-01 12:00:00    0.475000\n",
       "          2017-01-01 13:00:00    0.500000\n",
       "          2017-01-01 14:00:00    0.500000\n",
       "          2017-01-01 15:00:00    0.450000\n",
       "          2017-01-01 16:00:00    0.350000\n",
       "          2017-01-01 17:00:00    0.275000\n",
       "          2017-01-01 18:00:00    0.250000\n",
       "          2017-01-01 19:00:00    0.250000\n",
       "          2017-01-01 20:00:00    0.325000\n",
       "Name: CO, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2017 = pd.read_csv('data/madrid_2017.csv',  parse_dates=['date'], index_col=['station','date']).sort_index()\n",
    "\n",
    "display(df_2017['CO'].head(20))\n",
    "\n",
    "# Muestra la media de las cuatro ultimas horas\n",
    "df_2017['CO'].head(20).rolling(window=4, min_periods=2).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función `expanding` considera todas las entradas de las filas anteriores. Por defecto toma un parámetro, que es el equivalente a `min_periods` en `rolling`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station   date               \n",
       "28079004  2017-01-01 01:00:00         NaN\n",
       "          2017-01-01 02:00:00         NaN\n",
       "          2017-01-01 03:00:00         NaN\n",
       "          2017-01-01 04:00:00    0.550000\n",
       "          2017-01-01 05:00:00    0.520000\n",
       "          2017-01-01 06:00:00    0.500000\n",
       "          2017-01-01 07:00:00    0.485714\n",
       "          2017-01-01 08:00:00    0.500000\n",
       "          2017-01-01 09:00:00    0.488889\n",
       "          2017-01-01 10:00:00    0.480000\n",
       "          2017-01-01 11:00:00    0.481818\n",
       "          2017-01-01 12:00:00    0.491667\n",
       "          2017-01-01 13:00:00    0.492308\n",
       "          2017-01-01 14:00:00    0.485714\n",
       "          2017-01-01 15:00:00    0.473333\n",
       "          2017-01-01 16:00:00    0.456250\n",
       "          2017-01-01 17:00:00    0.441176\n",
       "          2017-01-01 18:00:00    0.433333\n",
       "          2017-01-01 19:00:00    0.426316\n",
       "          2017-01-01 20:00:00    0.430000\n",
       "Name: CO, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2017['CO'].head(20).expanding(4).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section8\"></a>\n",
    "# <font color=\"#7F000E\"> 8. Agrupamiento: <font face=\"monospace\"> groupby()</font></font>\n",
    "<br>\n",
    "\n",
    "La función `groupby()` permite agrupar los datos del `DataFrame` según valores de su índice o columnas. Devuelve una estructura del tipo `DataFrameGroupBy`, que implementa estructuras de datos necesarias para que las operaciones sobre grupos se apliquen de manera eficiente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K. Mbappé</th>\n",
       "      <td>231747</td>\n",
       "      <td>19</td>\n",
       "      <td>France</td>\n",
       "      <td>88</td>\n",
       "      <td>95</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>81.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>Jul 1, 2018</td>\n",
       "      <td>154.94</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€166.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O. Dembélé</th>\n",
       "      <td>231443</td>\n",
       "      <td>21</td>\n",
       "      <td>France</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>40.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Aug 28, 2017</td>\n",
       "      <td>154.94</td>\n",
       "      <td>67.131616</td>\n",
       "      <td>€90M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabriel Jesus</th>\n",
       "      <td>230666</td>\n",
       "      <td>21</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>41.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>Aug 3, 2016</td>\n",
       "      <td>175.26</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€84.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L. Sané</th>\n",
       "      <td>222492</td>\n",
       "      <td>22</td>\n",
       "      <td>Germany</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>61.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Aug 2, 2016</td>\n",
       "      <td>182.88</td>\n",
       "      <td>74.842680</td>\n",
       "      <td>€125.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>220834</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>54.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 1, 2015</td>\n",
       "      <td>182.88</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€121.5M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ID  Age Nationality  Overall  Potential  \\\n",
       "Name                                                         \n",
       "K. Mbappé      231747   19      France       88         95   \n",
       "O. Dembélé     231443   21      France       83         92   \n",
       "Gabriel Jesus  230666   21      Brazil       83         92   \n",
       "L. Sané        222492   22     Germany       86         92   \n",
       "Marco Asensio  220834   22       Spain       85         92   \n",
       "\n",
       "                              Club  Value (M)  Wage (K) Position  \\\n",
       "Name                                                               \n",
       "K. Mbappé      Paris Saint-Germain       81.0     100.0       RM   \n",
       "O. Dembélé            FC Barcelona       40.0     155.0       RW   \n",
       "Gabriel Jesus      Manchester City       41.0     130.0       ST   \n",
       "L. Sané            Manchester City       61.0     195.0       LW   \n",
       "Marco Asensio          Real Madrid       54.0     215.0       RW   \n",
       "\n",
       "                     Joined  Height     Weight Release Clause  \n",
       "Name                                                           \n",
       "K. Mbappé       Jul 1, 2018  154.94  73.028312        €166.1M  \n",
       "O. Dembélé     Aug 28, 2017  154.94  67.131616           €90M  \n",
       "Gabriel Jesus   Aug 3, 2016  175.26  73.028312         €84.1M  \n",
       "L. Sané         Aug 2, 2016  182.88  74.842680        €125.1M  \n",
       "Marco Asensio   Jul 1, 2015  182.88  76.203456        €121.5M  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fifa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La siguiente celda de código agrupa las entradas del conjunto de datos anterior en función del valor del campo `Nationality`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
     ]
    }
   ],
   "source": [
    "# grupos_df = df_fifa.groupby(df['Nationality']); # Las dos formas son equivalentes. La primera permite entender mejor el \n",
    "grupos_df = df_fifa.groupby('Nationality');       # funcionamiento de la función. La segunda es más cómoda. \n",
    "print(type(grupos_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es posible ___extraer___ un `DataFrame` con los elementos de cada grupo en `DataFrameGroupBy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>220834</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>54.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 1, 2015</td>\n",
       "      <td>182.88</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€121.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saúl</th>\n",
       "      <td>208421</td>\n",
       "      <td>23</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>50.5</td>\n",
       "      <td>77.0</td>\n",
       "      <td>RCM</td>\n",
       "      <td>Jul 1, 2013</td>\n",
       "      <td>182.88</td>\n",
       "      <td>77.110640</td>\n",
       "      <td>€107.3M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kepa</th>\n",
       "      <td>206585</td>\n",
       "      <td>23</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>91</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>28.5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Aug 8, 2018</td>\n",
       "      <td>185.42</td>\n",
       "      <td>84.821704</td>\n",
       "      <td>€58.4M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suso</th>\n",
       "      <td>202651</td>\n",
       "      <td>24</td>\n",
       "      <td>Spain</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>Milan</td>\n",
       "      <td>33.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jan 17, 2015</td>\n",
       "      <td>175.26</td>\n",
       "      <td>69.853168</td>\n",
       "      <td>€58.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isco</th>\n",
       "      <td>197781</td>\n",
       "      <td>26</td>\n",
       "      <td>Spain</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>73.5</td>\n",
       "      <td>315.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Jul 3, 2013</td>\n",
       "      <td>175.26</td>\n",
       "      <td>78.925008</td>\n",
       "      <td>€156.2M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ID  Age Nationality  Overall  Potential             Club  \\\n",
       "Name                                                                          \n",
       "Marco Asensio  220834   22       Spain       85         92      Real Madrid   \n",
       "Saúl           208421   23       Spain       85         90  Atlético Madrid   \n",
       "Kepa           206585   23       Spain       83         91          Chelsea   \n",
       "Suso           202651   24       Spain       83         86            Milan   \n",
       "Isco           197781   26       Spain       88         91      Real Madrid   \n",
       "\n",
       "               Value (M)  Wage (K) Position        Joined  Height     Weight  \\\n",
       "Name                                                                           \n",
       "Marco Asensio       54.0     215.0       RW   Jul 1, 2015  182.88  76.203456   \n",
       "Saúl                50.5      77.0      RCM   Jul 1, 2013  182.88  77.110640   \n",
       "Kepa                28.5      84.0       GK   Aug 8, 2018  185.42  84.821704   \n",
       "Suso                33.0     115.0       RW  Jan 17, 2015  175.26  69.853168   \n",
       "Isco                73.5     315.0       LW   Jul 3, 2013  175.26  78.925008   \n",
       "\n",
       "              Release Clause  \n",
       "Name                          \n",
       "Marco Asensio        €121.5M  \n",
       "Saúl                 €107.3M  \n",
       "Kepa                  €58.4M  \n",
       "Suso                  €58.6M  \n",
       "Isco                 €156.2M  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grupos_df.get_group('Spain').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También posible iterar sobre la estructura `DataFrameGroupBy` y obtener el `DataFrame` correspondiente a cada grupo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imprime el primer grupo. \n",
      "\n",
      "Grupo:  Algeria\n"
     ]
    },
    {
     "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R. Mahrez</th>\n",
       "      <td>204485</td>\n",
       "      <td>27</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>40.5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 10, 2018</td>\n",
       "      <td>154.94</td>\n",
       "      <td>67.131616</td>\n",
       "      <td>€74.9M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y. Brahimi</th>\n",
       "      <td>184267</td>\n",
       "      <td>28</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>FC Porto</td>\n",
       "      <td>39.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>LM</td>\n",
       "      <td>Jul 22, 2014</td>\n",
       "      <td>175.26</td>\n",
       "      <td>66.224432</td>\n",
       "      <td>€78M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID  Age Nationality  Overall  Potential             Club  \\\n",
       "Name                                                                       \n",
       "R. Mahrez   204485   27     Algeria       85         85  Manchester City   \n",
       "Y. Brahimi  184267   28     Algeria       85         85         FC Porto   \n",
       "\n",
       "            Value (M)  Wage (K) Position        Joined  Height     Weight  \\\n",
       "Name                                                                        \n",
       "R. Mahrez        40.5     205.0       RW  Jul 10, 2018  154.94  67.131616   \n",
       "Y. Brahimi       39.0      28.0       LM  Jul 22, 2014  175.26  66.224432   \n",
       "\n",
       "           Release Clause  \n",
       "Name                       \n",
       "R. Mahrez          €74.9M  \n",
       "Y. Brahimi           €78M  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Imprime el primer grupo. \\n\")\n",
    "for grupo, df_grupo in df_fifa.groupby('Nationality'):\n",
    "    print(\"Grupo: \",grupo)\n",
    "    display(df_grupo.head())\n",
    "    break  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La estructura `GroupBy.indices`, contiene un diccionario con las posiciones de las filas que corresponden a cada uno de los grupos. Otra estructura, `GroupBy.groups`, devuelve los índices con las filas correspondientes a cada grupo. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Índices\n",
      "<class 'dict'>\n",
      "\n",
      "Claves\n",
      "dict_keys(['Algeria', 'Argentina', 'Armenia', 'Austria', 'Belgium', 'Bosnia Herzegovina', 'Brazil', 'Chile', 'Colombia', 'Costa Rica', 'Croatia', 'Denmark', 'Egypt', 'England', 'Finland', 'France', 'Gabon', 'Germany', 'Greece', 'Guinea', 'Italy', 'Korea Republic', 'Montenegro', 'Morocco', 'Netherlands', 'Poland', 'Portugal', 'Senegal', 'Serbia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Uruguay', 'Wales'])\n",
      "\n",
      "Posiciones\n",
      "[  4  17  25  38  64  71  76  78  81  84  88  93 102 103 111 116 120 123\n",
      " 126 131 134 137 152 161 172 174 176 182 191]\n"
     ]
    }
   ],
   "source": [
    "print(\"Índices\")\n",
    "print(type(grupos_df.indices))\n",
    "\n",
    "print(\"\\nClaves\")\n",
    "print(grupos_df.indices.keys())\n",
    "\n",
    "print(\"\\nPosiciones\")\n",
    "print(grupos_df.indices['Spain'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grupos\n",
      "<class 'pandas.io.formats.printing.PrettyDict'>\n",
      "\n",
      "Claves\n",
      "dict_keys(['Algeria', 'Argentina', 'Armenia', 'Austria', 'Belgium', 'Bosnia Herzegovina', 'Brazil', 'Chile', 'Colombia', 'Costa Rica', 'Croatia', 'Denmark', 'Egypt', 'England', 'Finland', 'France', 'Gabon', 'Germany', 'Greece', 'Guinea', 'Italy', 'Korea Republic', 'Montenegro', 'Morocco', 'Netherlands', 'Poland', 'Portugal', 'Senegal', 'Serbia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Uruguay', 'Wales'])\n",
      "\n",
      "Entradas\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Marco Asensio', 'Saúl', 'Kepa', 'Suso', 'Isco', 'Koke', 'Carvajal',\n",
       "       'Sergi Roberto', 'Manu Trigueros', 'Gerard Moreno', 'De Gea', 'Thiago',\n",
       "       'Rodrigo', 'Lucas Vázquez', 'Azpilicueta', 'Illarramendi',\n",
       "       'Nacho Fernández', 'Sergio Busquets', 'Jordi Alba', 'Parejo',\n",
       "       'Diego Costa', 'Sergio Asenjo', 'Iago Aspas', 'Piqué', 'José Callejón',\n",
       "       'Sergio Ramos', 'David Silva', 'Raúl Albiol', 'Iniesta'],\n",
       "      dtype='object', name='Name')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Grupos\")\n",
    "print(type(grupos_df.groups))\n",
    "\n",
    "print(\"\\nClaves\")\n",
    "print(grupos_df.indices.keys())\n",
    "\n",
    "print(\"\\nEntradas\")\n",
    "grupos_df.groups['Spain']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "    \n",
    "<i class=\"fa fa-info-circle\" aria-hidden=\"true\"></i> __Nota__: Esta nomenclatura es totalmente contraintuitiva.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La estructura `DataFrameGroupBy` implementa la muchas de las funciones que implementa un `DataFrame`, pero éstas se aplican de manera independiente a cada uno de los grupos. El resultado de la aplicación es un `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "      <th>Value (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K. Mbappé</th>\n",
       "      <td>231747</td>\n",
       "      <td>19</td>\n",
       "      <td>France</td>\n",
       "      <td>88</td>\n",
       "      <td>95</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>81.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>Jul 1, 2018</td>\n",
       "      <td>154.94</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€166.1M</td>\n",
       "      <td>1.011969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O. Dembélé</th>\n",
       "      <td>231443</td>\n",
       "      <td>21</td>\n",
       "      <td>France</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>40.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Aug 28, 2017</td>\n",
       "      <td>154.94</td>\n",
       "      <td>67.131616</td>\n",
       "      <td>€90M</td>\n",
       "      <td>0.499738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabriel Jesus</th>\n",
       "      <td>230666</td>\n",
       "      <td>21</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>41.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>Aug 3, 2016</td>\n",
       "      <td>175.26</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€84.1M</td>\n",
       "      <td>0.512231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L. Sané</th>\n",
       "      <td>222492</td>\n",
       "      <td>22</td>\n",
       "      <td>Germany</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>61.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Aug 2, 2016</td>\n",
       "      <td>182.88</td>\n",
       "      <td>74.842680</td>\n",
       "      <td>€125.1M</td>\n",
       "      <td>0.762100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>220834</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>54.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 1, 2015</td>\n",
       "      <td>182.88</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€121.5M</td>\n",
       "      <td>0.674646</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M. Mandžukić</th>\n",
       "      <td>181783</td>\n",
       "      <td>32</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>25.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>Jul 1, 2015</td>\n",
       "      <td>190.50</td>\n",
       "      <td>84.821704</td>\n",
       "      <td>€41.3M</td>\n",
       "      <td>0.312336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falcao</th>\n",
       "      <td>167397</td>\n",
       "      <td>32</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>AS Monaco</td>\n",
       "      <td>25.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>RS</td>\n",
       "      <td>Jul 1, 2013</td>\n",
       "      <td>154.94</td>\n",
       "      <td>72.121128</td>\n",
       "      <td>€47.5M</td>\n",
       "      <td>0.312336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S. Handanovič</th>\n",
       "      <td>162835</td>\n",
       "      <td>33</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>Inter</td>\n",
       "      <td>30.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Jul 1, 2012</td>\n",
       "      <td>193.04</td>\n",
       "      <td>92.079176</td>\n",
       "      <td>€51M</td>\n",
       "      <td>0.374803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A. Robben</th>\n",
       "      <td>9014</td>\n",
       "      <td>34</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>15.5</td>\n",
       "      <td>110.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>Aug 28, 2009</td>\n",
       "      <td>154.94</td>\n",
       "      <td>79.832192</td>\n",
       "      <td>€25.6M</td>\n",
       "      <td>0.193648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z. Ibrahimović</th>\n",
       "      <td>41236</td>\n",
       "      <td>36</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>LA Galaxy</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>RS</td>\n",
       "      <td>Mar 23, 2018</td>\n",
       "      <td>195.58</td>\n",
       "      <td>94.800728</td>\n",
       "      <td>€21M</td>\n",
       "      <td>0.174908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ID  Age  Nationality  Overall  Potential  \\\n",
       "Name                                                           \n",
       "K. Mbappé       231747   19       France       88         95   \n",
       "O. Dembélé      231443   21       France       83         92   \n",
       "Gabriel Jesus   230666   21       Brazil       83         92   \n",
       "L. Sané         222492   22      Germany       86         92   \n",
       "Marco Asensio   220834   22        Spain       85         92   \n",
       "...                ...  ...          ...      ...        ...   \n",
       "M. Mandžukić    181783   32      Croatia       84         84   \n",
       "Falcao          167397   32     Colombia       84         84   \n",
       "S. Handanovič   162835   33     Slovenia       88         88   \n",
       "A. Robben         9014   34  Netherlands       84         84   \n",
       "Z. Ibrahimović   41236   36       Sweden       85         85   \n",
       "\n",
       "                               Club  Value (M)  Wage (K) Position  \\\n",
       "Name                                                                \n",
       "K. Mbappé       Paris Saint-Germain       81.0     100.0       RM   \n",
       "O. Dembélé             FC Barcelona       40.0     155.0       RW   \n",
       "Gabriel Jesus       Manchester City       41.0     130.0       ST   \n",
       "L. Sané             Manchester City       61.0     195.0       LW   \n",
       "Marco Asensio           Real Madrid       54.0     215.0       RW   \n",
       "...                             ...        ...       ...      ...   \n",
       "M. Mandžukić               Juventus       25.0     160.0       ST   \n",
       "Falcao                    AS Monaco       25.0     115.0       RS   \n",
       "S. Handanovič                 Inter       30.0     110.0       GK   \n",
       "A. Robben         FC Bayern München       15.5     110.0       RM   \n",
       "Z. Ibrahimović            LA Galaxy       14.0      15.0       RS   \n",
       "\n",
       "                      Joined  Height     Weight Release Clause  Value (%)  \n",
       "Name                                                                       \n",
       "K. Mbappé        Jul 1, 2018  154.94  73.028312        €166.1M   1.011969  \n",
       "O. Dembélé      Aug 28, 2017  154.94  67.131616           €90M   0.499738  \n",
       "Gabriel Jesus    Aug 3, 2016  175.26  73.028312         €84.1M   0.512231  \n",
       "L. Sané          Aug 2, 2016  182.88  74.842680        €125.1M   0.762100  \n",
       "Marco Asensio    Jul 1, 2015  182.88  76.203456        €121.5M   0.674646  \n",
       "...                      ...     ...        ...            ...        ...  \n",
       "M. Mandžukić     Jul 1, 2015  190.50  84.821704         €41.3M   0.312336  \n",
       "Falcao           Jul 1, 2013  154.94  72.121128         €47.5M   0.312336  \n",
       "S. Handanovič    Jul 1, 2012  193.04  92.079176           €51M   0.374803  \n",
       "A. Robben       Aug 28, 2009  154.94  79.832192         €25.6M   0.193648  \n",
       "Z. Ibrahimović  Mar 23, 2018  195.58  94.800728           €21M   0.174908  \n",
       "\n",
       "[98 rows x 14 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grupos_df = df_fifa.groupby('Nationality')\n",
    "grupos_df..mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El acceso a columnas también se aplica de manera independiente a cada grupo, de manera que genera un objeto `SeriesGroupBy` (o `DataFrameGroupBy` si se accede a varias columnas), en el que los datos están agrupados con el mismo criterio que el `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.generic.SeriesGroupBy'>\n"
     ]
    }
   ],
   "source": [
    "grupos_club = df_fifa.groupby(df_fifa['Nationality'])['Club'] # Equivalente\n",
    "#grupos_club = df_fifa['Club'].groupby(df_fifa['Nationality']) # Equivalente\n",
    "#rupos_club = df_fifa.groupby('Nationality')['Club']           # Equivalente\n",
    "print(type(grupos_club))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algeria :  2 \n",
      "\n",
      "Name\n",
      "R. Mahrez     Manchester City\n",
      "Y. Brahimi           FC Porto\n",
      "Name: Club, dtype: object\n"
     ]
    }
   ],
   "source": [
    "for grupo, serie_grupo in grupos_club:\n",
    "    print(grupo,\": \",len(serie_grupo),\"\\n\")\n",
    "    print(serie_grupo.head())\n",
    "    break;                                                      # Procesa solamente la primera iteración"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pueden utilizarse varias columnas para hacer la agrupación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Algeria', 'FC Porto'), ('Algeria', 'Manchester City'), ('Argentina', 'Atalanta'), ('Argentina', 'FC Barcelona'), ('Argentina', 'Inter')]\n",
      "\n",
      "                     ID  Age Nationality  Overall  Potential          Club  \\\n",
      "Name                                                                         \n",
      "Sergi Roberto    199564   26       Spain       83         86  FC Barcelona   \n",
      "Sergio Busquets  189511   29       Spain       89         89  FC Barcelona   \n",
      "Jordi Alba       189332   29       Spain       87         87  FC Barcelona   \n",
      "Piqué            152729   31       Spain       87         87  FC Barcelona   \n",
      "\n",
      "                 Value (M)  Wage (K) Position       Joined  Height     Weight  \\\n",
      "Name                                                                            \n",
      "Sergi Roberto         26.5     170.0       RB  Jul 1, 2013  154.94  68.038800   \n",
      "Sergio Busquets       51.5     315.0      CDM  Sep 1, 2008  187.96  76.203456   \n",
      "Jordi Alba            38.0     250.0       LB  Jul 1, 2012  170.18  68.038800   \n",
      "Piqué                 34.0     240.0      RCB  Jul 1, 2008  193.04  84.821704   \n",
      "\n",
      "                Release Clause  \n",
      "Name                            \n",
      "Sergi Roberto           €56.3M  \n",
      "Sergio Busquets        €105.6M  \n",
      "Jordi Alba              €77.9M  \n",
      "Piqué                   €69.7M  \n"
     ]
    }
   ],
   "source": [
    "grupos_df = df_fifa.groupby(['Nationality', 'Club'])\n",
    "print(list(grupos_df.groups.keys())[:5])\n",
    "print()\n",
    "\n",
    "print(grupos_df.get_group(('Spain','FC Barcelona')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es posible agrupar los datos según el resultado de una función aplicada sobre el índice del `DataFrame`. La siguiente celda de código implementa una función que devuelve el continente en el que está cada país."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Europa'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continente = dict.fromkeys(['Algeria', 'Egypt','Gabon','Guinea', 'Morocco', 'Senegal'],'África')\n",
    "continente.update(dict.fromkeys(['Argentina', 'Brazil', 'Chile', 'Colombia', \n",
    "                                 'Costa Rica', 'Uruguay'],'América'))\n",
    "continente.update(dict.fromkeys(['Armenia', 'Korea Republic'],'Asia'))\n",
    "continente.update(dict.fromkeys(['Austria', 'Belgium', 'Bosnia Herzegovina', 'Croatia', \n",
    "                                 'Denmark', 'England', 'Finland', 'France', 'Germany', \n",
    "                                 'Greece', 'Italy', 'Montenegro', 'Netherlands', 'Poland', \n",
    "                                 'Portugal', 'Serbia', 'Slovakia', 'Slovenia', 'Spain', \n",
    "                                 'Sweden', 'Wales'],'Europa'))\n",
    "def fun_continente(pais):\n",
    "    return continente[pais]\n",
    "\n",
    "fun_continente('Spain')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La siguiente celda de código agrupa las entradas en función del resultado de la función aplicada a cada uno de los valores del índice. Luego muestra, para cada grupo, el valor total de los jugadores. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "América: Valor total:  2037.0\n",
      "Asia: Valor total:  62.5\n",
      "Europa: Valor total:  5476.2\n",
      "África: Valor total:  428.5\n"
     ]
    }
   ],
   "source": [
    "for grupo, data in df_fifa.set_index('Nationality').groupby(fun_continente):\n",
    "    print(grupo+\": Valor total: \",data['Value (M)'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso, se puede obtener el mismo resultado accediendo directamente con el diccionario. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "América: Valor total:  2037.0\n",
      "Asia: Valor total:  62.5\n",
      "Europa: Valor total:  5476.2\n",
      "África: Valor total:  428.5\n"
     ]
    }
   ],
   "source": [
    "for grupo, data in df_fifa.set_index('Nationality').groupby(continente):\n",
    "    print(grupo+\": Valor total: \",data['Value (M)'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La operación anterior se puede sintetizar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "América    2037.0\n",
       "Asia         62.5\n",
       "Europa     5476.2\n",
       "África      428.5\n",
       "Name: Value (M), dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fifa.set_index('Nationality').groupby(continente)['Value (M)'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indirectamente, es posible agrupar a partir de una función aplicada sobre las columnas. El siguiente código es equivalente al anterior, pero no se establece la columna _Nationality_ como índice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "América: Valor total:  2037.0\n",
      "Asia: Valor total:  62.5\n",
      "Europa: Valor total:  5476.2\n",
      "África: Valor total:  428.5\n"
     ]
    }
   ],
   "source": [
    "#for grupo, data in df_fifa.groupby(df_fifa['Nationality'].apply(fun_continente)):\n",
    "#for grupo, data in df_fifa.groupby(df_fifa['Nationality'].map(fun_continente)):\n",
    "for grupo, data in df_fifa.groupby(df_fifa['Nationality'].map(continente)):\n",
    "    print(grupo+\": Valor total: \",data['Value (M)'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta forma de trabajar  permite hacer la agrupación mediante una función aplicada sobre varias columnas. El siguiente código agrupa los jugadores en función de si han alcanzado su  máximo potencial o no."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K. Mbappé</th>\n",
       "      <td>231747</td>\n",
       "      <td>19</td>\n",
       "      <td>France</td>\n",
       "      <td>88</td>\n",
       "      <td>95</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>81.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>Jul 1, 2018</td>\n",
       "      <td>154.94</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€166.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O. Dembélé</th>\n",
       "      <td>231443</td>\n",
       "      <td>21</td>\n",
       "      <td>France</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>40.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Aug 28, 2017</td>\n",
       "      <td>154.94</td>\n",
       "      <td>67.131616</td>\n",
       "      <td>€90M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabriel Jesus</th>\n",
       "      <td>230666</td>\n",
       "      <td>21</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>41.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>Aug 3, 2016</td>\n",
       "      <td>175.26</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€84.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L. Sané</th>\n",
       "      <td>222492</td>\n",
       "      <td>22</td>\n",
       "      <td>Germany</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>61.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Aug 2, 2016</td>\n",
       "      <td>182.88</td>\n",
       "      <td>74.842680</td>\n",
       "      <td>€125.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>220834</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>54.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 1, 2015</td>\n",
       "      <td>182.88</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€121.5M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ID  Age Nationality  Overall  Potential  \\\n",
       "Name                                                         \n",
       "K. Mbappé      231747   19      France       88         95   \n",
       "O. Dembélé     231443   21      France       83         92   \n",
       "Gabriel Jesus  230666   21      Brazil       83         92   \n",
       "L. Sané        222492   22     Germany       86         92   \n",
       "Marco Asensio  220834   22       Spain       85         92   \n",
       "\n",
       "                              Club  Value (M)  Wage (K) Position  \\\n",
       "Name                                                               \n",
       "K. Mbappé      Paris Saint-Germain       81.0     100.0       RM   \n",
       "O. Dembélé            FC Barcelona       40.0     155.0       RW   \n",
       "Gabriel Jesus      Manchester City       41.0     130.0       ST   \n",
       "L. Sané            Manchester City       61.0     195.0       LW   \n",
       "Marco Asensio          Real Madrid       54.0     215.0       RW   \n",
       "\n",
       "                     Joined  Height     Weight Release Clause  \n",
       "Name                                                           \n",
       "K. Mbappé       Jul 1, 2018  154.94  73.028312        €166.1M  \n",
       "O. Dembélé     Aug 28, 2017  154.94  67.131616           €90M  \n",
       "Gabriel Jesus   Aug 3, 2016  175.26  73.028312         €84.1M  \n",
       "L. Sané         Aug 2, 2016  182.88  74.842680        €125.1M  \n",
       "Marco Asensio   Jul 1, 2015  182.88  76.203456        €121.5M  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def max_potential(player):\n",
    "    return player['Potential']-player['Overall']<1\n",
    "\n",
    "grupos_df = df_fifa.groupby(df_fifa.apply(max_potential, axis=1))\n",
    "# Muestra los que no han alcanzado su máximo potencial.\n",
    "grupos_df.get_group(False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section81\"></a> \n",
    "## <font color=\"#7F000E\">Agregación: <font face=\"monospace\">GroupBy.agg()</font></font>\n",
    "<br>\n",
    "\n",
    "Una de los usos más frecuentes de la agrupación es la agregación por grupos. La función `agg()` lleva a cabo la agrupación de manera independiente para cada grupo. El resultado de la misma es un `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>&lt;lambda_0&gt;</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nationality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>79.5</td>\n",
       "      <td>40.5</td>\n",
       "      <td>116.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>502.5</td>\n",
       "      <td>110.5</td>\n",
       "      <td>202.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>25.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>543.5</td>\n",
       "      <td>102.0</td>\n",
       "      <td>179.727273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Value (M)               Wage (K)\n",
       "                  sum <lambda_0>        mean\n",
       "Nationality                                 \n",
       "Algeria          79.5       40.5  116.500000\n",
       "Argentina       502.5      110.5  202.666667\n",
       "Armenia          25.5       25.5  145.000000\n",
       "Austria          38.0       38.0  110.000000\n",
       "Belgium         543.5      102.0  179.727273"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_pais = df_fifa.groupby('Nationality').agg({'Value (M)': [np.sum, lambda pop: np.max(pop)], \n",
    "                                                 'Wage (K)':'mean'})\n",
    "media_pais.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En las últimas versiones de Pandas, se permite nombrar las columnas resultantes de las agregaciones. Existe una restricción, y es la necesidad de que los nombres sean compatibles con los identificadores en Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>Total_V</th>\n",
       "      <th>Máximo_V</th>\n",
       "      <th>Medio_W</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nationality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>79.5</td>\n",
       "      <td>40.5</td>\n",
       "      <td>116.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>502.5</td>\n",
       "      <td>110.5</td>\n",
       "      <td>202.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>25.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>543.5</td>\n",
       "      <td>102.0</td>\n",
       "      <td>179.727273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total_V  Máximo_V     Medio_W\n",
       "Nationality                               \n",
       "Algeria         79.5      40.5  116.500000\n",
       "Argentina      502.5     110.5  202.666667\n",
       "Armenia         25.5      25.5  145.000000\n",
       "Austria         38.0      38.0  110.000000\n",
       "Belgium        543.5     102.0  179.727273"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_pais = df_fifa.groupby('Nationality').agg(Total_V=('Value (M)',np.sum), \n",
    "                                                Máximo_V=('Value (M)',np.max),\n",
    "                                                Medio_W=('Wage (K)', np.mean))\n",
    "\n",
    "media_pais.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No obstante, se pueden renombrar las columnas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "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>Valor total</th>\n",
       "      <th>Valor máximo</th>\n",
       "      <th>Ganancia media</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nationality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>79.5</td>\n",
       "      <td>40.5</td>\n",
       "      <td>116.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>502.5</td>\n",
       "      <td>110.5</td>\n",
       "      <td>202.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>25.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>543.5</td>\n",
       "      <td>102.0</td>\n",
       "      <td>179.727273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Valor total  Valor máximo  Ganancia media\n",
       "Nationality                                           \n",
       "Algeria             79.5          40.5      116.500000\n",
       "Argentina          502.5         110.5      202.666667\n",
       "Armenia             25.5          25.5      145.000000\n",
       "Austria             38.0          38.0      110.000000\n",
       "Belgium            543.5         102.0      179.727273"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Este método, simple, todavía funciona, pero está obsoleto (deprecated) y dejará de funcionar. \n",
    "# media_pais = df_fifa.groupby('Nationality').agg({'Value (M)':{'Valor total':np.sum,\n",
    "#                                                              'Valor máximo':np.max},\n",
    "#                                                 'Wage (K)':{'Ganancia media':np.mean}})\n",
    "\n",
    "media_pais = (df_fifa.groupby('Nationality').agg(\n",
    "    Total_V=('Value (M)',np.sum), \n",
    "    Máximo_V=('Value (M)',np.max),\n",
    "    Medio_W=('Wage (K)', np.mean)\n",
    ").rename(columns = {\n",
    "    'Total_V':'Valor total',\n",
    "    'Máximo_V':'Valor máximo',\n",
    "    'Medio_W':'Ganancia media'\n",
    "}))\n",
    "\n",
    "media_pais.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Existe otro modo de llevar a cabo la agregación. Consiste en acceder a la columna determinada, y llevar a cabo la agregación sobre ella."
   ]
  },
  {
   "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",
       "    }\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>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nationality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>40.5</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>110.5</td>\n",
       "      <td>28.5</td>\n",
       "      <td>55.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>25.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>25.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>102.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>49.409091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              amax  amin       mean\n",
       "Nationality                        \n",
       "Algeria       40.5  39.0  39.750000\n",
       "Argentina    110.5  28.5  55.833333\n",
       "Armenia       25.5  25.5  25.500000\n",
       "Austria       38.0  38.0  38.000000\n",
       "Belgium      102.0  22.0  49.409091"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_pais = df_fifa.groupby('Nationality')['Value (M)'].agg([np.max, np.min, 'mean'])\n",
    "media_pais.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "ename": "SpecificationError",
     "evalue": "nested renamer is not supported",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mSpecificationError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-71-4d28e0d1bfae>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Esta forma funciona, pero está también obsoleta (deprecated)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmedia_pais\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_fifa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Nationality'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Value (M)'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'Valor máximo'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Valor medio'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda38\\lib\\site-packages\\pandas\\core\\groupby\\generic.py\u001b[0m in \u001b[0;36maggregate\u001b[1;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    244\u001b[0m             \u001b[1;31m# but not the class list / tuple itself.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    245\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaybe_mangle_lambdas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 246\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_aggregate_multiple_funcs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    247\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mrelabeling\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    248\u001b[0m                 \u001b[0mret\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda38\\lib\\site-packages\\pandas\\core\\groupby\\generic.py\u001b[0m in \u001b[0;36m_aggregate_multiple_funcs\u001b[1;34m(self, arg)\u001b[0m\n\u001b[0;32m    290\u001b[0m             \u001b[1;31m# GH 15931\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    291\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_selected_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 292\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mSpecificationError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"nested renamer is not supported\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    293\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    294\u001b[0m             \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mSpecificationError\u001b[0m: nested renamer is not supported"
     ]
    }
   ],
   "source": [
    "# Esta forma funciona, pero está también obsoleta (deprecated)\n",
    "# media_pais = df_fifa.groupby('Nationality')['Value (M)'].agg({'Valor máximo':np.max, 'Valor medio':np.mean})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section82\"></a> \n",
    "## <font color=\"#7F000E\">Transformación: <font face=\"monospace\">GroupBy.transform()</font> </font>\n",
    "<br>\n",
    "\n",
    "`GroupBy.transform` produce una transformación para cada columna del grupo, y devuelve un `Series` o `DataFrame` con las mismas filas que el `DataFrame` original, y los nuevos valores. Como parámetro acepta una función que actúa sobre el `Series`. Por tanto, dicha función puede devolver o bien un escalar o bien otro `Series`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La siguiente celda devuelve un `DataFrame` en el que, el valor para cada jugador, corresponde con la media de la columna para el grupo al que pertenece."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Value (M)</th>\n",
       "      <th>Overall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K. Mbappé</th>\n",
       "      <td>43.454545</td>\n",
       "      <td>85.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O. Dembélé</th>\n",
       "      <td>43.454545</td>\n",
       "      <td>85.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabriel Jesus</th>\n",
       "      <td>35.483333</td>\n",
       "      <td>84.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L. Sané</th>\n",
       "      <td>39.071429</td>\n",
       "      <td>85.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>37.965517</td>\n",
       "      <td>85.310345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Value (M)    Overall\n",
       "Name                               \n",
       "K. Mbappé      43.454545  85.090909\n",
       "O. Dembélé     43.454545  85.090909\n",
       "Gabriel Jesus  35.483333  84.966667\n",
       "L. Sané        39.071429  85.142857\n",
       "Marco Asensio  37.965517  85.310345"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fifa.groupby('Nationality')['Value (M)', 'Overall'].transform(lambda s: s.mean()).head()\n",
    "#df_fifa.groupby('Nationality')['Value (M)', 'Overall'].transform('mean').head() # Equivalente"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En esta celda, para cada jugador, se representa el valor original menos la media para su grupo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Value (M)</th>\n",
       "      <th>Overall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K. Mbappé</th>\n",
       "      <td>37.545455</td>\n",
       "      <td>2.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O. Dembélé</th>\n",
       "      <td>-3.454545</td>\n",
       "      <td>-2.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabriel Jesus</th>\n",
       "      <td>5.516667</td>\n",
       "      <td>-1.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L. Sané</th>\n",
       "      <td>21.928571</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>16.034483</td>\n",
       "      <td>-0.310345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Value (M)   Overall\n",
       "Name                              \n",
       "K. Mbappé      37.545455  2.909091\n",
       "O. Dembélé     -3.454545 -2.090909\n",
       "Gabriel Jesus   5.516667 -1.966667\n",
       "L. Sané        21.928571  0.857143\n",
       "Marco Asensio  16.034483 -0.310345"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fifa.groupby('Nationality')['Value (M)', 'Overall'].transform(lambda s: s-s.mean()).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section83\"></a> \n",
    "## <font color=\"#7F000E\"><font face=\"monospace\">GroupBy.apply()</font> </font>\n",
    "<br>\n",
    "\n",
    "Permite aplicar una función a cada uno de los grupos. La función tiene que tomar como entrada un `DataFrame` y puede devolver un escalar, un `Series`, o un `DataFrame`. Dependiendo de la salida de la función, `GroupBy.apply` devuelve un tipo u otro de estructura.  `GroupBy.apply` permite llevar a cabo tanto agregaciones como transformaciones. Por ejemplo, si la función devuelve un escalar, `GroupBy.apply` devuelve una serie con el valor correspondiente a cada grupo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nationality\n",
       "Algeria       2\n",
       "Argentina     9\n",
       "Armenia       1\n",
       "Austria       1\n",
       "Belgium      11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#len(df_fifa)\n",
    "df_fifa.groupby('Nationality').apply(lambda data: len(data)).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si la salida es un `Series`, depende de si el índice de ésta corresponde al índice o las columnas. En este caso, por ejemplo,  devuelve la media para cada columna.  Por tanto, el código devuelve la media para cada columna en cada grupo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Overall</th>\n",
       "      <th>Potential</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nationality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>87.111111</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>86.818182</td>\n",
       "      <td>87.545455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Overall  Potential\n",
       "Nationality                      \n",
       "Algeria      85.000000  85.000000\n",
       "Argentina    87.111111  88.000000\n",
       "Armenia      83.000000  83.000000\n",
       "Austria      85.000000  87.000000\n",
       "Belgium      86.818182  87.545455"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_fifa.mean()\n",
    "df_fifa.groupby('Nationality')['Overall','Potential'].apply(lambda data: data.mean()).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si la salida de la función es un `Series` cuyo índice es el índice del `DataFrame`, `GroupBy.apply()` devuelve un `Series` del tamaño del original. Esta funcionalidad se suele utilizar para calcular valores que dependen del grupo concreto. Por ejemplo, la siguiente celda crea una columna con el porcentaje que supone el valor de un jugador en el valor total de los jugadores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "K. Mbappé        1.011969\n",
       "O. Dembélé       0.499738\n",
       "Gabriel Jesus    0.512231\n",
       "L. Sané          0.762100\n",
       "Marco Asensio    0.674646\n",
       "Name: Value (M), dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pct_val_player(data):\n",
    "    return 100*data['Value (M)']/data['Value (M)'].sum()\n",
    "    \n",
    "pct_val_player(df_fifa).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al utilizarla con `GroupBy.apply`, para cada jugador representa el porcentaje de su valor en el valor total de los jugadores de su país. El `Series` resultante tiene un índice de dos niveles (se verá en el siguiente tutorial)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nationality  Name      \n",
       "Algeria      R. Mahrez      50.943396\n",
       "             Y. Brahimi     49.056604\n",
       "Argentina    P. Dybala      17.711443\n",
       "             M. Icardi      12.835821\n",
       "             S. Agüero      12.835821\n",
       "                              ...    \n",
       "Uruguay      J. Giménez     15.596330\n",
       "             L. Suárez      36.697248\n",
       "             E. Cavani      27.522936\n",
       "             D. Godín       20.183486\n",
       "Wales        G. Bale       100.000000\n",
       "Name: Value (M), Length: 200, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fifa.groupby('Nationality').apply(pct_val_player)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este ejemplo, la función devuelve el máximo y el mínimo para cada una de las columnas del `DataFrame`."
   ]
  },
  {
   "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",
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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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>amin</th>\n",
       "      <td>177509</td>\n",
       "      <td>23</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>154.94</td>\n",
       "      <td>63.956472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amax</th>\n",
       "      <td>220971</td>\n",
       "      <td>31</td>\n",
       "      <td>88</td>\n",
       "      <td>90</td>\n",
       "      <td>69.5</td>\n",
       "      <td>265.0</td>\n",
       "      <td>187.96</td>\n",
       "      <td>93.893544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  Age  Overall  Potential  Value (M)  Wage (K)  Height     Weight\n",
       "amin  177509   23       83         83       21.0      28.0  154.94  63.956472\n",
       "amax  220971   31       88         90       69.5     265.0  187.96  93.893544"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def max_min(data):\n",
    "    return data.select_dtypes(include=np.number).agg([np.min, np.max])\n",
    "\n",
    "max_min(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al utilizarla con `GroupBy.apply`, trata cada grupo por separado. El `DataFrame` resultante tiene un índice de dos niveles."
   ]
  },
  {
   "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></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nationality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Algeria</th>\n",
       "      <th>amin</th>\n",
       "      <td>184267</td>\n",
       "      <td>27</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>39.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>154.94</td>\n",
       "      <td>66.224432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amax</th>\n",
       "      <td>204485</td>\n",
       "      <td>28</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>40.5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>175.26</td>\n",
       "      <td>67.131616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Argentina</th>\n",
       "      <th>amin</th>\n",
       "      <td>143076</td>\n",
       "      <td>24</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>28.5</td>\n",
       "      <td>31.0</td>\n",
       "      <td>154.94</td>\n",
       "      <td>68.038800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amax</th>\n",
       "      <td>211110</td>\n",
       "      <td>31</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>110.5</td>\n",
       "      <td>565.0</td>\n",
       "      <td>185.42</td>\n",
       "      <td>88.904032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Armenia</th>\n",
       "      <th>amin</th>\n",
       "      <td>192883</td>\n",
       "      <td>29</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>25.5</td>\n",
       "      <td>145.0</td>\n",
       "      <td>154.94</td>\n",
       "      <td>74.842680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amax</th>\n",
       "      <td>192883</td>\n",
       "      <td>29</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>25.5</td>\n",
       "      <td>145.0</td>\n",
       "      <td>154.94</td>\n",
       "      <td>74.842680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Austria</th>\n",
       "      <th>amin</th>\n",
       "      <td>197445</td>\n",
       "      <td>26</td>\n",
       "      <td>85</td>\n",
       "      <td>87</td>\n",
       "      <td>38.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>154.94</td>\n",
       "      <td>76.203456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amax</th>\n",
       "      <td>197445</td>\n",
       "      <td>26</td>\n",
       "      <td>85</td>\n",
       "      <td>87</td>\n",
       "      <td>38.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>154.94</td>\n",
       "      <td>76.203456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Belgium</th>\n",
       "      <th>amin</th>\n",
       "      <td>139720</td>\n",
       "      <td>24</td>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>154.94</td>\n",
       "      <td>60.781328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amax</th>\n",
       "      <td>208418</td>\n",
       "      <td>32</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>102.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>198.12</td>\n",
       "      <td>96.161504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID  Age  Overall  Potential  Value (M)  Wage (K)  \\\n",
       "Nationality                                                              \n",
       "Algeria     amin  184267   27       85         85       39.0      28.0   \n",
       "            amax  204485   28       85         85       40.5     205.0   \n",
       "Argentina   amin  143076   24       84         84       28.5      31.0   \n",
       "            amax  211110   31       94         94      110.5     565.0   \n",
       "Armenia     amin  192883   29       83         83       25.5     145.0   \n",
       "            amax  192883   29       83         83       25.5     145.0   \n",
       "Austria     amin  197445   26       85         87       38.0     110.0   \n",
       "            amax  197445   26       85         87       38.0     110.0   \n",
       "Belgium     amin  139720   24       83         84       22.0      20.0   \n",
       "            amax  208418   32       91         92      102.0     355.0   \n",
       "\n",
       "                  Height     Weight  \n",
       "Nationality                          \n",
       "Algeria     amin  154.94  66.224432  \n",
       "            amax  175.26  67.131616  \n",
       "Argentina   amin  154.94  68.038800  \n",
       "            amax  185.42  88.904032  \n",
       "Armenia     amin  154.94  74.842680  \n",
       "            amax  154.94  74.842680  \n",
       "Austria     amin  154.94  76.203456  \n",
       "            amax  154.94  76.203456  \n",
       "Belgium     amin  154.94  60.781328  \n",
       "            amax  198.12  96.161504  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fifa.groupby('Nationality').apply(max_min).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este último ejemplo, similar al visto anteriormente,  la función transforma el `DataFrame` añadiendo una columna con el porcentaje del valor total que supone cada jugador."
   ]
  },
  {
   "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "      <th>Value (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K. Mbappé</th>\n",
       "      <td>231747</td>\n",
       "      <td>19</td>\n",
       "      <td>France</td>\n",
       "      <td>88</td>\n",
       "      <td>95</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>81.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>Jul 1, 2018</td>\n",
       "      <td>154.94</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€166.1M</td>\n",
       "      <td>1.011969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O. Dembélé</th>\n",
       "      <td>231443</td>\n",
       "      <td>21</td>\n",
       "      <td>France</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>40.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Aug 28, 2017</td>\n",
       "      <td>154.94</td>\n",
       "      <td>67.131616</td>\n",
       "      <td>€90M</td>\n",
       "      <td>0.499738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabriel Jesus</th>\n",
       "      <td>230666</td>\n",
       "      <td>21</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>41.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>Aug 3, 2016</td>\n",
       "      <td>175.26</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€84.1M</td>\n",
       "      <td>0.512231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L. Sané</th>\n",
       "      <td>222492</td>\n",
       "      <td>22</td>\n",
       "      <td>Germany</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>61.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Aug 2, 2016</td>\n",
       "      <td>182.88</td>\n",
       "      <td>74.842680</td>\n",
       "      <td>€125.1M</td>\n",
       "      <td>0.762100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>220834</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>54.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 1, 2015</td>\n",
       "      <td>182.88</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€121.5M</td>\n",
       "      <td>0.674646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ID  Age Nationality  Overall  Potential  \\\n",
       "Name                                                         \n",
       "K. Mbappé      231747   19      France       88         95   \n",
       "O. Dembélé     231443   21      France       83         92   \n",
       "Gabriel Jesus  230666   21      Brazil       83         92   \n",
       "L. Sané        222492   22     Germany       86         92   \n",
       "Marco Asensio  220834   22       Spain       85         92   \n",
       "\n",
       "                              Club  Value (M)  Wage (K) Position  \\\n",
       "Name                                                               \n",
       "K. Mbappé      Paris Saint-Germain       81.0     100.0       RM   \n",
       "O. Dembélé            FC Barcelona       40.0     155.0       RW   \n",
       "Gabriel Jesus      Manchester City       41.0     130.0       ST   \n",
       "L. Sané            Manchester City       61.0     195.0       LW   \n",
       "Marco Asensio          Real Madrid       54.0     215.0       RW   \n",
       "\n",
       "                     Joined  Height     Weight Release Clause  Value (%)  \n",
       "Name                                                                      \n",
       "K. Mbappé       Jul 1, 2018  154.94  73.028312        €166.1M   1.011969  \n",
       "O. Dembélé     Aug 28, 2017  154.94  67.131616           €90M   0.499738  \n",
       "Gabriel Jesus   Aug 3, 2016  175.26  73.028312         €84.1M   0.512231  \n",
       "L. Sané         Aug 2, 2016  182.88  74.842680        €125.1M   0.762100  \n",
       "Marco Asensio   Jul 1, 2015  182.88  76.203456        €121.5M   0.674646  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pct_val_player(data):\n",
    "    data['Value (%)'] = 100*data['Value (M)']/data['Value (M)'].sum()\n",
    "    return data\n",
    "    \n",
    "pct_val_player(df_fifa).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al aplicar esta función al grupo, añade el porcentaje del valor que supone el jugador dentro del total de jugadores de su país al `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value (M)</th>\n",
       "      <th>Wage (K)</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "      <th>Value (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K. Mbappé</th>\n",
       "      <td>231747</td>\n",
       "      <td>19</td>\n",
       "      <td>France</td>\n",
       "      <td>88</td>\n",
       "      <td>95</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>81.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>Jul 1, 2018</td>\n",
       "      <td>154.94</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€166.1M</td>\n",
       "      <td>8.472803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O. Dembélé</th>\n",
       "      <td>231443</td>\n",
       "      <td>21</td>\n",
       "      <td>France</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>40.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Aug 28, 2017</td>\n",
       "      <td>154.94</td>\n",
       "      <td>67.131616</td>\n",
       "      <td>€90M</td>\n",
       "      <td>4.184100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gabriel Jesus</th>\n",
       "      <td>230666</td>\n",
       "      <td>21</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>41.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>Aug 3, 2016</td>\n",
       "      <td>175.26</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€84.1M</td>\n",
       "      <td>3.851574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L. Sané</th>\n",
       "      <td>222492</td>\n",
       "      <td>22</td>\n",
       "      <td>Germany</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>61.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Aug 2, 2016</td>\n",
       "      <td>182.88</td>\n",
       "      <td>74.842680</td>\n",
       "      <td>€125.1M</td>\n",
       "      <td>7.434491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>220834</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>54.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 1, 2015</td>\n",
       "      <td>182.88</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€121.5M</td>\n",
       "      <td>4.904632</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pepe</th>\n",
       "      <td>120533</td>\n",
       "      <td>35</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>Beşiktaş JK</td>\n",
       "      <td>9.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>Jul 4, 2017</td>\n",
       "      <td>187.96</td>\n",
       "      <td>81.192968</td>\n",
       "      <td>€17.1M</td>\n",
       "      <td>2.898551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naldo</th>\n",
       "      <td>171919</td>\n",
       "      <td>35</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>FC Schalke 04</td>\n",
       "      <td>9.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Jul 1, 2016</td>\n",
       "      <td>198.12</td>\n",
       "      <td>91.171992</td>\n",
       "      <td>€15.3M</td>\n",
       "      <td>0.845467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z. Ibrahimović</th>\n",
       "      <td>41236</td>\n",
       "      <td>36</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>LA Galaxy</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>RS</td>\n",
       "      <td>Mar 23, 2018</td>\n",
       "      <td>195.58</td>\n",
       "      <td>94.800728</td>\n",
       "      <td>€21M</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A. Barzagli</th>\n",
       "      <td>137186</td>\n",
       "      <td>37</td>\n",
       "      <td>Italy</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>4.2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Jan 1, 2011</td>\n",
       "      <td>187.96</td>\n",
       "      <td>87.089664</td>\n",
       "      <td>€6.9M</td>\n",
       "      <td>1.309635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G. Buffon</th>\n",
       "      <td>1179</td>\n",
       "      <td>40</td>\n",
       "      <td>Italy</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>4.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Jul 6, 2018</td>\n",
       "      <td>193.04</td>\n",
       "      <td>92.079176</td>\n",
       "      <td>€7.4M</td>\n",
       "      <td>1.247272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ID  Age Nationality  Overall  Potential  \\\n",
       "Name                                                          \n",
       "K. Mbappé       231747   19      France       88         95   \n",
       "O. Dembélé      231443   21      France       83         92   \n",
       "Gabriel Jesus   230666   21      Brazil       83         92   \n",
       "L. Sané         222492   22     Germany       86         92   \n",
       "Marco Asensio   220834   22       Spain       85         92   \n",
       "...                ...  ...         ...      ...        ...   \n",
       "Pepe            120533   35    Portugal       85         85   \n",
       "Naldo           171919   35      Brazil       85         85   \n",
       "Z. Ibrahimović   41236   36      Sweden       85         85   \n",
       "A. Barzagli     137186   37       Italy       84         84   \n",
       "G. Buffon         1179   40       Italy       88         88   \n",
       "\n",
       "                               Club  Value (M)  Wage (K) Position  \\\n",
       "Name                                                                \n",
       "K. Mbappé       Paris Saint-Germain       81.0     100.0       RM   \n",
       "O. Dembélé             FC Barcelona       40.0     155.0       RW   \n",
       "Gabriel Jesus       Manchester City       41.0     130.0       ST   \n",
       "L. Sané             Manchester City       61.0     195.0       LW   \n",
       "Marco Asensio           Real Madrid       54.0     215.0       RW   \n",
       "...                             ...        ...       ...      ...   \n",
       "Pepe                    Beşiktaş JK        9.0      57.0      RCB   \n",
       "Naldo                 FC Schalke 04        9.0      38.0       CB   \n",
       "Z. Ibrahimović            LA Galaxy       14.0      15.0       RS   \n",
       "A. Barzagli                Juventus        4.2      95.0       CB   \n",
       "G. Buffon       Paris Saint-Germain        4.0      77.0       GK   \n",
       "\n",
       "                      Joined  Height     Weight Release Clause   Value (%)  \n",
       "Name                                                                        \n",
       "K. Mbappé        Jul 1, 2018  154.94  73.028312        €166.1M    8.472803  \n",
       "O. Dembélé      Aug 28, 2017  154.94  67.131616           €90M    4.184100  \n",
       "Gabriel Jesus    Aug 3, 2016  175.26  73.028312         €84.1M    3.851574  \n",
       "L. Sané          Aug 2, 2016  182.88  74.842680        €125.1M    7.434491  \n",
       "Marco Asensio    Jul 1, 2015  182.88  76.203456        €121.5M    4.904632  \n",
       "...                      ...     ...        ...            ...         ...  \n",
       "Pepe             Jul 4, 2017  187.96  81.192968         €17.1M    2.898551  \n",
       "Naldo            Jul 1, 2016  198.12  91.171992         €15.3M    0.845467  \n",
       "Z. Ibrahimović  Mar 23, 2018  195.58  94.800728           €21M  100.000000  \n",
       "A. Barzagli      Jan 1, 2011  187.96  87.089664          €6.9M    1.309635  \n",
       "G. Buffon        Jul 6, 2018  193.04  92.079176          €7.4M    1.247272  \n",
       "\n",
       "[200 rows x 14 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fifa.groupby('Nationality').apply(pct_val_player)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section84\"></a> \n",
    "## <font color=\"#7F000E\">Filtrado: <font face=\"monospace\">GroupBy.filter()</font> </font>\n",
    "<br>\n",
    "\n",
    "La función `filter` también permite filtrar grupos mediante alguna condición. Como resultado devuelve un `DataFrame` con los elementos correspondientes a los grupos que cumplen la condición."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K. Mbappé</th>\n",
       "      <td>231747</td>\n",
       "      <td>19</td>\n",
       "      <td>France</td>\n",
       "      <td>88</td>\n",
       "      <td>95</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>81.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>Jul 1, 2018</td>\n",
       "      <td>154.94</td>\n",
       "      <td>73.028312</td>\n",
       "      <td>€166.1M</td>\n",
       "      <td>1.011969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O. Dembélé</th>\n",
       "      <td>231443</td>\n",
       "      <td>21</td>\n",
       "      <td>France</td>\n",
       "      <td>83</td>\n",
       "      <td>92</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>40.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Aug 28, 2017</td>\n",
       "      <td>154.94</td>\n",
       "      <td>67.131616</td>\n",
       "      <td>€90M</td>\n",
       "      <td>0.499738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L. Sané</th>\n",
       "      <td>222492</td>\n",
       "      <td>22</td>\n",
       "      <td>Germany</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>61.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>Aug 2, 2016</td>\n",
       "      <td>182.88</td>\n",
       "      <td>74.842680</td>\n",
       "      <td>€125.1M</td>\n",
       "      <td>0.762100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marco Asensio</th>\n",
       "      <td>220834</td>\n",
       "      <td>22</td>\n",
       "      <td>Spain</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>54.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>Jul 1, 2015</td>\n",
       "      <td>182.88</td>\n",
       "      <td>76.203456</td>\n",
       "      <td>€121.5M</td>\n",
       "      <td>0.674646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N. Süle</th>\n",
       "      <td>212190</td>\n",
       "      <td>22</td>\n",
       "      <td>Germany</td>\n",
       "      <td>84</td>\n",
       "      <td>90</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>36.5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Jul 1, 2017</td>\n",
       "      <td>195.58</td>\n",
       "      <td>97.068688</td>\n",
       "      <td>€67.5M</td>\n",
       "      <td>0.456011</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iniesta</th>\n",
       "      <td>41</td>\n",
       "      <td>34</td>\n",
       "      <td>Spain</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>Vissel Kobe</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>LF</td>\n",
       "      <td>Jul 16, 2018</td>\n",
       "      <td>170.18</td>\n",
       "      <td>68.038800</td>\n",
       "      <td>€26.9M</td>\n",
       "      <td>0.268609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quaresma</th>\n",
       "      <td>20775</td>\n",
       "      <td>34</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>Beşiktaş JK</td>\n",
       "      <td>15.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>RM</td>\n",
       "      <td>Jul 22, 2015</td>\n",
       "      <td>175.26</td>\n",
       "      <td>67.131616</td>\n",
       "      <td>€29.5M</td>\n",
       "      <td>0.193648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pepe</th>\n",
       "      <td>120533</td>\n",
       "      <td>35</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>Beşiktaş JK</td>\n",
       "      <td>9.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>Jul 4, 2017</td>\n",
       "      <td>187.96</td>\n",
       "      <td>81.192968</td>\n",
       "      <td>€17.1M</td>\n",
       "      <td>0.112441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A. Barzagli</th>\n",
       "      <td>137186</td>\n",
       "      <td>37</td>\n",
       "      <td>Italy</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>4.2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Jan 1, 2011</td>\n",
       "      <td>187.96</td>\n",
       "      <td>87.089664</td>\n",
       "      <td>€6.9M</td>\n",
       "      <td>0.052472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G. Buffon</th>\n",
       "      <td>1179</td>\n",
       "      <td>40</td>\n",
       "      <td>Italy</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>4.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Jul 6, 2018</td>\n",
       "      <td>193.04</td>\n",
       "      <td>92.079176</td>\n",
       "      <td>€7.4M</td>\n",
       "      <td>0.049974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ID  Age Nationality  Overall  Potential  \\\n",
       "Name                                                         \n",
       "K. Mbappé      231747   19      France       88         95   \n",
       "O. Dembélé     231443   21      France       83         92   \n",
       "L. Sané        222492   22     Germany       86         92   \n",
       "Marco Asensio  220834   22       Spain       85         92   \n",
       "N. Süle        212190   22     Germany       84         90   \n",
       "...               ...  ...         ...      ...        ...   \n",
       "Iniesta            41   34       Spain       86         86   \n",
       "Quaresma        20775   34    Portugal       84         84   \n",
       "Pepe           120533   35    Portugal       85         85   \n",
       "A. Barzagli    137186   37       Italy       84         84   \n",
       "G. Buffon        1179   40       Italy       88         88   \n",
       "\n",
       "                              Club  Value (M)  Wage (K) Position  \\\n",
       "Name                                                               \n",
       "K. Mbappé      Paris Saint-Germain       81.0     100.0       RM   \n",
       "O. Dembélé            FC Barcelona       40.0     155.0       RW   \n",
       "L. Sané            Manchester City       61.0     195.0       LW   \n",
       "Marco Asensio          Real Madrid       54.0     215.0       RW   \n",
       "N. Süle          FC Bayern München       36.5      84.0       CB   \n",
       "...                            ...        ...       ...      ...   \n",
       "Iniesta                Vissel Kobe       21.5      21.0       LF   \n",
       "Quaresma               Beşiktaş JK       15.5      80.0       RM   \n",
       "Pepe                   Beşiktaş JK        9.0      57.0      RCB   \n",
       "A. Barzagli               Juventus        4.2      95.0       CB   \n",
       "G. Buffon      Paris Saint-Germain        4.0      77.0       GK   \n",
       "\n",
       "                     Joined  Height     Weight Release Clause  Value (%)  \n",
       "Name                                                                      \n",
       "K. Mbappé       Jul 1, 2018  154.94  73.028312        €166.1M   1.011969  \n",
       "O. Dembélé     Aug 28, 2017  154.94  67.131616           €90M   0.499738  \n",
       "L. Sané         Aug 2, 2016  182.88  74.842680        €125.1M   0.762100  \n",
       "Marco Asensio   Jul 1, 2015  182.88  76.203456        €121.5M   0.674646  \n",
       "N. Süle         Jul 1, 2017  195.58  97.068688         €67.5M   0.456011  \n",
       "...                     ...     ...        ...            ...        ...  \n",
       "Iniesta        Jul 16, 2018  170.18  68.038800         €26.9M   0.268609  \n",
       "Quaresma       Jul 22, 2015  175.26  67.131616         €29.5M   0.193648  \n",
       "Pepe            Jul 4, 2017  187.96  81.192968         €17.1M   0.112441  \n",
       "A. Barzagli     Jan 1, 2011  187.96  87.089664          €6.9M   0.052472  \n",
       "G. Buffon       Jul 6, 2018  193.04  92.079176          €7.4M   0.049974  \n",
       "\n",
       "[138 rows x 14 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_fifa.groupby('Nationality').filter(lambda x: len(x)>=15)             # Paises con más de 15 jugadores\n",
    "df_fifa.groupby('Nationality').filter(lambda x: x['Overall'].mean()>85)  # Filtra por la valoración media de los jugadores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#7F000E\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"section86\"></a> \n",
    "## <font color=\"#7F000E\">Eficiencia </font>\n",
    "<br>\n",
    "\n",
    "Aunque la agregación por grupos se puede llevar a cabo de otros modos, el uso de una estructura `GroupBy` permite hacerlo de manera más eficiente.  La siguiente celda obtiene el valor (`Overall`) medio de los jugadores de cada club sin utilizar la estructura `GroupBy`.\n",
    "\n",
    "Con el fin de que se aprecie la diferencia, se utilizarán 1000 registros del conjunto anterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fifa_all = pd.read_csv('./data/fifa19.csv', index_col=0).set_index('Name')\n",
    "df_fifa_all = df_fifa_all.iloc[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120 ms ± 4.77 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 10\n",
    "for club in df_fifa_all['Club'].unique():\n",
    "    avg = np.average(df_fifa_all[df_fifa_all['Club']==club]['Overall'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La siguiente celda hace la misma operación, pero iterando dobre una estructura `GroupBy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.4 ms ± 1.39 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 10\n",
    "for grupo, frame in df_fifa_all.groupby('Club'):\n",
    "    avg = np.average(frame['Overall'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por último, en esta celda se lleva a cabo el mismo cálculo, pero sin iterar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.13 ms ± 1.04 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 10\n",
    "avg = df_fifa_all.groupby('Club')['Overall'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">\n",
    "<a href=\"#indice\"><font size=5><i class=\"fa fa-arrow-circle-up\" aria-hidden=\"true\" style=\"color:#004D7F\"></i></font></a>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "<div style=\"text-align: right\"> <font size=6><i class=\"fa fa-coffee\" aria-hidden=\"true\" style=\"color:#004D7F\"></i> </font></div>"
   ]
  }
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