06_Pandas_Dataframe_Agregaciones_Ordenaciones.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6 - Pandas Dataframe - Agregaciones y Ordenaciones\n",
    "\n",
    "\n",
    "* En este Notebook vamos a ver las operaciones de ***Agregación de Datos y Ordenación***:\n",
    "\n",
    "    5. Agregaciones\n",
    "    6. Ordenaciones\n",
    "\n",
    "\n",
    "* Para más información ver los siguientes enlaces:\n",
    "    - https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html\n",
    "    - https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html\n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "\n",
    "## Funciones para el Análisis Exploratorio de Datos - Parte II\n",
    "\n",
    "\n",
    "* Veamos a continuación una serie de ejemplos de cada una de estas operaciones y/o funciones, usando los datos del fichero *'vehicles.txt'*\n",
    "\n",
    "    \n",
    "### Importamos la librería de Pandas:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pasamos los datos del Fichero a un DataFrame:\n",
    "\n",
    "\n",
    "* Fichero ***vehicles.txt***: Es un fichero de texto plano con formato CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>user_id</th>\n",
       "      <th>brand</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Cordoba</td>\n",
       "      <td>99999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>88888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id    brand    model  kilometers\n",
       "0        1  Renault     Clio          10\n",
       "1        2  Renault   Megane       23000\n",
       "2        3     Seat    Ibiza        9000\n",
       "3        2     Seat     Leon          20\n",
       "4        5     Opel    Corsa         999\n",
       "5        4  Renault     Clio       34000\n",
       "6        1     Seat    Ibiza        2000\n",
       "7        2     Seat  Cordoba       99999\n",
       "8        3  Renault     Clio       88888"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vehicles = pd.read_csv('./data/ejemplo_EDA/vehicles.txt', header=0)\n",
    "df_vehicles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## 5. Agregaciones\n",
    "\n",
    "\n",
    "* Las agregaciones son una de las operaciones más potentes del Análisis Exploratorio de Datos (EDA). \n",
    "\n",
    "\n",
    "* Esta operación nos permite ***obtener INFORMACIÓN DE LOS DATOS***, haciendo un ***resumen de los datos y viendo cuantos elementos (o valores de la fila) son iguales.***\n",
    "\n",
    "\n",
    "* Ejemplo conceptual de una agregación:\n",
    "\n",
    "\n",
    "<img src=\"./imgs/04_01_groupby.png\" style=\"width: 400px;\"/>\n",
    "\n",
    "\n",
    "* Posteriormente se pueden ***aplicar operaciones aritméticas o estadísticas***:\n",
    "\n",
    "    - ***count***: Número de observaciones no nulas\n",
    "    - ***sum***: Suma de los valores\n",
    "    - ***mean***: Media de los valores\n",
    "    - ***mad***: Desviación Media Absoluta de los valores\n",
    "    - ***median***: Mediana de los valores\n",
    "    - ***min***: Valor mínimo\n",
    "    - ***max***: Valor máximo\n",
    "    - ***first***: Primer valor encontrado en la agrupación\n",
    "    - ***last***: Último elemento encontrado en la agrupación\n",
    "    - ***mode***: Moda\n",
    "    - ***product***: Producto de los valores\n",
    "    - ***std***: Desviación Estandar\n",
    "    - ***var***: Varianza\n",
    "    - ***sem***: Error estandar\n",
    "    - ***skew***: Asimetria de los valores\n",
    "    - ***kurt***: La Curtosis\n",
    "    - ***quantile***: Cuantil\n",
    "    - ***value_counts***: Número de valores únicos\n",
    "\n",
    "\n",
    "* La ***sintaxis*** para realizar agrupaciones sería:\n",
    "\n",
    "\n",
    "```python\n",
    "df.groupby(['lista_columnas_agrupar'])['columna_aplicar_operacion'].agg(['lista_operaciones_agregación'])\n",
    "```\n",
    "    \n",
    "### 5.1 Ejemplo de agregación por un campo y conteo\n",
    "\n",
    "\n",
    "* Un primer ejemplo de agregación sería"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 tr th {\n",
       "        text-align: left;\n",
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       "\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>user_id</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>count</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Opel</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renault</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id model kilometers\n",
       "          count count      count\n",
       "brand                           \n",
       "Opel          1     1          1\n",
       "Renault       4     4          4\n",
       "Seat          4     4          4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vehicles.groupby(['brand']).agg(['count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Como se puede observar nos da como ***resultados de la agregación las diferentes Marcas de coches*** y un ***conteo de marcas por cada una de las columnas*** ya que ***no le hemos indicado sobre que columna tiene que operar***.\n",
    "\n",
    "\n",
    "* ***Si no se le indica un campo (o campos) sobre los que queremos aplicar la agregación, aplicará las operaciones de agregación sobre todos los campos (numéricos) del DataFrame*** que pueda aplicar la operación de agregación.\n",
    "\n",
    "\n",
    "* Para ello le podemos ***indicar*** después del \"groupBy\" ***sobre que columna vamos a aplicar la operación de agregación***:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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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       "        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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Opel</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renault</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count\n",
       "brand         \n",
       "Opel         1\n",
       "Renault      4\n",
       "Seat         4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vehicles.groupby(['brand'])['brand'].agg(['count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Ejemplo de agregación por un campo y aplicación de operaciones de agregación\n",
    "\n",
    "\n",
    "* Se pueden ***realizar cuantas operaciones de agregación se quieran sobre los campos que se quieran*** (siempre que tengan sentido).\n",
    "\n",
    "\n",
    "* Veamos a continuación un ***ejemplo de una agregación por marca***, y ***aplicamos sobre la columna Kilometros varias operaciones***:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>brand</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>std</th>\n",
       "      <th>var</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Opel</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>999.00</td>\n",
       "      <td>999</td>\n",
       "      <td>999</td>\n",
       "      <td>999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Renault</td>\n",
       "      <td>4</td>\n",
       "      <td>145898</td>\n",
       "      <td>36474.50</td>\n",
       "      <td>28500</td>\n",
       "      <td>88888</td>\n",
       "      <td>10</td>\n",
       "      <td>37702.873644</td>\n",
       "      <td>1.421507e+09</td>\n",
       "      <td>10</td>\n",
       "      <td>88888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seat</td>\n",
       "      <td>4</td>\n",
       "      <td>111019</td>\n",
       "      <td>27754.75</td>\n",
       "      <td>5500</td>\n",
       "      <td>99999</td>\n",
       "      <td>20</td>\n",
       "      <td>48316.649341</td>\n",
       "      <td>2.334499e+09</td>\n",
       "      <td>9000</td>\n",
       "      <td>99999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     brand  count     sum      mean  median    max  min           std  \\\n",
       "0     Opel      1     999    999.00     999    999  999           NaN   \n",
       "1  Renault      4  145898  36474.50   28500  88888   10  37702.873644   \n",
       "2     Seat      4  111019  27754.75    5500  99999   20  48316.649341   \n",
       "\n",
       "            var  first   last  \n",
       "0           NaN    999    999  \n",
       "1  1.421507e+09     10  88888  \n",
       "2  2.334499e+09   9000  99999  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vehicles.groupby(['brand'])['kilometers'] \\\n",
    "           .agg(['count', 'sum', 'mean', 'median', 'max', 'min', 'std', 'var', 'first', 'last']) \\\n",
    "           .reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Ejemplo de agregación por más de un campo\n",
    "\n",
    "\n",
    "* Veamos a continuación un ejemplo de agregación por los campos *'brand'* y *'model'*, aplicando las operaciones de agregación sobre el campo '*kilometers*':\n",
    "    - Conteo\n",
    "    - Suma\n",
    "    - Media\n",
    "    - Mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>brand</th>\n",
       "      <th>model</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>999</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>3</td>\n",
       "      <td>122898</td>\n",
       "      <td>40966</td>\n",
       "      <td>34000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>1</td>\n",
       "      <td>23000</td>\n",
       "      <td>23000</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seat</td>\n",
       "      <td>Cordoba</td>\n",
       "      <td>1</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "      <td>99999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>2</td>\n",
       "      <td>11000</td>\n",
       "      <td>5500</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     brand    model  count     sum   mean  median\n",
       "0     Opel    Corsa      1     999    999     999\n",
       "1  Renault     Clio      3  122898  40966   34000\n",
       "2  Renault   Megane      1   23000  23000   23000\n",
       "3     Seat  Cordoba      1   99999  99999   99999\n",
       "4     Seat    Ibiza      2   11000   5500    5500\n",
       "5     Seat     Leon      1      20     20      20"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vehicles.groupby(['brand', 'model'])['kilometers'] \\\n",
    "           .agg(['count', 'sum', 'mean', 'median']) \\\n",
    "           .reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## 6. Ordenaciones (Sort)\n",
    "\n",
    "\n",
    "* Otra de las operaciones interesantes a la hora de trabajar con datos, es poder ***realizar ordenaciones por alguno/s de sus campos***.\n",
    "\n",
    "\n",
    "* Con la función *'sort_values'* podemos oredenar el DataFrame por el/los campo/s que le indiquemos.\n",
    "\n",
    "\n",
    "* La ***sintaxis*** sería la siguiente:\n",
    "\n",
    "\n",
    "```python\n",
    "df.sort_values(by='nombre_columna_a_ordenar', ascending=Boolean)\n",
    "```\n",
    "\n",
    "\n",
    "* Por ejemplo podemos ordenar el DataFrame de vehiculos por el número de kilometros que han realizado:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>brand</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometers</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>88888</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
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       "      <td>34000</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10</td>\n",
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      "text/plain": [
       "   user_id    brand    model  kilometers\n",
       "7        2     Seat  Cordoba       99999\n",
       "8        3  Renault     Clio       88888\n",
       "5        4  Renault     Clio       34000\n",
       "1        2  Renault   Megane       23000\n",
       "2        3     Seat    Ibiza        9000\n",
       "6        1     Seat    Ibiza        2000\n",
       "4        5     Opel    Corsa         999\n",
       "3        2     Seat     Leon          20\n",
       "0        1  Renault     Clio          10"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df_vehicles.sort_values(by='kilometers', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* También nos permite hacer ordenaciones por valores que no son numéricos como por ejemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Seat</td>\n",
       "      <td>Cordoba</td>\n",
       "      <td>99999</td>\n",
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      "text/plain": [
       "   user_id    brand    model  kilometers\n",
       "4        5     Opel    Corsa         999\n",
       "0        1  Renault     Clio          10\n",
       "1        2  Renault   Megane       23000\n",
       "5        4  Renault     Clio       34000\n",
       "8        3  Renault     Clio       88888\n",
       "2        3     Seat    Ibiza        9000\n",
       "3        2     Seat     Leon          20\n",
       "6        1     Seat    Ibiza        2000\n",
       "7        2     Seat  Cordoba       99999"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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    }
   ],
   "source": [
    "df_vehicles.sort_values(by='brand', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "*Este Notebook ha sido desarrollado por **Ricardo Moya García** y registrado en Safe Creative como ***Atribución-NoComercial-CompartirIgual***.*\n",
    "\n",
    "<img src=\"./imgs/CC_BY-NC-SA.png\" alt=\"CC BY-NC\">"
   ]
  }
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