04_Pandas_Dataframe_Atributos_Metodos_Utiles.ipynb

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    "# 4 - Pandas Dataframe, Atributos y Métodos útiles\n",
    "\n",
    "\n",
    "* En este notebook vamos a ver algunos de los ***métodos y atributos más importantes de los DataFrames*** para poder:\n",
    "\n",
    "    - ***Obtener Información del DataFrame***\n",
    "    - ***Restructurar los datos del DataFrame***\n",
    "    - ***Limpiar los datos de DataFrame***\n",
    "    \n",
    "\n",
    "* Estos métodos y atributos los podemos clasificar en los siguientes grupos:\n",
    "\n",
    "\n",
    "* ***Nota: Para ver un ejemplo de cada método o función, pulsar en la celda del nombre de las tablas***\n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "\n",
    "## 1. Atributos y métodos importantes en DataFrames\n",
    "\n",
    "\n",
    "* A continuación se muestran algunos atributos y métodos útiles para obtener información del DataFrame:\n",
    "\n",
    "\n",
    "|Nombre Función/Atributo|Funcionalidad|\n",
    "|---|---|\n",
    "|[df.index](#M1_1)|Devuelve el rango de valores de los índices del DataFrame|\n",
    "|[df.columns](#M1_2)|Devuelve el nombre de las columnas del DataFrame|\n",
    "|[df.values](#M1_3)|Devuelve un numpy array con los valores del DataFrame|\n",
    "|[df.shape](#M1_4)|Devuelve una tupla con las dimensiones del DataFrame (Nº Filas, Nº Columnas)|\n",
    "|[len(df)](#M1_5)|Devuelve el número de filas que tiene el DataFrame|\n",
    "|[df.types](#M1_6)|Devuelve el tipo de dato de cada una de las columnas|\n",
    "|[df.head(n)](#M1_7)|Devuelve un DataFrame con los 'n' primeros elementos|\n",
    "|[df.tail(n)](#M1_8)|Devuelve un DataFrame con los 'n' últimos elementos|\n",
    "|[df.sample(n)](#M1_9)|Devuelve un DataFrame con 'n' filas seleccionadas al azar|\n",
    "|[df['nombre_columna'].unique()](#M1_10)|Devuelve los valores únicos de la columna del DataFrame|\n",
    "\n",
    "\n",
    "\n",
    "## 2. Manipulación de columnas en DataFrames\n",
    "\n",
    "\n",
    "* Para borrar y cambiar el nombre a las columnas tenemos las dos siguientes funciones:\n",
    "\n",
    "\n",
    "|Nombre Función|Funcionalidad|\n",
    "|---|---|\n",
    "|[df.rename(columns={old_name: new_name})](#M2_1)|Cambia el nombre de las columnas, indicando en un diccionario el nombre antiguo como clave y el nombre nuevo de la columna como valor|\n",
    "|[df.drop(columns=['col_1', ..., 'col_n'])](#M2_2)|Elimina las columnas que se pasan como parámetro en una lista|\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## 3. Manipulación de índices en DataFrames\n",
    "\n",
    "\n",
    "* Para manipular los índices de los DataFrames tenemos los siguientes métodos:\n",
    "\n",
    "\n",
    "|Nombre Función|Funcionalidad|\n",
    "|---|---|\n",
    "|[df.reindex(new_index)](#M3_1)|Reasigna los indices que se pasan en una lista como parámetro|\n",
    "|[df.drop(index)](#M3_2)|Elimina las filas con los indices que se pasan en una lista como parámetro|\n",
    "|[df.rename(index={old_index: new_index})](#M3_3)|Reasigna los índices del DataFrame indicando en un diccionario que se le pasa como parámetros el indice a sustituir y el valor del nuevo indice|\n",
    "|[df.sort_index(ascending=True/False)](#M3_4)|Ordena los índices del DataFrame de manera ascendente o descendente|\n",
    "|[df.reset_index()](#M3_5)|Reasigna de nuevo los índices al DataFrame|\n",
    "\n",
    "\n",
    "## 4. Manipulación de Datos en DataFrames\n",
    "\n",
    "\n",
    "* Para ordenar los DataFrames tenemos el siguiente método:\n",
    "\n",
    "|Nombre Función|Funcionalidad|\n",
    "|---|---|\n",
    "|[df.sort_values(col_name, ascending=True/False)](#M4_1)|Ordena de manera ascendente o descendente los valores de la columna indicada|\n",
    "\n",
    "\n",
    "## 5. Tratamiento de datos duplicados y nulos\n",
    "\n",
    "\n",
    "* Para tratar datos duplicados y nulos en los DataFrames tenemos los siguientes métodos:\n",
    "\n",
    "\n",
    "|Nombre Función|Funcionalidad|\n",
    "|---|---|\n",
    "|[df.duplicated()](#M5_1)|Devuelve un valor Booleano indicando aquellas columnas cuyo contenido esta o no repetido|\n",
    "|[df.index.duplicated()](#M5_2)|Devuelve un valor Booleano indicando aquellas columnas con índices repetidos|\n",
    "|[df.drop_duplicates(keep='first'/'last')](#M5_3)|Elimina las columnas del DataFrame que estén duplicadas excepto la primera o la última, indicando esto con el parámetro 'keep'|\n",
    "|[df.dropna()](#M5_4)|Elimina del DataFrame todas aquellas columnas que tengan valores nulos|\n",
    "|[df.fillna(df.xxx())](#M5_5)|Sustituye para las columnas de tipo numérico todos aquellos valores nulos por otro valor relativo a la columnas como puede ser su media, mediana, moda, etc, indicando esa función como parámetro|\n",
    "|[df.replace('a', 'b')](#M5_6)|Sustituye en el DataFrame todas aquellas ocurrencias 'a' por otro valor 'b'|\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## 6. Obtención de información numérica\n",
    "\n",
    "\n",
    "* Para obtener información numérica de las columnas con valores numéricos, tenemos los siguientes métodos:\n",
    "\n",
    "|Nombre Función|Funcionalidad|\n",
    "|---|---|\n",
    "|[df.sum()](#M6_1)|Devuelve la suma de cada una de las columnas de tipo numérico|\n",
    "|[df.cumsum()](#M6_2)|Devuelve la suma acumulada|\n",
    "|[df.min()](#M6_3)|Devuelve el valor mínimo de cada una de las columnas de tipo numérico|\n",
    "|[df.max()](#M6_4)|Devuelve el valor máximo de cada una de las columnas de tipo numérico|\n",
    "|[df.mean()](#M6_5)|Devuelve la media de cada una de las columnas de tipo numérico|\n",
    "|[df.median()](#M6_6)|Devuelve la mediana de cada una de las columnas de tipo numérico|\n",
    "\n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "\n",
    "* Para ver ejemplos de cada uno de estos métodos y atributos, vamos a utilizar el siguiente DataFrame que entre otras cosas contiene filas repetidas, valores nulos y todo lo necesario para ver ejemplos de los atributos y métodos mencionados:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>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>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    brand   model  kilometers\n",
       "0         1  Renault    Clio        10.0\n",
       "1         1  Renault    Clio        10.0\n",
       "2         1  Renault    Clio        10.0\n",
       "3         2  Renault  Megane     23000.0\n",
       "4         3     Seat   Ibiza      9000.0\n",
       "5         2     Seat    Leon        20.0\n",
       "6         5     Opel   Corsa       999.0\n",
       "7         5      NaN     NaN         NaN\n",
       "8         4  Renault    Clio     34000.0\n",
       "9         6  Renault  Megane         NaN\n",
       "10        2     Seat     NaN     99999.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('./data/ejemplo_EDA/ejemplo_limpieza_estructuracion.txt', header=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## 1. Atributos y métodos importantes en DataFrames\n",
    "\n",
    "\n",
    "### <a name=\"M1_1\">df.index</a>\n",
    "\n",
    "\n",
    "* Devuelve el rango de valores de los índices del DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=11, step=1)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Podemos pasarlo a una lista con los valores de los índices del DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M1_2\">df.columns</a>\n",
    "\n",
    "\n",
    "* Devuelve el nombre de las columnas del DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user_id', 'brand', 'model', 'kilometers'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Para un mejor tratamiento, podemos pasar los nombres de las columnas a una lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['user_id', 'brand', 'model', 'kilometers']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M1_3\">df.values</a>\n",
    "\n",
    "\n",
    "* Devuelve un numpy array con los valores del DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 'Renault', 'Clio', 10.0],\n",
       "       [1, 'Renault', 'Clio', 10.0],\n",
       "       [1, 'Renault', 'Clio', 10.0],\n",
       "       [2, 'Renault', 'Megane', 23000.0],\n",
       "       [3, 'Seat', 'Ibiza', 9000.0],\n",
       "       [2, 'Seat', 'Leon', 20.0],\n",
       "       [5, 'Opel', 'Corsa', 999.0],\n",
       "       [5, nan, nan, nan],\n",
       "       [4, 'Renault', 'Clio', 34000.0],\n",
       "       [6, 'Renault', 'Megane', nan],\n",
       "       [2, 'Seat', nan, 99999.0]], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M1_4\">df.shape</a>\n",
    "\n",
    "\n",
    "* Devuelve una tupla con las dimensiones del DataFrame (Nº Filas, Nº Columnas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 4)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M1_5\">len(df)</a>\n",
    "\n",
    "\n",
    "* Devuelve el número de filas que tiene el DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M1_6\">df.types</a>\n",
    "\n",
    "\n",
    "* Devuelve el tipo de dato de cada una de las columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id         int64\n",
       "brand          object\n",
       "model          object\n",
       "kilometers    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M1_7\">df.head(n)</a>\n",
    "\n",
    "\n",
    "* Devuelve un DataFrame con los 'n' primeros elementos\n",
    "\n",
    "\n",
    "* Si no indicamos el númbero de filas a mostrar, mostrará 5 por defecto."
   ]
  },
  {
   "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>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>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id    brand model  kilometers\n",
       "0        1  Renault  Clio        10.0\n",
       "1        1  Renault  Clio        10.0\n",
       "2        1  Renault  Clio        10.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M1_8\">df.tail(n)</a>\n",
    "\n",
    "\n",
    "* Devuelve un DataFrame con los 'n' últimos elementos\n",
    "\n",
    "\n",
    "* Si no indicamos el númbero de filas a mostrar, mostrará 5 por defecto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "    .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>8</th>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    brand   model  kilometers\n",
       "8         4  Renault    Clio     34000.0\n",
       "9         6  Renault  Megane         NaN\n",
       "10        2     Seat     NaN     99999.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M1_9\">df.sample(n)</a>\n",
    "\n",
    "\n",
    "* Devuelve un DataFrame con 'n' filas seleccionadas al azar\n",
    "\n",
    "\n",
    "* Si no indicamos el númbero de filas a mostrar, mostrará 5 por defecto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    brand model  kilometers\n",
       "1         1  Renault  Clio        10.0\n",
       "0         1  Renault  Clio        10.0\n",
       "10        2     Seat   NaN     99999.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M1_10\">df['nombre_columna'].unique()</a>\n",
    "\n",
    "* Devuelve los valores únicos en un numpy array de la columna del DataFrame que le indicamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Renault', 'Seat', 'Opel', nan], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['brand'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## 2. Manipulación de columnas en DataFrames\n",
    "\n",
    "\n",
    "### <a name=\"M2_1\">df.rename(columns={old_name: new_name})</a>\n",
    "\n",
    "\n",
    "* Cambia el nombre de las columnas, indicando en un diccionario el nombre antiguo como clave y el nombre nuevo de la columna como valor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>user_id</th>\n",
       "      <th>marca</th>\n",
       "      <th>modelo</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>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    marca  modelo  kilometers\n",
       "0         1  Renault    Clio        10.0\n",
       "1         1  Renault    Clio        10.0\n",
       "2         1  Renault    Clio        10.0\n",
       "3         2  Renault  Megane     23000.0\n",
       "4         3     Seat   Ibiza      9000.0\n",
       "5         2     Seat    Leon        20.0\n",
       "6         5     Opel   Corsa       999.0\n",
       "7         5      NaN     NaN         NaN\n",
       "8         4  Renault    Clio     34000.0\n",
       "9         6  Renault  Megane         NaN\n",
       "10        2     Seat     NaN     99999.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'brand': 'marca', 'model': 'modelo'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M2_2\"> df.drop(columns=['col_1', ..., 'col_n'])</a>\n",
    "\n",
    "\n",
    "* Elimina las columnas que se pasan como parámetro en una lista|"
   ]
  },
  {
   "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>brand</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      brand   model\n",
       "0   Renault    Clio\n",
       "1   Renault    Clio\n",
       "2   Renault    Clio\n",
       "3   Renault  Megane\n",
       "4      Seat   Ibiza\n",
       "5      Seat    Leon\n",
       "6      Opel   Corsa\n",
       "7       NaN     NaN\n",
       "8   Renault    Clio\n",
       "9   Renault  Megane\n",
       "10     Seat     NaN"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['user_id', 'kilometers'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## 3. Manipulación de índices en DataFrames\n",
    "\n",
    "\n",
    "### <a name=\"M3_1\">df.reindex(new_index)</a>\n",
    "\n",
    "\n",
    "* Reasigna los indices que se pasan en una lista como parámetro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    brand   model  kilometers\n",
       "0         1  Renault    Clio        10.0\n",
       "2         1  Renault    Clio        10.0\n",
       "4         3     Seat   Ibiza      9000.0\n",
       "6         5     Opel   Corsa       999.0\n",
       "8         4  Renault    Clio     34000.0\n",
       "10        2     Seat     NaN     99999.0\n",
       "1         1  Renault    Clio        10.0\n",
       "3         2  Renault  Megane     23000.0\n",
       "5         2     Seat    Leon        20.0\n",
       "7         5      NaN     NaN         NaN\n",
       "9         6  Renault  Megane         NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reindex([0, 2, 4, 6, 8, 10, 1, 3, 5, 7, 9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M3_2\">df.drop(index)</a>\n",
    "\n",
    "\n",
    "* Elimina las filas con los indices que se pasan en una lista como parámetro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id    brand   model  kilometers\n",
       "1        1  Renault    Clio        10.0\n",
       "3        2  Renault  Megane     23000.0\n",
       "5        2     Seat    Leon        20.0\n",
       "7        5      NaN     NaN         NaN\n",
       "9        6  Renault  Megane         NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([0, 2, 4, 6, 8, 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M3_3\">df.rename(df.rename(index={old_index: new_index})</a>\n",
    "\n",
    "\n",
    "* Reasigna los índices del DataFrame indicando en un diccionario que se le pasa como parámetros el indice a sustituir y el valor del nuevo indice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <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>100</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id    brand   model  kilometers\n",
       "100        1  Renault    Clio        10.0\n",
       "101        1  Renault    Clio        10.0\n",
       "102        1  Renault    Clio        10.0\n",
       "103        2  Renault  Megane     23000.0\n",
       "104        3     Seat   Ibiza      9000.0\n",
       "5          2     Seat    Leon        20.0\n",
       "6          5     Opel   Corsa       999.0\n",
       "7          5      NaN     NaN         NaN\n",
       "8          4  Renault    Clio     34000.0\n",
       "9          6  Renault  Megane         NaN\n",
       "10         2     Seat     NaN     99999.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(index={0: 100, 1: 101, 2: 102, 3: 103, 4: 104})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M3_4\">df.sort_index()</a>\n",
    "\n",
    "\n",
    "* Ordena los índices del DataFrame de manera ascendente o descendente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>user_id</th>\n",
       "      <th>brand</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometers</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.0</td>\n",
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       "      <th>7</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    brand   model  kilometers\n",
       "10        2     Seat     NaN     99999.0\n",
       "9         6  Renault  Megane         NaN\n",
       "8         4  Renault    Clio     34000.0\n",
       "7         5      NaN     NaN         NaN\n",
       "6         5     Opel   Corsa       999.0\n",
       "5         2     Seat    Leon        20.0\n",
       "4         3     Seat   Ibiza      9000.0\n",
       "3         2  Renault  Megane     23000.0\n",
       "2         1  Renault    Clio        10.0\n",
       "1         1  Renault    Clio        10.0\n",
       "0         1  Renault    Clio        10.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M3_5\">df.reset_index()</a>\n",
    "\n",
    "\n",
    "* Reasigna de nuevo los índices al DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
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       "      <th>3</th>\n",
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       "      <td>Megane</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  user_id    brand   model  kilometers\n",
       "0       0        1  Renault    Clio        10.0\n",
       "1       1        1  Renault    Clio        10.0\n",
       "2       2        1  Renault    Clio        10.0\n",
       "3       3        2  Renault  Megane     23000.0\n",
       "4       4        3     Seat   Ibiza      9000.0\n",
       "5       5        2     Seat    Leon        20.0\n",
       "6       6        5     Opel   Corsa       999.0\n",
       "7       7        5      NaN     NaN         NaN\n",
       "8       8        4  Renault    Clio     34000.0\n",
       "9       9        6  Renault  Megane         NaN\n",
       "10     10        2     Seat     NaN     99999.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## 4. Manipulación de Datos en DataFrames\n",
    "\n",
    "\n",
    "\n",
    "### <a name=\"M4_1\">df.sort_values(col_name, ascending=True/False)</a>\n",
    "\n",
    "\n",
    "* Ordena de manera ascendente o descendente los valores de la columna indicada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <th></th>\n",
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       "      <th>brand</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>Opel</td>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    brand   model  kilometers\n",
       "6         5     Opel   Corsa       999.0\n",
       "0         1  Renault    Clio        10.0\n",
       "1         1  Renault    Clio        10.0\n",
       "2         1  Renault    Clio        10.0\n",
       "3         2  Renault  Megane     23000.0\n",
       "8         4  Renault    Clio     34000.0\n",
       "9         6  Renault  Megane         NaN\n",
       "4         3     Seat   Ibiza      9000.0\n",
       "5         2     Seat    Leon        20.0\n",
       "10        2     Seat     NaN     99999.0\n",
       "7         5      NaN     NaN         NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['brand'], ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* También podemos ordenar los DataFrames por 2 o más columnas, indicando en una lista las columnas a ordenar por precedencia y luego indicando por cada una de las columnas si lo queremos en orden ascendente o descendente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    brand   model  kilometers\n",
       "5         2     Seat    Leon        20.0\n",
       "4         3     Seat   Ibiza      9000.0\n",
       "10        2     Seat     NaN     99999.0\n",
       "0         1  Renault    Clio        10.0\n",
       "1         1  Renault    Clio        10.0\n",
       "2         1  Renault    Clio        10.0\n",
       "3         2  Renault  Megane     23000.0\n",
       "8         4  Renault    Clio     34000.0\n",
       "9         6  Renault  Megane         NaN\n",
       "6         5     Opel   Corsa       999.0\n",
       "7         5      NaN     NaN         NaN"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['brand', 'kilometers'], ascending=[False, True])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## 5. Tratamiento de datos duplicados y nulos\n",
    "\n",
    "\n",
    "### <a name=\"M5_1\">df.duplicated()</a>\n",
    "\n",
    "\n",
    "* Devuelve un valor Booleano indicando aquellas columnas cuyo contenido esta o no repetido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1      True\n",
       "2      True\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M5_2\">df.index.duplicated()</a>\n",
    "\n",
    "\n",
    "* Devuelve un valor Booleano indicando aquellas columnas con índices repetidos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M5_3\">df.drop_duplicates(keep='first'/'last')</a>\n",
    "\n",
    "\n",
    "* Elimina las columnas del DataFrame que estén duplicadas excepto la primera o la última, indicando esto con el parámetro 'keep'."
   ]
  },
  {
   "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>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>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    brand   model  kilometers\n",
       "0         1  Renault    Clio        10.0\n",
       "3         2  Renault  Megane     23000.0\n",
       "4         3     Seat   Ibiza      9000.0\n",
       "5         2     Seat    Leon        20.0\n",
       "6         5     Opel   Corsa       999.0\n",
       "7         5      NaN     NaN         NaN\n",
       "8         4  Renault    Clio     34000.0\n",
       "9         6  Renault  Megane         NaN\n",
       "10        2     Seat     NaN     99999.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(keep='first')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M5_4\">df.dropna()</a>\n",
    "\n",
    "\n",
    "* Elimina del DataFrame todas aquellas columnas que tengan valores nulos."
   ]
  },
  {
   "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>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>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id    brand   model  kilometers\n",
       "0        1  Renault    Clio        10.0\n",
       "1        1  Renault    Clio        10.0\n",
       "2        1  Renault    Clio        10.0\n",
       "3        2  Renault  Megane     23000.0\n",
       "4        3     Seat   Ibiza      9000.0\n",
       "5        2     Seat    Leon        20.0\n",
       "6        5     Opel   Corsa       999.0\n",
       "8        4  Renault    Clio     34000.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M5_5\">df.fillna(df.xxx())</a>\n",
    "\n",
    "\n",
    "* Sustituye para las columnas de tipo numérico todos aquellos valores nulos por otro valor relativo a la columnas ### como puede ser su media, mediana, moda, etc, indicando esa función como parámetro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<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",
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       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18560.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>18560.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    brand   model    kilometers\n",
       "0         1  Renault    Clio     10.000000\n",
       "1         1  Renault    Clio     10.000000\n",
       "2         1  Renault    Clio     10.000000\n",
       "3         2  Renault  Megane  23000.000000\n",
       "4         3     Seat   Ibiza   9000.000000\n",
       "5         2     Seat    Leon     20.000000\n",
       "6         5     Opel   Corsa    999.000000\n",
       "7         5      NaN     NaN  18560.888889\n",
       "8         4  Renault    Clio  34000.000000\n",
       "9         6  Renault  Megane  18560.888889\n",
       "10        2     Seat     NaN  99999.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M5_6\">df.replace('a', 'b')</a>\n",
    "\n",
    "\n",
    "* Sustituye en el DataFrame todas aquellas ocurrencias 'a' por otro valor 'b'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
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       "      <th>brand</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometers</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>Apple</td>\n",
       "      <td>Clio</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
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       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Clio</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Megane</td>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Ibiza</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>Leon</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Opel</td>\n",
       "      <td>Corsa</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Clio</td>\n",
       "      <td>34000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Megane</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Seat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  brand   model  kilometers\n",
       "0         1  Apple    Clio        10.0\n",
       "1         1  Apple    Clio        10.0\n",
       "2         1  Apple    Clio        10.0\n",
       "3         2  Apple  Megane     23000.0\n",
       "4         3   Seat   Ibiza      9000.0\n",
       "5         2   Seat    Leon        20.0\n",
       "6         5   Opel   Corsa       999.0\n",
       "7         5    NaN     NaN         NaN\n",
       "8         4  Apple    Clio     34000.0\n",
       "9         6  Apple  Megane         NaN\n",
       "10        2   Seat     NaN     99999.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace('Renault', 'Apple')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## 6. Obtención de información numérica\n",
    "\n",
    "\n",
    "### <a name=\"M6_1\">df.sum()</a>\n",
    "\n",
    "\n",
    "* Devuelve la suma de cada una de las columnas de tipo numérico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id           32.0\n",
       "kilometers    167048.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M6_2\">df.cumsum()</a>\n",
    "\n",
    "\n",
    "* Devuelve la suma acumulada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         10.0\n",
       "1         20.0\n",
       "2         30.0\n",
       "3      23030.0\n",
       "4      32030.0\n",
       "5      32050.0\n",
       "6      33049.0\n",
       "7          NaN\n",
       "8      67049.0\n",
       "9          NaN\n",
       "10    167048.0\n",
       "Name: kilometers, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['kilometers'].cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M6_3\">df.min()</a>\n",
    "\n",
    "\n",
    "* Devuelve el valor mínimo de cada una de las columnas de tipo numérico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id        1.0\n",
       "kilometers    10.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M6_4\">df.max()</a>\n",
    "\n",
    "\n",
    "* Devuelve el valor máximo de cada una de las columnas de tipo numérico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id           6.0\n",
       "kilometers    99999.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M6_5\">df.mean()</a>\n",
    "\n",
    "\n",
    "* Devuelve la media de cada una de las columnas de tipo numérico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id           2.909091\n",
       "kilometers    18560.888889\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "### <a name=\"M6_6\">df.median()</a>\n",
    "\n",
    "\n",
    "* Devuelve la mediana de cada una de las columnas de tipo numérico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id         2.0\n",
       "kilometers    999.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median()"
   ]
  },
  {
   "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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