1.1-introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducción a la Ciencia de Datos con Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para cumplir las necesidades de analizar datos utilizando el lenguaje de programación Python hay que utilizar funciones que están fuera de las base contenidas en Python, al contrario de por ejemplo R, en donde la mayoría de las funciones que utilizaremos a lo largo del curso están dentro de las funciones base.\n",
    "\n",
    "Las bibliotecas de facto utilizadas en data science son `numpy`, `matplotlib`, `scipy` y `pandas`, las cuales nos aportaran con funciones desde programación vectorial hasta visualización de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np # Programación vectorial\n",
    "import matplotlib.pyplot as plt # Visualización\n",
    "import scipy.optimize # Programación científica\n",
    "import scipy.stats\n",
    "import pandas as pd # Estructura y manipulación de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los arreglos son zonas de almacenamiento contiguo que contienen una serie de elementos del mismo tipo, para utilizar esta estructura de datos utilizamos `numpy.array`.\n",
    "\n",
    "Con `numpy.array` podemos obtener las ventajas de velocidad que nos ofrece la programación vectorial de `numpy`, al aplicar funciones directamente a un arreglo sin recurrir a un ciclo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([0,1,2]) # Inicializamos un numpy.array desde una lista de python\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con la función `np.arange()` podemos crear un arreglo similar a la función `range()` de python en donde podemos especificar el largo del arreglo a generar y se devolverá un rango desde el 0 con el número de elementos especificado. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(10) # Creamos un arreglo de un rángo de 10 números\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = x * 2 # Directamente podemos multiplicar cada uno de los valores del arreglo\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generaremos datos al azar y para que nuestros resultados sean consistentes podemos bloquear el generador de números aleatorios con la función `np.random.seed(< number >)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(11) # Bloqueamos la generación de números aleatorios con la semilla 11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otra función que podemos utilizar para generar un arreglo es utilizar `np.random.normal(loc,scale,size)` para crear un arreglo al azar con distribución normal de largo `size`, con media `loc` y desviación estándar `scale`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.74945474, -0.286073  , -0.48456513, -2.65331856, -0.00828463,\n",
       "       -0.31963136, -0.53662936,  0.31540267,  0.42105072, -1.06560298])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = np.random.normal(0 , 1, 10) # Arreglo de largo 10, media 0 y desviación estándar 1\n",
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.74945474,  1.713927  ,  3.51543487,  3.34668144,  7.99171537,\n",
       "        9.68036864, 11.46337064, 14.31540267, 16.42105072, 16.93439702])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = a + e # Directamente podemos multiplicar cada uno de los valores del arreglo\n",
    "y"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No tan sólo podemos operar con arreglos unidimensionales, sino que también podemos operar sobre arreglos multidimensionales. Una de las funciones para generar un arreglo bidimensional es unir 2 arreglos como columnas, para realizar esto podemos utilizar la función `np.stack()` y concatenarlos por el eje 1.\n",
    "\n",
    "Una descripción gráfica de los ejes en numpy se describe en la siguiente imagen.\n",
    "\n",
    "<img src=\"attachment:1_Ikn1J6siiiCSk4ivYUhdgw.png\" width=\"400\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  1.74945474],\n",
       "       [ 1.        ,  1.713927  ],\n",
       "       [ 2.        ,  3.51543487],\n",
       "       [ 3.        ,  3.34668144],\n",
       "       [ 4.        ,  7.99171537],\n",
       "       [ 5.        ,  9.68036864],\n",
       "       [ 6.        , 11.46337064],\n",
       "       [ 7.        , 14.31540267],\n",
       "       [ 8.        , 16.42105072],\n",
       "       [ 9.        , 16.93439702]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.stack((x,y), axis=1) # Concatenamos los arreglos x e y por el eje 1 para generar el arreglo bidimensional data.\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si queremos extraer un dato o un arreglo de datos específicos utilizamos la siguiente sintáxis `np.array[ < row >,< column > ]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:,0] # Extraemos todas las filas, pero sólo la columna 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 1.74945474])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0,:] # Extraemos todas las columnas, pero sólo la fila 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0,0] # Extraemos el dato de la columna 0 y fila 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otra de las bibliotecas más utilizadas en ciencia de datos es matplotlib, con la cual podemos generar visualizaciones de los datos.\n",
    "\n",
    "Con la función `plt.scatter()` podemos crear un gráfico de dispersión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[:,0],data[:,1]) # Graficaremos la columna 0 sobre la columna 1 en un gráfico de dispersión\n",
    "plt.title(\"x vs. y\") # Título del gráfico\n",
    "plt.xlabel(\"x\") # Etiqueta del eje x\n",
    "plt.ylabel(\"y\") # Etiqueta del eje y\n",
    "plt.show() # Imprimimos el gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def linear_function(x,m,c):\n",
    "    \"\"\" \n",
    "    Función que recibe los parámetros de una función lineal y retorna el resultado de la función lineal f(x) = mx + c.\n",
    "    x: Variable real.\n",
    "    m: Constante que determina la pendiente de la linea.\n",
    "    c: Constante que determina el intercepto de la linea.\n",
    "    \"\"\"\n",
    "    return m * x + c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scipy es una biblioteca que contiene una serie de funciones de computación científica listas para aplicar.\n",
    "\n",
    "Para poder ajustar la función `linear_function()` a nuestros datos, utilizaremos la función `scipy.optimize.curve_fit()`, la cual recibe la función a ajustar y los datos a la cual la ajustaremos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.93729034, -0.00462623]), array([[ 0.01677671, -0.07549541],\n",
       "        [-0.07549541,  0.47814061]]))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = scipy.optimize.curve_fit(linear_function, # Función a ajustar\n",
    "                                 data[:,0],data[:,1] # Datos a los cuales ajustaremos la curva\n",
    "                                )\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función nos retorna una tupla de 2 objetos `np.array`, el primer arreglo son las constantes ajustadas de nuestra función lineal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "m = model[0][0] # Pendiente ajustada\n",
    "c = model[0][1] # Intercepto ajustado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para aprovechar las ventajas de la programación vectorial, debemos transformar nuestra función de Python `linear_function()` en una función vectorial, para que podamos aplicarla directamente sobre nuestro arreglo. Para esto utilizamos la función `np.vectorize()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "linear_function_vectorized = np.vectorize(linear_function) # Transformamos nuestra función en una vectorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4.62622679e-03,  1.93266411e+00,  3.86995446e+00,  5.80724480e+00,\n",
       "        7.74453514e+00,  9.68182548e+00,  1.16191158e+01,  1.35564062e+01,\n",
       "        1.54936965e+01,  1.74309868e+01])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat = linear_function_vectorized(x,m,c) # Aplicamos nuestra función vectorizada sobre el arreglo x, junto a nuestras constantes m y c\n",
    "y_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualmente evaluemos el ajuste de nuestro modelo a los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[:,0],data[:,1], label=\"data\") # Graficamos nuestros datos con la etiqueta data\n",
    "plt.plot(y_hat, color=\"red\", label=\"model\") # Graficamos los resultados de nuestro modelo ajustado\n",
    "plt.title(\"Real data and fitted model\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculemos las pérdidas de nuestro modelo al compararlo a nuestros datos reales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.75408097e+00, -2.18737112e-01, -3.54519589e-01, -2.46056336e+00,\n",
       "        2.47180231e-01, -1.45684473e-03, -1.55745185e-01,  7.58996504e-01,\n",
       "        9.27354211e-01, -4.96589828e-01])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss = y - y_hat\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.75408097e+00, 2.18737112e-01, 3.54519589e-01, 2.46056336e+00,\n",
       "       2.47180231e-01, 1.45684473e-03, 1.55745185e-01, 7.58996504e-01,\n",
       "       9.27354211e-01, 4.96589828e-01])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs_loss = np.abs(loss) # Calculamos el valor absoluto de las pérdidas\n",
    "abs_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7375223828644797"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae = abs_loss.mean() # Calculamos el error absoluto medio de nuestro modelo\n",
    "mae"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quizá la biblioteca más utilizada en ciencia de datos es `pandas`, la cual nos permite manejar datos de una manera muy simple gracias a su potente estructura de datos `pd.DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Siblings/Spouses Aboard</th>\n",
       "      <th>Parents/Children Aboard</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr. Owen Harris Braund</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs. John Bradley (Florence Briggs Thayer) Cum...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss. Laina Heikkinen</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs. Jacques Heath (Lily May Peel) Futrelle</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr. William Henry Allen</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Rev. Juozas Montvila</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss. Margaret Edith Graham</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss. Catherine Helen Johnston</td>\n",
       "      <td>female</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr. Karl Howell Behr</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr. Patrick Dooley</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>887 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass                                               Name  \\\n",
       "0           0       3                             Mr. Owen Harris Braund   \n",
       "1           1       1  Mrs. John Bradley (Florence Briggs Thayer) Cum...   \n",
       "2           1       3                              Miss. Laina Heikkinen   \n",
       "3           1       1        Mrs. Jacques Heath (Lily May Peel) Futrelle   \n",
       "4           0       3                            Mr. William Henry Allen   \n",
       "..        ...     ...                                                ...   \n",
       "882         0       2                               Rev. Juozas Montvila   \n",
       "883         1       1                        Miss. Margaret Edith Graham   \n",
       "884         0       3                     Miss. Catherine Helen Johnston   \n",
       "885         1       1                               Mr. Karl Howell Behr   \n",
       "886         0       3                                 Mr. Patrick Dooley   \n",
       "\n",
       "        Sex   Age  Siblings/Spouses Aboard  Parents/Children Aboard     Fare  \n",
       "0      male  22.0                        1                        0   7.2500  \n",
       "1    female  38.0                        1                        0  71.2833  \n",
       "2    female  26.0                        0                        0   7.9250  \n",
       "3    female  35.0                        1                        0  53.1000  \n",
       "4      male  35.0                        0                        0   8.0500  \n",
       "..      ...   ...                      ...                      ...      ...  \n",
       "882    male  27.0                        0                        0  13.0000  \n",
       "883  female  19.0                        0                        0  30.0000  \n",
       "884  female   7.0                        1                        2  23.4500  \n",
       "885    male  26.0                        0                        0  30.0000  \n",
       "886    male  32.0                        0                        0   7.7500  \n",
       "\n",
       "[887 rows x 8 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = pd.read_csv(\"data/titanic.csv\") # Importamos un CSV y Pandas nos retorna un pd.DataFrame\n",
    "titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para explorar rapidamente un conjunto de datos, `pandas` nos ofrece unas métodos muy útiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Siblings/Spouses Aboard</th>\n",
       "      <th>Parents/Children Aboard</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr. Owen Harris Braund</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs. John Bradley (Florence Briggs Thayer) Cum...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss. Laina Heikkinen</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs. Jacques Heath (Lily May Peel) Futrelle</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr. William Henry Allen</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                             Mr. Owen Harris Braund   \n",
       "1         1       1  Mrs. John Bradley (Florence Briggs Thayer) Cum...   \n",
       "2         1       3                              Miss. Laina Heikkinen   \n",
       "3         1       1        Mrs. Jacques Heath (Lily May Peel) Futrelle   \n",
       "4         0       3                            Mr. William Henry Allen   \n",
       "\n",
       "      Sex   Age  Siblings/Spouses Aboard  Parents/Children Aboard     Fare  \n",
       "0    male  22.0                        1                        0   7.2500  \n",
       "1  female  38.0                        1                        0  71.2833  \n",
       "2  female  26.0                        0                        0   7.9250  \n",
       "3  female  35.0                        1                        0  53.1000  \n",
       "4    male  35.0                        0                        0   8.0500  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head() # Con el método pd.DataFrame.head() podemos explorar las primeras filas de un pd.DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Siblings/Spouses Aboard</th>\n",
       "      <th>Parents/Children Aboard</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Rev. Juozas Montvila</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss. Margaret Edith Graham</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss. Catherine Helen Johnston</td>\n",
       "      <td>female</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr. Karl Howell Behr</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr. Patrick Dooley</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass                            Name     Sex   Age  \\\n",
       "882         0       2            Rev. Juozas Montvila    male  27.0   \n",
       "883         1       1     Miss. Margaret Edith Graham  female  19.0   \n",
       "884         0       3  Miss. Catherine Helen Johnston  female   7.0   \n",
       "885         1       1            Mr. Karl Howell Behr    male  26.0   \n",
       "886         0       3              Mr. Patrick Dooley    male  32.0   \n",
       "\n",
       "     Siblings/Spouses Aboard  Parents/Children Aboard   Fare  \n",
       "882                        0                        0  13.00  \n",
       "883                        0                        0  30.00  \n",
       "884                        1                        2  23.45  \n",
       "885                        0                        0  30.00  \n",
       "886                        0                        0   7.75  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.tail() # Con el método pd.DataFrame.tail() podemos explorar las últimas filas de un pd.DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Siblings/Spouses Aboard</th>\n",
       "      <th>Parents/Children Aboard</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr. Kristo Laleff</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass               Name   Sex   Age  Siblings/Spouses Aboard  \\\n",
       "874         0       3  Mr. Kristo Laleff  male  23.0                        0   \n",
       "\n",
       "     Parents/Children Aboard    Fare  \n",
       "874                        0  7.8958  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sample() # Con el método pd.DataFrame.tail() podemos explorar una fila al azar de un pd.DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al igual que en Numpy, en Pandas también podemos acceder a columnas específicas de nuestro conjunto de datos, pero en el caso de Pandas, podemos acceder facilmente mediante el nombre de la columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                 Mr. Owen Harris Braund\n",
       "1      Mrs. John Bradley (Florence Briggs Thayer) Cum...\n",
       "2                                  Miss. Laina Heikkinen\n",
       "3            Mrs. Jacques Heath (Lily May Peel) Futrelle\n",
       "4                                Mr. William Henry Allen\n",
       "                             ...                        \n",
       "882                                 Rev. Juozas Montvila\n",
       "883                          Miss. Margaret Edith Graham\n",
       "884                       Miss. Catherine Helen Johnston\n",
       "885                                 Mr. Karl Howell Behr\n",
       "886                                   Mr. Patrick Dooley\n",
       "Name: Name, Length: 887, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Name # Extraemos la columna Name  Pandas retorna un pd.Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "4      35.0\n",
       "       ... \n",
       "882    27.0\n",
       "883    19.0\n",
       "884     7.0\n",
       "885    26.0\n",
       "886    32.0\n",
       "Name: Age, Length: 887, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Age\"] # Otra sintaxis para extraer una columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>887 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sex  Pclass\n",
       "0      male       3\n",
       "1    female       1\n",
       "2    female       3\n",
       "3    female       1\n",
       "4      male       3\n",
       "..      ...     ...\n",
       "882    male       2\n",
       "883  female       1\n",
       "884  female       3\n",
       "885    male       1\n",
       "886    male       3\n",
       "\n",
       "[887 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[[\"Sex\",\"Pclass\"]] # Podemos también extraer una lista de columnas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para continuar con la exploración de nuetros datos, el método `pd.Series.describe()` nos devuelve una serie de descriptores de la columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    887.000000\n",
       "mean      29.471443\n",
       "std       14.121908\n",
       "min        0.420000\n",
       "25%       20.250000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      887\n",
       "unique       2\n",
       "top       male\n",
       "freq       573\n",
       "Name: Sex, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Sex.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El método `pd.Series.value_counts()` nos permite conocer la distribución de una columna de valores discretos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      573\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para poder filtrar un `pd.DataFrame` podemos entregar una expresión que nos retorne un arreglo de valores booleanos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Siblings/Spouses Aboard</th>\n",
       "      <th>Parents/Children Aboard</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Master. Gosta Leonard Palsson</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Master. Eugene Rice</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>29.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Master. Harald Skoog</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>27.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss. Ellis Anna Maria Andersson</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>31.2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Master. Eino Viljami Panula</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>39.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Master. Arthur Rice</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>29.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss. Telma Matilda Strom</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.4625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss. Helen Loraine Allison</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>151.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss. Stina Viola Palsson</td>\n",
       "      <td>female</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Master. Sidney Leonard Goodwin</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>46.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss. Ida Lefebre</td>\n",
       "      <td>female</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>25.4667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss. Margit Elizabeth Skoog</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>27.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Master. Urho Abraham Panula</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>39.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Master. Sigvard Harald Elias Andersson</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>31.2750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass                                    Name     Sex  Age  \\\n",
       "7           0       3           Master. Gosta Leonard Palsson    male  2.0   \n",
       "16          0       3                     Master. Eugene Rice    male  2.0   \n",
       "62          0       3                    Master. Harald Skoog    male  4.0   \n",
       "118         0       3        Miss. Ellis Anna Maria Andersson  female  2.0   \n",
       "163         0       3             Master. Eino Viljami Panula    male  1.0   \n",
       "170         0       3                     Master. Arthur Rice    male  4.0   \n",
       "204         0       3               Miss. Telma Matilda Strom  female  2.0   \n",
       "295         0       1             Miss. Helen Loraine Allison  female  2.0   \n",
       "372         0       3               Miss. Stina Viola Palsson  female  3.0   \n",
       "384         0       3          Master. Sidney Leonard Goodwin    male  1.0   \n",
       "407         0       3                       Miss. Ida Lefebre  female  3.0   \n",
       "639         0       3            Miss. Margit Elizabeth Skoog  female  2.0   \n",
       "820         0       3             Master. Urho Abraham Panula    male  2.0   \n",
       "846         0       3  Master. Sigvard Harald Elias Andersson    male  4.0   \n",
       "\n",
       "     Siblings/Spouses Aboard  Parents/Children Aboard      Fare  \n",
       "7                          3                        1   21.0750  \n",
       "16                         4                        1   29.1250  \n",
       "62                         3                        2   27.9000  \n",
       "118                        4                        2   31.2750  \n",
       "163                        4                        1   39.6875  \n",
       "170                        4                        1   29.1250  \n",
       "204                        0                        1   10.4625  \n",
       "295                        1                        2  151.5500  \n",
       "372                        3                        1   21.0750  \n",
       "384                        5                        2   46.9000  \n",
       "407                        3                        1   25.4667  \n",
       "639                        3                        2   27.9000  \n",
       "820                        4                        1   39.6875  \n",
       "846                        4                        2   31.2750  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[(titanic.Age < 5) & (titanic.Survived == 0)] # Filtramos sólo las personas menores a 5 años que no sobrevivieron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Siblings/Spouses Aboard</th>\n",
       "      <th>Parents/Children Aboard</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mrs. Sam (Leah Rosen) Aks</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass                       Name     Sex   Age  \\\n",
       "851         1       3  Mrs. Sam (Leah Rosen) Aks  female  18.0   \n",
       "\n",
       "     Siblings/Spouses Aboard  Parents/Children Aboard  Fare  \n",
       "851                        0                        1  9.35  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic.Name.str.contains(\"Rose\")] # Con el método pd.Series.str.contains() podemos filtrar una columna de texto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con el método `pd.DataFrame.groupby()` podemos agrupar los datos por los valores de una columna específica. Y con el método `pd.core.groupby.DataFrameGroupBy.agg()` podemos agregar los datos de cada grupo con un método específico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84.154687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.662183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.707707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Fare\n",
       "Pclass           \n",
       "1       84.154687\n",
       "2       20.662183\n",
       "3       13.707707"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_titanic = titanic[[\"Pclass\",\"Fare\"]].groupby(by = \"Pclass\") # Agrupamos los datos por la clase del pasajero\n",
    "grouped_titanic.agg(\"mean\") # Agregamos los datos utilizando el promedio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "values_per_Pclass = {col_name:col for col_name, col in grouped_titanic[\"Fare\"]} # Creamos un diccionario con los datos agrupados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El módulo `stats` de `scipy` contiene una serie de funciones estadísticas.\n",
    "\n",
    "Verificaremos si hay diferencias estadísticamente significativas entre las medias de las tarifas de los pasajeros de las distintas clases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=240.66041792910025, pvalue=3.6153954125446083e-84)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.f_oneway(*values_per_Pclass.values()) # Prueba ANOVA sobre la tarifa agrupada por clase del pasajero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para verificar si hay diferencias estadísticamente significativas en la proporción de sobrevivientes por clase. Lo primero que se debe hacer es construir una tabla de contingencia utilizando la función `pd.crosstab()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>368</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived    0    1\n",
       "Pclass            \n",
       "1          80  136\n",
       "2          97   87\n",
       "3         368  119"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table = pd.crosstab(titanic.Pclass,titanic.Survived) # Tabla de contingencia de la clase vs la sobrevivencia del pasajero\n",
    "contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101.21674250560609,\n",
       " 1.0496962042983216e-22,\n",
       " 2,\n",
       " array([[132.71702368,  83.28297632],\n",
       "        [113.05524239,  70.94475761],\n",
       "        [299.22773393, 187.77226607]]))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.chi2_contingency(contingency_table) # Aplicamos la prueba estadística chi cuadrado sobre la tabla de contingencia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El segundo elemento del resultado anterior el el `p_value`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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