02_Data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img\\dilbert.jpg\" alt=\"Python\" width=\"900\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La teoría de bases de datos incluye los principios formales para definir y manipular datos estructurados e interrelacionados. Para poder entender esta teoría, primero debemos comprender ¿qué son los datos? Los **datos** son las características que describen sucesos y objetos. Los datos son comunicados por varios tipos de símbolos tales como las letras del alfabeto, números, puntos y rayas, señales, dibujos, etc. Lo importante es considerar que estos símbolos se pueden ordenar y reordenar de forma utilizable, esto con el fin de obtener información a través de ellos.\n",
    "\n",
    "Un **dato** de manera individual no es capaz de aportar muchos detalles de la situación o del objeto. La importancia de los datos está en su capacidad de asociarse dentro de un contexto para convertirse en información. Para ser útiles, los datos\n",
    "deben convertirse en información para ofrecer un significado, conocimiento, ideas o conclusiones.\n",
    "\n",
    "A las agrupaciones de datos se les conoce como **bases de datos** (**DB** por sus siglas en ingles). Las DB son un almacén de datos relacionados con diferentes modos de organización. Una DB representa algunos aspectos del mundo real, aquellos que le interesan al usuario. Con la palabra “datos” se hace referencia a hechos conocidos que pueden registrarse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los **modelos de datos** facilitan el diseño de bases de datos. Para especificar la estructura y las restricciones (i.e. árboles, grafos y relaciones) se usa un **lenguaje de definición de datos** (**DDL**, por sus siglas en inglés) y para especificar la manipulación de los datos se utiliza el **lenguaje de manipulación de datos** (**DML**, por sus siglas en inglés). Un **DML** ofrece mecanismos para recuperar datos de la base de datos vigente y para actualizar datos produciendo un nuevo estado de la base de datos. Los DML tienen como propósito ofrecer facilidad, simplicidad y flexibilidad a la hora de utilizarlos para actualizar y recuperar información desde la base de datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un ***sistema de gestión de bases de datos** (**DBMS**) es una capa de software necesaria para crear, manipular y recuperar datos desde una base de datos. Un SGBD es una herramienta de propósito general útil para estructurar, almacenar y controlar los datos ofreciendo interfaces de acceso a la base de datos. Mediante el DDL y el DML, respectivamente, un usuario define una base de datos (tipos, estructura y restricciones) y puede recuperar, actualizar, insertar o borrar datos. Los usuarios no necesitan conocer detalles de almacenamiento de la base de datos, sólo requieren tener una vista abstracta de los datos. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por esta razón la arquitectura de un SGBD, generalmente, se basa en la arquitectura de tres niveles (externo, conceptual e interno):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img\\arq.png\" alt=\"Python\" width=\"1000\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **El nivel más bajo, el físico**, trata con los mecanismos de almacenamiento físico que el sistema operativo utiliza (dispositivos físicos).\n",
    "\n",
    "* **El nivel interno** describe la forma como los datos se almacenan en la base de datos (i.e. estructuras de datos, espacios de almacenamiento, índices, formato de registros).\n",
    "\n",
    "* **El nivel conceptual**, representado en la arquitectura, corresponde a la descripción de los datos y de las relaciones entre éstos. A este nivel, la base de datos se ve como la integración de todas las vistas de los usuarios de la base de datos.\n",
    "\n",
    "* **En el nivel externo** se representa cada una las partes de la bd que es relevante para cada uno de los diferentes usuarios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img\\arq2.png\" alt=\"Python\" width=\"1000\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acceso a base de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En Python, la librería para poder acceder y manipular bases de datos (DML) es **pandas**. Por ello es necesario hacer su importación. En la versión 3 de Python la librería ya está incluida, para mayor información, acceder a la documentación de la librería: https://pandas.pydata.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Primero importamos la librería de pandas junto con otras 2 \n",
    "# que nos servirán para el manejo y visualización de los datos:\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La librería de **numpy** nos ayudará para hacer uso de funciones de algebra lineal, creación de numeros aleatorios, manejo de arreglos, entre otras opciones. Para poder tener una mayor idea de las características disponibles en esta librería, se puede acceder a la documentación de la misma: https://numpy.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "d=np.array([1,2,3])\n",
    "print(type(d))\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "[[ 1  2  3]\n",
      " [ 3  4  5]\n",
      " [10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "nd=np.array([[1,2,3],[3,4,5],[10,11,12]])\n",
    "print(type(nd))\n",
    "print(nd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nd.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matriz de ceros, Matriz identidad, y Operaciones con matrices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros([3,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Matriz identidad\n",
    "identidad=np.eye(3)\n",
    "identidad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3., 2., 2.],\n",
       "       [2., 3., 2.],\n",
       "       [2., 2., 3.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e=identidad+2\n",
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.32335969,  1.90819827, -1.55613266],\n",
       "       [ 1.33237923,  1.02110628,  0.60439701],\n",
       "       [-0.34260421, -0.90957661,  0.87330235],\n",
       "       [ 0.07177562,  0.68608502, -1.90537474],\n",
       "       [ 0.27946616, -1.85372355, -0.44309259]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=np.random.randn(5,3)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Arreglo de números consecutivos del 0 a la posición indexada 20\n",
    "a=np.arange(0,20)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cambiando la forma de un arreglo:\n",
    "a45=a.reshape(4,5)\n",
    "a45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  5, 10, 15],\n",
       "       [ 1,  6, 11, 16],\n",
       "       [ 2,  7, 12, 17],\n",
       "       [ 3,  8, 13, 18],\n",
       "       [ 4,  9, 14, 19]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transponer una matriz\n",
    "b=a45.T\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.19596004,  0.50288153,  0.1403561 ,  1.05266583],\n",
       "       [ 0.09681345,  0.28954039, -0.35072035, -1.89172981],\n",
       "       [ 0.01521617, -1.40939476,  1.96229981, -1.28079158],\n",
       "       [ 0.91133647, -0.69037629,  0.265265  ,  0.51992358],\n",
       "       [-0.40784478,  1.24380599,  1.23844795, -0.26795633]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c54=np.random.randn(5,4)\n",
    "c54"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se pueden unir 2 arreglos, siempre y cuando tengan las mismas dimensiones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00,  5.00000000e+00,  1.00000000e+01,\n",
       "         1.50000000e+01],\n",
       "       [ 1.00000000e+00,  6.00000000e+00,  1.10000000e+01,\n",
       "         1.60000000e+01],\n",
       "       [ 2.00000000e+00,  7.00000000e+00,  1.20000000e+01,\n",
       "         1.70000000e+01],\n",
       "       [ 3.00000000e+00,  8.00000000e+00,  1.30000000e+01,\n",
       "         1.80000000e+01],\n",
       "       [ 4.00000000e+00,  9.00000000e+00,  1.40000000e+01,\n",
       "         1.90000000e+01],\n",
       "       [ 2.19596004e+00,  5.02881531e-01,  1.40356098e-01,\n",
       "         1.05266583e+00],\n",
       "       [ 9.68134473e-02,  2.89540394e-01, -3.50720352e-01,\n",
       "        -1.89172981e+00],\n",
       "       [ 1.52161693e-02, -1.40939476e+00,  1.96229981e+00,\n",
       "        -1.28079158e+00],\n",
       "       [ 9.11336468e-01, -6.90376287e-01,  2.65265004e-01,\n",
       "         5.19923578e-01],\n",
       "       [-4.07844780e-01,  1.24380599e+00,  1.23844795e+00,\n",
       "        -2.67956334e-01]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat1=np.concatenate([b,c54])\n",
    "print(concat1.shape)\n",
    "concat1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00,  5.00000000e+00,  1.00000000e+01,\n",
       "         1.50000000e+01,  2.19596004e+00,  5.02881531e-01,\n",
       "         1.40356098e-01,  1.05266583e+00],\n",
       "       [ 1.00000000e+00,  6.00000000e+00,  1.10000000e+01,\n",
       "         1.60000000e+01,  9.68134473e-02,  2.89540394e-01,\n",
       "        -3.50720352e-01, -1.89172981e+00],\n",
       "       [ 2.00000000e+00,  7.00000000e+00,  1.20000000e+01,\n",
       "         1.70000000e+01,  1.52161693e-02, -1.40939476e+00,\n",
       "         1.96229981e+00, -1.28079158e+00],\n",
       "       [ 3.00000000e+00,  8.00000000e+00,  1.30000000e+01,\n",
       "         1.80000000e+01,  9.11336468e-01, -6.90376287e-01,\n",
       "         2.65265004e-01,  5.19923578e-01],\n",
       "       [ 4.00000000e+00,  9.00000000e+00,  1.40000000e+01,\n",
       "         1.90000000e+01, -4.07844780e-01,  1.24380599e+00,\n",
       "         1.23844795e+00, -2.67956334e-01]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Al agregar el valor de axis=1 indicamos que la concatenación sea horizontal\n",
    "# y no una encima de la otra:\n",
    "\n",
    "concat2=np.concatenate([b,c54], axis=1)\n",
    "print(concat2.shape)\n",
    "concat2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Procedemos a verificar que cada una de las librerías funciona de forma correcta:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# La siguiente sintaxis sirve para crear una semilla que dará el mismo grupo de números aleatorios,\n",
    "# cada que se reinicie el Kernel\n",
    "\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5488135, 0.71518937, 0.60276338, 0.54488318)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Los siguientes son los primeros 4 números del conjunto con semilla:\n",
    "\n",
    "0.5488135 , 0.71518937, 0.60276338, 0.54488318"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97301163, 0.15848038, 0.47369496, 0.24520724])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conjunto de números aleatorios sin semilla:\n",
    "\n",
    "np.random.seed() ; np.random.rand(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.80163659, 0.14445707, 0.9345379, 0.02746618)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Los siguientes son los primeros 4 números del conjunto sin semilla:\n",
    "\n",
    "0.80163659, 0.14445707, 0.9345379 , 0.02746618"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,\n",
       "       0.64589411, 0.43758721, 0.891773  , 0.96366276, 0.38344152,\n",
       "       0.79172504, 0.52889492, 0.56804456, 0.92559664, 0.07103606,\n",
       "       0.0871293 , 0.0202184 , 0.83261985, 0.77815675, 0.87001215,\n",
       "       0.97861834, 0.79915856, 0.46147936, 0.78052918, 0.11827443,\n",
       "       0.63992102, 0.14335329, 0.94466892, 0.52184832, 0.41466194,\n",
       "       0.26455561, 0.77423369, 0.45615033, 0.56843395, 0.0187898 ,\n",
       "       0.6176355 , 0.61209572, 0.616934  , 0.94374808, 0.6818203 ,\n",
       "       0.3595079 , 0.43703195, 0.6976312 , 0.06022547, 0.66676672,\n",
       "       0.67063787, 0.21038256, 0.1289263 , 0.31542835, 0.36371077,\n",
       "       0.57019677, 0.43860151, 0.98837384, 0.10204481, 0.20887676,\n",
       "       0.16130952, 0.65310833, 0.2532916 , 0.46631077, 0.24442559,\n",
       "       0.15896958, 0.11037514, 0.65632959, 0.13818295, 0.19658236,\n",
       "       0.36872517, 0.82099323, 0.09710128, 0.83794491, 0.09609841,\n",
       "       0.97645947, 0.4686512 , 0.97676109, 0.60484552, 0.73926358,\n",
       "       0.03918779, 0.28280696, 0.12019656, 0.2961402 , 0.11872772,\n",
       "       0.31798318, 0.41426299, 0.0641475 , 0.69247212, 0.56660145,\n",
       "       0.26538949, 0.52324805, 0.09394051, 0.5759465 , 0.9292962 ,\n",
       "       0.31856895, 0.66741038, 0.13179786, 0.7163272 , 0.28940609,\n",
       "       0.18319136, 0.58651293, 0.02010755, 0.82894003, 0.00469548])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creamos un arreglo a partir de un grupo de números aleatorios con semilla:\n",
    "\n",
    "np.random.seed(0)\n",
    "arreglo=np.random.rand(100)\n",
    "arreglo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora revisaremos la librería de pandas, crearemos una serie a partir del arreglo de Numpy, para ello usaremos el método **Series()** de pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.548814\n",
       "1     0.715189\n",
       "2     0.602763\n",
       "3     0.544883\n",
       "4     0.423655\n",
       "5     0.645894\n",
       "6     0.437587\n",
       "7     0.891773\n",
       "8     0.963663\n",
       "9     0.383442\n",
       "10    0.791725\n",
       "11    0.528895\n",
       "12    0.568045\n",
       "13    0.925597\n",
       "14    0.071036\n",
       "15    0.087129\n",
       "16    0.020218\n",
       "17    0.832620\n",
       "18    0.778157\n",
       "19    0.870012\n",
       "20    0.978618\n",
       "21    0.799159\n",
       "22    0.461479\n",
       "23    0.780529\n",
       "24    0.118274\n",
       "25    0.639921\n",
       "26    0.143353\n",
       "27    0.944669\n",
       "28    0.521848\n",
       "29    0.414662\n",
       "        ...   \n",
       "70    0.976459\n",
       "71    0.468651\n",
       "72    0.976761\n",
       "73    0.604846\n",
       "74    0.739264\n",
       "75    0.039188\n",
       "76    0.282807\n",
       "77    0.120197\n",
       "78    0.296140\n",
       "79    0.118728\n",
       "80    0.317983\n",
       "81    0.414263\n",
       "82    0.064147\n",
       "83    0.692472\n",
       "84    0.566601\n",
       "85    0.265389\n",
       "86    0.523248\n",
       "87    0.093941\n",
       "88    0.575946\n",
       "89    0.929296\n",
       "90    0.318569\n",
       "91    0.667410\n",
       "92    0.131798\n",
       "93    0.716327\n",
       "94    0.289406\n",
       "95    0.183191\n",
       "96    0.586513\n",
       "97    0.020108\n",
       "98    0.828940\n",
       "99    0.004695\n",
       "Length: 100, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie = pd.Series(arreglo)\n",
    "serie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.548814\n",
       "1    0.715189\n",
       "2    0.602763\n",
       "3    0.544883\n",
       "4    0.423655\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Una forma de revisar los datos sin que se impriman todos en pantalla, es mediante los métodos\n",
    "# head() y tail()\n",
    "\n",
    "serie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.548814\n",
       "1    0.715189\n",
       "2    0.602763\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95    0.183191\n",
       "96    0.586513\n",
       "97    0.020108\n",
       "98    0.828940\n",
       "99    0.004695\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97    0.020108\n",
       "98    0.828940\n",
       "99    0.004695\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, revisamos que la importación de la librería matplotlib fuera correcta:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "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": [
    "# Utilizamos el método plot para obtener una gráfica de la serie\n",
    "\n",
    "serie.plot(kind='hist', title='Distribución de los datos')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100.000000\n",
       "mean       0.472794\n",
       "std        0.289754\n",
       "min        0.004695\n",
       "25%        0.205803\n",
       "50%        0.467481\n",
       "75%        0.684483\n",
       "max        0.988374\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtenemos las estadísticas descriptivas de la serie mediante el método describe()\n",
    "serie.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "arreglo1=np.random.rand(10)\n",
    "arreglo2=np.random.rand(10)\n",
    "arreglo3=np.random.rand(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "diccionario={\"A\":arreglo1,\"B\":arreglo2, \"C\":arreglo3}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando la librería de pandas y el diccionario anterior, crearemos un DataFrame. Los DataFrame son estructuras de datos de pandas de forma tabular:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.548814</td>\n",
       "      <td>0.791725</td>\n",
       "      <td>0.978618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.715189</td>\n",
       "      <td>0.528895</td>\n",
       "      <td>0.799159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.602763</td>\n",
       "      <td>0.568045</td>\n",
       "      <td>0.461479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.544883</td>\n",
       "      <td>0.925597</td>\n",
       "      <td>0.780529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.423655</td>\n",
       "      <td>0.071036</td>\n",
       "      <td>0.118274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  0.548814  0.791725  0.978618\n",
       "1  0.715189  0.528895  0.799159\n",
       "2  0.602763  0.568045  0.461479\n",
       "3  0.544883  0.925597  0.780529\n",
       "4  0.423655  0.071036  0.118274"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({\"A\":arreglo1,\"B\":arreglo2, \"C\":arreglo3})\n",
    "df.head()"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.548814</td>\n",
       "      <td>0.791725</td>\n",
       "      <td>0.978618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.715189</td>\n",
       "      <td>0.528895</td>\n",
       "      <td>0.799159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.602763</td>\n",
       "      <td>0.568045</td>\n",
       "      <td>0.461479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.544883</td>\n",
       "      <td>0.925597</td>\n",
       "      <td>0.780529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.423655</td>\n",
       "      <td>0.071036</td>\n",
       "      <td>0.118274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  0.548814  0.791725  0.978618\n",
       "1  0.715189  0.528895  0.799159\n",
       "2  0.602763  0.568045  0.461479\n",
       "3  0.544883  0.925597  0.780529\n",
       "4  0.423655  0.071036  0.118274"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(diccionario)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14ceeadbb70>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.plot(title='Datos dentro del DataFrame')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.615766</td>\n",
       "      <td>0.547343</td>\n",
       "      <td>0.580251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.194454</td>\n",
       "      <td>0.358835</td>\n",
       "      <td>0.304107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.383442</td>\n",
       "      <td>0.020218</td>\n",
       "      <td>0.118274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.464411</td>\n",
       "      <td>0.197571</td>\n",
       "      <td>0.426366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.575788</td>\n",
       "      <td>0.673101</td>\n",
       "      <td>0.580885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.697866</td>\n",
       "      <td>0.822396</td>\n",
       "      <td>0.794501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.963663</td>\n",
       "      <td>0.925597</td>\n",
       "      <td>0.978618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A          B          C\n",
       "count  10.000000  10.000000  10.000000\n",
       "mean    0.615766   0.547343   0.580251\n",
       "std     0.194454   0.358835   0.304107\n",
       "min     0.383442   0.020218   0.118274\n",
       "25%     0.464411   0.197571   0.426366\n",
       "50%     0.575788   0.673101   0.580885\n",
       "75%     0.697866   0.822396   0.794501\n",
       "max     0.963663   0.925597   0.978618"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un DataFrame puede ser creado utilizando estructuras de datos de diferente tipo, a continuación utilizaremos 3 arreglos diferentes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Los arreglos 5 y 6 contienen números aleatorios, pero dentro de un rango específico:\n",
    "# randint(min, max-1, cantidad)\n",
    "\n",
    "arreglo4=np.random.rand(10)\n",
    "arreglo5=np.random.randint(1,100,10)\n",
    "arreglo6=np.random.randint(1,4,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.26455561 0.77423369 0.45615033 0.56843395 0.0187898  0.6176355\n",
      " 0.61209572 0.616934   0.94374808 0.6818203 ]\n",
      "[59 32  2 66 42 58 36 12 47 83]\n",
      "[1 3 2 1 2 3 1 1 3 1]\n"
     ]
    }
   ],
   "source": [
    "print(arreglo4)\n",
    "print(arreglo5)\n",
    "print(arreglo6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x', 'z', 'y', 'x', 'y', 'z', 'x', 'x', 'z', 'x']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A partir del arreglo 5 crearemos una lista de letras, donde 1=x, 2=y, 3=z\n",
    "lista=[]\n",
    "\n",
    "for i in arreglo6:\n",
    "    if i==1:\n",
    "        lista.append(\"x\")\n",
    "    elif i==2:\n",
    "        lista.append(\"y\")\n",
    "    else:\n",
    "        lista.append(\"z\")\n",
    "\n",
    "lista"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos el DataFrame a partir de arreglo 4, arreglo5 y lista:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.264556</td>\n",
       "      <td>59</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.774234</td>\n",
       "      <td>32</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.456150</td>\n",
       "      <td>2</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.568434</td>\n",
       "      <td>66</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.018790</td>\n",
       "      <td>42</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A   B  C\n",
       "0  0.264556  59  x\n",
       "1  0.774234  32  z\n",
       "2  0.456150   2  y\n",
       "3  0.568434  66  x\n",
       "4  0.018790  42  y"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diccionario2={\"A\":arreglo4,\"B\":arreglo5, \"C\":lista}\n",
    "df1=pd.DataFrame(diccionario2)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es posible modificar el índice de un DataFrame utilizando una serie de la misma dimensión en filas que dicho DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "serie=[1,2,3,4,5,6,7,8,9,10]\n",
    "serie2=range(1,11) # Recordar que con range el límite superior hay que restarle 1, por tratarse de posición indexada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
       "      <td>0.264556</td>\n",
       "      <td>59</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.774234</td>\n",
       "      <td>32</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.456150</td>\n",
       "      <td>2</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.568434</td>\n",
       "      <td>66</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.018790</td>\n",
       "      <td>42</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.617635</td>\n",
       "      <td>58</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.612096</td>\n",
       "      <td>36</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.616934</td>\n",
       "      <td>12</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.943748</td>\n",
       "      <td>47</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.681820</td>\n",
       "      <td>83</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A   B  C\n",
       "1   0.264556  59  x\n",
       "2   0.774234  32  z\n",
       "3   0.456150   2  y\n",
       "4   0.568434  66  x\n",
       "5   0.018790  42  y\n",
       "6   0.617635  58  z\n",
       "7   0.612096  36  x\n",
       "8   0.616934  12  x\n",
       "9   0.943748  47  z\n",
       "10  0.681820  83  x"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.DataFrame(diccionario2, index=serie)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.264556</td>\n",
       "      <td>59</td>\n",
       "      <td>x</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.774234</td>\n",
       "      <td>32</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.456150</td>\n",
       "      <td>2</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.568434</td>\n",
       "      <td>66</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.018790</td>\n",
       "      <td>42</td>\n",
       "      <td>y</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.617635</td>\n",
       "      <td>58</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.612096</td>\n",
       "      <td>36</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.616934</td>\n",
       "      <td>12</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.943748</td>\n",
       "      <td>47</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.681820</td>\n",
       "      <td>83</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A   B  C\n",
       "1   0.264556  59  x\n",
       "2   0.774234  32  z\n",
       "3   0.456150   2  y\n",
       "4   0.568434  66  x\n",
       "5   0.018790  42  y\n",
       "6   0.617635  58  z\n",
       "7   0.612096  36  x\n",
       "8   0.616934  12  x\n",
       "9   0.943748  47  z\n",
       "10  0.681820  83  x"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.DataFrame(diccionario2, index=serie2)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.555440</td>\n",
       "      <td>43.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.259940</td>\n",
       "      <td>24.572116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.018790</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.484221</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.614515</td>\n",
       "      <td>44.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.665774</td>\n",
       "      <td>58.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.943748</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A          B\n",
       "count  10.000000  10.000000\n",
       "mean    0.555440  43.700000\n",
       "std     0.259940  24.572116\n",
       "min     0.018790   2.000000\n",
       "25%     0.484221  33.000000\n",
       "50%     0.614515  44.500000\n",
       "75%     0.665774  58.750000\n",
       "max     0.943748  83.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    5\n",
       "z    3\n",
       "y    2\n",
       "Name: C, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"C\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14ceeb87f98>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u08/nqffAEU0sftxruC6L/PEzMPu5xMZokm/qw/r96N+IMzbP/Cd30HHv7faFCbfaqAJfqvvbQ0n+SHTcH064TZPl3JeSe6zmilbB+B/DJy/q5Lvm9rUe91MoOgJevQE2LE1MjCb5NizRz+uQ0ZDXwnc0cQtGcs8tgK//FlbPhRl/9x1N9tm0QsdiJ6u/vbYRV0K3gdpFEdQp+tEovHyNLul64i/gqKubv89wjnbPuCp48YrMGvefyaY9DggccZnvSBolGMkddM2ZPsfB278N7hc+UzV1/famCudo3dXt6/VsLWicg/9eD7OehONvhWNvTNy+O/TRiV2R4sxeliFT7NwKM8dCv7N0Kes0EpzkLqKTXco3wuSGl4Y3CRQphrzW0LV/6o7Z9TA45gbtg/70jdQdtyHOwes36Vrdx94IX7s58ccYcCEceq42ZJbNTPz+TeJ89IzmpBHBXf1xb4KT3EHXRR76PV0nefU839Fkj0iJXvAL56T2uMf9RIeWvXpdMIYIOqcjeaY9pt0wI3+enJERIjq5qdU+8PwPtHVogsc5KH1UuxB7DvcdTaMFK7mDLriU31pP19NhNEW627pWF75KVX97TTn52j2zaTn871epP35NzmkMUx/Syjon/ya5Q94K28O5j+q1jiB2TRn47C1Yu0Bb7Wky/LGm4CX3Fh10NMXnb8OC131Hk/l2jW/3kNwBiobCiB/D9DGw+H0/MQBMvguK79czx1PvTs2Xuc+xusDYzLEwz8pPBk7pI7ocx6Hn+o6kSYKX3EG/YJ0Phom36XoOJnkiJZBTqMvU+jLydi188PLVunZLqr17ny5LPOgiOP0PqW2lnXA7dBug/+3pNLEr061dCAvf0HkNOfm+o2mSYCb3cC6cehes/0InD5jkibyvy0Dk5PmLIa8lnPVn7aJ4+3epPXbxA1oWr/8FGkOyx/nXlpMH543RH7WXfmRLYAdF6aMQztOGZpoKZnIHLXxw0Onaqtq80nc0mWn7Blg5p+nFsBNpv6/B4NG6zlCqRpBMfRje/IUu7nXOX/1V1enUF079nXZFlj7iJwaz2/YNMOtp/Vy06uI7miYLbnIHnRVYuQMm/dp3JJlpyVTApW58e0NO+Y2OIBl/lS7UlUzTHocJt0C/UXph03e5tCGXamPmf7/UH1zjz4dPQsVWvbCexoKd3DvuD0f+GGY9Bctm+I4m80SK9dSzaKjvSFRBWx0iuPoTvbiZLDPG6to2B52uXSLh3OQdK14iuu5OYXsdHunj2oPRWcPTHoVeR0L3gb6jaZZgJ3eAY3+iV6xfv9mGRiZapFgLpuQW+o5kt4NOg8O+Ce/cm5y5DrOehleu1eIh3/qH32sNtbXspN1Da+ZpERuTegte17Vk0rzVDumQ3Ava6LLAZR/A7Gd9R5M5dmyB5bOC0yVT02n36P/3RNddnf0cjL8S9jsezn8ymKMgDjgJhv9IW48L3/QdTfYpfUTX3j/4TN+RNFvwkzvAgG/rLLE3f2El+RJlaakuYOVrfHt9WnbSAhfLpifuAuMnL2kt032Phgue1sXqguqkX+nSyy/9GLas8R1N9lg5Bxa/B0f8IPWztZMgPZJ7KKRf9s0rktsXm00iJSBh6DnMdyR1O+w8OPBUmPQbHSLZHPP/C89/X5fbvfCZ4C/bmlsA5z2ha5q8fJV1R6bK1L9CbgsYfInvSBIiPZI76Nonh38Liv+c/kWWgyBSrBeM8lv7jqRuIlr1KJyrS+82NcF9+gb8Z7Se+X3nWchvldg4k2WfQ7Us4acTtCizSa7NK+Hj/8DA7+gs+QyQPskd4KQ7dMjamz/3HUl6q9iuo4+C2N9eU9secPKv9VR55tjGb79oEvz7Ik2UFz2v/fjpZNgVsP+JupjZmgW+o8ls0x6HaGVarv64N+mV3Nv20GVi546HL97zHU36KpsOVTuDMXmpIUO+C72PhTd+rguMxevzd+CZb0OnA+HiF6GwXdJCTJpQSEfP5LXQbiVbiiM5dm7TtY0OPkOHX2eI9EruAEddBW176QQUq2TTNJESQKDXCN+RNEwERv1Zi1S/ekN83TOREhh3AXTYDy4Zn96n2a27wqgHYeVsXSbBJN5H47RwzJFX+o4kodIvuecW6kzGVXOadqputL+962Hp05rtsB+M/Bl8+jrMeb7+9y6dBk99C9oWaWJv2TE1MSbTwafrDNaSv+gZiUmcaFQvpHYfpBOXMkj6JXeAQ87WLoVJv9FfXBO/yp2aAIM4BLI+I36kE65ev0nXoK/Lspnw5Hm6hMHoV9J6XZCv+PpvoeMB8OIPrQxlIi18A9YtgiOvSss12+uTnsldRFeNLN8Ak+/xHU16WTELKrenX3IPhbWwR/kmna1c24qP4F/naBfM6Fe0OyOT5LXU4ZFb12jlKhsemRhTHoQ2RdpgzDDpmdwBuvXXVQSnPQar5/uOJn1UF8QI+kiZunTpB8f9FOY8t2chl1WfwD/Pgfw2mtjb9vAXYzJ1H6jdU3PH6zIKpnmWz9KRWMOvCMb6QgmWvskd9IOe1womWkm+uEVKtBBKy06+I2maY66HLofqxdXyjfrDPnYU5BTA6JehXS/fESbXUVfr6KHXb4J1n/mOJr1N/avmjwyZtFRbeif3lp3g+Fu01uGnE31HE3xVlbrMbzq22qvl5MHZf4EtK3V6/j9HaZfN6Ff0wmumC4Xh3Ef09oXLdRSRabxNy/Xi/KCL02dgQSOld3IHGHaZjmWeeGvy1wBPd6tmw87N6dffXluPIXoBbP6rOhx29CvQ6QDfUaVO2yI46wFde+ede31Hk56mPQYuCiPSf/XHvUn/5B7Oha/fpeuPWBWb+i0u1tt0brlXO+E27aL57qvQ+SDf0aTeoefqgnrv/T5WdMXEbccWXdKh31lauzdDxZ3cRSQsIh+KyKuxx31EpFREForIv0XE38LYfU+Cvl/XVsyW1d7CCLxICbTvA226+46k+XILY6sn9vMdiT+n3aPXGF64TK8/mPjMelr/Xkde5TuSpGpMy/1aoGb1hHuAPznn+gLrge8nMrBG+/rvdIifleSrWzQKS0qgd5p3yZjdCtrANx6HjcvgtZ/6jiY9RKv0QmrREcFdETVB4kruIlIEnAE8EXsswEjgudhbxgLnJCPAuHU6QKunfPgkLP/QayiBtGaeTvhK9/52s6eew+BrN8PH/9ZiJKZ+C16H9V9kfKsd4m+53w/cBERjjzsCG5xzlbHHZYD/wcVfuwladITXb7GhkbXt6m+35J5xjr0Reg7X4aEblviOJtimPKhdWRlQaakhDSZ3ETkTWO2cq1mhuq55unVmUxG5XESmi8j0NWuSXFWmoC2c+AtYOrXhNUiyTaRYZ+Jl+jjwbBTOgXMf1dEfL1xhC+rtTdkMWDJFyxhmQKWlhsTTcj8aGCUii4Fn0O6Y+4F2IlL9FyoC6lyP1Tn3mHNuqHNuaOfOnRMQcgMGXQRd+2tJvp3bkn+8dOCcJvfeR2fc+hkmpkMfOOP3el3l/T/5jiaYpj6ks5gHXeQ7kpRoMLk75251zhU553oDFwBvOee+A7wNfDP2ttHA+KRF2RihsJbk27QMih/wHU0wrFuka5JkwhBIs3f9z9fyhJPv0mIsZrcNS7WO7uBL0q9oSxM1Z5z7zcANIrII7YMfk5iQEmDfI/VDXny//k/NdpHq/vY0KM5hmq66NGGrrvD8ZVZMvqZpj+rt8MydtFRbo5K7c26yc+7M2P3PnXPDnHMHOOe+5ZwLVpmYk+4AxErygV5Mbdklo6rMmL0obAffeFQn9U281Xc0wVC+CWaMhUPPgXY9fUeTMuk/Q3Vv2vWEY66DT17cPVIkG1l/e/bpfYzO3p35T5j3iu9o/PvwSdixCUZkVqWlhmRucgc46hodITLh5uwdQbAhotcfbAhkdjn+Vug2EF6+Gjat8B2NP1WVUPqwVlkqGuI7mpTK7OSe1wJO+bXWn/zwX76j8SNSoreW3LNLTp4W96jcAS/9UGcoZ6P5r+rY/yyYtFRbZid3gEO/ob/ak34D2zf4jib1FhdDYXtdw91kl059tWLZ55N1yn02mvKgrqd00Gm+I0m5zE/uInDq3bBtHbx7n+9oUi9SDL2OglDm/682dRg8Gg46A976Daxd5Dua1Fo6Dco+gBE/1iHSWSY7vvHdB8Lgi3VJ4LULfUeTOpuW6zoatlhY9hKBM/8IOfna/55N3TNTHtRZ6wO/7TsSL7IjuQOM/DnktoCJt/mOJHV29bfb5KWs1rqr1jxYUgLTgzMdJanWL9aRQkMuhfxWvqPxInuSe6suurDYwjfg0zd8R5Mai9+HvNa6HIPJbgO/DfuPhP/9Kjsm9pU+ChLS4tdZKnuSO8CwK6DD/tp6z4aSfJES6DUiK/sbTS0icOb9Ou/h1esye9XU7Rt0jP9h52VGYZomyq7knpOnowfWLYQPHvcdTXJtWQNrF1h/u9mt/b5w0i9h0f90/fdMNfOfsHOLXkjNYtmV3AH6ngIHnAST79EEmKmW2Ph2U4cjLoOeI2DCLZlZkrKqQrtkeh+rAymyWPYldxEtyVexFd6+03c0yRMp0QvI3bL7A25qCYVg1F90OexMLM03dzxsKsvKSUu1ZV9yB+h8EAy7XBcTWvGR72iSY3Gx1onM8Ve33ARU5wPh+Jth7kuZtfaMczr8seMBeoae5bIzuYPWnWzRASbennkXl7avh1VzdAEpY+py1DXQ9XD47436eckES6Zo/eQRP7ZJe2Rzci9spwl+8Xvw+du+o0msJVMBZ+Pbzd6Fc+Hsh2DrWpj4M9/RJMaUh3SpjQEX+o4kELI3uQMM+S607QmTfp1ZrfdIMYTzoMdQ35GYIOs2QJfFnvUkLJrkO5rmWfcZzP8vDP2+Lhhosjy55+Rr6335h/rByBSLizWx5xb4jsQE3XE3QacD4ZXr0rtyU+kjejYy7HLfkQRGdid30FO4jgfA27/NjDXfd2zWi8TWJWPikVugo2c2LtUz2HS07UstyHH4t6D1Pr6jCQxL7uEcLWywei7MecF3NM23tBRclU1eMvHrNUJbvNMei12vSTMz/gEV27J+0lJtltxB13zf5zBtvVdV+I6meSIlIGEoGuY7EpNOTvyFXn8afxVUlPuOJn6VO/VHab/joethvqMJFEvuoMOmRv5Ml8ed9ZTvaJpncTF0H5S1K+GZJspvBWfdr0tzvHOP72ji98mLsHkFHHm170gCx5J7tQNP1YuQ79ybXi2Xmiq2w7IZ1t9umuaAE2HgRVD8QHpM7nMOpvxFq4wdcKLvaALHkns1ETjx51pMesbffUfTNGUfQLTCJi+Zpvv6ndCyE4y/MvhdlIvf0/rII36s31+zB0vuNe13PPQ5Dt79fXoOC4uUAAI9h/uOxKSrwvZwxh80aZb82Xc09ZvyELToBP3P9x1JIFlyr23kL2DbWh03m24ixTqlvLCd70hMOut3Fhxyjq6cuuZT39HUbe1C+HQCDLvM5nPshSX32noeof3vJX/WRf/TReVOWPqBLfFrEuP0+3Sm58tXBbPu6tS/QjhfZ6SaOllyr8sJt0P5Rij5i+9I4rf8Q6jcbuPbTWK06gKn3q3zJoJW2GbrOpj1NAw4H1p19h1NYFlyr0u3/jr2ferD6VPQI/K+3vaykTImQfqfr4Vt/ncHrI/4jma36X+DynKbtNQAS+57c8Jt2hJ+/4++I4lPpAQ694OWHX1HYjJFdd1VEXjl2mAsrle5QyctHXASdOnnO5pAs+S+N536woBvwwdjYOMy39HUr6pSp43b+HaTaO16wsl36LLYQZjgN/s52LraKi3FwZJ7fb52E7govHuv70jqt/JjLQhs/e0mGYZ8Ty/UT7wNNq/0F4dzOvyxy6E6bNnUy5J7fdrvC0MvhZn/0vWigypSrLfW326SIRSCs/6sXSL/vdFf98znb8PqT+DIK23SUhwsuTfk2J9o4YvJd/uOZO8iJdBhP2jTzXckJlN1OkBXT53/qhah9mHKQ9BqHzj8m36On2YsuTek9T4w/HKY/Sysmus7mq+KRjW52/h2k2xHXgXdBsJrP9E11FNp9TxY9D+dtJSTn9pjpylL7vE4+jrIb61LAgfN6rlQvsGSu0m+cA6c/aDWno9UAAARVUlEQVQW1J5wa2qPPfWvkFOo/f8mLpbc49Gig7Za5r+qqy4GSXV/u11MNanQ9XA45gb4+BlY+GZqjrllNXz0bxh4oQ31bQRL7vEa8SMo7ABv3ek7kj1FirXIQrteviMx2eK4n+gyu69cB+Wbkn+8D8ZA1Q6btNRIltzjVdAGjrkePntLC2IEgXPW325SLycfRj2oy2NPuiO5x6rYDh88oes9deqb3GNlGEvujTHsMmjVFd76TTBm661dCFvX2OQlk3o9j9CW9AdPJLex8/F/dJVWm7TUaA0mdxHpKSJvi8g8EflERK6NPd9BRN4UkYWx2/bJD9ez3EL42k9hyRS9cu/brv52K85hPBh5O7TvDS9frS3sRItGdfhj1/72GW+CeFrulcCNzrl+wAjgShE5BLgFmOSc6wtMij3OfIMu0f7tILTeI8U67rfDfn7jMNkpr6VObvryM5h8V+L3/9kkWLtAW+02aanRGkzuzrkVzrmZsfubgXlAD+BsYGzsbWOBc5IVZKDk5OlkjhUfwbyX/cXhnJ4O73u0ffCNP/t9DQZfostjL5uZ2H1PeRBad4NDz03sfrNEo/rcRaQ3MAgoBfZxzq0A/QEAuuxlm8tFZLqITF+zJk2Wz21I//Oh04Hw1m8hWuUnhvWLYfNy6283/p1yp55Bjr9Ki8Ykwso58PlkGH6FNqhMo8Wd3EWkFfA8cJ1zLu7xT865x5xzQ51zQzt3zpCF9UNhLeixdoFe8PEhUqK31hdpfCtoC2f+Sdd9Kb4/Mfuc+lfIbQFDvpuY/WWhuJK7iOSiif0p59wLsadXiUi32OvdgNXJCTGg+o3SCz2T70pca6UxIsU67r7TQak/tjG1HXQaHHYevHMvrJ7fvH1tXqmNpkEXacFu0yTxjJYRYAwwzzlXs3LFy8Do2P3RgKfVhDwJhWDkz2FDBD78V+qPHynWLpmQjWY1AXHavbpMx/grm9ddOe1xiFbC8B8mLrYsFE9mOBq4GBgpIrNi/04H7gZOFpGFwMmxx9ml78nQczi8e19yhoLtzcZl2uduk5dMkLTspAl+2XQofbRp+9i5DaaPgYPPgI77Jza+LBPPaJn3nXPinOvvnBsY+/eac26dc+5E51zf2G2Kl4kLABE48ReweYVO5kiV6v52u5hqgubwb+ps0rd+A19+0fjtPxqnC5PZpKVms3P65up9DOx3Arz/J9ixOTXHjLwP+W10ESdjgkQEzvgjhHLglWsaNxckGtULqd0HQ68RyYsxS1hyT4SRP4dt62Dqw6k5XqREP/yhcGqOZ0xjtO0BJ/8avngXZv4z/u0WToR1i6zSUoJYck+EoiFw0Bk6kSPZRQy2rIa1n1p/uwm2waOh97Hwxs9g0/L4tpnyELQpgkPOTm5sWcKSe6KMvF27ZYofSO5xdvW3W3I3ARYKwag/Q1UFvHpDw90zy2fB4vdgxA8hnJuaGDOcJfdE2edQvZhU+ihsXpW840RKdHJH94HJO4YxidBhPxj5M/j0dZjzfP3vnfpXyGulSxmYhLDknkjH3wpVO+G9PyTvGJFi6DnMWjcmPYz4EfQYAq/fBFvX1f2ejcs0+Q++RGe7moSw5J5IHfeHQd+BGX+HDUsSv/9tX8KqT2BfW3LApIlQWAt7lG+CCTfX/Z5pj4GL6joyJmEsuSfa12If4HfuSfy+l0wFnI1vN+lln0O0NN/sZ2HBhD1f27FFG0P9Runa8CZhLLknWtsiGPp9mDUO1i5K7L4jxRDO19NcY9LJMTdAl0Pg1euhfOPu52c9rY9t0lLCWXJPhmNv0DqTk3+X2P1GiqFoKOQWJHa/xiRbTh6c/SBsWQlv/kKfi1bphdSiYVq2zySUJfdkaNVFLyTNeR5Wzk7MPnds1gIhNgTSpKseQ3SC0ox/6ASnBa/B+i/0OZNwltyT5airIb+tFvRIhCWletHJ+ttNOjv+Nh0i+fI18P79WrLy4DN9R5WRLLknS2F7OPpqHeO79IPm7y9SrOt19BzW/H0Z40teCxj1F22xL5sOI34M4RzfUWUkS+7JNPxH0KKTrpDXXJFi6D5IixIbk856H6NJvXV3LchhksKSezLlt4Jjb4Qv3oHP32n6fnZu0+LD1iVjMsWpd8G1s7S4h0kKS+7JNvR70KaHtt4bs/xpTWUfQLTCJi+ZzJKT7zuCjGbJPdlyC+C4n2qC/nRi0/YRKQEJQa/hiY3NGJOxLLmnwqCLoH0feOtOLUjQWJFiLcxh624YY+JkyT0Vwrlwwm2wajbMfalx21bu0Fa/jW83xjSCJfdUOew86NwP3v4tVFXGv93yD6Gy3JK7MaZRLLmnSiisBT3WLYKPn4l/u8Xv622vI5MTlzEmI1lyT6WDz9Sx6pPv0e6WeERKdMGllh2TG5sxJqNYck8lES2mvXFJfIWDqyphaamNbzfGNJol91Tbf6T2n797n05Oqs/Kj2DnFutvN8Y0miX3VKtuvW9ZpRVo6rO4WG8tuRtjGsmSuw/7HgkHnATF9+9ZuKC2SAl0PABa75O62IwxGcGSuy8jfwbb18OUv9b9erQKlpRYf7sxpkksufvSfZDWjZzyYN1V4VfP1Va9dckYY5rAkrtPJ9wOO7dC8Z+++pr1txtjmsGSu09dDob+58O0x2HTij1fixRD217Qrqef2Iwxac2Su2/H3wLRSnjv97ufc04vpva2VrsxpmksufvWoQ8MvkSLBq9frM+t/RS2rbWLqcaYJrPkHgTH/VTro06+Rx9HrL/dGNM8ltyDoE13OOIHuqDYmgV6MbVVV60Sb4wxTWDJPSiOuR5yW+iSwNX97SK+ozLGpClL7kHRspNWhJ87HjYvt/52Y0yzWHIPkqOugoJ2et+KYRtjmsGSe5AUtIWTfqWFOTof5DsaY0waa1ZyF5FTRWSBiCwSkVsSFVRWG3opfG+C9bcbk0LOOXZWRimvqGJnZZRo1PkOqdlymrqhiISBh4CTgTLgAxF52Tk3N1HB1cU5x47KqP6rqIrdr6K8Ys/bHRVRymO3O2L/02q/d0dFlKiD3LCQExZyQiFyw0JuOEROOERuSPR213NCbih2G3s+Z4/He3lPXfsKCeGQIElK4s45nIOoc0Rjt7sfOxzgorsfR51uE3XgiG0T3b0NQH5uiIKcMAW5YfJzQoRC2fkD5JyjospRXllF+c4qtlfE/sXuuzjzQlx/vTjeJPHtqVH0E9KoDRot6qCiKkpFVZTKqIvd19vKmvd3vRalssqxM3ZbGY2ys1Jvdz9f13Yu9vze91lZRzIPCeSEQoRD1flBCIf0u7v7sX6fwyGJPb/n/d3v2b3t7sex/BESwrH91XycW+PYTdHk5A4MAxY55z4HEJFngLOBvSb3tVt28NDbi3Yn2ooaiXaPBFxHMq7xWnPkhIT8nNCuBCUie/zPrv7QVFSl5pe7ZrKv/nHICYVqJd3qxPvVRF37tvo9qZCXE6Ig9rfUf7H7OWH9Iah+ftd7dj+XX3u7nFr7yA2RX+u53HD9J5rVSXd7RRXlNZJt9WN9LrrruR11vGf7Tv28feW5St22PPZ8VQa07NJNddLMq/6ehEO774dqNbDCIQpyQ7TKz9mzkVVH46tmYy4cFqJR/RxVRTXpV8XyQfXjyqpojdc0b+x6rfqHpjLKtp1Vux7XfL2qqvr+7n1VxPaVyM9Vc5J7D2BpjcdlwPD6NlixsZz7Ji5AhF0JoGai1S9ziMLcMO0Kc3c/H/ui5+fufk9+TrjWtjXu75Ecdr83P0f/J8bDueo/vqMiGqWi8qs/ANWthuqWQWW0RquiKkpF1MW2q/89e7Q2qhxVzhESCIm27EXY9Ti067G210Kh3Y/32IYaz9V6j1Brm9gZRPVru/cTe09If2Cqf3TLK2K31WdIu5Knni2VV1SxubySNZt31Nhm9+vxtmxrC4dkjx+T/JwQO6uieyTypnw3wiGhRW6Ygrwwhbn6r/oHpUPLPArbxZ7L0x+uwrxQ7D1hCvPCu+/n6mc0J9TwZ8zF8UeI5z8l3r+lwzW6hd/Yk8rGti9FZFdyrXnWm5tT+yxan8+GM0Xnav4QOKpi+afzPY3fV3OSe11/6a981ETkcuBygJ699uXTO08jN5y87ohE2f3Bg0LCvsPJGM65WELefeZW/YOgLeTdPwY7ary26welxnM7KqPkh0N1JuXCms/tSso1n9udoBs6IzAmVURiXT4JSDnNSe5lQM0lC4uA5bXf5Jx7DHgMYOjQoS4vx75I2UxEYmdSYSjM9R2OMRmrOZn2A6CviPQRkTzgAuDlxIRljDGmOZrccnfOVYrIVcBEIAz8zTn3ScIiM8YY02TN6ZbBOfca8FqCYjHGGJMg1gFujDEZyJK7McZkIEvuxhiTgSy5G2NMBrLkbowxGUjimQadsIOJbAYWpOyA8ekErPUdRC0WU/yCGJfFFB+LKX4HOedaN2aDZg2FbIIFzrmhKT5mvURkusXUsCDGBMGMy2KKj8UUPxGZ3thtrFvGGGMykCV3Y4zJQKlO7o+l+HjxsJjiE8SYIJhxWUzxsZji1+i4UnpB1RhjTGpYt4wxxmSglCR3EfmbiKwWkTmpOF48RKSniLwtIvNE5BMRuTYAMRWIyDQR+SgW0x2+Y6omImER+VBEXvUdC4CILBaR2SIyqykjCZJBRNqJyHMiMj/2uToyADEdFPsbVf/bJCLXBSCu62Of8TkiMk5ECgIQ07WxeD7x9TeqK1eKSAcReVNEFsZu28ezr1S13P8BnJqiY8WrErjROdcPGAFcKSKHeI5pBzDSOTcAGAicKiIjPMdU7Vpgnu8gajnBOTcwQEPXHgAmOOcOBgYQgL+Xc25B7G80EBgCbANe9BmTiPQArgGGOucOQ5cMv8BzTIcBl6G1oQcAZ4pIXw+h/IOv5spbgEnOub7ApNjjBqUkuTvn3gW+TMWx4uWcW+Gcmxm7vxn9IvbwHJNzzm2JPcyN/fN+UUREioAzgCd8xxJUItIGOA4YA+Cc2+mc2+A3qq84EfjMORfxHQg6x6ZQRHKAFtRRxS3F+gFTnXPbnHOVwDvAuakOYi+58mxgbOz+WOCcePZlfe6AiPQGBgGlfiPZ1f0xC1gNvOmc8x4TcD9wExD1HUgNDnhDRGbE6vT6th+wBvh7rPvqCRFp6TuoWi4AxvkOwjm3DPg9sARYAWx0zr3hNyrmAMeJSEcRaQGczp5lRH3axzm3ArRRCnSJZ6OsT+4i0gp4HrjOObfJdzzOuarYKXQRMCx2uuiNiJwJrHbOzfAZRx2Ods4NBk5Du9SO8xxPDjAYeNg5NwjYSpynz6kQK4U5Cng2ALG0R1ujfYDuQEsRuchnTM65ecA9wJvABOAjtOs2bWV1cheRXDSxP+Wce8F3PDXFTukn4/9axdHAKBFZDDwDjBSRJ/2GBM655bHb1Wgf8jC/EVEGlNU403oOTfZBcRow0zm3yncgwEnAF865Nc65CuAF4CjPMeGcG+OcG+ycOw7tGlnoO6aYVSLSDSB2uzqejbI2uYuIoP2j85xzf/QdD4CIdBaRdrH7heiXYL7PmJxztzrnipxzvdHT+recc15bWSLSUkRaV98HTkFPq71xzq0ElorIQbGnTgTmegyptgsJQJdMzBJghIi0iH0PTyQAF59FpEvsthfwDYLz93oZGB27PxoYH89GKVk4TETGAccDnUSkDPilc25MKo5dj6OBi4HZsT5ugNtidWF96QaMFZEw+sP7H+dcIIYeBsw+wIuaF8gBnnbOTfAbEgBXA0/FukA+By71HA8AsT7kk4ErfMcC4JwrFZHngJlo18eHBGNm6PMi0hGoAK50zq1PdQB15UrgbuA/IvJ99IfxW3Hty2aoGmNM5snabhljjMlkltyNMSYDWXI3xpgMZMndGGMykCV3Y4zJQJbcjTEmA1lyN8aYDGTJ3RhjMtD/A/xgxTUQ7CRQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14ceea85630>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df1.plot(y=\"A\", title=\"Solo columna A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    5\n",
       "z    3\n",
       "y    2\n",
       "Name: C, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"C\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14ceec451d0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df1[\"C\"].value_counts().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14ceecb7748>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Es importante seleccionar la gráfica correcta dependiendo los datos, para el caso de \n",
    "# variables cualitativas, usar un histograma generaría el siguiente resultado:\n",
    "\n",
    "df1[\"C\"].value_counts().plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cee9f7048>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# La mejor opción en este caso es el uso de una gráfica de barras:\n",
    "df1[\"C\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Actividad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diseña un DataFrame, el cuál contenga 6 columnas y 100 filas. Este DataFrame corresponde a una entrevista que se realizó a 100 personas. Los detalles de cada columna son los siguientes:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. La columna corresponde al índice, con valores de 1 a 100.\n",
    "\n",
    "2. La columna corresponde al sexo. Crear una lista aleatoria para 100 entradas para sexo.\n",
    "\n",
    "3. La columna corresponde a la edad. Crear un arreglo con límites entre 16 y 65.\n",
    "\n",
    "4. La columna corresponde a la estatura. Crear una lista con límites entre 1.55 y 1.95.\n",
    "\n",
    "5. La columna corresponde al peso. Crear un arreglo con límites entre 60 y 100.\n",
    "\n",
    "6. La columna corresponde a la nacionalidad. Crear una lista aleatoria considerando 5 naciones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/spyder.png\" alt=\"Spyder\" width=\"300\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "indice=range(1,101)\n",
    "\n",
    "sexo=np.random.randint(1,3,100)\n",
    "edad=np.random.randint(16,66,100)\n",
    "estatura=np.random.randint(155,195,100)\n",
    "peso=np.random.randint(60,101,100)\n",
    "pais=np.random.randint(1,6,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex=[]\n",
    "\n",
    "for i in sexo:\n",
    "    if i==1:\n",
    "        sex.append(\"M\")\n",
    "    else:\n",
    "        sex.append(\"F\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "est_m=[]\n",
    "\n",
    "for i in estatura:\n",
    "    est_m.append(i/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "nacionalidad=[]\n",
    "\n",
    "for i in pais:\n",
    "    if i==1:\n",
    "        nacionalidad.append(\"México\")\n",
    "    elif i==2:\n",
    "        nacionalidad.append(\"UK\")\n",
    "    elif i==3:\n",
    "        nacionalidad.append(\"EUA\")\n",
    "    elif i==4:\n",
    "        nacionalidad.append(\"Canada\")\n",
    "    else:\n",
    "        nacionalidad.append(\"Francia\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicc={\"Sexo\":sex, \"Edad\":edad, \"Estatura\":est_m, \"Peso\":peso, \"Nacionalidad\":nacionalidad}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe=pd.DataFrame(dicc, index=indice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Sexo</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Estatura</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Nacionalidad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>58</td>\n",
       "      <td>1.77</td>\n",
       "      <td>62</td>\n",
       "      <td>Francia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>36</td>\n",
       "      <td>1.68</td>\n",
       "      <td>87</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>27</td>\n",
       "      <td>1.66</td>\n",
       "      <td>81</td>\n",
       "      <td>EUA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>1.71</td>\n",
       "      <td>99</td>\n",
       "      <td>Francia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>22</td>\n",
       "      <td>1.79</td>\n",
       "      <td>99</td>\n",
       "      <td>México</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sexo  Edad  Estatura  Peso Nacionalidad\n",
       "1    M    58      1.77    62      Francia\n",
       "2    F    36      1.68    87           UK\n",
       "3    F    27      1.66    81          EUA\n",
       "4    M    20      1.71    99      Francia\n",
       "5    F    22      1.79    99       México"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo serie de tiempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creamos un periodo de 1 semana, en el que se considerará cada minuto:\n",
    "\n",
    "periodo=7*24*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-07-01 00:00:00', '2016-07-01 00:01:00',\n",
       "               '2016-07-01 00:02:00', '2016-07-01 00:03:00',\n",
       "               '2016-07-01 00:04:00', '2016-07-01 00:05:00',\n",
       "               '2016-07-01 00:06:00', '2016-07-01 00:07:00',\n",
       "               '2016-07-01 00:08:00', '2016-07-01 00:09:00',\n",
       "               ...\n",
       "               '2016-07-07 23:50:00', '2016-07-07 23:51:00',\n",
       "               '2016-07-07 23:52:00', '2016-07-07 23:53:00',\n",
       "               '2016-07-07 23:54:00', '2016-07-07 23:55:00',\n",
       "               '2016-07-07 23:56:00', '2016-07-07 23:57:00',\n",
       "               '2016-07-07 23:58:00', '2016-07-07 23:59:00'],\n",
       "              dtype='datetime64[ns]', length=10080, freq='T')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creamos un índice, utilizando la función date_range de pandas para crear fechas para cada minuto:\n",
    "# El primer argumento corresponde a la fecha inicial, el segundo corresponde al número de periodos\n",
    "# y el tercero corresponde a la frecuencia entre periodos, donde \"T\" equivale a cada minuto.\n",
    "\n",
    "indice_serie = pd.date_range(\"2016-07-01\", periods=periodo, freq=\"T\")\n",
    "\n",
    "indice_serie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creamos un arreglo de números aleatorios para los 10800 minutos de la semana:\n",
    "\n",
    "data = np.random.randn(periodo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10080.000000\n",
       "mean        -0.017357\n",
       "std          0.987360\n",
       "min         -3.740101\n",
       "25%         -0.687434\n",
       "50%         -0.026596\n",
       "75%          0.649883\n",
       "max          3.801660\n",
       "Name: Serie de tiempo, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_tiempo = pd.Series(data=data, index=indice_serie, name='Serie de tiempo')\n",
    "serie_tiempo.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14ceed39748>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "serie_tiempo.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "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": [
    "# Creamos una submuestra, que agrupe los datos en grupos de 15 minutos:\n",
    "\n",
    "serie_15m=serie_tiempo.resample(\"15T\").plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf1acc8d0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "serie_tiempo.resample('15T').agg(['min', 'mean', 'max']).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El método subplot, crea un area de gráfico, dividido en filas y columnas, el cuál sirve para poner varios plots al mismo tiempo.\n",
    "\n",
    "                            subplot(No. filas, No. columnas, kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '1 gráfica dentro de un subplot')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Método para crear un arrego de números espaciados usando igualmente dentro de un intervalo:\n",
    "# linspace(inicio, fin, cantidad)\n",
    "x = np.linspace(0, 2 * np.pi, 400)\n",
    "\n",
    "# Creamos un segundo arreglo, utilizando como base el primero y la función seno:\n",
    "y = np.sin(x ** 2)\n",
    "\n",
    "# La siguiente instancia es para crear el area del gráfico y los ejes de los subplots:\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y)\n",
    "ax.set_title(\"1 gráfica dentro de un subplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También utilizaremos un método precargado en Python llamado **enumerate()**. Este método se utiliza en conjunto con iteradores, y sirve para poder llevar la cuenta de las iteraciones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 'Hola ')\n",
      "(1, 'mundo ')\n",
      "(2, '!!!')\n",
      "10 Hola \n",
      "11 mundo \n",
      "12 !!!\n"
     ]
    }
   ],
   "source": [
    "l1 = [\"Hola \",\"mundo \",\"!!!\"] \n",
    "  \n",
    "for i in enumerate(l1): \n",
    "    print (i)\n",
    "\n",
    "for i,elemento in enumerate(l1,10): # El segundo valor, es el punto de partida para enumerar.\n",
    "    print (i,elemento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instanciamos al mismo tiempo una figura que contendrá al subplot, y los ejes de los plots:\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 4))\n",
    "\n",
    "# Utilizamos un ciclo for para generar los subplots:\n",
    "for i, freq in enumerate(['15T', '30T', '1H']):\n",
    "    serie_tiempo.resample(freq).agg(['max', 'mean', 'min']).plot(ax=axs[i], title=freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las librerías son grandes herramientas para el manejo y manipulación de los datos. Al tener tantas opciones disponibles es probable que se pueda llegar a confundir uno. Existen en internet diversas **cheat sheets** dependiendo la librería que se vaya a ocupar. En el repositorio de GitHub hay una carpeta especial en la que se irán agregando hojas conforme vayan apareciendo. A continuación se presenta un ejemplo de como son estas hojas:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img\\Pandas.png\" alt=\"Python\" width=\"1000\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uno de los formatos más comunes para trabajar bases de datos en Pandas es el csv (comma-separated values). Existen otros formatos soportados por pandas como son el json, excel, html, xml, pdf, docx, sql, entre otros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis (EDA)\n",
    "El objetivo de un EDA es comprender el tipo de datos que están presentes en el DataFrame\n",
    "\n",
    "* Tipo de variables (cualitativa o cuantitativa)\n",
    "* Cuantitativas: variables descriptivas\n",
    "* Cualitativas: categorías\n",
    "* ¿Hay valores faltantes? ¿NaN?\n",
    "* Determinar el tipo de distribución de las variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# En el siguiente ejemplo importaremos un data set de libre acceso. Los datos de los pasajeros del Titanic:\n",
    "\n",
    "import pandas as pd\n",
    "df=pd.read_csv(\"Datasets/titanic_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>female</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>A24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "5              6         0       3   \n",
       "6              7         0       1   \n",
       "7              8         0       3   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "12            13         0       3   \n",
       "13            14         0       3   \n",
       "14            15         0       3   \n",
       "15            16         1       2   \n",
       "16            17         0       3   \n",
       "17            18         1       2   \n",
       "18            19         0       3   \n",
       "19            20         1       3   \n",
       "20            21         0       2   \n",
       "21            22         1       2   \n",
       "22            23         1       3   \n",
       "23            24         1       1   \n",
       "24            25         0       3   \n",
       "25            26         1       3   \n",
       "26            27         0       3   \n",
       "27            28         0       1   \n",
       "28            29         1       3   \n",
       "29            30         0       3   \n",
       "..           ...       ...     ...   \n",
       "861          862         0       2   \n",
       "862          863         1       1   \n",
       "863          864         0       3   \n",
       "864          865         0       2   \n",
       "865          866         1       2   \n",
       "866          867         1       2   \n",
       "867          868         0       1   \n",
       "868          869         0       3   \n",
       "869          870         1       3   \n",
       "870          871         0       3   \n",
       "871          872         1       1   \n",
       "872          873         0       1   \n",
       "873          874         0       3   \n",
       "874          875         1       2   \n",
       "875          876         1       3   \n",
       "876          877         0       3   \n",
       "877          878         0       3   \n",
       "878          879         0       3   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "881          882         0       3   \n",
       "882          883         0       3   \n",
       "883          884         0       2   \n",
       "884          885         0       3   \n",
       "885          886         0       3   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                     Moran, Mr. James    male   NaN      0   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                       Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                      Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                         Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                                Rice, Master. Eugene    male   2.0      4   \n",
       "17                        Williams, Mr. Charles Eugene    male   NaN      0   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                             Masselmani, Mrs. Fatima  female   NaN      0   \n",
       "20                                Fynney, Mr. Joseph J    male  35.0      0   \n",
       "21                               Beesley, Mr. Lawrence    male  34.0      0   \n",
       "22                         McGowan, Miss. Anna \"Annie\"  female  15.0      0   \n",
       "23                        Sloper, Mr. William Thompson    male  28.0      0   \n",
       "24                       Palsson, Miss. Torborg Danira  female   8.0      3   \n",
       "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.0      1   \n",
       "26                             Emir, Mr. Farred Chehab    male   NaN      0   \n",
       "27                      Fortune, Mr. Charles Alexander    male  19.0      3   \n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female   NaN      0   \n",
       "29                                 Todoroff, Mr. Lalio    male   NaN      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "861                        Giles, Mr. Frederick Edward    male  21.0      1   \n",
       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female  48.0      0   \n",
       "863                  Sage, Miss. Dorothy Edith \"Dolly\"  female   NaN      8   \n",
       "864                             Gill, Mr. John William    male  24.0      0   \n",
       "865                           Bystrom, Mrs. (Karolina)  female  42.0      0   \n",
       "866                       Duran y More, Miss. Asuncion  female  27.0      1   \n",
       "867               Roebling, Mr. Washington Augustus II    male  31.0      0   \n",
       "868                        van Melkebeke, Mr. Philemon    male   NaN      0   \n",
       "869                    Johnson, Master. Harold Theodor    male   4.0      1   \n",
       "870                                  Balkic, Mr. Cerin    male  26.0      0   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.0      1   \n",
       "872                           Carlsson, Mr. Frans Olof    male  33.0      0   \n",
       "873                        Vander Cruyssen, Mr. Victor    male  47.0      0   \n",
       "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.0      1   \n",
       "875                   Najib, Miss. Adele Kiamie \"Jane\"  female  15.0      0   \n",
       "876                      Gustafsson, Mr. Alfred Ossian    male  20.0      0   \n",
       "877                               Petroff, Mr. Nedelio    male  19.0      0   \n",
       "878                                 Laleff, Mr. Kristo    male   NaN      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
       "881                                 Markun, Mr. Johann    male  33.0      0   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "883                      Banfield, Mr. Frederick James    male  28.0      0   \n",
       "884                             Sutehall, Mr. Henry Jr    male  25.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket      Fare        Cabin Embarked  \n",
       "0        0         A/5 21171    7.2500          NaN        S  \n",
       "1        0          PC 17599   71.2833          C85        C  \n",
       "2        0  STON/O2. 3101282    7.9250          NaN        S  \n",
       "3        0            113803   53.1000         C123        S  \n",
       "4        0            373450    8.0500          NaN        S  \n",
       "5        0            330877    8.4583          NaN        Q  \n",
       "6        0             17463   51.8625          E46        S  \n",
       "7        1            349909   21.0750          NaN        S  \n",
       "8        2            347742   11.1333          NaN        S  \n",
       "9        0            237736   30.0708          NaN        C  \n",
       "10       1           PP 9549   16.7000           G6        S  \n",
       "11       0            113783   26.5500         C103        S  \n",
       "12       0         A/5. 2151    8.0500          NaN        S  \n",
       "13       5            347082   31.2750          NaN        S  \n",
       "14       0            350406    7.8542          NaN        S  \n",
       "15       0            248706   16.0000          NaN        S  \n",
       "16       1            382652   29.1250          NaN        Q  \n",
       "17       0            244373   13.0000          NaN        S  \n",
       "18       0            345763   18.0000          NaN        S  \n",
       "19       0              2649    7.2250          NaN        C  \n",
       "20       0            239865   26.0000          NaN        S  \n",
       "21       0            248698   13.0000          D56        S  \n",
       "22       0            330923    8.0292          NaN        Q  \n",
       "23       0            113788   35.5000           A6        S  \n",
       "24       1            349909   21.0750          NaN        S  \n",
       "25       5            347077   31.3875          NaN        S  \n",
       "26       0              2631    7.2250          NaN        C  \n",
       "27       2             19950  263.0000  C23 C25 C27        S  \n",
       "28       0            330959    7.8792          NaN        Q  \n",
       "29       0            349216    7.8958          NaN        S  \n",
       "..     ...               ...       ...          ...      ...  \n",
       "861      0             28134   11.5000          NaN        S  \n",
       "862      0             17466   25.9292          D17        S  \n",
       "863      2          CA. 2343   69.5500          NaN        S  \n",
       "864      0            233866   13.0000          NaN        S  \n",
       "865      0            236852   13.0000          NaN        S  \n",
       "866      0     SC/PARIS 2149   13.8583          NaN        C  \n",
       "867      0          PC 17590   50.4958          A24        S  \n",
       "868      0            345777    9.5000          NaN        S  \n",
       "869      1            347742   11.1333          NaN        S  \n",
       "870      0            349248    7.8958          NaN        S  \n",
       "871      1             11751   52.5542          D35        S  \n",
       "872      0               695    5.0000  B51 B53 B55        S  \n",
       "873      0            345765    9.0000          NaN        S  \n",
       "874      0         P/PP 3381   24.0000          NaN        C  \n",
       "875      0              2667    7.2250          NaN        C  \n",
       "876      0              7534    9.8458          NaN        S  \n",
       "877      0            349212    7.8958          NaN        S  \n",
       "878      0            349217    7.8958          NaN        S  \n",
       "879      1             11767   83.1583          C50        C  \n",
       "880      1            230433   26.0000          NaN        S  \n",
       "881      0            349257    7.8958          NaN        S  \n",
       "882      0              7552   10.5167          NaN        S  \n",
       "883      0  C.A./SOTON 34068   10.5000          NaN        S  \n",
       "884      0   SOTON/OQ 392076    7.0500          NaN        S  \n",
       "885      5            382652   29.1250          NaN        Q  \n",
       "886      0            211536   13.0000          NaN        S  \n",
       "887      0            112053   30.0000          B42        S  \n",
       "888      2        W./C. 6607   23.4500          NaN        S  \n",
       "889      0            111369   30.0000         C148        C  \n",
       "890      0            370376    7.7500          NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para determinar el tamaño del Data Frame usamos el atributo shape:\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Podemos verificar el tipo de los datos de cada columna mediante el atributo dtypes:\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "Name: Cabin, dtype: bool"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# El método is null(), nos indica si el valor es faltante o no en el DataFrame:\n",
    "df[\"Cabin\"].isnull().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# La columna survived es dicótoma (2 valores), 1 corresponde a que sobrevivio mientras que cero significa que no:\n",
    "df[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "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": [
    "# Gráfica de barras de los pasajeros que sobreviviron(0) o murieron (1) en el Titanic.\n",
    "df[\"Survived\"].value_counts().plot(kind=\"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.616162\n",
       "1    0.383838\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para obtener la proporción de sobrevivientes, lo podemos hacer mediante el argumento normalize\n",
    "# de value_counts():\n",
    "\n",
    "df[\"Survived\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": [
    "df[\"Survived\"].value_counts(normalize=True).plot(kind=\"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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": [
    "# Podemos graficar la distribución de edad contra la columna de sobrevivientes:\n",
    "\n",
    "plt.scatter(df[\"Survived\"],df.Age, alpha=0.1) # El argumento alpha determina la transparencia de los datos.\n",
    "plt.title(\"Edad de los sobrevivientes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "4    35.0\n",
       "5     NaN\n",
       "6    54.0\n",
       "7     2.0\n",
       "8    27.0\n",
       "9    14.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Es posible llamar una columna de un DataFrame por medio de corchetes [] o usando punto:\n",
    "\n",
    "df.Age.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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": [
    "df.Embarked.value_counts(normalize=True).plot(kind=\"bar\", alpha=0.5)\n",
    "plt.title(\"Embarked\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf1e58d30>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.Fare.plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf2017a20>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.Fare.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf2073dd8>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df[df.Age<15].Survived.value_counts().plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf20d39e8>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df[df.Sex==\"female\"].Survived.value_counts().plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf215e128>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2,figsize=(13, 4))\n",
    "df[df.Sex==\"male\"].Survived.value_counts().plot(kind=\"barh\", ax=axs[0], title=\"Hombres\")\n",
    "df[df.Sex==\"female\"].Survived.value_counts().plot(kind=\"barh\", ax=axs[1], title=\"Mujeres\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf231a5f8>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2,figsize=(13, 4))\n",
    "df[(df.Age<15) & (df.Sex==\"male\")].Survived.value_counts().plot(kind=\"barh\", ax=axs[0], title=\"Niños\")\n",
    "df[(df.Age<15) & (df.Sex==\"female\")].Survived.value_counts().plot(kind=\"barh\", ax=axs[1], title=\"Niñas\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Consultas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los DataFrames son la estructura de Pandas mas comunmente utilizadas. Las series sirven para arreglos unidimensionales, mientras que los DataFrames sirven para arreglos multidimensionales. Un DataFrame tiene 2 diferentes tipos de identificadores: filas (índice) y columnas (características o variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data={\"name\":[\"AA\", \"IBM\", \"GOOG\"], \"date\":[\"2001-12-01\", \"20012-02-10\", \"2010-04-09\"], \"shares\": [100,30,90], \"price\":[12.3,10.3,32.2]}\n",
    "\n",
    "df=pd.DataFrame(data)\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>name</th>\n",
       "      <th>date</th>\n",
       "      <th>shares</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA</td>\n",
       "      <td>2001-12-01</td>\n",
       "      <td>100</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IBM</td>\n",
       "      <td>20012-02-10</td>\n",
       "      <td>30</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>2010-04-09</td>\n",
       "      <td>90</td>\n",
       "      <td>32.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name         date  shares  price\n",
       "0    AA   2001-12-01     100   12.3\n",
       "1   IBM  20012-02-10      30   10.3\n",
       "2  GOOG   2010-04-09      90   32.2"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es posible agregar columnas adicionales al DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>name</th>\n",
       "      <th>date</th>\n",
       "      <th>shares</th>\n",
       "      <th>price</th>\n",
       "      <th>owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA</td>\n",
       "      <td>2001-12-01</td>\n",
       "      <td>100</td>\n",
       "      <td>12.3</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IBM</td>\n",
       "      <td>20012-02-10</td>\n",
       "      <td>30</td>\n",
       "      <td>10.3</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>2010-04-09</td>\n",
       "      <td>90</td>\n",
       "      <td>32.2</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name         date  shares  price    owner\n",
       "0    AA   2001-12-01     100   12.3  Unknown\n",
       "1   IBM  20012-02-10      30   10.3  Unknown\n",
       "2  GOOG   2010-04-09      90   32.2  Unknown"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"owner\"]=\"Unknown\"\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>name</th>\n",
       "      <th>date</th>\n",
       "      <th>shares</th>\n",
       "      <th>price</th>\n",
       "      <th>owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>One</th>\n",
       "      <td>AA</td>\n",
       "      <td>2001-12-01</td>\n",
       "      <td>100</td>\n",
       "      <td>12.3</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Two</th>\n",
       "      <td>IBM</td>\n",
       "      <td>20012-02-10</td>\n",
       "      <td>30</td>\n",
       "      <td>10.3</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Three</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>2010-04-09</td>\n",
       "      <td>90</td>\n",
       "      <td>32.2</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name         date  shares  price    owner\n",
       "One      AA   2001-12-01     100   12.3  Unknown\n",
       "Two     IBM  20012-02-10      30   10.3  Unknown\n",
       "Three  GOOG   2010-04-09      90   32.2  Unknown"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Es posible modificar el index de un DataFrame ya creado:\n",
    "\n",
    "df.index=[\"One\", \"Two\", \"Three\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>date</th>\n",
       "      <th>shares</th>\n",
       "      <th>price</th>\n",
       "      <th>owner</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>2001-12-01</td>\n",
       "      <td>100</td>\n",
       "      <td>12.3</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>20012-02-10</td>\n",
       "      <td>30</td>\n",
       "      <td>10.3</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>2010-04-09</td>\n",
       "      <td>90</td>\n",
       "      <td>32.2</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  shares  price    owner\n",
       "name                                     \n",
       "AA     2001-12-01     100   12.3  Unknown\n",
       "IBM   20012-02-10      30   10.3  Unknown\n",
       "GOOG   2010-04-09      90   32.2  Unknown"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Así mismo, es posible escoger una de las columnas del DataFrame como índice:\n",
    "\n",
    "df=df.set_index([\"name\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loc(), sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      2001-12-01\n",
       "shares           100\n",
       "price           12.3\n",
       "owner        Unknown\n",
       "Name: AA, dtype: object"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para acceder a la información por filas, es necesario utilizar el método loc:\n",
    "\n",
    "df.loc[\"AA\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Es posible combinar el método con filas y columnas:\n",
    "df.loc[\"AA\",\"shares\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>title</th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Closet Monster</td>\n",
       "      <td>2015</td>\n",
       "      <td>Buffy #1</td>\n",
       "      <td>actor</td>\n",
       "      <td>Buffy 4</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Suuri illusioni</td>\n",
       "      <td>1985</td>\n",
       "      <td>Homo $</td>\n",
       "      <td>actor</td>\n",
       "      <td>Guests</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Battle of the Sexes</td>\n",
       "      <td>2017</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bobby Riggs Fan</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Secret in Their Eyes</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>2002 Dodger Fan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Steve Jobs</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>1988 Opera House Patron</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title  year      name   type                character     n\n",
       "0        Closet Monster  2015  Buffy #1  actor                  Buffy 4  31.0\n",
       "1       Suuri illusioni  1985    Homo $  actor                   Guests  22.0\n",
       "2   Battle of the Sexes  2017   $hutter  actor          Bobby Riggs Fan  10.0\n",
       "3  Secret in Their Eyes  2015   $hutter  actor          2002 Dodger Fan   NaN\n",
       "4            Steve Jobs  2015   $hutter  actor  1988 Opera House Patron   NaN"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "casts=pd.read_csv(\"Datasets/cast.csv\", index_col=None)\n",
    "casts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>Rebel</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>Suzanne</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>Bomba</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>Aao Jao Ghar Tumhara</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>Mrs. Munck</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      title  year\n",
       "49995                 Rebel  1970\n",
       "49996               Suzanne  1996\n",
       "49997                 Bomba  2013\n",
       "49998  Aao Jao Ghar Tumhara  1984\n",
       "49999            Mrs. Munck  1995"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles=pd.read_csv(\"Datasets/titles.csv\",index_col=None)\n",
    "titles.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# En Python, es posible especificar mediante pandas, el número de filas y columnas que queremos que nos muestre\n",
    "# cada que se imprime un DataFrame mediante el uso del método set_option()\n",
    "\n",
    "pd.set_option(\"max_rows\",10, \"max_columns\",10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Rising Son</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Thousand Plane Raid</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Crucea de piatra</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Country</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaiking II</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>Rebel</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>Suzanne</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>Bomba</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>Aao Jao Ghar Tumhara</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>Mrs. Munck</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         title  year\n",
       "0               The Rising Son  1990\n",
       "1      The Thousand Plane Raid  1969\n",
       "2             Crucea de piatra  1993\n",
       "3                      Country  2000\n",
       "4                   Gaiking II  2011\n",
       "...                        ...   ...\n",
       "49995                    Rebel  1970\n",
       "49996                  Suzanne  1996\n",
       "49997                    Bomba  2013\n",
       "49998     Aao Jao Ghar Tumhara  1984\n",
       "49999               Mrs. Munck  1995\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50000"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cada columna de un DataFrame, corresponde a una serie:\n",
    "\n",
    "t=titles[\"title\"]\n",
    "\n",
    "type(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             The Rising Son\n",
       "1    The Thousand Plane Raid\n",
       "2           Crucea de piatra\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Traemos la primera fila de titles:\n",
    "\n",
    "objeto=titles.loc[titles[\"year\"]<1950,  \"year\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13       1943\n",
       "20       1917\n",
       "26       1916\n",
       "43       1918\n",
       "46       1947\n",
       "         ... \n",
       "49966    1922\n",
       "49967    1923\n",
       "49970    1938\n",
       "49973    1935\n",
       "49986    1946\n",
       "Name: year, Length: 7173, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objeto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title    The Rising Son\n",
       "year               1990\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para poder filtrar datos a partir de un valor en una columna, es necesario utilizar instrucciones lógicas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Rising Son</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Crucea de piatra</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Country</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaiking II</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Medusa (IV)</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              title  year\n",
       "0    The Rising Son  1990\n",
       "2  Crucea de piatra  1993\n",
       "3           Country  2000\n",
       "4        Gaiking II  2011\n",
       "5       Medusa (IV)  2015"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "after85=titles[titles[\"year\"]>1985]\n",
    "after85.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29814"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(after85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Rising Son</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Crucea de piatra</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Poka Makorer Ghar Bosoti</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maa Durga Shakti</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Conflict of Interest</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       title  year\n",
       "0             The Rising Son  1990\n",
       "2           Crucea de piatra  1993\n",
       "12  Poka Makorer Ghar Bosoti  1996\n",
       "19          Maa Durga Shakti  1999\n",
       "24      Conflict of Interest  1993"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies90=titles[(titles[\"year\"]>=1990) & (titles[\"year\"]<2000)]\n",
    "movies90.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4803"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4226</th>\n",
       "      <td>Macbeth</td>\n",
       "      <td>1913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9322</th>\n",
       "      <td>Macbeth</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11722</th>\n",
       "      <td>Macbeth</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17166</th>\n",
       "      <td>Macbeth</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25847</th>\n",
       "      <td>Macbeth</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         title  year\n",
       "4226   Macbeth  1913\n",
       "9322   Macbeth  2006\n",
       "11722  Macbeth  2013\n",
       "17166  Macbeth  1997\n",
       "25847  Macbeth  1998"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macbeth=titles[titles[\"title\"]==\"Macbeth\"]\n",
    "macbeth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(macbeth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es posible ordenar un DataFrame mediante el método **sort_vales()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4226</th>\n",
       "      <td>Macbeth</td>\n",
       "      <td>1913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17166</th>\n",
       "      <td>Macbeth</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25847</th>\n",
       "      <td>Macbeth</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9322</th>\n",
       "      <td>Macbeth</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11722</th>\n",
       "      <td>Macbeth</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         title  year\n",
       "4226   Macbeth  1913\n",
       "17166  Macbeth  1997\n",
       "25847  Macbeth  1998\n",
       "9322   Macbeth  2006\n",
       "11722  Macbeth  2013"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macbeth=titles[titles[\"title\"]==\"Macbeth\"].sort_values(\"year\")\n",
    "macbeth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38880</th>\n",
       "      <td>Maa</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      title  year\n",
       "38880   Maa  1968"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para filtrar strings, a partir de cierta parte, hay que usar una herramienta diferente:\n",
    "titles[titles[\"title\"]==\"Maa\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maa Durga Shakti</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>Maarek hob</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3046</th>\n",
       "      <td>Maa Aur Mamta</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6304</th>\n",
       "      <td>Maampazhakkaalam</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7470</th>\n",
       "      <td>Maa Vaibhav Laxmi</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title  year\n",
       "19     Maa Durga Shakti  1999\n",
       "334          Maarek hob  2004\n",
       "3046      Maa Aur Mamta  1970\n",
       "6304   Maampazhakkaalam  2004\n",
       "7470  Maa Vaibhav Laxmi  1989"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles[titles[\"title\"].str.startswith(\"Maa\")].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf2db2198>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "serie_tiempo=titles[\"year\"].value_counts()\n",
    "serie_tiempo.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016    2363\n",
       "2017    2138\n",
       "2015    1849\n",
       "2014    1701\n",
       "2013    1609\n",
       "        ... \n",
       "1900       2\n",
       "1906       1\n",
       "2024       1\n",
       "2023       1\n",
       "1905       1\n",
       "Name: year, Length: 119, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_tiempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1900     2\n",
       "1905     1\n",
       "1906     1\n",
       "1909     3\n",
       "1910     5\n",
       "        ..\n",
       "2020    37\n",
       "2021     8\n",
       "2022     5\n",
       "2023     1\n",
       "2024     1\n",
       "Name: year, Length: 119, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie_tiempo.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf2df7a58>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Para poder realizar la gráfica de forma correcta, es necesario ordenar las fechas de la nueva serie:\n",
    "\n",
    "serie_tiempo.sort_index().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otra forma de realizar lo anterior es mediante el método **gruopby(\"argumento\").size()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1912       3\n",
       "1913      10\n",
       "1914      61\n",
       "1915      82\n",
       "1916     116\n",
       "        ... \n",
       "2017    2159\n",
       "2018     158\n",
       "2019      16\n",
       "2020       4\n",
       "2023       3\n",
       "Length: 110, dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Usando ahora el DataFrame casts:\n",
    "cg=casts.groupby([\"year\"]).size()\n",
    "cg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf2e02c50>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "cg.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2003    2\n",
       "2004    2\n",
       "2005    2\n",
       "2006    1\n",
       "2007    2\n",
       "       ..\n",
       "2011    5\n",
       "2013    1\n",
       "2015    2\n",
       "2017    2\n",
       "2018    1\n",
       "Length: 12, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c=casts\n",
    "cf=c[c[\"name\"]==\"Aaron Abrams\"]\n",
    "cf.groupby([\"year\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  title                               \n",
       "2003  The In-Laws                             1\n",
       "      The Visual Bible: The Gospel of John    1\n",
       "2004  Resident Evil: Apocalypse               1\n",
       "      Siblings                                1\n",
       "2005  Cinderella Man                          1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mediante este método, es posible agrupar data bajo varios parametros:\n",
    "\n",
    "# It is possible to group data under several parameters. For example:\n",
    "cf.groupby([\"year\", \"title\"]).size().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pandas.pydata.org/pandas-docs/stable/reference/groupby.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  type   \n",
       "1910  actor        384\n",
       "      actress      285\n",
       "1920  actor        710\n",
       "      actress      411\n",
       "1930  actor       2628\n",
       "                 ...  \n",
       "2000  actress     5831\n",
       "2010  actor      15523\n",
       "      actress     8853\n",
       "2020  actor          4\n",
       "      actress        3\n",
       "Length: 24, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para agrupar en intervalos de 10, podemos usar la fórma de división entera:\n",
    "serie2=c.groupby([c[\"year\"]//10*10, \"type\"]).size()\n",
    "serie2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type     year\n",
       "actor    1910     384\n",
       "         1920     710\n",
       "         1930    2628\n",
       "         1940    3014\n",
       "         1950    2877\n",
       "                 ... \n",
       "actress  1980    1989\n",
       "         1990    2544\n",
       "         2000    5831\n",
       "         2010    8853\n",
       "         2020       3\n",
       "Length: 24, dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_decade=c.groupby([\"type\", c[\"year\"]//10*10]).size()\n",
    "c_decade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1920</th>\n",
       "      <th>1930</th>\n",
       "      <th>1940</th>\n",
       "      <th>1950</th>\n",
       "      <th>...</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>actor</th>\n",
       "      <td>384</td>\n",
       "      <td>710</td>\n",
       "      <td>2628</td>\n",
       "      <td>3014</td>\n",
       "      <td>2877</td>\n",
       "      <td>...</td>\n",
       "      <td>3565</td>\n",
       "      <td>5108</td>\n",
       "      <td>10368</td>\n",
       "      <td>15523</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actress</th>\n",
       "      <td>285</td>\n",
       "      <td>411</td>\n",
       "      <td>820</td>\n",
       "      <td>983</td>\n",
       "      <td>1015</td>\n",
       "      <td>...</td>\n",
       "      <td>1989</td>\n",
       "      <td>2544</td>\n",
       "      <td>5831</td>\n",
       "      <td>8853</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year     1910  1920  1930  1940  1950  ...  1980  1990   2000   2010  2020\n",
       "type                                   ...                                \n",
       "actor     384   710  2628  3014  2877  ...  3565  5108  10368  15523     4\n",
       "actress   285   411   820   983  1015  ...  1989  2544   5831   8853     3\n",
       "\n",
       "[2 rows x 12 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# El método unstack() usará el segundo indice para crear un nuevo DataFrame con los valores como columnas:\n",
    "c_decade.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf2ee4cc0>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "c_decade.unstack().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf2f807b8>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "c_decade.unstack().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>type</th>\n",
       "      <th>actor</th>\n",
       "      <th>actress</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>384</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>710</td>\n",
       "      <td>411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>2628</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>3014</td>\n",
       "      <td>983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>2877</td>\n",
       "      <td>1015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>3565</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>5108</td>\n",
       "      <td>2544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>10368</td>\n",
       "      <td>5831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>15523</td>\n",
       "      <td>8853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "type  actor  actress\n",
       "year                \n",
       "1910    384      285\n",
       "1920    710      411\n",
       "1930   2628      820\n",
       "1940   3014      983\n",
       "1950   2877     1015\n",
       "...     ...      ...\n",
       "1980   3565     1989\n",
       "1990   5108     2544\n",
       "2000  10368     5831\n",
       "2010  15523     8853\n",
       "2020      4        3\n",
       "\n",
       "[12 rows x 2 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Usando como valor 0 en unstack() obtenemos la transpuesta:\n",
    "\n",
    "c_decade.unstack (0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14cf3042550>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "c_decade.unstack(0).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En casi todos los DataFrames es posible encontrarnos con valores faltantes, para poder manejar esto usaremos el método **isnull()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>title</th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Secret in Their Eyes</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>2002 Dodger Fan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Steve Jobs</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>1988 Opera House Patron</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title  year     name   type                character   n\n",
       "3  Secret in Their Eyes  2015  $hutter  actor          2002 Dodger Fan NaN\n",
       "4            Steve Jobs  2015  $hutter  actor  1988 Opera House Patron NaN"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casts.loc[3:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Secret in Their Eyes</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>2002 Dodger Fan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Steve Jobs</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>1988 Opera House Patron</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Straight Outta Compton</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Club Patron</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Straight Outta Compton</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Dopeman</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>For Thy Love 2</td>\n",
       "      <td>2009</td>\n",
       "      <td>Bee Moe $lim</td>\n",
       "      <td>actor</td>\n",
       "      <td>Thug 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Desire (III)</td>\n",
       "      <td>2014</td>\n",
       "      <td>Syaiful 'Ariffin</td>\n",
       "      <td>actor</td>\n",
       "      <td>Actor Playing Eteocles from 'Antigone'</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mixing Nia</td>\n",
       "      <td>1998</td>\n",
       "      <td>Michael 'babeepower' Viera</td>\n",
       "      <td>actor</td>\n",
       "      <td>Rapper</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>The Replacements</td>\n",
       "      <td>2000</td>\n",
       "      <td>Steven 'Bear'Boyd</td>\n",
       "      <td>actor</td>\n",
       "      <td>Defensive Tackle - Washington Sentinels</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>All Out Dysfunktion!</td>\n",
       "      <td>2016</td>\n",
       "      <td>Kirlew 'bliss' Vilbon</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bliss</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Gook</td>\n",
       "      <td>2017</td>\n",
       "      <td>Kirlew 'bliss' Vilbon</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bliss</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     title  year                        name   type  \\\n",
       "3     Secret in Their Eyes  2015                     $hutter  actor   \n",
       "4               Steve Jobs  2015                     $hutter  actor   \n",
       "5   Straight Outta Compton  2015                     $hutter  actor   \n",
       "6   Straight Outta Compton  2015                     $hutter  actor   \n",
       "7           For Thy Love 2  2009                Bee Moe $lim  actor   \n",
       "9             Desire (III)  2014            Syaiful 'Ariffin  actor   \n",
       "12              Mixing Nia  1998  Michael 'babeepower' Viera  actor   \n",
       "13        The Replacements  2000           Steven 'Bear'Boyd  actor   \n",
       "14    All Out Dysfunktion!  2016       Kirlew 'bliss' Vilbon  actor   \n",
       "15                    Gook  2017       Kirlew 'bliss' Vilbon  actor   \n",
       "\n",
       "                                  character   n  \n",
       "3                           2002 Dodger Fan NaN  \n",
       "4                   1988 Opera House Patron NaN  \n",
       "5                               Club Patron NaN  \n",
       "6                                   Dopeman NaN  \n",
       "7                                    Thug 1 NaN  \n",
       "9    Actor Playing Eteocles from 'Antigone' NaN  \n",
       "12                                   Rapper NaN  \n",
       "13  Defensive Tackle - Washington Sentinels NaN  \n",
       "14                                    Bliss NaN  \n",
       "15                                    Bliss NaN  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtramos el DataFrame casts por aquellas filas donde haya valores nulos en la columna n:\n",
    "casts[casts[\"n\"].isnull()].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>title</th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Closet Monster</td>\n",
       "      <td>2015</td>\n",
       "      <td>Buffy #1</td>\n",
       "      <td>actor</td>\n",
       "      <td>Buffy 4</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Suuri illusioni</td>\n",
       "      <td>1985</td>\n",
       "      <td>Homo $</td>\n",
       "      <td>actor</td>\n",
       "      <td>Guests</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Battle of the Sexes</td>\n",
       "      <td>2017</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bobby Riggs Fan</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lapis, Ballpen at Diploma, a True to Life Journey</td>\n",
       "      <td>2014</td>\n",
       "      <td>Jori ' Danilo' Jurado Jr.</td>\n",
       "      <td>actor</td>\n",
       "      <td>Jaime (young)</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>When the Man Went South</td>\n",
       "      <td>2014</td>\n",
       "      <td>Taipaleti 'Atu'ake</td>\n",
       "      <td>actor</td>\n",
       "      <td>Two Palms - Ua'i Paame</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Little Angel (Angelita)</td>\n",
       "      <td>2015</td>\n",
       "      <td>Michael 'babeepower' Viera</td>\n",
       "      <td>actor</td>\n",
       "      <td>Chico</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>My Song for You</td>\n",
       "      <td>2010</td>\n",
       "      <td>George 'Bootsy' Thomas</td>\n",
       "      <td>actor</td>\n",
       "      <td>Cooley's Customer</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>My Song for You</td>\n",
       "      <td>2010</td>\n",
       "      <td>George 'Bootsy' Thomas</td>\n",
       "      <td>actor</td>\n",
       "      <td>Celebration Guest</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Mariano Mison... NBI</td>\n",
       "      <td>1997</td>\n",
       "      <td>Joseph 'Byron' Sese</td>\n",
       "      <td>actor</td>\n",
       "      <td>Putik's Son</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>B-Girl</td>\n",
       "      <td>2009</td>\n",
       "      <td>Jesse 'Casper' Brown</td>\n",
       "      <td>actor</td>\n",
       "      <td>Battle Judge</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                title  year  \\\n",
       "0                                      Closet Monster  2015   \n",
       "1                                     Suuri illusioni  1985   \n",
       "2                                 Battle of the Sexes  2017   \n",
       "8   Lapis, Ballpen at Diploma, a True to Life Journey  2014   \n",
       "10                            When the Man Went South  2014   \n",
       "11                            Little Angel (Angelita)  2015   \n",
       "16                                    My Song for You  2010   \n",
       "17                                    My Song for You  2010   \n",
       "19                               Mariano Mison... NBI  1997   \n",
       "21                                             B-Girl  2009   \n",
       "\n",
       "                          name   type               character     n  \n",
       "0                     Buffy #1  actor                 Buffy 4  31.0  \n",
       "1                       Homo $  actor                  Guests  22.0  \n",
       "2                      $hutter  actor         Bobby Riggs Fan  10.0  \n",
       "8    Jori ' Danilo' Jurado Jr.  actor           Jaime (young)   9.0  \n",
       "10          Taipaleti 'Atu'ake  actor  Two Palms - Ua'i Paame   8.0  \n",
       "11  Michael 'babeepower' Viera  actor                   Chico   9.0  \n",
       "16      George 'Bootsy' Thomas  actor       Cooley's Customer  16.0  \n",
       "17      George 'Bootsy' Thomas  actor       Celebration Guest  16.0  \n",
       "19         Joseph 'Byron' Sese  actor             Putik's Son  31.0  \n",
       "21        Jesse 'Casper' Brown  actor            Battle Judge  25.0  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# El método contrario a isnull() es notnull():\n",
    "casts[casts[\"n\"].notnull()].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una técnica para trabajar valores nulos es cambiandolos por un valor promedio o algun valor definido. Esto se puede lograr usando el método **fillna()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>title</th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Secret in Their Eyes</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>2002 Dodger Fan</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Steve Jobs</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>1988 Opera House Patron</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Straight Outta Compton</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Club Patron</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Straight Outta Compton</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Dopeman</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>For Thy Love 2</td>\n",
       "      <td>2009</td>\n",
       "      <td>Bee Moe $lim</td>\n",
       "      <td>actor</td>\n",
       "      <td>Thug 1</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Desire (III)</td>\n",
       "      <td>2014</td>\n",
       "      <td>Syaiful 'Ariffin</td>\n",
       "      <td>actor</td>\n",
       "      <td>Actor Playing Eteocles from 'Antigone'</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mixing Nia</td>\n",
       "      <td>1998</td>\n",
       "      <td>Michael 'babeepower' Viera</td>\n",
       "      <td>actor</td>\n",
       "      <td>Rapper</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>The Replacements</td>\n",
       "      <td>2000</td>\n",
       "      <td>Steven 'Bear'Boyd</td>\n",
       "      <td>actor</td>\n",
       "      <td>Defensive Tackle - Washington Sentinels</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>All Out Dysfunktion!</td>\n",
       "      <td>2016</td>\n",
       "      <td>Kirlew 'bliss' Vilbon</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bliss</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Gook</td>\n",
       "      <td>2017</td>\n",
       "      <td>Kirlew 'bliss' Vilbon</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bliss</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     title  year                        name   type  \\\n",
       "3     Secret in Their Eyes  2015                     $hutter  actor   \n",
       "4               Steve Jobs  2015                     $hutter  actor   \n",
       "5   Straight Outta Compton  2015                     $hutter  actor   \n",
       "6   Straight Outta Compton  2015                     $hutter  actor   \n",
       "7           For Thy Love 2  2009                Bee Moe $lim  actor   \n",
       "9             Desire (III)  2014            Syaiful 'Ariffin  actor   \n",
       "12              Mixing Nia  1998  Michael 'babeepower' Viera  actor   \n",
       "13        The Replacements  2000           Steven 'Bear'Boyd  actor   \n",
       "14    All Out Dysfunktion!  2016       Kirlew 'bliss' Vilbon  actor   \n",
       "15                    Gook  2017       Kirlew 'bliss' Vilbon  actor   \n",
       "\n",
       "                                  character   n  \n",
       "3                           2002 Dodger Fan  NA  \n",
       "4                   1988 Opera House Patron  NA  \n",
       "5                               Club Patron  NA  \n",
       "6                                   Dopeman  NA  \n",
       "7                                    Thug 1  NA  \n",
       "9    Actor Playing Eteocles from 'Antigone'  NA  \n",
       "12                                   Rapper  NA  \n",
       "13  Defensive Tackle - Washington Sentinels  NA  \n",
       "14                                    Bliss  NA  \n",
       "15                                    Bliss  NA  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_fill=casts[casts[\"n\"].isnull()].fillna(\"NA\")\n",
    "c_fill.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 3.7.3 (default, Mar 27 2019, 17:13:21) [MSC v.1915 64 bit (AMD64)]\n",
      "numpy: 1.16.2\n",
      "matplotlib: 3.0.3\n",
      "pandas: 0.24.2\n",
      "sklearn: 0.20.3\n"
     ]
    }
   ],
   "source": [
    "# Python version\n",
    "import sys\n",
    "print(\"Python:\",sys.version)\n",
    "\n",
    "# numpy\n",
    "import numpy\n",
    "print(\"numpy:\",numpy.__version__)\n",
    "\n",
    "# matplotlib\n",
    "import matplotlib\n",
    "print(\"matplotlib:\",matplotlib.__version__)\n",
    "\n",
    "# pandas\n",
    "import pandas\n",
    "print(\"pandas:\",pandas.__version__)\n",
    "\n",
    "# scikit-learn\n",
    "import sklearn\n",
    "print(\"sklearn:\",sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>5.1</th>\n",
       "      <th>3.5</th>\n",
       "      <th>1.4</th>\n",
       "      <th>0.2</th>\n",
       "      <th>Iris-setosa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   5.1  3.5  1.4  0.2  Iris-setosa\n",
       "0  4.9  3.0  1.4  0.2  Iris-setosa\n",
       "1  4.7  3.2  1.3  0.2  Iris-setosa\n",
       "2  4.6  3.1  1.5  0.2  Iris-setosa\n",
       "3  5.0  3.6  1.4  0.2  Iris-setosa\n",
       "4  5.4  3.9  1.7  0.4  Iris-setosa"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=\"Datasets/iris.csv\"\n",
    "\n",
    "df = pd.read_csv(path)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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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       "\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>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width       target\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=\"Datasets/iris.csv\"\n",
    "\n",
    "# El argumento names, nos sirve para asignar nombres a las columnas de un DataFrame:\n",
    "df = pd.read_csv(path, names=['sepal length','sepal width','petal length','petal width','target'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una base de datos generica, por lo regular esta conformada por diferentes valores. Estos valores pueden ser representados por diferentes distribuciones, tener diferentes escalas, y en muchas ocaciones, contener valores atípicos. Es por ello que antes de trabajar una base de datos, es importante realizar un proceso de **normalización y escalado.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para entender esto mejor, supongamos que tenemos una base de datos con 2 variables dependientes: Peso y Estatura. La primera está medida en kilos, mientras que la segunda en metros. Debido a como están escaladas estas 2 variables, si realizamos un análisis de PCA, el programa supondrá que un cambio en la variable Peso (kg) supondría una mayor variación en los datos, que la variación que podría generar Estatura(m). Sin embargo un aumento de 1 metro en la estatura es mucho mas significativo que un cambio de 1 kilo en peso. Es por ello que para poder realizar este análisis de manera correcta ambas variables se deben estandarizar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "features = ['sepal length', 'sepal width', 'petal length', 'petal width']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width\n",
       "0           5.1          3.5           1.4          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2\n",
       "3           4.6          3.1           1.5          0.2\n",
       "4           5.0          3.6           1.4          0.2"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# La lista features, contiene los nombres de las columnas descriptoras que nos interesa analizar.\n",
    "df[features].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Por medio de la lista features, y utilizando el método loc, creamos un arreglo de los valores en dichas columnas.\n",
    "\n",
    "x = df.loc[:, features].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separando la columna de variable objetivo\n",
    "y = df.loc[:,['target']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estandarizando las características descriptoras\n",
    "x = StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.032057</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.124958</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.337848</td>\n",
       "      <td>-1.398138</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.106445</td>\n",
       "      <td>-1.284407</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.263460</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sepal length sepal width petal length petal width\n",
       "0    -0.900681    1.032057    -1.341272   -1.312977\n",
       "1    -1.143017   -0.124958    -1.341272   -1.312977\n",
       "2    -1.385353    0.337848    -1.398138   -1.312977\n",
       "3    -1.506521    0.106445    -1.284407   -1.312977\n",
       "4    -1.021849    1.263460    -1.341272   -1.312977"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm=pd.DataFrame(x, columns=[features]) \n",
    "df_norm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El siguiente código convierte los datos originales de 4 dimensiones en 2 dimensiones.\n",
    "Nota: Después de la reducción de dimensionalidad, no existe ninguna interpretación para cada componente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.264542</td>\n",
       "      <td>0.505704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.086426</td>\n",
       "      <td>-0.655405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.367950</td>\n",
       "      <td>-0.318477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.304197</td>\n",
       "      <td>-0.575368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.388777</td>\n",
       "      <td>0.674767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2\n",
       "0              -2.264542               0.505704\n",
       "1              -2.086426              -0.655405\n",
       "2              -2.367950              -0.318477\n",
       "3              -2.304197              -0.575368\n",
       "4              -2.388777               0.674767"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(x)\n",
    "principalDf = pd.DataFrame(data = principalComponents\n",
    "             , columns = ['principal component 1', 'principal component 2'])\n",
    "principalDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.264542</td>\n",
       "      <td>0.505704</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.086426</td>\n",
       "      <td>-0.655405</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.367950</td>\n",
       "      <td>-0.318477</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.304197</td>\n",
       "      <td>-0.575368</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.388777</td>\n",
       "      <td>0.674767</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2       target\n",
       "0              -2.264542               0.505704  Iris-setosa\n",
       "1              -2.086426              -0.655405  Iris-setosa\n",
       "2              -2.367950              -0.318477  Iris-setosa\n",
       "3              -2.304197              -0.575368  Iris-setosa\n",
       "4              -2.388777               0.674767  Iris-setosa"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDf = pd.concat([principalDf, df[['target']]], axis = 1)\n",
    "finalDf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizamos los resultados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "targets = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n",
    "colors = ['r', 'g', 'b']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = finalDf['target'] == target\n",
    "    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']\n",
    "               , finalDf.loc[indicesToKeep, 'principal component 2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El ratio de variación nos dice que tanta información se le puede atribuir a cada uno de los componentes. Esto se puede calcular por medio del siguiente método:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.72770452, 0.23030523])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este resultado nos indica que el componente principal 1 explica el **72.77%** de la variación, mientras que el segundo solo explica el **23.03%**. En conjunto explican el **95.8%**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}