{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas es un paquete de Python que proporciona estructuras de datos __rápidas, flexibles y expresivas__ diseñadas para que trabajar con datos __\"relacionales\" o \"etiquetados\"__ sea fácil e intuitivo, es una de las librerias más usadas debido a su potencia y además es de código abierto . Su función es ser una herramienta de alto nivel para realizar__ analisis de datos__ en el mundo real. \n",
    "\n",
    "Pandas es muy adecuado para muchos tipos diferentes de datos:\n",
    "\n",
    "- Datos tabulares con columnas de tipo heterogéneo, como en una tabla de SQL o una hoja de cálculo de Excel\n",
    "- Datos de series de tiempo ordenados y no ordenados (no necesariamente de frecuencia fija).\n",
    "- Datos de matriz arbitraria (homogéneamente tipados o heterogéneos) con etiquetas de fila y columna\n",
    "- Cualquier otra forma de conjuntos de datos observacionales / estadísticos. Los datos realmente no necesitan ser etiquetados en absoluto para ser colocados en una estructura de datos pandas.\n",
    "\n",
    "Pandas ofrece las siguientes estructuras de datos:\n",
    "\n",
    "* __Series__: Son arrays unidimensionales con indexación (arrays con índice o etiquetados), similar a los diccionarios. Pueden generarse a partir de diccionarios o de listas.\n",
    " \n",
    "* __DataFrame__: Son estructuras de datos similares a las tablas de bases de datos relacionales como SQL.\n",
    " \n",
    "* __Panel, Panel4D y PanelND__: Estas estructuras de datos permiten trabajar con más de dos dimensiones. Dado que es algo complejo y poco utilizado trabajar con arrays de más de dos dimensiones no trataremos los paneles en estos tutoriales de introdución a Pandas.\n",
    "\n",
    "Aquí están algunas de las cosas que los pandas hacen bien:\n",
    "\n",
    "- Manejo fácil de __datos faltantes__ (representados como __NaN__) en punto flotante así como datos de punto no flotante.\n",
    "- Cambios de tamaño: las columnas se pueden __insertar y eliminar__ de DataFrame y objetos de dimensiones superiores.\n",
    "- __Alineación automática y explícita de datos__: los objetos pueden alinearse explícitamente con un conjunto de etiquetas, o el usuario puede simplemente ignorar las etiquetas y dejar que Series, DataFrame, etc. alineen automáticamente los datos en cálculos.\n",
    "- Potente y flexible al __agrupar por funcionalidad__ para realizar operaciones __split-apply-combine__ en conjuntos de datos, tanto para la agregación como para la transformación de datos.\n",
    "- __Facilita la conversión__ de datos desiguales y diferenciados en otras estructuras de datos Python y NumPy en objetos DataFrame.\n",
    "- Recorte inteligente basado en __slicing, fancy indexing, y subsetting__ de grandes conjuntos de datos.\n",
    "- Robustas herramientas de IO para cargar datos de __archivos planos__ (CSV y delimitado), archivos de Excel, bases de datos y guardar / cargar datos desde el formato __HDF5 ultrarrápido__.\n",
    "- __Funciones específicas de series de tiempo__: generación de intervalos de fechas y conversión de frecuencia, estadísticas de ventanas en movimiento, regresiones lineales de ventanas en movimiento, cambio de fecha y retraso, etc.\n",
    "\n",
    "Para los científicos de datos, el trabajo con datos suele dividirse en múltiples etapas: muestrear y limpiar los datos, analizarlos o modelarlos, y luego organizar los resultados del análisis en una forma adecuada para representación gráfica o tabular. Pandas es la herramienta ideal para todas estas tareas.\n",
    "\n",
    "Otras notas:\n",
    "\n",
    "- Pandas es __rápido__. Muchos de los bits algorítmicos de bajo nivel se han modificado extensamente en el código de Cython. Sin embargo, como con cualquier otra cosa, la generalización suele sacrificar el rendimiento. Así que si usted se centra en una característica para su aplicación que puede ser capaz de crear una herramienta especializada más rápida.\n",
    "- Pandas es una dependencia de __statsmodels__, por lo que es una parte importante del ecosistema de computación estadística en Python.\n",
    "- Pandas ha sido ampliamente utilizado en la producción en __aplicaciones financieras__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora empecemos importando pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Forma convencional de importar pandas:\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importando matplotlib para graficar \n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (15, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estructuras de Datos\n",
    "------------------------------\n",
    "\n",
    "### Series\n",
    "\n",
    "La estructura de datos de Series en Pandas es una matriz etiquetada unidimensional.\n",
    "\n",
    "- Los datos de la matriz pueden ser de cualquier tipo (números enteros, cadenas, números de punto flotante, objetos Python, etc.).\n",
    "\n",
    "- Los datos dentro de la matriz son homogéneos.\n",
    "\n",
    "- Los datos pueden ser listas, arrays, o un diccionario.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    33\n",
       "1    19\n",
       "2    15\n",
       "3    89\n",
       "4    11\n",
       "5    -5\n",
       "6     9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Constructor de serie con datos como una lista de enteros\n",
    "s1 = pd.Series([33, 19, 15, 89, 11, -5, 9])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tipo de serie es la serie pandas\n",
    "type(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([33, 19, 15, 89, 11, -5,  9])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recupera los valores de la serie \n",
    "s1.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tipo de valores de datos es NumPy ndarray\n",
    "type(s1.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Alt text](../images/series.jpg \"Optional title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mon    33\n",
       "Tue    19\n",
       "Wed    15\n",
       "Thu    89\n",
       "Fri    11\n",
       "Sat    -5\n",
       "Sun     9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define los datos e indices como listas\n",
    "data1 = [33, 19, 15, 89, 11, -5, 9]\n",
    "index1 = ['Mon','Tue','Wed','Thu','Fri','Sat','Sun']\n",
    "\n",
    "# Crea la serie \n",
    "s2 = pd.Series(data1, index=index1)\n",
    "\n",
    "s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "![Alt text](../images/series2.jpg \"Optional title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weekday\n",
       "Mon    33\n",
       "Tue    19\n",
       "Wed    15\n",
       "Thu    89\n",
       "Fri    11\n",
       "Sat    -5\n",
       "Sun     9\n",
       "Name: Daily Temperatures, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# También podemos dar etiquetas significativas a los datos de la serie y el índice\n",
    "\n",
    "s2.name='Daily Temperatures'\n",
    "s2.index.name='Weekday'\n",
    "\n",
    "s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La representación más general de una serie es como un almacén de key-values ordenado.\n",
    "\n",
    "- El orden es representado por el offset.\n",
    "- El valor-clave es una asignación de índice o etiqueta a los valores de matriz de datos.\n",
    "- Indice como \"offset\" o \"posición\" vs índice como \"etiqueta\" o \"clave\".\n",
    "\n",
    "![Alt text](../images/series3.jpg \"Optional title\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al ser las series de tipo__NumPy-ndarray__ podemos efectuar las mismas operaciones que hicimos en Numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weekday\n",
       "Mon     66\n",
       "Tue     38\n",
       "Wed     30\n",
       "Thu    178\n",
       "Fri     22\n",
       "Sat    -10\n",
       "Sun     18\n",
       "Name: Daily Temperatures, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weekday\n",
       "Mon    33\n",
       "Tue    19\n",
       "Wed    15\n",
       "Name: Daily Temperatures, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Podemos usar el slicing usando la posicion\n",
    "s2[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weekday\n",
       "Mon    33\n",
       "Tue    19\n",
       "Wed    15\n",
       "Name: Daily Temperatures, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tambien podemos usar slicin usando sus etiquetas(labes)\n",
    "s2['Mon':'Wed']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Uniendo Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrs</th>\n",
       "      <th>bathrs</th>\n",
       "      <th>price_sqr_meter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>19510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>24508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrs  bathrs  price_sqr_meter\n",
       "0      1       2            19510\n",
       "1      2       1            15190\n",
       "2      1       2            16107\n",
       "3      1       1            22991\n",
       "4      2       2            24508"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "s1 = pd.Series(np.random.randint(1, high=5, size=100, dtype='l'))\n",
    "s2 = pd.Series(np.random.randint(1, high=4, size=100, dtype='l'))\n",
    "s3 = pd.Series(np.random.randint(10000, high=30001, size=100, dtype='l'))\n",
    "\n",
    "housemkt = pd.concat([s1, s2, s3], axis=1)\n",
    "housemkt.rename(columns = {0: 'bedrs', 1: 'bathrs', 2: 'price_sqr_meter'}, inplace=True)\n",
    "\n",
    "housemkt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataframes\n",
    "\n",
    "La estructura de datos de DataFrame en Pandas es una matriz etiquetada bidimensional.\n",
    "\n",
    "- Los datos de la matriz pueden ser de cualquier tipo (números enteros, cadenas, números de punto flotante, objetos Python, etc.).\n",
    "- Los datos dentro de cada columna son homogéneos\n",
    "- De forma predeterminada, Pandas crea un índice numérico para las filas en la secuencia 0 ... n\n",
    "\n",
    "![Alt text](../images/dataframe.jpg \"Optional title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "# Creamos una lista de fechas desde 12-01 to 12-10\n",
    "dt = datetime.datetime(2016,12,1)\n",
    "end = datetime.datetime(2016,12,10)\n",
    "step = datetime.timedelta(days=1)\n",
    "dates = []\n",
    "\n",
    "# Rellenar la lista\n",
    "while dt < end:\n",
    "    dates.append(dt.strftime('%m-%d'))\n",
    "    dt += step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['12-01',\n",
       " '12-02',\n",
       " '12-03',\n",
       " '12-04',\n",
       " '12-05',\n",
       " '12-06',\n",
       " '12-07',\n",
       " '12-08',\n",
       " '12-09']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Arequipa</th>\n",
       "      <th>Date</th>\n",
       "      <th>Lima</th>\n",
       "      <th>Puno</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>12-01</td>\n",
       "      <td>20</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>12-02</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>12-03</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>12-04</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>12-05</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>12-06</td>\n",
       "      <td>27</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>12-07</td>\n",
       "      <td>23</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>12-08</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>12-09</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Arequipa   Date  Lima  Puno\n",
       "0        15  12-01    20    -2\n",
       "1        19  12-02    18     0\n",
       "2        15  12-03    23     2\n",
       "3        11  12-04    19     5\n",
       "4         9  12-05    25     7\n",
       "5         8  12-06    27    -5\n",
       "6        13  12-07    23    -3\n",
       "7        14  12-08    29     4\n",
       "8        16  12-09    30     7"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'Date': dates, 'Arequipa' : [15,19,15,11,9,8,13,14,16], 'Puno': [-2,0,2,5,7,-5,-3,4,7], 'Lima':[20,18,23,19,25,27,23,29,30]}\n",
    "temps = pd.DataFrame(d)\n",
    "temps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Leyendo data de un archivo csv \n",
    "\n",
    "Puede leer datos de un archivo __CSV__ (comma-separated values) utilizando la función read_csv. \n",
    "\n",
    "Vamos a buscar algunos datos de avistamientos de ovnis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Leyendo el dataset de reportes de avistamientos en un dataframe\n",
    "ufo = pd.read_csv('../data/ufo.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examinamos las 5 primeras filas\n",
    "ufo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Documentacion de [read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También podemos leer data de una web, en este caso leeremos un archivo __TSV__ (Tabular-separated-values): con __read_table__:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4617</th>\n",
       "      <td>1833</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...</td>\n",
       "      <td>$11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4618</th>\n",
       "      <td>1833</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...</td>\n",
       "      <td>$11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4619</th>\n",
       "      <td>1834</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n",
       "      <td>$11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4620</th>\n",
       "      <td>1834</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...</td>\n",
       "      <td>$8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4621</th>\n",
       "      <td>1834</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n",
       "      <td>$8.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id  quantity           item_name  \\\n",
       "4617      1833         1       Steak Burrito   \n",
       "4618      1833         1       Steak Burrito   \n",
       "4619      1834         1  Chicken Salad Bowl   \n",
       "4620      1834         1  Chicken Salad Bowl   \n",
       "4621      1834         1  Chicken Salad Bowl   \n",
       "\n",
       "                                     choice_description item_price  \n",
       "4617  [Fresh Tomato Salsa, [Rice, Black Beans, Sour ...    $11.75   \n",
       "4618  [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...    $11.75   \n",
       "4619  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...    $11.25   \n",
       "4620  [Fresh Tomato Salsa, [Fajita Vegetables, Lettu...     $8.75   \n",
       "4621  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...     $8.75   "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leyendo el dataset de ordenes de Chipotle de una URL y guardar los resultados en un dataframe\n",
    "orders = pd.read_table('http://bit.ly/chiporders')\n",
    "\n",
    "#mostramos las ultimas filas\n",
    "orders.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para seleccionar una Columna o __\"Serie\"__ usamos la notacion []:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                  Ithaca\n",
       "1             Willingboro\n",
       "2                 Holyoke\n",
       "3                 Abilene\n",
       "4    New York Worlds Fair\n",
       "Name: City, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo['City'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tambien podemos usar la notación punto (__.__):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                  Ithaca\n",
       "1             Willingboro\n",
       "2                 Holyoke\n",
       "3                 Abilene\n",
       "4    New York Worlds Fair\n",
       "Name: City, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.City.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La notacion de brackets [] o corchetes siempre funciona mientras que la notación del punto tiene limitaciones:\n",
    "\n",
    "- La notación de puntos no funciona si hay espacios en el nombre de la serie\n",
    "- La notación de puntos no funciona si la Serie tiene el mismo nombre que un método o atributo de DataFrame (como 'head' o 'shape')\n",
    "- No se puede utilizar la notación de puntos para definir el nombre de una nueva serie (véase más adelante)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                     title content_rating   genre  duration  \\\n",
       "0          9.3  The Shawshank Redemption              R   Crime       142   \n",
       "1          9.2             The Godfather              R   Crime       175   \n",
       "2          9.1    The Godfather: Part II              R   Crime       200   \n",
       "3          9.0           The Dark Knight          PG-13  Action       152   \n",
       "4          8.9              Pulp Fiction              R   Crime       154   \n",
       "\n",
       "                                         actors_list  \n",
       "0  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "4  [u'John Travolta', u'Uma Thurman', u'Samuel L....  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leyendo un dataset de las top-rated IMDb movies en un dataframe\n",
    "\n",
    "movies = pd.read_csv('../data/imdb.csv')\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>979.000000</td>\n",
       "      <td>979.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.889785</td>\n",
       "      <td>120.979571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.336069</td>\n",
       "      <td>26.218010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.600000</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.800000</td>\n",
       "      <td>117.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.100000</td>\n",
       "      <td>134.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.300000</td>\n",
       "      <td>242.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       star_rating    duration\n",
       "count   979.000000  979.000000\n",
       "mean      7.889785  120.979571\n",
       "std       0.336069   26.218010\n",
       "min       7.400000   64.000000\n",
       "25%       7.600000  102.000000\n",
       "50%       7.800000  117.000000\n",
       "75%       8.100000  134.000000\n",
       "max       9.300000  242.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Método describe: calcula un resumen de estadísticas\n",
    "\n",
    "movies.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(979, 6)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Porqué algunos comandos de pandas terminan en parentesis y otros no?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los __métodos__ terminan con paréntesis, mientras que los __atributos__ no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "star_rating       float64\n",
       "title              object\n",
       "content_rating     object\n",
       "genre              object\n",
       "duration            int64\n",
       "actors_list        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ejemplo de atributo: obtener el data type de cada columna\n",
    "movies.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>979</td>\n",
       "      <td>976</td>\n",
       "      <td>979</td>\n",
       "      <td>979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>975</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>The Girl with the Dragon Tattoo</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama</td>\n",
       "      <td>[u'Daniel Radcliffe', u'Emma Watson', u'Rupert...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>278</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  title content_rating  genre  \\\n",
       "count                               979            976    979   \n",
       "unique                              975             12     16   \n",
       "top     The Girl with the Dragon Tattoo              R  Drama   \n",
       "freq                                  2            460    278   \n",
       "\n",
       "                                              actors_list  \n",
       "count                                                 979  \n",
       "unique                                                969  \n",
       "top     [u'Daniel Radcliffe', u'Emma Watson', u'Rupert...  \n",
       "freq                                                    6  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Utilice un parámetro opcional para describir el método para resumir sólo las columnas 'object'\n",
    "movies.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora vamos a obtener el numero de peliculas por clasificación de contenido ( R, PG-13, PG.. etc):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movie_ratings = movies['content_rating'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f46e0185128>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iE+bzN7h0nyQnJ1nZEFdVD0jy4iw2/j4vyWPvDr0KsAW2VdWLd3axu39tK4vZ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\n5P8HcC12GKrmj8YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46e0185d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movie_ratings.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examinamos las 5 ultimas filas\n",
    "ufo = pd.read_csv('../data/ufo.csv')\n",
    "ufo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los nombres de cada columna deben de tratar de no tener espacios, para ello podemos cambiar el nombre de las columnas de distintas formas como:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>colors_reported</th>\n",
       "      <th>shape_reported</th>\n",
       "      <th>state</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   city colors_reported shape_reported state             time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reemplazar todos los nombres de columnas sobrescribiendo el atributo 'columnas'\n",
    "ufo_cols = ['city', 'colors_reported', 'shape_reported', 'state', 'time']\n",
    "ufo.columns = ufo_cols\n",
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>Colors_Reported_test</th>\n",
       "      <th>Shape_Reported_test</th>\n",
       "      <th>state</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   city Colors_Reported_test Shape_Reported_test state  \\\n",
       "0                Ithaca                  NaN            TRIANGLE    NY   \n",
       "1           Willingboro                  NaN               OTHER    NJ   \n",
       "2               Holyoke                  NaN                OVAL    CO   \n",
       "3               Abilene                  NaN                DISK    KS   \n",
       "4  New York Worlds Fair                  NaN               LIGHT    NY   \n",
       "\n",
       "              time  \n",
       "0   6/1/1930 22:00  \n",
       "1  6/30/1930 20:00  \n",
       "2  2/15/1931 14:00  \n",
       "3   6/1/1931 13:00  \n",
       "4  4/18/1933 19:00  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renombrar dos de las columnas mediante el método 'rename'\n",
    "ufo.rename(columns={'colors_reported':'Colors_Reported_test', 'shape_reported':'Shape_Reported_test'}, inplace=True)\n",
    "ufo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Para remover una columna usamos el método __drop__ :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>Shape_Reported_test</th>\n",
       "      <th>state</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   city Shape_Reported_test state             time\n",
       "0                Ithaca            TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro               OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke                OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene                DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair               LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminar una columna (axis=1 se refiere a columnas)\n",
    "ufo.drop('Colors_Reported_test', axis=1, inplace=True)\n",
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>Shape_Reported_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>TRIANGLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>OTHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>OVAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>DISK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>LIGHT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   city Shape_Reported_test\n",
       "0                Ithaca            TRIANGLE\n",
       "1           Willingboro               OTHER\n",
       "2               Holyoke                OVAL\n",
       "3               Abilene                DISK\n",
       "4  New York Worlds Fair               LIGHT"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminar varias columnas a la vez\n",
    "ufo.drop(['state', 'time'], axis=1, inplace=True)\n",
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>Shape_Reported_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>OVAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>DISK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>LIGHT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Valley City</td>\n",
       "      <td>DISK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Crater Lake</td>\n",
       "      <td>CIRCLE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   city Shape_Reported_test\n",
       "2               Holyoke                OVAL\n",
       "3               Abilene                DISK\n",
       "4  New York Worlds Fair               LIGHT\n",
       "5           Valley City                DISK\n",
       "6           Crater Lake              CIRCLE"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminar varias filas a la vez (axis=0 se refiere a filas)\n",
    "ufo.drop([0, 1], axis=0, inplace=True)\n",
    "ufo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering y Sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                     title content_rating   genre  duration  \\\n",
       "0          9.3  The Shawshank Redemption              R   Crime       142   \n",
       "1          9.2             The Godfather              R   Crime       175   \n",
       "2          9.1    The Godfather: Part II              R   Crime       200   \n",
       "3          9.0           The Dark Knight          PG-13  Action       152   \n",
       "4          8.9              Pulp Fiction              R   Crime       154   \n",
       "\n",
       "                                         actors_list  \n",
       "0  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "4  [u'John Travolta', u'Uma Thurman', u'Samuel L....  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examinamos las 5 primeras filas\n",
    "movies = pd.read_csv('../data/imdb.csv')\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "864               [Rec]\n",
       "526                Zulu\n",
       "615          Zombieland\n",
       "677              Zodiac\n",
       "955    Zero Dark Thirty\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Si queremos ordenar usamos el metodo sort_values()\n",
    "\n",
    "movies.title.sort_values(ascending=False).head() # En este caso ordenamos de forma descendente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.9</td>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>201</td>\n",
       "      <td>[u'Elijah Wood', u'Viggo Mortensen', u'Ian McK...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.8</td>\n",
       "      <td>The Lord of the Rings: The Fellowship of the Ring</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>178</td>\n",
       "      <td>[u'Elijah Wood', u'Ian McKellen', u'Orlando Bl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.8</td>\n",
       "      <td>Inception</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>148</td>\n",
       "      <td>[u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.8</td>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Drama</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tom Hanks', u'Robin Wright', u'Gary Sinise']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    star_rating                                              title  \\\n",
       "3           9.0                                    The Dark Knight   \n",
       "7           8.9      The Lord of the Rings: The Return of the King   \n",
       "10          8.8  The Lord of the Rings: The Fellowship of the Ring   \n",
       "11          8.8                                          Inception   \n",
       "13          8.8                                       Forrest Gump   \n",
       "\n",
       "   content_rating      genre  duration  \\\n",
       "3           PG-13     Action       152   \n",
       "7           PG-13  Adventure       201   \n",
       "10          PG-13  Adventure       178   \n",
       "11          PG-13     Action       148   \n",
       "13          PG-13      Drama       142   \n",
       "\n",
       "                                          actors_list  \n",
       "3   [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "7   [u'Elijah Wood', u'Viggo Mortensen', u'Ian McK...  \n",
       "10  [u'Elijah Wood', u'Ian McKellen', u'Orlando Bl...  \n",
       "11  [u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'...  \n",
       "13    [u'Tom Hanks', u'Robin Wright', u'Gary Sinise']  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identificamos: los indices se mantienen con cada fila cuando filtramos el dataframe\n",
    "movies[movies.content_rating=='PG-13'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>7.8</td>\n",
       "      <td>(500) Days of Summer</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>95</td>\n",
       "      <td>[u'Zooey Deschanel', u'Joseph Gordon-Levitt', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.9</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Drama</td>\n",
       "      <td>96</td>\n",
       "      <td>[u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>8.1</td>\n",
       "      <td>12 Years a Slave</td>\n",
       "      <td>R</td>\n",
       "      <td>Biography</td>\n",
       "      <td>134</td>\n",
       "      <td>[u'Chiwetel Ejiofor', u'Michael Kenneth Willia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>7.6</td>\n",
       "      <td>127 Hours</td>\n",
       "      <td>R</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>94</td>\n",
       "      <td>[u'James Franco', u'Amber Tamblyn', u'Kate Mara']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>8.3</td>\n",
       "      <td>2001: A Space Odyssey</td>\n",
       "      <td>G</td>\n",
       "      <td>Mystery</td>\n",
       "      <td>160</td>\n",
       "      <td>[u'Keir Dullea', u'Gary Lockwood', u'William S...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     star_rating                  title content_rating      genre  duration  \\\n",
       "542          7.8   (500) Days of Summer          PG-13     Comedy        95   \n",
       "5            8.9           12 Angry Men      NOT RATED      Drama        96   \n",
       "201          8.1       12 Years a Slave              R  Biography       134   \n",
       "698          7.6              127 Hours              R  Adventure        94   \n",
       "110          8.3  2001: A Space Odyssey              G    Mystery       160   \n",
       "\n",
       "                                           actors_list  \n",
       "542  [u'Zooey Deschanel', u'Joseph Gordon-Levitt', ...  \n",
       "5    [u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...  \n",
       "201  [u'Chiwetel Ejiofor', u'Michael Kenneth Willia...  \n",
       "698  [u'James Franco', u'Amber Tamblyn', u'Kate Mara']  \n",
       "110  [u'Keir Dullea', u'Gary Lockwood', u'William S...  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ordenar todo el DataFrame por la serie 'title' (devuelve un DataFrame)\n",
    "\n",
    "movies.sort_values('title').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Objetivo:__ filtrar las filas de DataFrame para mostrar sólo películas con una \"duración\" de al menos 200 minutos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.9</td>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>201</td>\n",
       "      <td>[u'Elijah Wood', u'Viggo Mortensen', u'Ian McK...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8.7</td>\n",
       "      <td>Seven Samurai</td>\n",
       "      <td>UNRATED</td>\n",
       "      <td>Drama</td>\n",
       "      <td>207</td>\n",
       "      <td>[u'Toshir\\xf4 Mifune', u'Takashi Shimura', u'K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>8.4</td>\n",
       "      <td>Once Upon a Time in America</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>229</td>\n",
       "      <td>[u'Robert De Niro', u'James Woods', u'Elizabet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>8.4</td>\n",
       "      <td>Lawrence of Arabia</td>\n",
       "      <td>PG</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>216</td>\n",
       "      <td>[u\"Peter O'Toole\", u'Alec Guinness', u'Anthony...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>8.3</td>\n",
       "      <td>Lagaan: Once Upon a Time in India</td>\n",
       "      <td>PG</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>224</td>\n",
       "      <td>[u'Aamir Khan', u'Gracy Singh', u'Rachel Shell...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>8.2</td>\n",
       "      <td>Gone with the Wind</td>\n",
       "      <td>G</td>\n",
       "      <td>Drama</td>\n",
       "      <td>238</td>\n",
       "      <td>[u'Clark Gable', u'Vivien Leigh', u'Thomas Mit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>8.1</td>\n",
       "      <td>Ben-Hur</td>\n",
       "      <td>G</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>212</td>\n",
       "      <td>[u'Charlton Heston', u'Jack Hawkins', u'Stephe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>7.9</td>\n",
       "      <td>The Ten Commandments</td>\n",
       "      <td>APPROVED</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>220</td>\n",
       "      <td>[u'Charlton Heston', u'Yul Brynner', u'Anne Ba...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>7.8</td>\n",
       "      <td>Hamlet</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Drama</td>\n",
       "      <td>242</td>\n",
       "      <td>[u'Kenneth Branagh', u'Julie Christie', u'Dere...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>7.7</td>\n",
       "      <td>Malcolm X</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Biography</td>\n",
       "      <td>202</td>\n",
       "      <td>[u'Denzel Washington', u'Angela Bassett', u'De...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>7.6</td>\n",
       "      <td>It's a Mad, Mad, Mad, Mad World</td>\n",
       "      <td>APPROVED</td>\n",
       "      <td>Action</td>\n",
       "      <td>205</td>\n",
       "      <td>[u'Spencer Tracy', u'Milton Berle', u'Ethel Me...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     star_rating                                          title  \\\n",
       "2            9.1                         The Godfather: Part II   \n",
       "7            8.9  The Lord of the Rings: The Return of the King   \n",
       "17           8.7                                  Seven Samurai   \n",
       "78           8.4                    Once Upon a Time in America   \n",
       "85           8.4                             Lawrence of Arabia   \n",
       "142          8.3              Lagaan: Once Upon a Time in India   \n",
       "157          8.2                             Gone with the Wind   \n",
       "204          8.1                                        Ben-Hur   \n",
       "445          7.9                           The Ten Commandments   \n",
       "476          7.8                                         Hamlet   \n",
       "630          7.7                                      Malcolm X   \n",
       "767          7.6                It's a Mad, Mad, Mad, Mad World   \n",
       "\n",
       "    content_rating      genre  duration  \\\n",
       "2                R      Crime       200   \n",
       "7            PG-13  Adventure       201   \n",
       "17         UNRATED      Drama       207   \n",
       "78               R      Crime       229   \n",
       "85              PG  Adventure       216   \n",
       "142             PG  Adventure       224   \n",
       "157              G      Drama       238   \n",
       "204              G  Adventure       212   \n",
       "445       APPROVED  Adventure       220   \n",
       "476          PG-13      Drama       242   \n",
       "630          PG-13  Biography       202   \n",
       "767       APPROVED     Action       205   \n",
       "\n",
       "                                           actors_list  \n",
       "2    [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "7    [u'Elijah Wood', u'Viggo Mortensen', u'Ian McK...  \n",
       "17   [u'Toshir\\xf4 Mifune', u'Takashi Shimura', u'K...  \n",
       "78   [u'Robert De Niro', u'James Woods', u'Elizabet...  \n",
       "85   [u\"Peter O'Toole\", u'Alec Guinness', u'Anthony...  \n",
       "142  [u'Aamir Khan', u'Gracy Singh', u'Rachel Shell...  \n",
       "157  [u'Clark Gable', u'Vivien Leigh', u'Thomas Mit...  \n",
       "204  [u'Charlton Heston', u'Jack Hawkins', u'Stephe...  \n",
       "445  [u'Charlton Heston', u'Yul Brynner', u'Anne Ba...  \n",
       "476  [u'Kenneth Branagh', u'Julie Christie', u'Dere...  \n",
       "630  [u'Denzel Washington', u'Angela Bassett', u'De...  \n",
       "767  [u'Spencer Tracy', u'Milton Berle', u'Ethel Me...  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_long = movies.duration >= 200\n",
    "movies[is_long]\n",
    "\n",
    "# O de forma equivalente, escríbalo en una línea (no es necesario crear el objeto 'is_long')\n",
    "\n",
    "movies[movies.duration >= 200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2          Crime\n",
       "7      Adventure\n",
       "17         Drama\n",
       "78         Crime\n",
       "85     Adventure\n",
       "142    Adventure\n",
       "157        Drama\n",
       "204    Adventure\n",
       "445    Adventure\n",
       "476        Drama\n",
       "630    Biography\n",
       "767       Action\n",
       "Name: genre, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecciona la serie 'genre' del DataFrame filtrado\n",
    "movies[movies.duration >= 200].genre\n",
    "\n",
    "# O de forma equivalente, use el método 'loc'\n",
    "movies.loc[movies.duration >= 200, 'genre']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Meta:__ Filtrar aún más el DataFrame de películas largas (duration> = 200) para mostrar sólo películas que también tienen un 'genre' de 'Drama'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8.7</td>\n",
       "      <td>Seven Samurai</td>\n",
       "      <td>UNRATED</td>\n",
       "      <td>Drama</td>\n",
       "      <td>207</td>\n",
       "      <td>[u'Toshir\\xf4 Mifune', u'Takashi Shimura', u'K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>8.2</td>\n",
       "      <td>Gone with the Wind</td>\n",
       "      <td>G</td>\n",
       "      <td>Drama</td>\n",
       "      <td>238</td>\n",
       "      <td>[u'Clark Gable', u'Vivien Leigh', u'Thomas Mit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>7.8</td>\n",
       "      <td>Hamlet</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Drama</td>\n",
       "      <td>242</td>\n",
       "      <td>[u'Kenneth Branagh', u'Julie Christie', u'Dere...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     star_rating               title content_rating  genre  duration  \\\n",
       "17           8.7       Seven Samurai        UNRATED  Drama       207   \n",
       "157          8.2  Gone with the Wind              G  Drama       238   \n",
       "476          7.8              Hamlet          PG-13  Drama       242   \n",
       "\n",
       "                                           actors_list  \n",
       "17   [u'Toshir\\xf4 Mifune', u'Takashi Shimura', u'K...  \n",
       "157  [u'Clark Gable', u'Vivien Leigh', u'Thomas Mit...  \n",
       "476  [u'Kenneth Branagh', u'Julie Christie', u'Dere...  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[(movies.duration >=200) & (movies.genre == 'Drama')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Objetivo:__ Filtrar el DataFrame original para mostrar películas con un 'genre' de 'Crime' o 'Drama' o 'Action' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.9</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>NOT RATED</td>\n",
       "      <td>Drama</td>\n",
       "      <td>96</td>\n",
       "      <td>[u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Fight Club</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama</td>\n",
       "      <td>139</td>\n",
       "      <td>[u'Brad Pitt', u'Edward Norton', u'Helena Bonh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.8</td>\n",
       "      <td>Inception</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>148</td>\n",
       "      <td>[u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8.8</td>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back</td>\n",
       "      <td>PG</td>\n",
       "      <td>Action</td>\n",
       "      <td>124</td>\n",
       "      <td>[u'Mark Hamill', u'Harrison Ford', u'Carrie Fi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.8</td>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Drama</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tom Hanks', u'Robin Wright', u'Gary Sinise']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    star_rating                                           title  \\\n",
       "0           9.3                        The Shawshank Redemption   \n",
       "1           9.2                                   The Godfather   \n",
       "2           9.1                          The Godfather: Part II   \n",
       "3           9.0                                 The Dark Knight   \n",
       "4           8.9                                    Pulp Fiction   \n",
       "5           8.9                                    12 Angry Men   \n",
       "9           8.9                                      Fight Club   \n",
       "11          8.8                                       Inception   \n",
       "12          8.8  Star Wars: Episode V - The Empire Strikes Back   \n",
       "13          8.8                                    Forrest Gump   \n",
       "\n",
       "   content_rating   genre  duration  \\\n",
       "0               R   Crime       142   \n",
       "1               R   Crime       175   \n",
       "2               R   Crime       200   \n",
       "3           PG-13  Action       152   \n",
       "4               R   Crime       154   \n",
       "5       NOT RATED   Drama        96   \n",
       "9               R   Drama       139   \n",
       "11          PG-13  Action       148   \n",
       "12             PG  Action       124   \n",
       "13          PG-13   Drama       142   \n",
       "\n",
       "                                          actors_list  \n",
       "0   [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1     [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2   [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3   [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "4   [u'John Travolta', u'Uma Thurman', u'Samuel L....  \n",
       "5   [u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...  \n",
       "9   [u'Brad Pitt', u'Edward Norton', u'Helena Bonh...  \n",
       "11  [u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'...  \n",
       "12  [u'Mark Hamill', u'Harrison Ford', u'Carrie Fi...  \n",
       "13    [u'Tom Hanks', u'Robin Wright', u'Gary Sinise']  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Utiliza el '|' Operador para especificar que una fila puede coincidir con cualquiera de los tres criterios\n",
    "movies[(movies.genre == 'Crime') | (movies.genre == 'Drama') | (movies.genre == 'Action')].head(10)\n",
    "\n",
    "# O de forma equivalente, use el método 'isin'\n",
    "movies[movies.genre.isin(['Crime', 'Drama', 'Action'])].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lee el dataset de Chipotle orders en un DataFrame\n",
    "\n",
    "orders = pd.read_table('http://bit.ly/chiporders')\n",
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.39\n",
       "1     3.39\n",
       "2     3.39\n",
       "3     2.39\n",
       "4    16.98\n",
       "Name: item_price, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convierte un string a un numero para poder hacer operaciones matemáticas\n",
    "\n",
    "orders.item_price.str.replace('$', '').astype(float).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "Name: item_name, dtype: bool"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Método string 'contains' checkea si es que hay un substring que contenga 'Chicken' y retorna una serie booleana\n",
    "\n",
    "orders.item_name.str.contains('Chicken').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    1\n",
       "Name: item_name, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convierte una serie booleana a un entero (False = 0 , True = 1)\n",
    "\n",
    "orders.item_name.str.contains('Chicken').astype(int).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lee el dataset de consumo de alcohol en un dataframe\n",
    "\n",
    "drinks = pd.read_csv('http://bit.ly/drinksbycountry')\n",
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.471698113207545"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcula la media de cervezas servidas solo en paises del continente africano\n",
    "\n",
    "drinks[drinks.continent=='Africa'].beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa            61.471698\n",
       "Asia              37.045455\n",
       "Europe           193.777778\n",
       "North America    145.434783\n",
       "Oceania           89.687500\n",
       "South America    175.083333\n",
       "Name: beer_servings, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcula la media de cervezas servidas por cada continente\n",
    "\n",
    "drinks.groupby('continent').beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>61.471698</td>\n",
       "      <td>16.339623</td>\n",
       "      <td>16.264151</td>\n",
       "      <td>3.007547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>37.045455</td>\n",
       "      <td>60.840909</td>\n",
       "      <td>9.068182</td>\n",
       "      <td>2.170455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>193.777778</td>\n",
       "      <td>132.555556</td>\n",
       "      <td>142.222222</td>\n",
       "      <td>8.617778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>145.434783</td>\n",
       "      <td>165.739130</td>\n",
       "      <td>24.521739</td>\n",
       "      <td>5.995652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>89.687500</td>\n",
       "      <td>58.437500</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>3.381250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>175.083333</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>6.308333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               beer_servings  spirit_servings  wine_servings  \\\n",
       "continent                                                      \n",
       "Africa             61.471698        16.339623      16.264151   \n",
       "Asia               37.045455        60.840909       9.068182   \n",
       "Europe            193.777778       132.555556     142.222222   \n",
       "North America     145.434783       165.739130      24.521739   \n",
       "Oceania            89.687500        58.437500      35.625000   \n",
       "South America     175.083333       114.750000      62.416667   \n",
       "\n",
       "               total_litres_of_pure_alcohol  \n",
       "continent                                    \n",
       "Africa                             3.007547  \n",
       "Asia                               2.170455  \n",
       "Europe                             8.617778  \n",
       "North America                      5.995652  \n",
       "Oceania                            3.381250  \n",
       "South America                      6.308333  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Especificando una columna a la que se debe aplicar la función de agregación no se requiere\n",
    "\n",
    "drinks.groupby('continent').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f46dcdfa0f0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kydMzeyTFB6vqfZlNFW8GU3bnlCTvrKqfTXLRMLYls3tvnzZaKhaRY8sacE8c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\ngAlR4gAAACZEiQMAAJiQ/w3G3Mq4qTvOqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46dd41a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Diagrama de barras de lado a lado del DataFrame de arriba\n",
    "\n",
    "drinks.groupby('continent').beer_servings.mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f46dd3b38d0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GOEmSJElqiCFOkiRJkhpiiJMkSZKkhhjiJEmSJKkhhjhJkiRJaoghTpIkSZIaYoiTJEmS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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46dd3bc2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot of the 'value_counts' for the 'genre' Series\n",
    "movies.genre.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manejando Valores perdidos o Missing Values\n",
    "\n",
    "¿Qué significa \"NaN\"? \n",
    "\n",
    "- \"NaN\" no es una cadena, sino que es un valor especial: __numpy.nan__. \n",
    "- Representa \"Not a number\" e indica un valor faltante. \n",
    "- __read_csv__ detecta los valores perdidos (de forma predeterminada) al leer el archivo y los reemplaza con este valor especial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18236</th>\n",
       "      <td>Grant Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>IL</td>\n",
       "      <td>12/31/2000 23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18237</th>\n",
       "      <td>Spirit Lake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>IA</td>\n",
       "      <td>12/31/2000 23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18238</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WI</td>\n",
       "      <td>12/31/2000 23:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18239</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>WI</td>\n",
       "      <td>12/31/2000 23:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18240</th>\n",
       "      <td>Ybor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>FL</td>\n",
       "      <td>12/31/2000 23:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              City Colors Reported Shape Reported State              Time\n",
       "18236   Grant Park             NaN       TRIANGLE    IL  12/31/2000 23:00\n",
       "18237  Spirit Lake             NaN           DISK    IA  12/31/2000 23:00\n",
       "18238  Eagle River             NaN            NaN    WI  12/31/2000 23:45\n",
       "18239  Eagle River             RED          LIGHT    WI  12/31/2000 23:45\n",
       "18240         Ybor             NaN           OVAL    FL  12/31/2000 23:59"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leyendo el dataset de reportes de avistamientos en un dataframe\n",
    "ufo = pd.read_csv('../data/ufo.csv')\n",
    "ufo.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18236     True\n",
       "18237     True\n",
       "18238     True\n",
       "18239    False\n",
       "18240     True\n",
       "Name: Colors Reported, dtype: bool"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Si color reported es null retornara True\n",
    "ufo['Colors Reported'].isnull().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18236    False\n",
       "18237    False\n",
       "18238    False\n",
       "18239     True\n",
       "18240    False\n",
       "Name: Colors Reported, dtype: bool"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Caso contrario retornara False con notnull()\n",
    "ufo['Colors Reported'].notnull().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LA</td>\n",
       "      <td>8/15/1943 0:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>LA</td>\n",
       "      <td>8/15/1943 0:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>CA</td>\n",
       "      <td>7/15/1952 12:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BLUE</td>\n",
       "      <td>DISK</td>\n",
       "      <td>MT</td>\n",
       "      <td>7/4/1953 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>NV</td>\n",
       "      <td>7/1/1960 12:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    City Colors Reported Shape Reported State             Time\n",
       "21   NaN             NaN            NaN    LA   8/15/1943 0:00\n",
       "22   NaN             NaN          LIGHT    LA   8/15/1943 0:00\n",
       "204  NaN             NaN           DISK    CA  7/15/1952 12:30\n",
       "241  NaN            BLUE           DISK    MT   7/4/1953 14:00\n",
       "613  NaN             NaN           DISK    NV   7/1/1960 12:00"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nos devuelve el dataframe con las columnas vacias de City\n",
    "ufo[ufo.City.isnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18241, 5)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Devuelve el numero de filas y columnas\n",
    "ufo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2486, 5)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Si faltan 'algun (any)' valor en una fila entonces elimina esa fila esa fila\n",
    "ufo.dropna(how='any').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18241, 5)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Si faltan todos(all) los valores en una fila, entonces elimina esa fila (no se eliminan en este caso)\n",
    "ufo.dropna(how='all').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15576, 5)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Si falta algun valor en una fila (teniendo en cuenta sólo 'City' y 'Shape Reported'), entonces se elimina esa fila\n",
    "ufo.dropna(subset=['City', 'Shape Reported'], how='any').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18237, 5)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Si \"all\" los valores estan faltantes en una filla (considerando solo 'City' y 'Shape Reported') entonces elimina esa fila\n",
    "ufo.dropna(subset=['City', 'Shape Reported'], how='all').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIGHT       2803\n",
       "DISK        2122\n",
       "TRIANGLE    1889\n",
       "OTHER       1402\n",
       "CIRCLE      1365\n",
       "Name: Shape Reported, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'value_counts' no incluye missing values por defecto\n",
    "ufo['Shape Reported'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIGHT       2803\n",
       "NaN         2644\n",
       "DISK        2122\n",
       "TRIANGLE    1889\n",
       "OTHER       1402\n",
       "Name: Shape Reported, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Incluye explícitamente los missing values\n",
    "ufo['Shape Reported'].value_counts(dropna=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Rellenar los valores faltantes con un valor especificado\n",
    "ufo['Shape Reported'].fillna(value='VARIOUS', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VARIOUS     2977\n",
       "LIGHT       2803\n",
       "DISK        2122\n",
       "TRIANGLE    1889\n",
       "OTHER       1402\n",
       "Name: Shape Reported, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirmar que los valores faltantes fueron rellenados\n",
    "ufo['Shape Reported'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing \n",
    "\n",
    "El método __loc__ se utiliza para seleccionar filas y columnas por etiqueta. Puede pasar\n",
    ": \n",
    "- Una etiqueta única \n",
    "- Una lista de etiquetas \n",
    "- Una porción de etiquetas  \n",
    "- Una serie booleana \n",
    "- Dos puntos (que indica \"todas las etiquetas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Type of Crime</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Year</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1960</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1961</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1962</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1963</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1964</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State  Type of Crime                                 Crime  Year  Count\n",
       "0  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1960    406\n",
       "1  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1961    427\n",
       "2  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1962    316\n",
       "3  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1963    340\n",
       "4  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1964    316"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leyendo la data de crimen cometidos en los EEUU\n",
    "crime_data = pd.read_csv('../data/crime.csv')\n",
    "crime_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State                                         Alabama\n",
       "Type of Crime                           Violent Crime\n",
       "Crime            Murder and nonnegligent Manslaughter\n",
       "Year                                             1960\n",
       "Count                                             406\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fila 0, todas las columnas\n",
    "crime_data.loc[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Type of Crime</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Year</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1960</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1961</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1962</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State  Type of Crime                                 Crime  Year  Count\n",
       "0  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1960    406\n",
       "1  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1961    427\n",
       "2  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1962    316"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filas 0 , 1 y 2, all columns\n",
    "crime_data.loc[[0, 1, 2], :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Type of Crime</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Year</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1960</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1961</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1962</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1963</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1964</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1965</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1966</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1967</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1968</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State  Type of Crime                                 Crime  Year  Count\n",
       "0  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1960    406\n",
       "1  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1961    427\n",
       "2  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1962    316\n",
       "3  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1963    340\n",
       "4  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1964    316\n",
       "5  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1965    395\n",
       "6  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1966    384\n",
       "7  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1967    415\n",
       "8  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1968    421"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filas 0 a la 8, all columns\n",
    "crime_data.loc[0:8 ,  :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Type of Crime</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Year</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1960</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1961</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1962</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1963</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State  Type of Crime                                 Crime  Year  Count\n",
       "0  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1960    406\n",
       "1  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1961    427\n",
       "2  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1962    316\n",
       "3  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1963    340"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filas 0 a la 3, all columns\n",
    "crime_data.loc[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State  Count\n",
       "0  Alabama    406\n",
       "1  Alabama    427\n",
       "2  Alabama    316\n",
       "3  Alabama    340\n",
       "4  Alabama    316"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filas de la 0 a la 4 (incluyendo) las columnas 'State' y 'Count'\n",
    "crime_data.loc[0:4, ['State', 'Count']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El método __iloc__ se utiliza para seleccionar filas y columnas por posición entera. Se puede pasar: \n",
    "\n",
    "- Una sola posición entera \n",
    "- Una lista de posiciones enteras \n",
    "- Una porción de posiciones enteras \n",
    "- Dos puntos (que indica \"todas las posiciones enteras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Type of Crime</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Year</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1960</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Violent Crime</td>\n",
       "      <td>Murder and nonnegligent Manslaughter</td>\n",
       "      <td>1961</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State  Type of Crime                                 Crime  Year  Count\n",
       "0  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1960    406\n",
       "1  Alabama  Violent Crime  Murder and nonnegligent Manslaughter  1961    427"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filas en la posicion 0 al 2 (excluyente) todas las columnas\n",
    "crime_data.iloc[0:2, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El método __ix__ se utiliza para seleccionar filas y columnas por etiqueta o posición de número entero, y sólo debe utilizarse cuando se necesita mezclar selección basada en etiquetas y enteros en la misma llamada.\n",
    "\n",
    "Reglas para el uso de números con ix: \n",
    "\n",
    "- Si el índice es cadenas, los números se tratan como posiciones enteras, y por lo tanto los cortes son exclusivos a la derecha. \n",
    "- Si el índice es números enteros, los números se tratan como etiquetas y, por lo tanto, los cortes son inclusivos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua &amp; Barbuda</th>\n",
       "      <td>102</td>\n",
       "      <td>128</td>\n",
       "      <td>45</td>\n",
       "      <td>4.9</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>193</td>\n",
       "      <td>25</td>\n",
       "      <td>221</td>\n",
       "      <td>8.3</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>21</td>\n",
       "      <td>179</td>\n",
       "      <td>11</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>261</td>\n",
       "      <td>72</td>\n",
       "      <td>212</td>\n",
       "      <td>10.4</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>279</td>\n",
       "      <td>75</td>\n",
       "      <td>191</td>\n",
       "      <td>9.7</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   beer_servings  spirit_servings  wine_servings  \\\n",
       "country                                                            \n",
       "Afghanistan                    0                0              0   \n",
       "Albania                       89              132             54   \n",
       "Algeria                       25                0             14   \n",
       "Andorra                      245              138            312   \n",
       "Angola                       217               57             45   \n",
       "Antigua & Barbuda            102              128             45   \n",
       "Argentina                    193               25            221   \n",
       "Armenia                       21              179             11   \n",
       "Australia                    261               72            212   \n",
       "Austria                      279               75            191   \n",
       "\n",
       "                   total_litres_of_pure_alcohol      continent  \n",
       "country                                                         \n",
       "Afghanistan                                 0.0           Asia  \n",
       "Albania                                     4.9         Europe  \n",
       "Algeria                                     0.7         Africa  \n",
       "Andorra                                    12.4         Europe  \n",
       "Angola                                      5.9         Africa  \n",
       "Antigua & Barbuda                           4.9  North America  \n",
       "Argentina                                   8.3  South America  \n",
       "Armenia                                     3.8         Europe  \n",
       "Australia                                  10.4        Oceania  \n",
       "Austria                                     9.7         Europe  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leer el dataset de consumo de alcohol en un DataFrame y establecer 'país' como el índice\n",
    "drinks = pd.read_csv('http://bit.ly/drinksbycountry', index_col='country')\n",
    "drinks.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fila con la etiqueta 'Albania' columna en la posicion 0\n",
    "drinks.ix['Albania', 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fila en la posicion 1, columna con etiqueta 'beer_servings'\n",
    "drinks.ix[1, 'beer_servings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         beer_servings  spirit_servings\n",
       "country                                \n",
       "Albania             89              132\n",
       "Algeria             25                0\n",
       "Andorra            245              138"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filas 'Albania' hasta 'Andorra' (inclusiva), columnas en la posicion 0 hasta 2 (exclusiva)\n",
    "drinks.ix['Albania':'Andorra', 0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__ Entonces... ¿Cuáles son las diferencias entre loc, iloc, e ix? veamos... __\n",
    "\n",
    "Usemos la vieja confiable para contestar esta pregunta, es bueno buscar preguntas en internet ya que es imposible memorizar tantos metodos... ;) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://stackoverflow.com/questions/31593201/pandas-iloc-vs-ix-vs-loc-explanation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply\n",
    "\n",
    "Podemos aplicar funciones a data series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def alcoholics(x):\n",
    "    if x > 10:\n",
    "        return 'alcoholics!'\n",
    "    else:\n",
    "        return 'Sober people'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "      <th>Kind of people</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Sober people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Sober people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Sober people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "      <td>alcoholics!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Sober people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua &amp; Barbuda</th>\n",
       "      <td>102</td>\n",
       "      <td>128</td>\n",
       "      <td>45</td>\n",
       "      <td>4.9</td>\n",
       "      <td>North America</td>\n",
       "      <td>Sober people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>193</td>\n",
       "      <td>25</td>\n",
       "      <td>221</td>\n",
       "      <td>8.3</td>\n",
       "      <td>South America</td>\n",
       "      <td>Sober people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>21</td>\n",
       "      <td>179</td>\n",
       "      <td>11</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Sober people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>261</td>\n",
       "      <td>72</td>\n",
       "      <td>212</td>\n",
       "      <td>10.4</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>alcoholics!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>279</td>\n",
       "      <td>75</td>\n",
       "      <td>191</td>\n",
       "      <td>9.7</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Sober people</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   beer_servings  spirit_servings  wine_servings  \\\n",
       "country                                                            \n",
       "Afghanistan                    0                0              0   \n",
       "Albania                       89              132             54   \n",
       "Algeria                       25                0             14   \n",
       "Andorra                      245              138            312   \n",
       "Angola                       217               57             45   \n",
       "Antigua & Barbuda            102              128             45   \n",
       "Argentina                    193               25            221   \n",
       "Armenia                       21              179             11   \n",
       "Australia                    261               72            212   \n",
       "Austria                      279               75            191   \n",
       "\n",
       "                   total_litres_of_pure_alcohol      continent Kind of people  \n",
       "country                                                                        \n",
       "Afghanistan                                 0.0           Asia   Sober people  \n",
       "Albania                                     4.9         Europe   Sober people  \n",
       "Algeria                                     0.7         Africa   Sober people  \n",
       "Andorra                                    12.4         Europe    alcoholics!  \n",
       "Angola                                      5.9         Africa   Sober people  \n",
       "Antigua & Barbuda                           4.9  North America   Sober people  \n",
       "Argentina                                   8.3  South America   Sober people  \n",
       "Armenia                                     3.8         Europe   Sober people  \n",
       "Australia                                  10.4        Oceania    alcoholics!  \n",
       "Austria                                     9.7         Europe   Sober people  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks['Kind of people'] = drinks['total_litres_of_pure_alcohol'].apply(alcoholics)\n",
    "drinks.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creación de Dummy Variables\n",
    "\n",
    "En general: \n",
    "- Si tiene __\"K\" posibles valores__ para una característica categórica, sólo necesita __\"K-1\" dummy variables __ para capturar toda la información sobre esa característica. \n",
    "- Una convención es __eliminar la primera variable ficticia__, que define ese nivel como la \"baseline\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "  </tbody>\n",
       "</table>\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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leyendo la data de crimen cometidos en los EEUU\n",
    "titanic = pd.read_csv('../data/titanic.csv')\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Sex_male</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",
       "      <td>1</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "\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",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Sex_male  \n",
       "0      0         A/5 21171   7.2500   NaN        S         1  \n",
       "1      0          PC 17599  71.2833   C85        C         0  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S         0  \n",
       "3      0            113803  53.1000  C123        S         0  \n",
       "4      0            373450   8.0500   NaN        S         1  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the 'Sex_male' dummy variable using the 'map' method\n",
    "titanic['Sex_male'] = titanic.Sex.map({'female':0, 'male':1})\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   female  male\n",
       "0       0     1\n",
       "1       1     0\n",
       "2       1     0\n",
       "3       1     0\n",
       "4       0     1"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(titanic.Sex).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex_male\n",
       "0         1\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         1"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminamos la primera first dummy variable ('female') usando el metodo iloc\n",
    "pd.get_dummies(titanic.Sex, prefix='Sex').iloc[:, 1:].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Embarked_C  Embarked_Q  Embarked_S\n",
       "0           0           0           1\n",
       "1           1           0           0\n",
       "2           0           0           1\n",
       "3           0           0           1\n",
       "4           0           0           1"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Usando 'get_dummies' Con una característica que tiene 3 valores posibles\n",
    "pd.get_dummies(titanic.Embarked, prefix='Embarked').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Embarked_Q  Embarked_S\n",
       "0           0           1\n",
       "1           0           0\n",
       "2           0           1\n",
       "3           0           1\n",
       "4           0           1"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminamos la primera dummy variable ('C')\n",
    "\n",
    "pd.get_dummies(titanic.Embarked, prefix='Embarked').iloc[:, 1:].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cómo traducir estos valores de nuevo al valor original __'Embarked'__:\n",
    "* 0, 0 significa C\n",
    "* 1, 0 significa Q\n",
    "* 0, 1 significa S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</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>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "\n",
       "                                                Name   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin  Sex_female  Sex_male  Embarked_C  \\\n",
       "0         A/5 21171   7.2500   NaN           0         1           0   \n",
       "1          PC 17599  71.2833   C85           1         0           1   \n",
       "2  STON/O2. 3101282   7.9250   NaN           1         0           0   \n",
       "3            113803  53.1000  C123           1         0           0   \n",
       "4            373450   8.0500   NaN           0         1           0   \n",
       "\n",
       "   Embarked_Q  Embarked_S  \n",
       "0           0           1  \n",
       "1           0           0  \n",
       "2           0           1  \n",
       "3           0           1  \n",
       "4           0           1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pasa el DataFrame a 'get_dummies' y especifica qué columnas a dummy (descarta las columnas originales)\n",
    "\n",
    "titanic = pd.read_csv('../data/titanic.csv')\n",
    "pd.get_dummies(titanic, columns=['Sex', 'Embarked']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "\n",
       "                                                Name   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin  Sex_male  Embarked_Q  Embarked_S  \n",
       "0         A/5 21171   7.2500   NaN         1           0           1  \n",
       "1          PC 17599  71.2833   C85         0           0           0  \n",
       "2  STON/O2. 3101282   7.9250   NaN         0           0           1  \n",
       "3            113803  53.1000  C123         0           0           1  \n",
       "4            373450   8.0500   NaN         1           0           1  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Utiliza el parámetro 'drop_first' (nuevo en pandas 0.18) para eliminar la primera variable dummy para cada característica\n",
    "\n",
    "pd.get_dummies(titanic, columns=['Sex', 'Embarked'], drop_first=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo\n",
    "\n",
    "¿Son los niños más altos que las niñas en promedio a los X años? X puede ser 2 años, 9 años y 18 años de edad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Boys_2</th>\n",
       "      <th>Boys_9</th>\n",
       "      <th>Boys_18</th>\n",
       "      <th>Girls_2</th>\n",
       "      <th>Girls_9</th>\n",
       "      <th>Girls_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>87.1</td>\n",
       "      <td>136.8</td>\n",
       "      <td>182.4</td>\n",
       "      <td>94.0</td>\n",
       "      <td>136.1</td>\n",
       "      <td>175.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>91.4</td>\n",
       "      <td>140.6</td>\n",
       "      <td>185.8</td>\n",
       "      <td>89.7</td>\n",
       "      <td>135.8</td>\n",
       "      <td>167.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>89.7</td>\n",
       "      <td>138.6</td>\n",
       "      <td>180.7</td>\n",
       "      <td>86.4</td>\n",
       "      <td>131.9</td>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>92.2</td>\n",
       "      <td>140.0</td>\n",
       "      <td>178.7</td>\n",
       "      <td>86.4</td>\n",
       "      <td>130.9</td>\n",
       "      <td>161.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.8</td>\n",
       "      <td>126.3</td>\n",
       "      <td>153.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.4</td>\n",
       "      <td>135.5</td>\n",
       "      <td>173.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.6</td>\n",
       "      <td>134.0</td>\n",
       "      <td>166.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.4</td>\n",
       "      <td>138.2</td>\n",
       "      <td>162.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>168.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.2</td>\n",
       "      <td>140.8</td>\n",
       "      <td>169.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Boys_2  Boys_9  Boys_18  Girls_2  Girls_9  Girls_18\n",
       "22    87.1   136.8    182.4     94.0    136.1     175.6\n",
       "23    91.4   140.6    185.8     89.7    135.8     167.2\n",
       "24    89.7   138.6    180.7     86.4    131.9     164.0\n",
       "25    92.2   140.0    178.7     86.4    130.9     161.6\n",
       "26     NaN     NaN      NaN     81.8    126.3     153.6\n",
       "27     NaN     NaN      NaN     91.4    135.5     173.5\n",
       "28     NaN     NaN      NaN     88.6    134.0     166.2\n",
       "29     NaN     NaN      NaN     86.4    138.2     162.8\n",
       "30     NaN     NaN      NaN     94.0    142.0     168.6\n",
       "31     NaN     NaN      NaN     89.2    140.8     169.2"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "niños = pd.read_csv('../data/children_heights.txt',sep='\\t')\n",
    "niños.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Boys_2</th>\n",
       "      <th>Boys_9</th>\n",
       "      <th>Boys_18</th>\n",
       "      <th>Girls_2</th>\n",
       "      <th>Girls_9</th>\n",
       "      <th>Girls_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>88.400000</td>\n",
       "      <td>136.546154</td>\n",
       "      <td>180.038462</td>\n",
       "      <td>87.465625</td>\n",
       "      <td>135.275000</td>\n",
       "      <td>166.765625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.035918</td>\n",
       "      <td>5.316031</td>\n",
       "      <td>6.396191</td>\n",
       "      <td>3.506548</td>\n",
       "      <td>5.686884</td>\n",
       "      <td>6.371540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>81.300000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>169.400000</td>\n",
       "      <td>80.900000</td>\n",
       "      <td>121.400000</td>\n",
       "      <td>153.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>86.800000</td>\n",
       "      <td>133.225000</td>\n",
       "      <td>175.200000</td>\n",
       "      <td>85.850000</td>\n",
       "      <td>133.175000</td>\n",
       "      <td>163.525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>136.250000</td>\n",
       "      <td>178.850000</td>\n",
       "      <td>87.850000</td>\n",
       "      <td>136.300000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90.900000</td>\n",
       "      <td>139.775000</td>\n",
       "      <td>183.650000</td>\n",
       "      <td>89.325000</td>\n",
       "      <td>139.400000</td>\n",
       "      <td>170.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>92.200000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>195.100000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>144.800000</td>\n",
       "      <td>181.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Boys_2      Boys_9     Boys_18    Girls_2     Girls_9    Girls_18\n",
       "count  26.000000   26.000000   26.000000  32.000000   32.000000   32.000000\n",
       "mean   88.400000  136.546154  180.038462  87.465625  135.275000  166.765625\n",
       "std     3.035918    5.316031    6.396191   3.506548    5.686884    6.371540\n",
       "min    81.300000  125.400000  169.400000  80.900000  121.400000  153.600000\n",
       "25%    86.800000  133.225000  175.200000  85.850000  133.175000  163.525000\n",
       "50%    89.000000  136.250000  178.850000  87.850000  136.300000  167.000000\n",
       "75%    90.900000  139.775000  183.650000  89.325000  139.400000  170.300000\n",
       "max    92.200000  146.000000  195.100000  94.000000  144.800000  181.100000"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resumen de estadísticas\n",
    "niños.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ag+xnyuI9unuWZLaUSgAAAE4YZ8gAAAAGEcgAAAAGEcgAAAAGEcgAAAAGEcgAAAAGEcgA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\nyV/WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46dd2262b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_hist(df, columns):\n",
    "    df[columns].hist(color='r', alpha=0.3, normed=False)\n",
    "\n",
    "plot_hist(niños,['Boys_2', 'Girls_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WGGechptJDw8c2Nzt92hhaYn5o7zQ6kb11r8W3jem9LqYuIct9EKTOgQcWnamy7uAly4f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\nbEa1wSMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46dd079d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(niños,['Boys_9', 'Girls_9'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46dcd5b400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(niños, ['Boys_18', 'Girls_18'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "def plot_probs(df, boy_col, girl_col):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    # No considero los valores Nan\n",
    "    boy_values = df[boy_col].values\n",
    "    boy_values = boy_values[~np.isnan(boy_values)]\n",
    "    weights = np.ones_like(boy_values)/len(boy_values)\n",
    "    _ = ax.hist(boy_values, alpha=0.3, label=['Niños'], weights=weights)\n",
    "    girl_values = df[girl_col].values\n",
    "    girl_values = girl_values[~np.isnan(girl_values)]\n",
    "    weights = np.ones_like(girl_values)/len(girl_values)\n",
    "    _ = ax.hist(girl_values, color='red', alpha=.3, label='Niñas', weights=weights)\n",
    "    ax.set_xlabel('Altura(cm)', fontsize=14)\n",
    "    ax.set_ylabel('Probabilidad', fontsize=14)\n",
    "    ax.vlines(np.mean(boy_values), 0.0, ax.get_ylim()[1], colors='b', linestyle='--', linewidth=4)\n",
    "    ax.vlines(np.mean(girl_values), 0.0, ax.get_ylim()[1], colors='r', linestyle='--', linewidth=4)\n",
    "    ax.legend()\n",
    "    print(\"Resultado \", ttest_ind(boy_values, girl_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado  Ttest_indResult(statistic=1.0708580982445333, pvalue=0.28882695040683509)\n"
     ]
    },
    {
     "data": {
      "image/png": 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wNfAIcHgLc0mSJEmSOqTZB9xfUfN+JbBnRIwHfpeZ2epwkiSNWH19RSdoi+xbWnQESVIb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\n5CwMJUmSJKnkLAwlSZIkqeT+P2suiFlFIDp/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46dca5d0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_probs(niños, 'Boys_2', 'Girls_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado  Ttest_indResult(statistic=0.87148628022315633, pvalue=0.38720967132857143)\n"
     ]
    },
    {
     "data": {
      "image/png": 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hZv63ISeSJEmSJLVVIyOGd9RsjwHmAL8B/rPaNwvYG/hOa6NJkiRJktphp4VhZv7t9u2I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\nDCVJkiSp5CwMJUmSJKnk/j+FngzR5Py7PgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46dca5d780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_probs(niños, 'Boys_9', 'Girls_9')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado  Ttest_indResult(statistic=7.8761998332333256, pvalue=1.2361838873133691e-10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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OtNNgGBE3AHX921pm/uGIK5IkSZIktVQ9I4Y/rNqeAJwI/Bz4z6JvIbA38MXmliZJUq3r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46dd08f978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_probs(niños, 'Boys_18', 'Girls_18')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obsérvese que aunque hay diferencias en las alturas de niñas y niños a los 2 años 9, no se encuentran diferencias significativas en promedio. Sin embargo, a los 18 años de edad, en promedio los niños son más altos que las niñas y esta diferencia es estadísticamente significativa a nivel de 0,05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leyendo el dataset de reportes de avistamientos en un dataframe\n",
    "ufo = pd.read_csv('../data/ufo.csv')\n",
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    20\n",
       "2    14\n",
       "3    13\n",
       "4    19\n",
       "Name: Time, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hora se puede acceder usando el corte de cadena, pero este enfoque se rompe con demasiada facilidad\n",
    "ufo.Time.str.slice(-5, -3).astype(int).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   1930-06-01 22:00:00\n",
       "1   1930-06-30 20:00:00\n",
       "2   1931-02-15 14:00:00\n",
       "3   1931-06-01 13:00:00\n",
       "4   1933-04-18 19:00:00\n",
       "Name: Time, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convierte 'Time' a un datetime format\n",
    "ufo['Time'] = pd.to_datetime(ufo.Time)\n",
    "ufo['Time'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convertir una sola cadena al formato datetime (sale un objeto timestamp)\n",
    "\n",
    "ts = pd.to_datetime('1/1/1999')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12832</th>\n",
       "      <td>Loma Rica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>CA</td>\n",
       "      <td>1999-01-01 02:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12833</th>\n",
       "      <td>Bauxite</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AR</td>\n",
       "      <td>1999-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12834</th>\n",
       "      <td>Florence</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CYLINDER</td>\n",
       "      <td>SC</td>\n",
       "      <td>1999-01-01 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12835</th>\n",
       "      <td>Lake Henshaw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CIGAR</td>\n",
       "      <td>CA</td>\n",
       "      <td>1999-01-01 15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12836</th>\n",
       "      <td>Wilmington Island</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>GA</td>\n",
       "      <td>1999-01-01 17:15:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    City Colors Reported Shape Reported State  \\\n",
       "12832          Loma Rica             NaN          LIGHT    CA   \n",
       "12833            Bauxite             NaN            NaN    AR   \n",
       "12834           Florence             NaN       CYLINDER    SC   \n",
       "12835       Lake Henshaw             NaN          CIGAR    CA   \n",
       "12836  Wilmington Island             NaN          LIGHT    GA   \n",
       "\n",
       "                     Time  \n",
       "12832 1999-01-01 02:30:00  \n",
       "12833 1999-01-01 03:00:00  \n",
       "12834 1999-01-01 14:00:00  \n",
       "12835 1999-01-01 15:00:00  \n",
       "12836 1999-01-01 17:15:00  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparar una serie de fecha y hora con una marca de tiempo\n",
    "ufo.loc[ufo.Time >= ts, :].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1930\n",
       "1    1930\n",
       "2    1931\n",
       "3    1931\n",
       "4    1933\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cuenta el numero de avistamientos de ovnis por año\n",
    "\n",
    "ufo['Year'] = ufo.Time.dt.year\n",
    "ufo['Year'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f46dd1ad400>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sZfDqb5rZG94Q0ZlZkA2SupNe0+OVNXjH55fPycweNLNWM2sdGBhYQhUBAAAAYGmcc3q+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\nAQAAAGCNIAACAAAAwBrx/wMrS0Pw+0rwZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46dd1994a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafica el número de informes de OVNI por año\n",
    "\n",
    "ufo.Year.value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "/* This template is inspired in the one used by Lorena Barba\n",
       "in the numerical-mooc repository: https://github.com/numerical-mooc/numerical-mooc\n",
       "We thank her work and hope you also enjoy the look of the notobooks with this style */\n",
       "\n",
       "<link href='http://fonts.googleapis.com/css?family=Source+Sans+Pro|Josefin+Sans:400,700,400italic|Ubuntu+Condensed' rel='stylesheet' type='text/css'>\n",
       "Estilo aplicado\n",
       "<style>\n",
       "\n",
       "\n",
       "\n",
       "#notebook_panel { /* main background */\n",
       "    background: #f7f7f7;\n",
       "}\n",
       "\n",
       "div.cell { /* set cell width */\n",
       "    width: 1000px;\n",
       "}\n",
       "\n",
       "div #notebook { /* centre the content */\n",
       "    background: #fff; /* white background for content */\n",
       "    width: 1100px;\n",
       "    margin: auto;\n",
       "    padding-left: 0em;\n",
       "}\n",
       "\n",
       "#notebook li { /* More space between bullet points */\n",
       "    margin-top:0.6em;\n",
       "}\n",
       "\n",
       "/* draw border around running cells */\n",
       "div.cell.border-box-sizing.code_cell.running { \n",
       "    border: 1px solid #111;\n",
       "}\n",
       "\n",
       "/* Put a solid color box around each cell and its output, visually linking them*/\n",
       "div.cell.code_cell {\n",
       "    font-family: 'Source Sans Pro', sans-serif;\n",
       "    background-color: rgb(256,256,256);\n",
       "    font-size: 110%;\n",
       "    border-radius: 0px; \n",
       "    padding: 0.5em;\n",
       "    margin-left:1em;\n",
       "    margin-top: 1em;\n",
       "}\n",
       "\n",
       "div.text_cell_render{\n",
       "    font-family: 'Josefin Sans', serif;\n",
       "    line-height: 145%;\n",
       "    font-size: 125%;\n",
       "    font-weight: 500;\n",
       "    width:850px;\n",
       "    margin-left:auto;\n",
       "    margin-right:auto;\n",
       "}\n",
       "\n",
       "\n",
       "/* Formatting for header cells */\n",
       ".text_cell_render h1, .text_cell_render h2, .text_cell_render h3,\n",
       ".text_cell_render h4, .text_cell_render h5 {\n",
       "    font-family: 'Ubuntu Condensed', sans-serif;\n",
       "}\n",
       "/*\n",
       ".text_cell_render h1 {\n",
       "    font-family: Flux, 'Ubuntu Condensed', serif;\n",
       "    font-style:regular;\n",
       "    font-weight: 400;    \n",
       "    font-size: 30pt;\n",
       "    text-align: center;\n",
       "    line-height: 100%;\n",
       "    color: #335082;\n",
       "    margin-bottom: 0.5em;\n",
       "    margin-top: 0.5em;\n",
       "    display: block;\n",
       "}\n",
       "*/\n",
       ".text_cell_render h1 {\n",
       "\tfont-family: Flux, 'Ubuntu Condensed', serif;\n",
       "    font-style:regular;\n",
       "    font-weight: 600;\n",
       "    font-size: 35pt;\n",
       "\ttext-align: center;\n",
       "    line-height: 100%;\n",
       "    color: #2c7638;\n",
       "    margin-bottom: 0.2em;\n",
       "    margin-top: 0.3em;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render h2 {\n",
       "    margin-top:16px;\n",
       "    font-size: 27pt;\n",
       "    font-weight: 550;\n",
       "    margin-bottom: 0.1em;\n",
       "    margin-top: 0.3em;\n",
       "    font-style: regular;\n",
       "    color: #498550\t;\n",
       "}\t\n",
       "\n",
       ".text_cell_render h3 {\n",
       "    font-size: 20pt;\n",
       "    font-weight: 550\n",
       "    text-align: left;\n",
       "    margin-bottom: 0.1em;\n",
       "    margin-top: 0.3em;\n",
       "    font-style: regular;\n",
       "    color:  #1a66ab;\n",
       "}\n",
       "\n",
       ".text_cell_render h4 {    /*Use this for captions*/\n",
       "    font-size: 18pt;\n",
       "    font-weight: 450\n",
       "    text-align: left;\n",
       "    margin-bottom: 0.1em;\n",
       "    margin-top: 0.3em;\n",
       "    font-style: regular;\n",
       "    color:  #578cb4;\n",
       "}\n",
       "\n",
       ".text_cell_render h5 {  /*Use this for small titles*/\n",
       "    font-size: 18pt;\n",
       "    font-weight: 550;\n",
       "    color: rgb(163,0,0);\n",
       "    font-style: italic;\n",
       "    margin-bottom: .1em;\n",
       "    margin-top: 0.8em;\n",
       "    display: block;\n",
       "    color:  #b21c0d;\n",
       "}\n",
       "\n",
       ".text_cell_render h6 { /*use this for copyright note*/\n",
       "    font-family: 'Ubuntu Condensed', sans-serif;\n",
       "    font-weight: 300;\n",
       "    font-size: 14pt;\n",
       "    line-height: 100%;\n",
       "    color: #252525;\n",
       "    text-align: right;\n",
       "    margin-bottom: 1px;\n",
       "    margin-top: 1px;\n",
       "}\n",
       "\n",
       ".CodeMirror{\n",
       "        font-family: 'Duru Sans', sans-serif;\n",
       "        font-size: 100%;\n",
       "}\n",
       "\n",
       "</style>\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
       "                        TeX: {\n",
       "                           extensions: [\"AMSmath.js\"],\n",
       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
       "                           },\n",
       "                tex2jax: {\n",
       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
       "                },\n",
       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                \"HTML-CSS\": {\n",
       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
       "                }\n",
       "        });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Esta celda da el estilo al notebook\n",
    "from IPython.core.display import HTML\n",
    "css_file = '../styles/StyleCursoPython.css'\n",
    "HTML(open(css_file, \"r\").read())"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}