Álvaro López - Ejercicios Bloque 5-6 - Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Ejercicio 1 **\n",
    "\n",
    "- Carga en un DataFrame este dataset: https://archive.ics.uci.edu/ml/datasets/Automobile "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(205, 26)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make fuel-type aspiration  \\\n",
       "0          3                NaN  alfa-romero       gas        std   \n",
       "1          3                NaN  alfa-romero       gas        std   \n",
       "2          1                NaN  alfa-romero       gas        std   \n",
       "3          2              164.0         audi       gas        std   \n",
       "4          2              164.0         audi       gas        std   \n",
       "\n",
       "  num-of-doors   body-style drive-wheels engine-location  wheel-base   ...     \\\n",
       "0          two  convertible          rwd           front        88.6   ...      \n",
       "1          two  convertible          rwd           front        88.6   ...      \n",
       "2          two    hatchback          rwd           front        94.5   ...      \n",
       "3         four        sedan          fwd           front        99.8   ...      \n",
       "4         four        sedan          4wd           front        99.4   ...      \n",
       "\n",
       "   engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "0          130         mpfi  3.47    2.68               9.0      111.0   \n",
       "1          130         mpfi  3.47    2.68               9.0      111.0   \n",
       "2          152         mpfi  2.68    3.47               9.0      154.0   \n",
       "3          109         mpfi  3.19    3.40              10.0      102.0   \n",
       "4          136         mpfi  3.19    3.40               8.0      115.0   \n",
       "\n",
       "   peak-rpm city-mpg  highway-mpg    price  \n",
       "0    5000.0       21           27  13495.0  \n",
       "1    5000.0       21           27  16500.0  \n",
       "2    5000.0       19           26  16500.0  \n",
       "3    5500.0       24           30  13950.0  \n",
       "4    5500.0       18           22  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "cols = [\"symboling\", \"normalized-losses\", \"make\", \"fuel-type\",  \"aspiration\",\n",
    "        \"num-of-doors\", \"body-style\", \"drive-wheels\", \"engine-location\", \"wheel-base\",\n",
    "        \"length\", \"width\", \"height\",\"curb-weight\", \"engine-type\", \"num-of-cylinders\",\n",
    "        \"engine-size\", \"fuel-system\", \"bore\", \"stroke\", \"compression-ratio\", \"horsepower\",                \n",
    "        \"peak-rpm\", \"city-mpg\", \"highway-mpg\",  \"price\"]\n",
    "\n",
    "#En imports-85.names indica: Missing Attribute Values: (denoted by \"?\")\n",
    "autosdf = pd.read_csv(\"autos/imports-85.data\", sep=',', names=cols, na_values=[\"?\"])\n",
    "#autosdf.columns = cols\n",
    "\n",
    "print(autosdf.shape)\n",
    "autosdf.head()\n",
    "\n",
    "#http://stackoverflow.com/questions/29247712/pandas-how-to-replace-with-nan-handling-non-standard-missing-values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Comprueba si tiene datos faltantes y trátalos de la manera que creas más conveniente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values:  59\n",
      "Rows with missing values:  46\n"
     ]
    }
   ],
   "source": [
    "#Existen 59 datos faltantes, repartidos en 46 filas\n",
    "#Se mantiene el df original y, en función de los cálculos a realizar se suprimirán los NaN\n",
    "\n",
    "count_nan = len(autosdf) - autosdf.count()\n",
    "print(\"Missing values: \", sum(count_nan))\n",
    "print(\"Rows with missing values: \", autosdf.shape[0] - autosdf.dropna().shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Obtén la longitud y anchuras medias por cada categoría (“body-style”)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": 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>length</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>convertible</th>\n",
       "      <td>170.383333</td>\n",
       "      <td>65.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardtop</th>\n",
       "      <td>176.937500</td>\n",
       "      <td>66.612500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hatchback</th>\n",
       "      <td>166.592857</td>\n",
       "      <td>65.247143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sedan</th>\n",
       "      <td>177.585417</td>\n",
       "      <td>66.260417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wagon</th>\n",
       "      <td>181.304000</td>\n",
       "      <td>66.256000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 length      width\n",
       "body-style                        \n",
       "convertible  170.383333  65.583333\n",
       "hardtop      176.937500  66.612500\n",
       "hatchback    166.592857  65.247143\n",
       "sedan        177.585417  66.260417\n",
       "wagon        181.304000  66.256000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selección de las columnas con las que trabajar y descarte de NaN\n",
    "bodyStyleDf = autosdf[[\"body-style\", \"length\", \"width\"]]\n",
    "bodyStyleDf = bodyStyleDf.dropna()\n",
    "#bodyStyleDf.head()\n",
    "\n",
    "bodyStyleDf.groupby([\"body-style\"]).mean()\n",
    "\n",
    "#http://stackoverflow.com/questions/30328646/python-pandas-group-by-in-group-by-and-average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Analiza el atributo peso (“curb-weight”) y dibuja su histograma para apreciar si sigue alguna distribución concreta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a6179765f8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAg4AAAFyCAYAAACds1IeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfA5r1EwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a617af8e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#Selección de las columnas con las que trabajar y descarte de NaN\n",
    "curbWeightDf = autosdf[[\"curb-weight\"]]\n",
    "curbWeightDf = curbWeightDf.dropna()\n",
    "#curbWeightDf.head()\n",
    "\n",
    "_ = plt.hist(curbWeightDf.values, label=\"curb-weight\", bins=20)\n",
    "plt.title(\"curb-weight\")\n",
    "#curbWeightDf.hist(bins=20)\n",
    "\n",
    "#Se asemeja a una distribución normal, desplazada hacia la izquierda (skew)\n",
    "\n",
    "#http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.hist.html\n",
    "#https://en.wikipedia.org/wiki/Skew_normal_distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Analiza cuál es el número de cilindros que se da con más frecuencia en el dataset.\n",
    "- Obtén una lista ordenada de las diferentes marcas de coches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "four      159\n",
      "six        24\n",
      "five       11\n",
      "eight       5\n",
      "two         4\n",
      "three       1\n",
      "twelve      1\n",
      "Name: num-of-cylinders, dtype: int64\n",
      "['alfa-romero' 'audi' 'bmw' 'chevrolet' 'dodge' 'honda' 'isuzu' 'jaguar'\n",
      " 'mazda' 'mercedes-benz' 'mercury' 'mitsubishi' 'nissan' 'peugot'\n",
      " 'plymouth' 'porsche' 'renault' 'saab' 'subaru' 'toyota' 'volkswagen'\n",
      " 'volvo']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "#Se obtiene la columna y se descartan los NaN\n",
    "numCylDf = autosdf[[\"num-of-cylinders\"]]\n",
    "numCylDf = numCylDf.dropna()\n",
    "\n",
    "#Cuatro cilindros, con 159 muestras es la más frecuente\n",
    "print(numCylDf['num-of-cylinders'].value_counts())\n",
    "#http://stackoverflow.com/questions/22391433/count-the-frequency-that-a-value-occurs-in-a-dataframe-column\n",
    "\n",
    "#Se obtiene la columna y se descartan los NaN\n",
    "makeDf = autosdf[[\"make\"]]\n",
    "makeDf = makeDf.dropna()\n",
    "\n",
    "#Se obtiene un array con las únicas marcas\n",
    "uniqueMakes = makeDf[\"make\"].unique()\n",
    "#print(type(uniqueMakes))\n",
    "\n",
    "#Se ordenan alfabéticamente\n",
    "print(np.sort(uniqueMakes))\n",
    "\n",
    "#https://docs.scipy.org/doc/numpy/reference/generated/numpy.sort.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Ejercicio 2 **\n",
    "- Carga en un DataFrame este dataset: https://archive.ics.uci.edu/ml/datasets/Census+Income\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32561, 17)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "      <th>low_income</th>\n",
       "      <th>high_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  education-num  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "5   37           Private  284582    Masters             14   \n",
       "6   49           Private  160187        9th              5   \n",
       "7   52  Self-emp-not-inc  209642    HS-grad              9   \n",
       "8   31           Private   45781    Masters             14   \n",
       "9   42           Private  159449  Bachelors             13   \n",
       "\n",
       "          marital-status         occupation   relationship   race     sex  \\\n",
       "0          Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1     Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2               Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3     Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4     Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "5     Married-civ-spouse    Exec-managerial           Wife  White  Female   \n",
       "6  Married-spouse-absent      Other-service  Not-in-family  Black  Female   \n",
       "7     Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "8          Never-married     Prof-specialty  Not-in-family  White  Female   \n",
       "9     Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week native-country income  \\\n",
       "0          2174             0              40  United-States  <=50K   \n",
       "1             0             0              13  United-States  <=50K   \n",
       "2             0             0              40  United-States  <=50K   \n",
       "3             0             0              40  United-States  <=50K   \n",
       "4             0             0              40           Cuba  <=50K   \n",
       "5             0             0              40  United-States  <=50K   \n",
       "6             0             0              16        Jamaica  <=50K   \n",
       "7             0             0              45  United-States   >50K   \n",
       "8         14084             0              50  United-States   >50K   \n",
       "9          5178             0              40  United-States   >50K   \n",
       "\n",
       "   low_income  high_income  \n",
       "0           1            0  \n",
       "1           1            0  \n",
       "2           1            0  \n",
       "3           1            0  \n",
       "4           1            0  \n",
       "5           1            0  \n",
       "6           1            0  \n",
       "7           0            1  \n",
       "8           0            1  \n",
       "9           0            1  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "adultcols = [\"age\", \"workclass\", \"fnlwgt\", \"education\",  \"education-num\",\n",
    "        \"marital-status\", \"occupation\", \"relationship\", \"race\", \"sex\",\n",
    "        \"capital-gain\", \"capital-loss\", \"hours-per-week\",\"native-country\", \"income\"]\n",
    "\n",
    "#En este caso, las variables van separadas por una coma y hay un espacio detrás (na_values!!)\n",
    "adultsdf = pd.read_csv(\"adult/adult.data\", sep=',', names=adultcols, na_values=[\"?\"], skipinitialspace=True)\n",
    "\n",
    "adultsdf['low_income'] = np.where(adultsdf[\"income\"] == \"<=50K\", 1, 0)\n",
    "adultsdf['high_income'] = np.where(adultsdf[\"income\"] == \">50K\", 1, 0)\n",
    "\n",
    "print(adultsdf.shape)\n",
    "adultsdf.head(10)\n",
    "\n",
    "#http://stackoverflow.com/questions/10715519/conditionally-fill-column-values-based-on-another-columns-value-in-pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Estudia si hay variables como el nivel educativo o el país que influyen en que se tuviese un salario mayor o menor de 50K. Puedes simplemente obtener la cuenta de las veces que sucede una u otra cosa de los diferentes grupos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>low_income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Cambodia</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Canada</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">China</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Columbia</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Cuba</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Dominican-Republic</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Ecuador</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">El-Salvador</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">England</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">France</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Germany</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Greece</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Guatemala</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Haiti</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Holand-Netherlands</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honduras</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Nicaragua</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlying-US(Guam-USVI-etc)</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Peru</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Philippines</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Poland</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Portugal</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Puerto-Rico</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Scotland</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">South</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Taiwan</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thailand</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Trinadad&amp;Tobago</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">United-States</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>21999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>7171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Vietnam</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Yugoslavia</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  low_income\n",
       "                                       count\n",
       "native-country             income           \n",
       "Cambodia                   <=50K          12\n",
       "                           >50K            7\n",
       "Canada                     <=50K          82\n",
       "                           >50K           39\n",
       "China                      <=50K          55\n",
       "                           >50K           20\n",
       "Columbia                   <=50K          57\n",
       "                           >50K            2\n",
       "Cuba                       <=50K          70\n",
       "                           >50K           25\n",
       "Dominican-Republic         <=50K          68\n",
       "                           >50K            2\n",
       "Ecuador                    <=50K          24\n",
       "                           >50K            4\n",
       "El-Salvador                <=50K          97\n",
       "                           >50K            9\n",
       "England                    <=50K          60\n",
       "                           >50K           30\n",
       "France                     <=50K          17\n",
       "                           >50K           12\n",
       "Germany                    <=50K          93\n",
       "                           >50K           44\n",
       "Greece                     <=50K          21\n",
       "                           >50K            8\n",
       "Guatemala                  <=50K          61\n",
       "                           >50K            3\n",
       "Haiti                      <=50K          40\n",
       "                           >50K            4\n",
       "Holand-Netherlands         <=50K           1\n",
       "Honduras                   <=50K          12\n",
       "...                                      ...\n",
       "Mexico                     >50K           33\n",
       "Nicaragua                  <=50K          32\n",
       "                           >50K            2\n",
       "Outlying-US(Guam-USVI-etc) <=50K          14\n",
       "Peru                       <=50K          29\n",
       "                           >50K            2\n",
       "Philippines                <=50K         137\n",
       "                           >50K           61\n",
       "Poland                     <=50K          48\n",
       "                           >50K           12\n",
       "Portugal                   <=50K          33\n",
       "                           >50K            4\n",
       "Puerto-Rico                <=50K         102\n",
       "                           >50K           12\n",
       "Scotland                   <=50K           9\n",
       "                           >50K            3\n",
       "South                      <=50K          64\n",
       "                           >50K           16\n",
       "Taiwan                     <=50K          31\n",
       "                           >50K           20\n",
       "Thailand                   <=50K          15\n",
       "                           >50K            3\n",
       "Trinadad&Tobago            <=50K          17\n",
       "                           >50K            2\n",
       "United-States              <=50K       21999\n",
       "                           >50K         7171\n",
       "Vietnam                    <=50K          62\n",
       "                           >50K            5\n",
       "Yugoslavia                 <=50K          10\n",
       "                           >50K            6\n",
       "\n",
       "[80 rows x 1 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TODO - REVISAR\n",
    "\n",
    "nativeCountryDf = adultsdf[[\"native-country\", \"income\", \"low_income\"]]\n",
    "nativeCountryDf = nativeCountryDf.dropna()\n",
    "\n",
    "#Con esto se obtiene la cuenta total de items por país, pero hay que diferenciar\n",
    "nativeCountryDf.groupby([\"native-country\", \"income\"]).agg(['count'])\n",
    "\n",
    "#byNatCountry = nativeCountryDf.groupby(\"native-country\")\n",
    "#byNatCountry.boxplot(column=\"income\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}