{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Examples\n",
    "\n",
    "http://thinkstats2.com\n",
    "\n",
    "Copyright 2017 Allen B. Downey\n",
    "\n",
    "MIT License: https://opensource.org/licenses/MIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import nsfg\n",
    "import first\n",
    "import analytic\n",
    "\n",
    "import thinkstats2\n",
    "\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's read data from the BRFSS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import brfss\n",
    "df = brfss.ReadBrfss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>wtyrago</th>\n",
       "      <th>finalwt</th>\n",
       "      <th>wtkg2</th>\n",
       "      <th>htm3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>410856.000000</td>\n",
       "      <td>414509.000000</td>\n",
       "      <td>390399.000000</td>\n",
       "      <td>414509.000000</td>\n",
       "      <td>398484.000000</td>\n",
       "      <td>409129.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.862180</td>\n",
       "      <td>1.624368</td>\n",
       "      <td>79.721319</td>\n",
       "      <td>561.774700</td>\n",
       "      <td>78.992453</td>\n",
       "      <td>168.825190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.737702</td>\n",
       "      <td>0.484286</td>\n",
       "      <td>20.565164</td>\n",
       "      <td>1076.538764</td>\n",
       "      <td>19.546157</td>\n",
       "      <td>10.352653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.727273</td>\n",
       "      <td>1.695143</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.545455</td>\n",
       "      <td>97.006804</td>\n",
       "      <td>64.550000</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>77.272727</td>\n",
       "      <td>234.010543</td>\n",
       "      <td>77.270000</td>\n",
       "      <td>168.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>90.909091</td>\n",
       "      <td>590.775576</td>\n",
       "      <td>90.910000</td>\n",
       "      <td>175.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>342.272727</td>\n",
       "      <td>60995.111700</td>\n",
       "      <td>309.090000</td>\n",
       "      <td>236.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age            sex        wtyrago        finalwt  \\\n",
       "count  410856.000000  414509.000000  390399.000000  414509.000000   \n",
       "mean       54.862180       1.624368      79.721319     561.774700   \n",
       "std        16.737702       0.484286      20.565164    1076.538764   \n",
       "min        18.000000       1.000000      22.727273       1.695143   \n",
       "25%        43.000000       1.000000      64.545455      97.006804   \n",
       "50%        55.000000       2.000000      77.272727     234.010543   \n",
       "75%        67.000000       2.000000      90.909091     590.775576   \n",
       "max        99.000000       2.000000     342.272727   60995.111700   \n",
       "\n",
       "               wtkg2           htm3  \n",
       "count  398484.000000  409129.000000  \n",
       "mean       78.992453     168.825190  \n",
       "std        19.546157      10.352653  \n",
       "min        20.000000      61.000000  \n",
       "25%        64.550000     160.000000  \n",
       "50%        77.270000     168.000000  \n",
       "75%        90.910000     175.000000  \n",
       "max       309.090000     236.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we group by sex, we get a `DataFrameGroupBy` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x7f6c6c1cf2d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby = df.groupby('sex')\n",
    "groupby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we select a particular column from the `GroupBy`, we get a `SeriesGroupBy` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.SeriesGroupBy object at 0x7f6c3c88d590>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seriesgroupby = groupby.htm3\n",
    "seriesgroupby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you invoke a reduce method on a DataFrameGroupBy, you get a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>age</th>\n",
       "      <th>wtyrago</th>\n",
       "      <th>finalwt</th>\n",
       "      <th>wtkg2</th>\n",
       "      <th>htm3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</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>1</th>\n",
       "      <td>54.114237</td>\n",
       "      <td>89.880639</td>\n",
       "      <td>727.426119</td>\n",
       "      <td>89.016966</td>\n",
       "      <td>178.066221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.314401</td>\n",
       "      <td>73.278344</td>\n",
       "      <td>462.115407</td>\n",
       "      <td>72.684712</td>\n",
       "      <td>163.223475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age    wtyrago     finalwt      wtkg2        htm3\n",
       "sex                                                         \n",
       "1    54.114237  89.880639  727.426119  89.016966  178.066221\n",
       "2    55.314401  73.278344  462.115407  72.684712  163.223475"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you invoke a reduce method on a SeriesGroupBy, you get a Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "1    178.066221\n",
       "2    163.223475\n",
       "Name: htm3, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seriesgroupby.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use `aggregate` to apply a collection of reduce methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">age</th>\n",
       "      <th colspan=\"2\" halign=\"left\">wtyrago</th>\n",
       "      <th colspan=\"2\" halign=\"left\">finalwt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">wtkg2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">htm3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.114237</td>\n",
       "      <td>16.383712</td>\n",
       "      <td>89.880639</td>\n",
       "      <td>19.260805</td>\n",
       "      <td>727.426119</td>\n",
       "      <td>1370.544678</td>\n",
       "      <td>89.016966</td>\n",
       "      <td>18.240528</td>\n",
       "      <td>178.066221</td>\n",
       "      <td>7.723563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.314401</td>\n",
       "      <td>16.932158</td>\n",
       "      <td>73.278344</td>\n",
       "      <td>18.678964</td>\n",
       "      <td>462.115407</td>\n",
       "      <td>836.457063</td>\n",
       "      <td>72.684712</td>\n",
       "      <td>17.609006</td>\n",
       "      <td>163.223475</td>\n",
       "      <td>7.269156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age               wtyrago                finalwt               \\\n",
       "          mean        std       mean        std        mean          std   \n",
       "sex                                                                        \n",
       "1    54.114237  16.383712  89.880639  19.260805  727.426119  1370.544678   \n",
       "2    55.314401  16.932158  73.278344  18.678964  462.115407   836.457063   \n",
       "\n",
       "         wtkg2                   htm3            \n",
       "          mean        std        mean       std  \n",
       "sex                                              \n",
       "1    89.016966  18.240528  178.066221  7.723563  \n",
       "2    72.684712  17.609006  163.223475  7.269156  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby.aggregate(['mean', 'std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>178.066221</td>\n",
       "      <td>7.723563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>163.223475</td>\n",
       "      <td>7.269156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean       std\n",
       "sex                      \n",
       "1    178.066221  7.723563\n",
       "2    163.223475  7.269156"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seriesgroupby.aggregate(['mean', 'std'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the reduce method you want is not available, you can make your own:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def trimmed_mean(series):\n",
    "    lower, upper = series.quantile([0.05, 0.95])\n",
    "    return series.clip(lower, upper).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's how it works when we apply it directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "168.65401132650092"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trimmed_mean(df.htm3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can use `apply` to apply it to each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "1    178.109121\n",
       "2    163.210210\n",
       "Name: htm3, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seriesgroupby.apply(trimmed_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The digitize-groupby combo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to group people into deciles (bottom 10%, next 10%, and so on).\n",
    "\n",
    "We can start by defining the cumulative probabilities that mark the borders between deciles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = np.linspace(0, 1, 11)\n",
    "ps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then use `deciles` to find the values that correspond to those cumulative probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     61.0\n",
       "0.1    157.0\n",
       "0.2    160.0\n",
       "0.3    163.0\n",
       "0.4    165.0\n",
       "0.5    168.0\n",
       "0.6    170.0\n",
       "0.7    175.0\n",
       "0.8    178.0\n",
       "0.9    183.0\n",
       "1.0    236.0\n",
       "Name: htm3, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = df.htm3\n",
    "bins = series.quantile(ps)\n",
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`digitize` takes a series and a sequence of bin boundaries, and computes the bin index for each element in the series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 5, ..., 9, 2, 9])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.digitize(series, bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Collect the code snippets from the previous cells to write a function called `digitize` that takes a Series and a number of bins and return the results from `np.digitize`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def digitize(series, n=11):\n",
    "    ps = np.linspace(0, 1, n)\n",
    "    bins = series.quantile(ps)\n",
    "    return np.digitize(series, bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, if your `digitize` function is working, we can assign the results to a new column in the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    414509.000000\n",
       "mean          5.897512\n",
       "std           2.924237\n",
       "min           1.000000\n",
       "25%           4.000000\n",
       "50%           6.000000\n",
       "75%           9.000000\n",
       "max          11.000000\n",
       "Name: height_decile, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['height_decile'] = digitize(df.htm3)\n",
    "df.height_decile.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then group by `height_decile`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "groupby = df.groupby('height_decile')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compute means for each variable in each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>wtyrago</th>\n",
       "      <th>finalwt</th>\n",
       "      <th>wtkg2</th>\n",
       "      <th>htm3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height_decile</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>1</th>\n",
       "      <td>59.343555</td>\n",
       "      <td>1.978576</td>\n",
       "      <td>65.732170</td>\n",
       "      <td>482.633403</td>\n",
       "      <td>65.071502</td>\n",
       "      <td>152.147807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.708848</td>\n",
       "      <td>1.976869</td>\n",
       "      <td>68.925145</td>\n",
       "      <td>460.942597</td>\n",
       "      <td>68.415169</td>\n",
       "      <td>157.001891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56.693958</td>\n",
       "      <td>1.964062</td>\n",
       "      <td>71.014838</td>\n",
       "      <td>461.123901</td>\n",
       "      <td>70.420280</td>\n",
       "      <td>160.002595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55.411945</td>\n",
       "      <td>1.943195</td>\n",
       "      <td>72.962619</td>\n",
       "      <td>481.562164</td>\n",
       "      <td>72.417512</td>\n",
       "      <td>163.000728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55.010687</td>\n",
       "      <td>1.890204</td>\n",
       "      <td>74.834351</td>\n",
       "      <td>498.016131</td>\n",
       "      <td>74.251253</td>\n",
       "      <td>165.002383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54.469226</td>\n",
       "      <td>1.781678</td>\n",
       "      <td>77.115205</td>\n",
       "      <td>534.779185</td>\n",
       "      <td>76.563233</td>\n",
       "      <td>168.000509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>53.708454</td>\n",
       "      <td>1.573876</td>\n",
       "      <td>80.716256</td>\n",
       "      <td>570.787161</td>\n",
       "      <td>80.072338</td>\n",
       "      <td>171.405279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>53.354186</td>\n",
       "      <td>1.306171</td>\n",
       "      <td>85.399642</td>\n",
       "      <td>643.930094</td>\n",
       "      <td>84.663829</td>\n",
       "      <td>175.001213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>53.838675</td>\n",
       "      <td>1.134095</td>\n",
       "      <td>89.774340</td>\n",
       "      <td>651.359825</td>\n",
       "      <td>88.932201</td>\n",
       "      <td>178.888785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>51.531986</td>\n",
       "      <td>1.034134</td>\n",
       "      <td>98.338009</td>\n",
       "      <td>714.941775</td>\n",
       "      <td>97.387332</td>\n",
       "      <td>186.214057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>55.246926</td>\n",
       "      <td>1.758967</td>\n",
       "      <td>71.630761</td>\n",
       "      <td>792.074824</td>\n",
       "      <td>71.236649</td>\n",
       "      <td>236.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age       sex    wtyrago     finalwt      wtkg2  \\\n",
       "height_decile                                                          \n",
       "1              59.343555  1.978576  65.732170  482.633403  65.071502   \n",
       "2              57.708848  1.976869  68.925145  460.942597  68.415169   \n",
       "3              56.693958  1.964062  71.014838  461.123901  70.420280   \n",
       "4              55.411945  1.943195  72.962619  481.562164  72.417512   \n",
       "5              55.010687  1.890204  74.834351  498.016131  74.251253   \n",
       "6              54.469226  1.781678  77.115205  534.779185  76.563233   \n",
       "7              53.708454  1.573876  80.716256  570.787161  80.072338   \n",
       "8              53.354186  1.306171  85.399642  643.930094  84.663829   \n",
       "9              53.838675  1.134095  89.774340  651.359825  88.932201   \n",
       "10             51.531986  1.034134  98.338009  714.941775  97.387332   \n",
       "11             55.246926  1.758967  71.630761  792.074824  71.236649   \n",
       "\n",
       "                     htm3  \n",
       "height_decile              \n",
       "1              152.147807  \n",
       "2              157.001891  \n",
       "3              160.002595  \n",
       "4              163.000728  \n",
       "5              165.002383  \n",
       "6              168.000509  \n",
       "7              171.405279  \n",
       "8              175.001213  \n",
       "9              178.888785  \n",
       "10             186.214057  \n",
       "11             236.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like:\n",
    "\n",
    "1. The shortest people are older than the tallest people, on average.\n",
    "\n",
    "2. The shortest people are much more likely to be female (no surprise there).\n",
    "\n",
    "3. The shortest people are lighter than the tallest people (`wtkg2`), and they were lighter last year, too (`wtyrago`).\n",
    "\n",
    "4. Shorter people are more oversampled, so they have lower final weights.  This is at least partly, and maybe entirely, due to the relationship with sex.\n",
    "\n",
    "The fact that all of these variables are associates with height suggests that it will be important to control for age and sex for almost any analysis we want to do with this data.\n",
    "\n",
    "Nevertheless, we'll start with a simple analysis looking at weights within each height group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.SeriesGroupBy object at 0x7f6c34e6a6d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = groupby.wtkg2\n",
    "weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we apply `quantile` to a `SeriesGroupBy`, we get back a `Series` with a `MultiIndex`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "height_decile      \n",
       "1              0.25     54.55\n",
       "               0.50     62.73\n",
       "               0.75     72.73\n",
       "2              0.25     57.73\n",
       "               0.50     65.91\n",
       "               0.75     76.36\n",
       "3              0.25     59.09\n",
       "               0.50     68.18\n",
       "               0.75     78.64\n",
       "4              0.25     61.36\n",
       "               0.50     68.18\n",
       "               0.75     81.36\n",
       "5              0.25     63.18\n",
       "               0.50     71.36\n",
       "               0.75     81.82\n",
       "6              0.25     64.09\n",
       "               0.50     72.73\n",
       "               0.75     84.09\n",
       "7              0.25     68.18\n",
       "               0.50     77.27\n",
       "               0.75     88.64\n",
       "8              0.25     72.73\n",
       "               0.50     81.82\n",
       "               0.75     90.91\n",
       "9              0.25     77.27\n",
       "               0.50     86.36\n",
       "               0.75     97.27\n",
       "10             0.25     84.09\n",
       "               0.50     95.45\n",
       "               0.75    106.82\n",
       "11             0.25     59.09\n",
       "               0.50     69.09\n",
       "               0.75     80.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles = weights.quantile([0.25, 0.5, 0.75])\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.indexes.multi.MultiIndex"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(quantiles.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you unstack a MultiIndex, the inner level of the MultIndex gets broken out into columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>0.25</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height_decile</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.55</td>\n",
       "      <td>62.73</td>\n",
       "      <td>72.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.73</td>\n",
       "      <td>65.91</td>\n",
       "      <td>76.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59.09</td>\n",
       "      <td>68.18</td>\n",
       "      <td>78.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61.36</td>\n",
       "      <td>68.18</td>\n",
       "      <td>81.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>63.18</td>\n",
       "      <td>71.36</td>\n",
       "      <td>81.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64.09</td>\n",
       "      <td>72.73</td>\n",
       "      <td>84.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>68.18</td>\n",
       "      <td>77.27</td>\n",
       "      <td>88.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>72.73</td>\n",
       "      <td>81.82</td>\n",
       "      <td>90.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>77.27</td>\n",
       "      <td>86.36</td>\n",
       "      <td>97.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>84.09</td>\n",
       "      <td>95.45</td>\n",
       "      <td>106.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>59.09</td>\n",
       "      <td>69.09</td>\n",
       "      <td>80.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0.25   0.50    0.75\n",
       "height_decile                      \n",
       "1              54.55  62.73   72.73\n",
       "2              57.73  65.91   76.36\n",
       "3              59.09  68.18   78.64\n",
       "4              61.36  68.18   81.36\n",
       "5              63.18  71.36   81.82\n",
       "6              64.09  72.73   84.09\n",
       "7              68.18  77.27   88.64\n",
       "8              72.73  81.82   90.91\n",
       "9              77.27  86.36   97.27\n",
       "10             84.09  95.45  106.82\n",
       "11             59.09  69.09   80.00"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which makes it convenient to plot each of the columns as a line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c34e43890>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YzaO/cPvdgId9NZ36eW/zfl6u2UiYLoRv53wDrDo6rGP/s5+rj+dFzKW68yg7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\njwycBdzE5OIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c34e430d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantiles.unstack().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The other view of this data we might like is the CDF of weight within each height group.\n",
    "\n",
    "We can use `apply` with the `Cdf` constructor from `thinkstats2`.  The results is a Series of Cdf objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "height_decile\n",
       "1     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "2     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "3     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "4     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "5     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "6     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "7     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "8     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "9     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "10    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "11    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "Name: wtkg2, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from thinkstats2 import Cdf\n",
    "\n",
    "cdfs = weights.apply(Cdf)\n",
    "cdfs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we can plot the CDFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FAECOIuQBAMhRhDwAADkqaxrUxKqBn88aGxv1N3/zN7r88stlGIbG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\nFCEPAECOIuQBAMhRhDwAADnq/wNI1dW2DrFpywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c3c5e3310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import thinkplot\n",
    "\n",
    "thinkplot.Cdfs(cdfs[1:11:2])\n",
    "thinkplot.Config(xlabel='Weight (kg)', ylabel='Cdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Plot CDFs of weight for men and women separately, broken out by decile of height."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "groupby = df.groupby(['sex', 'height_decile'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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></th>\n",
       "      <th>age</th>\n",
       "      <th>wtyrago</th>\n",
       "      <th>finalwt</th>\n",
       "      <th>wtkg2</th>\n",
       "      <th>htm3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>height_decile</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 rowspan=\"11\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>53.844743</td>\n",
       "      <td>71.977922</td>\n",
       "      <td>1191.234678</td>\n",
       "      <td>71.514913</td>\n",
       "      <td>150.233293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56.595658</td>\n",
       "      <td>71.746929</td>\n",
       "      <td>1003.079353</td>\n",
       "      <td>71.003320</td>\n",
       "      <td>157.025469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54.869454</td>\n",
       "      <td>72.944564</td>\n",
       "      <td>964.772919</td>\n",
       "      <td>72.326525</td>\n",
       "      <td>160.028037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56.042489</td>\n",
       "      <td>74.553472</td>\n",
       "      <td>879.420399</td>\n",
       "      <td>74.016273</td>\n",
       "      <td>163.005126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55.165862</td>\n",
       "      <td>76.385110</td>\n",
       "      <td>917.796857</td>\n",
       "      <td>75.760722</td>\n",
       "      <td>165.013377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56.300059</td>\n",
       "      <td>79.337985</td>\n",
       "      <td>814.286624</td>\n",
       "      <td>78.624502</td>\n",
       "      <td>168.001398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>56.170706</td>\n",
       "      <td>82.956049</td>\n",
       "      <td>705.872453</td>\n",
       "      <td>82.235605</td>\n",
       "      <td>171.753016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>55.213608</td>\n",
       "      <td>86.572882</td>\n",
       "      <td>706.591295</td>\n",
       "      <td>85.843598</td>\n",
       "      <td>175.001515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>54.734034</td>\n",
       "      <td>90.392173</td>\n",
       "      <td>676.266016</td>\n",
       "      <td>89.530755</td>\n",
       "      <td>178.925710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>51.677645</td>\n",
       "      <td>98.621484</td>\n",
       "      <td>721.615568</td>\n",
       "      <td>97.668112</td>\n",
       "      <td>186.240144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50.650927</td>\n",
       "      <td>79.295724</td>\n",
       "      <td>1195.072324</td>\n",
       "      <td>78.971384</td>\n",
       "      <td>236.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2</th>\n",
       "      <th>1</th>\n",
       "      <td>59.464339</td>\n",
       "      <td>65.595065</td>\n",
       "      <td>467.120026</td>\n",
       "      <td>64.928121</td>\n",
       "      <td>152.189721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.735134</td>\n",
       "      <td>68.857631</td>\n",
       "      <td>448.105460</td>\n",
       "      <td>68.351735</td>\n",
       "      <td>157.001333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56.762345</td>\n",
       "      <td>70.942543</td>\n",
       "      <td>442.349122</td>\n",
       "      <td>70.347362</td>\n",
       "      <td>160.001647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55.373809</td>\n",
       "      <td>72.864145</td>\n",
       "      <td>457.600597</td>\n",
       "      <td>72.317857</td>\n",
       "      <td>163.000463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>54.991480</td>\n",
       "      <td>74.636768</td>\n",
       "      <td>446.241040</td>\n",
       "      <td>74.057330</td>\n",
       "      <td>165.001027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53.955434</td>\n",
       "      <td>76.467033</td>\n",
       "      <td>456.712813</td>\n",
       "      <td>75.963038</td>\n",
       "      <td>168.000260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>51.872079</td>\n",
       "      <td>78.972655</td>\n",
       "      <td>470.481305</td>\n",
       "      <td>78.390760</td>\n",
       "      <td>171.147071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>49.126449</td>\n",
       "      <td>82.582291</td>\n",
       "      <td>501.930594</td>\n",
       "      <td>81.844168</td>\n",
       "      <td>175.000528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>48.030870</td>\n",
       "      <td>85.524509</td>\n",
       "      <td>490.531017</td>\n",
       "      <td>84.845692</td>\n",
       "      <td>178.650349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>47.388489</td>\n",
       "      <td>89.769177</td>\n",
       "      <td>526.098271</td>\n",
       "      <td>89.063530</td>\n",
       "      <td>185.475904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>56.722523</td>\n",
       "      <td>69.371896</td>\n",
       "      <td>664.090555</td>\n",
       "      <td>68.941935</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         age    wtyrago      finalwt      wtkg2        htm3\n",
       "sex height_decile                                                          \n",
       "1   1              53.844743  71.977922  1191.234678  71.514913  150.233293\n",
       "    2              56.595658  71.746929  1003.079353  71.003320  157.025469\n",
       "    3              54.869454  72.944564   964.772919  72.326525  160.028037\n",
       "    4              56.042489  74.553472   879.420399  74.016273  163.005126\n",
       "    5              55.165862  76.385110   917.796857  75.760722  165.013377\n",
       "    6              56.300059  79.337985   814.286624  78.624502  168.001398\n",
       "    7              56.170706  82.956049   705.872453  82.235605  171.753016\n",
       "    8              55.213608  86.572882   706.591295  85.843598  175.001515\n",
       "    9              54.734034  90.392173   676.266016  89.530755  178.925710\n",
       "    10             51.677645  98.621484   721.615568  97.668112  186.240144\n",
       "    11             50.650927  79.295724  1195.072324  78.971384  236.000000\n",
       "2   1              59.464339  65.595065   467.120026  64.928121  152.189721\n",
       "    2              57.735134  68.857631   448.105460  68.351735  157.001333\n",
       "    3              56.762345  70.942543   442.349122  70.347362  160.001647\n",
       "    4              55.373809  72.864145   457.600597  72.317857  163.000463\n",
       "    5              54.991480  74.636768   446.241040  74.057330  165.001027\n",
       "    6              53.955434  76.467033   456.712813  75.963038  168.000260\n",
       "    7              51.872079  78.972655   470.481305  78.390760  171.147071\n",
       "    8              49.126449  82.582291   501.930594  81.844168  175.000528\n",
       "    9              48.030870  85.524509   490.531017  84.845692  178.650349\n",
       "    10             47.388489  89.769177   526.098271  89.063530  185.475904\n",
       "    11             56.722523  69.371896   664.090555  68.941935         NaN"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  height_decile\n",
       "1    1                71.514913\n",
       "     2                71.003320\n",
       "     3                72.326525\n",
       "     4                74.016273\n",
       "     5                75.760722\n",
       "     6                78.624502\n",
       "     7                82.235605\n",
       "     8                85.843598\n",
       "     9                89.530755\n",
       "     10               97.668112\n",
       "     11               78.971384\n",
       "2    1                64.928121\n",
       "     2                68.351735\n",
       "     3                70.347362\n",
       "     4                72.317857\n",
       "     5                74.057330\n",
       "     6                75.963038\n",
       "     7                78.390760\n",
       "     8                81.844168\n",
       "     9                84.845692\n",
       "     10               89.063530\n",
       "     11               68.941935\n",
       "Name: wtkg2, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby.wtkg2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  height_decile\n",
       "1    1                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     2                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     3                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     4                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     5                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     6                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     7                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     8                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     9                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     10               [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     11               [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "2    1                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     2                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     3                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     4                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     5                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     6                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     7                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     8                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     9                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     10               [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "     11               [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "Name: wtkg2, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdfs = groupby.wtkg2.apply(Cdf)\n",
    "cdfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>height_decile</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "height_decile                                                 1   \\\n",
       "sex                                                                \n",
       "1              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "height_decile                                                 2   \\\n",
       "sex                                                                \n",
       "1              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "height_decile                                                 3   \\\n",
       "sex                                                                \n",
       "1              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "height_decile                                                 4   \\\n",
       "sex                                                                \n",
       "1              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "height_decile                                                 5   \\\n",
       "sex                                                                \n",
       "1              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "height_decile                                                 6   \\\n",
       "sex                                                                \n",
       "1              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "height_decile                                                 7   \\\n",
       "sex                                                                \n",
       "1              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "height_decile                                                 8   \\\n",
       "sex                                                                \n",
       "1              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "height_decile                                                 9   \\\n",
       "sex                                                                \n",
       "1              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "height_decile                                                 10  \\\n",
       "sex                                                                \n",
       "1              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "height_decile                                                 11  \n",
       "sex                                                               \n",
       "1              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "2              [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdfs.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "height_decile\n",
       "1     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "2     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "3     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "4     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "5     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "6     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "7     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "8     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "9     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "10    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "11    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "men = cdfs.unstack().loc[1]\n",
    "men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NxejxDQCuEdQ1zLqI7IGNt6bnp63sl2VZlaPsbS15S4ymAaAYviXrhFNIV4K1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\nQQ0AgI/9f3dqlXg5zAMnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c3c5e3ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Cdfs(men[1:11:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "height_decile\n",
       "1     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "2     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "3     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "4     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "5     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "6     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "7     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "8     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "9     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "10    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "11    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "women = cdfs.unstack().loc[2]\n",
    "women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L3F4NACgBghpphmJxmQbdmt8QnLmTAQCPI6g9oO9kOK/nW0fT8whqAJgxBLUH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\nY3d9jh8/rpUrV6qhoUF1dXV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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c38fc0210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Cdfs(women[1:11:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}