ch09third.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from pandas import DataFrame, Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>cty</th>\n",
       "      <th colspan=\"3\" halign=\"left\">US</th>\n",
       "      <th colspan=\"2\" halign=\"left\">JP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenor</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.026181</td>\n",
       "      <td>-0.343651</td>\n",
       "      <td>-1.839707</td>\n",
       "      <td>-0.239827</td>\n",
       "      <td>0.554371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.342152</td>\n",
       "      <td>1.696171</td>\n",
       "      <td>0.911705</td>\n",
       "      <td>-0.610586</td>\n",
       "      <td>0.795040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.229819</td>\n",
       "      <td>0.623547</td>\n",
       "      <td>-0.721125</td>\n",
       "      <td>1.040916</td>\n",
       "      <td>0.937521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.023564</td>\n",
       "      <td>-0.023763</td>\n",
       "      <td>-0.024780</td>\n",
       "      <td>-0.815674</td>\n",
       "      <td>0.752994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cty          US                            JP          \n",
       "tenor         1         3         5         1         3\n",
       "0      0.026181 -0.343651 -1.839707 -0.239827  0.554371\n",
       "1     -0.342152  1.696171  0.911705 -0.610586  0.795040\n",
       "2     -0.229819  0.623547 -0.721125  1.040916  0.937521\n",
       "3      0.023564 -0.023763 -0.024780 -0.815674  0.752994"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = pd.MultiIndex.from_arrays([['US', 'US', 'US', 'JP', 'JP'],\n",
    "                                    [1, 3, 5, 1, 3]], names=['cty', 'tenor'])\n",
    "hier_df = DataFrame(np.random.randn(4, 5), columns=columns)\n",
    "hier_df"
   ]
  },
  {
   "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>cty</th>\n",
       "      <th>JP</th>\n",
       "      <th>US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cty  JP  US\n",
       "0     2   3\n",
       "1     2   3\n",
       "2     2   3\n",
       "3     2   3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hier_df.groupby(level='cty', axis=1).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.059447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.160542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.166587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.139780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.146808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.29</td>\n",
       "      <td>4.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.186240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size   tip_pct\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2  0.059447\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3  0.160542\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3  0.166587\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2  0.139780\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4  0.146808\n",
       "5       25.29  4.71    Male     No  Sun  Dinner     4  0.186240"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = pd.read_csv('ch08/tips.csv')\n",
    "# 添加\"小费占总额百分比\"的列\n",
    "tips['tip_pct'] = tips['tip'] / tips['total_bill']\n",
    "tips[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex     smoker\n",
       "Female  No        0.156921\n",
       "        Yes       0.182150\n",
       "Male    No        0.160669\n",
       "        Yes       0.152771\n",
       "Name: tip_pct, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = tips.groupby(['sex', 'smoker'])\n",
    "grouped_pct = grouped['tip_pct']\n",
    "grouped_pct.agg('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>peak_to_peak</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>No</th>\n",
       "      <td>0.156921</td>\n",
       "      <td>0.036421</td>\n",
       "      <td>0.195876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.182150</td>\n",
       "      <td>0.071595</td>\n",
       "      <td>0.360233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>No</th>\n",
       "      <td>0.160669</td>\n",
       "      <td>0.041849</td>\n",
       "      <td>0.220186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.152771</td>\n",
       "      <td>0.090588</td>\n",
       "      <td>0.674707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean       std  peak_to_peak\n",
       "sex    smoker                                  \n",
       "Female No      0.156921  0.036421      0.195876\n",
       "       Yes     0.182150  0.071595      0.360233\n",
       "Male   No      0.160669  0.041849      0.220186\n",
       "       Yes     0.152771  0.090588      0.674707"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def peak_to_peak(arr):\n",
    "    return arr.max() - arr.min()\n",
    "grouped_pct.agg(['mean', 'std', peak_to_peak])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>foo</th>\n",
       "      <th>bar</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>No</th>\n",
       "      <td>0.156921</td>\n",
       "      <td>0.036421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.182150</td>\n",
       "      <td>0.071595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>No</th>\n",
       "      <td>0.160669</td>\n",
       "      <td>0.041849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.152771</td>\n",
       "      <td>0.090588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    foo       bar\n",
       "sex    smoker                    \n",
       "Female No      0.156921  0.036421\n",
       "       Yes     0.182150  0.071595\n",
       "Male   No      0.160669  0.041849\n",
       "       Yes     0.152771  0.090588"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pct.agg([('foo', 'mean'), ('bar', np.std)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 colspan=\"3\" halign=\"left\">tip_pct</th>\n",
       "      <th colspan=\"3\" halign=\"left\">total_bill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</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 rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>No</th>\n",
       "      <td>54</td>\n",
       "      <td>0.156921</td>\n",
       "      <td>0.252672</td>\n",
       "      <td>54</td>\n",
       "      <td>18.105185</td>\n",
       "      <td>35.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>33</td>\n",
       "      <td>0.182150</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>33</td>\n",
       "      <td>17.977879</td>\n",
       "      <td>44.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>No</th>\n",
       "      <td>97</td>\n",
       "      <td>0.160669</td>\n",
       "      <td>0.291990</td>\n",
       "      <td>97</td>\n",
       "      <td>19.791237</td>\n",
       "      <td>48.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>60</td>\n",
       "      <td>0.152771</td>\n",
       "      <td>0.710345</td>\n",
       "      <td>60</td>\n",
       "      <td>22.284500</td>\n",
       "      <td>50.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tip_pct                     total_bill                  \n",
       "                count      mean       max      count       mean    max\n",
       "sex    smoker                                                         \n",
       "Female No          54  0.156921  0.252672         54  18.105185  35.83\n",
       "       Yes         33  0.182150  0.416667         33  17.977879  44.30\n",
       "Male   No          97  0.160669  0.291990         97  19.791237  48.33\n",
       "       Yes         60  0.152771  0.710345         60  22.284500  50.81"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "functions = ['count', 'mean', 'max']\n",
    "result = grouped['tip_pct', 'total_bill'].agg(functions)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>No</th>\n",
       "      <td>54</td>\n",
       "      <td>0.156921</td>\n",
       "      <td>0.252672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>33</td>\n",
       "      <td>0.182150</td>\n",
       "      <td>0.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>No</th>\n",
       "      <td>97</td>\n",
       "      <td>0.160669</td>\n",
       "      <td>0.291990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>60</td>\n",
       "      <td>0.152771</td>\n",
       "      <td>0.710345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count      mean       max\n",
       "sex    smoker                           \n",
       "Female No         54  0.156921  0.252672\n",
       "       Yes        33  0.182150  0.416667\n",
       "Male   No         97  0.160669  0.291990\n",
       "       Yes        60  0.152771  0.710345"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['tip_pct']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 colspan=\"2\" halign=\"left\">tip_pct</th>\n",
       "      <th colspan=\"2\" halign=\"left\">total_bill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Durchschnitt</th>\n",
       "      <th>Abweichung</th>\n",
       "      <th>Durchschnitt</th>\n",
       "      <th>Abweichung</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>No</th>\n",
       "      <td>0.156921</td>\n",
       "      <td>0.001327</td>\n",
       "      <td>18.105185</td>\n",
       "      <td>53.092422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.182150</td>\n",
       "      <td>0.005126</td>\n",
       "      <td>17.977879</td>\n",
       "      <td>84.451517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>No</th>\n",
       "      <td>0.160669</td>\n",
       "      <td>0.001751</td>\n",
       "      <td>19.791237</td>\n",
       "      <td>76.152961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.152771</td>\n",
       "      <td>0.008206</td>\n",
       "      <td>22.284500</td>\n",
       "      <td>98.244673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   tip_pct              total_bill           \n",
       "              Durchschnitt Abweichung Durchschnitt Abweichung\n",
       "sex    smoker                                                \n",
       "Female No         0.156921   0.001327    18.105185  53.092422\n",
       "       Yes        0.182150   0.005126    17.977879  84.451517\n",
       "Male   No         0.160669   0.001751    19.791237  76.152961\n",
       "       Yes        0.152771   0.008206    22.284500  98.244673"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftuples = [('Durchschnitt', 'mean'), ('Abweichung', np.var)]\n",
    "grouped['tip_pct', 'total_bill'].agg(ftuples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>No</th>\n",
       "      <td>5.2</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>6.5</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>No</th>\n",
       "      <td>9.0</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>10.0</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tip  size\n",
       "sex    smoker            \n",
       "Female No       5.2   140\n",
       "       Yes      6.5    74\n",
       "Male   No       9.0   263\n",
       "       Yes     10.0   150"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.agg({'tip': np.max, 'size' : 'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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 colspan=\"4\" halign=\"left\">tip_pct</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</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=\"2\" valign=\"top\">Female</th>\n",
       "      <th>No</th>\n",
       "      <td>0.056797</td>\n",
       "      <td>0.252672</td>\n",
       "      <td>0.156921</td>\n",
       "      <td>0.036421</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.056433</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.182150</td>\n",
       "      <td>0.071595</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>No</th>\n",
       "      <td>0.071804</td>\n",
       "      <td>0.291990</td>\n",
       "      <td>0.160669</td>\n",
       "      <td>0.041849</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.035638</td>\n",
       "      <td>0.710345</td>\n",
       "      <td>0.152771</td>\n",
       "      <td>0.090588</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tip_pct                               size\n",
       "                    min       max      mean       std  sum\n",
       "sex    smoker                                             \n",
       "Female No      0.056797  0.252672  0.156921  0.036421  140\n",
       "       Yes     0.056433  0.416667  0.182150  0.071595   74\n",
       "Male   No      0.071804  0.291990  0.160669  0.041849  263\n",
       "       Yes     0.035638  0.710345  0.152771  0.090588  150"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.agg({'tip_pct':['min', 'max', 'mean', 'std'],\n",
    "            'size' : 'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>18.105185</td>\n",
       "      <td>2.773519</td>\n",
       "      <td>2.592593</td>\n",
       "      <td>0.156921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>17.977879</td>\n",
       "      <td>2.931515</td>\n",
       "      <td>2.242424</td>\n",
       "      <td>0.182150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>19.791237</td>\n",
       "      <td>3.113402</td>\n",
       "      <td>2.711340</td>\n",
       "      <td>0.160669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>22.284500</td>\n",
       "      <td>3.051167</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.152771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex smoker  total_bill       tip      size   tip_pct\n",
       "0  Female     No   18.105185  2.773519  2.592593  0.156921\n",
       "1  Female    Yes   17.977879  2.931515  2.242424  0.182150\n",
       "2    Male     No   19.791237  3.113402  2.711340  0.160669\n",
       "3    Male    Yes   22.284500  3.051167  2.500000  0.152771"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(['sex', 'smoker'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def top(df, n=5, column='tip_pct'):\n",
    "    return df.sort_values(by=column)[-n:]"
   ]
  },
  {
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>14.31</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.279525</td>\n",
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       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>23.17</td>\n",
       "      <td>6.50</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.280535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>11.61</td>\n",
       "      <td>3.39</td>\n",
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       "      <td>No</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.291990</td>\n",
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       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>3.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>1</td>\n",
       "      <td>0.325733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>9.60</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>7.25</td>\n",
       "      <td>5.15</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.710345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker  day    time  size   tip_pct\n",
       "109       14.31  4.00  Female    Yes  Sat  Dinner     2  0.279525\n",
       "183       23.17  6.50    Male    Yes  Sun  Dinner     4  0.280535\n",
       "232       11.61  3.39    Male     No  Sat  Dinner     2  0.291990\n",
       "67         3.07  1.00  Female    Yes  Sat  Dinner     1  0.325733\n",
       "178        9.60  4.00  Female    Yes  Sun  Dinner     2  0.416667\n",
       "172        7.25  5.15    Male    Yes  Sun  Dinner     2  0.710345"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top(tips, n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>total_bill</th>\n",
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       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
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       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</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",
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       "      <th rowspan=\"5\" valign=\"top\">No</th>\n",
       "      <th>88</th>\n",
       "      <td>24.71</td>\n",
       "      <td>5.85</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "      <td>0.236746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>20.69</td>\n",
       "      <td>5.00</td>\n",
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       "      <td>No</td>\n",
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       "      <td>5</td>\n",
       "      <td>0.241663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>10.29</td>\n",
       "      <td>2.60</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.252672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>7.51</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "      <td>0.266312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>11.61</td>\n",
       "      <td>3.39</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.291990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Yes</th>\n",
       "      <th>109</th>\n",
       "      <td>14.31</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.279525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>23.17</td>\n",
       "      <td>6.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.280535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>3.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>1</td>\n",
       "      <td>0.325733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>9.60</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>7.25</td>\n",
       "      <td>5.15</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.710345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_bill   tip     sex smoker   day    time  size   tip_pct\n",
       "smoker                                                                   \n",
       "No     88        24.71  5.85    Male     No  Thur   Lunch     2  0.236746\n",
       "       185       20.69  5.00    Male     No   Sun  Dinner     5  0.241663\n",
       "       51        10.29  2.60  Female     No   Sun  Dinner     2  0.252672\n",
       "       149        7.51  2.00    Male     No  Thur   Lunch     2  0.266312\n",
       "       232       11.61  3.39    Male     No   Sat  Dinner     2  0.291990\n",
       "Yes    109       14.31  4.00  Female    Yes   Sat  Dinner     2  0.279525\n",
       "       183       23.17  6.50    Male    Yes   Sun  Dinner     4  0.280535\n",
       "       67         3.07  1.00  Female    Yes   Sat  Dinner     1  0.325733\n",
       "       178        9.60  4.00  Female    Yes   Sun  Dinner     2  0.416667\n",
       "       172        7.25  5.15    Male    Yes   Sun  Dinner     2  0.710345"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('smoker').apply(top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th>day</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 rowspan=\"4\" valign=\"top\">No</th>\n",
       "      <th>Fri</th>\n",
       "      <th>94</th>\n",
       "      <td>22.75</td>\n",
       "      <td>3.25</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <th>212</th>\n",
       "      <td>48.33</td>\n",
       "      <td>9.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.186220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <th>156</th>\n",
       "      <td>48.17</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>6</td>\n",
       "      <td>0.103799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <th>142</th>\n",
       "      <td>41.19</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>5</td>\n",
       "      <td>0.121389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Yes</th>\n",
       "      <th>Fri</th>\n",
       "      <th>95</th>\n",
       "      <td>40.17</td>\n",
       "      <td>4.73</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.117750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <th>170</th>\n",
       "      <td>50.81</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.196812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <th>182</th>\n",
       "      <td>45.35</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.077178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <th>197</th>\n",
       "      <td>43.11</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>4</td>\n",
       "      <td>0.115982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 total_bill    tip     sex smoker   day    time  size  \\\n",
       "smoker day                                                              \n",
       "No     Fri  94        22.75   3.25  Female     No   Fri  Dinner     2   \n",
       "       Sat  212       48.33   9.00    Male     No   Sat  Dinner     4   \n",
       "       Sun  156       48.17   5.00    Male     No   Sun  Dinner     6   \n",
       "       Thur 142       41.19   5.00    Male     No  Thur   Lunch     5   \n",
       "Yes    Fri  95        40.17   4.73    Male    Yes   Fri  Dinner     4   \n",
       "       Sat  170       50.81  10.00    Male    Yes   Sat  Dinner     3   \n",
       "       Sun  182       45.35   3.50    Male    Yes   Sun  Dinner     3   \n",
       "       Thur 197       43.11   5.00  Female    Yes  Thur   Lunch     4   \n",
       "\n",
       "                  tip_pct  \n",
       "smoker day                 \n",
       "No     Fri  94   0.142857  \n",
       "       Sat  212  0.186220  \n",
       "       Sun  156  0.103799  \n",
       "       Thur 142  0.121389  \n",
       "Yes    Fri  95   0.117750  \n",
       "       Sat  170  0.196812  \n",
       "       Sun  182  0.077178  \n",
       "       Thur 197  0.115982  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(['smoker', 'day']).apply(top, n=1, column='total_bill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoker       \n",
       "No      count    151.000000\n",
       "        mean       0.159328\n",
       "        std        0.039910\n",
       "        min        0.056797\n",
       "        25%        0.136906\n",
       "        50%        0.155625\n",
       "        75%        0.185014\n",
       "        max        0.291990\n",
       "Yes     count     93.000000\n",
       "        mean       0.163196\n",
       "        std        0.085119\n",
       "        min        0.035638\n",
       "        25%        0.106771\n",
       "        50%        0.153846\n",
       "        75%        0.195059\n",
       "        max        0.710345\n",
       "Name: tip_pct, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = tips.groupby('smoker')['tip_pct'].describe()\n",
    "result"
   ]
  },
  {
   "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>smoker</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>151.000000</td>\n",
       "      <td>93.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.159328</td>\n",
       "      <td>0.163196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.039910</td>\n",
       "      <td>0.085119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.056797</td>\n",
       "      <td>0.035638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.136906</td>\n",
       "      <td>0.106771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.155625</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.185014</td>\n",
       "      <td>0.195059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.291990</td>\n",
       "      <td>0.710345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoker          No        Yes\n",
       "count   151.000000  93.000000\n",
       "mean      0.159328   0.163196\n",
       "std       0.039910   0.085119\n",
       "min       0.056797   0.035638\n",
       "25%       0.136906   0.106771\n",
       "50%       0.155625   0.153846\n",
       "75%       0.185014   0.195059\n",
       "max       0.291990   0.710345"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.unstack('smoker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>smoker</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>151.000000</td>\n",
       "      <td>93.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.159328</td>\n",
       "      <td>0.163196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.039910</td>\n",
       "      <td>0.085119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.056797</td>\n",
       "      <td>0.035638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.136906</td>\n",
       "      <td>0.106771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.155625</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.185014</td>\n",
       "      <td>0.195059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.291990</td>\n",
       "      <td>0.710345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoker          No        Yes\n",
       "count   151.000000  93.000000\n",
       "mean      0.159328   0.163196\n",
       "std       0.039910   0.085119\n",
       "min       0.056797   0.035638\n",
       "25%       0.136906   0.106771\n",
       "50%       0.155625   0.153846\n",
       "75%       0.185014   0.195059\n",
       "max       0.291990   0.710345"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = lambda x: x.describe()\n",
    "tips.groupby('smoker')['tip_pct'].apply(f).unstack('smoker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>24.71</td>\n",
       "      <td>5.85</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "      <td>0.236746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>20.69</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>5</td>\n",
       "      <td>0.241663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>10.29</td>\n",
       "      <td>2.60</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.252672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>7.51</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "      <td>0.266312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>11.61</td>\n",
       "      <td>3.39</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.291990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>14.31</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.279525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>23.17</td>\n",
       "      <td>6.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.280535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>3.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>1</td>\n",
       "      <td>0.325733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>9.60</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>7.25</td>\n",
       "      <td>5.15</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.710345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size   tip_pct\n",
       "88        24.71  5.85    Male     No  Thur   Lunch     2  0.236746\n",
       "185       20.69  5.00    Male     No   Sun  Dinner     5  0.241663\n",
       "51        10.29  2.60  Female     No   Sun  Dinner     2  0.252672\n",
       "149        7.51  2.00    Male     No  Thur   Lunch     2  0.266312\n",
       "232       11.61  3.39    Male     No   Sat  Dinner     2  0.291990\n",
       "109       14.31  4.00  Female    Yes   Sat  Dinner     2  0.279525\n",
       "183       23.17  6.50    Male    Yes   Sun  Dinner     4  0.280535\n",
       "67         3.07  1.00  Female    Yes   Sat  Dinner     1  0.325733\n",
       "178        9.60  4.00  Female    Yes   Sun  Dinner     2  0.416667\n",
       "172        7.25  5.15    Male    Yes   Sun  Dinner     2  0.710345"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('smoker', group_keys=False).apply(top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>size</th>\n",
       "      <th>tip</th>\n",
       "      <th>tip_pct</th>\n",
       "      <th>total_bill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>No</th>\n",
       "      <td>2.592593</td>\n",
       "      <td>2.773519</td>\n",
       "      <td>0.156921</td>\n",
       "      <td>18.105185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>2.242424</td>\n",
       "      <td>2.931515</td>\n",
       "      <td>0.182150</td>\n",
       "      <td>17.977879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>No</th>\n",
       "      <td>2.711340</td>\n",
       "      <td>3.113402</td>\n",
       "      <td>0.160669</td>\n",
       "      <td>19.791237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.051167</td>\n",
       "      <td>0.152771</td>\n",
       "      <td>22.284500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   size       tip   tip_pct  total_bill\n",
       "sex    smoker                                          \n",
       "Female No      2.592593  2.773519  0.156921   18.105185\n",
       "       Yes     2.242424  2.931515  0.182150   17.977879\n",
       "Male   No      2.711340  3.113402  0.160669   19.791237\n",
       "       Yes     2.500000  3.051167  0.152771   22.284500"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(index=['sex', 'smoker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">tip_pct</th>\n",
       "      <th colspan=\"2\" halign=\"left\">size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>smoker</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Female</th>\n",
       "      <th>Fri</th>\n",
       "      <td>0.165296</td>\n",
       "      <td>0.209129</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>0.147993</td>\n",
       "      <td>0.163817</td>\n",
       "      <td>2.307692</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0.165710</td>\n",
       "      <td>0.237075</td>\n",
       "      <td>3.071429</td>\n",
       "      <td>2.500000</td>\n",
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       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>0.155971</td>\n",
       "      <td>0.163073</td>\n",
       "      <td>2.480000</td>\n",
       "      <td>2.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Male</th>\n",
       "      <th>Fri</th>\n",
       "      <td>0.138005</td>\n",
       "      <td>0.144730</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>0.162132</td>\n",
       "      <td>0.139067</td>\n",
       "      <td>2.656250</td>\n",
       "      <td>2.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0.158291</td>\n",
       "      <td>0.173964</td>\n",
       "      <td>2.883721</td>\n",
       "      <td>2.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>0.165706</td>\n",
       "      <td>0.164417</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tip_pct                size          \n",
       "smoker             No       Yes        No       Yes\n",
       "sex    day                                         \n",
       "Female Fri   0.165296  0.209129  2.500000  2.000000\n",
       "       Sat   0.147993  0.163817  2.307692  2.200000\n",
       "       Sun   0.165710  0.237075  3.071429  2.500000\n",
       "       Thur  0.155971  0.163073  2.480000  2.428571\n",
       "Male   Fri   0.138005  0.144730  2.000000  2.125000\n",
       "       Sat   0.162132  0.139067  2.656250  2.629630\n",
       "       Sun   0.158291  0.173964  2.883721  2.600000\n",
       "       Thur  0.165706  0.164417  2.500000  2.300000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(['tip_pct', 'size'], index=['sex', 'day'],\n",
    "                columns='smoker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">tip_pct</th>\n",
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       "    <tr>\n",
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       "      <th>smoker</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Female</th>\n",
       "      <th>Fri</th>\n",
       "      <td>0.165296</td>\n",
       "      <td>0.209129</td>\n",
       "      <td>0.199388</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>0.147993</td>\n",
       "      <td>0.163817</td>\n",
       "      <td>0.156470</td>\n",
       "      <td>2.307692</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.250000</td>\n",
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       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0.165710</td>\n",
       "      <td>0.237075</td>\n",
       "      <td>0.181569</td>\n",
       "      <td>3.071429</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>0.155971</td>\n",
       "      <td>0.163073</td>\n",
       "      <td>0.157525</td>\n",
       "      <td>2.480000</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>2.468750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Male</th>\n",
       "      <th>Fri</th>\n",
       "      <td>0.138005</td>\n",
       "      <td>0.144730</td>\n",
       "      <td>0.143385</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.125000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>0.162132</td>\n",
       "      <td>0.139067</td>\n",
       "      <td>0.151577</td>\n",
       "      <td>2.656250</td>\n",
       "      <td>2.629630</td>\n",
       "      <td>2.644068</td>\n",
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       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0.158291</td>\n",
       "      <td>0.173964</td>\n",
       "      <td>0.162344</td>\n",
       "      <td>2.883721</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.810345</td>\n",
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       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>0.165706</td>\n",
       "      <td>0.164417</td>\n",
       "      <td>0.165276</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>0.159328</td>\n",
       "      <td>0.163196</td>\n",
       "      <td>0.160803</td>\n",
       "      <td>2.668874</td>\n",
       "      <td>2.408602</td>\n",
       "      <td>2.569672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tip_pct                          size                    \n",
       "smoker             No       Yes       All        No       Yes       All\n",
       "sex    day                                                             \n",
       "Female Fri   0.165296  0.209129  0.199388  2.500000  2.000000  2.111111\n",
       "       Sat   0.147993  0.163817  0.156470  2.307692  2.200000  2.250000\n",
       "       Sun   0.165710  0.237075  0.181569  3.071429  2.500000  2.944444\n",
       "       Thur  0.155971  0.163073  0.157525  2.480000  2.428571  2.468750\n",
       "Male   Fri   0.138005  0.144730  0.143385  2.000000  2.125000  2.100000\n",
       "       Sat   0.162132  0.139067  0.151577  2.656250  2.629630  2.644068\n",
       "       Sun   0.158291  0.173964  0.162344  2.883721  2.600000  2.810345\n",
       "       Thur  0.165706  0.164417  0.165276  2.500000  2.300000  2.433333\n",
       "All          0.159328  0.163196  0.160803  2.668874  2.408602  2.569672"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(['tip_pct', 'size'], index=['sex', 'day'],\n",
    "                columns='smoker', margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>day</th>\n",
       "      <th>Fri</th>\n",
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       "      <th>sex</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>No</th>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>54.0</td>\n",
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       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>7.0</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>33.0</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>No</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>20.0</td>\n",
       "      <td>97.0</td>\n",
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       "      <th>Yes</th>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.0</td>\n",
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       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>19.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>244.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "day             Fri   Sat   Sun  Thur    All\n",
       "sex    smoker                               \n",
       "Female No       2.0  13.0  14.0  25.0   54.0\n",
       "       Yes      7.0  15.0   4.0   7.0   33.0\n",
       "Male   No       2.0  32.0  43.0  20.0   97.0\n",
       "       Yes      8.0  27.0  15.0  10.0   60.0\n",
       "All            19.0  87.0  76.0  62.0  244.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table('tip_pct', index=['sex', 'smoker'],\n",
    "                columns='day', aggfunc=len, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>time</th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"4\" valign=\"top\">Dinner</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>No</th>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>77.0</td>\n",
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       "      <th>Yes</th>\n",
       "      <td>8.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>No</th>\n",
       "      <td>4.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>213.0</td>\n",
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       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>12.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>122.0</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Lunch</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>No</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>63.0</td>\n",
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       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>23.0</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>No</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
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       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <td>40.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>627.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "day                    Fri    Sat    Sun   Thur    All\n",
       "time   sex    smoker                                  \n",
       "Dinner Female No       2.0   30.0   43.0    2.0   77.0\n",
       "              Yes      8.0   33.0   10.0    0.0   51.0\n",
       "       Male   No       4.0   85.0  124.0    0.0  213.0\n",
       "              Yes     12.0   71.0   39.0    0.0  122.0\n",
       "Lunch  Female No       3.0    0.0    0.0   60.0   63.0\n",
       "              Yes      6.0    0.0    0.0   17.0   23.0\n",
       "       Male   No       0.0    0.0    0.0   50.0   50.0\n",
       "              Yes      5.0    0.0    0.0   23.0   28.0\n",
       "All                   40.0  219.0  216.0  152.0  627.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table('size', index=['time', 'sex', 'smoker'],\n",
    "                columns='day', aggfunc='sum', fill_value=0, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>smoker</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Dinner</th>\n",
       "      <th>Fri</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>45</td>\n",
       "      <td>42</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>57</td>\n",
       "      <td>19</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Lunch</th>\n",
       "      <th>Fri</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>44</td>\n",
       "      <td>17</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>151</td>\n",
       "      <td>93</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoker        No  Yes  All\n",
       "time   day                \n",
       "Dinner Fri     3    9   12\n",
       "       Sat    45   42   87\n",
       "       Sun    57   19   76\n",
       "       Thur    1    0    1\n",
       "Lunch  Fri     1    6    7\n",
       "       Thur   44   17   61\n",
       "All          151   93  244"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab([tips.time, tips.day], tips.smoker, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VMX92AfNnF2DVRdn9gReL1T7yASPQx8Hn9RIKjr+9TyQcRKczEAz4CAWjEzomFPRP+Hkm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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbfad780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "tips = sns.load_dataset( \"tips\" )\n",
    "\n",
    "sns.jointplot( \"total_bill\" , \"tip\" , tips, kind = 'reg' );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2t+FUZyR7TJElvPvSely/qREeV2FqvBU9KDZNE/fccw86Ozuhqiq+9KUvYeXKlcUeBhER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\nEb2Io7oJy7IQi0cQj0VRXe7ERzcuzTimN6wn6oqHn0ogUWe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dx8BgGDHDhuZ0Z7RVSwrH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb9313c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot( \"total_bill\" , \"tip\" , tips, col = \"smoker\" );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "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
}