example_lousy_tipper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "plt.matplotlib.style.use('fivethirtyeight')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## I/O and Format Conversions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = (\n",
    "    'https://raw.github.com/pandas-dev/'\n",
    "    'pandas/master/pandas/tests/data/tips.csv'\n",
    ")\n",
    "df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"total_bill\":{\"0\":16.99,\"1\":10.34,\"2\":21.01,\"3\":23.68,\"4\":24.59,\"5\":25.29,\"6\":8.77,\"7\":26.88,\"8\":15.04,\"9\":14.78,\"10\":10.27,\"11\":35.26,\"12\":15.42,\"13\":18.43,\"14\":14.83,\"15\":21.58,\"16\":10.33,\"17\":16.29,\"18\":16.97,\"19\":20.65,\"20\":17.92,\"21\":20.29,\"22\":15.77,\"23\":39.42,\"24\":19.82,\"25\":17.81,\"26\":13.37,\"27\":12.69,\"28\":21.7,\"29\":19.65,\"30\":9.55,\"31\":18.35,\"32\":15.06,\"33\":20.69,\"34\":17.78,\"35\":24.06,\"36\":16.31,\"37\":16.93,\"38\":18.69,\"39\":31.27,\"40\":16.04,\"41\":17.46,\"42\":13.94,\"43\":9.68,\"44\":30.4,\"45\":...\n"
     ]
    }
   ],
   "source": [
    "fn = 'tips.jsoin'\n",
    "df.to_json(fn)\n",
    "\n",
    "with open(fn) as f:\n",
    "    print(f.read()[:500] + '...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "if os.path.isfile(fn):\n",
    "    os.remove(fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## Descriptive Info"
   ]
  },
  {
   "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>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 244 entries, 0 to 243\n",
      "Data columns (total 7 columns):\n",
      "total_bill    244 non-null float64\n",
      "tip           244 non-null float64\n",
      "sex           244 non-null object\n",
      "smoker        244 non-null object\n",
      "day           244 non-null object\n",
      "time          244 non-null object\n",
      "size          244 non-null int64\n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 13.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>244.000000</td>\n",
       "      <td>244.000000</td>\n",
       "      <td>244.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.785943</td>\n",
       "      <td>2.998279</td>\n",
       "      <td>2.569672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.902412</td>\n",
       "      <td>1.383638</td>\n",
       "      <td>0.951100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.070000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.347500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.795000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.127500</td>\n",
       "      <td>3.562500</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.810000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_bill         tip        size\n",
       "count  244.000000  244.000000  244.000000\n",
       "mean    19.785943    2.998279    2.569672\n",
       "std      8.902412    1.383638    0.951100\n",
       "min      3.070000    1.000000    1.000000\n",
       "25%     13.347500    2.000000    2.000000\n",
       "50%     17.795000    2.900000    2.000000\n",
       "75%     24.127500    3.562500    3.000000\n",
       "max     50.810000   10.000000    6.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>day</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>244</td>\n",
       "      <td>244</td>\n",
       "      <td>244</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>157</td>\n",
       "      <td>151</td>\n",
       "      <td>87</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sex smoker  day    time\n",
       "count    244    244  244     244\n",
       "unique     2      2    4       2\n",
       "top     Male     No  Sat  Dinner\n",
       "freq     157    151   87     176"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## Mutability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['tip_percent'] = df['tip'] / df['total_bill']\n",
    "df['totally_dumb_column'] = np.random.random(df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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_percent</th>\n",
       "      <th>totally_dumb_column</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",
       "      <td>0.465515</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",
       "      <td>0.743808</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",
       "      <td>0.847701</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",
       "      <td>0.354460</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",
       "      <td>0.776685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size  tip_percent  \\\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",
       "\n",
       "   totally_dumb_column  \n",
       "0             0.465515  \n",
       "1             0.743808  \n",
       "2             0.847701  \n",
       "3             0.354460  \n",
       "4             0.776685  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del df['totally_dumb_column']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size', 'tip_percent']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## Filtering"
   ]
  },
  {
   "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>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_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip   sex smoker  day    time  size  tip_percent\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"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (df['sex'] == 'Male') & (df['total_bill'] > 10.00)\n",
    "df[mask].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>size</th>\n",
       "      <th>tip_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.657123</td>\n",
       "      <td>3.175205</td>\n",
       "      <td>2.684932</td>\n",
       "      <td>0.151715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.940516</td>\n",
       "      <td>1.482820</td>\n",
       "      <td>0.966711</td>\n",
       "      <td>0.046967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.070000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.035638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.435000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.119297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.465000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.149856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25.492500</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.184942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.810000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.291990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_bill         tip        size  tip_percent\n",
       "count  146.000000  146.000000  146.000000   146.000000\n",
       "mean    21.657123    3.175205    2.684932     0.151715\n",
       "std      8.940516    1.482820    0.966711     0.046967\n",
       "min     10.070000    1.000000    2.000000     0.035638\n",
       "25%     15.435000    2.000000    2.000000     0.119297\n",
       "50%     19.465000    3.000000    2.000000     0.149856\n",
       "75%     25.492500    3.880000    3.000000     0.184942\n",
       "max     50.810000   10.000000    6.000000     0.291990"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[mask].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## Flexible Accessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------\n",
    "\n",
    "**[ ]** the get item access is intentially flexible for a variety of access methods\n",
    "\n",
    "------\n",
    "\n",
    "**iloc** is for accessing **by integer** & integers are **exclusive** of the end value\n",
    "\n",
    "**loc** is for accessing **by label** & labels are **inclusive** of the end value\n",
    "\n",
    "------\n",
    "**ix** is for accessing **by integer and label**\n",
    "\n",
    "integers are **exclusive** of the end value\n",
    "\n",
    "labels are **inclusive** of the end value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.01\n",
       "1    1.66\n",
       "2    3.50\n",
       "3    3.31\n",
       "4    3.61\n",
       "Name: tip, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['tip'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.01\n",
       "1    1.66\n",
       "2    3.50\n",
       "Name: tip, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['tip'][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.01\n",
       "57     1.50\n",
       "102    2.50\n",
       "237    1.17\n",
       "Name: tip, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['tip'][df['tip_percent'] < 0.06]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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_percent</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size  tip_percent\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"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker\n",
       "0       16.99  1.01  Female     No\n",
       "1       10.34  1.66    Male     No\n",
       "2       21.01  3.50    Male     No"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:3, :4]"
   ]
  },
  {
   "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>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tip     sex smoker\n",
       "0  1.01  Female     No\n",
       "1  1.66    Male     No\n",
       "2  3.50    Male     No"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, 'tip': 'smoker'].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.ix[df['size'] == 2, 'size'] = None"
   ]
  },
  {
   "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></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_percent</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>NaN</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>3.0</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>3.0</td>\n",
       "      <td>0.166587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size  tip_percent\n",
       "0       16.99  1.01  Female     No  Sun  Dinner   NaN     0.059447\n",
       "1       10.34  1.66    Male     No  Sun  Dinner   3.0     0.160542\n",
       "2       21.01  3.50    Male     No  Sun  Dinner   3.0     0.166587"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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_percent</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>-99.0</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>3.0</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>3.0</td>\n",
       "      <td>0.166587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size  tip_percent\n",
       "0       16.99  1.01  Female     No  Sun  Dinner -99.0     0.059447\n",
       "1       10.34  1.66    Male     No  Sun  Dinner   3.0     0.160542\n",
       "2       21.01  3.50    Male     No  Sun  Dinner   3.0     0.166587"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(-99).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>3.0</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>3.0</td>\n",
       "      <td>0.166587</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>4.0</td>\n",
       "      <td>0.146808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size  tip_percent\n",
       "1       10.34  1.66    Male     No  Sun  Dinner   3.0     0.160542\n",
       "2       21.01  3.50    Male     No  Sun  Dinner   3.0     0.166587\n",
       "4       24.59  3.61  Female     No  Sun  Dinner   4.0     0.146808"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.fillna(2, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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_percent</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>2.0</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>3.0</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>3.0</td>\n",
       "      <td>0.166587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size  tip_percent\n",
       "0       16.99  1.01  Female     No  Sun  Dinner   2.0     0.059447\n",
       "1       10.34  1.66    Male     No  Sun  Dinner   3.0     0.160542\n",
       "2       21.01  3.50    Male     No  Sun  Dinner   3.0     0.166587"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<br>\n",
    "## Statistics and Math "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_bill          3.07\n",
       "tip                    1\n",
       "sex               Female\n",
       "smoker                No\n",
       "day                  Fri\n",
       "time              Dinner\n",
       "size                   1\n",
       "tip_percent    0.0356381\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_bill        50.81\n",
       "tip                  10\n",
       "sex                Male\n",
       "smoker              Yes\n",
       "day                Thur\n",
       "time              Lunch\n",
       "size                  6\n",
       "tip_percent    0.710345\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_bill     19.785943\n",
       "tip             2.998279\n",
       "size            2.569672\n",
       "tip_percent     0.160803\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_bill     8.902412\n",
       "tip            1.383638\n",
       "size           0.951100\n",
       "tip_percent    0.061072\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_bill     14.249000\n",
       "tip             2.000000\n",
       "size            2.000000\n",
       "tip_percent     0.137238\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.quantile(0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>size</th>\n",
       "      <th>tip_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_bill</th>\n",
       "      <td>79.252939</td>\n",
       "      <td>8.323502</td>\n",
       "      <td>5.065983</td>\n",
       "      <td>-0.184107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip</th>\n",
       "      <td>8.323502</td>\n",
       "      <td>1.914455</td>\n",
       "      <td>0.643906</td>\n",
       "      <td>0.028931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>5.065983</td>\n",
       "      <td>0.643906</td>\n",
       "      <td>0.904591</td>\n",
       "      <td>-0.008298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip_percent</th>\n",
       "      <td>-0.184107</td>\n",
       "      <td>0.028931</td>\n",
       "      <td>-0.008298</td>\n",
       "      <td>0.003730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             total_bill       tip      size  tip_percent\n",
       "total_bill    79.252939  8.323502  5.065983    -0.184107\n",
       "tip            8.323502  1.914455  0.643906     0.028931\n",
       "size           5.065983  0.643906  0.904591    -0.008298\n",
       "tip_percent   -0.184107  0.028931 -0.008298     0.003730"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>size</th>\n",
       "      <th>tip_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_bill</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.675734</td>\n",
       "      <td>0.598315</td>\n",
       "      <td>-0.338624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip</th>\n",
       "      <td>0.675734</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.489299</td>\n",
       "      <td>0.342370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>0.598315</td>\n",
       "      <td>0.489299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.142860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip_percent</th>\n",
       "      <td>-0.338624</td>\n",
       "      <td>0.342370</td>\n",
       "      <td>-0.142860</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             total_bill       tip      size  tip_percent\n",
       "total_bill     1.000000  0.675734  0.598315    -0.338624\n",
       "tip            0.675734  1.000000  0.489299     0.342370\n",
       "size           0.598315  0.489299  1.000000    -0.142860\n",
       "tip_percent   -0.338624  0.342370 -0.142860     1.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## Groupby Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "Female    0.166491\n",
       "Male      0.157651\n",
       "Name: tip_percent, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = df.groupby('sex')\n",
    "gb['tip_percent'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_percent</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>14.145556</td>\n",
       "      <td>2.781111</td>\n",
       "      <td>2.111111</td>\n",
       "      <td>0.199388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>19.680357</td>\n",
       "      <td>2.801786</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>0.156470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>19.872222</td>\n",
       "      <td>3.367222</td>\n",
       "      <td>2.944444</td>\n",
       "      <td>0.181569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>16.715312</td>\n",
       "      <td>2.575625</td>\n",
       "      <td>2.468750</td>\n",
       "      <td>0.157525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Male</th>\n",
       "      <th>Fri</th>\n",
       "      <td>19.857000</td>\n",
       "      <td>2.693000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.143385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>20.802542</td>\n",
       "      <td>3.083898</td>\n",
       "      <td>2.644068</td>\n",
       "      <td>0.151577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>21.887241</td>\n",
       "      <td>3.220345</td>\n",
       "      <td>2.810345</td>\n",
       "      <td>0.162344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>18.714667</td>\n",
       "      <td>2.980333</td>\n",
       "      <td>2.433333</td>\n",
       "      <td>0.165276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             total_bill       tip      size  tip_percent\n",
       "sex    day                                              \n",
       "Female Fri    14.145556  2.781111  2.111111     0.199388\n",
       "       Sat    19.680357  2.801786  2.250000     0.156470\n",
       "       Sun    19.872222  3.367222  2.944444     0.181569\n",
       "       Thur   16.715312  2.575625  2.468750     0.157525\n",
       "Male   Fri    19.857000  2.693000  2.100000     0.143385\n",
       "       Sat    20.802542  3.083898  2.644068     0.151577\n",
       "       Sun    21.887241  3.220345  2.810345     0.162344\n",
       "       Thur   18.714667  2.980333  2.433333     0.165276"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = df.groupby(('sex', 'day'))\n",
    "gb.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## Data Alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>size</th>\n",
       "      <th>tip_percent</th>\n",
       "    </tr>\n",
       "    <tr>\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>Fri</th>\n",
       "      <td>17.151579</td>\n",
       "      <td>2.734737</td>\n",
       "      <td>2.105263</td>\n",
       "      <td>0.169913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>20.441379</td>\n",
       "      <td>2.993103</td>\n",
       "      <td>2.517241</td>\n",
       "      <td>0.153152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>21.410000</td>\n",
       "      <td>3.255132</td>\n",
       "      <td>2.842105</td>\n",
       "      <td>0.166897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill       tip      size  tip_percent\n",
       "day                                             \n",
       "Fri   17.151579  2.734737  2.105263     0.169913\n",
       "Sat   20.441379  2.993103  2.517241     0.153152\n",
       "Sun   21.410000  3.255132  2.842105     0.166897"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_by_day = df.groupby('day').mean()\n",
    "mean_by_day.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>takehome_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>0.169295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>0.675815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>0.457629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>0.482166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>0.483651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>0.912389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0.741331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      takehome_percent\n",
       "Mon           0.169295\n",
       "Tue           0.675815\n",
       "Wed           0.457629\n",
       "Thur          0.482166\n",
       "Fri           0.483651\n",
       "Sat           0.912389\n",
       "Sun           0.741331"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = [\n",
    "    'Mon', 'Tue', 'Wed', \n",
    "    'Thur', 'Fri', 'Sat', 'Sun'\n",
    "]\n",
    "data = {\n",
    "    'takehome_percent': np.random.random(len(index)),\n",
    "}\n",
    "\n",
    "df_percent_takehome = pd.DataFrame(data, index=index)\n",
    "df_percent_takehome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>takehome_percent</th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>0.284653</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>0.232131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>0.998957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>0.256711</td>\n",
       "      <td>17.682742</td>\n",
       "      <td>2.771452</td>\n",
       "      <td>2.451613</td>\n",
       "      <td>0.161276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>0.659085</td>\n",
       "      <td>17.151579</td>\n",
       "      <td>2.734737</td>\n",
       "      <td>2.105263</td>\n",
       "      <td>0.169913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>0.365878</td>\n",
       "      <td>20.441379</td>\n",
       "      <td>2.993103</td>\n",
       "      <td>2.517241</td>\n",
       "      <td>0.153152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0.909365</td>\n",
       "      <td>21.410000</td>\n",
       "      <td>3.255132</td>\n",
       "      <td>2.842105</td>\n",
       "      <td>0.166897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      takehome_percent  total_bill       tip      size  tip_percent\n",
       "Mon           0.284653         NaN       NaN       NaN          NaN\n",
       "Tue           0.232131         NaN       NaN       NaN          NaN\n",
       "Wed           0.998957         NaN       NaN       NaN          NaN\n",
       "Thur          0.256711   17.682742  2.771452  2.451613     0.161276\n",
       "Fri           0.659085   17.151579  2.734737  2.105263     0.169913\n",
       "Sat           0.365878   20.441379  2.993103  2.517241     0.153152\n",
       "Sun           0.909365   21.410000  3.255132  2.842105     0.166897"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_percent_takehome.join(mean_by_day)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NwHMMTi/YbGZ3A+vc/QfAzWb2uwSLSh8EbhjJtRpZvknwixdXOVM1Rku/iURLqYNRhjNj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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e4af98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot.scatter('total_bill', 'tip_percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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poavr6abzGkqRzzcWcSZ6rKL3nXbRNytA0h5kBf+WiFiSN3dLmhUR3ZJmAy8MFaPIRrp1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\ngiGoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117f244e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.hist('tip_percent', bins=20, by='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more check out http://pandas.pydata.org/pandas-docs/version/0.18.1/visualization.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}