ch10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Aggregation and Group Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "PREVIOUS_MAX_ROWS = pd.options.display.max_rows\n",
    "pd.options.display.max_rows = 20\n",
    "np.random.seed(12345)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "np.set_printoptions(precision=4, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GroupBy Mechanics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.204708</td>\n",
       "      <td>1.393406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "      <td>0.478943</td>\n",
       "      <td>0.092908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.519439</td>\n",
       "      <td>0.281746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.555730</td>\n",
       "      <td>0.769023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>1.965781</td>\n",
       "      <td>1.246435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2     data1     data2\n",
       "0    a  one -0.204708  1.393406\n",
       "1    a  two  0.478943  0.092908\n",
       "2    b  one -0.519439  0.281746\n",
       "3    b  two -0.555730  0.769023\n",
       "4    a  one  1.965781  1.246435"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key1' : ['a', 'a', 'b', 'b', 'a'],\n",
    "                   'key2' : ['one', 'two', 'one', 'two', 'one'],\n",
    "                   'data1' : np.random.randn(5),\n",
    "                   'data2' : np.random.randn(5)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.SeriesGroupBy object at 0x00000193447EE860>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df['data1'].groupby(df['key1'])\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "a    0.746672\n",
       "b   -0.537585\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1  key2\n",
       "a     one     0.880536\n",
       "      two     0.478943\n",
       "b     one    -0.519439\n",
       "      two    -0.555730\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means = df['data1'].groupby([df['key1'], df['key2']]).mean()\n",
    "means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>key2</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.880536</td>\n",
       "      <td>0.478943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-0.519439</td>\n",
       "      <td>-0.555730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "key2       one       two\n",
       "key1                    \n",
       "a     0.880536  0.478943\n",
       "b    -0.519439 -0.555730"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2005    0.478943\n",
       "            2006   -0.519439\n",
       "Ohio        2005   -0.380219\n",
       "            2006    1.965781\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = np.array(['Ohio', 'California', 'California', 'Ohio', 'Ohio'])\n",
    "years = np.array([2005, 2005, 2006, 2005, 2006])\n",
    "df['data1'].groupby([states, years]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x0000019344BDA5C0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.746672</td>\n",
       "      <td>0.910916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-0.537585</td>\n",
       "      <td>0.525384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         data1     data2\n",
       "key1                    \n",
       "a     0.746672  0.910916\n",
       "b    -0.537585  0.525384"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key1').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>one</th>\n",
       "      <td>0.880536</td>\n",
       "      <td>1.319920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.478943</td>\n",
       "      <td>0.092908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.519439</td>\n",
       "      <td>0.281746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.555730</td>\n",
       "      <td>0.769023</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "              data1     data2\n",
       "key1 key2                    \n",
       "a    one   0.880536  1.319920\n",
       "     two   0.478943  0.092908\n",
       "b    one  -0.519439  0.281746\n",
       "     two  -0.555730  0.769023"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1', 'key2']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1  key2\n",
       "a     one     2\n",
       "      two     1\n",
       "b     one     1\n",
       "      two     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1', 'key2']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterating Over Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.204708</td>\n",
       "      <td>1.393406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "      <td>0.478943</td>\n",
       "      <td>0.092908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.519439</td>\n",
       "      <td>0.281746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.555730</td>\n",
       "      <td>0.769023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>1.965781</td>\n",
       "      <td>1.246435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2     data1     data2\n",
       "0    a  one -0.204708  1.393406\n",
       "1    a  two  0.478943  0.092908\n",
       "2    b  one -0.519439  0.281746\n",
       "3    b  two -0.555730  0.769023\n",
       "4    a  one  1.965781  1.246435"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "  key1 key2     data1     data2\n",
      "0    a  one -0.204708  1.393406\n",
      "1    a  two  0.478943  0.092908\n",
      "4    a  one  1.965781  1.246435\n",
      "b\n",
      "  key1 key2     data1     data2\n",
      "2    b  one -0.519439  0.281746\n",
      "3    b  two -0.555730  0.769023\n"
     ]
    }
   ],
   "source": [
    "for name, group in df.groupby('key1'):\n",
    "    print(name)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('a', 'one')\n",
      "  key1 key2     data1     data2\n",
      "0    a  one -0.204708  1.393406\n",
      "4    a  one  1.965781  1.246435\n",
      "('a', 'two')\n",
      "  key1 key2     data1     data2\n",
      "1    a  two  0.478943  0.092908\n",
      "('b', 'one')\n",
      "  key1 key2     data1     data2\n",
      "2    b  one -0.519439  0.281746\n",
      "('b', 'two')\n",
      "  key1 key2    data1     data2\n",
      "3    b  two -0.55573  0.769023\n"
     ]
    }
   ],
   "source": [
    "for (k1, k2), group in df.groupby(['key1', 'key2']):\n",
    "    print((k1, k2))\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('a',   key1 key2     data1     data2\n",
       "  0    a  one -0.204708  1.393406\n",
       "  1    a  two  0.478943  0.092908\n",
       "  4    a  one  1.965781  1.246435), ('b',   key1 key2     data1     data2\n",
       "  2    b  one -0.519439  0.281746\n",
       "  3    b  two -0.555730  0.769023)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.groupby('key1'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a':   key1 key2     data1     data2\n",
       " 0    a  one -0.204708  1.393406\n",
       " 1    a  two  0.478943  0.092908\n",
       " 4    a  one  1.965781  1.246435, 'b':   key1 key2     data1     data2\n",
       " 2    b  one -0.519439  0.281746\n",
       " 3    b  two -0.555730  0.769023}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pieces = dict(list(df.groupby('key1')))\n",
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.519439</td>\n",
       "      <td>0.281746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.555730</td>\n",
       "      <td>0.769023</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2     data1     data2\n",
       "2    b  one -0.519439  0.281746\n",
       "3    b  two -0.555730  0.769023"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pieces['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1      object\n",
       "key2      object\n",
       "data1    float64\n",
       "data2    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = df.groupby(df.dtypes, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n",
      "      data1     data2\n",
      "0 -0.204708  1.393406\n",
      "1  0.478943  0.092908\n",
      "2 -0.519439  0.281746\n",
      "3 -0.555730  0.769023\n",
      "4  1.965781  1.246435\n",
      "object\n",
      "  key1 key2\n",
      "0    a  one\n",
      "1    a  two\n",
      "2    b  one\n",
      "3    b  two\n",
      "4    a  one\n"
     ]
    }
   ],
   "source": [
    "for dtype, group in grouped:\n",
    "    print(dtype)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting a Column or Subset of Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x0000019344C08828>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key1')['data1']\n",
    "df.groupby('key1')[['data2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x0000019344C089E8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['data1'].groupby(df['key1'])\n",
    "df[['data2']].groupby(df['key1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>one</th>\n",
       "      <td>1.319920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.092908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>one</th>\n",
       "      <td>0.281746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.769023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              data2\n",
       "key1 key2          \n",
       "a    one   1.319920\n",
       "     two   0.092908\n",
       "b    one   0.281746\n",
       "     two   0.769023"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1', 'key2'])[['data2']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1  key2\n",
       "a     one     1.319920\n",
       "      two     0.092908\n",
       "b     one     0.281746\n",
       "      two     0.769023\n",
       "Name: data2, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_grouped = df.groupby(['key1', 'key2'])['data2']\n",
    "s_grouped\n",
    "s_grouped.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping with Dicts and Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>1.007189</td>\n",
       "      <td>-1.296221</td>\n",
       "      <td>0.274992</td>\n",
       "      <td>0.228913</td>\n",
       "      <td>1.352917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>0.886429</td>\n",
       "      <td>-2.001637</td>\n",
       "      <td>-0.371843</td>\n",
       "      <td>1.669025</td>\n",
       "      <td>-0.438570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>-0.539741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.021228</td>\n",
       "      <td>-0.577087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim</th>\n",
       "      <td>0.124121</td>\n",
       "      <td>0.302614</td>\n",
       "      <td>0.523772</td>\n",
       "      <td>0.000940</td>\n",
       "      <td>1.343810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travis</th>\n",
       "      <td>-0.713544</td>\n",
       "      <td>-0.831154</td>\n",
       "      <td>-2.370232</td>\n",
       "      <td>-1.860761</td>\n",
       "      <td>-0.860757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               a         b         c         d         e\n",
       "Joe     1.007189 -1.296221  0.274992  0.228913  1.352917\n",
       "Steve   0.886429 -2.001637 -0.371843  1.669025 -0.438570\n",
       "Wes    -0.539741       NaN       NaN -1.021228 -0.577087\n",
       "Jim     0.124121  0.302614  0.523772  0.000940  1.343810\n",
       "Travis -0.713544 -0.831154 -2.370232 -1.860761 -0.860757"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people = pd.DataFrame(np.random.randn(5, 5),\n",
    "                      columns=['a', 'b', 'c', 'd', 'e'],\n",
    "                      index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis'])\n",
    "people.iloc[2:3, [1, 2]] = np.nan # Add a few NA values\n",
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {'a': 'red', 'b': 'red', 'c': 'blue',\n",
    "           'd': 'blue', 'e': 'red', 'f' : 'orange'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>blue</th>\n",
       "      <th>red</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>0.503905</td>\n",
       "      <td>1.063885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>1.297183</td>\n",
       "      <td>-1.553778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>-1.021228</td>\n",
       "      <td>-1.116829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim</th>\n",
       "      <td>0.524712</td>\n",
       "      <td>1.770545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travis</th>\n",
       "      <td>-4.230992</td>\n",
       "      <td>-2.405455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            blue       red\n",
       "Joe     0.503905  1.063885\n",
       "Steve   1.297183 -1.553778\n",
       "Wes    -1.021228 -1.116829\n",
       "Jim     0.524712  1.770545\n",
       "Travis -4.230992 -2.405455"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_column = people.groupby(mapping, axis=1)\n",
    "by_column.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a       red\n",
       "b       red\n",
       "c      blue\n",
       "d      blue\n",
       "e       red\n",
       "f    orange\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_series = pd.Series(mapping)\n",
    "map_series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>blue</th>\n",
       "      <th>red</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travis</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        blue  red\n",
       "Joe        2    3\n",
       "Steve      2    3\n",
       "Wes        1    2\n",
       "Jim        2    3\n",
       "Travis     2    3"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.groupby(map_series, axis=1).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping with Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>1.007189</td>\n",
       "      <td>-1.296221</td>\n",
       "      <td>0.274992</td>\n",
       "      <td>0.228913</td>\n",
       "      <td>1.352917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>0.886429</td>\n",
       "      <td>-2.001637</td>\n",
       "      <td>-0.371843</td>\n",
       "      <td>1.669025</td>\n",
       "      <td>-0.438570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>-0.539741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.021228</td>\n",
       "      <td>-0.577087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim</th>\n",
       "      <td>0.124121</td>\n",
       "      <td>0.302614</td>\n",
       "      <td>0.523772</td>\n",
       "      <td>0.000940</td>\n",
       "      <td>1.343810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travis</th>\n",
       "      <td>-0.713544</td>\n",
       "      <td>-0.831154</td>\n",
       "      <td>-2.370232</td>\n",
       "      <td>-1.860761</td>\n",
       "      <td>-0.860757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               a         b         c         d         e\n",
       "Joe     1.007189 -1.296221  0.274992  0.228913  1.352917\n",
       "Steve   0.886429 -2.001637 -0.371843  1.669025 -0.438570\n",
       "Wes    -0.539741       NaN       NaN -1.021228 -0.577087\n",
       "Jim     0.124121  0.302614  0.523772  0.000940  1.343810\n",
       "Travis -0.713544 -0.831154 -2.370232 -1.860761 -0.860757"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.591569</td>\n",
       "      <td>-0.993608</td>\n",
       "      <td>0.798764</td>\n",
       "      <td>-0.791374</td>\n",
       "      <td>2.119639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.886429</td>\n",
       "      <td>-2.001637</td>\n",
       "      <td>-0.371843</td>\n",
       "      <td>1.669025</td>\n",
       "      <td>-0.438570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.713544</td>\n",
       "      <td>-0.831154</td>\n",
       "      <td>-2.370232</td>\n",
       "      <td>-1.860761</td>\n",
       "      <td>-0.860757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d         e\n",
       "3  0.591569 -0.993608  0.798764 -0.791374  2.119639\n",
       "5  0.886429 -2.001637 -0.371843  1.669025 -0.438570\n",
       "6 -0.713544 -0.831154 -2.370232 -1.860761 -0.860757"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.groupby(len).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.539741</td>\n",
       "      <td>-1.296221</td>\n",
       "      <td>0.274992</td>\n",
       "      <td>-1.021228</td>\n",
       "      <td>-0.577087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.124121</td>\n",
       "      <td>0.302614</td>\n",
       "      <td>0.523772</td>\n",
       "      <td>0.000940</td>\n",
       "      <td>1.343810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>one</th>\n",
       "      <td>0.886429</td>\n",
       "      <td>-2.001637</td>\n",
       "      <td>-0.371843</td>\n",
       "      <td>1.669025</td>\n",
       "      <td>-0.438570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>two</th>\n",
       "      <td>-0.713544</td>\n",
       "      <td>-0.831154</td>\n",
       "      <td>-2.370232</td>\n",
       "      <td>-1.860761</td>\n",
       "      <td>-0.860757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              a         b         c         d         e\n",
       "3 one -0.539741 -1.296221  0.274992 -1.021228 -0.577087\n",
       "  two  0.124121  0.302614  0.523772  0.000940  1.343810\n",
       "5 one  0.886429 -2.001637 -0.371843  1.669025 -0.438570\n",
       "6 two -0.713544 -0.831154 -2.370232 -1.860761 -0.860757"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_list = ['one', 'one', 'one', 'two', 'two']\n",
    "people.groupby([len, key_list]).min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping by Index Levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\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>0.560145</td>\n",
       "      <td>-1.265934</td>\n",
       "      <td>0.119827</td>\n",
       "      <td>-1.063512</td>\n",
       "      <td>0.332883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.359419</td>\n",
       "      <td>-0.199543</td>\n",
       "      <td>-1.541996</td>\n",
       "      <td>-0.970736</td>\n",
       "      <td>-1.307030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.286350</td>\n",
       "      <td>0.377984</td>\n",
       "      <td>-0.753887</td>\n",
       "      <td>0.331286</td>\n",
       "      <td>1.349742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.069877</td>\n",
       "      <td>0.246674</td>\n",
       "      <td>-0.011862</td>\n",
       "      <td>1.004812</td>\n",
       "      <td>1.327195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cty          US                            JP          \n",
       "tenor         1         3         5         1         3\n",
       "0      0.560145 -1.265934  0.119827 -1.063512  0.332883\n",
       "1     -2.359419 -0.199543 -1.541996 -0.970736 -1.307030\n",
       "2      0.286350  0.377984 -0.753887  0.331286  1.349742\n",
       "3      0.069877  0.246674 -0.011862  1.004812  1.327195"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = pd.MultiIndex.from_arrays([['US', 'US', 'US', 'JP', 'JP'],\n",
    "                                    [1, 3, 5, 1, 3]],\n",
    "                                    names=['cty', 'tenor'])\n",
    "hier_df = pd.DataFrame(np.random.randn(4, 5), columns=columns)\n",
    "hier_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hier_df.groupby(level='cty', axis=1).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.204708</td>\n",
       "      <td>1.393406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "      <td>0.478943</td>\n",
       "      <td>0.092908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.519439</td>\n",
       "      <td>0.281746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.555730</td>\n",
       "      <td>0.769023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>1.965781</td>\n",
       "      <td>1.246435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2     data1     data2\n",
       "0    a  one -0.204708  1.393406\n",
       "1    a  two  0.478943  0.092908\n",
       "2    b  one -0.519439  0.281746\n",
       "3    b  two -0.555730  0.769023\n",
       "4    a  one  1.965781  1.246435"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.SeriesGroupBy object at 0x0000019344BDA320>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby('key1')\n",
    "grouped['data1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "a    1.668413\n",
       "b   -0.523068\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped['data1'].quantile(0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2.170488</td>\n",
       "      <td>1.300498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.036292</td>\n",
       "      <td>0.487276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         data1     data2\n",
       "key1                    \n",
       "a     2.170488  1.300498\n",
       "b     0.036292  0.487276"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def peak_to_peak(arr):\n",
    "    return arr.max() - arr.min()\n",
    "grouped.agg(peak_to_peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">data1</th>\n",
       "      <th colspan=\"8\" halign=\"left\">data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.746672</td>\n",
       "      <td>1.109736</td>\n",
       "      <td>-0.204708</td>\n",
       "      <td>0.137118</td>\n",
       "      <td>0.478943</td>\n",
       "      <td>1.222362</td>\n",
       "      <td>1.965781</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.910916</td>\n",
       "      <td>0.712217</td>\n",
       "      <td>0.092908</td>\n",
       "      <td>0.669671</td>\n",
       "      <td>1.246435</td>\n",
       "      <td>1.319920</td>\n",
       "      <td>1.393406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.537585</td>\n",
       "      <td>0.025662</td>\n",
       "      <td>-0.555730</td>\n",
       "      <td>-0.546657</td>\n",
       "      <td>-0.537585</td>\n",
       "      <td>-0.528512</td>\n",
       "      <td>-0.519439</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.525384</td>\n",
       "      <td>0.344556</td>\n",
       "      <td>0.281746</td>\n",
       "      <td>0.403565</td>\n",
       "      <td>0.525384</td>\n",
       "      <td>0.647203</td>\n",
       "      <td>0.769023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data1                                                              \\\n",
       "     count      mean       std       min       25%       50%       75%   \n",
       "key1                                                                     \n",
       "a      3.0  0.746672  1.109736 -0.204708  0.137118  0.478943  1.222362   \n",
       "b      2.0 -0.537585  0.025662 -0.555730 -0.546657 -0.537585 -0.528512   \n",
       "\n",
       "               data2                                                    \\\n",
       "           max count      mean       std       min       25%       50%   \n",
       "key1                                                                     \n",
       "a     1.965781   3.0  0.910916  0.712217  0.092908  0.669671  1.246435   \n",
       "b    -0.519439   2.0  0.525384  0.344556  0.281746  0.403565  0.525384   \n",
       "\n",
       "                          \n",
       "           75%       max  \n",
       "key1                      \n",
       "a     1.319920  1.393406  \n",
       "b     0.647203  0.769023  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Column-Wise and Multiple Function Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\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>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>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>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>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>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>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>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 smoker  day    time  size   tip_pct\n",
       "0       16.99  1.01     No  Sun  Dinner     2  0.059447\n",
       "1       10.34  1.66     No  Sun  Dinner     3  0.160542\n",
       "2       21.01  3.50     No  Sun  Dinner     3  0.166587\n",
       "3       23.68  3.31     No  Sun  Dinner     2  0.139780\n",
       "4       24.59  3.61     No  Sun  Dinner     4  0.146808\n",
       "5       25.29  4.71     No  Sun  Dinner     4  0.186240"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = pd.read_csv('C:/Users/owolf/Desktop/pydata/examples/tips.csv')\n",
    "# Add tip percentage of total bill\n",
    "tips['tip_pct'] = tips['tip'] / tips['total_bill']\n",
    "tips[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = tips.groupby(['day', 'smoker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day   smoker\n",
       "Fri   No        0.151650\n",
       "      Yes       0.174783\n",
       "Sat   No        0.158048\n",
       "      Yes       0.147906\n",
       "Sun   No        0.160113\n",
       "      Yes       0.187250\n",
       "Thur  No        0.160298\n",
       "      Yes       0.163863\n",
       "Name: tip_pct, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pct = grouped['tip_pct']\n",
    "grouped_pct.agg('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
       "      <th>No</th>\n",
       "      <td>0.151650</td>\n",
       "      <td>0.028123</td>\n",
       "      <td>0.067349</td>\n",
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       "      <td>0.174783</td>\n",
       "      <td>0.051293</td>\n",
       "      <td>0.159925</td>\n",
       "    </tr>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>No</th>\n",
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       "      <th>Yes</th>\n",
       "      <td>0.147906</td>\n",
       "      <td>0.061375</td>\n",
       "      <td>0.290095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>No</th>\n",
       "      <td>0.160113</td>\n",
       "      <td>0.042347</td>\n",
       "      <td>0.193226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.187250</td>\n",
       "      <td>0.154134</td>\n",
       "      <td>0.644685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
       "      <th>No</th>\n",
       "      <td>0.160298</td>\n",
       "      <td>0.038774</td>\n",
       "      <td>0.193350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.163863</td>\n",
       "      <td>0.039389</td>\n",
       "      <td>0.151240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean       std  peak_to_peak\n",
       "day  smoker                                  \n",
       "Fri  No      0.151650  0.028123      0.067349\n",
       "     Yes     0.174783  0.051293      0.159925\n",
       "Sat  No      0.158048  0.039767      0.235193\n",
       "     Yes     0.147906  0.061375      0.290095\n",
       "Sun  No      0.160113  0.042347      0.193226\n",
       "     Yes     0.187250  0.154134      0.644685\n",
       "Thur No      0.160298  0.038774      0.193350\n",
       "     Yes     0.163863  0.039389      0.151240"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pct.agg(['mean', 'std', peak_to_peak])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.028123</td>\n",
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       "      <td>0.174783</td>\n",
       "      <td>0.051293</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>No</th>\n",
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       "      <td>0.039767</td>\n",
       "    </tr>\n",
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       "      <th>Yes</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>No</th>\n",
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       "      <td>0.042347</td>\n",
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       "    <tr>\n",
       "      <th>Yes</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
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       "      <td>0.038774</td>\n",
       "    </tr>\n",
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       "      <th>Yes</th>\n",
       "      <td>0.163863</td>\n",
       "      <td>0.039389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  foo       bar\n",
       "day  smoker                    \n",
       "Fri  No      0.151650  0.028123\n",
       "     Yes     0.174783  0.051293\n",
       "Sat  No      0.158048  0.039767\n",
       "     Yes     0.147906  0.061375\n",
       "Sun  No      0.160113  0.042347\n",
       "     Yes     0.187250  0.154134\n",
       "Thur No      0.160298  0.038774\n",
       "     Yes     0.163863  0.039389"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pct.agg([('foo', 'mean'), ('bar', np.std)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>max</th>\n",
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       "      <th>day</th>\n",
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       "      <td>0.151650</td>\n",
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       "      <td>18.420000</td>\n",
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       "      <th>Yes</th>\n",
       "      <td>15</td>\n",
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       "      <td>15</td>\n",
       "      <td>16.813333</td>\n",
       "      <td>40.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>No</th>\n",
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       "      <td>0.158048</td>\n",
       "      <td>0.291990</td>\n",
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       "      <td>19.661778</td>\n",
       "      <td>48.33</td>\n",
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       "      <td>0.147906</td>\n",
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       "      <td>21.276667</td>\n",
       "      <td>50.81</td>\n",
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       "      <th>No</th>\n",
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       "      <td>0.252672</td>\n",
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       "      <td>45.35</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
       "      <th>No</th>\n",
       "      <td>45</td>\n",
       "      <td>0.160298</td>\n",
       "      <td>0.266312</td>\n",
       "      <td>45</td>\n",
       "      <td>17.113111</td>\n",
       "      <td>41.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>17</td>\n",
       "      <td>0.163863</td>\n",
       "      <td>0.241255</td>\n",
       "      <td>17</td>\n",
       "      <td>19.190588</td>\n",
       "      <td>43.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tip_pct                     total_bill                  \n",
       "              count      mean       max      count       mean    max\n",
       "day  smoker                                                         \n",
       "Fri  No           4  0.151650  0.187735          4  18.420000  22.75\n",
       "     Yes         15  0.174783  0.263480         15  16.813333  40.17\n",
       "Sat  No          45  0.158048  0.291990         45  19.661778  48.33\n",
       "     Yes         42  0.147906  0.325733         42  21.276667  50.81\n",
       "Sun  No          57  0.160113  0.252672         57  20.506667  48.17\n",
       "     Yes         19  0.187250  0.710345         19  24.120000  45.35\n",
       "Thur No          45  0.160298  0.266312         45  17.113111  41.19\n",
       "     Yes         17  0.163863  0.241255         17  19.190588  43.11"
      ]
     },
     "execution_count": 53,
     "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": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
       "      <th>No</th>\n",
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       "      <td>0.160298</td>\n",
       "      <td>0.266312</td>\n",
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       "      <th>Yes</th>\n",
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       "      <td>0.163863</td>\n",
       "      <td>0.241255</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count      mean       max\n",
       "day  smoker                           \n",
       "Fri  No          4  0.151650  0.187735\n",
       "     Yes        15  0.174783  0.263480\n",
       "Sat  No         45  0.158048  0.291990\n",
       "     Yes        42  0.147906  0.325733\n",
       "Sun  No         57  0.160113  0.252672\n",
       "     Yes        19  0.187250  0.710345\n",
       "Thur No         45  0.160298  0.266312\n",
       "     Yes        17  0.163863  0.241255"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['tip_pct']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>day</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\">Fri</th>\n",
       "      <th>No</th>\n",
       "      <td>0.151650</td>\n",
       "      <td>0.000791</td>\n",
       "      <td>18.420000</td>\n",
       "      <td>25.596333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.174783</td>\n",
       "      <td>0.002631</td>\n",
       "      <td>16.813333</td>\n",
       "      <td>82.562438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>No</th>\n",
       "      <td>0.158048</td>\n",
       "      <td>0.001581</td>\n",
       "      <td>19.661778</td>\n",
       "      <td>79.908965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.147906</td>\n",
       "      <td>0.003767</td>\n",
       "      <td>21.276667</td>\n",
       "      <td>101.387535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>No</th>\n",
       "      <td>0.160113</td>\n",
       "      <td>0.001793</td>\n",
       "      <td>20.506667</td>\n",
       "      <td>66.099980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.187250</td>\n",
       "      <td>0.023757</td>\n",
       "      <td>24.120000</td>\n",
       "      <td>109.046044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
       "      <th>No</th>\n",
       "      <td>0.160298</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>17.113111</td>\n",
       "      <td>59.625081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.163863</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>19.190588</td>\n",
       "      <td>69.808518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tip_pct              total_bill            \n",
       "            Durchschnitt Abweichung Durchschnitt  Abweichung\n",
       "day  smoker                                                 \n",
       "Fri  No         0.151650   0.000791    18.420000   25.596333\n",
       "     Yes        0.174783   0.002631    16.813333   82.562438\n",
       "Sat  No         0.158048   0.001581    19.661778   79.908965\n",
       "     Yes        0.147906   0.003767    21.276667  101.387535\n",
       "Sun  No         0.160113   0.001793    20.506667   66.099980\n",
       "     Yes        0.187250   0.023757    24.120000  109.046044\n",
       "Thur No         0.160298   0.001503    17.113111   59.625081\n",
       "     Yes        0.163863   0.001551    19.190588   69.808518"
      ]
     },
     "execution_count": 55,
     "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": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>day</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\">Fri</th>\n",
       "      <th>No</th>\n",
       "      <td>3.50</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>4.73</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>No</th>\n",
       "      <td>9.00</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>10.00</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>No</th>\n",
       "      <td>6.00</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>6.50</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
       "      <th>No</th>\n",
       "      <td>6.70</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>5.00</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tip  size\n",
       "day  smoker             \n",
       "Fri  No       3.50     9\n",
       "     Yes      4.73    31\n",
       "Sat  No       9.00   115\n",
       "     Yes     10.00   104\n",
       "Sun  No       6.00   167\n",
       "     Yes      6.50    49\n",
       "Thur No       6.70   112\n",
       "     Yes      5.00    40"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.agg({'tip' : np.max, 'size' : 'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>day</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\">Fri</th>\n",
       "      <th>No</th>\n",
       "      <td>0.120385</td>\n",
       "      <td>0.187735</td>\n",
       "      <td>0.151650</td>\n",
       "      <td>0.028123</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.103555</td>\n",
       "      <td>0.263480</td>\n",
       "      <td>0.174783</td>\n",
       "      <td>0.051293</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>No</th>\n",
       "      <td>0.056797</td>\n",
       "      <td>0.291990</td>\n",
       "      <td>0.158048</td>\n",
       "      <td>0.039767</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.035638</td>\n",
       "      <td>0.325733</td>\n",
       "      <td>0.147906</td>\n",
       "      <td>0.061375</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>No</th>\n",
       "      <td>0.059447</td>\n",
       "      <td>0.252672</td>\n",
       "      <td>0.160113</td>\n",
       "      <td>0.042347</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.065660</td>\n",
       "      <td>0.710345</td>\n",
       "      <td>0.187250</td>\n",
       "      <td>0.154134</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
       "      <th>No</th>\n",
       "      <td>0.072961</td>\n",
       "      <td>0.266312</td>\n",
       "      <td>0.160298</td>\n",
       "      <td>0.038774</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.090014</td>\n",
       "      <td>0.241255</td>\n",
       "      <td>0.163863</td>\n",
       "      <td>0.039389</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tip_pct                               size\n",
       "                  min       max      mean       std  sum\n",
       "day  smoker                                             \n",
       "Fri  No      0.120385  0.187735  0.151650  0.028123    9\n",
       "     Yes     0.103555  0.263480  0.174783  0.051293   31\n",
       "Sat  No      0.056797  0.291990  0.158048  0.039767  115\n",
       "     Yes     0.035638  0.325733  0.147906  0.061375  104\n",
       "Sun  No      0.059447  0.252672  0.160113  0.042347  167\n",
       "     Yes     0.065660  0.710345  0.187250  0.154134   49\n",
       "Thur No      0.072961  0.266312  0.160298  0.038774  112\n",
       "     Yes     0.090014  0.241255  0.163863  0.039389   40"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.agg({'tip_pct' : ['min', 'max', 'mean', 'std'],\n",
    "             'size' : 'sum'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Returning Aggregated Data Without Row Indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
       "      <th>No</th>\n",
       "      <td>18.420000</td>\n",
       "      <td>2.812500</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>0.151650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>16.813333</td>\n",
       "      <td>2.714000</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>No</th>\n",
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       "      <td>3.102889</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>0.158048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>21.276667</td>\n",
       "      <td>2.875476</td>\n",
       "      <td>2.476190</td>\n",
       "      <td>0.147906</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>No</th>\n",
       "      <td>20.506667</td>\n",
       "      <td>3.167895</td>\n",
       "      <td>2.929825</td>\n",
       "      <td>0.160113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>24.120000</td>\n",
       "      <td>3.516842</td>\n",
       "      <td>2.578947</td>\n",
       "      <td>0.187250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
       "      <th>No</th>\n",
       "      <td>17.113111</td>\n",
       "      <td>2.673778</td>\n",
       "      <td>2.488889</td>\n",
       "      <td>0.160298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>19.190588</td>\n",
       "      <td>3.030000</td>\n",
       "      <td>2.352941</td>\n",
       "      <td>0.163863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             total_bill       tip      size   tip_pct\n",
       "day  smoker                                          \n",
       "Fri  No       18.420000  2.812500  2.250000  0.151650\n",
       "     Yes      16.813333  2.714000  2.066667  0.174783\n",
       "Sat  No       19.661778  3.102889  2.555556  0.158048\n",
       "     Yes      21.276667  2.875476  2.476190  0.147906\n",
       "Sun  No       20.506667  3.167895  2.929825  0.160113\n",
       "     Yes      24.120000  3.516842  2.578947  0.187250\n",
       "Thur No       17.113111  2.673778  2.488889  0.160298\n",
       "     Yes      19.190588  3.030000  2.352941  0.163863"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(['day', 'smoker'], as_index=True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>day</th>\n",
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       "      <th>tip</th>\n",
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       "      <td>Fri</td>\n",
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       "      <td>2.250000</td>\n",
       "      <td>0.151650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fri</td>\n",
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       "      <td>2.066667</td>\n",
       "      <td>0.174783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sat</td>\n",
       "      <td>No</td>\n",
       "      <td>19.661778</td>\n",
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       "      <td>0.158048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sat</td>\n",
       "      <td>Yes</td>\n",
       "      <td>21.276667</td>\n",
       "      <td>2.875476</td>\n",
       "      <td>2.476190</td>\n",
       "      <td>0.147906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sun</td>\n",
       "      <td>No</td>\n",
       "      <td>20.506667</td>\n",
       "      <td>3.167895</td>\n",
       "      <td>2.929825</td>\n",
       "      <td>0.160113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sun</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24.120000</td>\n",
       "      <td>3.516842</td>\n",
       "      <td>2.578947</td>\n",
       "      <td>0.187250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thur</td>\n",
       "      <td>No</td>\n",
       "      <td>17.113111</td>\n",
       "      <td>2.673778</td>\n",
       "      <td>2.488889</td>\n",
       "      <td>0.160298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thur</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.190588</td>\n",
       "      <td>3.030000</td>\n",
       "      <td>2.352941</td>\n",
       "      <td>0.163863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    day smoker  total_bill       tip      size   tip_pct\n",
       "0   Fri     No   18.420000  2.812500  2.250000  0.151650\n",
       "1   Fri    Yes   16.813333  2.714000  2.066667  0.174783\n",
       "2   Sat     No   19.661778  3.102889  2.555556  0.158048\n",
       "3   Sat    Yes   21.276667  2.875476  2.476190  0.147906\n",
       "4   Sun     No   20.506667  3.167895  2.929825  0.160113\n",
       "5   Sun    Yes   24.120000  3.516842  2.578947  0.187250\n",
       "6  Thur     No   17.113111  2.673778  2.488889  0.160298\n",
       "7  Thur    Yes   19.190588  3.030000  2.352941  0.163863"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(['day', 'smoker'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply: General split-apply-combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>14.31</td>\n",
       "      <td>4.00</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>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",
       "      <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>67</th>\n",
       "      <td>3.07</td>\n",
       "      <td>1.00</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>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>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 smoker  day    time  size   tip_pct\n",
       "109       14.31  4.00    Yes  Sat  Dinner     2  0.279525\n",
       "183       23.17  6.50    Yes  Sun  Dinner     4  0.280535\n",
       "232       11.61  3.39     No  Sat  Dinner     2  0.291990\n",
       "67         3.07  1.00    Yes  Sat  Dinner     1  0.325733\n",
       "178        9.60  4.00    Yes  Sun  Dinner     2  0.416667\n",
       "172        7.25  5.15    Yes  Sun  Dinner     2  0.710345"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def top(df, n=5, column='tip_pct'):\n",
    "    return df.sort_values(by=column)[-n:]\n",
    "top(tips, n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
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       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">No</th>\n",
       "      <th>88</th>\n",
       "      <td>24.71</td>\n",
       "      <td>5.85</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>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>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>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>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>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",
       "      <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>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>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>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 smoker   day    time  size   tip_pct\n",
       "smoker                                                           \n",
       "No     88        24.71  5.85     No  Thur   Lunch     2  0.236746\n",
       "       185       20.69  5.00     No   Sun  Dinner     5  0.241663\n",
       "       51        10.29  2.60     No   Sun  Dinner     2  0.252672\n",
       "       149        7.51  2.00     No  Thur   Lunch     2  0.266312\n",
       "       232       11.61  3.39     No   Sat  Dinner     2  0.291990\n",
       "Yes    109       14.31  4.00    Yes   Sat  Dinner     2  0.279525\n",
       "       183       23.17  6.50    Yes   Sun  Dinner     4  0.280535\n",
       "       67         3.07  1.00    Yes   Sat  Dinner     1  0.325733\n",
       "       178        9.60  4.00    Yes   Sun  Dinner     2  0.416667\n",
       "       172        7.25  5.15    Yes   Sun  Dinner     2  0.710345"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('smoker').apply(top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>No</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.142857</td>\n",
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       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <th>212</th>\n",
       "      <td>48.33</td>\n",
       "      <td>9.00</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>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>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>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>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>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>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 smoker   day    time  size   tip_pct\n",
       "smoker day                                                             \n",
       "No     Fri  94        22.75   3.25     No   Fri  Dinner     2  0.142857\n",
       "       Sat  212       48.33   9.00     No   Sat  Dinner     4  0.186220\n",
       "       Sun  156       48.17   5.00     No   Sun  Dinner     6  0.103799\n",
       "       Thur 142       41.19   5.00     No  Thur   Lunch     5  0.121389\n",
       "Yes    Fri  95        40.17   4.73    Yes   Fri  Dinner     4  0.117750\n",
       "       Sat  170       50.81  10.00    Yes   Sat  Dinner     3  0.196812\n",
       "       Sun  182       45.35   3.50    Yes   Sun  Dinner     3  0.077178\n",
       "       Thur 197       43.11   5.00    Yes  Thur   Lunch     4  0.115982"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(['smoker', 'day']).apply(top, n=1, column='total_bill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>No</th>\n",
       "      <td>151.0</td>\n",
       "      <td>0.159328</td>\n",
       "      <td>0.039910</td>\n",
       "      <td>0.056797</td>\n",
       "      <td>0.136906</td>\n",
       "      <td>0.155625</td>\n",
       "      <td>0.185014</td>\n",
       "      <td>0.291990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>93.0</td>\n",
       "      <td>0.163196</td>\n",
       "      <td>0.085119</td>\n",
       "      <td>0.035638</td>\n",
       "      <td>0.106771</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.195059</td>\n",
       "      <td>0.710345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count      mean       std       min       25%       50%       75%  \\\n",
       "smoker                                                                      \n",
       "No      151.0  0.159328  0.039910  0.056797  0.136906  0.155625  0.185014   \n",
       "Yes      93.0  0.163196  0.085119  0.035638  0.106771  0.153846  0.195059   \n",
       "\n",
       "             max  \n",
       "smoker            \n",
       "No      0.291990  \n",
       "Yes     0.710345  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = tips.groupby('smoker')['tip_pct'].describe()\n",
    "result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       smoker\n",
       "count  No        151.000000\n",
       "       Yes        93.000000\n",
       "mean   No          0.159328\n",
       "       Yes         0.163196\n",
       "std    No          0.039910\n",
       "       Yes         0.085119\n",
       "min    No          0.056797\n",
       "       Yes         0.035638\n",
       "25%    No          0.136906\n",
       "       Yes         0.106771\n",
       "50%    No          0.155625\n",
       "       Yes         0.153846\n",
       "75%    No          0.185014\n",
       "       Yes         0.195059\n",
       "max    No          0.291990\n",
       "       Yes         0.710345\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.unstack('smoker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = lambda x: x.describe()\n",
    "grouped.apply(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suppressing the Group Keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>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></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=\"5\" valign=\"top\">No</th>\n",
       "      <th>88</th>\n",
       "      <td>24.71</td>\n",
       "      <td>5.85</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>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>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>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>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>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>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>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>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>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 smoker   day    time  size   tip_pct\n",
       "smoker                                                           \n",
       "No     88        24.71  5.85     No  Thur   Lunch     2  0.236746\n",
       "       185       20.69  5.00     No   Sun  Dinner     5  0.241663\n",
       "       51        10.29  2.60     No   Sun  Dinner     2  0.252672\n",
       "       149        7.51  2.00     No  Thur   Lunch     2  0.266312\n",
       "       232       11.61  3.39     No   Sat  Dinner     2  0.291990\n",
       "Yes    109       14.31  4.00    Yes   Sat  Dinner     2  0.279525\n",
       "       183       23.17  6.50    Yes   Sun  Dinner     4  0.280535\n",
       "       67         3.07  1.00    Yes   Sat  Dinner     1  0.325733\n",
       "       178        9.60  4.00    Yes   Sun  Dinner     2  0.416667\n",
       "       172        7.25  5.15    Yes   Sun  Dinner     2  0.710345"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('smoker', group_keys=True).apply(top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>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>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>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>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>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>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>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>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>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>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>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 smoker   day    time  size   tip_pct\n",
       "88        24.71  5.85     No  Thur   Lunch     2  0.236746\n",
       "185       20.69  5.00     No   Sun  Dinner     5  0.241663\n",
       "51        10.29  2.60     No   Sun  Dinner     2  0.252672\n",
       "149        7.51  2.00     No  Thur   Lunch     2  0.266312\n",
       "232       11.61  3.39     No   Sat  Dinner     2  0.291990\n",
       "109       14.31  4.00    Yes   Sat  Dinner     2  0.279525\n",
       "183       23.17  6.50    Yes   Sun  Dinner     4  0.280535\n",
       "67         3.07  1.00    Yes   Sat  Dinner     1  0.325733\n",
       "178        9.60  4.00    Yes   Sun  Dinner     2  0.416667\n",
       "172        7.25  5.15    Yes   Sun  Dinner     2  0.710345"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('smoker', group_keys=False).apply(top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantile and Bucket Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     (-0.06, 1.733]\n",
       "1    (-1.853, -0.06]\n",
       "2     (-0.06, 1.733]\n",
       "3    (-1.853, -0.06]\n",
       "4    (-1.853, -0.06]\n",
       "5     (-0.06, 1.733]\n",
       "6    (-1.853, -0.06]\n",
       "7    (-1.853, -0.06]\n",
       "8    (-1.853, -0.06]\n",
       "9     (-0.06, 1.733]\n",
       "Name: data1, dtype: category\n",
       "Categories (4, interval[float64]): [(-3.653, -1.853] < (-1.853, -0.06] < (-0.06, 1.733] < (1.733, 3.526]]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame({'data1': np.random.randn(1000),\n",
    "                      'data2': np.random.randn(1000)})\n",
    "quartiles = pd.cut(frame.data1, 4)\n",
    "quartiles[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.SeriesGroupBy object at 0x0000019344D0C630>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_stats(group):\n",
    "    return {'min': group.min(), 'max': group.max(),\n",
    "            'count': group.count(), 'mean': group.mean()}\n",
    "grouped = frame.data2.groupby(quartiles)\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data1                  \n",
       "(-3.653, -1.853]  count     34.000000\n",
       "                  max        2.135719\n",
       "                  mean       0.000529\n",
       "                  min       -1.236664\n",
       "(-1.853, -0.06]   count    456.000000\n",
       "                  max        2.903300\n",
       "                  mean       0.007797\n",
       "                  min       -3.018842\n",
       "(-0.06, 1.733]    count    481.000000\n",
       "                  max        2.771398\n",
       "                  mean       0.013224\n",
       "                  min       -3.333767\n",
       "(1.733, 3.526]    count     29.000000\n",
       "                  max        1.676926\n",
       "                  mean      -0.078567\n",
       "                  min       -2.494075\n",
       "Name: data2, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.apply(get_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(-3.653, -1.853]</th>\n",
       "      <td>34.0</td>\n",
       "      <td>2.135719</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>-1.236664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(-1.853, -0.06]</th>\n",
       "      <td>456.0</td>\n",
       "      <td>2.903300</td>\n",
       "      <td>0.007797</td>\n",
       "      <td>-3.018842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(-0.06, 1.733]</th>\n",
       "      <td>481.0</td>\n",
       "      <td>2.771398</td>\n",
       "      <td>0.013224</td>\n",
       "      <td>-3.333767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1.733, 3.526]</th>\n",
       "      <td>29.0</td>\n",
       "      <td>1.676926</td>\n",
       "      <td>-0.078567</td>\n",
       "      <td>-2.494075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count       max      mean       min\n",
       "data1                                                \n",
       "(-3.653, -1.853]   34.0  2.135719  0.000529 -1.236664\n",
       "(-1.853, -0.06]   456.0  2.903300  0.007797 -3.018842\n",
       "(-0.06, 1.733]    481.0  2.771398  0.013224 -3.333767\n",
       "(1.733, 3.526]     29.0  1.676926 -0.078567 -2.494075"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.apply(get_stats).unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (0.847, 1.212]\n",
       "1    (-1.267, -0.884]\n",
       "2    (-0.0381, 0.224]\n",
       "3    (-0.884, -0.531]\n",
       "4    (-0.531, -0.272]\n",
       "5      (0.847, 1.212]\n",
       "Name: data1, dtype: category\n",
       "Categories (10, interval[float64]): [(-3.647, -1.267] < (-1.267, -0.884] < (-0.884, -0.531] < (-0.531, -0.272] ... (0.224, 0.514] < (0.514, 0.847] < (0.847, 1.212] < (1.212, 3.526]]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouping = pd.qcut(frame.data1, 10)\n",
    "grouping[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    8\n",
       "1    1\n",
       "2    5\n",
       "3    2\n",
       "4    3\n",
       "5    8\n",
       "Name: data1, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Return quantile numbers\n",
    "grouping = pd.qcut(frame.data1, 10, labels=False)\n",
    "grouping[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data1       \n",
       "0      count    100.000000\n",
       "       max        2.146716\n",
       "       mean      -0.079868\n",
       "       min       -3.018842\n",
       "1      count    100.000000\n",
       "       max        2.038456\n",
       "       mean      -0.047797\n",
       "       min       -2.641014\n",
       "2      count    100.000000\n",
       "       max        2.497899\n",
       "                   ...    \n",
       "7      mean       0.030854\n",
       "       min       -3.333767\n",
       "8      count    100.000000\n",
       "       max        2.445125\n",
       "       mean       0.036268\n",
       "       min       -2.428129\n",
       "9      count    100.000000\n",
       "       max        1.716892\n",
       "       mean      -0.067369\n",
       "       min       -2.494075\n",
       "Name: data2, Length: 40, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = frame.data2.groupby(grouping)\n",
    "grouped.apply(get_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.146716</td>\n",
       "      <td>-0.079868</td>\n",
       "      <td>-3.018842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.038456</td>\n",
       "      <td>-0.047797</td>\n",
       "      <td>-2.641014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.497899</td>\n",
       "      <td>0.128559</td>\n",
       "      <td>-2.453248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.903300</td>\n",
       "      <td>0.050232</td>\n",
       "      <td>-2.901831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.209647</td>\n",
       "      <td>-0.015985</td>\n",
       "      <td>-2.251291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.497837</td>\n",
       "      <td>0.055541</td>\n",
       "      <td>-3.108915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.771398</td>\n",
       "      <td>-0.013879</td>\n",
       "      <td>-1.866563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100.0</td>\n",
       "      <td>1.786051</td>\n",
       "      <td>0.030854</td>\n",
       "      <td>-3.333767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.445125</td>\n",
       "      <td>0.036268</td>\n",
       "      <td>-2.428129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100.0</td>\n",
       "      <td>1.716892</td>\n",
       "      <td>-0.067369</td>\n",
       "      <td>-2.494075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count       max      mean       min\n",
       "data1                                     \n",
       "0      100.0  2.146716 -0.079868 -3.018842\n",
       "1      100.0  2.038456 -0.047797 -2.641014\n",
       "2      100.0  2.497899  0.128559 -2.453248\n",
       "3      100.0  2.903300  0.050232 -2.901831\n",
       "4      100.0  2.209647 -0.015985 -2.251291\n",
       "5      100.0  2.497837  0.055541 -3.108915\n",
       "6      100.0  2.771398 -0.013879 -1.866563\n",
       "7      100.0  1.786051  0.030854 -3.333767\n",
       "8      100.0  2.445125  0.036268 -2.428129\n",
       "9      100.0  1.716892 -0.067369 -2.494075"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.apply(get_stats).unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Filling Missing Values with Group-Specific       Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1    0.615507\n",
       "2         NaN\n",
       "3   -0.047085\n",
       "4         NaN\n",
       "5    1.351335\n",
       "dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.randn(6))\n",
    "s[::2] = np.nan\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.639919\n",
       "1    0.615507\n",
       "2    0.639919\n",
       "3   -0.047085\n",
       "4    0.639919\n",
       "5    1.351335\n",
       "dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(s.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio         -0.776652\n",
       "New York     -0.022289\n",
       "Vermont      -0.232862\n",
       "Florida      -0.930217\n",
       "Oregon        1.890817\n",
       "Nevada        1.714924\n",
       "California    0.310738\n",
       "Idaho         0.151633\n",
       "dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = ['Ohio', 'New York', 'Vermont', 'Florida',\n",
    "          'Oregon', 'Nevada', 'California', 'Idaho']\n",
    "group_key = ['East'] * 4 + ['West'] * 4\n",
    "data = pd.Series(np.random.randn(8), index=states)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio         -0.776652\n",
       "New York     -0.022289\n",
       "Vermont            NaN\n",
       "Florida      -0.930217\n",
       "Oregon        1.890817\n",
       "Nevada             NaN\n",
       "California    0.310738\n",
       "Idaho              NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Vermont', 'Nevada', 'Idaho']] = np.nan\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "East   -0.576386\n",
       "West    1.100777\n",
       "dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(group_key).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio         -0.776652\n",
       "New York     -0.022289\n",
       "Vermont      -0.576386\n",
       "Florida      -0.930217\n",
       "Oregon        1.890817\n",
       "Nevada        1.100777\n",
       "California    0.310738\n",
       "Idaho         1.100777\n",
       "dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill_mean = lambda g: g.fillna(g.mean())\n",
    "data.groupby(group_key).apply(fill_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio         -0.776652\n",
       "New York     -0.022289\n",
       "Vermont       0.500000\n",
       "Florida      -0.930217\n",
       "Oregon        1.890817\n",
       "Nevada       -1.000000\n",
       "California    0.310738\n",
       "Idaho        -1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill_values = {'East': 0.5, 'West': -1}\n",
    "fill_func = lambda g: g.fillna(fill_values[g.name])\n",
    "data.groupby(group_key).apply(fill_func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Random Sampling and Permutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AH', '2H', '3H', '4H', '5H', '6H', '7H', '8H', '9H', '10H', 'JH', 'KH', 'QH']"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hearts, Spades, Clubs, Diamonds\n",
    "suits = ['H', 'S', 'C', 'D']\n",
    "card_val = (list(range(1, 11)) + [10] * 3) * 4\n",
    "base_names = ['A'] + list(range(2, 11)) + ['J', 'K', 'Q']\n",
    "cards = []\n",
    "for suit in ['H', 'S', 'C', 'D']:\n",
    "    cards.extend(str(num) + suit for num in base_names)\n",
    "cards[:13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "deck = pd.Series(card_val, index=cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AH      1\n",
       "2H      2\n",
       "3H      3\n",
       "4H      4\n",
       "5H      5\n",
       "6H      6\n",
       "7H      7\n",
       "8H      8\n",
       "9H      9\n",
       "10H    10\n",
       "JH     10\n",
       "KH     10\n",
       "QH     10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck[:13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5D     5\n",
       "6D     6\n",
       "7H     7\n",
       "JS    10\n",
       "3H     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def draw(deck, n=5):\n",
    "    return deck.sample(n)\n",
    "draw(deck)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C  4C     4\n",
       "   QC    10\n",
       "D  3D     3\n",
       "   JD    10\n",
       "H  7H     7\n",
       "   2H     2\n",
       "S  9S     9\n",
       "   KS    10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_suit = lambda card: card[-1] # last letter is suit\n",
    "deck.groupby(get_suit).apply(draw, n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4C      4\n",
       "AC      1\n",
       "7D      7\n",
       "KD     10\n",
       "10H    10\n",
       "6H      6\n",
       "5S      5\n",
       "7S      7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck.groupby(get_suit, group_keys=False).apply(draw, n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C  10C    10\n",
       "   AC      1\n",
       "D  KD     10\n",
       "   3D      3\n",
       "H  6H      6\n",
       "   2H      2\n",
       "S  3S      3\n",
       "   2S      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck.groupby(get_suit, group_keys=True).apply(draw, n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Group Weighted Average and Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>data</th>\n",
       "      <th>weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>-0.204708</td>\n",
       "      <td>0.748907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>0.478943</td>\n",
       "      <td>0.653570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>-0.519439</td>\n",
       "      <td>0.747715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>-0.555730</td>\n",
       "      <td>0.961307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>1.965781</td>\n",
       "      <td>0.008388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>1.393406</td>\n",
       "      <td>0.106444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b</td>\n",
       "      <td>0.092908</td>\n",
       "      <td>0.298704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b</td>\n",
       "      <td>0.281746</td>\n",
       "      <td>0.656411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category      data   weights\n",
       "0        a -0.204708  0.748907\n",
       "1        a  0.478943  0.653570\n",
       "2        a -0.519439  0.747715\n",
       "3        a -0.555730  0.961307\n",
       "4        b  1.965781  0.008388\n",
       "5        b  1.393406  0.106444\n",
       "6        b  0.092908  0.298704\n",
       "7        b  0.281746  0.656411"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'category': ['a', 'a', 'a', 'a',\n",
    "                                'b', 'b', 'b', 'b'],\n",
    "                   'data': np.random.randn(8),\n",
    "                   'weights': np.random.rand(8)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "a   -0.245188\n",
       "b    0.352824\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby('category')\n",
    "get_wavg = lambda g: np.average(g['data'], weights=g['weights'])\n",
    "grouped.apply(get_wavg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2214 entries, 2003-01-02 to 2011-10-14\n",
      "Data columns (total 4 columns):\n",
      "AAPL    2214 non-null float64\n",
      "MSFT    2214 non-null float64\n",
      "XOM     2214 non-null float64\n",
      "SPX     2214 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 86.5 KB\n"
     ]
    }
   ],
   "source": [
    "close_px = pd.read_csv('C:/Users/owolf/Desktop/pydata/examples/stock_px_2.csv', parse_dates=True,\n",
    "                       index_col=0)\n",
    "close_px.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "      <th>SPX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>400.29</td>\n",
       "      <td>27.00</td>\n",
       "      <td>76.27</td>\n",
       "      <td>1195.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12</th>\n",
       "      <td>402.19</td>\n",
       "      <td>26.96</td>\n",
       "      <td>77.16</td>\n",
       "      <td>1207.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-13</th>\n",
       "      <td>408.43</td>\n",
       "      <td>27.18</td>\n",
       "      <td>76.37</td>\n",
       "      <td>1203.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-14</th>\n",
       "      <td>422.00</td>\n",
       "      <td>27.27</td>\n",
       "      <td>78.11</td>\n",
       "      <td>1224.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL   MSFT    XOM      SPX\n",
       "2011-10-11  400.29  27.00  76.27  1195.54\n",
       "2011-10-12  402.19  26.96  77.16  1207.25\n",
       "2011-10-13  408.43  27.18  76.37  1203.66\n",
       "2011-10-14  422.00  27.27  78.11  1224.58"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_px[-4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "spx_corr = lambda x: x.corrwith(x['SPX'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rets = close_px.pct_change().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "      <th>SPX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.001421</td>\n",
       "      <td>0.000684</td>\n",
       "      <td>-0.000484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017975</td>\n",
       "      <td>0.024624</td>\n",
       "      <td>0.022474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>-0.002685</td>\n",
       "      <td>0.019052</td>\n",
       "      <td>-0.033712</td>\n",
       "      <td>-0.006545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>-0.020188</td>\n",
       "      <td>-0.028272</td>\n",
       "      <td>-0.004145</td>\n",
       "      <td>-0.014086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-09</th>\n",
       "      <td>0.008242</td>\n",
       "      <td>0.029094</td>\n",
       "      <td>0.021159</td>\n",
       "      <td>0.019386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-10</th>\n",
       "      <td>0.002725</td>\n",
       "      <td>0.001824</td>\n",
       "      <td>-0.013927</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-13</th>\n",
       "      <td>-0.005435</td>\n",
       "      <td>0.008648</td>\n",
       "      <td>-0.004134</td>\n",
       "      <td>-0.001412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-14</th>\n",
       "      <td>-0.002732</td>\n",
       "      <td>0.010379</td>\n",
       "      <td>0.008993</td>\n",
       "      <td>0.005830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-15</th>\n",
       "      <td>-0.010959</td>\n",
       "      <td>-0.012506</td>\n",
       "      <td>-0.013713</td>\n",
       "      <td>-0.014426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-16</th>\n",
       "      <td>0.012465</td>\n",
       "      <td>-0.016282</td>\n",
       "      <td>0.004519</td>\n",
       "      <td>-0.003942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-03</th>\n",
       "      <td>-0.017623</td>\n",
       "      <td>-0.014464</td>\n",
       "      <td>-0.020377</td>\n",
       "      <td>-0.028451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04</th>\n",
       "      <td>-0.005606</td>\n",
       "      <td>0.033021</td>\n",
       "      <td>0.023612</td>\n",
       "      <td>0.022488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05</th>\n",
       "      <td>0.015436</td>\n",
       "      <td>0.021705</td>\n",
       "      <td>0.015378</td>\n",
       "      <td>0.017866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06</th>\n",
       "      <td>-0.002327</td>\n",
       "      <td>0.017381</td>\n",
       "      <td>-0.000811</td>\n",
       "      <td>0.018304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-07</th>\n",
       "      <td>-0.020060</td>\n",
       "      <td>-0.003417</td>\n",
       "      <td>-0.004466</td>\n",
       "      <td>-0.008163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-10</th>\n",
       "      <td>0.051406</td>\n",
       "      <td>0.026286</td>\n",
       "      <td>0.036977</td>\n",
       "      <td>0.034125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>0.029526</td>\n",
       "      <td>0.002227</td>\n",
       "      <td>-0.000131</td>\n",
       "      <td>0.000544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12</th>\n",
       "      <td>0.004747</td>\n",
       "      <td>-0.001481</td>\n",
       "      <td>0.011669</td>\n",
       "      <td>0.009795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-13</th>\n",
       "      <td>0.015515</td>\n",
       "      <td>0.008160</td>\n",
       "      <td>-0.010238</td>\n",
       "      <td>-0.002974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-14</th>\n",
       "      <td>0.033225</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>0.022784</td>\n",
       "      <td>0.017380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2213 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL      MSFT       XOM       SPX\n",
       "2003-01-03  0.006757  0.001421  0.000684 -0.000484\n",
       "2003-01-06  0.000000  0.017975  0.024624  0.022474\n",
       "2003-01-07 -0.002685  0.019052 -0.033712 -0.006545\n",
       "2003-01-08 -0.020188 -0.028272 -0.004145 -0.014086\n",
       "2003-01-09  0.008242  0.029094  0.021159  0.019386\n",
       "2003-01-10  0.002725  0.001824 -0.013927  0.000000\n",
       "2003-01-13 -0.005435  0.008648 -0.004134 -0.001412\n",
       "2003-01-14 -0.002732  0.010379  0.008993  0.005830\n",
       "2003-01-15 -0.010959 -0.012506 -0.013713 -0.014426\n",
       "2003-01-16  0.012465 -0.016282  0.004519 -0.003942\n",
       "...              ...       ...       ...       ...\n",
       "2011-10-03 -0.017623 -0.014464 -0.020377 -0.028451\n",
       "2011-10-04 -0.005606  0.033021  0.023612  0.022488\n",
       "2011-10-05  0.015436  0.021705  0.015378  0.017866\n",
       "2011-10-06 -0.002327  0.017381 -0.000811  0.018304\n",
       "2011-10-07 -0.020060 -0.003417 -0.004466 -0.008163\n",
       "2011-10-10  0.051406  0.026286  0.036977  0.034125\n",
       "2011-10-11  0.029526  0.002227 -0.000131  0.000544\n",
       "2011-10-12  0.004747 -0.001481  0.011669  0.009795\n",
       "2011-10-13  0.015515  0.008160 -0.010238 -0.002974\n",
       "2011-10-14  0.033225  0.003311  0.022784  0.017380\n",
       "\n",
       "[2213 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "      <th>SPX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>0.541124</td>\n",
       "      <td>0.745174</td>\n",
       "      <td>0.661265</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.374283</td>\n",
       "      <td>0.588531</td>\n",
       "      <td>0.557742</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>0.467540</td>\n",
       "      <td>0.562374</td>\n",
       "      <td>0.631010</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>0.428267</td>\n",
       "      <td>0.406126</td>\n",
       "      <td>0.518514</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>0.508118</td>\n",
       "      <td>0.658770</td>\n",
       "      <td>0.786264</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.681434</td>\n",
       "      <td>0.804626</td>\n",
       "      <td>0.828303</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>0.707103</td>\n",
       "      <td>0.654902</td>\n",
       "      <td>0.797921</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.710105</td>\n",
       "      <td>0.730118</td>\n",
       "      <td>0.839057</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.691931</td>\n",
       "      <td>0.800996</td>\n",
       "      <td>0.859975</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AAPL      MSFT       XOM  SPX\n",
       "2003  0.541124  0.745174  0.661265  1.0\n",
       "2004  0.374283  0.588531  0.557742  1.0\n",
       "2005  0.467540  0.562374  0.631010  1.0\n",
       "2006  0.428267  0.406126  0.518514  1.0\n",
       "2007  0.508118  0.658770  0.786264  1.0\n",
       "2008  0.681434  0.804626  0.828303  1.0\n",
       "2009  0.707103  0.654902  0.797921  1.0\n",
       "2010  0.710105  0.730118  0.839057  1.0\n",
       "2011  0.691931  0.800996  0.859975  1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_year = lambda x: x.year\n",
    "by_year = rets.groupby(get_year)\n",
    "by_year.apply(spx_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2003    0.480868\n",
       "2004    0.259024\n",
       "2005    0.300093\n",
       "2006    0.161735\n",
       "2007    0.417738\n",
       "2008    0.611901\n",
       "2009    0.432738\n",
       "2010    0.571946\n",
       "2011    0.581987\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_year.apply(lambda g: g['AAPL'].corr(g['MSFT']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.274992\n",
       "1    0.228913\n",
       "2    1.352917\n",
       "3    0.886429\n",
       "4   -2.001637\n",
       "5   -0.371843\n",
       "6    1.669025\n",
       "7   -0.438570\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser = pd.Series(np.random.randn(8))\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1   -0.167564\n",
       "2    4.910182\n",
       "3   -0.344801\n",
       "4   -3.258090\n",
       "5   -0.814231\n",
       "6   -5.488527\n",
       "7   -1.262770\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.pct_change()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Group-Wise Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>intercept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1.195406</td>\n",
       "      <td>0.000710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>1.363463</td>\n",
       "      <td>0.004201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1.766415</td>\n",
       "      <td>0.003246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1.645496</td>\n",
       "      <td>0.000080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.198761</td>\n",
       "      <td>0.003438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.968016</td>\n",
       "      <td>-0.001110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>0.879103</td>\n",
       "      <td>0.002954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.052608</td>\n",
       "      <td>0.001261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.806605</td>\n",
       "      <td>0.001514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SPX  intercept\n",
       "2003  1.195406   0.000710\n",
       "2004  1.363463   0.004201\n",
       "2005  1.766415   0.003246\n",
       "2006  1.645496   0.000080\n",
       "2007  1.198761   0.003438\n",
       "2008  0.968016  -0.001110\n",
       "2009  0.879103   0.002954\n",
       "2010  1.052608   0.001261\n",
       "2011  0.806605   0.001514"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_year.apply(regress, 'AAPL', ['SPX'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot Tables and Cross-Tabulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\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>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>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>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>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>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>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.146808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip smoker  day    time  size   tip_pct\n",
       "0       16.99  1.01     No  Sun  Dinner     2  0.059447\n",
       "1       10.34  1.66     No  Sun  Dinner     3  0.160542\n",
       "2       21.01  3.50     No  Sun  Dinner     3  0.166587\n",
       "3       23.68  3.31     No  Sun  Dinner     2  0.139780\n",
       "4       24.59  3.61     No  Sun  Dinner     4  0.146808"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>day</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\">Fri</th>\n",
       "      <th>No</th>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.812500</td>\n",
       "      <td>0.151650</td>\n",
       "      <td>18.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>2.066667</td>\n",
       "      <td>2.714000</td>\n",
       "      <td>0.174783</td>\n",
       "      <td>16.813333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>No</th>\n",
       "      <td>2.555556</td>\n",
       "      <td>3.102889</td>\n",
       "      <td>0.158048</td>\n",
       "      <td>19.661778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>2.476190</td>\n",
       "      <td>2.875476</td>\n",
       "      <td>0.147906</td>\n",
       "      <td>21.276667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>No</th>\n",
       "      <td>2.929825</td>\n",
       "      <td>3.167895</td>\n",
       "      <td>0.160113</td>\n",
       "      <td>20.506667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>2.578947</td>\n",
       "      <td>3.516842</td>\n",
       "      <td>0.187250</td>\n",
       "      <td>24.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
       "      <th>No</th>\n",
       "      <td>2.488889</td>\n",
       "      <td>2.673778</td>\n",
       "      <td>0.160298</td>\n",
       "      <td>17.113111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>2.352941</td>\n",
       "      <td>3.030000</td>\n",
       "      <td>0.163863</td>\n",
       "      <td>19.190588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 size       tip   tip_pct  total_bill\n",
       "day  smoker                                          \n",
       "Fri  No      2.250000  2.812500  0.151650   18.420000\n",
       "     Yes     2.066667  2.714000  0.174783   16.813333\n",
       "Sat  No      2.555556  3.102889  0.158048   19.661778\n",
       "     Yes     2.476190  2.875476  0.147906   21.276667\n",
       "Sun  No      2.929825  3.167895  0.160113   20.506667\n",
       "     Yes     2.578947  3.516842  0.187250   24.120000\n",
       "Thur No      2.488889  2.673778  0.160298   17.113111\n",
       "     Yes     2.352941  3.030000  0.163863   19.190588"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(index=['day', 'smoker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">size</th>\n",
       "      <th colspan=\"2\" halign=\"left\">tip_pct</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>time</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\">Dinner</th>\n",
       "      <th>Fri</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.222222</td>\n",
       "      <td>0.139622</td>\n",
       "      <td>0.165347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2.555556</td>\n",
       "      <td>2.476190</td>\n",
       "      <td>0.158048</td>\n",
       "      <td>0.147906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>2.929825</td>\n",
       "      <td>2.578947</td>\n",
       "      <td>0.160113</td>\n",
       "      <td>0.187250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.159744</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Lunch</th>\n",
       "      <th>Fri</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>0.187735</td>\n",
       "      <td>0.188937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.352941</td>\n",
       "      <td>0.160311</td>\n",
       "      <td>0.163863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 size             tip_pct          \n",
       "smoker             No       Yes        No       Yes\n",
       "time   day                                         \n",
       "Dinner Fri   2.000000  2.222222  0.139622  0.165347\n",
       "       Sat   2.555556  2.476190  0.158048  0.147906\n",
       "       Sun   2.929825  2.578947  0.160113  0.187250\n",
       "       Thur  2.000000       NaN  0.159744       NaN\n",
       "Lunch  Fri   3.000000  1.833333  0.187735  0.188937\n",
       "       Thur  2.500000  2.352941  0.160311  0.163863"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(['tip_pct', 'size'], index=['time', 'day'],\n",
    "                 columns='smoker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th colspan=\"3\" halign=\"left\">size</th>\n",
       "      <th colspan=\"3\" halign=\"left\">tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>smoker</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>All</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th>day</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\">Dinner</th>\n",
       "      <th>Fri</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.222222</td>\n",
       "      <td>2.166667</td>\n",
       "      <td>0.139622</td>\n",
       "      <td>0.165347</td>\n",
       "      <td>0.158916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2.555556</td>\n",
       "      <td>2.476190</td>\n",
       "      <td>2.517241</td>\n",
       "      <td>0.158048</td>\n",
       "      <td>0.147906</td>\n",
       "      <td>0.153152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>2.929825</td>\n",
       "      <td>2.578947</td>\n",
       "      <td>2.842105</td>\n",
       "      <td>0.160113</td>\n",
       "      <td>0.187250</td>\n",
       "      <td>0.166897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.159744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.159744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Lunch</th>\n",
       "      <th>Fri</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.187735</td>\n",
       "      <td>0.188937</td>\n",
       "      <td>0.188765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.352941</td>\n",
       "      <td>2.459016</td>\n",
       "      <td>0.160311</td>\n",
       "      <td>0.163863</td>\n",
       "      <td>0.161301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>2.668874</td>\n",
       "      <td>2.408602</td>\n",
       "      <td>2.569672</td>\n",
       "      <td>0.159328</td>\n",
       "      <td>0.163196</td>\n",
       "      <td>0.160803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 size                       tip_pct                    \n",
       "smoker             No       Yes       All        No       Yes       All\n",
       "time   day                                                             \n",
       "Dinner Fri   2.000000  2.222222  2.166667  0.139622  0.165347  0.158916\n",
       "       Sat   2.555556  2.476190  2.517241  0.158048  0.147906  0.153152\n",
       "       Sun   2.929825  2.578947  2.842105  0.160113  0.187250  0.166897\n",
       "       Thur  2.000000       NaN  2.000000  0.159744       NaN  0.159744\n",
       "Lunch  Fri   3.000000  1.833333  2.000000  0.187735  0.188937  0.188765\n",
       "       Thur  2.500000  2.352941  2.459016  0.160311  0.163863  0.161301\n",
       "All          2.668874  2.408602  2.569672  0.159328  0.163196  0.160803"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(['tip_pct', 'size'], index=['time', 'day'],\n",
    "                 columns='smoker', margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\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",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "      <th>Thur</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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\">Dinner</th>\n",
       "      <th>No</th>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>9.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Lunch</th>\n",
       "      <th>No</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "day             Fri   Sat   Sun  Thur    All\n",
       "time   smoker                               \n",
       "Dinner No       3.0  45.0  57.0   1.0  106.0\n",
       "       Yes      9.0  42.0  19.0   NaN   70.0\n",
       "Lunch  No       1.0   NaN   NaN  44.0   45.0\n",
       "       Yes      6.0   NaN   NaN  17.0   23.0\n",
       "All            19.0  87.0  76.0  62.0  244.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table('tip_pct', index=['time', 'smoker'], columns='day',\n",
    "                 aggfunc=len, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th rowspan=\"10\" valign=\"top\">Dinner</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>No</th>\n",
       "      <td>0.139622</td>\n",
       "      <td>0.162705</td>\n",
       "      <td>0.168859</td>\n",
       "      <td>0.159744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.171297</td>\n",
       "      <td>0.148668</td>\n",
       "      <td>0.207893</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.152663</td>\n",
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       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.152660</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>Yes</th>\n",
       "      <td>0.117750</td>\n",
       "      <td>0.124515</td>\n",
       "      <td>0.193370</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.206928</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106572</td>\n",
       "      <td>0.065660</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Lunch</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>No</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.181728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.223776</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>No</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.181969</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.158843</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>No</th>\n",
       "      <td>0.187735</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.084246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.204952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>No</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.138919</td>\n",
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       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.155410</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.121389</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>No</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.173706</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "day                      Fri       Sat       Sun      Thur\n",
       "time   size smoker                                        \n",
       "Dinner 1    No      0.000000  0.137931  0.000000  0.000000\n",
       "            Yes     0.000000  0.325733  0.000000  0.000000\n",
       "       2    No      0.139622  0.162705  0.168859  0.159744\n",
       "            Yes     0.171297  0.148668  0.207893  0.000000\n",
       "       3    No      0.000000  0.154661  0.152663  0.000000\n",
       "            Yes     0.000000  0.144995  0.152660  0.000000\n",
       "       4    No      0.000000  0.150096  0.148143  0.000000\n",
       "            Yes     0.117750  0.124515  0.193370  0.000000\n",
       "       5    No      0.000000  0.000000  0.206928  0.000000\n",
       "            Yes     0.000000  0.106572  0.065660  0.000000\n",
       "...                      ...       ...       ...       ...\n",
       "Lunch  1    No      0.000000  0.000000  0.000000  0.181728\n",
       "            Yes     0.223776  0.000000  0.000000  0.000000\n",
       "       2    No      0.000000  0.000000  0.000000  0.166005\n",
       "            Yes     0.181969  0.000000  0.000000  0.158843\n",
       "       3    No      0.187735  0.000000  0.000000  0.084246\n",
       "            Yes     0.000000  0.000000  0.000000  0.204952\n",
       "       4    No      0.000000  0.000000  0.000000  0.138919\n",
       "            Yes     0.000000  0.000000  0.000000  0.155410\n",
       "       5    No      0.000000  0.000000  0.000000  0.121389\n",
       "       6    No      0.000000  0.000000  0.000000  0.173706\n",
       "\n",
       "[21 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table('tip_pct', index=['time', 'size', 'smoker'],\n",
    "                 columns='day', aggfunc='mean', fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-Tabulations: Crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import StringIO\n",
    "data = \"\"\"\\\n",
    "Sample  Nationality  Handedness\n",
    "1   USA  Right-handed\n",
    "2   Japan    Left-handed\n",
    "3   USA  Right-handed\n",
    "4   Japan    Right-handed\n",
    "5   Japan    Left-handed\n",
    "6   Japan    Right-handed\n",
    "7   USA  Right-handed\n",
    "8   USA  Left-handed\n",
    "9   Japan    Right-handed\n",
    "10  USA  Right-handed\"\"\"\n",
    "data = pd.read_table(StringIO(data), sep='\\s+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Right-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>Japan</td>\n",
       "      <td>Left-handed</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Right-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>USA</td>\n",
       "      <td>Right-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>USA</td>\n",
       "      <td>Left-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Right-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>USA</td>\n",
       "      <td>Right-handed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sample Nationality    Handedness\n",
       "0       1         USA  Right-handed\n",
       "1       2       Japan   Left-handed\n",
       "2       3         USA  Right-handed\n",
       "3       4       Japan  Right-handed\n",
       "4       5       Japan   Left-handed\n",
       "5       6       Japan  Right-handed\n",
       "6       7         USA  Right-handed\n",
       "7       8         USA   Left-handed\n",
       "8       9       Japan  Right-handed\n",
       "9      10         USA  Right-handed"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Handedness</th>\n",
       "      <th>Left-handed</th>\n",
       "      <th>Right-handed</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nationality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
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      ],
      "text/plain": [
       "Handedness   Left-handed  Right-handed  All\n",
       "Nationality                                \n",
       "Japan                  2             3    5\n",
       "USA                    1             4    5\n",
       "All                    3             7   10"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data.Nationality, data.Handedness, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smoker</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab([tips.time, tips.day], tips.smoker, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = PREVIOUS_MAX_ROWS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  }
 ],
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