05.17_Pandas_数据聚合与分组运算.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:31:03.046668Z",
     "start_time": "2019-11-11T02:31:03.034940Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GroupBy机制"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "+    使用一个或多个键（形式可以是函数、数组或DataFrame列名）分割pandas对象。\n",
    "+   计算分组的概述统计，比如数量、平均值或标准差，或是用户定义的函数。\n",
    "+  应用组内转换或其他运算，如规格化、线性回归、排名或选取子集等。\n",
    "+ 计算透视表或交叉表。\n",
    "+ 执行分位数分析以及其它统计分组分析。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GroupBy的工作原理\n",
    "1. 拆分(split)：根据传入的分组方式进行分组\n",
    "2. 应用(apply)：对每个分组运用对应的函数\n",
    "3. 合并(combine)：合并数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://camo.githubusercontent.com/8e5549c8c55780a51555d0c193f8568e73796143/687474703a2f2f75706c6f61642d696d616765732e6a69616e7368752e696f2f75706c6f61645f696d616765732f373137383639312d653563363731653039656366393462652e706e673f696d6167654d6f6772322f6175746f2d6f7269656e742f7374726970253743696d61676556696577322f322f772f31323430)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分组键可以有很多中形式， 而且类型可以不相同\n",
    "+ 列表或者数组，其长度与待分组的轴一样\n",
    "+ DataFrame的每个列名值\n",
    "+ 字典或者Series，给出待分组轴上的值与分组名之间对应关系\n",
    "+ 函数，用于处理轴索引或者索引中的各个标签"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分组操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T06:32:00.948984Z",
     "start_time": "2019-11-10T06:32:00.870635Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>1.341073</td>\n",
       "      <td>2.355754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.385922</td>\n",
       "      <td>0.466002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "      <td>-2.474644</td>\n",
       "      <td>1.456787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
       "      <td>0.654396</td>\n",
       "      <td>1.274367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>-1.891275</td>\n",
       "      <td>1.906919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2     data1     data2\n",
       "0    a  one  1.341073  2.355754\n",
       "1    a  two -1.385922  0.466002\n",
       "2    b  one -2.474644  1.456787\n",
       "3    b  two  0.654396  1.274367\n",
       "4    a  one -1.891275  1.906919"
      ]
     },
     "execution_count": 3,
     "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": "markdown",
   "metadata": {},
   "source": [
    "示例1：按key1进行分组，并计算data1列的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T06:33:56.696769Z",
     "start_time": "2019-11-10T06:33:56.684937Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x000001D18C895588>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df['data1'].groupby(df['key1'])\n",
    "grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对`DataFrame`进行分组，返回一个GroupBy对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T06:34:38.962324Z",
     "start_time": "2019-11-10T06:34:38.941953Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "a   -0.645375\n",
       "b   -0.910124\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例2：根据key1，key2进行分组，并计算data1的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T06:36:04.490432Z",
     "start_time": "2019-11-10T06:36:04.407779Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1  key2\n",
       "a     one    -0.275101\n",
       "      two    -1.385922\n",
       "b     one    -2.474644\n",
       "      two     0.654396\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means = df['data1'].groupby([df['key1'], df['key2']]).mean()\n",
    "means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T06:36:36.199602Z",
     "start_time": "2019-11-10T06:36:36.174631Z"
    }
   },
   "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",
       "\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>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>-0.275101</td>\n",
       "      <td>-1.385922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-2.474644</td>\n",
       "      <td>0.654396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "key2       one       two\n",
       "key1                    \n",
       "a    -0.275101 -1.385922\n",
       "b    -2.474644  0.654396"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例3：根据适当长度的数组进行分组\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T06:38:45.536727Z",
     "start_time": "2019-11-10T06:38:45.505475Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2005   -1.385922\n",
       "            2006   -2.474644\n",
       "Ohio        2005    0.997735\n",
       "            2006   -1.891275\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df['data1'].size  5\n",
    "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": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T06:44:17.624941Z",
     "start_time": "2019-11-10T06:44:17.593515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "states      years\n",
       "California  2005    -1.385922\n",
       "            2006    -2.474644\n",
       "Ohio        2005     0.997735\n",
       "            2006    -1.891275\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 上面代码可以看出如下：\n",
    "df2 = pd.concat([df['data1'],\n",
    "                 pd.Series(states, name='states'),\n",
    "                 pd.Series(years, name='years')],\n",
    "                axis=1)\n",
    "df2['data1'].groupby([df2['states'], df2['years']]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例4：根据df的key1进行分组，并计算平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T06:45:54.706497Z",
     "start_time": "2019-11-10T06:45:54.669839Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>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>-0.645375</td>\n",
       "      <td>1.576225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-0.910124</td>\n",
       "      <td>1.365577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         data1     data2\n",
       "key1                    \n",
       "a    -0.645375  1.576225\n",
       "b    -0.910124  1.365577"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key1').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：**不相关的数据(非数组型数列)会被过滤掉。（如，上面的key2）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T06:46:12.251638Z",
     "start_time": "2019-11-10T06:46:12.242763Z"
    }
   },
   "source": [
    "示例5：根据df的key1, key2进行分组，并计算平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T06:46:30.910999Z",
     "start_time": "2019-11-10T06:46:30.881794Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>-0.275101</td>\n",
       "      <td>2.131336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-1.385922</td>\n",
       "      <td>0.466002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>one</th>\n",
       "      <td>-2.474644</td>\n",
       "      <td>1.456787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.654396</td>\n",
       "      <td>1.274367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              data1     data2\n",
       "key1 key2                    \n",
       "a    one  -0.275101  2.131336\n",
       "     two  -1.385922  0.466002\n",
       "b    one  -2.474644  1.456787\n",
       "     two   0.654396  1.274367"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1', 'key2']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例5：根据df的key1，key2进行分组，并计算每个小组里面数组的个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T06:50:02.787998Z",
     "start_time": "2019-11-10T06:50:02.758728Z"
    }
   },
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1', 'key2']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对分组进行迭代"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GroupBy对象支持迭代，可以产生一组二元元组\n",
    "GroupBy--->(name1, group1),(name2, group2)...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：根据df的key1进行分组，并遍历分组产生的GroupBy对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:00:53.607872Z",
     "start_time": "2019-11-10T07:00:53.527905Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "  key1 key2     data1     data2\n",
      "0    a  one  1.341073  2.355754\n",
      "1    a  two -1.385922  0.466002\n",
      "4    a  one -1.891275  1.906919\n",
      "b\n",
      "  key1 key2     data1     data2\n",
      "2    b  one -2.474644  1.456787\n",
      "3    b  two  0.654396  1.274367\n"
     ]
    }
   ],
   "source": [
    "for name, group in df.groupby('key1'):\n",
    "    print(name)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：根据df的key1,key2进行分组，并遍历分组产生的GroupBy对象.  \n",
    "（这里有点特殊，因为根据多个键值进行分组，返回的name是一个多元元组）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:03:07.685965Z",
     "start_time": "2019-11-10T07:03:07.645784Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a one\n",
      "  key1 key2     data1     data2\n",
      "0    a  one  1.341073  2.355754\n",
      "4    a  one -1.891275  1.906919\n",
      "a two\n",
      "  key1 key2     data1     data2\n",
      "1    a  two -1.385922  0.466002\n",
      "b one\n",
      "  key1 key2     data1     data2\n",
      "2    b  one -2.474644  1.456787\n",
      "b two\n",
      "  key1 key2     data1     data2\n",
      "3    b  two  0.654396  1.274367\n"
     ]
    }
   ],
   "source": [
    "# for name, group in df.groupby(['key1', 'key2']):\n",
    "for (key1, key2), group in df.groupby(['key1', 'key2']):\n",
    "    print(key1, key2)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 示例3：根据df的key1进行分组，并将数据片段变为一个字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:04:19.732866Z",
     "start_time": "2019-11-10T07:04:19.695354Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a':   key1 key2     data1     data2\n",
       " 0    a  one  1.341073  2.355754\n",
       " 1    a  two -1.385922  0.466002\n",
       " 4    a  one -1.891275  1.906919, 'b':   key1 key2     data1     data2\n",
       " 2    b  one -2.474644  1.456787\n",
       " 3    b  two  0.654396  1.274367}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pieces = dict(list(df.groupby('key1')))\n",
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:04:32.906084Z",
     "start_time": "2019-11-10T07:04:32.886581Z"
    }
   },
   "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>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>-2.474644</td>\n",
       "      <td>1.456787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
       "      <td>0.654396</td>\n",
       "      <td>1.274367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2     data1     data2\n",
       "2    b  one -2.474644  1.456787\n",
       "3    b  two  0.654396  1.274367"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pieces['b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例4：根据df的dtype进行分类(**注意：**可以通过axis=1分类)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:05:46.129518Z",
     "start_time": "2019-11-10T07:05:46.104400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1      object\n",
       "key2      object\n",
       "data1    float64\n",
       "data2    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:06:58.456575Z",
     "start_time": "2019-11-10T07:06:58.425591Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    2\n",
       "object     2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby(df.dtypes, axis=1)\n",
    "grouped.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:07:23.505112Z",
     "start_time": "2019-11-10T07:07:23.474152Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n",
      "      data1     data2\n",
      "0  1.341073  2.355754\n",
      "1 -1.385922  0.466002\n",
      "2 -2.474644  1.456787\n",
      "3  0.654396  1.274367\n",
      "4 -1.891275  1.906919\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": [
    "### 选取一列或者列的子集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：根据df的key1进行分组，并选取其中的某一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:11:40.796813Z",
     "start_time": "2019-11-10T07:11:28.320389Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "154 µs ± 3.49 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "df.groupby('key1')['data1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:10:23.340750Z",
     "start_time": "2019-11-10T07:10:23.321842Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001D18DF62828>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key1')[['data2']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面两行代码相当于"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:13:20.085237Z",
     "start_time": "2019-11-10T07:13:20.072606Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x000001D18DDCC630>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['data1'].groupby(df['key1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:13:46.855792Z",
     "start_time": "2019-11-10T07:13:46.833636Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001D18E58BBA8>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['data2']].groupby(df['key1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例2：根据df的key1，key2进行分组，选取其中的data2进行求平均，返回一个DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:15:58.937280Z",
     "start_time": "2019-11-10T07:15:58.915168Z"
    }
   },
   "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>data1</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>-0.275101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-1.385922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>one</th>\n",
       "      <td>-2.474644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.654396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              data1\n",
       "key1 key2          \n",
       "a    one  -0.275101\n",
       "     two  -1.385922\n",
       "b    one  -2.474644\n",
       "     two   0.654396"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1', 'key2'])[['data1']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例3：根据df的key1，key2进行分组，选取其中的data2进行求平均，返回一个Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:16:37.976738Z",
     "start_time": "2019-11-10T07:16:37.948150Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1  key2\n",
       "a     one    -0.275101\n",
       "      two    -1.385922\n",
       "b     one    -2.474644\n",
       "      two     0.654396\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1','key2'])['data1'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 传入映射字典进行分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:19:39.933269Z",
     "start_time": "2019-11-10T07:19:39.893845Z"
    }
   },
   "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>0.522318</td>\n",
       "      <td>-0.340390</td>\n",
       "      <td>-0.659717</td>\n",
       "      <td>1.107929</td>\n",
       "      <td>0.744146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>0.660020</td>\n",
       "      <td>1.161863</td>\n",
       "      <td>-1.165410</td>\n",
       "      <td>0.773331</td>\n",
       "      <td>-0.517003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>-0.673866</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.098838</td>\n",
       "      <td>1.279254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim</th>\n",
       "      <td>0.729824</td>\n",
       "      <td>1.138708</td>\n",
       "      <td>-0.597597</td>\n",
       "      <td>1.027640</td>\n",
       "      <td>-0.219603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travis</th>\n",
       "      <td>1.474624</td>\n",
       "      <td>-0.685122</td>\n",
       "      <td>1.403698</td>\n",
       "      <td>1.502232</td>\n",
       "      <td>1.371654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               a         b         c         d         e\n",
       "Joe     0.522318 -0.340390 -0.659717  1.107929  0.744146\n",
       "Steve   0.660020  1.161863 -1.165410  0.773331 -0.517003\n",
       "Wes    -0.673866       NaN       NaN -1.098838  1.279254\n",
       "Jim     0.729824  1.138708 -0.597597  1.027640 -0.219603\n",
       "Travis  1.474624 -0.685122  1.403698  1.502232  1.371654"
      ]
     },
     "execution_count": 64,
     "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": "markdown",
   "metadata": {},
   "source": [
    "示例1：假设已知列的分组关系，根据分组计算列的和。  \n",
    "(**Note：**如果存在未使用的键，程序不会报错)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:22:37.752115Z",
     "start_time": "2019-11-10T07:22:37.725243Z"
    }
   },
   "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",
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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>blue</th>\n",
       "      <th>red</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>0.448212</td>\n",
       "      <td>0.926074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>-0.392080</td>\n",
       "      <td>1.304880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>-1.098838</td>\n",
       "      <td>0.605388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim</th>\n",
       "      <td>0.430043</td>\n",
       "      <td>1.648930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travis</th>\n",
       "      <td>2.905929</td>\n",
       "      <td>2.161155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            blue       red\n",
       "Joe     0.448212  0.926074\n",
       "Steve  -0.392080  1.304880\n",
       "Wes    -1.098838  0.605388\n",
       "Jim     0.430043  1.648930\n",
       "Travis  2.905929  2.161155"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping = {'a':'red','b':'red','c':'blue','d':'blue','e':'red','f':'orange'}\n",
    "by_column = people.groupby(mapping, axis=1)\n",
    "by_column.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例2：传入Series作为映射，并计算每组的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:23:51.100458Z",
     "start_time": "2019-11-10T07:23:51.082016Z"
    }
   },
   "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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_series = pd.Series(mapping)\n",
    "map_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:24:15.911778Z",
     "start_time": "2019-11-10T07:24:15.885968Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.groupby(map_series, axis=1).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 通过函数进行分组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：根据索引的长度进行分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:25:07.701122Z",
     "start_time": "2019-11-10T07:25:07.657881Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>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>0.578276</td>\n",
       "      <td>0.798319</td>\n",
       "      <td>-1.257314</td>\n",
       "      <td>1.036730</td>\n",
       "      <td>1.803797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.660020</td>\n",
       "      <td>1.161863</td>\n",
       "      <td>-1.165410</td>\n",
       "      <td>0.773331</td>\n",
       "      <td>-0.517003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.474624</td>\n",
       "      <td>-0.685122</td>\n",
       "      <td>1.403698</td>\n",
       "      <td>1.502232</td>\n",
       "      <td>1.371654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d         e\n",
       "3  0.578276  0.798319 -1.257314  1.036730  1.803797\n",
       "5  0.660020  1.161863 -1.165410  0.773331 -0.517003\n",
       "6  1.474624 -0.685122  1.403698  1.502232  1.371654"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.groupby(len).sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例2：根据索引最后一个字母进行分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:28:08.110496Z",
     "start_time": "2019-11-10T07:28:08.078854Z"
    }
   },
   "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",
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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>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>e</th>\n",
       "      <td>1.182338</td>\n",
       "      <td>0.821473</td>\n",
       "      <td>-1.825127</td>\n",
       "      <td>1.881259</td>\n",
       "      <td>0.227144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>0.729824</td>\n",
       "      <td>1.138708</td>\n",
       "      <td>-0.597597</td>\n",
       "      <td>1.027640</td>\n",
       "      <td>-0.219603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.800758</td>\n",
       "      <td>-0.685122</td>\n",
       "      <td>1.403698</td>\n",
       "      <td>0.403393</td>\n",
       "      <td>2.650908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d         e\n",
       "e  1.182338  0.821473 -1.825127  1.881259  0.227144\n",
       "m  0.729824  1.138708 -0.597597  1.027640 -0.219603\n",
       "s  0.800758 -0.685122  1.403698  0.403393  2.650908"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.groupby(lambda x:x[-1]).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将函数、数组、列表等混合使用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：根据people索引的长度和key_list进行分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:30:22.820877Z",
     "start_time": "2019-11-10T07:30:22.775763Z"
    }
   },
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.151548</td>\n",
       "      <td>-0.340390</td>\n",
       "      <td>-0.659717</td>\n",
       "      <td>0.009090</td>\n",
       "      <td>2.023400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.729824</td>\n",
       "      <td>1.138708</td>\n",
       "      <td>-0.597597</td>\n",
       "      <td>1.027640</td>\n",
       "      <td>-0.219603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>one</th>\n",
       "      <td>0.660020</td>\n",
       "      <td>1.161863</td>\n",
       "      <td>-1.165410</td>\n",
       "      <td>0.773331</td>\n",
       "      <td>-0.517003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>two</th>\n",
       "      <td>1.474624</td>\n",
       "      <td>-0.685122</td>\n",
       "      <td>1.403698</td>\n",
       "      <td>1.502232</td>\n",
       "      <td>1.371654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              a         b         c         d         e\n",
       "3 one -0.151548 -0.340390 -0.659717  0.009090  2.023400\n",
       "  two  0.729824  1.138708 -0.597597  1.027640 -0.219603\n",
       "5 one  0.660020  1.161863 -1.165410  0.773331 -0.517003\n",
       "6 two  1.474624 -0.685122  1.403698  1.502232  1.371654"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_list = ['one', 'one', 'one', 'two', 'two']\n",
    "people.groupby([len, key_list]).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 根据索引级别进行分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:32:04.766320Z",
     "start_time": "2019-11-10T07:32:04.714300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>-0.640946</td>\n",
       "      <td>0.938213</td>\n",
       "      <td>0.332981</td>\n",
       "      <td>-2.128284</td>\n",
       "      <td>0.074242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.245719</td>\n",
       "      <td>-1.602921</td>\n",
       "      <td>-0.475766</td>\n",
       "      <td>0.032530</td>\n",
       "      <td>-0.647863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.544264</td>\n",
       "      <td>0.097320</td>\n",
       "      <td>-0.237489</td>\n",
       "      <td>-0.094617</td>\n",
       "      <td>-2.402969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.917560</td>\n",
       "      <td>-1.276469</td>\n",
       "      <td>-0.481658</td>\n",
       "      <td>-0.786999</td>\n",
       "      <td>-0.801427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cty          US                            JP          \n",
       "tenor         1         3         5         1         3\n",
       "0     -0.640946  0.938213  0.332981 -2.128284  0.074242\n",
       "1     -1.245719 -1.602921 -0.475766  0.032530 -0.647863\n",
       "2      1.544264  0.097320 -0.237489 -0.094617 -2.402969\n",
       "3     -0.917560 -1.276469 -0.481658 -0.786999 -0.801427"
      ]
     },
     "execution_count": 77,
     "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": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:32:44.501969Z",
     "start_time": "2019-11-10T07:32:44.474219Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<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",
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       "    <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",
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       "    <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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hier_df.groupby(level=0, axis=1).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据聚合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**聚合指的是任何能够从数组产生标量值的数据转换过程。**之前的例子已经用过一些，比如mean、count、min以及sum等。你可能想知道在GroupBy对象上调用mean()时究竟发生了什么。许多常见的聚合运算（如表10-1所示）都有进行优化。然而，除了这些方法，你还可以使用其它的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://camo.githubusercontent.com/ceb83fe40f3025fa5a8d17aa6b50e74407b6d95a/687474703a2f2f75706c6f61642d696d616765732e6a69616e7368752e696f2f75706c6f61645f696d616765732f373137383639312d626138646535323465303862316236662e706e673f696d6167654d6f6772322f6175746f2d6f7269656e742f7374726970253743696d61676556696577322f322f772f31323430)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 普通聚合操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:38:48.659016Z",
     "start_time": "2019-11-10T07:38:48.644391Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
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       "      <td>1.341073</td>\n",
       "      <td>2.355754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.385922</td>\n",
       "      <td>0.466002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "      <td>-2.474644</td>\n",
       "      <td>1.456787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
       "      <td>0.654396</td>\n",
       "      <td>1.274367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>-1.891275</td>\n",
       "      <td>1.906919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2     data1     data2\n",
       "0    a  one  1.341073  2.355754\n",
       "1    a  two -1.385922  0.466002\n",
       "2    b  one -2.474644  1.456787\n",
       "3    b  two  0.654396  1.274367\n",
       "4    a  one -1.891275  1.906919"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：根据df的key1进行分组，并计算data1的上四分位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:40:19.737492Z",
     "start_time": "2019-11-10T07:40:19.711059Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "a   -0.022425\n",
       "b   -0.127864\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby('key1')\n",
    "grouped['data1'].quantile(0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**如果想要自己定义聚合函数可以通过`GroupBy.aggregate(func)`实现"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例2：根据df的key1进行分组，并计算分组数据的极差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:45:24.068721Z",
     "start_time": "2019-11-10T07:45:24.006424Z"
    }
   },
   "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",
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       "    }\n",
       "\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>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>3.232349</td>\n",
       "      <td>1.889752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3.129040</td>\n",
       "      <td>0.182420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         data1     data2\n",
       "key1                    \n",
       "a     3.232349  1.889752\n",
       "b     3.129040  0.182420"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def func(arr):\n",
    "    \"\"\"\n",
    "    由于分组的聚合操作是将每个小组的每一列当作整体进行聚合操作，\n",
    "    所以这里用arr接收\n",
    "    \"\"\"\n",
    "    return arr.max() - arr.min()\n",
    "df.groupby('key1').agg(func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例3：根据df的key1进行分组，并对每组进行描述(describe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T07:46:20.099608Z",
     "start_time": "2019-11-10T07:46:19.941792Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<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>-0.645375</td>\n",
       "      <td>1.738772</td>\n",
       "      <td>-1.891275</td>\n",
       "      <td>-1.638599</td>\n",
       "      <td>-1.385922</td>\n",
       "      <td>-0.022425</td>\n",
       "      <td>1.341073</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.576225</td>\n",
       "      <td>0.987324</td>\n",
       "      <td>0.466002</td>\n",
       "      <td>1.186461</td>\n",
       "      <td>1.906919</td>\n",
       "      <td>2.131336</td>\n",
       "      <td>2.355754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.910124</td>\n",
       "      <td>2.212565</td>\n",
       "      <td>-2.474644</td>\n",
       "      <td>-1.692384</td>\n",
       "      <td>-0.910124</td>\n",
       "      <td>-0.127864</td>\n",
       "      <td>0.654396</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.365577</td>\n",
       "      <td>0.128990</td>\n",
       "      <td>1.274367</td>\n",
       "      <td>1.319972</td>\n",
       "      <td>1.365577</td>\n",
       "      <td>1.411182</td>\n",
       "      <td>1.456787</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 -0.645375  1.738772 -1.891275 -1.638599 -1.385922 -0.022425   \n",
       "b      2.0 -0.910124  2.212565 -2.474644 -1.692384 -0.910124 -0.127864   \n",
       "\n",
       "               data2                                                    \\\n",
       "           max count      mean       std       min       25%       50%   \n",
       "key1                                                                     \n",
       "a     1.341073   3.0  1.576225  0.987324  0.466002  1.186461  1.906919   \n",
       "b     0.654396   2.0  1.365577  0.128990  1.274367  1.319972  1.365577   \n",
       "\n",
       "                          \n",
       "           75%       max  \n",
       "key1                      \n",
       "a     2.131336  2.355754  \n",
       "b     1.411182  1.456787  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key1').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Note:***如果能用优化过的函数(mean,sum等)就用优化过的函数，因为自定义函数会消耗很多的内存"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 面向列的多函数应用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T15:44:00.933896Z",
     "start_time": "2019-11-10T15:44:00.754201Z"
    }
   },
   "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",
       "\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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = pd.read_csv('examples/tips.csv')\n",
    "# 添加小费百分比\n",
    "tips['tip_pct'] = tips['tip'] / tips['total_bill']\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你已经看到，对Series或DataFrame列的聚合运算其实就是使用aggregate（使用自定义函数）或调用诸如mean、std之类的方法。然而，你可能希望对不同的列使用不同的聚合函数，或一次应用多个函数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先对数据进行分组, 根据(day, smoker)对tips进行分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T08:00:26.138888Z",
     "start_time": "2019-11-10T08:00:26.124127Z"
    }
   },
   "outputs": [],
   "source": [
    "grouped = tips.groupby(['day', 'smoker'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：根据分组，采集其中的tip_pct，求平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T08:03:06.275196Z",
     "start_time": "2019-11-10T08:03:06.235737Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th>smoker</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.174783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>No</th>\n",
       "      <td>0.158048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.147906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>No</th>\n",
       "      <td>0.160113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.187250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
       "      <th>No</th>\n",
       "      <td>0.160298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.163863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tip_pct\n",
       "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"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pct = grouped[['tip_pct']]\n",
    "grouped_pct.agg('mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例2：根据分组，采集其中的tip_pct，求平均值, 标准差, 极差(func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "传入一组函数或函数名，得到的DataFrame的列就会以相应的函数命名："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T08:06:25.747181Z",
     "start_time": "2019-11-10T08:06:25.685419Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>func</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
       "      <th>No</th>\n",
       "      <td>0.151650</td>\n",
       "      <td>0.028123</td>\n",
       "      <td>0.067349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.174783</td>\n",
       "      <td>0.051293</td>\n",
       "      <td>0.159925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>No</th>\n",
       "      <td>0.158048</td>\n",
       "      <td>0.039767</td>\n",
       "      <td>0.235193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": [
       "              tip_pct                    \n",
       "                 mean       std      func\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": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pct.agg(['mean', 'std', func])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例3：根据分组，采集其中的tip_pct，求平均值, 标准差, 极差(func)。并为此起别名  \n",
    "**Note:**可以通过传入列表元组起别名[(name, func), (name,func)...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T08:10:22.405303Z",
     "start_time": "2019-11-10T08:10:22.364884Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>day</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
       "      <th>No</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>No</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>No</th>\n",
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       "      <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",
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       "      <th>Yes</th>\n",
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       "      <td>0.039389</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              tip_pct                    \n",
       "                  foo       bar     range\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": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pct.agg([('foo','mean'), ('bar',np.std), ('range',func)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例4：根据分组后(grouped)的对象采集tip_pct、total_bill数据，对计算每列数据的统计信息  \n",
    "（相当于，对每一列进行聚合，然后concat起来）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T08:14:07.899527Z",
     "start_time": "2019-11-10T08:14:07.790878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>max</th>\n",
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       "      <th>Yes</th>\n",
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       "      <td>19.661778</td>\n",
       "      <td>48.33</td>\n",
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       "      <td>50.81</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>No</th>\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",
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       "      <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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "functions = ['count', 'mean', 'max']\n",
    "result = grouped['tip_pct', 'total_bill'].agg(functions)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例5：根据分组后(grouped)的对象采集tip_pct、total_bill数据，对计算每列数据的统计信息。并起别名  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T08:16:48.253500Z",
     "start_time": "2019-11-10T08:16:48.199193Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>平均值</th>\n",
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       "      <th>平均值</th>\n",
       "      <th>方差</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
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       "      <td>0.151650</td>\n",
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       "      <td>18.420000</td>\n",
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       "      <td>82.562438</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>No</th>\n",
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       "      <td>79.908965</td>\n",
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       "      <td>0.003767</td>\n",
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       "      <td>101.387535</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>No</th>\n",
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       "      <td>20.506667</td>\n",
       "      <td>66.099980</td>\n",
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       "      <td>109.046044</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
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       "      <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",
       "                  平均值        方差        平均值          方差\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": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftuples = [('平均值', 'mean'), ('方差', np.var)]\n",
    "grouped['tip_pct', 'total_bill'].agg(ftuples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例5：根据分组后(grouped)的对象采集tip_pct、total_bill数据，计算tip_pct的个数(count)，total_bill的平均值和方差(mean,var)。并起别名.  \n",
    "**Note:**可以通过字典完成\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T08:20:43.342127Z",
     "start_time": "2019-11-10T08:20:43.287298Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Yes</th>\n",
       "      <td>15</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>45</td>\n",
       "      <td>19.661778</td>\n",
       "      <td>79.908965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>42</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>57</td>\n",
       "      <td>20.506667</td>\n",
       "      <td>66.099980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>19</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>45</td>\n",
       "      <td>17.113111</td>\n",
       "      <td>59.625081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>17</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",
       "              count        平均值          方差\n",
       "day  smoker                               \n",
       "Fri  No           4  18.420000   25.596333\n",
       "     Yes         15  16.813333   82.562438\n",
       "Sat  No          45  19.661778   79.908965\n",
       "     Yes         42  21.276667  101.387535\n",
       "Sun  No          57  20.506667   66.099980\n",
       "     Yes         19  24.120000  109.046044\n",
       "Thur No          45  17.113111   59.625081\n",
       "     Yes         17  19.190588   69.808518"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_dict = {\n",
    "    'tip_pct':'count',\n",
    "    'total_bill':[('平均值','mean'), ('方差', np.var)]\n",
    "}\n",
    "grouped['tip_pct', 'total_bill'].agg(map_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 取消索引的聚合模式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：对tips进行分组并取消分组的索引(即，分组完成后，保留分组的数据，而不是把分组的依据当成索引)，求分组后的平均值。  \n",
    "**Note:**as_index=Fasle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T15:48:20.489719Z",
     "start_time": "2019-11-10T15:48:20.455404Z"
    }
   },
   "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>day</th>\n",
       "      <th>smoker</th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fri</td>\n",
       "      <td>No</td>\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>1</th>\n",
       "      <td>Fri</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.813333</td>\n",
       "      <td>2.714000</td>\n",
       "      <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>2.555556</td>\n",
       "      <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = tips.groupby(['day', 'smoker'], as_index=False).mean()\n",
    "grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T15:49:13.820420Z",
     "start_time": "2019-11-10T15:49:13.785902Z"
    }
   },
   "source": [
    "上面操作相当于"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T15:49:58.806315Z",
     "start_time": "2019-11-10T15:49:58.777037Z"
    }
   },
   "outputs": [
    {
     "data": {
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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",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
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       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
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       "      <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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped2 =tips.groupby(['day', 'smoker']).mean()\n",
    "grouped2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T15:50:17.995021Z",
     "start_time": "2019-11-10T15:50:17.961823Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>Fri</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Sat</td>\n",
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       "      <th>3</th>\n",
       "      <td>Sat</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sun</td>\n",
       "      <td>No</td>\n",
       "      <td>20.506667</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thur</td>\n",
       "      <td>Yes</td>\n",
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       "    </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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped2.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## filter:过滤操作"
   ]
  },
  {
   "attachments": {
    "%E5%9B%BE%E7%89%87.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "attachments": {
    "%E5%9B%BE%E7%89%87.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：根据smoker，day对tips进行分组，并选取其中中的count小于16的的所有组的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T01:04:15.046513Z",
     "start_time": "2019-11-11T01:04:15.018405Z"
    }
   },
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">No</th>\n",
       "      <th>Fri</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Yes</th>\n",
       "      <th>Fri</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             total_bill  tip  time  size  tip_pct\n",
       "smoker day                                       \n",
       "No     Fri            4    4     4     4        4\n",
       "       Sat           45   45    45    45       45\n",
       "       Sun           57   57    57    57       57\n",
       "       Thur          45   45    45    45       45\n",
       "Yes    Fri           15   15    15    15       15\n",
       "       Sat           42   42    42    42       42\n",
       "       Sun           19   19    19    19       19\n",
       "       Thur          17   17    17    17       17"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(['smoker', 'day']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T01:04:38.249708Z",
     "start_time": "2019-11-11T01:04:38.194113Z"
    },
    "scrolled": false
   },
   "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>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>90</th>\n",
       "      <td>28.97</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.103555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>22.49</td>\n",
       "      <td>3.50</td>\n",
       "      <td>No</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.155625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.173913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>16.32</td>\n",
       "      <td>4.30</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.263480</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "    </tr>\n",
       "    <tr>\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>96</th>\n",
       "      <td>27.28</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.146628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>12.03</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.124688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.142789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>12.46</td>\n",
       "      <td>1.50</td>\n",
       "      <td>No</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.120385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>11.35</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.220264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>15.38</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.195059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>12.16</td>\n",
       "      <td>2.20</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "      <td>0.180921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>13.42</td>\n",
       "      <td>3.48</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "      <td>0.259314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>8.58</td>\n",
       "      <td>1.92</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>1</td>\n",
       "      <td>0.223776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>15.98</td>\n",
       "      <td>3.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>3</td>\n",
       "      <td>0.187735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>13.42</td>\n",
       "      <td>1.58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "      <td>0.117735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>16.27</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "      <td>0.153657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>10.09</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "      <td>0.198216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip smoker  day    time  size   tip_pct\n",
       "90        28.97  3.00    Yes  Fri  Dinner     2  0.103555\n",
       "91        22.49  3.50     No  Fri  Dinner     2  0.155625\n",
       "92         5.75  1.00    Yes  Fri  Dinner     2  0.173913\n",
       "93        16.32  4.30    Yes  Fri  Dinner     2  0.263480\n",
       "94        22.75  3.25     No  Fri  Dinner     2  0.142857\n",
       "95        40.17  4.73    Yes  Fri  Dinner     4  0.117750\n",
       "96        27.28  4.00    Yes  Fri  Dinner     2  0.146628\n",
       "97        12.03  1.50    Yes  Fri  Dinner     2  0.124688\n",
       "98        21.01  3.00    Yes  Fri  Dinner     2  0.142789\n",
       "99        12.46  1.50     No  Fri  Dinner     2  0.120385\n",
       "100       11.35  2.50    Yes  Fri  Dinner     2  0.220264\n",
       "101       15.38  3.00    Yes  Fri  Dinner     2  0.195059\n",
       "220       12.16  2.20    Yes  Fri   Lunch     2  0.180921\n",
       "221       13.42  3.48    Yes  Fri   Lunch     2  0.259314\n",
       "222        8.58  1.92    Yes  Fri   Lunch     1  0.223776\n",
       "223       15.98  3.00     No  Fri   Lunch     3  0.187735\n",
       "224       13.42  1.58    Yes  Fri   Lunch     2  0.117735\n",
       "225       16.27  2.50    Yes  Fri   Lunch     2  0.153657\n",
       "226       10.09  2.00    Yes  Fri   Lunch     2  0.198216"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def filter_func(x):\n",
    "    return x['size'].count() < 16\n",
    "tips.groupby(['smoker', 'day']).filter(filter_func)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如上述例子：分组后有些组的count会小于16，filter可以可以每一组进行判断，如果满足条件的组会被挑选出来\n",
    "## transform:转换\n",
    "Aggregate操作返回的是对组内全变量缩减的过程。\n",
    "而转换则会返回一个新的**全量数据**  \n",
    "示例：根据smoker对tips进行分组，对每个小组的size求平均误差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:00:12.171961Z",
     "start_time": "2019-11-11T02:00:12.153252Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'key':['a','b','a','a','b'],'data':[1,2,3,4,5], 'data2':[100,200,300,400,500]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:00:26.303593Z",
     "start_time": "2019-11-11T02:00:26.243800Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data</th>\n",
       "      <th>data2</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>a</td>\n",
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       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>4</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data  data2\n",
       "0   a     1    100\n",
       "1   b     2    200\n",
       "2   a     3    300\n",
       "3   a     4    400\n",
       "4   b     5    500"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\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>data</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2.666667</td>\n",
       "      <td>266.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>350.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         data       data2\n",
       "key                      \n",
       "a    2.666667  266.666667\n",
       "b    3.500000  350.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.666667</td>\n",
       "      <td>-166.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.500000</td>\n",
       "      <td>-150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>133.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       data       data2\n",
       "0 -1.666667 -166.666667\n",
       "1 -1.500000 -150.000000\n",
       "2  0.333333   33.333333\n",
       "3  1.333333  133.333333\n",
       "4  1.500000  150.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df, df.groupby('key').mean(),\n",
    "        df.groupby('key').transform(lambda x: x-x.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## apply:一般性的‘拆分-应用-合并’"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "apply会将待处理的对象拆分成多个片段，然后对各片段调用传入的函数，最后尝试将各片段组合到一起。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://camo.githubusercontent.com/75efe2c5f4cd3b5580b7561558bfb575f7f3092c/687474703a2f2f75706c6f61642d696d616765732e6a69616e7368752e696f2f75706c6f61645f696d616765732f373137383639312d376538626232313766353939623461652e706e673f696d6167654d6f6772322f6175746f2d6f7269656e742f7374726970253743696d61676556696577322f322f772f31323430)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：根据分组选出最高5个tip_pct。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T16:14:26.483411Z",
     "start_time": "2019-11-10T16:14:26.442863Z"
    }
   },
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
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       "    <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",
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       "      <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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def top(df, n=5, column='tip_pct'):\n",
    "    return df.sort_values(by=column)[-n:]\n",
    "\n",
    "top(tips, n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T16:16:01.518716Z",
     "start_time": "2019-11-10T16:16:01.474372Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"5\" valign=\"top\">No</th>\n",
       "      <th>88</th>\n",
       "      <td>24.71</td>\n",
       "      <td>5.85</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
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       "      <th>185</th>\n",
       "      <td>20.69</td>\n",
       "      <td>5.00</td>\n",
       "      <td>No</td>\n",
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       "      <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",
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       "      <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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('smoker').apply(top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**apply传入的函数对tips分组的DataFrame的各个片段进行调用，然后用pd.concat组装到一起，并以分组名称进行标记"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例2：根据分组选出total_bill中最高的7个  \n",
    "（即，往top里面传参数）  \n",
    "**Note:**可以在apply后面添加参数\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T16:23:47.086905Z",
     "start_time": "2019-11-10T16:23:47.042643Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">No</th>\n",
       "      <th>238</th>\n",
       "      <td>35.83</td>\n",
       "      <td>4.67</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.130338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>38.07</td>\n",
       "      <td>4.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.105070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>39.42</td>\n",
       "      <td>7.58</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.192288</td>\n",
       "    </tr>\n",
       "    <tr>\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>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>59</th>\n",
       "      <td>48.27</td>\n",
       "      <td>6.73</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.139424</td>\n",
       "    </tr>\n",
       "    <tr>\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 rowspan=\"7\" valign=\"top\">Yes</th>\n",
       "      <th>207</th>\n",
       "      <td>38.73</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.077459</td>\n",
       "    </tr>\n",
       "    <tr>\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>184</th>\n",
       "      <td>40.55</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.073983</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>44.30</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.056433</td>\n",
       "    </tr>\n",
       "    <tr>\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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_bill    tip smoker   day    time  size   tip_pct\n",
       "smoker                                                            \n",
       "No     238       35.83   4.67     No   Sat  Dinner     3  0.130338\n",
       "       112       38.07   4.00     No   Sun  Dinner     3  0.105070\n",
       "       23        39.42   7.58     No   Sat  Dinner     4  0.192288\n",
       "       142       41.19   5.00     No  Thur   Lunch     5  0.121389\n",
       "       156       48.17   5.00     No   Sun  Dinner     6  0.103799\n",
       "       59        48.27   6.73     No   Sat  Dinner     4  0.139424\n",
       "       212       48.33   9.00     No   Sat  Dinner     4  0.186220\n",
       "Yes    207       38.73   3.00    Yes   Sat  Dinner     4  0.077459\n",
       "       95        40.17   4.73    Yes   Fri  Dinner     4  0.117750\n",
       "       184       40.55   3.00    Yes   Sun  Dinner     2  0.073983\n",
       "       197       43.11   5.00    Yes  Thur   Lunch     4  0.115982\n",
       "       102       44.30   2.50    Yes   Sat  Dinner     3  0.056433\n",
       "       182       45.35   3.50    Yes   Sun  Dinner     3  0.077178\n",
       "       170       50.81  10.00    Yes   Sat  Dinner     3  0.196812"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('smoker').apply(top, n=7, column='total_bill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "apply可以传入自定义函数，只要返回一个**pandas对象或者标量**就可以"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例3：根据smoker对tips进行分组，去分组中的tip_pct，并描述(describe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T16:27:23.540591Z",
     "start_time": "2019-11-10T16:27:23.499975Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>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>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
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       "  </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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = tips.groupby('smoker')['tip_pct'].describe()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T16:27:31.371021Z",
     "start_time": "2019-11-10T16:27:31.349148Z"
    }
   },
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面代码相当于："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T16:28:22.668693Z",
     "start_time": "2019-11-10T16:28:22.554576Z"
    },
    "scrolled": true
   },
   "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>total_bill</th>\n",
       "      <th>tip</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">No</th>\n",
       "      <th>count</th>\n",
       "      <td>151.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>151.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.188278</td>\n",
       "      <td>2.991854</td>\n",
       "      <td>2.668874</td>\n",
       "      <td>0.159328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.255582</td>\n",
       "      <td>1.377190</td>\n",
       "      <td>1.017984</td>\n",
       "      <td>0.039910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.056797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.325000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.136906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.590000</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.155625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.755000</td>\n",
       "      <td>3.505000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.185014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.330000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.291990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Yes</th>\n",
       "      <th>count</th>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.756344</td>\n",
       "      <td>3.008710</td>\n",
       "      <td>2.408602</td>\n",
       "      <td>0.163196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.832154</td>\n",
       "      <td>1.401468</td>\n",
       "      <td>0.810751</td>\n",
       "      <td>0.085119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.070000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.420000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.106771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.920000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26.860000</td>\n",
       "      <td>3.680000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.195059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.810000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.710345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              total_bill         tip        size     tip_pct\n",
       "smoker                                                      \n",
       "No     count  151.000000  151.000000  151.000000  151.000000\n",
       "       mean    19.188278    2.991854    2.668874    0.159328\n",
       "       std      8.255582    1.377190    1.017984    0.039910\n",
       "       min      7.250000    1.000000    1.000000    0.056797\n",
       "       25%     13.325000    2.000000    2.000000    0.136906\n",
       "       50%     17.590000    2.740000    2.000000    0.155625\n",
       "       75%     22.755000    3.505000    3.000000    0.185014\n",
       "       max     48.330000    9.000000    6.000000    0.291990\n",
       "Yes    count   93.000000   93.000000   93.000000   93.000000\n",
       "       mean    20.756344    3.008710    2.408602    0.163196\n",
       "       std      9.832154    1.401468    0.810751    0.085119\n",
       "       min      3.070000    1.000000    1.000000    0.035638\n",
       "       25%     13.420000    2.000000    2.000000    0.106771\n",
       "       50%     17.920000    3.000000    2.000000    0.153846\n",
       "       75%     26.860000    3.680000    3.000000    0.195059\n",
       "       max     50.810000   10.000000    5.000000    0.710345"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('smoker').apply(lambda x:x.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例4：根据smoker对tips进行分组，并添加一列(tip_multi_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T01:09:40.184887Z",
     "start_time": "2019-11-11T01:09:40.101165Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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",
       "      <th>tip_multi_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.059447</td>\n",
       "      <td>2.02</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",
       "      <td>4.98</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",
       "      <td>10.50</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",
       "      <td>6.62</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",
       "      <td>14.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip smoker  day    time  size   tip_pct  tip_multi_size\n",
       "0       16.99  1.01     No  Sun  Dinner     2  0.059447            2.02\n",
       "1       10.34  1.66     No  Sun  Dinner     3  0.160542            4.98\n",
       "2       21.01  3.50     No  Sun  Dinner     3  0.166587           10.50\n",
       "3       23.68  3.31     No  Sun  Dinner     2  0.139780            6.62\n",
       "4       24.59  3.61     No  Sun  Dinner     4  0.146808           14.44"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def apply_func(x):\n",
    "    x['tip_multi_size'] = x['tip'] * x['size']\n",
    "    return x\n",
    "tips.groupby('smoker').apply(apply_func).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 禁止分组键"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上面例子可以看出：通过分组apply后分组的依据会当作索引。可以通过传入group_keys,修改操作。  \n",
    "\n",
    "示例1：根据smoker对tips进行分组，并把分组的的依据还原。  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T16:32:44.838607Z",
     "start_time": "2019-11-10T16:32:44.767394Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('smoker', group_keys=False).apply(top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-10T16:32:58.284946Z",
     "start_time": "2019-11-10T16:32:58.235287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>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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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</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\">1</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",
       "0 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",
       "1 109       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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('smoker', as_index=False).apply(top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对比一下group_keys和as_index可以看出，as_index只是把分组依据还原回去后，用默认索引代替。  \n",
    "而group_keys一旦设定就不会用层级索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分位数和桶分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T01:40:23.499341Z",
     "start_time": "2019-11-11T01:40:23.429834Z"
    }
   },
   "outputs": [],
   "source": [
    "frame = pd.DataFrame({'data1': np.random.randn(1000),\n",
    "                       'data2': np.random.randn(1000)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T01:41:47.519226Z",
     "start_time": "2019-11-11T01:41:47.485945Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     (0.332, 1.828]\n",
       "1    (-1.164, 0.332]\n",
       "2     (0.332, 1.828]\n",
       "3    (-1.164, 0.332]\n",
       "4     (0.332, 1.828]\n",
       "5    (-1.164, 0.332]\n",
       "6    (-1.164, 0.332]\n",
       "7     (1.828, 3.324]\n",
       "Name: data1, dtype: category\n",
       "Categories (4, interval[float64]): [(-2.667, -1.164] < (-1.164, 0.332] < (0.332, 1.828] < (1.828, 3.324]]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quartiles = pd.cut(frame.data1, 4)\n",
    "quartiles[:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：根据区间划分对frame.data2进行分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T01:43:22.470042Z",
     "start_time": "2019-11-11T01:43:22.444798Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = frame.data2.groupby(quartiles)\n",
    "len(list(grouped))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例2：根据区间划分对frame.data2进行分类。对获取每个小组的统计数据(count, max, mean, max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T01:48:53.919714Z",
     "start_time": "2019-11-11T01:48:53.877049Z"
    }
   },
   "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>(-2.667, -1.164]</th>\n",
       "      <td>106.0</td>\n",
       "      <td>3.210119</td>\n",
       "      <td>0.039615</td>\n",
       "      <td>-2.360351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(-1.164, 0.332]</th>\n",
       "      <td>523.0</td>\n",
       "      <td>2.841288</td>\n",
       "      <td>0.009685</td>\n",
       "      <td>-2.591931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.332, 1.828]</th>\n",
       "      <td>336.0</td>\n",
       "      <td>3.141762</td>\n",
       "      <td>-0.055392</td>\n",
       "      <td>-3.204247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1.828, 3.324]</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1.244815</td>\n",
       "      <td>-0.102135</td>\n",
       "      <td>-1.828422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count       max      mean       min\n",
       "data1                                                \n",
       "(-2.667, -1.164]  106.0  3.210119  0.039615 -2.360351\n",
       "(-1.164, 0.332]   523.0  2.841288  0.009685 -2.591931\n",
       "(0.332, 1.828]    336.0  3.141762 -0.055392 -3.204247\n",
       "(1.828, 3.324]     35.0  1.244815 -0.102135 -1.828422"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_stats(group):\n",
    "    return {\n",
    "        'min':group.min(),\n",
    "        'max':group.max(),\n",
    "        'count':group.count(),\n",
    "        'mean':group.mean()\n",
    "    }\n",
    "grouped.apply(get_stats).unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T01:49:39.075778Z",
     "start_time": "2019-11-11T01:49:39.063173Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_stats2(group):\n",
    "    group['min'] = group.min()\n",
    "    group['max'] = group.max()\n",
    "    return group\n",
    "# 两种方法不一样\n",
    "# grouped.apply(get_stats2).unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T01:52:54.601128Z",
     "start_time": "2019-11-11T01:52:54.541735Z"
    }
   },
   "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>3.210119</td>\n",
       "      <td>0.050064</td>\n",
       "      <td>-2.360351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.841288</td>\n",
       "      <td>0.027774</td>\n",
       "      <td>-2.586979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.482167</td>\n",
       "      <td>-0.028772</td>\n",
       "      <td>-2.016180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.721582</td>\n",
       "      <td>-0.004746</td>\n",
       "      <td>-1.954934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.433149</td>\n",
       "      <td>0.093108</td>\n",
       "      <td>-2.591931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.062076</td>\n",
       "      <td>-0.068910</td>\n",
       "      <td>-1.997119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.216600</td>\n",
       "      <td>0.061283</td>\n",
       "      <td>-2.645708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100.0</td>\n",
       "      <td>1.914626</td>\n",
       "      <td>0.045205</td>\n",
       "      <td>-3.204247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.312653</td>\n",
       "      <td>-0.132874</td>\n",
       "      <td>-2.353333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100.0</td>\n",
       "      <td>3.141762</td>\n",
       "      <td>-0.171351</td>\n",
       "      <td>-2.529357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count       max      mean       min\n",
       "data1                                     \n",
       "0      100.0  3.210119  0.050064 -2.360351\n",
       "1      100.0  2.841288  0.027774 -2.586979\n",
       "2      100.0  2.482167 -0.028772 -2.016180\n",
       "3      100.0  2.721582 -0.004746 -1.954934\n",
       "4      100.0  2.433149  0.093108 -2.591931\n",
       "5      100.0  2.062076 -0.068910 -1.997119\n",
       "6      100.0  2.216600  0.061283 -2.645708\n",
       "7      100.0  1.914626  0.045205 -3.204247\n",
       "8      100.0  2.312653 -0.132874 -2.353333\n",
       "9      100.0  3.141762 -0.171351 -2.529357"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouping = pd.qcut(frame.data1, 10, labels=False)\n",
    "grouped = frame.data2.groupby(grouping)\n",
    "grouped.apply(get_stats).unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例一：用特定于分组的值填充缺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在数据处理难免会遇到缺失值，一般可以通过`fillna(value)`去填充缺失值。但如果我们想按分组填充缺失值的话。可以通过apply进行不同组的填充.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:03:29.207361Z",
     "start_time": "2019-11-11T02:03:29.195153Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1    1.020236\n",
       "2         NaN\n",
       "3   -0.844332\n",
       "4         NaN\n",
       "5    0.172252\n",
       "dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.randn(6))\n",
    "s[::2] = np.nan\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：普通填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:03:52.541476Z",
     "start_time": "2019-11-11T02:03:52.528950Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.116052\n",
       "1    1.020236\n",
       "2    0.116052\n",
       "3   -0.844332\n",
       "4    0.116052\n",
       "5    0.172252\n",
       "dtype: float64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(s.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例2：根据分组填充缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:04:59.314884Z",
     "start_time": "2019-11-11T02:04:59.287970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio          0.422509\n",
       "New York     -0.000253\n",
       "Vermont            NaN\n",
       "Florida       0.748856\n",
       "Oregon        0.303337\n",
       "Nevada             NaN\n",
       "California    1.161443\n",
       "Idaho              NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 148,
     "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[['Vermont', 'Nevada', 'Idaho']] = np.nan\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:05:54.801907Z",
     "start_time": "2019-11-11T02:05:54.791360Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "East    0.390371\n",
       "West    0.732390\n",
       "dtype: float64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(group_key).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:06:21.782112Z",
     "start_time": "2019-11-11T02:06:21.755818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio          0.422509\n",
       "New York     -0.000253\n",
       "Vermont       0.390371\n",
       "Florida       0.748856\n",
       "Oregon        0.303337\n",
       "Nevada        0.732390\n",
       "California    1.161443\n",
       "Idaho         0.732390\n",
       "dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(group_key).apply(lambda g:g.fillna(g.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例3：根据分组名称进行不同分组填充缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:09:12.401704Z",
     "start_time": "2019-11-11T02:09:12.374607Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio            0.422509\n",
       "New York       -0.000253\n",
       "Vermont       111.000000\n",
       "Florida         0.748856\n",
       "Oregon          0.303337\n",
       "Nevada        222.000000\n",
       "California      1.161443\n",
       "Idaho         222.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill_values = {\n",
    "    'East':111,\n",
    "    'West':222\n",
    "}\n",
    "def fill_func(g):\n",
    "    \"\"\"\n",
    "    由于分组后，每个小组都有自己的名称(可以通过g.name获得)\n",
    "    我们可以根据名称填充不同的值\n",
    "    \"\"\"\n",
    "    return g.fillna(fill_values[g.name])\n",
    "data.groupby(group_key).apply(fill_func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例二：随机采样与排列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先创建一副扑克牌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:17:22.942780Z",
     "start_time": "2019-11-11T02:17:22.927316Z"
    }
   },
   "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     11\n",
       "QH     12\n",
       "KH     13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hearts, Spades, Clubs, Diamonds\n",
    "suits = ['H', 'S', 'C', 'D']\n",
    "card_val = (list(range(1, 11)) + [11, 12, 13]) * 4\n",
    "base_names = ['A'] + list(range(2, 11)) + ['J', 'Q', 'K']\n",
    "cards = []\n",
    "for suit in suits:\n",
    "    cards.extend(str(num) + suit for num in base_names)\n",
    "deck = pd.Series(card_val, index=cards)\n",
    "deck[:13]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：从扑克牌中随机抽取n张牌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:18:45.274573Z",
     "start_time": "2019-11-11T02:18:45.264349Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9D     9\n",
       "5D     5\n",
       "AC     1\n",
       "QH    12\n",
       "7S     7\n",
       "4C     4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def draw(deck, n=5):\n",
    "    \"\"\"\n",
    "    从扑克牌deck中抽取n张牌\n",
    "    \"\"\"\n",
    "    return deck.sample(n)\n",
    "\n",
    "draw(deck, n=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例2：从每种花色随机抽取2张"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:22:38.137257Z",
     "start_time": "2019-11-11T02:22:38.113154Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C  6C     6\n",
       "   QC    12\n",
       "D  AD     1\n",
       "   JD    11\n",
       "H  AH     1\n",
       "   9H     9\n",
       "S  4S     4\n",
       "   JS    11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_same_suit(card):\n",
    "    \"\"\"\n",
    "    由于花色的索引为Series的索引字符串-1位置\n",
    "    所以可以通过访问字符串-1判别花色\n",
    "    \"\"\"\n",
    "    return card[-1]\n",
    "deck.groupby(get_same_suit).apply(draw, n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以这样写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:23:30.509674Z",
     "start_time": "2019-11-11T02:23:30.491618Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9C    9\n",
       "6C    6\n",
       "AD    1\n",
       "5D    5\n",
       "4H    4\n",
       "9H    9\n",
       "AS    1\n",
       "7S    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck.groupby(get_same_suit, group_keys=False).apply(draw, n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例3：分组加权平均数和相关系数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：加权平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:26:44.430139Z",
     "start_time": "2019-11-11T02:26:44.351105Z"
    }
   },
   "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>0.753510</td>\n",
       "      <td>0.838128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>-1.073305</td>\n",
       "      <td>0.605984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>-0.948689</td>\n",
       "      <td>0.870206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>1.278804</td>\n",
       "      <td>0.886689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>0.035030</td>\n",
       "      <td>0.276160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>0.874235</td>\n",
       "      <td>0.789520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b</td>\n",
       "      <td>-0.369767</td>\n",
       "      <td>0.730400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b</td>\n",
       "      <td>-1.270240</td>\n",
       "      <td>0.909210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category      data   weights\n",
       "0        a  0.753510  0.838128\n",
       "1        a -1.073305  0.605984\n",
       "2        a -0.948689  0.870206\n",
       "3        a  1.278804  0.886689\n",
       "4        b  0.035030  0.276160\n",
       "5        b  0.874235  0.789520\n",
       "6        b -0.369767  0.730400\n",
       "7        b -1.270240  0.909210"
      ]
     },
     "execution_count": 168,
     "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": 171,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:29:11.690942Z",
     "start_time": "2019-11-11T02:29:11.663624Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "a    0.090434\n",
       "b   -0.268028\n",
       "dtype: float64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_wavg(g):\n",
    "    \"\"\"\n",
    "    定义加权平均函数\n",
    "    \"\"\"\n",
    "    return np.average(g.data, weights=g.weights)\n",
    "df.groupby('category').apply(get_wavg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例2：计算股票日均收益率与标普500之间的年度相关系数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取股票信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:30:15.335434Z",
     "start_time": "2019-11-11T02:30:15.307442Z"
    }
   },
   "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('examples/stock_px_2.csv', parse_dates=True,\n",
    "                           index_col=0)\n",
    "close_px.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:32:05.479959Z",
     "start_time": "2019-11-11T02:32:05.460688Z"
    }
   },
   "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>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "      <th>SPX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-02</th>\n",
       "      <td>7.40</td>\n",
       "      <td>21.11</td>\n",
       "      <td>29.22</td>\n",
       "      <td>909.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>7.45</td>\n",
       "      <td>21.14</td>\n",
       "      <td>29.24</td>\n",
       "      <td>908.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>7.45</td>\n",
       "      <td>21.52</td>\n",
       "      <td>29.96</td>\n",
       "      <td>929.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>7.43</td>\n",
       "      <td>21.93</td>\n",
       "      <td>28.95</td>\n",
       "      <td>922.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>7.28</td>\n",
       "      <td>21.31</td>\n",
       "      <td>28.83</td>\n",
       "      <td>909.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL   MSFT    XOM     SPX\n",
       "2003-01-02  7.40  21.11  29.22  909.03\n",
       "2003-01-03  7.45  21.14  29.24  908.59\n",
       "2003-01-06  7.45  21.52  29.96  929.01\n",
       "2003-01-07  7.43  21.93  28.95  922.93\n",
       "2003-01-08  7.28  21.31  28.83  909.93"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_px.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:46:47.598242Z",
     "start_time": "2019-11-11T02:46:47.593299Z"
    }
   },
   "outputs": [],
   "source": [
    "# 创建函数，计算每支股票于标普500的相关系数\n",
    "spx_corr = lambda x: x.corrwith(x['SPX'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:46:48.430591Z",
     "start_time": "2019-11-11T02:46:48.412131Z"
    }
   },
   "outputs": [],
   "source": [
    "# 获取股票的日均收益率，由于第一行没有对比数据所以为na，这里用dropna删除\n",
    "rets = close_px.pct_change().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:46:48.924850Z",
     "start_time": "2019-11-11T02:46:48.919515Z"
    }
   },
   "outputs": [],
   "source": [
    "# 获取所有的年份\n",
    "get_year = lambda x:x.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:46:50.291573Z",
     "start_time": "2019-11-11T02:46:50.283596Z"
    }
   },
   "outputs": [],
   "source": [
    "# 用于没有传入键，默认以索引为键-->get_year(index)\n",
    "by_year = rets.groupby(get_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:52:57.124742Z",
     "start_time": "2019-11-11T02:52:57.057911Z"
    },
    "scrolled": true
   },
   "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>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": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# def _(g):\n",
    "#     print(type(g))\n",
    "#     return g.corrwith(g['SPX'])\n",
    "# by_year.apply(_)\n",
    "by_year.apply(spx_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例3：计算Apple于Micosoft的年相关系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:58:10.325784Z",
     "start_time": "2019-11-11T02:58:10.306829Z"
    }
   },
   "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": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# def _(g):\n",
    "#     print(type(g))\n",
    "#     return g['AAPL'].corr(g['MSFT'])\n",
    "# by_year.apply()\n",
    "by_year.apply(lambda g:g['AAPL'].corr(g['MSFT']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**corrwith用于两个DataFrame之间的比较，corr用于两个Series之间的比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T02:59:33.056209Z",
     "start_time": "2019-11-11T02:59:33.029814Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32732683535398854"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame([1,2,3],index=['2011','2012','2013'], columns=['c1'])\n",
    "df2 = pd.DataFrame([2,5,3],index=['2011','2012','2013'], columns=['c2'])\n",
    "df1.corrwith(df2)\n",
    "df1['c1'].corr(df2['c2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例4：组级别的线性回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "顺着上一个例子继续，你可以用groupby执行更为复杂的分组统计分析，只要函数返回的是pandas对象或标量值即可。例如，我可以定义下面这个regress函数（利用statsmodels计量经济学库）对各数据块执行普通最小二乘法（Ordinary Least Squares，OLS）回归："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:07:01.128268Z",
     "start_time": "2019-11-11T03:07:01.033540Z"
    }
   },
   "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>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": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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\n",
    "\n",
    "by_year.apply(regress, 'AAPL', ['SPX'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 透视表(pivot table)和交叉表\n",
    "![](https://camo.githubusercontent.com/63b3d687e5ff4cc334b0b72ce681c58c89482ff3/687474703a2f2f75706c6f61642d696d616765732e6a69616e7368752e696f2f75706c6f61645f696d616765732f373137383639312d633965303138343463343830336134322e706e673f696d6167654d6f6772322f6175746f2d6f7269656e742f7374726970253743696d61676556696577322f322f772f31323430)\n",
    ">数据透视表（pivot table） 是一种类似的操作方法， 常见于 Excel 与\n",
    "类似的表格应用中。 数据透视表将每一列数据作为输入， 输出将数据不\n",
    "断细分成多个维度累计信息的二维数据表。 人们有时容易弄混数据透视\n",
    "表与 GroupBy， 但我觉得数据透视表更像是一种多维的 GroupBy 累计\n",
    "操作。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 演示数据透视表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:18:16.318145Z",
     "start_time": "2019-11-11T03:18:16.065629Z"
    }
   },
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "titanic = sns.load_dataset('titanic')\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 手工制作数据透视表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：根据sex对titanic进行分组选取其中的survived，对其求平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:20:04.526194Z",
     "start_time": "2019-11-11T03:20:04.506247Z"
    }
   },
   "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>survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived\n",
       "sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('sex')[['survived']].mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:20:36.524832Z",
     "start_time": "2019-11-11T03:20:36.517230Z"
    }
   },
   "source": [
    "示例2：将船舱等级（ 'class'） 与性别（ 'sex'） 分组， 然后选\n",
    "择生还状态（ 'survived'） 列， 应用均值（ 'mean'） 累计函数， 再将\n",
    "各组结果组合， 最后通过行索引转列索引操作将最里层的行索引转换成\n",
    "列索引， 形成二维数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:23:56.875931Z",
     "start_time": "2019-11-11T03:23:56.853391Z"
    }
   },
   "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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(['class','sex'])['survived'].mean().unstack(level=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据透视表语法\n",
    "用 DataFrame 的 pivot_table 实现的效果等同于上一节的管道命令的\n",
    "代码："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`df.pivot_table(value=[想要观察的数据], index=[分组名，并且把分组放在索引中], columns=[分组名, 并且把分组放在列索引中],aggfunc='要聚合的函数'，fill_value='填充na值', margins='boolean添加边缘密度函数', margins_name='ALL 对边缘密度函数起别名')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:23:45.479121Z",
     "start_time": "2019-11-11T03:23:45.401919Z"
    }
   },
   "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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(values='survived', index=['sex'], columns=['class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多级数据透视表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:29:15.368674Z",
     "start_time": "2019-11-11T03:29:15.361692Z"
    }
   },
   "source": [
    "与 GroupBy 类似， 数据透视表中的分组也可以通过各种参数指定\n",
    "多个等级。 例如， 我们可能想把年龄（'age'） 也加进去作为第三\n",
    "个维度， 这就可以通过 pd.cut 函数将年龄进行分段："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:30:53.444936Z",
     "start_time": "2019-11-11T03:30:53.366447Z"
    }
   },
   "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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.423729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.133663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class               First    Second     Third\n",
       "sex    age                                   \n",
       "female (0, 18]   0.909091  1.000000  0.511628\n",
       "       (18, 80]  0.972973  0.900000  0.423729\n",
       "male   (0, 18]   0.800000  0.600000  0.215686\n",
       "       (18, 80]  0.375000  0.071429  0.133663"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.cut(titanic['age'], [0, 18, 80])\n",
    "titanic.pivot_table('survived', index=['sex', age], columns='class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用 pd.qcut 将船票价\n",
    "格按照计数项等分为两份， 加入数据透视表看看："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:33:58.732801Z",
     "start_time": "2019-11-11T03:33:58.275562Z"
    }
   },
   "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",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">(-0.001, 14.454]</th>\n",
       "      <th colspan=\"3\" halign=\"left\">(14.454, 512.329]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.318182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>0.391304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0</td>\n",
       "      <td>0.098039</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.192308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fare            (-0.001, 14.454]                     (14.454, 512.329]  \\\n",
       "class                      First    Second     Third             First   \n",
       "sex    age                                                               \n",
       "female (0, 18]                 0  1.000000  0.714286          0.909091   \n",
       "       (18, 80]                0  0.880000  0.444444          0.972973   \n",
       "male   (0, 18]                 0  0.000000  0.260870          0.800000   \n",
       "       (18, 80]                0  0.098039  0.125000          0.391304   \n",
       "\n",
       "fare                                 \n",
       "class              Second     Third  \n",
       "sex    age                           \n",
       "female (0, 18]   1.000000  0.318182  \n",
       "       (18, 80]  0.914286  0.391304  \n",
       "male   (0, 18]   0.818182  0.178571  \n",
       "       (18, 80]  0.030303  0.192308  "
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare = pd.qcut(titanic['fare'], 2)\n",
    "titanic.pivot_table('survived', ['sex', age], [fare, 'class'], fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 其他数据透视表选项"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "aggfunc 参数用于设置累计函数类型， 默认值是均值（mean） 。\n",
    "与 GroupBy 的用法一样， 累计函数可以用一些常见的字符串\n",
    "（'sum'、 'mean'、 'count'、 'min'、 'max' 等） 表示， 也可以\n",
    "用标准的累计函数（np.sum()、 min()、 sum() 等） 表示。 另外，还可以通过字典为不同的列指定不同的累计函数："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### aggfunc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:36:26.374172Z",
     "start_time": "2019-11-11T03:36:26.319922Z"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>106.125798</td>\n",
       "      <td>21.970121</td>\n",
       "      <td>16.118810</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>67.226127</td>\n",
       "      <td>19.741782</td>\n",
       "      <td>12.661633</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare                       survived             \n",
       "class        First     Second      Third    First Second Third\n",
       "sex                                                           \n",
       "female  106.125798  21.970121  16.118810       91     70    72\n",
       "male     67.226127  19.741782  12.661633       45     17    47"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(['survived', 'fare'], index='sex', columns='class',\n",
    "                   aggfunc={'survived':sum, 'fare':'mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面相当于："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:38:06.766250Z",
     "start_time": "2019-11-11T03:38:06.706068Z"
    }
   },
   "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",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>106.125798</td>\n",
       "      <td>21.970121</td>\n",
       "      <td>16.118810</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>67.226127</td>\n",
       "      <td>19.741782</td>\n",
       "      <td>12.661633</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare                       survived             \n",
       "class        First     Second      Third    First Second Third\n",
       "sex                                                           \n",
       "female  106.125798  21.970121  16.118810       91     70    72\n",
       "male     67.226127  19.741782  12.661633       45     17    47"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由于aggfunc里面传入参数，pandas可以自动识别出要聚合的values\n",
    "\n",
    "titanic.pivot_table(index='sex', columns='class',\n",
    "                   aggfunc={'survived':sum, 'fare':'mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### margins:边缘密度函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:39:58.141165Z",
     "start_time": "2019-11-11T03:39:58.048385Z"
    }
   },
   "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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third       All\n",
       "sex                                           \n",
       "female  0.968085  0.921053  0.500000  0.742038\n",
       "male    0.368852  0.157407  0.135447  0.188908\n",
       "All     0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(values='survived',index='sex',\n",
    "                   columns='class', margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### margins_name:起别名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:40:17.678848Z",
     "start_time": "2019-11-11T03:40:17.584185Z"
    }
   },
   "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",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>margins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>margins</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class       First    Second     Third   margins\n",
       "sex                                            \n",
       "female   0.968085  0.921053  0.500000  0.742038\n",
       "male     0.368852  0.157407  0.135447  0.188908\n",
       "margins  0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(values='survived',index='sex',\n",
    "                   columns='class', margins=True,\n",
    "                   margins_name='margins')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:12:22.111370Z",
     "start_time": "2019-11-11T03:12:22.083143Z"
    }
   },
   "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>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": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = pd.read_csv('examples/tips.csv')\n",
    "# 添加小费百分比\n",
    "tips['tip_pct'] = tips['tip'] / tips['total_bill']\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例1：根据day，smoker对tips进行分组，并把分组依据放在index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T03:41:48.827012Z",
     "start_time": "2019-11-11T03:41:48.791565Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <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": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(index=['day', 'smoker'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示例2：根据time，day，smoker对tips进行分组，并且把time，day放在index，\n",
    "把smoker放在columns.index，观察tip_pct, size的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T04:32:52.846897Z",
     "start_time": "2019-11-11T04:32:52.821399Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <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",
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       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th>day</th>\n",
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       "      <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": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(['tip_pct', 'size'], index=['time','day'], columns=['smoker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T04:40:08.950594Z",
     "start_time": "2019-11-11T04:40:08.825916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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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",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Dinner</th>\n",
       "      <th>Fri</th>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26</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>115.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>219</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>167.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>216</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>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</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>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14</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>110.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>150</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>403.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>627</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     6.0   20.0   26  0.139622  0.165347  0.158916\n",
       "       Sat   115.0  104.0  219  0.158048  0.147906  0.153152\n",
       "       Sun   167.0   49.0  216  0.160113  0.187250  0.166897\n",
       "       Thur    2.0    NaN    2  0.159744       NaN  0.159744\n",
       "Lunch  Fri     3.0   11.0   14  0.187735  0.188937  0.188765\n",
       "       Thur  110.0   40.0  150  0.160311  0.163863  0.161301\n",
       "All          403.0  224.0  627  0.159328  0.163196  0.160803"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(['tip_pct', 'size'],aggfunc={'size':'sum', 'tip_pct':'mean'},\n",
    "                 index=['time', 'day'], columns=['smoker'],\n",
    "                 margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 交叉表(cross-tabulation)\n",
    "用于计算分组频率的特殊透视表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T04:42:16.989664Z",
     "start_time": "2019-11-11T04:42:16.962845Z"
    }
   },
   "outputs": [
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       "    </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": 307,
     "metadata": {},
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   ],
   "source": [
    "data = pd.read_csv('examples/crosstab.csv')\n",
    "data"
   ]
  },
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     "end_time": "2019-11-11T04:41:53.704321Z",
     "start_time": "2019-11-11T04:41:53.644019Z"
    }
   },
   "source": [
    "示例1：根据国籍和用手习惯对这段数据进行统计汇总。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T04:44:19.562123Z",
     "start_time": "2019-11-11T04:44:19.484403Z"
    }
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   "outputs": [
    {
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       "      <th>Handedness</th>\n",
       "      <th>Left-handed</th>\n",
       "      <th>Right-handed</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
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       "      <th>Nationality</th>\n",
       "      <th></th>\n",
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       "      <th>Japan</th>\n",
       "      <td>2</td>\n",
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       "      <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",
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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": 309,
     "metadata": {},
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   ],
   "source": [
    "pd.crosstab(data.Nationality, data.Handedness, margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "crosstab可以是几个Series连接在一起"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-11T04:45:31.011785Z",
     "start_time": "2019-11-11T04:45:30.930142Z"
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   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>smoker</th>\n",
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       "      <th>Thur</th>\n",
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       "    <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",
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       "      <th>Thur</th>\n",
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      "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": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=[tips.time, tips.day], columns=tips.smoker, margins=True)"
   ]
  }
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