ch09_数据聚合与分组运算.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.sans-serif'] = [u'SimHei']\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False\n",
    "import os\n",
    "\n",
    "path =str(os.getcwd()).replace('\\\\','/')\n",
    "# filename = path +'/datasets/ch08_data/spx.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GroupBy技术"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.236473</td>\n",
       "      <td>-0.605849</td>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.520080</td>\n",
       "      <td>0.407974</td>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.663590</td>\n",
       "      <td>0.700334</td>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.172694</td>\n",
       "      <td>-1.207168</td>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.630407</td>\n",
       "      <td>0.725371</td>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      data1     data2 key1 key2\n",
       "0 -0.236473 -0.605849    a  one\n",
       "1 -0.520080  0.407974    a  two\n",
       "2 -1.663590  0.700334    b  one\n",
       "3 -0.172694 -1.207168    b  two\n",
       "4  0.630407  0.725371    a  one"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key1':['a','a','b','b','a'],\n",
    "                   'key2':['one','two','one','two','one'],\n",
    "                   'data1':np.random.randn(5),\n",
    "                   'data2':np.random.randn(5)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.SeriesGroupBy object at 0x05EC2250>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按key1进行分组，并计算data1列的平均值\n",
    "grouped = df['data1'].groupby(df['key1'])  \n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "a   -0.042049\n",
       "b   -0.918142\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调用GroupBy的mean方法来计算分组平均值\n",
    "# 数据（Series）根据分组键进行聚合，产生一个新的Series,其索引为key1列中的唯一值\n",
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1  key2\n",
       "a     one     0.196967\n",
       "      two    -0.520080\n",
       "b     one    -1.663590\n",
       "      two    -0.172694\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一次传入多个数组\n",
    "means = df['data1'].groupby([df['key1'],df['key2']]).mean()\n",
    "means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>key2</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
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       "      <th>a</th>\n",
       "      <td>0.196967</td>\n",
       "      <td>-0.520080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-1.663590</td>\n",
       "      <td>-0.172694</td>\n",
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      ],
      "text/plain": [
       "key2       one       two\n",
       "key1                    \n",
       "a     0.196967 -0.520080\n",
       "b    -1.663590 -0.172694"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "means.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2005   -0.520080\n",
       "            2006   -1.663590\n",
       "Ohio        2005   -0.204584\n",
       "            2006    0.630407\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分组键可以是任何长度适当的数组\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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "      <th>a</th>\n",
       "      <td>-0.042049</td>\n",
       "      <td>0.175832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
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       "      <td>-0.253417</td>\n",
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      ],
      "text/plain": [
       "         data1     data2\n",
       "key1                    \n",
       "a    -0.042049  0.175832\n",
       "b    -0.918142 -0.253417"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "# 可以将列名用作分组键\n",
    "df.groupby('key1').mean() # 默认情况下，所有数值列都会被聚合，会过滤掉非数值数据的列，所以把key2列过滤了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>0.196967</td>\n",
       "      <td>0.059761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.520080</td>\n",
       "      <td>0.407974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>one</th>\n",
       "      <td>-1.663590</td>\n",
       "      <td>0.700334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.172694</td>\n",
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      ],
      "text/plain": [
       "              data1     data2\n",
       "key1 key2                    \n",
       "a    one   0.196967  0.059761\n",
       "     two  -0.520080  0.407974\n",
       "b    one  -1.663590  0.700334\n",
       "     two  -0.172694 -1.207168"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1','key2']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1  key2\n",
       "a     one     2\n",
       "      two     1\n",
       "b     one     1\n",
       "      two     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GroupBy的size 返回一个含有分组大小的Series\n",
    "df.groupby(['key1','key2']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对分组进行迭代"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "      data1     data2 key1 key2\n",
      "0 -0.236473 -0.605849    a  one\n",
      "1 -0.520080  0.407974    a  two\n",
      "4  0.630407  0.725371    a  one\n",
      "b\n",
      "      data1     data2 key1 key2\n",
      "2 -1.663590  0.700334    b  one\n",
      "3 -0.172694 -1.207168    b  two\n"
     ]
    }
   ],
   "source": [
    "for name,group in df.groupby('key1'):\n",
    "    print(name)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a one\n",
      "      data1     data2 key1 key2\n",
      "0 -0.236473 -0.605849    a  one\n",
      "4  0.630407  0.725371    a  one\n",
      "a two\n",
      "     data1     data2 key1 key2\n",
      "1 -0.52008  0.407974    a  two\n",
      "b one\n",
      "     data1     data2 key1 key2\n",
      "2 -1.66359  0.700334    b  one\n",
      "b two\n",
      "      data1     data2 key1 key2\n",
      "3 -0.172694 -1.207168    b  two\n"
     ]
    }
   ],
   "source": [
    "# 对于多重键，元组的第一个元素会是由键值组成的元组\n",
    "for (k1,k2),group in df.groupby(['key1','key2']):\n",
    "    print(k1,k2)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.663590</td>\n",
       "      <td>0.700334</td>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.172694</td>\n",
       "      <td>-1.207168</td>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
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      ],
      "text/plain": [
       "      data1     data2 key1 key2\n",
       "2 -1.663590  0.700334    b  one\n",
       "3 -0.172694 -1.207168    b  two"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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    }
   ],
   "source": [
    "pieces = dict(list(df.groupby('key1')))\n",
    "pieces['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data1    float64\n",
       "data2    float64\n",
       "key1      object\n",
       "key2      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# groupby默认是在axis=1上进行分组的，当axis=1表示在列上进行分组\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{dtype('float64'):       data1     data2\n",
       " 0 -0.236473 -0.605849\n",
       " 1 -0.520080  0.407974\n",
       " 2 -1.663590  0.700334\n",
       " 3 -0.172694 -1.207168\n",
       " 4  0.630407  0.725371, dtype('O'):   key1 key2\n",
       " 0    a  one\n",
       " 1    a  two\n",
       " 2    b  one\n",
       " 3    b  two\n",
       " 4    a  one}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据dtype对列进行分组\n",
    "grouped = df.groupby(df.dtypes,axis=1)\n",
    "dict(list(grouped))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选取一个或一组列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>one</th>\n",
       "      <td>0.059761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.407974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>one</th>\n",
       "      <td>0.700334</td>\n",
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       "    <tr>\n",
       "      <th>two</th>\n",
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      "text/plain": [
       "              data2\n",
       "key1 key2          \n",
       "a    one   0.059761\n",
       "     two   0.407974\n",
       "b    one   0.700334\n",
       "     two  -1.207168"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1','key2'])[['data2']].mean() # 传入[['data2']] 是列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.SeriesGroupBy object at 0x073D8B10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_grouped = df.groupby(['key1','key2'])['data2']  # ['data2'] 传入的是单个列名\n",
    "s_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1  key2\n",
       "a     one     0.059761\n",
       "      two     0.407974\n",
       "b     one     0.700334\n",
       "      two    -1.207168\n",
       "Name: data2, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>-0.620131</td>\n",
       "      <td>-1.030947</td>\n",
       "      <td>0.719033</td>\n",
       "      <td>0.051255</td>\n",
       "      <td>1.384048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>0.021453</td>\n",
       "      <td>0.095205</td>\n",
       "      <td>0.792497</td>\n",
       "      <td>0.186442</td>\n",
       "      <td>-0.895837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>0.593875</td>\n",
       "      <td>-1.175601</td>\n",
       "      <td>0.274483</td>\n",
       "      <td>2.231851</td>\n",
       "      <td>0.047239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim</th>\n",
       "      <td>0.012906</td>\n",
       "      <td>0.748557</td>\n",
       "      <td>0.147786</td>\n",
       "      <td>-0.841786</td>\n",
       "      <td>0.086021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travis</th>\n",
       "      <td>0.044333</td>\n",
       "      <td>-0.386799</td>\n",
       "      <td>0.541179</td>\n",
       "      <td>0.170206</td>\n",
       "      <td>0.043394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               a         b         c         d         e\n",
       "Joe    -0.620131 -1.030947  0.719033  0.051255  1.384048\n",
       "Steve   0.021453  0.095205  0.792497  0.186442 -0.895837\n",
       "Wes     0.593875 -1.175601  0.274483  2.231851  0.047239\n",
       "Jim     0.012906  0.748557  0.147786 -0.841786  0.086021\n",
       "Travis  0.044333 -0.386799  0.541179  0.170206  0.043394"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过字典或Series进行分组\n",
    "people = pd.DataFrame(np.random.randn(5,5),\n",
    "                      columns=['a','b','c','d','e'],\n",
    "                      index =['Joe','Steve','Wes','Jim','Travis'])\n",
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>-0.620131</td>\n",
       "      <td>-1.030947</td>\n",
       "      <td>0.719033</td>\n",
       "      <td>0.051255</td>\n",
       "      <td>1.384048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>0.021453</td>\n",
       "      <td>0.095205</td>\n",
       "      <td>0.792497</td>\n",
       "      <td>0.186442</td>\n",
       "      <td>-0.895837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>0.593875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.231851</td>\n",
       "      <td>0.047239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim</th>\n",
       "      <td>0.012906</td>\n",
       "      <td>0.748557</td>\n",
       "      <td>0.147786</td>\n",
       "      <td>-0.841786</td>\n",
       "      <td>0.086021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travis</th>\n",
       "      <td>0.044333</td>\n",
       "      <td>-0.386799</td>\n",
       "      <td>0.541179</td>\n",
       "      <td>0.170206</td>\n",
       "      <td>0.043394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               a         b         c         d         e\n",
       "Joe    -0.620131 -1.030947  0.719033  0.051255  1.384048\n",
       "Steve   0.021453  0.095205  0.792497  0.186442 -0.895837\n",
       "Wes     0.593875       NaN       NaN  2.231851  0.047239\n",
       "Jim     0.012906  0.748557  0.147786 -0.841786  0.086021\n",
       "Travis  0.044333 -0.386799  0.541179  0.170206  0.043394"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.iloc[2:3,1:3] = np.nan  # 添加几个NA值\n",
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>blue</th>\n",
       "      <th>red</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>0.770288</td>\n",
       "      <td>-0.267029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>0.978938</td>\n",
       "      <td>-0.779180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>2.231851</td>\n",
       "      <td>0.641114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim</th>\n",
       "      <td>-0.694001</td>\n",
       "      <td>0.847483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travis</th>\n",
       "      <td>0.711385</td>\n",
       "      <td>-0.299072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            blue       red\n",
       "Joe     0.770288 -0.267029\n",
       "Steve   0.978938 -0.779180\n",
       "Wes     2.231851  0.641114\n",
       "Jim    -0.694001  0.847483\n",
       "Travis  0.711385 -0.299072"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据分组计算列的总计\n",
    "mapping = {'a':'red','b':'red','c':'blue','d':'blue','e':'red','f':'orange'}\n",
    "# 只需将这个字典传给groupby\n",
    "by_column = people.groupby(mapping,axis=1)\n",
    "by_column.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a       red\n",
       "b       red\n",
       "c      blue\n",
       "d      blue\n",
       "e       red\n",
       "f    orange\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果用Series作为分组，则pandas会检查Series以确保其索引跟分组轴是对齐的\n",
    "map_series = pd.Series(mapping)\n",
    "map_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <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": 23,
     "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": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>-0.013351</td>\n",
       "      <td>-0.282390</td>\n",
       "      <td>0.866819</td>\n",
       "      <td>1.441320</td>\n",
       "      <td>1.517309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.021453</td>\n",
       "      <td>0.095205</td>\n",
       "      <td>0.792497</td>\n",
       "      <td>0.186442</td>\n",
       "      <td>-0.895837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.044333</td>\n",
       "      <td>-0.386799</td>\n",
       "      <td>0.541179</td>\n",
       "      <td>0.170206</td>\n",
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      ],
      "text/plain": [
       "          a         b         c         d         e\n",
       "3 -0.013351 -0.282390  0.866819  1.441320  1.517309\n",
       "5  0.021453  0.095205  0.792497  0.186442 -0.895837\n",
       "6  0.044333 -0.386799  0.541179  0.170206  0.043394"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过函数进行分组,任何被当做分组键的函数都会在各个索引值上被调用一次，返回值就会被用作分组名称\n",
    "# 根据人名的长度进行分组，可传入len函数即可\n",
    "people.groupby(len).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.620131</td>\n",
       "      <td>-1.030947</td>\n",
       "      <td>0.719033</td>\n",
       "      <td>0.051255</td>\n",
       "      <td>0.047239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.012906</td>\n",
       "      <td>0.748557</td>\n",
       "      <td>0.147786</td>\n",
       "      <td>-0.841786</td>\n",
       "      <td>0.086021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>one</th>\n",
       "      <td>0.021453</td>\n",
       "      <td>0.095205</td>\n",
       "      <td>0.792497</td>\n",
       "      <td>0.186442</td>\n",
       "      <td>-0.895837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>two</th>\n",
       "      <td>0.044333</td>\n",
       "      <td>-0.386799</td>\n",
       "      <td>0.541179</td>\n",
       "      <td>0.170206</td>\n",
       "      <td>0.043394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              a         b         c         d         e\n",
       "3 one -0.620131 -1.030947  0.719033  0.051255  0.047239\n",
       "  two  0.012906  0.748557  0.147786 -0.841786  0.086021\n",
       "5 one  0.021453  0.095205  0.792497  0.186442 -0.895837\n",
       "6 two  0.044333 -0.386799  0.541179  0.170206  0.043394"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 函数跟数组、列表、字典、Series混合使用也可以\n",
    "key_list=['one','one','one','two','two']\n",
    "people.groupby([len,key_list]).min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 根据索引级别分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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",
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       "      <th>0</th>\n",
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       "      <td>-0.268056</td>\n",
       "      <td>-1.209800</td>\n",
       "      <td>0.281393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.778349</td>\n",
       "      <td>-1.329415</td>\n",
       "      <td>-1.335615</td>\n",
       "      <td>-0.152545</td>\n",
       "      <td>-1.310159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.609961</td>\n",
       "      <td>2.689072</td>\n",
       "      <td>0.677165</td>\n",
       "      <td>0.381002</td>\n",
       "      <td>0.627624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.155070</td>\n",
       "      <td>1.825284</td>\n",
       "      <td>-3.076447</td>\n",
       "      <td>-0.763961</td>\n",
       "      <td>-0.789791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cty          US                            JP          \n",
       "tenor         1         3         5         1         3\n",
       "0     -0.038260  0.492372 -0.268056 -1.209800  0.281393\n",
       "1      0.778349 -1.329415 -1.335615 -0.152545 -1.310159\n",
       "2      0.609961  2.689072  0.677165  0.381002  0.627624\n",
       "3      2.155070  1.825284 -3.076447 -0.763961 -0.789791"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 层次化索引数据集根据索引级别进行聚合，通过level关键字传入级别编号或名称即可\n",
    "columns = pd.MultiIndex.from_arrays([['US','US','US','JP','JP'],\n",
    "                                    [1,3,5,1,3]],names=['cty','tenor'])\n",
    "hier_df = pd.DataFrame(np.random.randn(4,5),columns=columns)\n",
    "hier_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cty</th>\n",
       "      <th>JP</th>\n",
       "      <th>US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cty  JP  US\n",
       "0     2   3\n",
       "1     2   3\n",
       "2     2   3\n",
       "3     2   3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hier_df.groupby(level='cty',axis=1).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据聚合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\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",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.236473</td>\n",
       "      <td>-0.605849</td>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.520080</td>\n",
       "      <td>0.407974</td>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.663590</td>\n",
       "      <td>0.700334</td>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.172694</td>\n",
       "      <td>-1.207168</td>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.630407</td>\n",
       "      <td>0.725371</td>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      data1     data2 key1 key2\n",
       "0 -0.236473 -0.605849    a  one\n",
       "1 -0.520080  0.407974    a  two\n",
       "2 -1.663590  0.700334    b  one\n",
       "3 -0.172694 -1.207168    b  two\n",
       "4  0.630407  0.725371    a  one"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 任何能够从数组产生标量值的数据转换过程\n",
    "# 用quantile计算Series或DataFrame列的样本分位数\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "a    0.457031\n",
       "b   -0.321783\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby('key1')\n",
    "grouped['data1'].quantile(0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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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>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>1.150487</td>\n",
       "      <td>1.331220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1.490896</td>\n",
       "      <td>1.907502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         data1     data2\n",
       "key1                    \n",
       "a     1.150487  1.331220\n",
       "b     1.490896  1.907502"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入自己的聚合函数 只需将其传入aggregate方法即可\n",
    "def peak_to_peak(arr):\n",
    "    return arr.max()-arr.min()\n",
    "grouped.agg(peak_to_peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">a</th>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.042049</td>\n",
       "      <td>0.175832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.599379</td>\n",
       "      <td>0.695309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.520080</td>\n",
       "      <td>-0.605849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.378277</td>\n",
       "      <td>-0.098938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.236473</td>\n",
       "      <td>0.407974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.196967</td>\n",
       "      <td>0.566672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.630407</td>\n",
       "      <td>0.725371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">b</th>\n",
       "      <th>count</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.918142</td>\n",
       "      <td>-0.253417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.054223</td>\n",
       "      <td>1.348807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.663590</td>\n",
       "      <td>-1.207168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.290866</td>\n",
       "      <td>-0.730293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.918142</td>\n",
       "      <td>-0.253417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.545418</td>\n",
       "      <td>0.223458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-0.172694</td>\n",
       "      <td>0.700334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               data1     data2\n",
       "key1                          \n",
       "a    count  3.000000  3.000000\n",
       "     mean  -0.042049  0.175832\n",
       "     std    0.599379  0.695309\n",
       "     min   -0.520080 -0.605849\n",
       "     25%   -0.378277 -0.098938\n",
       "     50%   -0.236473  0.407974\n",
       "     75%    0.196967  0.566672\n",
       "     max    0.630407  0.725371\n",
       "b    count  2.000000  2.000000\n",
       "     mean  -0.918142 -0.253417\n",
       "     std    1.054223  1.348807\n",
       "     min   -1.663590 -1.207168\n",
       "     25%   -1.290866 -0.730293\n",
       "     50%   -0.918142 -0.253417\n",
       "     75%   -0.545418  0.223458\n",
       "     max   -0.172694  0.700334"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.describe().stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip smoker  day    time  size\n",
       "0       16.99  1.01     No  Sun  Dinner     2\n",
       "1       10.34  1.66     No  Sun  Dinner     3\n",
       "2       21.01  3.50     No  Sun  Dinner     3\n",
       "3       23.68  3.31     No  Sun  Dinner     2\n",
       "4       24.59  3.61     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 餐馆小费的数据集\n",
    "tips = pd.read_csv(path +'/datasets/ch08_data/tips.csv',engine = 'python')\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.059447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.160542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.166587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.139780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.146808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.29</td>\n",
       "      <td>4.71</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.186240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip smoker  day    time  size   tip_pct\n",
       "0       16.99  1.01     No  Sun  Dinner     2  0.059447\n",
       "1       10.34  1.66     No  Sun  Dinner     3  0.160542\n",
       "2       21.01  3.50     No  Sun  Dinner     3  0.166587\n",
       "3       23.68  3.31     No  Sun  Dinner     2  0.139780\n",
       "4       24.59  3.61     No  Sun  Dinner     4  0.146808\n",
       "5       25.29  4.71     No  Sun  Dinner     4  0.186240"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加表示小费比例的列tip_pct\n",
    "tips['tip_pct'] = tips['tip']/tips['total_bill']\n",
    "tips[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoker\n",
       "No     0.159328\n",
       "Yes    0.163196\n",
       "Name: tip_pct, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 面向列的多函数应用\n",
    "# 根据smoker对tips进行分组\n",
    "grouped = tips.groupby(['smoker'])\n",
    "grouped_pct = grouped['tip_pct']\n",
    "grouped_pct.agg('mean')  # 函数名以字符串的形式传入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe 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>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>peak_to_peak</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>0.159328</td>\n",
       "      <td>0.039910</td>\n",
       "      <td>0.235193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.163196</td>\n",
       "      <td>0.085119</td>\n",
       "      <td>0.674707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean       std  peak_to_peak\n",
       "smoker                                  \n",
       "No      0.159328  0.039910      0.235193\n",
       "Yes     0.163196  0.085119      0.674707"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果传入一组函数或函数名，得到的DataFrame列就会以相应的函数命名\n",
    "grouped_pct.agg(['mean','std',peak_to_peak])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foo</th>\n",
       "      <th>bar</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>0.159328</td>\n",
       "      <td>0.039910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.163196</td>\n",
       "      <td>0.085119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             foo       bar\n",
       "smoker                    \n",
       "No      0.159328  0.039910\n",
       "Yes     0.163196  0.085119"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果传入的是一个由（name,function）元组组成的列表，则各元组的第一个元素就会被用作于DataFrame的列名，可以看做一个有序映射\n",
    "grouped_pct.agg([('foo','mean'),('bar',np.std)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">tip_pct</th>\n",
       "      <th colspan=\"3\" halign=\"left\">total_bill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>151</td>\n",
       "      <td>0.159328</td>\n",
       "      <td>0.291990</td>\n",
       "      <td>151</td>\n",
       "      <td>19.188278</td>\n",
       "      <td>48.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>93</td>\n",
       "      <td>0.163196</td>\n",
       "      <td>0.710345</td>\n",
       "      <td>93</td>\n",
       "      <td>20.756344</td>\n",
       "      <td>50.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tip_pct                     total_bill                  \n",
       "         count      mean       max      count       mean    max\n",
       "smoker                                                         \n",
       "No         151  0.159328  0.291990        151  19.188278  48.33\n",
       "Yes         93  0.163196  0.710345         93  20.756344  50.81"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义一组应用于全部列的函数，或不同的列应用于不同的函数\n",
    "# 对tip_pct和total_bill列计算三个统计信息\n",
    "functions = ['count','mean','max']\n",
    "result = grouped['tip_pct','total_bill'].agg(functions)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>151</td>\n",
       "      <td>0.159328</td>\n",
       "      <td>0.291990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>93</td>\n",
       "      <td>0.163196</td>\n",
       "      <td>0.710345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count      mean       max\n",
       "smoker                           \n",
       "No        151  0.159328  0.291990\n",
       "Yes        93  0.163196  0.710345"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['tip_pct']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">tip_pct</th>\n",
       "      <th colspan=\"2\" halign=\"left\">total_bill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Durchschnitt</th>\n",
       "      <th>Abweichung</th>\n",
       "      <th>Durchschnitt</th>\n",
       "      <th>Abweichung</th>\n",
       "    </tr>\n",
       "    <tr>\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>No</th>\n",
       "      <td>0.159328</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>19.188278</td>\n",
       "      <td>68.154632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.163196</td>\n",
       "      <td>0.007245</td>\n",
       "      <td>20.756344</td>\n",
       "      <td>96.671256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tip_pct              total_bill           \n",
       "       Durchschnitt Abweichung Durchschnitt Abweichung\n",
       "smoker                                                \n",
       "No         0.159328   0.001593    19.188278  68.154632\n",
       "Yes        0.163196   0.007245    20.756344  96.671256"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftuple=[('Durchschnitt','mean'),('Abweichung',np.var)] # 元组列表\n",
    "grouped['tip_pct','total_bill'].agg(ftuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>tip_pct</th>\n",
       "      <th>total_bill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>0.291990</td>\n",
       "      <td>2897.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.710345</td>\n",
       "      <td>1930.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tip_pct  total_bill\n",
       "smoker                      \n",
       "No      0.291990     2897.43\n",
       "Yes     0.710345     1930.34"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对不同列应用不同的函数\n",
    "grouped.agg({'tip_pct':np.max,'total_bill':'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\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=\"4\" halign=\"left\">tip_pct</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>No</th>\n",
       "      <td>0.056797</td>\n",
       "      <td>0.291990</td>\n",
       "      <td>0.159328</td>\n",
       "      <td>0.039910</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.035638</td>\n",
       "      <td>0.710345</td>\n",
       "      <td>0.163196</td>\n",
       "      <td>0.085119</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tip_pct                               size\n",
       "             min       max      mean       std  sum\n",
       "smoker                                             \n",
       "No      0.056797  0.291990  0.159328  0.039910  403\n",
       "Yes     0.035638  0.710345  0.163196  0.085119  224"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.agg({'tip_pct':['min','max','mean','std'],'size':'sum'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 以“无索引”的形式返回聚合数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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>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>No</td>\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>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>20.756344</td>\n",
       "      <td>3.008710</td>\n",
       "      <td>2.408602</td>\n",
       "      <td>0.163196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  smoker  total_bill       tip      size   tip_pct\n",
       "0     No   19.188278  2.991854  2.668874  0.159328\n",
       "1    Yes   20.756344  3.008710  2.408602  0.163196"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 向groupby传入as_index=False以禁用该功能  # 对结果调用reset_index也可以得到同样效果\n",
    "tips.groupby(['smoker'],as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分组级运算和转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.236473</td>\n",
       "      <td>-0.605849</td>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.520080</td>\n",
       "      <td>0.407974</td>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.663590</td>\n",
       "      <td>0.700334</td>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.172694</td>\n",
       "      <td>-1.207168</td>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.630407</td>\n",
       "      <td>0.725371</td>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      data1     data2 key1 key2\n",
       "0 -0.236473 -0.605849    a  one\n",
       "1 -0.520080  0.407974    a  two\n",
       "2 -1.663590  0.700334    b  one\n",
       "3 -0.172694 -1.207168    b  two\n",
       "4  0.630407  0.725371    a  one"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_data1</th>\n",
       "      <th>mean_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>-0.042049</td>\n",
       "      <td>0.175832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-0.918142</td>\n",
       "      <td>-0.253417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mean_data1  mean_data2\n",
       "key1                        \n",
       "a      -0.042049    0.175832\n",
       "b      -0.918142   -0.253417"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k1_means = df.groupby('key1').mean().add_prefix('mean_')\n",
    "k1_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>data2</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>mean_data1</th>\n",
       "      <th>mean_data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.236473</td>\n",
       "      <td>-0.605849</td>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.042049</td>\n",
       "      <td>0.175832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.520080</td>\n",
       "      <td>0.407974</td>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.042049</td>\n",
       "      <td>0.175832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.630407</td>\n",
       "      <td>0.725371</td>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.042049</td>\n",
       "      <td>0.175832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.663590</td>\n",
       "      <td>0.700334</td>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.918142</td>\n",
       "      <td>-0.253417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.172694</td>\n",
       "      <td>-1.207168</td>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.918142</td>\n",
       "      <td>-0.253417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      data1     data2 key1 key2  mean_data1  mean_data2\n",
       "0 -0.236473 -0.605849    a  one   -0.042049    0.175832\n",
       "1 -0.520080  0.407974    a  two   -0.042049    0.175832\n",
       "4  0.630407  0.725371    a  one   -0.042049    0.175832\n",
       "2 -1.663590  0.700334    b  one   -0.918142   -0.253417\n",
       "3 -0.172694 -1.207168    b  two   -0.918142   -0.253417"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df,k1_means,left_on='key1',right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0.006026</td>\n",
       "      <td>-0.708873</td>\n",
       "      <td>0.630106</td>\n",
       "      <td>0.817771</td>\n",
       "      <td>0.491561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.017179</td>\n",
       "      <td>0.421881</td>\n",
       "      <td>0.470141</td>\n",
       "      <td>-0.327672</td>\n",
       "      <td>-0.404908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            a         b         c         d         e\n",
       "one  0.006026 -0.708873  0.630106  0.817771  0.491561\n",
       "two  0.017179  0.421881  0.470141 -0.327672 -0.404908"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key = ['one','two','one','two','one']\n",
    "people.groupby(key).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>0.006026</td>\n",
       "      <td>-0.708873</td>\n",
       "      <td>0.630106</td>\n",
       "      <td>0.817771</td>\n",
       "      <td>0.491561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>0.017179</td>\n",
       "      <td>0.421881</td>\n",
       "      <td>0.470141</td>\n",
       "      <td>-0.327672</td>\n",
       "      <td>-0.404908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>0.006026</td>\n",
       "      <td>-0.708873</td>\n",
       "      <td>0.630106</td>\n",
       "      <td>0.817771</td>\n",
       "      <td>0.491561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim</th>\n",
       "      <td>0.017179</td>\n",
       "      <td>0.421881</td>\n",
       "      <td>0.470141</td>\n",
       "      <td>-0.327672</td>\n",
       "      <td>-0.404908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travis</th>\n",
       "      <td>0.006026</td>\n",
       "      <td>-0.708873</td>\n",
       "      <td>0.630106</td>\n",
       "      <td>0.817771</td>\n",
       "      <td>0.491561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               a         b         c         d         e\n",
       "Joe     0.006026 -0.708873  0.630106  0.817771  0.491561\n",
       "Steve   0.017179  0.421881  0.470141 -0.327672 -0.404908\n",
       "Wes     0.006026 -0.708873  0.630106  0.817771  0.491561\n",
       "Jim     0.017179  0.421881  0.470141 -0.327672 -0.404908\n",
       "Travis  0.006026 -0.708873  0.630106  0.817771  0.491561"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tranform会将一个函数应用到各个分组中，然后将结果放置到适当的位置上，如果各分组产生的是一个标量，则该值就会被广播出去\n",
    "people.groupby(key).transform(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>-0.626157</td>\n",
       "      <td>-0.322074</td>\n",
       "      <td>0.088927</td>\n",
       "      <td>-0.766515</td>\n",
       "      <td>0.892488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>0.004274</td>\n",
       "      <td>-0.326676</td>\n",
       "      <td>0.322355</td>\n",
       "      <td>0.514114</td>\n",
       "      <td>-0.490929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes</th>\n",
       "      <td>0.587849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.414080</td>\n",
       "      <td>-0.444321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim</th>\n",
       "      <td>-0.004274</td>\n",
       "      <td>0.326676</td>\n",
       "      <td>-0.322355</td>\n",
       "      <td>-0.514114</td>\n",
       "      <td>0.490929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travis</th>\n",
       "      <td>0.038308</td>\n",
       "      <td>0.322074</td>\n",
       "      <td>-0.088927</td>\n",
       "      <td>-0.647565</td>\n",
       "      <td>-0.448167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               a         b         c         d         e\n",
       "Joe    -0.626157 -0.322074  0.088927 -0.766515  0.892488\n",
       "Steve   0.004274 -0.326676  0.322355  0.514114 -0.490929\n",
       "Wes     0.587849       NaN       NaN  1.414080 -0.444321\n",
       "Jim    -0.004274  0.326676 -0.322355 -0.514114  0.490929\n",
       "Travis  0.038308  0.322074 -0.088927 -0.647565 -0.448167"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从各组中减去平均值\n",
    "def demean(arr):\n",
    "    return arr-arr.mean()\n",
    "demeaned = people.groupby(key).transform(demean)\n",
    "demeaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
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       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1.156482e-17</td>\n",
       "      <td>5.551115e-17</td>\n",
       "      <td>-5.551115e-17</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>3.700743e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>8.673617e-19</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.775558e-17</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                a             b             c             d             e\n",
       "one  1.156482e-17  5.551115e-17 -5.551115e-17  1.110223e-16  3.700743e-17\n",
       "two  8.673617e-19  0.000000e+00  2.775558e-17  0.000000e+00  0.000000e+00"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demeaned.groupby(key).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply 一般性的“拆分-应用-合并”\n",
    "# apply会将待处理的对象拆分成多个片段，然后对各个片段调用传入的函数，最后尝试将各个片段组合在一起\n",
    "\n",
    "# 根据分组，选出最高的5个tip_pct值\n",
    "def top(df,n=5,column='tip_pct'):\n",
    "    return df.sort_values(by=column)[-n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.059447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.160542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.166587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.139780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.146808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.29</td>\n",
       "      <td>4.71</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.186240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.77</td>\n",
       "      <td>2.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.228050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.88</td>\n",
       "      <td>3.12</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.116071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.04</td>\n",
       "      <td>1.96</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.130319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.78</td>\n",
       "      <td>3.23</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.218539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.27</td>\n",
       "      <td>1.71</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.166504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>35.26</td>\n",
       "      <td>5.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.141804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.42</td>\n",
       "      <td>1.57</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.101816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18.43</td>\n",
       "      <td>3.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.162778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.83</td>\n",
       "      <td>3.02</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.203641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>21.58</td>\n",
       "      <td>3.92</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.181650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.33</td>\n",
       "      <td>1.67</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.161665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.29</td>\n",
       "      <td>3.71</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.227747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16.97</td>\n",
       "      <td>3.50</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.206246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.65</td>\n",
       "      <td>3.35</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.162228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>17.92</td>\n",
       "      <td>4.08</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.227679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20.29</td>\n",
       "      <td>2.75</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.135535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.77</td>\n",
       "      <td>2.23</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.141408</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>24</th>\n",
       "      <td>19.82</td>\n",
       "      <td>3.18</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.160444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.81</td>\n",
       "      <td>2.34</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.131387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13.37</td>\n",
       "      <td>2.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.149589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12.69</td>\n",
       "      <td>2.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.157604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>21.70</td>\n",
       "      <td>4.30</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.198157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>19.65</td>\n",
       "      <td>3.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.152672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>28.17</td>\n",
       "      <td>6.50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.230742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>12.90</td>\n",
       "      <td>1.10</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.085271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>28.15</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>5</td>\n",
       "      <td>0.106572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>11.59</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.129422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>7.74</td>\n",
       "      <td>1.44</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.186047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>30.14</td>\n",
       "      <td>3.09</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.102522</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",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>20.45</td>\n",
       "      <td>3.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.146699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>13.28</td>\n",
       "      <td>2.72</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.204819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>22.12</td>\n",
       "      <td>2.88</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.130199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>24.01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.083299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>15.69</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.191205</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>233</th>\n",
       "      <td>10.77</td>\n",
       "      <td>1.47</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.136490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>15.53</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.193175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>10.07</td>\n",
       "      <td>1.25</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.124131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>12.60</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.079365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>32.83</td>\n",
       "      <td>1.17</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.035638</td>\n",
       "    </tr>\n",
       "    <tr>\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>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.203927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.073584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.088222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.098204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.159744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip smoker   day    time  size   tip_pct\n",
       "0         16.99  1.01     No   Sun  Dinner     2  0.059447\n",
       "1         10.34  1.66     No   Sun  Dinner     3  0.160542\n",
       "2         21.01  3.50     No   Sun  Dinner     3  0.166587\n",
       "3         23.68  3.31     No   Sun  Dinner     2  0.139780\n",
       "4         24.59  3.61     No   Sun  Dinner     4  0.146808\n",
       "5         25.29  4.71     No   Sun  Dinner     4  0.186240\n",
       "6          8.77  2.00     No   Sun  Dinner     2  0.228050\n",
       "7         26.88  3.12     No   Sun  Dinner     4  0.116071\n",
       "8         15.04  1.96     No   Sun  Dinner     2  0.130319\n",
       "9         14.78  3.23     No   Sun  Dinner     2  0.218539\n",
       "10        10.27  1.71     No   Sun  Dinner     2  0.166504\n",
       "11        35.26  5.00     No   Sun  Dinner     4  0.141804\n",
       "12        15.42  1.57     No   Sun  Dinner     2  0.101816\n",
       "13        18.43  3.00     No   Sun  Dinner     4  0.162778\n",
       "14        14.83  3.02     No   Sun  Dinner     2  0.203641\n",
       "15        21.58  3.92     No   Sun  Dinner     2  0.181650\n",
       "16        10.33  1.67     No   Sun  Dinner     3  0.161665\n",
       "17        16.29  3.71     No   Sun  Dinner     3  0.227747\n",
       "18        16.97  3.50     No   Sun  Dinner     3  0.206246\n",
       "19        20.65  3.35     No   Sat  Dinner     3  0.162228\n",
       "20        17.92  4.08     No   Sat  Dinner     2  0.227679\n",
       "21        20.29  2.75     No   Sat  Dinner     2  0.135535\n",
       "22        15.77  2.23     No   Sat  Dinner     2  0.141408\n",
       "23        39.42  7.58     No   Sat  Dinner     4  0.192288\n",
       "24        19.82  3.18     No   Sat  Dinner     2  0.160444\n",
       "25        17.81  2.34     No   Sat  Dinner     4  0.131387\n",
       "26        13.37  2.00     No   Sat  Dinner     2  0.149589\n",
       "27        12.69  2.00     No   Sat  Dinner     2  0.157604\n",
       "28        21.70  4.30     No   Sat  Dinner     2  0.198157\n",
       "29        19.65  3.00     No   Sat  Dinner     2  0.152672\n",
       "..          ...   ...    ...   ...     ...   ...       ...\n",
       "214       28.17  6.50    Yes   Sat  Dinner     3  0.230742\n",
       "215       12.90  1.10    Yes   Sat  Dinner     2  0.085271\n",
       "216       28.15  3.00    Yes   Sat  Dinner     5  0.106572\n",
       "217       11.59  1.50    Yes   Sat  Dinner     2  0.129422\n",
       "218        7.74  1.44    Yes   Sat  Dinner     2  0.186047\n",
       "219       30.14  3.09    Yes   Sat  Dinner     4  0.102522\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\n",
       "227       20.45  3.00     No   Sat  Dinner     4  0.146699\n",
       "228       13.28  2.72     No   Sat  Dinner     2  0.204819\n",
       "229       22.12  2.88    Yes   Sat  Dinner     2  0.130199\n",
       "230       24.01  2.00    Yes   Sat  Dinner     4  0.083299\n",
       "231       15.69  3.00    Yes   Sat  Dinner     3  0.191205\n",
       "232       11.61  3.39     No   Sat  Dinner     2  0.291990\n",
       "233       10.77  1.47     No   Sat  Dinner     2  0.136490\n",
       "234       15.53  3.00    Yes   Sat  Dinner     2  0.193175\n",
       "235       10.07  1.25     No   Sat  Dinner     2  0.124131\n",
       "236       12.60  1.00    Yes   Sat  Dinner     2  0.079365\n",
       "237       32.83  1.17    Yes   Sat  Dinner     2  0.035638\n",
       "238       35.83  4.67     No   Sat  Dinner     3  0.130338\n",
       "239       29.03  5.92     No   Sat  Dinner     3  0.203927\n",
       "240       27.18  2.00    Yes   Sat  Dinner     2  0.073584\n",
       "241       22.67  2.00    Yes   Sat  Dinner     2  0.088222\n",
       "242       17.82  1.75     No   Sat  Dinner     2  0.098204\n",
       "243       18.78  3.00     No  Thur  Dinner     2  0.159744\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <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",
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       "      <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",
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       "    <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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top(tips,n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">No</th>\n",
       "      <th>88</th>\n",
       "      <td>24.71</td>\n",
       "      <td>5.85</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "      <td>0.236746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>20.69</td>\n",
       "      <td>5.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>5</td>\n",
       "      <td>0.241663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>10.29</td>\n",
       "      <td>2.60</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.252672</td>\n",
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       "    <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",
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       "      <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",
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       "      <td>Yes</td>\n",
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       "      <td>Dinner</td>\n",
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       "      <td>0.279525</td>\n",
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       "      <th>183</th>\n",
       "      <td>23.17</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.280535</td>\n",
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       "    <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",
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       "    <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",
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       "    <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",
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       "</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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 在dataframe的各个片段上调用，然后结果由pandas.concat组装到一起，并以分组名称进行标记\n",
    "tips.groupby('smoker').apply(top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>3.25</td>\n",
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       "      <th>Sat</th>\n",
       "      <th>212</th>\n",
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       "      <td>9.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.186220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <th>156</th>\n",
       "      <td>48.17</td>\n",
       "      <td>5.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>6</td>\n",
       "      <td>0.103799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <th>142</th>\n",
       "      <td>41.19</td>\n",
       "      <td>5.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>5</td>\n",
       "      <td>0.121389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Yes</th>\n",
       "      <th>Fri</th>\n",
       "      <th>95</th>\n",
       "      <td>40.17</td>\n",
       "      <td>4.73</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.117750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <th>170</th>\n",
       "      <td>50.81</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.196812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <th>182</th>\n",
       "      <td>45.35</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.077178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <th>197</th>\n",
       "      <td>43.11</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>4</td>\n",
       "      <td>0.115982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 total_bill    tip smoker   day    time  size   tip_pct\n",
       "smoker day                                                             \n",
       "No     Fri  94        22.75   3.25     No   Fri  Dinner     2  0.142857\n",
       "       Sat  212       48.33   9.00     No   Sat  Dinner     4  0.186220\n",
       "       Sun  156       48.17   5.00     No   Sun  Dinner     6  0.103799\n",
       "       Thur 142       41.19   5.00     No  Thur   Lunch     5  0.121389\n",
       "Yes    Fri  95        40.17   4.73    Yes   Fri  Dinner     4  0.117750\n",
       "       Sat  170       50.81  10.00    Yes   Sat  Dinner     3  0.196812\n",
       "       Sun  182       45.35   3.50    Yes   Sun  Dinner     3  0.077178\n",
       "       Thur 197       43.11   5.00    Yes  Thur   Lunch     4  0.115982"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果传给apply的函数能够接受其他参数或关键字，则将这些内容放在函数名后面一并传入\n",
    "tips.groupby(['smoker','day']).apply(top,n=1,column='total_bill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>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",
       "      <th></th>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count      mean       std       min       25%       50%       75%  \\\n",
       "smoker                                                                      \n",
       "No      151.0  0.159328  0.039910  0.056797  0.136906  0.155625  0.185014   \n",
       "Yes      93.0  0.163196  0.085119  0.035638  0.106771  0.153846  0.195059   \n",
       "\n",
       "             max  \n",
       "smoker            \n",
       "No      0.291990  \n",
       "Yes     0.710345  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = tips.groupby('smoker')['tip_pct'].describe()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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></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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = lambda x:x.describe()\n",
    "grouped.apply(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 禁止分组键"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "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",
       "    </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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将group_keys=False传入groupby即可禁止分组键\n",
    "tips.groupby('smoker',group_keys=False).apply(top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分位数与分桶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (1.605, 3.175]\n",
       "1     (0.0345, 1.605]\n",
       "2     (0.0345, 1.605]\n",
       "3     (0.0345, 1.605]\n",
       "4    (-1.536, 0.0345]\n",
       "5     (0.0345, 1.605]\n",
       "6      (1.605, 3.175]\n",
       "7     (0.0345, 1.605]\n",
       "8     (0.0345, 1.605]\n",
       "9    (-1.536, 0.0345]\n",
       "Name: data1, dtype: category\n",
       "Categories (4, interval[float64]): [(-3.113, -1.536] < (-1.536, 0.0345] < (0.0345, 1.605] < (1.605, 3.175]]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用cut将其加入长度相等的桶中\n",
    "frame = pd.DataFrame({'data1':np.random.randn(1000),\n",
    "                      'data2':np.random.randn(1000)})\n",
    "factor = pd.cut(frame.data1,4)\n",
    "factor[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(-3.113, -1.536]</th>\n",
       "      <td>71.0</td>\n",
       "      <td>2.629703</td>\n",
       "      <td>0.112675</td>\n",
       "      <td>-2.546319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(-1.536, 0.0345]</th>\n",
       "      <td>443.0</td>\n",
       "      <td>3.392151</td>\n",
       "      <td>0.069770</td>\n",
       "      <td>-3.583700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.0345, 1.605]</th>\n",
       "      <td>411.0</td>\n",
       "      <td>2.507156</td>\n",
       "      <td>-0.027153</td>\n",
       "      <td>-3.069521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1.605, 3.175]</th>\n",
       "      <td>75.0</td>\n",
       "      <td>3.190842</td>\n",
       "      <td>0.073438</td>\n",
       "      <td>-3.223812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count       max      mean       min\n",
       "data1                                                \n",
       "(-3.113, -1.536]   71.0  2.629703  0.112675 -2.546319\n",
       "(-1.536, 0.0345]  443.0  3.392151  0.069770 -3.583700\n",
       "(0.0345, 1.605]   411.0  2.507156 -0.027153 -3.069521\n",
       "(1.605, 3.175]     75.0  3.190842  0.073438 -3.223812"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由cut返回的Factor对象可直接用于groupby\n",
    "def get_stats(group):\n",
    "    return {'min':group.min(),'max':group.max(),'count':group.count(),'mean':group.mean()}\n",
    "grouped = frame.data2.groupby(factor)\n",
    "grouped.apply(get_stats).unstack()  # 长度相等的桶  是指区间大小相等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.629703</td>\n",
       "      <td>0.083684</td>\n",
       "      <td>-2.546319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>3.392151</td>\n",
       "      <td>0.011848</td>\n",
       "      <td>-2.245350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>1.887499</td>\n",
       "      <td>0.021290</td>\n",
       "      <td>-2.645953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.558449</td>\n",
       "      <td>0.078630</td>\n",
       "      <td>-3.583700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.016598</td>\n",
       "      <td>0.175763</td>\n",
       "      <td>-2.269272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.989172</td>\n",
       "      <td>0.051021</td>\n",
       "      <td>-3.069521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100.0</td>\n",
       "      <td>1.831858</td>\n",
       "      <td>-0.094109</td>\n",
       "      <td>-2.725016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.240216</td>\n",
       "      <td>-0.045485</td>\n",
       "      <td>-2.168530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.365300</td>\n",
       "      <td>0.046422</td>\n",
       "      <td>-2.686625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100.0</td>\n",
       "      <td>3.190842</td>\n",
       "      <td>0.003498</td>\n",
       "      <td>-3.223812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count       max      mean       min\n",
       "data1                                     \n",
       "0      100.0  2.629703  0.083684 -2.546319\n",
       "1      100.0  3.392151  0.011848 -2.245350\n",
       "2      100.0  1.887499  0.021290 -2.645953\n",
       "3      100.0  2.558449  0.078630 -3.583700\n",
       "4      100.0  2.016598  0.175763 -2.269272\n",
       "5      100.0  2.989172  0.051021 -3.069521\n",
       "6      100.0  1.831858 -0.094109 -2.725016\n",
       "7      100.0  2.240216 -0.045485 -2.168530\n",
       "8      100.0  2.365300  0.046422 -2.686625\n",
       "9      100.0  3.190842  0.003498 -3.223812"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要根据样本分位数得到大小相等的桶，使用qcut即可，传入labels=False即可只获取分位数的编号\n",
    "# 大小相等的桶 是指 数据点数量相等\n",
    "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：用特定于分组的值填充缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1    1.066180\n",
       "2         NaN\n",
       "3    0.307974\n",
       "4         NaN\n",
       "5   -1.071731\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用平均值去填充缺失值\n",
    "s = pd.Series(np.random.randn(6))\n",
    "s[::2]=np.nan   # s[::2]从开始以步长为2到结束的值为nan\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.100808\n",
       "1    1.066180\n",
       "2    0.100808\n",
       "3    0.307974\n",
       "4    0.100808\n",
       "5   -1.071731\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(s.mean())  # 填充均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio          0.125561\n",
       "New York      0.375881\n",
       "Vermont            NaN\n",
       "Florida       2.662314\n",
       "Oregen       -0.844430\n",
       "Nevada             NaN\n",
       "California   -0.452775\n",
       "Idaho              NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对不同的分组填充不同的值，只需要将数据分组，并使用apply和fillna即可\n",
    "# 用分组平均值去填充缺失值\n",
    "states =['Ohio','New York','Vermont','Florida','Oregen','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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "East    1.054585\n",
       "West   -0.648603\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(group_key).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio          0.125561\n",
       "New York      0.375881\n",
       "Vermont       1.054585\n",
       "Florida       2.662314\n",
       "Oregen       -0.844430\n",
       "Nevada       -0.648603\n",
       "California   -0.452775\n",
       "Idaho        -0.648603\n",
       "dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill_mean = lambda g: g.fillna(g.mean())\n",
    "data.groupby(group_key).apply(fill_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio          0.125561\n",
       "New York      0.375881\n",
       "Vermont       0.500000\n",
       "Florida       2.662314\n",
       "Oregen       -0.844430\n",
       "Nevada       -1.000000\n",
       "California   -0.452775\n",
       "Idaho        -1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可以在代码中预定义各组的填充值\n",
    "fill_value = {'East':0.5,'West':-1}\n",
    "fill_func = lambda g:g.fillna(fill_value[g.name])\n",
    "data.groupby(group_key).apply(fill_func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 示例：随机采样和排列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AH      1\n",
       "2H      2\n",
       "3H      3\n",
       "4H      4\n",
       "5H      5\n",
       "6H      6\n",
       "7H      7\n",
       "8H      8\n",
       "9H      9\n",
       "10H    10\n",
       "JH     10\n",
       "KH     10\n",
       "QH     10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从一个大数据集中随机抽取样本，取np.random.permutation(N)的前k个元素，其中N为数据的大小，K为期望选取的样本大小\n",
    "# 构造一副英语型扑克牌\n",
    "# 红桃（Hearts）、黑桃（Spades）、梅花（Clubs）、方片（Diamonds）\n",
    "suits = ['H','S','C','D']\n",
    "card_val = (np.arange(1,11).tolist() + [10] * 3 ) * 4\n",
    "base_names=['A'] + np.arange(2,11).tolist() + ['J','K','Q']\n",
    "cards = []\n",
    "for suit in suits:\n",
    "    cards.extend(str(num)+suit for num in base_names)\n",
    "# 长度为52的Series,其索引为牌名，值则是21点或其他游戏用计分的点数，这里记A的点数为1\n",
    "deck = pd.Series(card_val,index = cards)   \n",
    "deck[:13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3D    3\n",
       "3C    3\n",
       "7H    7\n",
       "9S    9\n",
       "2S    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从整副牌中随机抽取5张\n",
    "def draw(deck,n=5):\n",
    "    return deck.take(np.random.permutation(len(deck))[:n])\n",
    "draw(deck)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C  4C     4\n",
       "   7C     7\n",
       "D  QD    10\n",
       "   2D     2\n",
       "H  9H     9\n",
       "   3H     3\n",
       "S  JS    10\n",
       "   9S     9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果想要从每种花色中随机抽取两张牌，由于花色是牌名的最后一个字符，则可以据此进行分组，并使用apply\n",
    "get_suit = lambda card:card[-1] # 只要最后一个字母就行\n",
    "deck.groupby(get_suit).apply(draw,n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KC     10\n",
       "3C      3\n",
       "2D      2\n",
       "10D    10\n",
       "5H      5\n",
       "3H      3\n",
       "2S      2\n",
       "AS      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 另一种方法\n",
    "deck.groupby(get_suit,group_keys=False).apply(draw,n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 示例：分组加权平均数和相关系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>data</th>\n",
       "      <th>weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>-0.778784</td>\n",
       "      <td>0.156788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>-0.954083</td>\n",
       "      <td>0.679618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>-0.067018</td>\n",
       "      <td>0.254644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>0.083499</td>\n",
       "      <td>-1.535433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>1.189532</td>\n",
       "      <td>0.310784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>0.317738</td>\n",
       "      <td>0.145978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b</td>\n",
       "      <td>-0.369658</td>\n",
       "      <td>1.657384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b</td>\n",
       "      <td>0.623439</td>\n",
       "      <td>0.097687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category      data   weights\n",
       "0        a -0.778784  0.156788\n",
       "1        a -0.954083  0.679618\n",
       "2        a -0.067018  0.254644\n",
       "3        a  0.083499 -1.535433\n",
       "4        b  1.189532  0.310784\n",
       "5        b  0.317738  0.145978\n",
       "6        b -0.369658  1.657384\n",
       "7        b  0.623439  0.097687"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 下面这个数据集，含有分组键、值及一些权重值\n",
    "df = pd.DataFrame({'category':['a','a','a','a','b','b','b','b'],\n",
    "                   'data':np.random.randn(8),\n",
    "                   'weights':np.random.randn(8)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "a    2.060806\n",
       "b   -0.061348\n",
       "dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用category计算分组加权平均数\n",
    "grouped = df.groupby('category')\n",
    "get_wavg = lambda g:np.average(g['data'],weights=g['weights'])\n",
    "grouped.apply(get_wavg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "      <th>SPX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 来自Yahoo Finance的数据集，其中含有标准普尔500指数（SPX字段）和几只股票的收盘价\n",
    "# 第0列是索引列\n",
    "close_px = pd.read_csv(path+'/datasets/ch09_data/stock_px_2.csv',parse_dates=True,index_col=0,engine = 'python')\n",
    "close_px.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "      <th>SPX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.001421</td>\n",
       "      <td>0.000684</td>\n",
       "      <td>-0.000484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017975</td>\n",
       "      <td>0.024624</td>\n",
       "      <td>0.022474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>-0.002685</td>\n",
       "      <td>0.019052</td>\n",
       "      <td>-0.033712</td>\n",
       "      <td>-0.006545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>-0.020188</td>\n",
       "      <td>-0.028272</td>\n",
       "      <td>-0.004145</td>\n",
       "      <td>-0.014086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-09</th>\n",
       "      <td>0.008242</td>\n",
       "      <td>0.029094</td>\n",
       "      <td>0.021159</td>\n",
       "      <td>0.019386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-10</th>\n",
       "      <td>0.002725</td>\n",
       "      <td>0.001824</td>\n",
       "      <td>-0.013927</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-13</th>\n",
       "      <td>-0.005435</td>\n",
       "      <td>0.008648</td>\n",
       "      <td>-0.004134</td>\n",
       "      <td>-0.001412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-14</th>\n",
       "      <td>-0.002732</td>\n",
       "      <td>0.010379</td>\n",
       "      <td>0.008993</td>\n",
       "      <td>0.005830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-15</th>\n",
       "      <td>-0.010959</td>\n",
       "      <td>-0.012506</td>\n",
       "      <td>-0.013713</td>\n",
       "      <td>-0.014426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-16</th>\n",
       "      <td>0.012465</td>\n",
       "      <td>-0.016282</td>\n",
       "      <td>0.004519</td>\n",
       "      <td>-0.003942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-17</th>\n",
       "      <td>-0.035568</td>\n",
       "      <td>-0.070345</td>\n",
       "      <td>-0.010381</td>\n",
       "      <td>-0.014017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-21</th>\n",
       "      <td>-0.005674</td>\n",
       "      <td>-0.002473</td>\n",
       "      <td>-0.023077</td>\n",
       "      <td>-0.015702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-22</th>\n",
       "      <td>-0.009986</td>\n",
       "      <td>-0.006445</td>\n",
       "      <td>-0.012885</td>\n",
       "      <td>-0.010432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-23</th>\n",
       "      <td>0.021614</td>\n",
       "      <td>0.024950</td>\n",
       "      <td>-0.002175</td>\n",
       "      <td>0.010224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-24</th>\n",
       "      <td>-0.026798</td>\n",
       "      <td>-0.046251</td>\n",
       "      <td>-0.021439</td>\n",
       "      <td>-0.029233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-27</th>\n",
       "      <td>0.024638</td>\n",
       "      <td>-0.013783</td>\n",
       "      <td>-0.026736</td>\n",
       "      <td>-0.016160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-28</th>\n",
       "      <td>0.031117</td>\n",
       "      <td>-0.007246</td>\n",
       "      <td>0.026326</td>\n",
       "      <td>0.013050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-29</th>\n",
       "      <td>0.024691</td>\n",
       "      <td>0.022419</td>\n",
       "      <td>0.036431</td>\n",
       "      <td>0.006779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-30</th>\n",
       "      <td>-0.041499</td>\n",
       "      <td>-0.033656</td>\n",
       "      <td>-0.018293</td>\n",
       "      <td>-0.022849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-31</th>\n",
       "      <td>0.002793</td>\n",
       "      <td>-0.015831</td>\n",
       "      <td>0.027768</td>\n",
       "      <td>0.013130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-03</th>\n",
       "      <td>0.020891</td>\n",
       "      <td>0.023056</td>\n",
       "      <td>0.013864</td>\n",
       "      <td>0.005399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-04</th>\n",
       "      <td>-0.004093</td>\n",
       "      <td>-0.025681</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.014088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-05</th>\n",
       "      <td>-0.010959</td>\n",
       "      <td>-0.007531</td>\n",
       "      <td>-0.014376</td>\n",
       "      <td>-0.005435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-06</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009756</td>\n",
       "      <td>-0.008538</td>\n",
       "      <td>-0.006449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-07</th>\n",
       "      <td>-0.020776</td>\n",
       "      <td>-0.017713</td>\n",
       "      <td>-0.007535</td>\n",
       "      <td>-0.010094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-10</th>\n",
       "      <td>0.015559</td>\n",
       "      <td>0.017486</td>\n",
       "      <td>0.007592</td>\n",
       "      <td>0.007569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-11</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.019871</td>\n",
       "      <td>-0.007176</td>\n",
       "      <td>-0.008098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-12</th>\n",
       "      <td>0.002786</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.019877</td>\n",
       "      <td>-0.012687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-13</th>\n",
       "      <td>0.009722</td>\n",
       "      <td>0.011507</td>\n",
       "      <td>0.012906</td>\n",
       "      <td>-0.001600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-14</th>\n",
       "      <td>0.009629</td>\n",
       "      <td>0.028169</td>\n",
       "      <td>0.009465</td>\n",
       "      <td>0.021435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-02</th>\n",
       "      <td>-0.018319</td>\n",
       "      <td>-0.015643</td>\n",
       "      <td>-0.018370</td>\n",
       "      <td>-0.025282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-06</th>\n",
       "      <td>0.015212</td>\n",
       "      <td>-0.011240</td>\n",
       "      <td>-0.013723</td>\n",
       "      <td>-0.007436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-07</th>\n",
       "      <td>0.011034</td>\n",
       "      <td>0.019208</td>\n",
       "      <td>0.035137</td>\n",
       "      <td>0.028646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-08</th>\n",
       "      <td>0.000547</td>\n",
       "      <td>0.008462</td>\n",
       "      <td>-0.011270</td>\n",
       "      <td>-0.010612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-09</th>\n",
       "      <td>-0.017337</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>-0.024856</td>\n",
       "      <td>-0.026705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-12</th>\n",
       "      <td>0.006517</td>\n",
       "      <td>0.005828</td>\n",
       "      <td>0.011688</td>\n",
       "      <td>0.006966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-13</th>\n",
       "      <td>0.012318</td>\n",
       "      <td>0.005794</td>\n",
       "      <td>-0.002645</td>\n",
       "      <td>0.009120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-14</th>\n",
       "      <td>0.012168</td>\n",
       "      <td>0.017665</td>\n",
       "      <td>0.013817</td>\n",
       "      <td>0.013480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-15</th>\n",
       "      <td>0.009401</td>\n",
       "      <td>0.018491</td>\n",
       "      <td>0.018860</td>\n",
       "      <td>0.017187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-16</th>\n",
       "      <td>0.019188</td>\n",
       "      <td>0.004817</td>\n",
       "      <td>0.007296</td>\n",
       "      <td>0.005707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-19</th>\n",
       "      <td>0.027790</td>\n",
       "      <td>0.003319</td>\n",
       "      <td>-0.011402</td>\n",
       "      <td>-0.009803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-20</th>\n",
       "      <td>0.004421</td>\n",
       "      <td>-0.008453</td>\n",
       "      <td>0.004206</td>\n",
       "      <td>-0.001661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-21</th>\n",
       "      <td>-0.003168</td>\n",
       "      <td>-0.036694</td>\n",
       "      <td>-0.027564</td>\n",
       "      <td>-0.029390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-22</th>\n",
       "      <td>-0.025040</td>\n",
       "      <td>-0.035783</td>\n",
       "      <td>-0.037932</td>\n",
       "      <td>-0.031883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-23</th>\n",
       "      <td>0.006172</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001011</td>\n",
       "      <td>0.006082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-26</th>\n",
       "      <td>-0.002795</td>\n",
       "      <td>0.015164</td>\n",
       "      <td>0.034771</td>\n",
       "      <td>0.023336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-27</th>\n",
       "      <td>-0.009698</td>\n",
       "      <td>0.009041</td>\n",
       "      <td>0.016592</td>\n",
       "      <td>0.010688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-28</th>\n",
       "      <td>-0.005635</td>\n",
       "      <td>-0.003506</td>\n",
       "      <td>-0.011521</td>\n",
       "      <td>-0.020691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-29</th>\n",
       "      <td>-0.016221</td>\n",
       "      <td>-0.005082</td>\n",
       "      <td>0.025114</td>\n",
       "      <td>0.008114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-30</th>\n",
       "      <td>-0.023683</td>\n",
       "      <td>-0.022004</td>\n",
       "      <td>-0.016919</td>\n",
       "      <td>-0.024974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-03</th>\n",
       "      <td>-0.017623</td>\n",
       "      <td>-0.014464</td>\n",
       "      <td>-0.020377</td>\n",
       "      <td>-0.028451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04</th>\n",
       "      <td>-0.005606</td>\n",
       "      <td>0.033021</td>\n",
       "      <td>0.023612</td>\n",
       "      <td>0.022488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05</th>\n",
       "      <td>0.015436</td>\n",
       "      <td>0.021705</td>\n",
       "      <td>0.015378</td>\n",
       "      <td>0.017866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06</th>\n",
       "      <td>-0.002327</td>\n",
       "      <td>0.017381</td>\n",
       "      <td>-0.000811</td>\n",
       "      <td>0.018304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-07</th>\n",
       "      <td>-0.020060</td>\n",
       "      <td>-0.003417</td>\n",
       "      <td>-0.004466</td>\n",
       "      <td>-0.008163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-10</th>\n",
       "      <td>0.051406</td>\n",
       "      <td>0.026286</td>\n",
       "      <td>0.036977</td>\n",
       "      <td>0.034125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>0.029526</td>\n",
       "      <td>0.002227</td>\n",
       "      <td>-0.000131</td>\n",
       "      <td>0.000544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12</th>\n",
       "      <td>0.004747</td>\n",
       "      <td>-0.001481</td>\n",
       "      <td>0.011669</td>\n",
       "      <td>0.009795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-13</th>\n",
       "      <td>0.015515</td>\n",
       "      <td>0.008160</td>\n",
       "      <td>-0.010238</td>\n",
       "      <td>-0.002974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-14</th>\n",
       "      <td>0.033225</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>0.022784</td>\n",
       "      <td>0.017380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2213 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL      MSFT       XOM       SPX\n",
       "2003-01-03  0.006757  0.001421  0.000684 -0.000484\n",
       "2003-01-06  0.000000  0.017975  0.024624  0.022474\n",
       "2003-01-07 -0.002685  0.019052 -0.033712 -0.006545\n",
       "2003-01-08 -0.020188 -0.028272 -0.004145 -0.014086\n",
       "2003-01-09  0.008242  0.029094  0.021159  0.019386\n",
       "2003-01-10  0.002725  0.001824 -0.013927  0.000000\n",
       "2003-01-13 -0.005435  0.008648 -0.004134 -0.001412\n",
       "2003-01-14 -0.002732  0.010379  0.008993  0.005830\n",
       "2003-01-15 -0.010959 -0.012506 -0.013713 -0.014426\n",
       "2003-01-16  0.012465 -0.016282  0.004519 -0.003942\n",
       "2003-01-17 -0.035568 -0.070345 -0.010381 -0.014017\n",
       "2003-01-21 -0.005674 -0.002473 -0.023077 -0.015702\n",
       "2003-01-22 -0.009986 -0.006445 -0.012885 -0.010432\n",
       "2003-01-23  0.021614  0.024950 -0.002175  0.010224\n",
       "2003-01-24 -0.026798 -0.046251 -0.021439 -0.029233\n",
       "2003-01-27  0.024638 -0.013783 -0.026736 -0.016160\n",
       "2003-01-28  0.031117 -0.007246  0.026326  0.013050\n",
       "2003-01-29  0.024691  0.022419  0.036431  0.006779\n",
       "2003-01-30 -0.041499 -0.033656 -0.018293 -0.022849\n",
       "2003-01-31  0.002793 -0.015831  0.027768  0.013130\n",
       "2003-02-03  0.020891  0.023056  0.013864  0.005399\n",
       "2003-02-04 -0.004093 -0.025681  0.000000 -0.014088\n",
       "2003-02-05 -0.010959 -0.007531 -0.014376 -0.005435\n",
       "2003-02-06  0.000000  0.009756 -0.008538 -0.006449\n",
       "2003-02-07 -0.020776 -0.017713 -0.007535 -0.010094\n",
       "2003-02-10  0.015559  0.017486  0.007592  0.007569\n",
       "2003-02-11  0.000000 -0.019871 -0.007176 -0.008098\n",
       "2003-02-12  0.002786  0.000000 -0.019877 -0.012687\n",
       "2003-02-13  0.009722  0.011507  0.012906 -0.001600\n",
       "2003-02-14  0.009629  0.028169  0.009465  0.021435\n",
       "...              ...       ...       ...       ...\n",
       "2011-09-02 -0.018319 -0.015643 -0.018370 -0.025282\n",
       "2011-09-06  0.015212 -0.011240 -0.013723 -0.007436\n",
       "2011-09-07  0.011034  0.019208  0.035137  0.028646\n",
       "2011-09-08  0.000547  0.008462 -0.011270 -0.010612\n",
       "2011-09-09 -0.017337 -0.018307 -0.024856 -0.026705\n",
       "2011-09-12  0.006517  0.005828  0.011688  0.006966\n",
       "2011-09-13  0.012318  0.005794 -0.002645  0.009120\n",
       "2011-09-14  0.012168  0.017665  0.013817  0.013480\n",
       "2011-09-15  0.009401  0.018491  0.018860  0.017187\n",
       "2011-09-16  0.019188  0.004817  0.007296  0.005707\n",
       "2011-09-19  0.027790  0.003319 -0.011402 -0.009803\n",
       "2011-09-20  0.004421 -0.008453  0.004206 -0.001661\n",
       "2011-09-21 -0.003168 -0.036694 -0.027564 -0.029390\n",
       "2011-09-22 -0.025040 -0.035783 -0.037932 -0.031883\n",
       "2011-09-23  0.006172  0.000000  0.001011  0.006082\n",
       "2011-09-26 -0.002795  0.015164  0.034771  0.023336\n",
       "2011-09-27 -0.009698  0.009041  0.016592  0.010688\n",
       "2011-09-28 -0.005635 -0.003506 -0.011521 -0.020691\n",
       "2011-09-29 -0.016221 -0.005082  0.025114  0.008114\n",
       "2011-09-30 -0.023683 -0.022004 -0.016919 -0.024974\n",
       "2011-10-03 -0.017623 -0.014464 -0.020377 -0.028451\n",
       "2011-10-04 -0.005606  0.033021  0.023612  0.022488\n",
       "2011-10-05  0.015436  0.021705  0.015378  0.017866\n",
       "2011-10-06 -0.002327  0.017381 -0.000811  0.018304\n",
       "2011-10-07 -0.020060 -0.003417 -0.004466 -0.008163\n",
       "2011-10-10  0.051406  0.026286  0.036977  0.034125\n",
       "2011-10-11  0.029526  0.002227 -0.000131  0.000544\n",
       "2011-10-12  0.004747 -0.001481  0.011669  0.009795\n",
       "2011-10-13  0.015515  0.008160 -0.010238 -0.002974\n",
       "2011-10-14  0.033225  0.003311  0.022784  0.017380\n",
       "\n",
       "[2213 rows x 4 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算一个由日收益（通过百分数变化计算）与SPX之间的年度相关系数组成的Dataframe\n",
    "rets = close_px.pct_change().dropna()  # pct_change()表示当前元素与前一天元素的相差百分比\n",
    "rets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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>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": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spx_corr = lambda x:x.corrwith(x['SPX']) # 计算每一列与‘SPX’列之间的相关系数\n",
    "by_year = rets.groupby(lambda x:x.year)  # 按年分组\n",
    "by_year.apply(spx_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2003    0.480868\n",
       "2004    0.259024\n",
       "2005    0.300093\n",
       "2006    0.161735\n",
       "2007    0.417738\n",
       "2008    0.611901\n",
       "2009    0.432738\n",
       "2010    0.571946\n",
       "2011    0.581987\n",
       "dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 苹果和微软的年度相关系数\n",
    "by_year.apply(lambda g:g['AAPL'].corr(g['MSFT']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 示例：面向分组的线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regress(data,yvar,xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept']=1.\n",
    "    result = sm.OLS(Y,X).fit()  # OLS 是 普通最小二乘法  Ordinary Least Squares\n",
    "    return result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 为了计算AAPL对SPX收益率的线性回归\n",
    "by_year.apply(regress,'AAPL',['SPX'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 透视表和交叉表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></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": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 透视表（pivot table）:根据一个或多个键对数据进行聚合，并根据行和列上的分组键将数据分配到各个矩阵区域中\n",
    "# 透视表可用dataframe中的pivot_table函数，该函数还可以添加分项小计\n",
    "# 根据小费数据中day和smoker计算分组平均数，并将day和smoker放在行上\n",
    "tips.pivot_table(index=['day','smoker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "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=\"2\" halign=\"left\">size</th>\n",
       "      <th colspan=\"2\" halign=\"left\">tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.066667</td>\n",
       "      <td>0.151650</td>\n",
       "      <td>0.174783</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>2.488889</td>\n",
       "      <td>2.352941</td>\n",
       "      <td>0.160298</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",
       "day                                           \n",
       "Fri     2.250000  2.066667  0.151650  0.174783\n",
       "Sat     2.555556  2.476190  0.158048  0.147906\n",
       "Sun     2.929825  2.578947  0.160113  0.187250\n",
       "Thur    2.488889  2.352941  0.160298  0.163863"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 假设只想聚合tip_pct和size，而且想根据day进行分组，故需将day放在行，smoker 放在列\n",
    "tips.pivot_table(['tip_pct','size'],index=['day'],columns=['smoker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">size</th>\n",
       "      <th colspan=\"3\" halign=\"left\">tip_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>All</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.066667</td>\n",
       "      <td>2.105263</td>\n",
       "      <td>0.151650</td>\n",
       "      <td>0.174783</td>\n",
       "      <td>0.169913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2.555556</td>\n",
       "      <td>2.476190</td>\n",
       "      <td>2.517241</td>\n",
       "      <td>0.158048</td>\n",
       "      <td>0.147906</td>\n",
       "      <td>0.153152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>2.929825</td>\n",
       "      <td>2.578947</td>\n",
       "      <td>2.842105</td>\n",
       "      <td>0.160113</td>\n",
       "      <td>0.187250</td>\n",
       "      <td>0.166897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>2.488889</td>\n",
       "      <td>2.352941</td>\n",
       "      <td>2.451613</td>\n",
       "      <td>0.160298</td>\n",
       "      <td>0.163863</td>\n",
       "      <td>0.161276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>2.668874</td>\n",
       "      <td>2.408602</td>\n",
       "      <td>2.569672</td>\n",
       "      <td>0.159328</td>\n",
       "      <td>0.163196</td>\n",
       "      <td>0.160803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            size                       tip_pct                    \n",
       "smoker        No       Yes       All        No       Yes       All\n",
       "day                                                               \n",
       "Fri     2.250000  2.066667  2.105263  0.151650  0.174783  0.169913\n",
       "Sat     2.555556  2.476190  2.517241  0.158048  0.147906  0.153152\n",
       "Sun     2.929825  2.578947  2.842105  0.160113  0.187250  0.166897\n",
       "Thur    2.488889  2.352941  2.451613  0.160298  0.163863  0.161276\n",
       "All     2.668874  2.408602  2.569672  0.159328  0.163196  0.160803"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入margins=True添加分项小计\n",
    "tips.pivot_table(['tip_pct','size'],index=['day'],columns=['smoker'],margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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>day</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "      <th>Thur</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>No</th>\n",
       "      <td>4.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>15.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>19.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>244.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "day      Fri   Sat   Sun  Thur    All\n",
       "smoker                               \n",
       "No       4.0  45.0  57.0  45.0  151.0\n",
       "Yes     15.0  42.0  19.0  17.0   93.0\n",
       "All     19.0  87.0  76.0  62.0  244.0"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要使用其他的聚合函数，可使用aggfunc即可，例如 使用count或len可以得到有关分组大小的较差表\n",
    "tips.pivot_table('tip_pct',index=['smoker'],columns=['day'],aggfunc=len,margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>day</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "      <th>Thur</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Dinner</th>\n",
       "      <th>No</th>\n",
       "      <td>6</td>\n",
       "      <td>115</td>\n",
       "      <td>167</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>20</td>\n",
       "      <td>104</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Lunch</th>\n",
       "      <th>No</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "day            Fri  Sat  Sun  Thur\n",
       "time   smoker                     \n",
       "Dinner No        6  115  167     2\n",
       "       Yes      20  104   49     0\n",
       "Lunch  No        3    0    0   110\n",
       "       Yes      11    0    0    40"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果出现空的组合（NA），希望设置一个fill_value\n",
    "tips.pivot_table('size',index=['time','smoker'],columns=['day'],aggfunc='sum',fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 交叉表  crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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>Sample</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Handedness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>Right-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>Left-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>Right-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>Left-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Female</td>\n",
       "      <td>Right-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Female</td>\n",
       "      <td>Left-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right-handed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>Right-handed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sample  Gender    Handedness\n",
       "0       1  Female  Right-handed\n",
       "1       2    Male   Left-handed\n",
       "2       3  Female  Right-handed\n",
       "3       4    Male  Right-handed\n",
       "4       5    Male   Left-handed\n",
       "5       6    Male  Right-handed\n",
       "6       7  Female  Right-handed\n",
       "7       8  Female   Left-handed\n",
       "8       9    Male  Right-handed\n",
       "9      10  Female  Right-handed"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 交叉表：cross tabulation 简称crosstab：是一种用于计算分组频率的特殊透视表\n",
    "data = [[1,'Female','Right-handed'],\n",
    "        [2,'Male','Left-handed'],\n",
    "        [3,'Female','Right-handed'],\n",
    "        [4,'Male','Right-handed'],\n",
    "        [5,'Male','Left-handed'],\n",
    "        [6,'Male','Right-handed'],\n",
    "        [7,'Female','Right-handed'],\n",
    "        [8,'Female','Left-handed'],\n",
    "        [9,'Male','Right-handed'],\n",
    "        [10,'Female','Right-handed']]\n",
    "\n",
    "data = pd.DataFrame(data,columns=['Sample','Gender','Handedness'],index=np.arange(10))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Handedness</th>\n",
       "      <th>Left-handed</th>\n",
       "      <th>Right-handed</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Handedness  Left-handed  Right-handed  All\n",
       "Gender                                    \n",
       "Female                1             4    5\n",
       "Male                  2             3    5\n",
       "All                   3             7   10"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据性别和用手习惯对数据进行统计汇总\n",
    "pd.crosstab(data.Gender,data.Handedness,margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smoker</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Dinner</th>\n",
       "      <th>Fri</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>45</td>\n",
       "      <td>42</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>57</td>\n",
       "      <td>19</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Lunch</th>\n",
       "      <th>Fri</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>44</td>\n",
       "      <td>17</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>151</td>\n",
       "      <td>93</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoker        No  Yes  All\n",
       "time   day                \n",
       "Dinner Fri     3    9   12\n",
       "       Sat    45   42   87\n",
       "       Sun    57   19   76\n",
       "       Thur    1    0    1\n",
       "Lunch  Fri     1    6    7\n",
       "       Thur   44   17   61\n",
       "All          151   93  244"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# crosstab前两个参数可以是数组，Series或数组列表\n",
    "# 比如对小费数据集\n",
    "pd.crosstab([tips.time,tips.day],tips.smoker,margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 示例：2012联邦选举委员会数据库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmte_id</th>\n",
       "      <th>cand_id</th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_city</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_zip</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>contb_receipt_dt</th>\n",
       "      <th>receipt_desc</th>\n",
       "      <th>memo_cd</th>\n",
       "      <th>memo_text</th>\n",
       "      <th>form_tp</th>\n",
       "      <th>file_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>366010290</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>366010290</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>SMITH, LANIER</td>\n",
       "      <td>LANETT</td>\n",
       "      <td>AL</td>\n",
       "      <td>368633403</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>05-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>PIGGOTT</td>\n",
       "      <td>AR</td>\n",
       "      <td>724548253</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>01-AUG-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>WARDENBURG, HAROLD</td>\n",
       "      <td>HOT SPRINGS NATION</td>\n",
       "      <td>AR</td>\n",
       "      <td>719016467</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cmte_id    cand_id             cand_nm           contbr_nm  \\\n",
       "0  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
       "1  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
       "2  C00410118  P20002978  Bachmann, Michelle       SMITH, LANIER   \n",
       "3  C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   \n",
       "4  C00410118  P20002978  Bachmann, Michelle  WARDENBURG, HAROLD   \n",
       "\n",
       "          contbr_city contbr_st contbr_zip        contbr_employer  \\\n",
       "0              MOBILE        AL  366010290                RETIRED   \n",
       "1              MOBILE        AL  366010290                RETIRED   \n",
       "2              LANETT        AL  368633403  INFORMATION REQUESTED   \n",
       "3             PIGGOTT        AR  724548253                   NONE   \n",
       "4  HOT SPRINGS NATION        AR  719016467                   NONE   \n",
       "\n",
       "       contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \\\n",
       "0                RETIRED              250.0        20-JUN-11          NaN   \n",
       "1                RETIRED               50.0        23-JUN-11          NaN   \n",
       "2  INFORMATION REQUESTED              250.0        05-JUL-11          NaN   \n",
       "3                RETIRED              250.0        01-AUG-11          NaN   \n",
       "4                RETIRED              300.0        20-JUN-11          NaN   \n",
       "\n",
       "  memo_cd memo_text form_tp  file_num  \n",
       "0     NaN       NaN   SA17A    736166  \n",
       "1     NaN       NaN   SA17A    736166  \n",
       "2     NaN       NaN   SA17A    749073  \n",
       "3     NaN       NaN   SA17A    749073  \n",
       "4     NaN       NaN   SA17A    736166  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 美国联邦选举委员会发布了有关政治精选赞助方面的数据，包括赞助者的姓名、职业、雇主、地址以及出资额等信息\n",
    "# 下面提供的是2012年美国总统大选的数据集\n",
    "fec = pd.read_csv(path +'/datasets/fec/P00000001-ALL.csv',engine = 'python')\n",
    "fec.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "cmte_id                             C00431445\n",
       "cand_id                             P80003338\n",
       "cand_nm                         Obama, Barack\n",
       "contbr_nm                         ELLMAN, IRA\n",
       "contbr_city                             TEMPE\n",
       "contbr_st                                  AZ\n",
       "contbr_zip                          852816719\n",
       "contbr_employer      ARIZONA STATE UNIVERSITY\n",
       "contbr_occupation                   PROFESSOR\n",
       "contb_receipt_amt                          50\n",
       "contb_receipt_dt                    01-DEC-11\n",
       "receipt_desc                              NaN\n",
       "memo_cd                                   NaN\n",
       "memo_text                                 NaN\n",
       "form_tp                                 SA17A\n",
       "file_num                               772372\n",
       "Name: 123456, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.ix[123456]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',\n",
       "       \"Roemer, Charles E. 'Buddy' III\", 'Pawlenty, Timothy',\n",
       "       'Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick',\n",
       "       'Cain, Herman', 'Gingrich, Newt', 'McCotter, Thaddeus G',\n",
       "       'Huntsman, Jon', 'Perry, Rick'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 该数据集中没有党派信息，通过unique，可以获取全部的候选人名单\n",
    "unique_cands = fec.cand_nm.unique()\n",
    "unique_cands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Obama, Barack'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cands[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 利用字典说明党派关系\n",
    "parties = {'Bachmann, Michelle':'Republican', # Republican:共和党员\n",
    "           'Cain, Herman':'Republican',\n",
    "           'Gingrich, Newt':'Republican',\n",
    "           'Huntsman, Jon':'Republican',\n",
    "           'Johnson, Gary Earl':'Republican',\n",
    "           'McCotter, Thaddeus G':'Republican',\n",
    "           'Obama, Barack':'Democrat',  # 民主党员\n",
    "           'Paul, Ron':'Republican',\n",
    "           'Pawlenty, Timothy':'Republican',\n",
    "           'Perry, Rick':'Republican',\n",
    "           \"Roemer, Charles E. 'Buddy' III\":'Republican',\n",
    "           'Romney, Mitt':'Republican',\n",
    "           'Santorum, Rick':'Republican'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123456    Obama, Barack\n",
       "123457    Obama, Barack\n",
       "123458    Obama, Barack\n",
       "123459    Obama, Barack\n",
       "123460    Obama, Barack\n",
       "Name: cand_nm, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用这个映射和Series对象的map方法，根据候选人姓名得到一组党派信息\n",
    "fec.cand_nm[123456:123461]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123456    Democrat\n",
       "123457    Democrat\n",
       "123458    Democrat\n",
       "123459    Democrat\n",
       "123460    Democrat\n",
       "Name: cand_nm, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.cand_nm[123456:123461].map(parties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Democrat      593746\n",
       "Republican    407985\n",
       "Name: party, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将其添加为新的列\n",
    "fec['party']=fec.cand_nm.map(parties)\n",
    "fec['party'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     991475\n",
       "False     10256\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 该数据集中既包括赞助也包括退款（负的出资额）\n",
    "(fec.contb_receipt_amt>0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为简化分析过程，限定改数据集只能有正的出资额\n",
    "fec = fec[fec.contb_receipt_amt>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 由于Obama, Barack 和 Romney, Mitt 是最主要的两名候选人，故专门准备一个子集，只包含这两个人的竞选活动的赞助信息\n",
    "fec_mrbo = fec[fec.cand_nm.isin(['Obama, Barack','Romney, Mitt'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RETIRED                                   233990\n",
       "INFORMATION REQUESTED                      35107\n",
       "ATTORNEY                                   34286\n",
       "HOMEMAKER                                  29931\n",
       "PHYSICIAN                                  23432\n",
       "INFORMATION REQUESTED PER BEST EFFORTS     21138\n",
       "ENGINEER                                   14334\n",
       "TEACHER                                    13990\n",
       "CONSULTANT                                 13273\n",
       "PROFESSOR                                  12555\n",
       "Name: contbr_occupation, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据职业和雇主统计赞助信息\n",
    "# 律师们更倾向于资助民主党，而企业家更倾向于资助共和党\n",
    "# 根据职业计算出资总额\n",
    "fec.contbr_occupation.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    990569\n",
       "True        906\n",
       "Name: contbr_occupation, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.contbr_occupation.isin(['INFORMATION REQUESTED (BEST EFFORTS)']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从上面可以看到，许多信息都设计相同的基本工作类型，或者同一样东子有多种变体\n",
    "# 利用dict.get，它允许没有映射关系的职业也能“通过”\n",
    "# INFORMATION REQUESTED:信息不明\n",
    "occ_mapping = {'INFORMATION REQUESTED PER BEST EFFORTS':'NOT PROVIDED',\n",
    "               'INFORMATION REQUESTED':'NOT PROVIDED',\n",
    "               'INFORMATION REQUESTED (BEST EFFORTS)':'NOT PROVIDED',\n",
    "               'C.E.O':'CEO'}\n",
    "# 如果没有提供相关映射，则返回x\n",
    "f = lambda x:occ_mapping.get(x,x)\n",
    "fec.contbr_occupation = fec.contbr_occupation.map(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对雇主信息也进行同样处理\n",
    "emp_mapping={'INFORMATION REQUESTED PER BEST EFFORTS':'NOT PROVIDED',\n",
    "             'INFORMATION REQUESTED':'NOT PROVIDED',\n",
    "             'SELF':'SELF-EMPLOYED',\n",
    "             'SELF EMPLOYED':'SELF-EMPLOYED',}\n",
    "# 如果没有提供相关映射，则返回x\n",
    "f = lambda x:emp_mapping.get(x)\n",
    "fec.contbr_employer = fec.contbr_employer.map(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>party</th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Republican</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATTORNEY</th>\n",
       "      <td>11141982.97</td>\n",
       "      <td>7.477194e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C.E.O.</th>\n",
       "      <td>1690.00</td>\n",
       "      <td>2.592983e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEO</th>\n",
       "      <td>2074284.79</td>\n",
       "      <td>1.640758e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONSULTANT</th>\n",
       "      <td>2459912.71</td>\n",
       "      <td>2.544725e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENGINEER</th>\n",
       "      <td>951525.55</td>\n",
       "      <td>1.818374e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXECUTIVE</th>\n",
       "      <td>1355161.05</td>\n",
       "      <td>4.138850e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOMEMAKER</th>\n",
       "      <td>4248875.80</td>\n",
       "      <td>1.363428e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INVESTOR</th>\n",
       "      <td>884133.00</td>\n",
       "      <td>2.431769e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAWYER</th>\n",
       "      <td>3160478.87</td>\n",
       "      <td>3.912243e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANAGER</th>\n",
       "      <td>762883.22</td>\n",
       "      <td>1.444532e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOT PROVIDED</th>\n",
       "      <td>4866973.96</td>\n",
       "      <td>2.056547e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWNER</th>\n",
       "      <td>1001567.36</td>\n",
       "      <td>2.408287e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHYSICIAN</th>\n",
       "      <td>3735124.94</td>\n",
       "      <td>3.594320e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRESIDENT</th>\n",
       "      <td>1878509.95</td>\n",
       "      <td>4.720924e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROFESSOR</th>\n",
       "      <td>2165071.08</td>\n",
       "      <td>2.967027e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REAL ESTATE</th>\n",
       "      <td>528902.09</td>\n",
       "      <td>1.625902e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIRED</th>\n",
       "      <td>25305116.38</td>\n",
       "      <td>2.356124e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <td>672393.40</td>\n",
       "      <td>1.640253e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "party                 Democrat    Republican\n",
       "contbr_occupation                           \n",
       "ATTORNEY           11141982.97  7.477194e+06\n",
       "C.E.O.                 1690.00  2.592983e+06\n",
       "CEO                 2074284.79  1.640758e+06\n",
       "CONSULTANT          2459912.71  2.544725e+06\n",
       "ENGINEER             951525.55  1.818374e+06\n",
       "EXECUTIVE           1355161.05  4.138850e+06\n",
       "HOMEMAKER           4248875.80  1.363428e+07\n",
       "INVESTOR             884133.00  2.431769e+06\n",
       "LAWYER              3160478.87  3.912243e+05\n",
       "MANAGER              762883.22  1.444532e+06\n",
       "NOT PROVIDED        4866973.96  2.056547e+07\n",
       "OWNER               1001567.36  2.408287e+06\n",
       "PHYSICIAN           3735124.94  3.594320e+06\n",
       "PRESIDENT           1878509.95  4.720924e+06\n",
       "PROFESSOR           2165071.08  2.967027e+05\n",
       "REAL ESTATE          528902.09  1.625902e+06\n",
       "RETIRED            25305116.38  2.356124e+07\n",
       "SELF-EMPLOYED        672393.40  1.640253e+06"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过pivot_table根据党派和职业对数据进行聚合，然后过滤掉总出资额不足200万美元的数据\n",
    "by_occupation = fec.pivot_table('contb_receipt_amt',index='contbr_occupation',columns='party',aggfunc='sum')\n",
    "over_2mm = by_occupation[by_occupation.sum(1)>2000000]\n",
    "over_2mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa562910>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x55dff90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将这些数据做成柱状图，‘barh’表示水平柱状图，即条形图\n",
    "over_2mm.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm        contbr_occupation                     \n",
       "Obama, Barack  RETIRED                                   25305116.38\n",
       "               ATTORNEY                                  11141982.97\n",
       "               INFORMATION REQUESTED                      4866973.96\n",
       "               HOMEMAKER                                  4248875.80\n",
       "               PHYSICIAN                                  3735124.94\n",
       "               LAWYER                                     3160478.87\n",
       "               CONSULTANT                                 2459912.71\n",
       "Romney, Mitt   RETIRED                                   11508473.59\n",
       "               INFORMATION REQUESTED PER BEST EFFORTS    11396894.84\n",
       "               HOMEMAKER                                  8147446.22\n",
       "               ATTORNEY                                   5364718.82\n",
       "               PRESIDENT                                  2491244.89\n",
       "               EXECUTIVE                                  2300947.03\n",
       "               C.E.O.                                     1968386.11\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果想了解下Obama, Barack 和 Romney, Mitt 总出资额最高的职业和企业\n",
    "# 需要先对候选人进行分组，然后取最大值\n",
    "def get_top_amounts(group,key,n=5):\n",
    "    totals = group.groupby(key)['contb_receipt_amt'].sum()\n",
    "    # 根据key对totals进行降序排列\n",
    "    return totals.sort_values(ascending=False)[:n]\n",
    "\n",
    "grouped = fec_mrbo.groupby('cand_nm')\n",
    "# 根据职业进行聚合，得到出资额top7的职业\n",
    "grouped.apply(get_top_amounts,'contbr_occupation',n=7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm        contbr_employer                       \n",
       "Obama, Barack  RETIRED                                   22694358.85\n",
       "               SELF-EMPLOYED                             17080985.96\n",
       "               NOT EMPLOYED                               8586308.70\n",
       "               INFORMATION REQUESTED                      5053480.37\n",
       "               HOMEMAKER                                  2605408.54\n",
       "               SELF                                       1076531.20\n",
       "               SELF EMPLOYED                               469290.00\n",
       "               STUDENT                                     318831.45\n",
       "               VOLUNTEER                                   257104.00\n",
       "               MICROSOFT                                   215585.36\n",
       "Romney, Mitt   INFORMATION REQUESTED PER BEST EFFORTS    12059527.24\n",
       "               RETIRED                                   11506225.71\n",
       "               HOMEMAKER                                  8147196.22\n",
       "               SELF-EMPLOYED                              7409860.98\n",
       "               STUDENT                                     496490.94\n",
       "               CREDIT SUISSE                               281150.00\n",
       "               MORGAN STANLEY                              267266.00\n",
       "               GOLDMAN SACH & CO.                          238250.00\n",
       "               BARCLAYS CAPITAL                            162750.00\n",
       "               H.I.G. CAPITAL                              139500.00\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据雇主进行聚合，得到出资额top10的雇主\n",
    "grouped.apply(get_top_amounts,'contbr_employer',n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "411           (10, 100]\n",
       "412         (100, 1000]\n",
       "413         (100, 1000]\n",
       "414           (10, 100]\n",
       "415           (10, 100]\n",
       "416           (10, 100]\n",
       "417         (100, 1000]\n",
       "418           (10, 100]\n",
       "419         (100, 1000]\n",
       "420           (10, 100]\n",
       "421           (10, 100]\n",
       "422         (100, 1000]\n",
       "423         (100, 1000]\n",
       "424         (100, 1000]\n",
       "425         (100, 1000]\n",
       "426         (100, 1000]\n",
       "427       (1000, 10000]\n",
       "428         (100, 1000]\n",
       "429         (100, 1000]\n",
       "430           (10, 100]\n",
       "431       (1000, 10000]\n",
       "432         (100, 1000]\n",
       "433         (100, 1000]\n",
       "434         (100, 1000]\n",
       "435         (100, 1000]\n",
       "436         (100, 1000]\n",
       "437           (10, 100]\n",
       "438         (100, 1000]\n",
       "439         (100, 1000]\n",
       "440           (10, 100]\n",
       "              ...      \n",
       "701356        (10, 100]\n",
       "701357          (1, 10]\n",
       "701358        (10, 100]\n",
       "701359        (10, 100]\n",
       "701360        (10, 100]\n",
       "701361        (10, 100]\n",
       "701362      (100, 1000]\n",
       "701363        (10, 100]\n",
       "701364        (10, 100]\n",
       "701365        (10, 100]\n",
       "701366        (10, 100]\n",
       "701367        (10, 100]\n",
       "701368      (100, 1000]\n",
       "701369        (10, 100]\n",
       "701370        (10, 100]\n",
       "701371        (10, 100]\n",
       "701372        (10, 100]\n",
       "701373        (10, 100]\n",
       "701374        (10, 100]\n",
       "701375        (10, 100]\n",
       "701376    (1000, 10000]\n",
       "701377        (10, 100]\n",
       "701378        (10, 100]\n",
       "701379      (100, 1000]\n",
       "701380    (1000, 10000]\n",
       "701381        (10, 100]\n",
       "701382      (100, 1000]\n",
       "701383          (1, 10]\n",
       "701384        (10, 100]\n",
       "701385      (100, 1000]\n",
       "Name: contb_receipt_amt, Length: 694282, dtype: category\n",
       "Categories (8, interval[int64]): [(0, 1] < (1, 10] < (10, 100] < (100, 1000] < (1000, 10000] < (10000, 100000] < (100000, 1000000] < (1000000, 10000000]]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对出资额分组\n",
    "# 根据出资额大小将数据离散化到多个面元中\n",
    "bins = np.array([0,1,10,100,1000,10000,100000,1000000,10000000])\n",
    "labels = pd.cut(fec_mrbo.contb_receipt_amt,bins)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>493.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 10]</th>\n",
       "      <td>40070.0</td>\n",
       "      <td>3681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10, 100]</th>\n",
       "      <td>372280.0</td>\n",
       "      <td>31853.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>153991.0</td>\n",
       "      <td>43357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 10000]</th>\n",
       "      <td>22284.0</td>\n",
       "      <td>26186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10000, 100000]</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000, 1000000]</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000000, 10000000]</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                       493.0          77.0\n",
       "(1, 10]                    40070.0        3681.0\n",
       "(10, 100]                 372280.0       31853.0\n",
       "(100, 1000]               153991.0       43357.0\n",
       "(1000, 10000]              22284.0       26186.0\n",
       "(10000, 100000]                2.0           1.0\n",
       "(100000, 1000000]              3.0           NaN\n",
       "(1000000, 10000000]            4.0           NaN"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 然后根据候选人姓名以及面元标签对数据进行分组\n",
    "grouped = fec_mrbo.groupby(['cand_nm',labels])\n",
    "grouped.size().unstack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>318.24</td>\n",
       "      <td>77.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 10]</th>\n",
       "      <td>337267.62</td>\n",
       "      <td>29819.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10, 100]</th>\n",
       "      <td>20288981.41</td>\n",
       "      <td>1987783.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>54798531.46</td>\n",
       "      <td>22363381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 10000]</th>\n",
       "      <td>51753705.67</td>\n",
       "      <td>63942145.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10000, 100000]</th>\n",
       "      <td>59100.00</td>\n",
       "      <td>12700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000, 1000000]</th>\n",
       "      <td>1490683.08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000000, 10000000]</th>\n",
       "      <td>7148839.76</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                      318.24         77.00\n",
       "(1, 10]                  337267.62      29819.66\n",
       "(10, 100]              20288981.41    1987783.76\n",
       "(100, 1000]            54798531.46   22363381.69\n",
       "(1000, 10000]          51753705.67   63942145.42\n",
       "(10000, 100000]           59100.00      12700.00\n",
       "(100000, 1000000]       1490683.08           NaN\n",
       "(1000000, 10000000]     7148839.76           NaN"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从上面可以看到，小额赞助方面，Obama比Romney获得的数量多得多\n",
    "# 还可以对出资额求和并在面元内规格化，以便用图形化显示两位候选人各种赞助额度的比例\n",
    "bucket_sums = grouped.contb_receipt_amt.sum().unstack(0)\n",
    "bucket_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>0.805182</td>\n",
       "      <td>0.194818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 10]</th>\n",
       "      <td>0.918767</td>\n",
       "      <td>0.081233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10, 100]</th>\n",
       "      <td>0.910769</td>\n",
       "      <td>0.089231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>0.710176</td>\n",
       "      <td>0.289824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 10000]</th>\n",
       "      <td>0.447326</td>\n",
       "      <td>0.552674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10000, 100000]</th>\n",
       "      <td>0.823120</td>\n",
       "      <td>0.176880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000, 1000000]</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000000, 10000000]</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                    0.805182      0.194818\n",
       "(1, 10]                   0.918767      0.081233\n",
       "(10, 100]                 0.910769      0.089231\n",
       "(100, 1000]               0.710176      0.289824\n",
       "(1000, 10000]             0.447326      0.552674\n",
       "(10000, 100000]           0.823120      0.176880\n",
       "(100000, 1000000]         1.000000           NaN\n",
       "(1000000, 10000000]       1.000000           NaN"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normed_sums = bucket_sums.div(bucket_sums.sum(axis=1),axis=0)  # 每行占比和为1\n",
    "normed_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa5cca30>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa596470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normed_sums[:-2]:最后两个中有缺失值 所以不能用来画图\n",
    "normed_sums[:-2].plot(kind='barh',stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 根据州统计赞助信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_st</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>281840.15</td>\n",
       "      <td>86204.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>543123.48</td>\n",
       "      <td>527303.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>359247.28</td>\n",
       "      <td>105556.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>1506476.98</td>\n",
       "      <td>1888436.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>23824984.24</td>\n",
       "      <td>11237636.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>2132429.49</td>\n",
       "      <td>1506714.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>2068291.26</td>\n",
       "      <td>3499475.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>4373538.80</td>\n",
       "      <td>1025137.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>336669.14</td>\n",
       "      <td>82712.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>7318178.58</td>\n",
       "      <td>8338458.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm    Obama, Barack  Romney, Mitt\n",
       "contbr_st                             \n",
       "AK             281840.15      86204.24\n",
       "AL             543123.48     527303.51\n",
       "AR             359247.28     105556.00\n",
       "AZ            1506476.98    1888436.23\n",
       "CA           23824984.24   11237636.60\n",
       "CO            2132429.49    1506714.12\n",
       "CT            2068291.26    3499475.45\n",
       "DC            4373538.80    1025137.50\n",
       "DE             336669.14      82712.00\n",
       "FL            7318178.58    8338458.81"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 首先根据候选人和州对数据进行聚合\n",
    "grouped = fec_mrbo.groupby(['cand_nm','contbr_st'])\n",
    "totals = grouped.contb_receipt_amt.sum().unstack(0).fillna(0)\n",
    "totals = totals[totals.sum(1)>100000] # 过滤掉总出资额小于10万的\n",
    "totals[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_st</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>0.765778</td>\n",
       "      <td>0.234222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>0.507390</td>\n",
       "      <td>0.492610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>0.772902</td>\n",
       "      <td>0.227098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>0.443745</td>\n",
       "      <td>0.556255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>0.679498</td>\n",
       "      <td>0.320502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>0.585970</td>\n",
       "      <td>0.414030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>0.371476</td>\n",
       "      <td>0.628524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>0.810113</td>\n",
       "      <td>0.189887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.802776</td>\n",
       "      <td>0.197224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>0.467417</td>\n",
       "      <td>0.532583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm    Obama, Barack  Romney, Mitt\n",
       "contbr_st                             \n",
       "AK              0.765778      0.234222\n",
       "AL              0.507390      0.492610\n",
       "AR              0.772902      0.227098\n",
       "AZ              0.443745      0.556255\n",
       "CA              0.679498      0.320502\n",
       "CO              0.585970      0.414030\n",
       "CT              0.371476      0.628524\n",
       "DC              0.810113      0.189887\n",
       "DE              0.802776      0.197224\n",
       "FL              0.467417      0.532583"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对各行除以总赞助额，就会得到各候选人在各州的总赞助额比例\n",
    "percent = totals.div(totals.sum(1),axis=0)\n",
    "percent[:10]"
   ]
  }
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