{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分组聚合透视\n",
    "# 很多时候属性是相似的\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>Hand</th>\n",
       "      <th>Smoke</th>\n",
       "      <th>sex</th>\n",
       "      <th>weight</th>\n",
       "      <th>IQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>right</td>\n",
       "      <td>yes</td>\n",
       "      <td>male</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>left</td>\n",
       "      <td>yes</td>\n",
       "      <td>female</td>\n",
       "      <td>50</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>left</td>\n",
       "      <td>no</td>\n",
       "      <td>female</td>\n",
       "      <td>48</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>right</td>\n",
       "      <td>no</td>\n",
       "      <td>male</td>\n",
       "      <td>75</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>right</td>\n",
       "      <td>yes</td>\n",
       "      <td>male</td>\n",
       "      <td>68</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>right</td>\n",
       "      <td>no</td>\n",
       "      <td>male</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>right</td>\n",
       "      <td>no</td>\n",
       "      <td>female</td>\n",
       "      <td>40</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>right</td>\n",
       "      <td>no</td>\n",
       "      <td>female</td>\n",
       "      <td>90</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>left</td>\n",
       "      <td>no</td>\n",
       "      <td>male</td>\n",
       "      <td>88</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>right</td>\n",
       "      <td>yes</td>\n",
       "      <td>female</td>\n",
       "      <td>76</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hand Smoke     sex  weight   IQ\n",
       "0  right   yes    male      80  100\n",
       "1   left   yes  female      50  120\n",
       "2   left    no  female      48   90\n",
       "3  right    no    male      75  130\n",
       "4  right   yes    male      68  140\n",
       "5  right    no    male     100   80\n",
       "6  right    no  female      40   94\n",
       "7  right    no  female      90  110\n",
       "8   left    no    male      88  100\n",
       "9  right   yes  female      76  160"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 走右手习惯，是否抽烟，性别，对体重，智商，有一定影响\n",
    "\n",
    "df = DataFrame({'Hand':['right','left','left','right','right','right','right','right','left','right'],\n",
    "               'Smoke':['yes','yes','no','no','yes','no','no','no','no','yes'],\n",
    "               'sex':['male','female','female','male','male','male','female','female','male','female'],\n",
    "               'weight':[80,50,48,75,68,100,40,90,88,76],\n",
    "               'IQ':[100,120,90,130,140,80,94,110,100,160]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分组聚合查看规律，某一条件下规律"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>weight</th>\n",
       "      <th>IQ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hand</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>62.0</td>\n",
       "      <td>103.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>75.6</td>\n",
       "      <td>116.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       weight     IQ\n",
       "Hand                \n",
       "left     62.0  103.3\n",
       "right    75.6  116.3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.groupby(by = ['Hand'])[['weight','IQ']].mean().round(1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hand</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       weight\n",
       "Hand         \n",
       "left     62.0\n",
       "right    75.6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by = ['Hand'])[['weight']].apply(np.mean).round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.groupby(by = ['Hand'])[['weight']].transform(np.mean).round(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75.6</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight_mean\n",
       "0         75.6\n",
       "1         62.0\n",
       "2         62.0\n",
       "3         75.6\n",
       "4         75.6\n",
       "5         75.6\n",
       "6         75.6\n",
       "7         75.6\n",
       "8         62.0\n",
       "9         75.6"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2.add_suffix('_mean')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hand</th>\n",
       "      <th>Smoke</th>\n",
       "      <th>sex</th>\n",
       "      <th>weight</th>\n",
       "      <th>IQ</th>\n",
       "      <th>weight_mean</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>male</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>left</td>\n",
       "      <td>yes</td>\n",
       "      <td>female</td>\n",
       "      <td>50</td>\n",
       "      <td>120</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>left</td>\n",
       "      <td>no</td>\n",
       "      <td>female</td>\n",
       "      <td>48</td>\n",
       "      <td>90</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>right</td>\n",
       "      <td>no</td>\n",
       "      <td>male</td>\n",
       "      <td>75</td>\n",
       "      <td>130</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>right</td>\n",
       "      <td>yes</td>\n",
       "      <td>male</td>\n",
       "      <td>68</td>\n",
       "      <td>140</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>right</td>\n",
       "      <td>no</td>\n",
       "      <td>male</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>right</td>\n",
       "      <td>no</td>\n",
       "      <td>female</td>\n",
       "      <td>40</td>\n",
       "      <td>94</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>right</td>\n",
       "      <td>no</td>\n",
       "      <td>female</td>\n",
       "      <td>90</td>\n",
       "      <td>110</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>left</td>\n",
       "      <td>no</td>\n",
       "      <td>male</td>\n",
       "      <td>88</td>\n",
       "      <td>100</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>right</td>\n",
       "      <td>yes</td>\n",
       "      <td>female</td>\n",
       "      <td>76</td>\n",
       "      <td>160</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hand Smoke     sex  weight   IQ  weight_mean\n",
       "0  right   yes    male      80  100         75.6\n",
       "1   left   yes  female      50  120         62.0\n",
       "2   left    no  female      48   90         62.0\n",
       "3  right    no    male      75  130         75.6\n",
       "4  right   yes    male      68  140         75.6\n",
       "5  right    no    male     100   80         75.6\n",
       "6  right    no  female      40   94         75.6\n",
       "7  right    no  female      90  110         75.6\n",
       "8   left    no    male      88  100         62.0\n",
       "9  right   yes  female      76  160         75.6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df.merge(df2,left_index=True,right_index=True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hand\n",
       "left     ([3, 3], [62.0, 103.3])\n",
       "right    ([7, 7], [75.6, 116.3])\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def count(x):\n",
    "    \n",
    "    return (x.count(),x.mean().round(1))\n",
    "\n",
    "df.groupby(by = ['Hand'])[['weight','IQ']].apply(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>IQ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hand</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">left</th>\n",
       "      <th>female</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">right</th>\n",
       "      <th>female</th>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               IQ\n",
       "Hand  sex        \n",
       "left  female  120\n",
       "      male    100\n",
       "right female  160\n",
       "      male    140"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by = ['Hand','sex'])[['IQ']].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000019E24051EF0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.groupby(by = ['Hand'])['IQ','weight']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    .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=\"2\" halign=\"left\">IQ</th>\n",
       "      <th colspan=\"2\" halign=\"left\">weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hand</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>120</td>\n",
       "      <td>103.3</td>\n",
       "      <td>88</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>160</td>\n",
       "      <td>116.3</td>\n",
       "      <td>100</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        IQ        weight      \n",
       "       max   mean    max  mean\n",
       "Hand                          \n",
       "left   120  103.3     88  62.0\n",
       "right  160  116.3    100  75.6"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.agg(['max','mean']).round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>IQ</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hand</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>120</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>160</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        IQ  weight\n",
       "Hand              \n",
       "left   120    62.0\n",
       "right  160    75.6"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.agg({'IQ':'max','weight':'mean'}).round(1)"
   ]
  }
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