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