878 lines
22 KiB
Plaintext
878 lines
22 KiB
Plaintext
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{
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"cells": [
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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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},
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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>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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"</div>"
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],
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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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"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>weight</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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" </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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" </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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" </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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" 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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]
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},
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"execution_count": 11,
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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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"df.groupby(by = ['Hand'])[['weight']].apply(np.mean).round(1)"
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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": 14,
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"metadata": {},
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"outputs": [],
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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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"execution_count": 17,
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"metadata": {
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"collapsed": 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>weight_mean</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>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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" <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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" <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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" <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",
|
||
|
|
" </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>"
|
||
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|
],
|
||
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"text/plain": [
|
||
|
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" weight_mean\n",
|
||
|
|
"0 75.6\n",
|
||
|
|
"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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]
|
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},
|
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"execution_count": 17,
|
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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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|
"df2 = df2.add_suffix('_mean')\n",
|
||
|
|
"df2"
|
||
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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": 19,
|
||
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"metadata": {
|
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"collapsed": 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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||
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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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|
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" vertical-align: top;\n",
|
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|
" }\n",
|
||
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|
"\n",
|
||
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|
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|
||
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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>Hand</th>\n",
|
||
|
|
" <th>Smoke</th>\n",
|
||
|
|
" <th>sex</th>\n",
|
||
|
|
" <th>weight</th>\n",
|
||
|
|
" <th>IQ</th>\n",
|
||
|
|
" <th>weight_mean</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",
|
||
|
|
" <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": [
|
||
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|
{
|
||
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|
"data": {
|
||
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|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<style scoped>\n",
|
||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
||
|
|
" vertical-align: middle;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe tbody tr th {\n",
|
||
|
|
" vertical-align: top;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe thead th {\n",
|
||
|
|
" text-align: right;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>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": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<style scoped>\n",
|
||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
||
|
|
" vertical-align: middle;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe tbody tr th {\n",
|
||
|
|
" vertical-align: top;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe thead tr th {\n",
|
||
|
|
" text-align: left;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe thead tr:last-of-type th {\n",
|
||
|
|
" text-align: right;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th colspan=\"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",
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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>160</td>\n",
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" <td>116.3</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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" </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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" IQ weight \n",
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" max mean max mean\n",
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"Hand \n",
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"left 120 103.3 88 62.0\n",
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"right 160 116.3 100 75.6"
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]
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},
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"execution_count": 31,
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"metadata": {},
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}
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],
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"source": [
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"data.agg(['max','mean']).round(1)"
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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": 35,
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"metadata": {},
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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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" <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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|
||
|
|
" <tr>\n",
|
||
|
|
" <th>right</th>\n",
|
||
|
|
" <td>160</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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|
||
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