【数据分析可视化】透视表

前提要对数据信息之间的关系要了解

代码语言：javascript复制

import numpy as np
import pandas as pd
from pandas import Series, DataFrame

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# 读入excel文件
df = pd.read_excel('/Users/bennyrhys/Desktop/数据分析可视化-数据集/homework/sales-funnel.xlsx')
df

	Account	Name	Rep	Manager	Product	Quantity	Price	Status
0	714466	Trantow-Barrows	Craig Booker	Debra Henley	CPU	1	30000	presented
1	714466	Trantow-Barrows	Craig Booker	Debra Henley	Software	1	10000	presented
2	714466	Trantow-Barrows	Craig Booker	Debra Henley	Maintenance	2	5000	pending
3	737550	Fritsch, Russel and Anderson	Craig Booker	Debra Henley	CPU	1	35000	declined
4	146832	Kiehn-Spinka	Daniel Hilton	Debra Henley	CPU	2	65000	won
5	218895	Kulas Inc	Daniel Hilton	Debra Henley	CPU	2	40000	pending
6	218895	Kulas Inc	Daniel Hilton	Debra Henley	Software	1	10000	presented
7	412290	Jerde-Hilpert	John Smith	Debra Henley	Maintenance	2	5000	pending
8	740150	Barton LLC	John Smith	Debra Henley	CPU	1	35000	declined
9	141962	Herman LLC	Cedric Moss	Fred Anderson	CPU	2	65000	won
10	163416	Purdy-Kunde	Cedric Moss	Fred Anderson	CPU	1	30000	presented
11	239344	Stokes LLC	Cedric Moss	Fred Anderson	Maintenance	1	5000	pending
12	239344	Stokes LLC	Cedric Moss	Fred Anderson	Software	1	10000	presented
13	307599	Kassulke, Ondricka and Metz	Wendy Yule	Fred Anderson	Maintenance	3	7000	won
14	688981	Keeling LLC	Wendy Yule	Fred Anderson	CPU	5	100000	won
15	729833	Koepp Ltd	Wendy Yule	Fred Anderson	CPU	2	65000	declined
16	729833	Koepp Ltd	Wendy Yule	Fred Anderson	Monitor	2	5000	presented

代码语言：javascript复制

# 透视表-去重复（聚合默认 agg平均值）
pd.pivot_table(df, index=['Name'])

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# 透视表-去重复（聚合默认 agg-sum）
pd.pivot_table(df, index=['Name'], aggfunc='sum')

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# 透视表-多列索引（基本不变）
pd.pivot_table(df, index=['Name','Rep','Manager'])

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# 透视表-包含关系
pd.pivot_table(df, index=['Manager','Rep'])

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# 透视表指定 values默认是None
pd.pivot_table(df, index=['Manager','Rep'], values=['Price','Quantity'])

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# 透视表-col
pd.pivot_table(df, index=['Manager','Rep'], values=['Price','Quantity'], columns=['Product'])

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# 透视表-填充nan
pd.pivot_table(df, index=['Manager','Rep'], values=['Price','Quantity'], columns=['Product'],fill_value=0)

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