pandas-10 pd.pivot_table()透视表功能
和excel一样，pandas也有一个透视表的功能，具体demo如下：
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
#显示所有列
pd.set_option('display.max_columns', None)
#显示所有行
pd.set_option('display.max_rows', None)
#设置value的显示长度为100，默认为50
pd.set_option('max_colwidth',100)
df = pd.read_excel('./sales-funnel.xlsx')
print(df.head())
'''
Account Name Rep Manager
0 714466 Trantow-Barrows Craig Booker Debra Henley
1 714466 Trantow-Barrows Craig Booker Debra Henley
2 714466 Trantow-Barrows Craig Booker Debra Henley
3 737550 Fritsch, Russel and Anderson Craig Booker Debra Henley
4 146832 Kiehn-Spinka Daniel Hilton Debra Henley
Product Quantity Price Status
0 CPU 1 30000 presented
1 Software 1 10000 presented
2 Maintenance 2 5000 pending
3 CPU 1 35000 declined
4 CPU 2 65000 won
'''
print(pd.pivot_table(df, index=['Name']))
'''
Account Price Quantity
Name
Barton LLC 740150 35000 1.000000
Fritsch, Russel and Anderson 737550 35000 1.000000
Herman LLC 141962 65000 2.000000
Jerde-Hilpert 412290 5000 2.000000
Kassulke, Ondricka and Metz 307599 7000 3.000000
Keeling LLC 688981 100000 5.000000
Kiehn-Spinka 146832 65000 2.000000
Koepp Ltd 729833 35000 2.000000
Kulas Inc 218895 25000 1.500000
Purdy-Kunde 163416 30000 1.000000
Stokes LLC 239344 7500 1.000000
Trantow-Barrows 714466 15000 1.333333
对名字进行了去重，将每个人的销售记录取进行统计，上例是求了均值。
这是由aggfunc参数来决定的。
'''
print(pd.pivot_table(df, index=['Name'], aggfunc='sum'))
'''
Account Price Quantity
Name
Barton LLC 740150 35000 1
Fritsch, Russel and Anderson 737550 35000 1
Herman LLC 141962 65000 2
Jerde-Hilpert 412290 5000 2
Kassulke, Ondricka and Metz 307599 7000 3
Keeling LLC 688981 100000 5
Kiehn-Spinka 146832 65000 2
Koepp Ltd 1459666 70000 4
Kulas Inc 437790 50000 3
Purdy-Kunde 163416 30000 1
Stokes LLC 478688 15000 2
Trantow-Barrows 2143398 45000 4
'''
print(pd.pivot_table(df, index=['Name', 'Rep', 'Manager']))
'''
Account ... Quantity
Name Rep Manager ...
Barton LLC John Smith Debra Henley 740150 ... 1.000000
Fritsch, Russel and Anderson Craig Booker Debra Henley 737550 ... 1.000000
Herman LLC Cedric Moss Fred Anderson 141962 ... 2.000000
Jerde-Hilpert John Smith Debra Henley 412290 ... 2.000000
Kassulke, Ondricka and Metz Wendy Yule Fred Anderson 307599 ... 3.000000
Keeling LLC Wendy Yule Fred Anderson 688981 ... 5.000000
Kiehn-Spinka Daniel Hilton Debra Henley 146832 ... 2.000000
Koepp Ltd Wendy Yule Fred Anderson 729833 ... 2.000000
Kulas Inc Daniel Hilton Debra Henley 218895 ... 1.500000
Purdy-Kunde Cedric Moss Fred Anderson 163416 ... 1.000000
Stokes LLC Cedric Moss Fred Anderson 239344 ... 1.000000
Trantow-Barrows Craig Booker Debra Henley 714466 ... 1.333333
'''
print(pd.pivot_table(df, index=['Manager', 'Rep']))
# manager 和 rep 之间 存在 一对多的 关系
'''
Account Price Quantity
Manager Rep
Debra Henley Craig Booker 720237.0 20000.000000 1.250000
Daniel Hilton 194874.0 38333.333333 1.666667
John Smith 576220.0 20000.000000 1.500000
Fred Anderson Cedric Moss 196016.5 27500.000000 1.250000
Wendy Yule 614061.5 44250.000000 3.000000
'''
print(pd.pivot_table(df, index=['Manager', 'Rep'], values=['Price', 'Quantity']))
'''
Price Quantity
Manager Rep
Debra Henley Craig Booker 20000.000000 1.250000
Daniel Hilton 38333.333333 1.666667
John Smith 20000.000000 1.500000
Fred Anderson Cedric Moss 27500.000000 1.250000
Wendy Yule 44250.000000 3.000000
'''
print(pd.pivot_table(df, index=['Manager', 'Rep'], values=['Price', 'Quantity'], columns=['Product']))
'''
Price ... Quantity
Product CPU Maintenance ... Monitor Software
Manager Rep ...
Debra Henley Craig Booker 32500.0 5000.0 ... NaN 1.0
Daniel Hilton 52500.0 NaN ... NaN 1.0
John Smith 35000.0 5000.0 ... NaN NaN
Fred Anderson Cedric Moss 47500.0 5000.0 ... NaN 1.0
Wendy Yule 82500.0 7000.0 ... 2.0 NaN
由以上输出可以看出，当column指定为product之后，price和quantity进行了细分，将每个product的详情列出。
另外还可以设置一个fill_value的参数，可以将nan填充为某个值。
'''
'''
总结：
使用透视表之前，需要对原始数据有一个大概的了解，这样生成的透视表才能够有意义。
'''