大熊猫按月计算唯一出现次数

Question

我有一些月度数据,我试图用Pandas总结,我需要计算每月发生的唯一条目数.这是一些示例代码,显示了我正在尝试做的事情:

import pandas as pd

mnths = ['JAN','FEB','MAR','APR']
custs = ['A','B','C',]

testFrame = pd.DataFrame(index=custs, columns=mnths)
testFrame['JAN']['A'] = 'purchased Prod'
testFrame['JAN']['B'] = 'No Data'
testFrame['JAN']['C'] = 'Purchased Competitor'
testFrame['FEB']['A'] = 'purchased Prod'
testFrame['FEB']['B'] = 'purchased Prod'
testFrame['FEB']['C'] = 'purchased Prod'
testFrame['MAR']['A'] = 'No Data'
testFrame['MAR']['B'] = 'No Data'
testFrame['MAR']['C'] = 'Purchased Competitor'
testFrame['APR']['A'] = 'Purchased Competitor'
testFrame['APR']['B'] = 'purchased Prod'
testFrame['APR']['C'] = 'Purchased Competitor'

uniqueValues = pd.Series(testFrame.values.ravel()).unique()

#CODE TO GET COUNT OF ENTRIES IN testFrame BY UNIQUE VALUE

期望的输出:

                JAN    FEB    MAR    APR
purchased Prod   ?     ?       ?      ?
Purchased Competitor ? ?       ?      ?
No Data          ?     ?       ?      ?

我可以获得唯一值并使用正确的轴/列创建新的数据框

我从这里开始: Pandas:计算数据 框中的唯一值在Pandas数据框中查找唯一值,无论行或列位置如何

但仍然不能完全得到我需要的格式的输出.我不太确定如何将df.groupby语法或df.apply语法应用于我正在使用的语法.

Answer 1

填充是可选的.

In [40]: testFrame.apply(Series.value_counts).fillna(0)
Out[40]: 
                      JAN  FEB  MAR  APR
No Data                 1    0    2    0
Purchased Competitor    1    0    1    2
purchased Prod          1    3    0    1

这是一个巧妙的应用技巧.我将创建一个函数并打印出传入的内容(甚至可以调试它们).然后很容易看出发生了什么.

In [20]: def f(x):
   ....:     print(x)
   ....:     return x.value_counts()
   ....: 

In [21]: testFrame.apply(f)
A          purchased Prod
B                 No Data
C    Purchased Competitor
Name: JAN, dtype: object
A          purchased Prod
B                 No Data
C    Purchased Competitor
Name: JAN, dtype: object
A    purchased Prod
B    purchased Prod
C    purchased Prod
Name: FEB, dtype: object
A                 No Data
B                 No Data
C    Purchased Competitor
Name: MAR, dtype: object
A    Purchased Competitor
B          purchased Prod
C    Purchased Competitor
Name: APR, dtype: object
Out[21]: 
                      JAN  FEB  MAR  APR
No Data                 1  NaN    2  NaN
Purchased Competitor    1  NaN    1    2
purchased Prod          1    3  NaN    1

[3 rows x 4 columns]

所以它执行此操作然后将它们连接在一起(使用正确的标签)

In [22]: testFrame.iloc[0].value_counts()
Out[22]: 
purchased Prod          2
Purchased Competitor    1
No Data                 1
dtype: int64