Question

我有一些月度数据，我正在尝试使用Pandas进行总结，我需要计算每个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          ?     ?       ?      ?

我可以获取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: Counting unique values in a dataframe Find unique values in a Pandas dataframe, irrespective of row or column location

但仍然无法将输出完全转换为我需要的格式。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

Answer 2

li = [testFrame.ix[:,i].value_counts() for i in range(len(mnths))]
frame = pd.DataFrame(li, index=mnths)

frame.fillna(value=0).swapaxes(0,1)
Out[42]: 
                      JAN  FEB  MAR  APR
No Data                 1    0    2    0
Purchased Competitor    1    0    1    2
purchased Prod          1    3    0    1

[3 rows x 4 columns]

大熊猫按月计算唯一出现次数

2 个答案: