如何基于python中的多个条件计算列中的唯一行

Question

I have a data frame that looks like that : (the treatment has multiple possibily of character variable, I just simplified for the question)

ID              Position            Treatment
--20AxECvv-         0           A
--20AxECvv-         -1          A
--20AxECvv-         -2          A
--h9INKewQf-        0           A
--h9INKewQf-        -1          B
zZU7a@8jN           0           B
QUeSNEXmdB          0           C
QUeSNEXmdB          -1          C
qu72Ql@h79          0           C

I just want to keep the ID with exclusif treatment, in other word keep ID who was treated by only one treatment even if it was several times. After, I want to sum the number of ID for each treatment. The result would be :

ID              Position            Treatment
--20AxECvv-         0           A
--20AxECvv-         -1          A
--20AxECvv-         -2          A
zZU7a@8jN           0           B
QUeSNEXmdB          0           C
QUeSNEXmdB          -1          C   
qu72Ql@h79          0           C

And the sum :

A : 1 
B : 1
C : 2

I have any ida how to resolve this, maybe with a loop within a loop but I am a beginner with Python/panda Thanks

Answer 1

You can groupby ID and filter the rows based on the condition number of unique rows == 1

df1 = df.loc[df.groupby('ID').Treatment.filter(lambda x: x.nunique()==1).index]

Or as @Igor Raush suggested,

df1 = df.groupby('ID').filter(lambda g: g.Treatment.nunique() == 1)    

    ID          Position    Treatment
0   --20AxECvv-     0           A
1   --20AxECvv-     -1          A
2   --20AxECvv-     -2          A
5   zZU7a@8jN       0           B
6   QUeSNEXmdB      0           C
7   QUeSNEXmdB      -1          C
8   qu72Ql@h79      0           C

And to get the unique count

df1.groupby('Treatment').ID.nunique()

Treatment
A        1
B        1
C        2