Pandas: Row wise count for multiple columns after filter based on condition

Question

I have a dataframe df as

Decile    2_Con    3_Con    6_Con   Pred
   1        0        0        0       0
   1        0        0        1       1
   1        0        0        1       1
   2        0        0        0       0
   2        0        1        0       1
   2        0        1        1       1

Objective:

I want to compute the count of 6_Con, 3_Con and 2_Con when I filter the above df by Pred and set 6_Con ,3_con and 2_Con to 1 for each Decile.

So the resultant Dataframe should look like:

   Decile      2_Con     3_Con      6_Con    Pred
     1           0         0          2        2      <--We are only counting 1 in the columns for each Decile
     2           0         2          1        2

How to achieve this? I am not able to generate a plausible code here.

Thanks in advance

if you want take count where not is 0: `df.ne(0).gropuby('decile',as_index = False).sum()` — ansev, Feb 13 '20 at 17:49

score 0 · Accepted Answer · answered Feb 13 '20 at 17:46

0

Did you try:

df = df.groupby('Decile').sum()

This makes sense because of the 0-1 values, hence, the sum will be the count of non 0 values (rows) for each different decile.

answered Feb 13 '20 at 17:46

Celius Stingher

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I have used `groupby().agg()` for suitable Transformation – pythondumb Feb 14 '20 at 02:25

Pandas: Row wise count for multiple columns after filter based on condition

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