My df

id    var1
A      9
A      0
A      2
A      1
B      2
B      5
B      2
B      1
C      1
C      9
D      7
D      2
D      0
..

desired output will have a dummy =1 if only var1 contains non-zeros, =0 otherwise.

id    var1
A      0
B      1
C      1
D      0

CodePudding user response：

There are many ways, one could be to clip to only have 0/1 in the input:

df['var1'].clip(upper=1).groupby(df['id']).min()
# or
# df.groupby('id')['var1'].min().clip(upper=1)

Alternatively, use booleans:

df['var1'].ne(0).groupby(df['id']).all().astype(int)

output:

id
A    0
B    1
C    1
D    0
Name: var1, dtype: int64

for a DataFrame as output add .reset_index(name='var1'):

  id  var1
0  A     0
1  B     1
2  C     1
3  D     0