Filtering DataFrame on groups where count of element is different than 1

Question

I'm working with a DataFrame having the following structure:

import pandas as pd

df = pd.DataFrame({'group':[1,1,1,2,2,2,2,3,3,3],
                   'brand':['A','B','X','C','D','X','X','E','F','X']})

print(df)

   group brand
0      1     A
1      1     B
2      1     X
3      2     C
4      2     D
5      2     X
6      2     X
7      3     E
8      3     F
9      3     X

My goal is to view only the groups having exactly one brand X associated to them. Since group number 2 has two observations equal to brand X, it should be filtered out from the resulting DataFrame.

The output should look like this:

   group brand
0      1     A
1      1     B
2      1     X
3      3     E
4      3     F
5      3     X

I know I should do a groupby on the group column and then filter those groups having a count of X different than 1. The filtering part is where I struggle. Any help would be appreciated.

Answer 1

Use series.eq to check if brand is equal to X , then groupby and transform sum and filter groups in which X count is equal to 1:

df[df['brand'].eq('X').groupby(df['group']).transform('sum').eq(1)]

---

   group brand
0      1     A
1      1     B
2      1     X
7      3     E
8      3     F
9      3     X

Answer 2

This should work as well

df[df.groupby(['group'])['brand'].transform('sum').str.count('X').eq(1)]

Output

 group  brand
0   1   A
1   1   B
2   1   X
7   3   E
8   3   F
9   3   X