Filter out rows that are the same in one column but have multiple values in another columns respectively in dataframe

Question

To say I have a dataframe with three columns like:

index	A	B	C
1	foo	One	1
2	foo	Two	2
3	foo	Three	3
4	bar	One	2
5	bar	One	1
6	num	Two	3
7	num	Three	3

In this case, how may I filter out the rows that have the same value in column B but more than one respective value in column C by using Python Pandas?

The rows that I need is 1, 2, 4, 5, 6 because "One" in column B has two corresponding values (1 and 2) in column C and "Two" in column B has two corresponding values as well. Eventually I want to group them by column A if possible.

Answer 1

You can try groupby B column then filter by the value_counts of C column.

out = df.groupby('B').filter(lambda group: len(group['C'].value_counts()) > 1)

print(out)

   index    A    B  C
0      1  foo  One  1
1      2  foo  Two  2
3      4  bar  One  2
4      5  bar  One  1
5      6  num  Two  3