Select rows from a Pandas DataFrame with same values in one column but different value in the other column

You can try groupby() + filter + drop_duplicates():

>>> df.groupby('A').filter(lambda g: len(g) > 1).drop_duplicates(subset=['A', 'B'], keep="first")     A      B  C   D0  foo    one  0   02  foo    two  4   84  bar   four  6  125  bar  three  7  14

OR, in case you want to drop duplicates between the subset of columns A & B then can use below but that will have the row having cat as well.

>>> df.drop_duplicates(subset=['A', 'B'], keep="first")     A      B  C   D0  foo    one  0   02  foo    two  4   83  cat    one  8   44  bar   four  6  125  bar  three  7  14

Use groupby + filter + head:

result = df.groupby('A').filter(lambda g: len(g) > 1).groupby(['A', 'B']).head(1)print(result)

Output

     A      B  C   D0  foo    one  0   02  foo    two  4   84  bar   four  6  125  bar  three  7  14

The first group-by and filter will remove the rows with no duplicated A values (i.e. cat), the second will create groups with same A, B and for each of those get the first element.

The current answers are correct and may be more sophisticated too. If you have complex criteria, filter function will be very useful. If you are like me and want to keep things simple, i feel following is more beginner friendly way

>>> df = pd.DataFrame({    'A': ['foo', 'foo', 'foo', 'cat', 'bar', 'bar', 'bar'],    'B': ['one', 'one', 'two', 'one', 'four', 'three', 'four'],    'C': [0,2,4,8,6,7,7],    'D': [0,4,8,4,12,14,14]}, index=[1,2,3,4,5,6,7])>>> df = df.drop_duplicates(['A', 'B'], keep='last')    A       B       C   D2   foo     one     2   43   foo     two     4   84   cat     one     8   46   bar     three   7   147   bar     four    7   14>>> df = df[df.duplicated(['A'], keep=False)]    A       B       C   D2   foo     one     2   43   foo     two     4   86   bar     three   7   147   bar     four    7   14

keep='last' is optional here