Dev Stack Source

Answer : Use groupby and agg , and aggregate only unique values by calling Series.unique : df.astype(str).groupby('prop1').agg(lambda x: ','.join(x.unique())) prop2 prop3 prop4 prop1 K20 12,1,66 travis,leo 10.0,4.0 L30 3,54,11,10 bob,john 11.2,10.0 df.astype(str).groupby('prop1', sort=False).agg(lambda x: ','.join(x.unique())) prop2 prop3 prop4 prop1 L30 3,54,11,10 bob,john 11.2,10.0 K20 12,1,66 travis,leo 10.0,4.0 If handling NaNs is important, call fillna in advance: import re df.fillna('').astype(str).groupby('prop1').agg( lambda x: re.sub(',+', ',', ','.join(x.unique())) ) prop2 prop3 prop4 prop1 K20 12,1,66 travis,leo 10.0,4.0 L30 3,54,11,10 bob,john 11.2,10.0

Answer : You need nunique : df = df.groupby('domain')['ID'].nunique() print (df) domain 'facebook.com' 1 'google.com' 1 'twitter.com' 2 'vk.com' 3 Name: ID, dtype: int64 If you need to strip ' characters: df = df.ID.groupby([df.domain.str.strip("'")]).nunique() print (df) domain facebook.com 1 google.com 1 twitter.com 2 vk.com 3 Name: ID, dtype: int64 Or as Jon Clements commented: df.groupby(df.domain.str.strip("'"))['ID'].nunique() You can retain the column name like this: df = df.groupby(by='domain', as_index=False).agg({'ID': pd.Series.nunique}) print(df) domain ID 0 fb 1 1 ggl 1 2 twitter 2 3 vk 3 The difference is that nunique() returns a Series and agg() returns a DataFrame. Generally to count distinct values in single column, you can use Series.value_counts : df.domain.value_counts() #'vk.com...