How to get unique values from multiple columns in a pandas groupby

Question

Starting from this dataframe df:

df = pd.DataFrame({'c':[1,1,1,2,2,2],'l1':['a','a','b','c','c','b'],'l2':['b','d','d','f','e','f']})     c l1 l2 0  1  a  b 1  1  a  d 2  1  b  d 3  2  c  f 4  2  c  e 5  2  b  f 

I would like to perform a groupby over the c column to get unique values of the l1 and l2 columns. For one columns I can do:

g = df.groupby('c')['l1'].unique() 

that correctly returns:

c 1    [a, b] 2    [c, b] Name: l1, dtype: object 

but using:

g = df.groupby('c')['l1','l2'].unique() 

returns:

AttributeError: 'DataFrameGroupBy' object has no attribute 'unique' 

I know I can get the unique values for the two columns with (among others):

In [12]: np.unique(df[['l1','l2']]) Out[12]: array(['a', 'b', 'c', 'd', 'e', 'f'], dtype=object) 

Is there a way to apply this method to the groupby in order to get something like:

c 1    [a, b, d] 2    [c, b, e, f] Name: l1, dtype: object 

Answer 1

You can do it with apply:

import numpy as np g = df.groupby('c')['l1','l2'].apply(lambda x: list(np.unique(x))) 

Answer 2

Alternatively, you can use agg:

g = df.groupby('c')['l1','l2'].agg(['unique'])