CODEWITHSUNDEEP
pythonpandasmodefillna
Filipe Carvalho
Filipe Carvalho

Reputation: 109

Pandas: Fill na with mode of a group

I have a df with multiple columns.

df = pd.DataFrame({'Store':['M1','M2','M3','M1','M1','M2','M2','M3','M3'],
                   'Category':['A','A','A','B','B','B','C','C','C'],
                   'Price_Category':[np.nan,X,np.nan,np.nan,Y,Y,Z,np.nan,Z]})

How do I fill the NaN on Price_Category with the mode, based only on Category?

I tried to use:

df['Price_Category'] = df.groupby('Category')['Price_Category'].apply(lambda x: x.fillna(x.mode()[0]))

But I get this error: KeyError: 0

Other ways that I tried, started to fill the NaN with the name from the Store.

Thanks for your help!

Upvotes: 4

Views: 480

Answers (1)

jezrael
jezrael

Reputation: 863291

Use Series.iat for first value of Series by position:

f = lambda x: x.fillna(x.mode().iat[0])
df['Price_Category'] = df.groupby('Category')['Price_Category'].apply(f)

print (df)
  Store Category Price_Category
0    M1        A              X
1    M2        A              X
2    M3        A              X
3    M1        B              Y
4    M1        B              Y
5    M2        B              Y
6    M2        C              Z
7    M3        C              Z
8    M3        C              Z

If error:

IndexError: index 0 is out of bounds for axis 0 with size 0

it means only NaNs one or more groups (changed data for raise error):

df = pd.DataFrame({'Store':['M1','M2','M3','M1','M1','M2','M2','M3','M3'],
                   'Category':['A','A','A','B','B','B','C','C','C'],
                  'Price_Category':[np.nan,'X',np.nan,np.nan,'Y','Y',np.nan,np.nan,np.nan]})

#test if at least one non NaN else return same values (NaNs)
f = lambda x: x.fillna(x.mode().iat[0]) if x.notna().any() else x
df['Price_Category'] = df.groupby('Category')['Price_Category'].apply(f)

print (df)
  Store Category Price_Category
0    M1        A              X
1    M2        A              X
2    M3        A              X
3    M1        B              Y
4    M1        B              Y
5    M2        B              Y
6    M2        C            NaN
7    M3        C            NaN
8    M3        C            NaN

Upvotes: 3

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