Can I apply a function to multiple columns in Pandas dataframe without a for loop?

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Is there a way to apply a function to multiple columns in a Pandas dataframe without a loop? For example, give this dataframe:

df = pd.DataFrame({'id': [1,2,3,4,5,6,7,8,9,10],
                   'val1': [2,2,np.nan,np.nan,4,1,np.nan,np.nan,np.nan,2],
                   'val2': [7,0.2,5,8,np.nan,1,0,np.nan,1,1],
                  })
display(df)

enter image description here

If I want to operate on both val1 and val2 columns, I can do the following. Is there a one-liner to replace the for-loop?

f = lambda x: 'ERR' if x < 5 else x
cols = ['val1', 'val2']
for c in cols:
    df[c] = df[c].apply(f) 
display(df)

enter image description here

pandas apply lambda python
1 Answers

applymap

df.assign(**df[cols].applymap(f))

   id val1 val2
0   1  ERR  7.0
1   2  ERR  ERR
2   3  NaN  5.0
3   4  NaN  8.0
4   5  ERR  NaN
5   6  ERR  ERR
6   7  NaN  ERR
7   8  NaN  NaN
8   9  NaN  ERR
9  10  ERR  ERR

mask

df.assign(**df[cols].mask(df[cols] < 5, 'ERR'))

   id val1 val2
0   1  ERR  7.0
1   2  ERR  ERR
2   3  NaN  5.0
3   4  NaN  8.0
4   5  ERR  NaN
5   6  ERR  ERR
6   7  NaN  ERR
7   8  NaN  NaN
8   9  NaN  ERR
9  10  ERR  ERR
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