How to apply lambda function to multiple columns

Question

I have a dataframe, need to apply the same lambda function to multiple columns.

sample data:

col1                    col2                  col3 
xxx;#2;yyy              zzz;#46;zyzcz        1
aaa;#3;bbbccc           bbbb;cccc;dd#5        2

I need to clean up and result should be as below:

col1                    col2                  col3 
xxx;yyy                 zzz;zyzcz             1
aaa;bbbccc              bbbb;cccc;dd          2

function I used:

def cleanDigit(row):
    replacements = [('\d', ''), ('#', ''), (';;', ';')]

    for (old, new) in replacements: 
        row = re.sub(old, new, row)

    return row

code to apply function to multiple columns:

df[['col1', 'col2']] = df[['col1', 'col2']] .apply(lambda r: cleanDigit(r))

Error message:

TypeError: ('expected string or buffer', u'occurred at index col1')

jezrael · Accepted Answer · 2019-08-16 07:04:38Z

2

Use DataFrame.applymap, also lambda function should be omit and pass only function:

df[['col1', 'col2']] = df[['col1', 'col2']].applymap(cleanDigit)
print (df)
         col1          col2  col3
0     xxx;yyy     zzz;zyzcz     1
1  aaa;bbbccc  bbbb;cccc;dd     2

answered Aug 16, 2019 at 7:04

jezrael

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3 Comments

jezrael Over a year ago

@iamklaus - yes, if question is about another solutions, then seems good.

Ling Over a year ago

@jezrael applymap is not working for my situation, I actually found reason is col2 has Nan, and both 'apply' and 'applymap' do not work when row is Nan. I tried below but not working.... df[['col1', 'col2']] = df[['col1', 'col2']] .apply(lambda r: cleanDigit(r) if pd.notnull(r) else r)

jezrael Over a year ago

@Ling - Use np.nan != np.nan from definition - df[['col1', 'col2']] = df[['col1', 'col2']].applymap(lambda x: cleanDigit(x) if x == x else np.nan)

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