Replace missing values from another column - pandas

Question

I'm fairly new to python and was wondering whether it is possible to loop through a bunch of columns and replace missing values with another column that has the same name but with a suffix.

For example:

---obs---|---col1---|---col2---|---col1_suffix---|---col2_suffix---|

----1-----|---NaN---|----50-----|------20----------| -----60------------

----2-----|---200---|----NaN----|------30---------| ------100---------

Will want to loop through from col1 to up to colN and replace the NaN with the value in col1_suffix up to colN_suffix. So in above example, col1 NaN will be replaced with a value of 20 and col2 NaN will be replaced with 100.

jpp · Accepted Answer · 2018-04-04 12:08:07Z

3

Assuming you are using panda, you could do this:

for col in ['col1', 'col2']:
    df[col] = df[col].fillna(df[col+'_suffix'])

A more generic version:

for col in df.columns:
    if col+'_suffix' in df.columns:
        df[col] = df[col].fillna(df[col+'_suffix'])

answered Apr 4, 2018 at 12:08

jpp

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