How to pick specific columns Pandas dataframe when checking against NaN

Question

I have a pandas dataframe in Python that looks something like

       AccountID_x    AccountId  AmountCD_x  AmountDOC_x  AmountDoc_x  
1              NaN  4001001         


        

Answer 1

You can use combine_first to combine the two

df['new_col'] = df['AccountId'].combine_first(df['AccountID_x'])

df['new_col']

1    4001001copa
2    4001001copa
3    4001001copa
4    4001001copa

Answer 2

Try this:

df['new_column'] = df.apply(lambda x: x['AccountId'] if pd.isnull(x['AccountID_x']) else x['AccountID_x'], axis=1)

Answer 3

You can also propagate fillna using apply:

df2['newcolumn'] = df2[['AccountID_x','AccountId']].apply(lambda x: x.fillna(method='ffill')[-1], axis=1)

Or equivalently (in your case):

df2['newcolumn'] = df2[['AccountID_x','AccountId']].apply(lambda x: x.fillna(method='bfill')[0], axis=1)