Applying Lambda Row on Multiple Columns Pandas

Question

I am creating a sample dataframe:

tp = pd.DataFrame({'source':['a','s','f'], 
                   'target':['b','n','m'], 
                   'count':[0,8,4]})

And creating a column 'col' based on condition of 'target' column >> same as source, if matching condition, else to a default, as below:

tp['col'] = tp.apply(lambda row:row['source'] if row['target'] in ['b','n'] else 'x')

But it's throwing me this error: KeyError: ('target', 'occurred at index count')

How can I make it work, without defining a function?

Accepted Answer

You need to use axis=1 to tell Pandas you want to apply a function to each row. The default is axis=0.

tp['col'] = tp.apply(lambda row: row['source'] if row['target'] in ['b', 'n'] else 'x',
                     axis=1)

However, for this specific task, you should use vectorised operations. For example, using numpy.where:

tp['col'] = np.where(tp['target'].isin(['b', 'n']), tp['source'], 'x')

pd.Series.isin returns a Boolean series which tells numpy.where whether to select the second or third argument.

1 Answer