Using lambda if condition on different columns in Pandas dataframe

Question

I have simple dataframe:

import pandas as pd
frame = pd.DataFrame(np.random.randn(4, 3), columns=list(\'abc\'))

Thus for example:

Answer 1

Solution

use a vectorized approach

frame['d'] = frame.b + (frame.c > 0) * (frame.c - frame.b)

Explanation

This is derived from the sum of

(frame.c > 0) * frame.c  # frame.c if positive

Plus

(frame.c <= 0) * frame.b  # frame.b if c is not positive

However

(frame.c <=0 )

is equivalent to

(1 - frame.c > 0)

and when combined you get

frame['d'] = frame.b + (frame.c > 0) * (frame.c - frame.b)

Answer 2

is that what you want?

In [300]: frame[['b','c']].apply(lambda x: x['c'] if x['c']>0 else x['b'], axis=1)
Out[300]:
0   -1.099891
1    0.582815
2    0.901591
3    0.900856
dtype: float64