Python using lambda to apply pd.DataFrame instead for nested loop is it possible?

Question

I'm trying to avoid nested loop in python here by using lambda apply to create a new column using this argument below :

from pandas import *
import pandas as pd    
df = pd.DataFrame((np.random.rand(100, 4)*100), columns=list('ABCD'))
df['C'] = df.apply(lambda A,B: A+B)

TypeError: ('() takes exactly 2 arguments (1 given)', u'occurred at index A')

Obviously this doesn't work any recommendation ?

Answer 1

Do you want to add column A and column B and store the result in C? Then you can have it simpler:

df.C = df.A + df.B

---

As @EdChum points out in the comment, the argument to the function in apply is a series, by default on axis 0 which are rows (axis 1 means columns):

>>> df.apply(lambda s: s)[:3]
           A          B          C          D
0  57.890858  72.344298  16.348960  84.109071
1  85.534617  53.067682  95.212719  36.677814
2  23.202907   3.788458  66.717430   1.466331

Here, we add the first and the second row:

>>> df.apply(lambda s: s[0] + s[1])
A    143.425475
B    125.411981
C    111.561680
D    120.786886
dtype: float64

To work on columns, use axis=1 keyword parameter:

>>> df.apply(lambda s: s[0] + s[1], axis=1)
0     130.235156
1     138.602299
2      26.991364
3     143.229523
...
98    152.640811
99     90.266934

Which yield the same result as referring to the columns by name:

>>> (df.apply(lambda s: s[0] + s[1], axis=1) == 
     df.apply(lambda s: s['A'] + s['B'], axis=1))
0     True
1     True
2     True
3     True
...
98    True
99    True