Applying function across dataframe columns for each row

Question

I have a pandas dataframe which appears as:

df1['A'].ix[1:3]       Sims          Grade    SleepNumber
2017-01-01 02:00:00    [33, 34, 39]  5        6
2017-01-01 03:00:00    [3, 43, 9]    1        12

I have a function defined as:

def Fn(S, G, SL):
 #some complicated operation but for example it returns only product
 return S*G*SL

I want to do the following for each row of df1 to get a dataframe as:

df1['A'].ix[1:3]       FnResult
2017-01-01 02:00:00    [Fn(33, 5, 6), Fn(34, 5, 6), Fn(39, 5, 6)]  
2017-01-01 03:00:00    [Fn(3, 1, 12), Fn(43, 1, 12), Fn(9, 1, 12)]   

I tried the following:

z1 = df1.apply(map(lambda x:Fn([x, x.Grade, x.SleepNumber]), x.Sims))

But I am not setting it up correctly so it errors out.

Answer 1

You might rewrite your Fn as, since S is a column of lists:

def Fn(S, G, SL):
    return [s*G*SL for s in S]

df['FnResult'] = df.apply(lambda r: Fn(r.Sims, r.Grade, r.SleepNumber), axis=1).values.tolist()

df
#                           Sims    Grade   SleepNumber          FnResult
#2017-01-01 02:00:00    [33, 34, 39]    5           6   [990, 1020, 1170]
#2017-01-01 03:00:00      [3, 43, 9]    1           12     [36, 516, 108]

Or without modifying Fn , modify the map function in the apply method; Recall that map by itself is not callable, you need to wrap it in another lambda :

df['FnResult'] = df.apply(lambda r: list(map(lambda s: Fn(s, r.Grade, r.SleepNumber), r.Sims)), axis=1)

df
#                               Sims    Grade   SleepNumber         FnResult
#2017-01-01 02:00:00    [33, 34, 39]        5           6    [990, 1020, 1170]
#2017-01-01 03:00:00    [3, 43, 9]          1           12      [36, 516, 108]