Cfrgtkky

I've got this long algebra formula that I need to apply to a dataframe:

def experience_mod(A, B, C, D, T, W):

    E = (T-A)

    F = (C-D)



    xmod = (A + B + (E*W) + ((1-W)*F))/(D + B + (F*W) + ((1-W)*F))



    return xmod



A = loss['actual_primary_losses']

B = loss['ballast']

C = loss['ExpectedLosses']

D = loss['ExpectedPrimaryLosses']

T = loss['ActualIncurred']

W = loss['weight']

How would I write this to calculate the experience_mod() for every row?

something like this?

loss['ExperienceRating'] = loss.apply(experience_mod(A,B,C,D,T,W) axis = 0)

Pandas and the underlying library, numpy, it's using, support vectorized operations, so given two dataframes A and B, operations like A + B, A - B etc are valid.

Your code works fine, you need to apply the function to the columns directly and assign the results back to the new column ExperienceRating,

Here's a working example:

In [1]: import pandas as pd 



In [2]: import numpy as np 



In [3]: df = pd.DataFrame(np.random.randn(6,6), columns=list('ABCDTW'))



In [4]: df

Out[4]: 

          A         B         C         D         T         W

0  0.049617  0.082861  2.289549 -0.783082 -0.691990 -0.071152

1  0.722605  0.209683 -0.347372  0.254951  0.468615 -0.132794

2 -0.301469 -1.849026 -0.334381 -0.365116 -0.238384 -1.999025

3 -0.554925 -0.859044 -0.637079 -1.040336  0.627027 -0.955889

4 -2.024621 -0.539384  0.006734  0.117628 -0.215070 -0.661466

5  1.942926 -0.433067 -1.034814 -0.292179  0.744039  0.233953



In [5]: def experience_mod(A, B, C, D, T, W):

   ...:     E = (T-A)

   ...:     F = (C-D)

   ...: 

   ...:     xmod = (A + B + (E*W) + ((1-W)*F))/(D + B + (F*W) + ((1-W)*F))

   ...: 

   ...:     return xmod

   ...: 



In [6]: experience_mod(df["A"], df["B"], df["C"], df["D"], df["T"], df["W"])

Out[6]: 

0    1.465387

1   -2.060483

2    1.000469

3    1.173070

4    7.406756

5   -0.449957

dtype: float64



In [7]: df['ExperienceRating'] = experience_mod(df["A"], df["B"], df["C"], df["D"], df["T"], df["W"])



In [8]: df

Out[8]: 

          A         B         C         D         T         W  ExperienceRating

0  0.049617  0.082861  2.289549 -0.783082 -0.691990 -0.071152          1.465387

1  0.722605  0.209683 -0.347372  0.254951  0.468615 -0.132794         -2.060483

2 -0.301469 -1.849026 -0.334381 -0.365116 -0.238384 -1.999025          1.000469

3 -0.554925 -0.859044 -0.637079 -1.040336  0.627027 -0.955889          1.173070

4 -2.024621 -0.539384  0.006734  0.117628 -0.215070 -0.661466          7.406756

5  1.942926 -0.433067 -1.034814 -0.292179  0.744039  0.233953         -0.449957