Apply rolling function on pandas dataframe with multiple arguments

I am trying to apply a rolling function, with a 3 year window, on a pandas dataframe.

import pandas as pd

# Dummy data
df = pd.DataFrame({'Product': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
                   'Year': [2015, 2016, 2017, 2018, 2015, 2016, 2017, 2018],
                   'IB': [2, 5, 8, 10, 7, 5, 10, 14],
                   'OB': [5, 8, 10, 12, 5, 10, 14, 20],
                   'Delta': [2, 2, 1, 3, -1, 3, 2, 4]})

# The function to be applied
def get_ln_rate(ib, ob, delta):
    n_years = len(ib)
    return sum(delta)*np.log(ob[-1]/ib[0]) / (n_years * (ob[-1] - ib[0]))

The expected output is

  Product  Year  IB  OB  Delta  Ln_Rate
0       A  2015   2   5      2     
1       A  2016   5   8      2    
2       A  2017   8  10      1   0.3353
3       A  2018  10  12      3   0.2501
4       B  2015   7   5     -1  
5       B  2016   5  10      3
6       B  2017  10  14      2   0.1320
7       B  2018  14  20      4   0.2773

I have tried

df['Ln_Rate'] = df.groupby('Product').rolling(3).apply(lambda x: get_ln_rate(x['IB'], x['OB'], x['Delta']))

But this does not work.

I have found several similar posts

https://stackoverflow.com/questions/45517686/applying-custom-rolling-function-to-dataframe - this one does not have a clear answer

https://stackoverflow.com/questions/40954560/pandas-rolling-apply-custom - this one does not have multiple arguments

https://stackoverflow.com/questions/30806838/apply-custom-function-on-pandas-dataframe-on-a-rolling-window - this one has rolling.apply... but it doesn't show the syntax.

Neither seems to be spot on. Any pointers towards the correct syntax would be greatly appreciated.

I solved this by reusing the rolling window.

import numpy as np

WINDOW_SIZE = 3

rw = df.groupby('Product').rolling(WINDOW_SIZE)

df = df.assign(delta_sum=rw['Delta'].agg(np.sum).reset_index()['Delta'],
               ib_first=rw['IB'].apply(lambda xs: xs[0]).reset_index()['IB'],
               ob_last=rw['OB'].apply(lambda xs: xs[-1]).reset_index()['OB'])

df['ln_rate'] = df['delta_sum']*np.log(df['ob_last']/df['ib_first']) / (WINDOW_SIZE * (df['ob_last'] - df['ib_first']))

Which yields:

  Product  Year  IB  OB  Delta  delta_sum  ib_first  ob_last   ln_rate
0       A  2015   2   5      2        NaN       NaN      NaN       NaN
1       A  2016   5   8      2        NaN       NaN      NaN       NaN
2       A  2017   8  10      1        5.0       2.0     10.0  0.335300
3       A  2018  10  12      3        6.0       5.0     12.0  0.250134
4       B  2015   7   5     -1        NaN       NaN      NaN       NaN
5       B  2016   5  10      3        NaN       NaN      NaN       NaN
6       B  2017  10  14      2        4.0       7.0     14.0  0.132028
7       B  2018  14  20      4        9.0       5.0     20.0  0.277259

Resetting indices is necessary, to transform the grouped DataFrame back to its initial shape.

Hope that helps.

Another answer came up my mind: Create rolling windows on the grouped indices, and pass partial dfs to your custom function. Of course, the function is not exactly called with multiple arguments, but nevertheless with all data needed.

import numpy as np
import pandas as pd

df = pd.DataFrame({'Product': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
                   'Year': [2015, 2016, 2017, 2018, 2015, 2016, 2017, 2018],
                   'IB': [2, 5, 8, 10, 7, 5, 10, 14],
                   'OB': [5, 8, 10, 12, 5, 10, 14, 20],
                   'Delta': [2, 2, 1, 3, -1, 3, 2, 4]})

# The function to be applied
def get_ln_rate(df):
    n_years = len(df['IB'])
    return df['Delta'].sum() * np.log(df['OB'].iloc[-1] / df['IB'].iloc[0]) / (n_years * (df['OB'].iloc[-1] - df['IB'].iloc[0]))

ln_rate = df.groupby('Product').apply(lambda grp: pd.Series(grp.index).rolling(3).agg({'Ln_Rate': lambda window: get_ln_rate(grp.loc[window])})).reset_index()['Ln_Rate']
df.assign(Ln_Rate=ln_rate)