Combining two pandas series with changing logic

Question

I have two bool data series that I would like to combine with a new series object, but the combination logic depends on the “history” (previous values).

Series 1 contains mostly False, but single True values. Series 2 usually contains periods of True or False values ​​- the probability of a repeat of the values ​​is quite high.

In the resulting series, I need periods of bool values ​​that start with the True section, when both are True and end when the section ends in Series2, i.e. e. no longer contains True.

e. g.

s1 s2 result 0 False False False 1 False True False 2 True True True 3 False True True 4 False True True 5 True False False 6 False False False 

On line 2, the result switches to True and remains on until the true phase in series 2 ends on line 5.

This is what I came up with so far:

 import pandas as pd import numpy as np x = pd.DataFrame() x['s1'] = [False, False, True, False, False, True, False] x['s2'] = [False, True, True, True, True, False, False] x['start'] = (x['s1'] & x['s2']).replace(False, np.nan) x['end'] = (~ (x['s2'].shift() & (~ x['s2']))).replace(True, np.nan) x['result'] = x['start'].fillna(x['end']).fillna(method='ffill').fillna(0) > 0 x 

Despite the fact that my solution works, I got the impression that I'm too hard for this !?

Any suggestions?

Answer 1

First, we know for sure that result always False when s2 is False, and always True when both s1 and s2 are True. It does not depend on the previous values:

 x.loc[~x['s2'], 'result'] = False x.loc[x['s1'] & x['s2'], 'result'] = True 

Then we fill NA "forward fill":

 x['result'].fillna(method = 'ffill', inplace = True) 

And if some NA remains at the beginning of the column, we will replace them with False:

 x['result'].fillna(False, inplace = True)