Combining two pandas series with changing logic

Question

I've two series of bool data, that I'd like to combine to a new series object, but the combination logic depends from "history" (previous values).

Series1 contains mostly False, but single True values. Series2 usually contains periods of True or False values - the probability of repeating values is quite high.

In the resulting series I need periods of bool values that begin with a True-section whenever both are True and end when the section in Series2 ends, i. 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

In row 2 the result switches to True and stays on, until the True-phase in Series2 ends on row 5.

This is what I have come 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

Even though my solution works, I have the impression I am thinking way too difficult to achieve this!?

Any suggestions?

Answer 1

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

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

Then we fill NA's with "forward fill":

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

And in case there are some NA's remaining at the beginning of the column, we replace them with False:

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