Streaks of True or False in pandas Series

Question

I'm trying to work out how to show streaks of True or False in a pandas Series.

Data:

p = pd.Series([True,False,True,True,True,True,False,False,True])

0     True
1    False
2     True
3     True
4     True
5     True
6    False
7    False
8     True
dtype: bool

I tried p.diff() but not sure how to count the False values this generates to show my desired output which is as follows:.

0     0
1     0
2     0
3     1
4     2
5     3
6     0
7     1
8     0

Answer 1

You can use cumcount of consecutives groups created by compare if p is not equal with shifted p and cumsum:

print (p.ne(p.shift()))
0     True
1     True
2     True
3    False
4    False
5    False
6     True
7    False
8     True
dtype: bool

print (p.ne(p.shift()).cumsum())
0    1
1    2
2    3
3    3
4    3
5    3
6    4
7    4
8    5
dtype: int32

print (p.groupby(p.ne(p.shift()).cumsum()).cumcount())
0    0
1    0
2    0
3    1
4    2
5    3
6    0
7    1
8    0
dtype: int64

Thank you MaxU for another solution:

print (p.groupby(p.diff().cumsum()).cumcount())
0    0
1    0
2    0
3    1
4    2
5    3
6    0
7    1
8    0
dtype: int64 

Answer 2

Another alternative solution is create the cumulative sum of p Series and subtract the most recent cumulative sum where p is 0. Then invert p and do same. Last multiple Series together:

c = p.cumsum()
a = c.sub(c.mask(p).ffill(), fill_value=0).sub(1).abs()
c = (~p).cumsum()
d = c.sub(c.mask(~(p)).ffill(), fill_value=0).sub(1).abs()

print (a)
0    0.0
1    1.0
2    0.0
3    1.0
4    2.0
5    3.0
6    1.0
7    1.0
8    0.0
dtype: float64

print (d)
0    1.0
1    0.0
2    1.0
3    1.0
4    1.0
5    1.0
6    0.0
7    1.0
8    1.0
dtype: float64

print (a.mul(d).astype(int))
0    0
1    0
2    0
3    1
4    2
5    3
6    0
7    1
8    0
dtype: int32