[FIXED] How to assign unique grouping value for each sequence of consecutive True values in pandas boolean mask

Issue

I am trying to generate an appropriate pandas groupBy

Say I have a boolean mask like so [false, false, true, false, true, true, false, true, true]

I would like the groupings to be like so [0,0,1,0,2,2,0,3,3]

I can certainly create this array via a loop through the mask but I would like if possible to use the pandas or numpy builtins for ease of use and perhaps vectorization.

(If no builtin exists I would appreciate a more pythonic way of doing this than via a straight loop with a state flag and rank counter)

Solution

Original Answer:

l = [False, False, True, False, True, True, False, True, True]
s = pd.Series(l)
d = s.astype(int).diff().ne(0).cumsum()
d.loc[~(s)] = 0
pd.factorize(d)[0].tolist()

Slightly Modified Original Answer (works if first item is True):

l = [False, False, True, False, True, True, False, True, True]
s = pd.Series(l)
d = s.astype(int).diff().ne(0).cumsum()
d.loc[~(s)] = 0
dsort = d.sort_values()
dindex = dsort.index
pd.Series(pd.factorize(dsort)[0],index = dindex).sort_index().tolist()

Alternative way:

Generate List and put into series.

l = [False, False, True, False, True, True, False, True, True]
s = pd.Series(l)

Find items that are sequential

d = s.astype(int).diff().ne(0).cumsum().reset_index()

Locate the first True in each group

d.loc[s].groupby(0)['index'].first().rename_axis(None)

Factorize new grouping and put into series

f = pd.factorize(d.loc[s].groupby(0)['index'].first().rename_axis(None))
s2 = pd.Series(f[0]+1,index = f[1])

Use reindex and forward fill all the missing spaces. Fill any NaN's with 0. Lastly replace all places that were False with zeros.

s2 = s2.reindex(s.index).fillna(method='ffill').fillna(0)
s2.loc[~(s)] = 0
s2.tolist()

Answered By - rhug123