[FIXED] Count Instances Until Value Repeats

Issue

I have a list/array/series of booleans, and I want a list of the same length that contains NaNs wherever there was a False value in the original, and on True indices I want to plug the number of False elements until the next True. If there is no subsequent True than the last "count" should be np.inf.
For example:

input: [False, False, True, True, False, False, True, False, True, False , False, False]
output: [NaN, NaN, 0, 2, NaN, NaN, 1, NaN, np.inf, NaN, NaN, NaN]

Of course, I could iterate over the array/list and count (better to start the iteration from the end in that case). I'm interested in a vectorized implementation.

Solution

Assuming a numpy array:

a = np.array([False, False, True, True, False, False, True, False, True, False, False, False])

# identify True
idx = np.nonzero(a)[0]
# array([2, 3, 6, 8])

# set up output
out = np.full_like(a, np.nan, dtype=float)

# replace first n-1 True with distance to next
out[idx[:-1]] = np.diff(idx)-1
# last True (if any) is np.inf
out[idx[-1:]] = np.inf

Output:

array([nan, nan,  0.,  2., nan, nan,  1., nan, inf, nan, nan, nan])

Assuming a pandas Series with a range index:

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

out = pd.Series(index=s.index)
out[idx] = (-s[s].index.diff(-1)-1).fillna(np.inf)

Or more complex approach with a groupby

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

# reverse Series
s2 = s[::-1]
# mask last True/Falses
m2 = s2.shift(fill_value=False).cummax()

# compute distance to next True
out = s2.groupby(s2.cumsum()).cumcount().shift()[::-1].where(s2)
# mask last True with np.inf
out[s2 & ~ m2] = np.inf

Output:

0     NaN
1     NaN
2     0.0
3     2.0
4     NaN
5     NaN
6     1.0
7     NaN
8     inf
9     NaN
10    NaN
11    NaN
dtype: float64

Answered By - mozway