Cumsum reset at NaN
cumsum, numpy, pandas, python
Solution
A simple Numpy translation of your Matlab code is this:
import numpy as np
v = np.array([1., 1., 1., np.nan, 1., 1., 1., 1., np.nan, 1.])
n = np.isnan(v)
a = ~n
c = np.cumsum(a)
d = np.diff(np.concatenate(([0.], c[n])))
v[n] = -d
np.cumsum(v)
Executing this code returns the result `array([ 1., 2., 3., 0., 1., 2., 3., 4., 0., 1.])`. This solution will only be as valid as the original one, but maybe it will help you come up with something better if it isn't sufficient for your purposes.
Problem
If I have a `pandas.core.series.Series` named `ts` of either 1's or NaN's like this: ``` 3382 NaN 3381 NaN ... 3369 NaN 3368 NaN ... 15 1 10 NaN 11 1 12 1 13 1 9 NaN 8 NaN 7 NaN 6 NaN 3 NaN 4 1 5 1 2 NaN 1 NaN 0 NaN ``` I would like to calculate cumsum of this serie but it should be reset (set to zero) at the location of the NaNs like below: ``` 3382 0 3381 0 ... 3369 0 3368 0 ... 15 1 10 0 11 1 12 2 13 3 9 0 8 0 7 0 6 0 3 0 4 1 5 2 2 0 1 0 0 0 ``` Ideally I would like to have a vectorized solution ! I ever see a similar question with Matlab : Matlab cumsum reset at NaN? but I don't know how to translate this line `d = diff([0 c(n)]);`