Pandas vectorised function cumsum versus numpy

Question

While answering the question Vectorize calculation of a Pandas Dataframe, I noticed an interesting issue regarding performance.

I was under the impression that funct

Answer 1

Pandas can deal with NaN, you can check the difference by:

a = np.random.randn(1000000)
%timeit np.nancumsum(a)
%timeit np.cumsum(a)

outputs:

9.02 ms ± 189 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
4.37 ms ± 18.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)