Most efficient (and pythonic) way to count False values in 2D numpy arrays?

Question

I am trying to count False value in an np.array like this:

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

I usually use this method:

number_of_false=np.size(a)-np.sum(a)

Is there a better way?

Answer 1

Use count_nonzero to count non-zero (e.g. not False) values:

>>> np.size(a) - np.count_nonzero(a)
2

Answer 2

The clearer is surely to ask exactly what is needed, but that doesn't mean it is the most efficient:

Using %%timeit in jupyter with python 2.7 on the proposed answers gives a clear winner:

    seq = [[True, True, False, True, False, False, False] * 10 for _ in range(100)]
    a = np.array(seq)

    np.size(a) - np.count_nonzero(a) 1000000 loops, best of 3: 1.34 µs per loop  - Antti Haapala
    (~a).sum()                        100000 loops, best of 3: 18.5 µs per loop  - Paul H
    np.size(a) - np.sum(a)             10000 loops, best of 3: 18.8 µs per loop  - OP
    len(a[a == False])                 10000 loops, best of 3: 52.4 µs per loop
    len(np.where(a==False))            10000 loops, best of 3: 77 µs per loop    - Forzaa
.

The clear winner is Antti Haapala, by an order of magnitude, with np.size(a) - np.count_nonzero(a)

len(np.where(a==False)) seems to be penalized by the nested structure of the array; the same benchmark on a 1 D array gives 10000 loops, best of 3: 27 µs per loop