Most efficient (and pythonic) way to count False values in 2D numpy arrays?
Solution 1
Use count_nonzero
to count non-zero (e.g. not False
) values:
>>> np.size(a) - np.count_nonzero(a)
2
Solution 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
Solution 3
This would do that:
len(np.where(a==False))
Maybe there are other ways that are faster or look better.
Solution 4
One alternative would be:
np.bitwise_not(a).sum()
Even shorter, but maybe less clear is:
(~a).sum()
Solution 5
Count Number of False Compare
number_of_false = np.size(out_putArray) - np.count_nonzero(out_putArray[0] >= out_putArray[1])
Count of Numbers of True Compare
number_of_true = np.count_nonzero(out_putArray[0] >= out_putArray[1])
G M
About me I am an Italian analytical chemist specializing in the conservation of Cultural Heritage. I have a strong interest in science and IT for problem solving and divulgation. I enjoy learning new things and applying my knowledge to create new ones. Why am I here? I've learned Python and GIS on my own (but S.E. community really has helped me) so I'm trying to share my chemistry knowledge. I like helping people and I constantly try to improve my knowledge and skills ( so please correct my English mistakes!).
Updated on June 25, 2022Comments
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G M almost 2 years
I am trying to count
False
value in annp.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?
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G M about 8 yearsYes but this is not the same solution that I use more or less?
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Reblochon Masque about 8 yearsand from my little benchmark, by an order of magnitude at that. :)
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BottleNick about 4 yearsBtw:
a.size
is ~10x faster thannp.size(a)
. An alternative to benchmark could benp.count_nonzero(~a)
, which will not be the most efficient, though.