CODEWITHSUNDEEP
pythonnumpy
Donbeo
Donbeo

Reputation: 17617

How to apply a function to all the column of a numpy matrix?

It should be a standard question but I am not able find the answer :(

I have a numpy darray n samples (raw) and p variables (observation). I would like to count how many times each variables is non 0.

I would use a function like 

sum([1 for i in column if i!=0])

but how can I apply this function to all the columns of my matrix?

Upvotes: 0

Views: 4731

Answers (2)

Jaime
Jaime

Reputation: 67427

You can use np.sum over a boolean array created from comparing your original array to zero, using the axis keyword argument to indicate whether you want to count over rows or columns. In your case:

>>> a = np.array([[0, 1, 1, 0],[1, 1, 0, 0]])
>>> a
array([[0, 1, 1, 0],
       [1, 1, 0, 0]])
>>> np.sum(a != 0, axis=0)
array([1, 2, 1, 0])

Upvotes: 1

gongzhitaao
gongzhitaao

Reputation: 6682

from this post: How to apply numpy.linalg.norm to each row of a matrix?

If the operation supports axis, use the axis parameter, it's usually faster,

Otherwise, np.apply_along_axis could help.

Here is the numpy.count_nonzero.

So here is the simple answer:

import numpy as np

arr = np.eye(3)
np.apply_along_axis(np.count_nonzero, 0, arr)

Upvotes: 2

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