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Concatenating data along one axis in numpy

I have a large 2-D array arr

that I would like to fit on the second axis using numpy. Since it np.histogram

aligns the array where I am using the for loop:

import numpy as np

arr = np.random.randn(100, 100)

nbins = 10
binned = np.empty((arr.shape[0], nbins))

for i in range(arr.shape[0]):
    binned[i,:] = np.histogram(arr[i,:], bins=nbins)[0]

I feel like there should be a more direct and more efficient way to do this in numpy, but I haven't been able to find one.

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2 answers


You can use np.apply_along_axis

:

x = np.array([range(20), range(1, 21), range(2, 22)])

nbins = 2
>>> np.apply_along_axis(lambda a: np.histogram(a, bins=nbins)[0], 1, x)
array([[10, 10],
       [10, 10],
       [10, 10]])


The main benefit (if any) is that it is a little shorter, but I wouldn't expect much performance benefit. It is arguably marginally more efficient in collecting the results of each row.

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You should be using numpy.histogramdd specifically for your problem



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