Binning of data along one axis in numpy

Question

I have a large two dimensional array arr which I would like to bin over the second axis using numpy. Because np.histogram flattens the array I'm currently using a 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 that within numpy but I failed to find one.

Answer 1

You could 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 advantage (if any) is that it's slightly shorter, but I wouldn't expect much of a performance gain. It's possibly marginally more efficient in the assembly of the per-row results.

Answer 2

I was a bit confused by the lambda in Ami's solution so I expanded it out to show what it's doing:

def hist_1d(a):
    return np.histogram(a, bins=bins)[0]

counts = np.apply_along_axis(hist_1d, axis=1, arr=x)

Answer 3

You have to use numpy.histogramdd specifically meant for your problem