vectorizing numpy bincount

Question

I have a 2d numpy array., A I want to apply np.bincount() to each column of the matrix A to generate another 2d array B that is composed of the bincounts of each column of the original matrix A.

My problem is that np.bincount() is a function that takes a 1d array-like. It's not an array method like B = A.max(axis=1) for example.

Is there a more pythonic/numpythic way to generate this B array other than a nasty for-loop?

import numpy as np

states = 4
rows = 8
cols = 4

A = np.random.randint(0,states,(rows,cols))
B = np.zeros((states,cols))

for x in range(A.shape[1]):
    B[:,x] =  np.bincount(A[:,x])

Answer 1

Using the same philosophy as in this post, here's a vectorized approach -

m = A.shape[1]    
n = A.max()+1
A1 = A + (n*np.arange(m))
out = np.bincount(A1.ravel(),minlength=n*m).reshape(m,-1).T