Can numpy bincount work with 2D arrays?

Question

I am seeing behaviour with numpy bincount that I cannot make sense of. I want to bin the values in a 2D array in a row-wise manner and see the behaviour below. Why would i

Answer 1

The problem is that bincount isn't always returning the same shaped objects, in particular when values are missing. For example:

>>> m = np.array([[0,0,1],[1,1,0],[1,1,1]])
>>> np.apply_along_axis(np.bincount, 1, m)
array([[2, 1],
       [1, 2],
       [0, 3]])
>>> [np.bincount(m[i]) for i in range(m.shape[1])]
[array([2, 1]), array([1, 2]), array([0, 3])]

works, but:

>>> m = np.array([[0,0,0],[1,1,0],[1,1,0]])
>>> m
array([[0, 0, 0],
       [1, 1, 0],
       [1, 1, 0]])
>>> [np.bincount(m[i]) for i in range(m.shape[1])]
[array([3]), array([1, 2]), array([1, 2])]
>>> np.apply_along_axis(np.bincount, 1, m)
Traceback (most recent call last):
  File "", line 1, in 
    np.apply_along_axis(np.bincount, 1, m)
  File "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py", line 117, in apply_along_axis
    outarr[tuple(i.tolist())] = res
ValueError: could not broadcast input array from shape (2) into shape (1)

won't.

You could use the minlength parameter and pass it using a lambda or partial or something:

>>> np.apply_along_axis(lambda x: np.bincount(x, minlength=2), axis=1, arr=m)
array([[3, 0],
       [1, 2],
       [1, 2]])