Can numpy work in two languages ​​with 2D arrays?

Question

I see a behavior with numpy bincount that I cannot understand. I want to put the values ​​in a 2D array differently and see the behavior below. Why will it work with dbArray, but not with simarray?

>>> dbArray array([[1, 0, 1, 0, 1], [1, 1, 1, 1, 1], [1, 1, 0, 1, 1], [1, 0, 0, 0, 0], [0, 0, 0, 1, 1], [0, 1, 0, 1, 0]]) >>> N.apply_along_axis(N.bincount,1,dbArray) array([[2, 3], [0, 5], [1, 4], [4, 1], [3, 2], [3, 2]], dtype=int64) >>> simarray array([[2, 0, 2, 0, 2], [2, 1, 2, 1, 2], [2, 1, 1, 1, 2], [2, 0, 1, 0, 1], [1, 0, 1, 1, 2], [1, 1, 1, 1, 1]]) >>> N.apply_along_axis(N.bincount,1,simarray) Traceback (most recent call last): File "<pyshell#31>", line 1, in <module> N.apply_along_axis(N.bincount,1,simarray) File "C:\Python27\lib\site-packages\numpy\lib\shape_base.py", line 118, in apply_along_axis outarr[tuple(i.tolist())] = res ValueError: could not broadcast input array from shape (2) into shape (3) 

Answer 1

The problem is that bincount does not always return the same shaped objects, in particular when values ​​are missing. For instance:

 >>> 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 "<ipython-input-49-72e06e26a718>", line 1, in <module> 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) 

will not be.

You can use the minlength parameter and pass it using 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]])