BUG: np.array fails on a list of arrays with partially matching dimensions

The functions numpy.array and numpy.asarray have a well defined behaviour when applied to lists of arrays: if the listed arrays have the same dimensions and size, the list is turned in one of the dimensions of the resulting array (let’s call it “mode 1”). If not, an array of arrays is returned (“mode 2”).

However, the behaviour of numpy.array and numpy.asarray in “mode 2” seems to be dependent on the number of items in the arrays. The following code is not very elegant relative to numpy usefulness, but works:

>>> a = np.array([1, 2, 3])
>>> b = np.array([[1, 0], [0, 1]])
>>> np.asarray([a, b])
array([array([1, 2, 3]), array([[1, 0],
       [0, 1]])], dtype=object)

But the following doesn’t:

>>> a = np.array([1, 2])
>>> b = np.array([[1, 0], [0, 1]])
>>> np.asarray([a, b])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.4/dist-packages/numpy/core/numeric.py", line 474, in asarray
    return array(a, dtype, copy=False, order=order)
ValueError: could not broadcast input array from shape (2,2) into shape (2)

Clearly, the problem is that, when numpy.asarray sees the first dimension has the same length in a and b, it tries to go for “mode 1”, which is impossible here.

EDIT: I’m using numpy 1.10.4 and python 3.4.3.

[roman-kh]

Providing dtype=object does not help - you still get the same error. However, adding an empty array lets you avoid it:

arr_of_arr = np.array([np.array([]), a, b])[1:]

[ppwwyyxx]

Still exists in 1.14.4