BUG: numpy.full does not fill all types of data

[jerasman]

doesnt work

v= numpy.full(shape, [], dtype='object')

works

v=numpy.empty(shape, dtype='object')
v.fill([])

[ewmoore]

Yes. ndarray.fill fills will a scalar, but np.full can do more complicated fills and coerces its second argument to an array. So for instance, this means that

In [21]: np.full((6,2), [1,2])
Out[21]: 
array([[ 1.,  2.],
       [ 1.,  2.],
       [ 1.,  2.],
       [ 1.,  2.],
       [ 1.,  2.],
       [ 1.,  2.]])

works, but it cannot create an object array filled with an empty list.

Looking at the docstring for np.full its not obvious to me that this was by design, since it says fill_value : scalar, but this is how it works in practice. The docs could be improved here, since it really takes array_like input.

[ewmoore]

Broadcasting of the fill value is also untested, so seems unintentional.

[jerasman]

Also, with
v=numpy.empty((3,3), dtype='object')
v.fill([])
v[0,0].extend([1])
extend() changes all the elements of the object array.

I wonder if there is functionality that can be added to fill/full that would avoid extra code like
filler=numpy.frompyfunc(list, 0, 1)
filler(v)
to fill an object array with empty lists that are not the same in memory.

[sursu]

Might be wrong, but I think one can achieve that with: np.tile(list, shape)

[eric-wieser]

@ewmoore: Created #14387 to track documenting the broadcasting

[vmarkovtsev]

np.tile(list, shape) does not work:

In [3]: np.tile([], 5)                                                                                                                                                
Out[3]: array([], dtype=float64)