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``np.r_[]`` and ``np.c_[]`` with certain scalar values
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In rare cases, using mainly ``np.r_`` with scalars can lead to different
results. The main potential changes are highlighted by the following::
>>> np.r_[np.arange(5, dtype=np.uint8), -1].dtype
int16 # rather than the default integer (int64 or int32)
>>> np.r_[np.arange(5, dtype=np.int8), 255]
array([ 0, 1, 2, 3, 4, 255], dtype=int16)
Where the second example returned::
array([ 0, 1, 2, 3, 4, -1], dtype=int8)
The first one is due to a signed integer scalar with an unsigned integer
array, while the second is due to ``255`` not fitting into ``int8`` and
NumPy currently inspecting values to make this work.
(Note that the second example is expected to change in the future due to
:ref:`NEP 50 <NEP50>`; it will then raise an error.)
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