On Thu, May 20, 2010 at 9:00 PM, Keith Goodman <kwgood...@gmail.com> wrote:
> While automating some unit tests for my labeled array class, larry, I
> assumed that
>
> np.array([1, 2], dtype=dtype)
>
> would give the same result as
>
> np.array([1, 2]).astype(dtype)
>
> But it doesn't for dtype=None:
>
>>> np.array([1, 2, 3], dtype=None)
> array([1, 2, 3])
>>> np.array([1, 2, 3]).astype(None)
> array([ 1., 2., 3.])
>
> I prefer the behavior of array where dtype=None is a no-op.
Since nobody who knows this answered, I try my explanation
It's all in the docs
astype(None) cast to a specified type
here the dtype is "None"
None is by default float_
>>> np.dtype(None)
dtype('float64')
np.array([1, 2, 3], dtype=None)
np.asarray([1, 2, 3], dtype=None)
here dtype is a keyword argument where None is not a dtype but
triggers the default, which is:
dtype : data-type, optional
By default, the data-type is inferred from the input data.
Shall we start a list of inconsistent looking corner cases ?)
Josef
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