On Thu, May 20, 2010 at 9:00 PM, Keith Goodman
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.
np.dtype(None)
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_ 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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