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Neal Becker wrote:
> I've been browsing the numpy source. I'm wondering about mixed-mode
> arithmetic on arrays. I believe the way numpy handles this is that it
> never does mixed arithmetic, but instead converts arrays to a common type.
> Arguably, that might be efficient for a mix of say, doub
When a number is passed to random.seed(), which is otherwise numerically valid,
but is of the wrong data type (e.g. a string passed into a Flask app that was
not cast to an int type or perhaps a float), a VauleError is raised with an
ambiguous caption: `The value [...] cannot be used to seed a
On 9/7/23 23:34, glaserj--- via NumPy-Discussion wrote:
Reviving this old thread - I note that numpy.dot supports in-place
computation for performance reasons like this
c = np.empty_like(a, order='C')
np.dot(a, b, out=c)
However, the data type of the pre-allocated c array must match the result
