> On 13 Dec 2020, at 6:25 am, Sebastian Berg <sebast...@sipsolutions.net> wrote:
>
> But "default" in NumPy really doesn't mean a whole lot? I can think of
> three places where "defaults" exists:
Huh? There are platform-specific defaults for literally every array creation
function in NumPy?
In [1]: np.array([4, 9]).dtype
Out[1]: dtype('int64')
In [2]: np.array([3., 0.]).dtype
Out[2]: dtype('float64')
In [3]: np.arange(5).dtype
Out[3]: dtype('int64')
In [4]: np.arange(5.).dtype
Out[4]: dtype('float64')
In [5]: np.empty(5).dtype
Out[5]: dtype('float64')
In [6]: np.zeros(5).dtype
Out[6]: dtype('float64')
In [7]: np.full(5, 5).dtype
Out[7]: dtype('int64')
In [8]: np.full(5, 5.).dtype
Out[8]: dtype('float64’)
The list goes on…
And, indeed, mixing types can cause implicit casting, and thus both slowness
and unexpected type promotion, which brings with it its own bugs… Again, I
think it is valuable to have syntax to express `np.zeros(…,
dtype=<whatever-dtype-np.array(…)-would-give-for-my-data>)`.
Juan._______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@python.org
https://mail.python.org/mailman/listinfo/numpy-discussion
