On Sat, Jul 13, 2019 at 12:48 AM Mark Mikofski
wrote:
> This slide deck from Matthew Rocklin at SciPy 2019 might be relevant:
> https://matthewrocklin.com/slides/scipy-2019#/
>
That was a very nice talk indeed. It's also up on Youtube, worth watching:
https://www.youtube.com/watch?v=Q0DsdiY-jiw
Hi,
With respect to this call for contributions:
https://github.com/numpy/numpy/pull/13988/files
I would like to help with improving the website of numpy (and maybe scipy
as well).
I have also applied for the Google Season of Docs 2019, and if accepted, I
will be starting by the beginning of Augus
Hi Omry!
You're looking for `.view()`:
In [1]: import numpy as np
In [2]: b = np.arange(1, 13).astype(np.uint8)
In [4]: y = b.view(np.uint16).reshape((3, 2))
In [5]: y
Out[5]:
array([[ 513, 1027],
[1541, 2055],
[2569, 3083]], dtype=uint16)
You can also change the endianness by replacing `np.
Hi All,
I know how to reshape arrays, my problem is a little more complicated than
that.
I am looking for the most efficient way to do the following and an example
will help:
1) I have a an array of bytes b = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
This bytes array represents a width of 2 and a
https://github.com/kritisingh1/numpy/wiki/Now-You-Know-It!
Worth a read, see what applicants go through to get accepted. Feel free
to re-publish it.
There will be more coming as she progresses through the summer.
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