The next NumPy community meeting will be held this Wednesday, February 28th
at 6pm UTC.
Join us via Zoom:
https://numfocus-org.zoom.us/j/83278611437?pwd=ekhoLzlHRjdWc0NOY2FQM0NPemdkZz09
.
Everyone is welcome and encouraged to attend.
To add to the meeting agenda the topics you’d like to discuss, fo
Hi all,
Our next Documentation Team meeting will happen on *Monday, February 26* at
*11PM UTC*. If this time slot is inconvenient for you to join, please let me
know in the replies or Slack and we will work something out.
All are welcome - you don't need to already be a contributor to join. If
Does it have native bytes support? To me, it's either having fast conversion to
`string` or data format with native bytes support.
Sometimes readability is important, sometimes speed takes priority. Even with a
good, unified data structure for arrays, indexed arrays, etc., it is always
good to
On Sun, Feb 25, 2024 at 1:52 PM Dom Grigonis wrote:
> Thank you for your answer,
>
> Yeah, I was unsure if it ever existed in the first place.
>
> Space is less of an issue and something like `a.data.hex()` would be fine
> as long as its speed was on par with `a.tobytes()`. However, it is 10x
> s
Thank you for your answer,
Yeah, I was unsure if it ever existed in the first place.
Space is less of an issue and something like `a.data.hex()` would be fine as
long as its speed was on par with `a.tobytes()`. However, it is 10x slower on
my machine.
This e-mail is pretty much a final check (
On Sat, Feb 24, 2024 at 7:17 PM Dom Grigonis wrote:
> Hello,
>
> I am seeking a bit of help.
>
> I need a fast way to transfer numpy arrays in json format.
>
> Thus, converting array to list is not an option and I need something
> similar to:
>
> a = np.ones(1000)%timeit a.tobytes()17.6 ms
>
