On Fri, Feb 16, 2024 at 12:40 AM Marten van Kerkwijk
wrote:
> > From my experience, calling methods is generally faster than
> > functions. I figure it is due to having less overhead figuring out the
> > input. Maybe it is not significant for large data, but it does make a
> > difference even whe
Good to know it is not only on my PC.
I have done a fair bit of work trying to find more efficient sum.
The only faster option that I have found was PyTorch. (although thinking about
it now maybe it’s because it was using MKL, don’t remember)
MKL is faster, but I use OpenBLAS.
Scipp library is
This idea looks interesting, but I think that having a pipeline method like
`Sequential in PyTorch` would be more intuitive than this approach.
On Thu, Feb 15, 2024, 8:48 PM wrote:
> Hello Numpy community,
>
> I'm proposing the introduction of a `pipe` method for NumPy arrays to
> enhance their
We could expand this topic for a broader perspective.
Pandas offers "custom accessors," empowering users to extend DataFrame
functionality, while Polars introduces "Expression plugins" for customization,
enhancing DataFrame operations. These features are pretty awesome.
The obvious advantage, the
