NumPy 2.0

The thing I want most is a more sane and more memorable way to compose non-element-wise operations. There are so many different ways to build views and multiply arrays that I can’t remember them and never know which to use, and have to relearn them every time I use numpy… broadcasting, padding, repeating, slicing, stacking, transposing, outers, inners, dots of all sorts, and half the stack overflow answers lead to the most confusing pickaxe of all: einsum. Am I alone? I love numpy, but every time I reach for it I somehow get stuck for hours on what ought to be really simple indexing problems.

&gt; The default integer type on Windows is now int64 rather than int32, matching the behavior on other platforms<p>This was a footgun due to C long being int32 in win64. Glad that they changed it.

Any notable highlights for a consumer of Numpy who rarely interfaces directly with it? Most of my work is pandas+scipy, with occasionally dropping into the specific numpy algorithm when required.<p>I am much more of an &quot;upgrade when there is a X.1&quot; release kind of guy, so my hat off to those who will bravely be testing the version on my behalf.

it feels like the first major release in 18 years which introduces lots of breaking changes should just be a fork rather than a version.<p>let me do `pip install numpy2` and not have to worry about whether or not some other library in my project requires numpy&lt;2.

<a href="https:&#x2F;&#x2F;stackoverflow.com&#x2F;questions&#x2F;78641150&#x2F;a-module-that-was-compiled-using-numpy-1-x-cannot-be-run-in-numpy-2-0-0" rel="nofollow">https:&#x2F;&#x2F;stackoverflow.com&#x2F;questions&#x2F;78641150&#x2F;a-module-that-w...</a>

I&#x27;m starting to see some packages break due to not pinning 1.x in their dependencies. `pip install numpy==1.*` is a quick and hacky way to work around those issues until the ecosystem catches up.

&gt; X months of work by Y contributors?<p>Makes it look like they pressed publish before filling in their template, or is this on purpose?

I wish numpy pushed their structured arrays (and thereby also improvements to their interface) more aggressively.<p>Most people are simply unaware of them, which is why we get stuff like pandas on top of everything.

So apparently this is what broke my CI job since it was indirectly installed. One of the downsides of using loose version locking with requirements.txt rather than something like poetry I guess.

What are the implications of the new stringdtype? If I remember correctly, string performance was a big part of the pandas switch to arrow.

I would love for numpy to be ported as a typescript project personally. So I can do ml in ts. The python ecosystem feels a bit insane to me (more so than the js one). Venv helps but is still inferior to a half decent npm project imo. I feel there is no strict reason why this migration couldn&#x27;t happen, only the inertia that makes it unlikely

Here&#x27;s a link to the release notes: <a href="https:&#x2F;&#x2F;numpy.org&#x2F;devdocs&#x2F;release&#x2F;2.0.0-notes.html" rel="nofollow">https:&#x2F;&#x2F;numpy.org&#x2F;devdocs&#x2F;release&#x2F;2.0.0-notes.html</a>