The most important changes are deprecations of certain public APIs: https://numpy.org/devdocs/release/2.0.0-notes.html#deprecati...

One new interesting feature, though, is the support for string routines: https://numpy.org/devdocs/reference/routines.strings.html#mo...

> One new interesting feature, though, is the support for string routines

Sounds almost like they're building a language inside a language.

No. Native python ops in string suck in performance. String support is absolutely interesting and will enable abstractions for many NLP and LLM use cases without writing native C extensions.

> Native python ops in string suck in performance.

That’s not true? Python string implementation is very optimized, probably have similar performance to C.

stringzilla[1] has 10x perf on some string operations - maybe they don't suck, but there's definitely room for improvement

[1] - https://github.com/ashvardanian/StringZilla?tab=readme-ov-fi...

It is absolutely true that there is massive amounts of room for performance improvements for Python strings and that performance is generally subpar due to implementation decisions/restrictions.

Strings are immutable, so no efficient truncation, concatenation, or modifications of any time, you're always reallocating.

There's no native support for a view of string, so operations like iteration over windows or ranges have to allocate or throw away all the string abstractions.

By nature of how the interpreter stores objects, Strings are always going to have an extra level of indirection compared to what you can do with a language like C.

Python strings have multiple potential underlying representations, and thus have some overhead for managing and dealing with those multiple representations without exposing those details to user code

There is a built in memoryview. But it only works on bytes or other objects supporting the buffer protocol, not on strings.

For numpy applications you have to always box a value to get a new python string. It quite far from fast.

Yeah, operating on strings has historically been a major weak point of Numpy's. I'm looking forward seeing benchmarks for the new implementation.

It's already very much a DSL, and has been for the decade-ish that I've used it.

They're not building a language. They're carefully adding a newly-in-demand feature to a mature, already-built language.

This one will be rough :|

> arange’s start argument is positional-only

Looks like that might get reverted [0].

[0] https://github.com/numpy/numpy/pull/25955

Does numpy use GPU?

No.

You may want to check out cupy

https://cupy.dev/

Interesting that the new string library mirrors the introduction of variable-length string arrays in Matlab in 2016 (https://www.mathworks.com/help/matlab/ref/string.html).

As a more or less daily user, I was surprised at how not-breaking the 2.0 changes will be for 90% of Numpy users. Unless their dependencies/environments break, I expect that casual users won't even notice the upgrade.

Even the new string dtype I expect would go unnoticed by half of users or more, because they won't be using it (because Numpy historically only had fixed-length strings and generally poor support for them) and so won't even think to try it. Pandas meanwhile has had a proper string dtype for a while, so anyone interested in doing serious work on strings in data frames / arrays would presumably be using Pandas anyway.

Most of the breaking changes are in long-deprecated oddball functions that I literally have never seen used in the wild, and in the internal parts that will be a headache for library developers.

The only change that a casual user might actually notice is the change in repr(np.float64(3.0)), from "3.0" to "np.float64(3.0)".

I suspect the C ABI break to be the biggest issue, though maybe fewer packages than I imagine compile against the numpy C ABI...