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

The most important changes are deprecations of certain public APIs: https://numpy.org/devdocs/release/2.0.0-notes.html#deprecations 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#module-numpy.strings https://numpy.org/devdocs/reference/routines.strings.html#mo...

ahurmazda 2y ago
This one will be rough :| > arange’s start argument is positional-only
- haiguise 2y ago
  Looks like that might get reverted [0]. [0] https://github.com/numpy/numpy/pull/25955 https://github.com/numpy/numpy/pull/25955
amelius 2y ago
> One new interesting feature, though, is the support for string routines Sounds almost like they're building a language inside a language.
- ssahoo 2y ago
  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.
  - topper-123 2y ago
    Yeah, operating on strings has historically been a major weak point of Numpy's. I'm looking forward seeing benchmarks for the new implementation.
  - ayhanfuat 2y ago
    > Native python ops in string suck in performance. That’s not true? Python string implementation is very optimized, probably have similar performance to C.
    - csjh 2y ago
      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-file#performance https://github.com/ashvardanian/StringZilla?tab=readme-ov-fi...
    - mirashii 2y ago
      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
      - Too 2y ago
        There is a built in memoryview. But it only works on bytes or other objects supporting the buffer protocol, not on strings.
    - bvrmn 2y ago
      For numpy applications you have to always box a value to get a new python string. It quite far from fast.
- nerdponx 2y ago
  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.
brcmthrowaway 2y ago
Does numpy use GPU?
- ahurmazda 2y ago
  No. You may want to check out cupy https://cupy.dev/ https://cupy.dev/
etbebl 2y ago
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 https://www.mathworks.com/help/matlab/ref/string.html).

ahurmazda 2y ago

This one will be rough :| > arange’s start argument is positional-only

haiguise 2y ago
Looks like that might get reverted [0]. [0] https://github.com/numpy/numpy/pull/25955 https://github.com/numpy/numpy/pull/25955

amelius 2y ago

> One new interesting feature, though, is the support for string routines Sounds almost like they're building a language inside a language.

ssahoo 2y ago
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.
- topper-123 2y ago
  Yeah, operating on strings has historically been a major weak point of Numpy's. I'm looking forward seeing benchmarks for the new implementation.
- ayhanfuat 2y ago
  > Native python ops in string suck in performance. That’s not true? Python string implementation is very optimized, probably have similar performance to C.
  - csjh 2y ago
    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-file#performance https://github.com/ashvardanian/StringZilla?tab=readme-ov-fi...
  - mirashii 2y ago
    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
    - Too 2y ago
      There is a built in memoryview. But it only works on bytes or other objects supporting the buffer protocol, not on strings.
  - bvrmn 2y ago
    For numpy applications you have to always box a value to get a new python string. It quite far from fast.
nerdponx 2y ago
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.

brcmthrowaway 2y ago

Does numpy use GPU?

ahurmazda 2y ago
No. You may want to check out cupy https://cupy.dev/ https://cupy.dev/

etbebl 2y ago

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 https://www.mathworks.com/help/matlab/ref/string.html).