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Road to NumPy 2.0

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Re: Road to NumPy 2.0

#3

Post by fouronnes3 »

I think that ndarray is the most successful abstraction I've come across. Numerical computing is a domain ripe for terrible code, but multi-indexing, broadcasting, mask arrays, .reshape(), .where(), linspace(), and all that are so well made and useful they are now the standard grammar of data science. Yes you've seen horrible numpy code before, but how much worse would it be if it had been written by the same person in raw C with only malloc and pointer arithmetic?

Re: Road to NumPy 2.0

#4

Post by ktpsns »

fouronnes3 wrote: post #3

I think that ndarray is the most successful abstraction I've come across. Numerical computing is a domain ripe for terrible code, but multi-indexing, broadcasting, mask arrays, .reshape(), .where(), linspace(), and all that are so well made and useful they are now the standard grammar of data science. Yes you've seen horrible numpy code before, but how much worse would it be if it had been written by the same person…

Just FYI, numpy by no means pioneered this concept. There was Fortran before, as a language built around n-dimensional arrays. And even Fortran was not the first one, as some stack based programming languages (such as APL, IIRC) have similar concepts. Also languages such as R and evventually Matlab (as a kind-of nicer frontend to Fortran libs) pioneered this concept. However, Numpy was the first library bringing this into a general-purpose language as Python is.

Re: Road to NumPy 2.0

#5

Post by mattip »

While we are trying to minimize the disruptions, there is one thing project maintainers should do right now: pin the maximum NumPy to <2.0 in their ~`pyproject.toml`~ project dependencies. This will ensure they do not inadvertently upgrade before they are ready to do so. Once numpy2.0 is released, you can check that your code works with it, and then release the pin.

Re: Road to NumPy 2.0

#6

Post by jcarrano »

fouronnes3 wrote: post #3

I think that ndarray is the most successful abstraction I've come across. Numerical computing is a domain ripe for terrible code, but multi-indexing, broadcasting, mask arrays, .reshape(), .where(), linspace(), and all that are so well made and useful they are now the standard grammar of data science. Yes you've seen horrible numpy code before, but how much worse would it be if it had been written by the same person…

I remember implementing a sliding overlapping 2d windowing by playing with the strides. I don't know how common is that trick but at the time it felt like magic.

Re: Road to NumPy 2.0

#8

Post by LeanderK »

numpy is a great library, but I find the pytorch take on it superior. It allows for much more method-chaining, which I find to be very readable in longer computations. I frequently want to reach out for it in numpy, only to see that it's a np.* function. In functional languages I would use an infix pipeline-combinator like (|>) but that's not possible in python.
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