NumPy VS SymPy

Unmatched integration with ML/AI ecosystems through NumPy, TensorFlow, and PyTorch

> Let’s take a pause here for a second - the ‘CanIndex’ and ‘SupportsIndex’ from the looks are just “int”.

The PR for the change is https://github.com/numpy/numpy/pull/28913 - The details of files changed[0] shows the change was made in 'numpy/__init__.pyi'. Looking at the whole file[1] shows SupportsIndex is being imported from the standard library's typing module[2].

Where are you seeing SupportsIndex being defined as an int?

> I have a hard time dealing with these custom types because they are so obscure.

SupportsIndex is obscure, I agree, but it's not a custom type. It's defined in stdlib's typing module[2], and was added in Python 3.8.

[0]: https://github.com/numpy/numpy/pull/28913/files

[1]: https://github.com/charris/numpy/blob/c906f847f8ebfe0adec896...

[2]: https://docs.python.org/3/library/typing.html#typing.Support...

Know (or check) tells of older versions, such as the python sdk of OpenAI changing from a client with global state in v0.x.x, to a declared instance in v1.x.x, or numpy's change in how random generators are declared.

The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A…

AI starts with math and coding. You don’t need a PhD—just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Python’s syntax is straightforward.

As is the case with most Python libraries, it is open-source and free-to-use, making it easily accessible by anyone willing to learn machine learning, and it is built upon other open-source libraries within Python, like SciPy for advanced scientific operations, NumPy for efficient numerical computations, Matplotlib for data visualization, and Cython for increased efficiency and speed, similar to that of C/C++.

It looks like Narwhals; "Narwhals and scikit-Lego came together to achieve dataframe-agnosticism" https://news.ycombinator.com/item?id=40950813 :

> Narwhals: https://narwhals-dev.github.io/narwhals/ :

>> Extremely lightweight compatibility layer between [pandas, Polars, cuDF, Modin]

> Lancedb/lance works with [Pandas, DuckDB, Polars, Pyarrow,]; https://github.com/lancedb/lance

SymPy has Solvers for ODEs and PDEs and convex optimization. SymPy also has lambdify to compile from a relatively slow symbolic expression tree to faster 'vectorized' functions

From https://news.ycombinator.com/item?id=40683777 re: warp :

> sympy.utilities.lambdify.lambdify() https://github.com/sympy/sympy/blob/main/sympy/utilities/lam... :

>>> """Convert a SymPy expression into a function that allows for fast numeric evaluation""" [with e.g. the CPython math module, mpmath, NumPy, SciPy, CuPy, JAX, TensorFlow, PyTorch (*), SymPy, numexpr, but not yet cmath]

I have been using sympy while learning electron physics to automatically integrate linear charge densities. It works great symbolically, but often fails silently when the symbols are substituted with floats before integration.

https://github.com/sympy/sympy/issues/27675

It's an interesting exercise to think about why the performance of Sum[i, {i, 1, 100000}] differs between Mathics and MMA: Mathics just calls down to sympy, which I think just does the sum in Python [1]; Mathematica (likely) pattern-matches and computes the 100000th triangular number directly, since I know Mathematica relies heavily on standard tables of summations/integrals/etc.

[1] https://github.com/sympy/sympy/blob/master/sympy/concrete/su....

From https://news.ycombinator.com/item?id=37686351 :

>> sympy.utilities.lambdify.lambdify() https://github.com/sympy/sympy/blob/a76b02fcd3a8b7f79b3a88df... :

>> """Convert a SymPy expression into a function that allows for fast numeric evaluation [e.g. the CPython math module, mpmath, NumPy, SciPy, CuPy, JAX, TensorFlow, SymPy, numexpr,]

Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:

- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...

- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.

There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).

That's interesting. You should consider yourself lucky to have met Wolfram employees, as they are obviously vastly outnumbered by users of Mathematica.

I have not met any developers for either of these products but I know that SymPy has a huge list of contributors for a project of its size. See: https://github.com/sympy/sympy/blob/master/AUTHORS

You may not be hearing about SymPy users because SymPy is not a monolithic product. It is a library. If you know mathematicians big into using Python, they are probably aware of SymPy as it is the main attraction when it comes to symbolic computation in Python.

https://github.com/sympy/sympy/issues/9479 suggests that multivariate inequalities are still unsolved in SymPy, though it looks like https://github.com/sympy/sympy/pull/21687 was merged in August. This probably isn't yet implemented in C++ in SymForce yet?

bug report opened https://github.com/sympy/sympy/issues/25507

https://news.ycombinator.com/item?id=36463580

From https://news.ycombinator.com/item?id=36159017 :

> sympy.utilities.lambdify.lambdify() https://github.com/sympy/sympy/blob/a76b02fcd3a8b7f79b3a88df... :

>> """Convert a SymPy expression into a function that allows for fast numeric evaluation [with the CPython math module, mpmath, NumPy, SciPy, CuPy, JAX, TensorFlow, SymPy, numexpr,]*

From https://westurner.github.io/hnlog/#comment-19084622 :

> "latex2sympy parses LaTeX math expressions and converts it into the equivalent SymPy form" and is now merged into SymPy master and callable with sympy.parsing.latex.parse_latex(). It requires antlr-python-runtime to be installed. https://github.com/augustt198/latex2sympy https://github.com/sympy/sympy/pull/13706

ENH: 'generate a Jupyter notebook' (nbformat .ipynb JSON) function from this stem formula