Pandas VS SymPy

Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.

there is pull request to integrate in both pydantic extra types and into pandas cose [1]

[1]: https://github.com/pandas-dev/pandas/issues/53999

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

pandas: A library for data analysis in Python

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.

So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:

Weirdly most of my time spent with data analysis was in the C layers in pandas.

Explore here.

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

https://en.wikipedia.org/wiki/Vectorization :

> Array programming, a style of computer programming where operations are applied to whole arrays instead of individual elements

> Automatic vectorization, a compiler optimization that transforms loops to vector operations

> Image tracing, the creation of vector from raster graphics

> Word embedding, mapping words to vectors, in natural language processing

> Vectorization (mathematics), a linear transformation which converts a matrix into a column vector

Vector (disambiguation) https://en.wikipedia.org/wiki/Vector

> Vector (mathematics and physics):

> Row and column vectors, single row or column matrices

> Vector space

> Vector field, a vector for each point

And then there are a number of CS usages of the word vector for 1D arrays.

Compute kernel: https://en.m.wikipedia.org/wiki/Compute_kernel

GPGPU > Vectorization, Stream Processing > Compute kernels: https://en.wikipedia.org/wiki/General-purpose_computing_on_g...

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, SymPt, numexpr,]

pyorch lambdify PR, sympytorch: https://github.com/sympy/sympy/pull/20516#issuecomment-78428...

Sympytorch:

> Turn SymPy expressions into PyTorch Modules.

> SymPy floats (optionally) become trainable parameters. SymPy symbols are inputs to the Module.

sympy2jax https://github.com/MilesCranmer/sympy2jax :

> Turn SymPy expressions into parametrized, differentiable, vectorizable, JAX functions.

> All SymPy floats become trainable input parameters. SymPy symbols become columns of a passed matrix.

Look at sympy.isprime for a carefully-optimized pure-Python solution (though if gmpy2 is installed, which it usually is, it will use that instead after trying the easiest cases)