SymPy
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SymPy | Pandas | |
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34 | 393 | |
12,365 | 41,923 | |
3.8% | 1.4% | |
10.0 | 10.0 | |
6 days ago | 3 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
SymPy
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AutoCodeRover resolves 22% of real-world GitHub in SWE-bench lite
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).
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SymPy: Symbolic Mathematics in Python
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.
- Matrix Cookbook examples using SymPy
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Fast Symbolic Computation for Robotics
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?
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Solving a simple puzzle using SymPy
bug report opened https://github.com/sympy/sympy/issues/25507
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Stem Formulas
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
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Vectorization: Introduction
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.
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Has anyone solved the prime number problem on SPOJ yet using pure python?
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)
- What can I contribute to SciPy (or other) with my pure math skill? I’m pen and paper mathematician
- Quantum Monism Could Save the Soul of Physics
Pandas
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Deploying a Serverless Dash App with AWS SAM and Lambda
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.
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Help Us Build Our Roadmap – Pydantic
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
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Stuff I Learned during Hanukkah of Data 2023
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.
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Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
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.
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Mastering Pandas read_csv() with Examples - A Tutorial by Codes With Pankaj
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.
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What Would Go in Your Dream Documentation Solution?
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:
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How do people know when to use what programming language?
Weirdly most of my time spent with data analysis was in the C layers in pandas.
- Read files from s3 using Pandas/s3fs or AWS Data Wrangler?
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10 Github repositories to achieve Python mastery
Explore here.
What are some alternatives?
SciPy - SciPy library main repository
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
NumPy - The fundamental package for scientific computing with Python.
tensorflow - An Open Source Machine Learning Framework for Everyone
Numba - NumPy aware dynamic Python compiler using LLVM
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
NetworkX - Network Analysis in Python
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
ti84-forth - A Forth implementation for the TI-84+ calculator.
Keras - Deep Learning for humans
Ndless - The TI-Nspire calculator extension for native applications
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration