SciPy
Pandas
SciPy | Pandas | |
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50 | 396 | |
12,459 | 41,983 | |
1.0% | 0.6% | |
9.9 | 10.0 | |
5 days ago | 5 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
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SciPy
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What Is a Schur Decomposition?
I guess it is a rite of passage to rewrite it. I'm doing it for SciPy too together with Propack in [1]. Somebody already mentioned your repo. Thank you for your efforts.
[1]: https://github.com/scipy/scipy/issues/18566
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Fortran codes are causing problems
Fortran codes have caused many problems for the Python package Scipy, and some of them are now being rewritten in C: e.g., https://github.com/scipy/scipy/pull/19121. Not only does R have many Fortran codes, there are also many R packages using Fortran codes: https://github.com/r-devel/r-svn, https://github.com/cran?q=&type=&language=fortran&sort=. Modern Fortran is a fine language but most legacy Fortran codes use the F77 style. When I update the R package quantreg, which uses many Fortran codes, I get a lot of warning messages. Not sure how the Fortran codes in the R ecosystem will be dealt with in the future, but they recently caused an issue in R due to the lack of compiler support for Fortran: https://blog.r-project.org/2023/08/23/will-r-work-on-64-bit-arm-windows/index.html. Some renowned packages like glmnet already have their Fortran codes rewritten in C/C++: https://cran.r-project.org/web/packages/glmnet/news/news.html
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[D] Which BLAS library to choose for apple silicon?
There are several lessons here: a) vanilla conda-forge numpy and scipy versions come with openblas, and it works pretty well, b) do not use netlib unless your matrices are small and you need to do a lot of SVDs, or idek why c) Apple's veclib/accelerate is super fast, but it is also numerically unstable. So much so that the scipy's devs dropped any support of it back in 2018. Like dang. That said, they are apparently are bring it back in, since the 13.3 release of macOS Ventura saw some major improvements in accelerate performance.
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SciPy: Interested in adopting PRIMA, but little appetite for more Fortran code
First, if you read through that scipy issue (https://github.com/scipy/scipy/issues/18118 ) the author was willing and able to relicense PRIMA under a 3-clause BSD license which is perfectly acceptable for scipy.
For the numerical recipes reference, there is a mention that scipy uses a slightly improved version of Powell's algorithm that is originally due to Forman Acton and presumably published in his popular book on numerical analysis, and that also happens to be described & included in numerical recipes. That is, unless the code scipy uses is copied from numerical recipes, which I presume it isn't, NR having the same algorithm doesn't mean that every other independent implementation of that algorithm falls under NR copyright.
- numerically evaluating wavelets?
- Fortran in SciPy: Get rid of linalg.interpolative Fortran code
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Optimization Without Using Derivatives
Reading the discussions under a previous thread titled "More Descent, Less Gradient"( https://news.ycombinator.com/item?id=23004026 ), I guess people might be interested in PRIMA ( www.libprima.net ), which provides the reference implementation for Powell's renowned gradient/derivative-free (zeroth-order) optimization methods, namely COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA.
PRIMA solves general nonlinear optimizaton problems without using derivatives. It implements Powell's solvers in modern Fortran, compling with the Fortran 2008 standard. The implementation is faithful, in the sense of being mathmatically equivalent to Powell's Fortran 77 implementation, but with a better numerical performance. In contrast to the 7939 lines of Fortran 77 code with 244 GOTOs, the new implementation is structured and modularized.
There is a discussion to include the PRIMA solvers into SciPy ( https://github.com/scipy/scipy/issues/18118 ), replacing the buggy and unmaintained Fortran 77 version of COBYLA, and making the other four solvers available to all SciPy users.
- What can I contribute to SciPy (or other) with my pure math skill? I’m pen and paper mathematician
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Emerging Technologies: Rust in HPC
if that makes your eyes bleed, what do you think about this? https://github.com/scipy/scipy/blob/main/scipy/special/specfun/specfun.f (heh)
- Python
Pandas
- PHP Doesn't Suck Anymore
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AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.
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Pandas reset_index(): How To Reset Indexes in Pandas
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.
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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:
What are some alternatives?
SymPy - A computer algebra system written in pure Python
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
statsmodels - Statsmodels: statistical modeling and econometrics in Python
tensorflow - An Open Source Machine Learning Framework for Everyone
NumPy - The fundamental package for scientific computing with Python.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
astropy - Astronomy and astrophysics core library
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
or-tools - Google's Operations Research tools:
Keras - Deep Learning for humans
PyMC - Bayesian Modeling and Probabilistic Programming in Python
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration