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80个通俗易懂的Python爬虫练手项目 🧲https://www.hezuclub.com/6783.html #Python

Version 3.10 of the legendary programming language is now here: https://www.python.org/downloads/release/python-3100 No rush to update, though. #Python

PYTHON en menos de una semana - Con ejercicios de práctica | #Python

#python Our internet is broken again, and this time by Python setuptools. [BUG] Version 78.0.1 breaks install of ansible-vault package · Issue #4910 · pypa/setuptools https://github.com/pypa/setuptools/issues/4910

Python 基金会在放弃美政府的 150 万美元拨款后收到了大量捐款 2025-11-10 16:38 by 另一个艾米 上月底，Python 软件基金会（PSF）宣布坚守 DEI(多元化、平等及包容)价值观以及考虑到无法预测的财务风险，放弃了美国政府的 150 万美元拨款。此事备受关注而被广泛报道，当天基金会就收到了大约 300 笔捐款，第二天还有 Reddit 用户抱怨尝试捐款时遭遇超时。上周五，基金会执行董事 Deb Nicholson 披露他们至今收到了逾 15.7 万美元捐款，包括 295 名每年捐款 99 美元的新支持会员。虽然这些捐款尚不足以填补 150 万美元的缺口，但基金会表示意义重大，他们感受到了来自社区的强有力支持。 https://thenewstack.io/psf-gets-a-donor-surge-after-rejecting-anti-dei-federal-grant/ #Python

#Python is the main language of data science, per this analysis on 10M Jupyter Notebooks: https://blog.jetbrains.com/datalore/2020/12/17/we-downloaded-10-000-000-jupyter-notebooks-from-github-this-is-what-we-learned/

#python I had the wrong idea for a long time that IDEs treat method/function without return type hint as returning None type. I was wrong. In PEP484, it says IDE should treat such a method/function as a type that is as general as possible. Ah that just makes sense. https://peps.python.org/pep-0484/#the-meaning-of-annotations

#python Faster conda https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community

#python Guidelines for research coding. It is not the highest standard but is easy to follow. https://goodresearch.dev/

#python This post is a retro on how I learned Python. Disclaimer: I can not claim that I am a master of Python. This post is a retrospective of how I learned Python in different stages. I started using Python back in 2012. Before this, I was mostly a Matlab/C user. Python is easy to get started, yet it is hard to master. People coming from other languages can easily make it work but will write some "disgusting" python code. And this is because Python people talk about "pythonic" all the time. Instead of being an actual style guide, it is rather a philosophy of styles. When we get started, we are most likely not interested in [PEP8](https://peps.python.org/pep-0008/) and [PEP257](https://peps.python.org/pep-0257/). Instead, we focus on making things work. After some lectures from the university (or whatever sources), we started to get some sense of styles. Following these lectures, people will probably write code and use Python in some projects. Then we began to realize that Python is strange, sometimes even doesn't make sense. Then we started leaning about the philosophy behind it. At some point, we will get some peer reviews and probably fight against each other on some philosophies we accumulated throughout the years. The attached drawing (in comments) somehow captures this path that I went through. It is not a monotonic path of any sort. This path is most likely to be permutation invariant and cyclic. But the bottom line is that mastering Python requires a lot of struggle, fights, and relearning. And one of the most effective methods is peer review, just as in any other learning task in our life. Peer review makes us think, and it is very important to find some good reviewers. Don't just stay in a silo and admire our own code. To me, the whole journey helped me building one of the most important philosophies of my life: embrace open source and collaborate.

#python Anaconda open sourced this... I have no idea what this is for... https://github.com/pyscript/pyscript

#python I find poetry a great tool to manage Python requirements. I used to manage Python requirements using requirements.txt(environment.yaml) and install them using pip(conda). The thing is, in this stack, we have to pin the version ranges manually. It is quite tedious, and we easily run into version problems for a large project. Poetry is the savior here. When developing a package, we add some initial dependencies to the pyproject.yaml, a PEP standard. Whenever a new package is needed, we run poetry add package-name. Poetry tries to figure out the compatible versions. A lock file for the dependencies with restricted versions will be created or updated. To recreate an identical python environment, we only need to run poetry install. There's one drawback and may be quite painful at some point. Recreating the lock file for dependencies is extremely slow when the complexity grows in the requirements. But this is not a problem if poetry but rather constraints from pypi. One solution to this problem is to use cache. https://python-poetry.org/