GitHub - astral-sh/ruff: An extremely fast Python linter and code formatter, written in Rust.
Docs | Playground An extremely fast Python linter and code formatter, written in Rust. Linting the CPython codebase from scratch. âš¡ï¸ 10-100x faster than existing linters (like Flake8) and formatters (like Black) ðŸ Installable via pip ðŸ› ï¸ pyproject.toml support 🤠Python 3.12 compatibility âš–ï¸ Drop-in parity with Flake8, isort, and Black 📦 Built-in caching, to avoid re-analyzing unchanged files 🔧
GitHub - bloomberg/memray: Memray is a memory profiler for Python
Memray is a memory profiler for Python. It can track memory allocations in Python code, in native extension modules, and in the Python interpreter itself. It can generate several different types of reports to help you analyze the captured memory usage data. While commonly used as a CLI tool, it can also be used as a library to perform more fine-grained profiling tasks. Notable features: 🕵ï¸â€â™€ï¸ Tra
Processing large JSON files in Python without running out of memory
Processing large JSON files in Python without running out of memory by Itamar Turner-Trauring Last updated 06 Jan 2023, originally created 14 Mar 2022 If you need to process a large JSON file in Python, it’s very easy to run out of memory. Even if the raw data fits in memory, the Python representation can increase memory usage even more. And that means either slow processing, as your program swaps
GitHub - facebookresearch/Kats: Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change point
Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis. Time series analysis is an essential component of Data Science and Engineering work at industry, from understanding the key statistics and characteristics, detecting regressions and anomalies, to forecasting future trends. Kats aims to provide the one-stop shop fo
How to make an awesome Python package
If you are like me, every once in a while you write a useful python utility and want to share it with your colleagues. The best way to do this is to make a package: it easy to install and saves from copy-pasting. If you are like me, you might be thinking that creating packages is a real headache. Well, that's not the case anymore. And I am going to prove it with this step-by-step guide. Just three
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- ã¯ã˜ã‚ã« - Pythonã®ãƒ‘ッケージ管ç†ãƒ„ールã¯ã€é•·ã‚‰ã乱世ã«ã‚ã‚‹ã¨è¨€ãˆã‚‹ã€‚ 特ã«pipã€pipenvã€poetryã¨ã„ã†ãƒ„ールã®ç™»å ´ã‚·ãƒ¼ãƒ³å‰å¾Œã§ã¯ã€å¤šãã®å¤‰é©ãŒã‚‚ãŸã‚‰ã•ã‚ŒãŸã€‚ 本記事ã¯ã€Pythonパッケージ管ç†ãƒ„ールã§ã‚ã‚‹pipã€pipenvã€poetryã®3ã¤ã«ç€ç›®ã—ã€ãã‚Œãžã‚Œã®ãƒ„ールã«å¯¾ã—ã¦ãƒ•ãƒ©ãƒƒãƒˆãªèƒŒæ™¯ã€æŠ€è¡“çš„ãªèª¬æ˜Žã‚’示ã—ãªãŒã‚‰ã€æ‰€å±žä¼æ¥å†…ã«ã¦poetry移行大臣ã¨ã—ã¦1年活動ã—ãŸä¸Šã§ã®çµŒé¨“ã€ç§»è¡Œã®æ„図ã«ã¤ã„ã¦ç¶´ã‚Šã€ä»Šå¾Œã®Pythonパッケージ管ç†ã®å±•æœ›ã«ã¤ã„ã¦å¦„想ã™ã‚‹ã‚‚ã®ã§ã‚る。 注æ„：本記事ã¯Pythonパッケージ管ç†ã®ãƒ™ã‚¹ãƒˆãƒ—ラクティスを主張ã™ã‚‹è¨˜äº‹ã§ã¯ã‚ã‚Šã¾ã›ã‚“。背景をç†è§£ã—自らã®é–‹ç™ºç’°å¢ƒã‚„状態ã«å¿œã˜ã¦é©åˆ‡ã«æŠ€è¡“é¸å®šã§ãるソフトウェアエンジニアã“ã良ã„ソフトウェアエンジニアã§ã‚ã‚‹ã¨ç†è€…ã¯è€ƒãˆã¦ã„ã¾ã™ã€‚ é‡è¦ãªãƒã‚¤ãƒ³ãƒˆã®ã¿æŠŠæ¡ã—ãŸã„å ´åˆã¯ã€å„ç« ã®æœ€å¾Œã®ã¾ã¨ã‚ã‚’èªã‚“ã§é ‚
GitHub - thebjorn/pydeps: Python Module Dependency graphs
positional arguments: fname filename optional arguments: -h, --help show this help message and exit --debug turn on all the show and verbose options (mainly for debugging pydeps itself) --config FILE specify config file --no-config disable processing of config files --version print pydeps version -L LOG, --log LOG set log-level to one of CRITICAL, ERROR, WARNING, INFO, DEBUG, NOTSET. --find-packag
今ã‹ã‚‰å§‹ã‚る型安全 Python / Start Python Type Hints
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Welcoming Google as a Visionary Sponsor of the PSF
Our top sponsors—companies who step forward to make the biggest investment in Python and its community—not only use Python for their own internal development, but also offer Python as a crucial part of the products they offer to their own customers. That is certainly true of Google, the Python Software Foundation’s first Visionary Sponsor. Google's donations and sponsorship funds will be used to s
Web Scraping with Python: Everything you need to know (2022)
Introduction: In this post, which can be read as a follow-up to our guide about web scraping without getting blocked, we will cover almost all of the tools to do web scraping in Python. We will go from the basic to advanced ones, covering the pros and cons of each. Of course, we won't be able to cover every aspect of every tool we discuss, but this post should give you a good idea of what each too
Pattern matching accepted for Python [LWN.net]
The Python steering council has, after some discussion, accepted the controversial proposal to add a pattern-matching primitive to the language. "We acknowledge that Pattern Matching is an extensive change to Python and that reaching consensus across the entire community is close to impossible. Different people have reservations or concerns around different aspects of the semantics and the syntax
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GitHub - nottheswimmer/pytago: A source-to-source transpiler for Python to Go translation
GitHub - Nuitka/Nuitka: Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4-3.12. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
GitHub - capitalone/DataProfiler: What's in your data? Extract schema, statistics and entities from datasets
GitHub - Pytlicek/sheet2dict: Simple XLSX and CSV to dictionary converter
GitHub - gruns/icecream: 🦠Never use print() to debug again.
GitHub - pfnet/pysen: Python linting made easy. Also a casual yet honorific way to address individuals who have entered an organization prior to you.
GitHub - replicate/keepsake: Version control for machine learning
GitHub - nipunsadvilkar/pySBD: ðŸðŸ’¯pySBD (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box.
Spyder | The Python IDE that scientists and data analysts deserve
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