Quart has moved! See https://github.com/pallets/quart
Phil Jones / quart · GitLab
Quart has moved! See https://github.com/pallets/quart
Advanced usage of Python requests - timeouts, retries, hooks
Advanced usage of Python requests - timeouts, retries, hooks The Python HTTP library requests is probably my favourite HTTP utility in all the languages I program in. It's simple, intuitive and ubiquitous in the Python community. Most of the programs that interface with HTTP use either requests or urllib3 from the standard library. While it's easy to immediately be productive with requests because
Advanced Flask Patterns
Presentation about advanced Flask patterns at PyCon Russia 2013.
PythonSpeed/PerformanceTips - Python Wiki
This page is devoted to various tips and tricks that help improve the performance of your Python programs. Wherever the information comes from someone else, I've tried to identify the source. Python has changed in some significant ways since I first wrote my "fast python" page in about 1996, which means that some of the orderings will have changed. I migrated it to the Python wiki in hopes others
One-lined Python
Convert any* Python 2 script into a single line. No newlines allowed. No semicolons, either. No silly file I/O tricks, or eval or exec. Just good, old-fashioned λ. Oh dear God, why? Yeah, I know, I’m a terrible person. Why no semicolons? That would be against the spirit of this exercise. Why pass up a perfectly good excuse to abuse lambda functions, ternary expressions, list comprehensions, and ev
Pythonã§ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚° / fujimisakari blog
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Get Started | Intel® Distribution for Python* | Intel® Software
The Intel® Distribution for Python* provides: Scalable performance using all available CPU cores on laptops, desktops, and powerful servers Support for the latest CPU instructions Near-native performance through acceleration of core numerical and machine learning packages with libraries like the Intel® oneAPI Math Kernel Library (oneMKL) and Intel® oneAPI Data Analytics Library Productivity tools
What the mock? - A cheatsheet for mocking in Python
It’s just a fact of life, as code grows eventually you will need to start adding mocks to your test suite. What started as a cute little two class project is now talking to external services and you cannot test it comfortably anymore. That’s why Python ships with unittest.mock, a powerful part of the standard library for stubbing dependencies and mocking side effects. However, unittest.mock is not
20 万行超ã®ã‚³ãƒ¼ãƒ‰ãƒ™ãƒ¼ã‚¹ã‚’テストã›ãšã«ãƒªãƒ•ã‚¡ã‚¯ã‚¿ãƒªãƒ³ã‚°ãƒªãƒªãƒ¼ã‚¹ã—ãŸè©± - MonotaRO Tech Blog
ã“ã‚“ã«ã¡ã¯ã€éˆ´æœ¨ã§ã™ã€‚ 20 万行を超ãˆã‚‹ã‚¢ãƒ—リケーションã®ã»ã¨ã‚“ã©å…¨ã¦ã®ã‚½ãƒ¼ã‚¹ã‚³ãƒ¼ãƒ‰ã‚’変更ã—ã€ãƒ†ã‚¹ãƒˆã‚’è¡Œã‚ãšã«æœ¬ç•ªãƒªãƒªãƒ¼ã‚¹ã—ã¾ã—ãŸã€‚ 「ãã‚Œã£ã¦ãƒ†ã‚¹ãƒˆã„ã‚‹ã‚“ã§ã™ã‹ï¼Ÿã€å•é¡Œ ã„ããªã‚Šã§ã™ãŒè³ªå•ã§ã™ã€‚ソースコードを 1 ãƒã‚¤ãƒˆã§ã‚‚変更ã—ãŸã‚‰å†ãƒ†ã‚¹ãƒˆã™ã‚‹å¿…è¦ã¯ã‚ã‚‹ã§ã—ょã†ã‹ã€‚「絶対ã«å†ãƒ†ã‚¹ãƒˆã™ã¹ãã€ã¨ã„ã†æ–¹ã‚‚ã„ã‚Œã°ã€ã€ŒçŠ¶æ³ã«ã‚ˆã‚‹ã—ケースãƒã‚¤ã‚±ãƒ¼ã‚¹ã‹ãªãƒ»ãƒ»ã€ã¨ã„ã†æ–¹ã‚‚ã„らã£ã—ゃるã¨æ€ã„ã¾ã™ã€‚ ケースãƒã‚¤ã‚±ãƒ¼ã‚¹ã¨è€ƒãˆã‚‹æ–¹ã¯ã€ã©ã®ã‚ˆã†ãªå ´åˆã«ãƒ†ã‚¹ãƒˆã‚’è¡Œã‚ãªãã¦è‰¯ã„ã¨è€ƒãˆã‚‹ã§ã—ょã†ã‹ã€‚例ãˆã°ã€ã‚³ãƒ¡ãƒ³ãƒˆå†…ã®èª¤å—ã‚’ä¿®æ£ã—ãŸå ´åˆã¯ã©ã†ã§ã—ょã†ã‹ã€‚ãƒãƒ¼ã‚«ãƒ«å¤‰æ•°ã®åå‰ã‚’ typo ã—ã¦ã„ãŸã®ã§ä¿®æ£ã—ãŸå ´åˆã€ãƒ‡ãƒƒãƒ‰ã‚³ãƒ¼ãƒ‰ã‚’削除ã—ãŸå ´åˆã¯ã©ã†ã§ã—ょã†ã‹ã€‚ ã“ã‚“ãªã“ã¨ãŒã‚ã‚Šã¾ã—㟠ã‚ã‚‹æ—¥ã€Python ã®ã‚½ãƒ¼ã‚¹ã‚³ãƒ¼ãƒ‰ã‚’眺ã‚ã¦ã„ã‚‹ã¨ã€ã€Œ# $Idã€ã®ã‚ˆã†ãª CVS 時代ã®ã‚³ãƒ¡ãƒ³ãƒˆãŒã‚ã‚Šã¾ã—ãŸã€‚ã„ã¾ã‚„ソースコード㯠Gi
高速化ã®ãŸã‚ã®Python Tips - ã®ã‚“ã³ã‚Šã—ã¦ã„るエンジニアã®æ—¥è¨˜
皆ã•ã‚“ã“ã‚“ã«ã¡ã¯ ãŠå…ƒæ°—ã§ã™ã‹ï¼Ÿç§ã¯å…ƒæ°—ã§ã™ã€‚ Pythonã«ãŠã‘る高速化手法を掲載ã—ã¦ã¿ã¾ã™ã€‚ ç°¡å˜ãªã‚³ãƒ¼ãƒ‰ä¸¦ã³ã«ç´¢å¼•ã®ã‚ˆã†ãªæ„Ÿã˜ã§å¼•ã‘るよã†ãªã‚¤ãƒ¡ãƒ¼ã‚¸ã§ä½œæˆã—ã¾ã—ãŸã€‚ 本日ã®ç›®æ¬¡ã§ã™ã€‚ Pythonã«ãŠã‘る高速化ã®å¿…è¦æ€§ Pythonã®é«˜é€ŸåŒ– 高速化ã®æ‰‹é † Profiling 基本的ãªæ¡ä»¶ 計測コード Pythonã®åŸºæœ¬çš„ãªæ›¸ã方部分 rangeよりxrangeを（Python2.7） リストã®ç”Ÿæˆ æ–‡å—列çµåˆ Importæ–‡ã®ã‚³ã‚¹ãƒˆ 関数呼ã³å‡ºã—ã®ã‚³ã‚¹ãƒˆ ドットをé¿ã‘ã‚‹ yieldを使ㆠNumpyã«é–¢ã™ã‚‹Tips Numpyを使用ã—ã¦åŸºæœ¬æ¼”算を高速化ã™ã‚‹ Numpyã®è¦ç´ ã«ã‚¢ã‚¯ã‚»ã‚¹ã™ã‚‹æ¼”ç®—ã‚’ã—ãªã„ Numbaã§æ‰‹æ—©ã高速化 ãã®ä»–高速化ツール Cython Dask PyPy 感想並ã³ã«å±•æœ› å‚考文献 Pythonã«ãŠã‘る高速化ã®å¿…è¦æ€§ Pythonã¯C++ã‚„Javaã¨æ¯”較ã™ã‚‹ã¨éž
Authlib
Generic RFCsAuthlib offers generic implementations of RFCs, including: RFC5849: The OAuth 1.0 ProtocolRFC6749: The OAuth 2.0 Authorization FrameworkRFC7519: JSON Web TokenOIDC: OpenID Connect 1.0More details in documentation. Framework IntegrationsVarious built-in high-level framework integrations for both clients and servers, aiming to create a seamless experience. Flask OAuth 1.0/2.0 clients and
Dropbox releases PyAnnotate -- auto-generate type annotations for mypy
Updates about mypy, an optional static type checker for Python For statically checking Python code, mypy is great, but it only works after you have added type annotations to your codebase. When you have a large codebase, this can be painful. At Dropbox we’ve annotated over 1.2 million lines of code (about 20% of our total Python codebase), so we know how much work this can be. It’s worth it though
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Git Internals - Learn by Building Your Own Git
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Google Colab
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