Most testing is ineffective - Hypothesis
Normal “automated†software testing is surprisingly manual. Every scenario the computer runs, someone had to write by hand. Hypothesis can fix this. Hypothesis is a new generation of tools for automating your testing process. It combines human understanding of your problem domain with machine intelligence to improve the quality of your testing process while spending less time writing tests. Don’t
ã€é•·ç·¨ãƒã‚¨ãƒ 】関数型言語ãŒåˆ†ã‹ã‚‰ã‚“ãŠå‰ã§ã‚‚Pythonãªã‚‰åˆ†ã‹ã‚‹ã‚„ã‚：第1回 関数をå—ã‘å–る関数ã¯ä¾¿åˆ©ã よ編 - Qiita
ã“ã‚Œã¯ã€é–¢æ•°åž‹ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ã®ç‰¹å¾´ã‚’ Python ã§èª¬æ˜Žã—ãŸé•·ç·¨ãƒã‚¨ãƒ ã§ã™ã€‚ IQ145 ã®ç¾Žå°‘女ã¯å‡ºã¦ãã¾ã›ã‚“ã®ã§ã€éŽåº¦ãªæœŸå¾…ã¯ã—ãªã„ã§ãã ã•ã„。 ã€å¯¾è±¡èªè€…】 関数型言語ã¯çŸ¥ã‚‰ãªã„ã‘ã©é–¢æ•°åž‹ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ã®åˆæ©ã‚’勉強ã—ãŸã„人 (Python を知らãªãã¦ã‚‚ãªã‚“ã¨ãªã分ã‹ã‚‹ã‚ˆã†ã«æ›¸ã„ãŸã®ã§ã€PHP ã‚„ Java ã‚„ JavaScript ã®äººã‚‚èªã‚“ã§ã¿ã¦ãã ã•ã„) ã€é€£è¼‰è¨˜äº‹ã€‘ 第1回 関数をå—ã‘å–る関数ã¯ä¾¿åˆ©ã よ編 (â†ä»Šã‚³ã‚³) 第2回 関数を生æˆã™ã‚‹é–¢æ•°ã¯ã™ã”ã„よ編 ã¯ã˜ã‚㫠「ãªãœé–¢æ•°ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ã¯é‡è¦ã‹ã€ã¨ã„ã†æ–‡ç« ãŒã‚ã‚Šã¾ã™ã€‚タイトルã®é€šã‚Šã€é–¢æ•°åž‹è¨€èªžãŒã„ã‹ã«å½¹ã«ç«‹ã¤ã‹ã‚’紹介ã—ã¦ã„ã‚‹æ–‡ç« ã§ã™ã€‚é•·ã„ã§ã™ãŒã€3 è¡Œã§ã¾ã¨ã‚ã‚‹ã¨ã“ã‚“ãªæ„Ÿã˜ï¼š ソフトウェアãŒã©ã‚“ã©ã‚“複雑ã«ãªã‚‹ã®ã§ã€ãƒ¢ã‚¸ãƒ¥ãƒ¼ãƒ«åŒ–ãŒé‡è¦ 関数型言語ã¯ã€å¼·åŠ›ãªãƒ¢ã‚¸ãƒ¥ãƒ¼ãƒ«åŒ–機能をæä¾›ã—ã¦ã„ã‚‹ 特ã«ã€Œé«˜éšŽé–¢æ•°ã€ã¨ã€Œé…延評価ã€ãŒ
What is Gradual Typing: 漸進的型付ã‘ã¨ã¯ä½•ã‹ - Qiita
本稿㯠Python ã«åž‹ã‚¢ãƒŽãƒ†ãƒ¼ã‚·ãƒ§ãƒ³ã‚’è¿½åŠ ã™ã‚‹ã¨ã„ㆠPEP 483 - The Theory of Type Hinting ã®æ案ã§å‚ç…§ã•ã‚Œã¦ã„ã‚‹ Jeremy Siek (@jeremysiek) æ°ã¨ Walid Taha æ°ãŒé–‹ç™ºã—ãŸæ¼¸é€²çš„型付ã‘ã«ã¤ã„ã¦ã®å…¥é–€è¨˜äº‹ã®ç¿»è¨³ã§ã™ã€‚ What is Gradual Typing Python 3.5 ã§å°Žå…¥ã•ã‚ŒãŸåž‹ã‚¢ãƒŽãƒ†ãƒ¼ã‚·ãƒ§ãƒ³ã«ã¤ã„ã¦èˆˆå‘³ãŒã‚ã‚‹æ–¹ã¯ä»¥ä¸‹ã‚’å‚考ã«ã—ã¦ãã ã•ã„。 Python ã¨åž‹ãƒ’ント (Type Hints) 㨠#pyconjp [翻訳] PEP 0484 -- 型ヒント (Type Hints) Revenge of the Types: åž‹ã®å¾©è® ç§è‡ªèº«ã€åž‹ã‚·ã‚¹ãƒ†ãƒ ã«æ˜Žã‚‹ããªã„ãŸã‚ã€ä¸€éƒ¨æœªè¨³ã®éƒ¨åˆ†ãŒã‚ã£ãŸã‚Šã€å‹˜é•ã„や誤訳もã‚ã‚‹ã¨æ€ã„ã¾ã™ã€‚ãã†ã„ã£ãŸèª¤ã‚Šã‚’見ã¤ã‘ãŸã‚‰ç·¨é›†ãƒªã‚¯ã‚¨ã‚¹ãƒˆã‚’é€ã£ã¦ã‚‚らãˆã‚‹ã¨åŠ©ã‹ã‚Šã¾ã™ã€‚
Effective Numerical Computation in NumPy and SciPy
This document provides an overview of effective numerical computation in NumPy and SciPy. It discusses how Python can be used for numerical computation tasks like differential equations, simulations, and machine learning. While Python is initially slower than languages like C, libraries like NumPy and SciPy allow Python code to achieve sufficient speed through techniques like broadcasting, indexin
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GitHub - borzunov/dontasq: âš¡ðŸ Extends built-in Python collections with LINQ-style methods
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