Å·hat | Introducing db.py
Data analysis libraries for Python keep getting better and better. pandas is now on 0.15, scikit-learn continues to pick up converts, and a number of visualization libraries have started to emerge: for example, our own baby, ggplot, and others like seaborn. But there's still one subject that's practically synonymous with data analysis where there aren't any new, killer libraries: databases. There
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僕ã¯Oracleã§RDBMSã¨ã‹SQLを勉強ã—ãŸäººé–“ãªã®ã§ã€çµ¶å¯¾ã«å¤–部ã‚ーを張りã€å¯èƒ½ã§ã‚ã‚Œã°ãƒã‚§ãƒƒã‚¯åˆ¶ç´„ã‚‚ã‹ã‘ã¦ã€çµ¶å¯¾ã«ä¸æ£ãªãƒ‡ãƒ¼ã‚¿ã¯å…¥ã‚Œã•ã›ãŸããªã„ã¨æ€ã†äººé–“ãªã®ã ãŒã€LAMPサーãƒãƒ¼ã‚’並ã¹ã¦ã‚¹ã‚±ãƒ¼ãƒ«ã•ã›ã‚‹ã£ã¦ã„ã†ä»Šæ™‚ã®ã‚µã‚¤ãƒˆã§ã¯ã€å¤–部ã‚ーを張らãªã„è¨è¨ˆã®æ–¹ãŒä¸»æµã‚‰ã—ã„。・・・本当ï¼ï¼Ÿç¢ºã‹ã«ã€ã‚¢ãƒ—リケーションやORMã§é ‘張れã°ã€å¤–部ã‚ーを張るメリットãŒæ¶ˆãˆã€å¤–部ã‚ーを張るデメリットã ã‘ãŒæ®‹ã‚Šã€ãã—ã¦MySQLã¯RDBMSã§ã¯ãªãã€SQLをサãƒãƒ¼ãƒˆã™ã‚‹å˜ãªã‚‹ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã«ãªã‚‹ã ã‚ã†ãŒãƒ»ãƒ»ãƒ»ã¡ãªã¿ã«SQLAlchemyãªã‚‰ã°ãƒ†ãƒ¼ãƒ–ル定義ã‹ã‚‰å¤–部ã‚ーを消ã—ã¦ã‚‚ã€mapperã®å®šç¾©ã§æ˜Žç¤ºçš„ã«ç¤ºã—ã¦ã‚„ã‚Œã°ã€ã„ã¾ã¾ã§é€šã‚Šã®ã‚³ãƒ¼ãƒ‰ãŒå‹•ãã¯ãšã€‚以下ã¯ã€Œé£¼ã„主(Owner)ã¨çŠ¬(Dog)ã®é–“ã«ä¸€å¯¾å¤šã®é–¢ä¿‚ãŒã‚ã‚‹ã€ã¨ã„ã†å ´åˆã®ä¾‹ã€‚ # -*- coding: utf-8 -*- from sqlalchem
A step-by-step SQLAlchemy tutorial
About This Tutorial This tutorial is for SQLAlchemy version 0.2. You may notice that some sections are marked "New in 0.2". If this is the first time you're reading this tutorial, you can safely skip those sections. On the other hand, if you read the previous version of this tutorial and are now trying to learn SQLAlchemy 0.2, then just search for the text "New in 0.2" and you'll have a lot less r
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