Understanding all of Python, through its builtins
Python as a language is comparatively simple. And I believe, that you can learn quite a lot about Python and its features, just by learning what all of its builtins are, and what they do. And to back up that claim, I'll be doing just that. Just to be clear, this is not going to be a tutorial post. Covering such a vast amount of material in a single blog post, while starting from the beginning is p
GitHub - microsoft/ML-For-Beginners: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
🌠Travel around the world as we explore Machine Learning by means of world cultures 🌠Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our AI for Beginners
Announcing the new Jupyter Book
Jupyter Book is an open source project for building beautiful, publication-quality books, websites, and documents from source material that contains computational content. With this post, we’re happy to announce that Jupyter Book has been re-written from the ground up, making it easier to install, faster to use, and able to create more complex publishing content in your books. It is now supported
GCPã§åŸºæœ¬ã«æˆ»ã£ã¦å§‹ã‚る実践 Infrastructure as codeå†å…¥é–€#2 - VISASQ Dev Blog
ã“ã‚“ã«ã¡ã¯! 2020å¹´2月ã‹ã‚‰SREãƒãƒ¼ãƒ ã«Joinã—ã¾ã—ãŸæœ¨æ‘ã§ã™! 仕事をã™ã‚‹ä¸Šã§ã®åº§å³ã®éŠ˜ã¯ã€Œæ˜Žæ—¥äº¤é€šäº‹æ•…ã«ã‚ã£ã¦ã‚‚システムã¨ä»•äº‹ã‚’回ã›ã‚‹ã‚ˆã†ã«ã™ã‚‹ã“ã¨ã€ã§ã™ã€‚ 基本ã«æˆ»ã£ã¦å§‹ã‚る。ã¨è¡¨é¡Œã§ã¯æ›¸ã„ã¦ã„ã¾ã™ãŒã€ç§å…ƒã€…ã¯AWSè·äººã§GCPã«æœ¬æ ¼çš„ã«ã‚³ãƒŸãƒƒãƒˆã—ã¦ã‹ã‚‰ã¾ã 3ヶ月ä½ã§ã™! ãªã®ã§ãƒ’ィヒィ?言ã„ãªãŒã‚‰GCPã®ã‚ャッãƒã‚¢ãƒƒãƒ—ã«åŠªã‚ã¦ã„ã‚‹ã‚ã‘ã§ã™ãŒä»Šå›žã¯éŽåŽ»ã«AWSã§å¾—ãŸInfrastructure as Codeã®çŸ¥è˜ã¨ãƒ“ザスクã«å…¥ç¤¾ã—ã¦ã‚ャッãƒã‚¢ãƒƒãƒ—ã§åŸ¹ã£ãŸGCPã®çŸ¥è˜ã‚’å…ƒã«åŸºæœ¬ã«æˆ»ã£ã¦å§‹ã‚ã‚‹GCPã®Infrastructure as Codeå†å…¥é–€ã¨ã„ã†ã“ã¨ã§æ›¸ã‹ã›ã¦ã„ãŸã ãã¾ã™ã€‚ å‰å›žã¯GCPã«Compute Engineã®ã‚¤ãƒ³ã‚¹ã‚¿ãƒ³ã‚¹ã¨ã‚µãƒ¼ãƒ“スアカウント作æˆã¾ã§ã§ãã¾ã—ãŸã®ã§æ¬¡ã¯Ansibleを使ã£ã¦ä½œæˆã—ãŸã‚¤ãƒ³ã‚¹ã‚¿ãƒ³ã‚¹ã«å¯¾ã—ã¦Provisionを実行ã—ã¦ã„ããŸã„ã¨æ€ã„ã¾
Things you're probably not using in Python 3 - but should - Data, what now? turns
Many people started switching their Python versions from 2 to 3 as a result of Python EOL. Unfortunately, most Python 3 I find still looks like Python 2, but with parentheses (even I am guilty of that in my code examples in previous posts – Introduction to web scraping with Python). Below, I show some examples of exciting features you can only use in Python 3 in the hopes that it will make solving
Python Development in Visual Studio Code – Real Python
Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Python Development in Visual Studio Code (Setup Guide) One of the coolest code editors available to programmers, Visual Studio Code, is an open-source, extensible, light-weight editor available on all platforms. It’s these qualities that make
Python ã§éŸ³æ¥½ã‚’作ã£ã¦æ¥½ã—もㆠ- Qiita
æ¦‚è¦ ã“ã®è¨˜äº‹ã§ã¯ã€Python を使ã£ã¦ シンセã£ã½ã„音をåˆæˆã—ãŸã‚Šã€MIDI 入出力をã„ã˜ã£ãŸã‚Šã€å’ŒéŸ³ã‚’扱ã£ãŸã‚Šã—ã¦ã€éŸ³æ¥½ã‚’楽ã—む方法を紹介ã—ã¾ã™ã€‚音楽や MIDI ã«é–¢ã™ã‚‹åŸºæœ¬çš„ãªçŸ¥è˜ã¯æŒã£ã¦ã„ã‚‹ã‘ã©ãƒ—ãƒã‚°ãƒ©ãƒ ã‹ã‚‰åˆ¶å¾¡ã™ã‚‹æ–¹æ³•ã¯åˆ†ã‹ã‚‰ãªã„ã¨ã„ã†æ–¹ã‚„ã€Python ã§éŸ³ãŒå‡ºã›ã‚‹ã®ã£ã¦é¢ç™½ãã†ã ã¨æ€ã†æ–¹ã«èªã‚“ã§ã„ãŸã ã‘ã‚‹ã¨å¬‰ã—ã„ã§ã™ã€‚ Python ã§ã‚·ãƒ³ã‚»ã£ã½ã„音をåˆæˆã™ã‚‹ Python ã§ã‚¢ãƒŠãƒã‚°ã‚·ãƒ³ã‚»ã®ã‚ˆã†ãªæ³¢å½¢ã‚’生æˆã—ã¦å†ç”Ÿã—ãŸã‚Šã€éŸ³å£°ãƒ•ã‚¡ã‚¤ãƒ«ã¨ã—ã¦æ›¸ã出ã—ãŸã‚Šã§ãã‚‹ synthesizer ã¨ã„ã†ãƒ©ã‚¤ãƒ–ラリを作りã¾ã—ãŸ(macOS 㨠Ubuntu ã—ã‹ã‚µãƒãƒ¼ãƒˆã—ã¦ã„ã¾ã›ã‚“ã€Windows ã®æ–¹ã€ã”ã‚ã‚“ãªã•ã„)。ã“ã®ãƒ©ã‚¤ãƒ–ラリ㯠Python ã‹ã‚‰ç°¡å˜ã«æ³¢å½¢ã‚’生æˆãƒ»ç·¨é›†ã§ãã‚‹ã®ãŒç‰¹å¾´ã§ã™ãŒã€ã‚µãƒãƒ¼ãƒˆã—ã¦ã„る機能ã¯ç¾åœ¨ã®ã¨ã“ã‚éžå¸¸ã«é™å®šçš„ã§ã™ã€‚オープンソースã®ã‚¢ãƒŠãƒã‚°ã‚·ãƒ³ã‚»ã‚·ãƒŸãƒ¥
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SCOOPã¨ã¯ ssh ã¨Python ã®è¨å®šã‚’é©åˆ‡ã«ã™ã‚‹ã ã‘ã§ã€ç°¡å˜ã«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯é–“ã§ã®åˆ†æ•£å‡¦ç†ãŒå®Ÿè¡Œã§ãる。 ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯åˆ†æ•£ã«å¯¾å¿œã—ã¦ã„ãªã„ライブラリã«ä¾¿åˆ©ã€‚ 関数ã®å‡¦ç†ã‚’分散ã—ã¦è¡Œã„ã€è¿”り値をã¾ã¨ã‚ã¦è¿”ã—ã¦ãれる。 Core æ•°ã«å¿œã˜ãŸ Worker æ•°ã®è¨å®šãŒå¯èƒ½ Docker ã¨ã®ç›¸æ€§ãŒæŠœç¾¤ 基本動作 python 標準㮠map 関数ã®ä½¿ã„方㧠ssh ã§ç¹‹ã„ã å…ˆ(ノード)ã§ã‚‚分散処ç†ã—ã¦ãれる。multiprocessing.Pool.map ã®ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯å‡¦ç†ã«å¯¾å¿œã—ãŸç‰ˆã€‚ map 関数ã«é…列を渡ã™ã¨é…列ã®å„è¦ç´ を引数ã«é–¢æ•°ã‚’実行ã™ã‚‹ã€‚ãã®éš›ã®å„処ç†ã‚’å„ノードã§ãƒžãƒ«ãƒãƒ—ãƒã‚»ã‚¹ã§å®Ÿè¡Œã—ã¦ã€ãƒ›ã‚¹ãƒˆPCã«å¤‰æ•°ã‚’è¿”ã™ã€‚ 通信ã«ã¯ pickle 化ã•ã‚ŒãŸæƒ…å ±ã‚’é€ä¿¡ã—ã¦ã„る。 ssh ã§æŽ¥ç¶šå…ˆã®é€šä¿¡ç”¨ãƒãƒ¼ãƒˆã‚’é–‹ã‘ã¦ã€ãƒãƒ¼ãƒˆã®æƒ…å ±ã‚’ãƒ›ã‚¹ãƒˆã«é€ã£ã¦é€šä¿¡ã€‚--tunnelオプションを使ãˆã°
Pythonã§å°‘ãªãメモリを使用ã™ã‚‹æ–¹æ³• - ã®ã‚“ã³ã‚Šã—ã¦ã„るエンジニアã®æ—¥è¨˜
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Python pandas データé¸æŠžå‡¦ç†ã‚’ã¡ã‚‡ã£ã¨è©³ã—ã <å‰ç·¨> - StatsFragments
æ¦‚è¦ æ›¸ã„ã¦ã„ã¦é•·ããªã£ãŸãŸã‚ã€ã¾ãšå‰ç·¨ã¨ã—㦠pandas 㧠データを行 / 列ã‹ã‚‰é¸æŠžã™ã‚‹æ–¹æ³•ã‚’å°‘ã—詳ã—ã書ã。特ã«ã€å€‹äººçš„ã«ã¯ã‘ã£ã“ã†é‡è¦ã ã¨æ€ã£ã¦ã„ã‚‹ loc 㨠iloc ã«ã¤ã„㦠日本語ã§æ•´ç†ã—ãŸã‚‚ã®ãŒãªã•ãã†ãªã®ã§ã€‚ サンプルデータã®æº–å‚™ import pandas as pd s = pd.Series([1, 2, 3], index = ['I1', 'I2', 'I3']) df = pd.DataFrame({'C1': [11, 21, 31], 'C2': [12, 22, 32], 'C3': [13, 23, 33]}, index = ['I1', 'I2', 'I3']) s # I1 1 # I2 2 # I3 3 # dtype: int64 df # C1 C2 C3 # I1 11 12 13 # I2 21 22 23 # I3 31 32
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