GitHub - elkowar/pipr: A tool to interactively write shell pipelines.
Pipr is a commandline pipe-building tool, written in Rust! Pipr can automatically evaluate the pipeline you're editing in the background, showing you the results as you go. This makes writing complex sed and awk chains a lot easier, as you'll immediately see what they do. Because this could be dangerous, (imagine typing rm ./*.txt to delete all text files, but it already being executed at rm ./*,
第723回　複雑ãªã‚³ãƒžãƒ³ãƒ‰ãƒ‘イプラインを簡å˜ã«çµ„ã¿ç«‹ã¦ã‚‹æ–¹æ³• | gihyo.jp
パイプライン処ç†ã¨ã¯ GUIã¯éžå¸¸ã«ç›´æ„Ÿçš„ã§ã™ã€‚ã¯ã˜ã‚ã¦ä½¿ã†ã‚¢ãƒ—リã§ã‚ã£ã¦ã‚‚ã€ãªã‚“ã¨ãªããã‚Œãªã‚Šã«å‹•ã‹ã›ã¦ã—ã¾ã†ã¨ã„ã†ç‚¹ã§ã€å„ªã‚ŒãŸã‚¤ãƒ³ã‚¿ãƒ¼ãƒ•ã‚§ã‚¤ã‚¹ã¨è¨€ãˆã¾ã™ã€‚ã—ã‹ã—効率をçªãè©°ã‚ã‚‹ã¨ã€è»é…ãŒä¸ŠãŒã‚‹ã®ã¯GUIよりもCLIã§ã—ょã†ã€‚本連載ã®èªè€…ã§ã‚ã‚Œã°ã€UnixライクãªOSã®CLIãŒæŒã¤ãƒ‘ワーã«ã¤ã„ã¦ã¯å½“然ã”å˜çŸ¥ã‹ã¨æ€ã„ã¾ã™ã€‚ ã¨ã¯ã„ãˆã€å¤å…¸çš„ãªUnixコマンドã®å¤šãã¯ã€å˜ä½“ã§ã¯ãã‚Œã»ã©å¼·åŠ›ãªã‚‚ã®ã§ã¯ã‚ã‚Šã¾ã›ã‚“。ã¨ã„ã†ã®ã‚‚ã€ã²ã¨ã¤ã²ã¨ã¤ã®ã‚³ãƒžãƒ³ãƒ‰ã¯ã‚·ãƒ³ãƒ—ルã«ã€ç‰¹å®šã®ç”¨é€”ã«ãŠã„ã¦ã®ã¿ä¸Šæ‰‹ã動作ã™ã‚‹ã‚ˆã†è¨è¨ˆã•ã‚Œã¦ã„ã‚‹ã“ã¨ãŒã»ã¨ã‚“ã©ã ã‹ã‚‰ã§ã™ã€‚ã“ã†ã—ãŸã‚³ãƒžãƒ³ãƒ‰ç¾¤ã«ç„¡é™ã®ã‚·ãƒŠã‚¸ãƒ¼ã‚’与ãˆã‚‹ã®ãŒã€Œãƒ‘イプライン処ç†ã€ã§ã™ã€‚標準入出力を通ã˜ã¦è¤‡æ•°ã®ã‚³ãƒžãƒ³ãƒ‰ã‚’直列ã«ç¹‹ã’ã‚‹ã“ã¨ã§ã€è¤‡é›‘ãªå‡¦ç†ã‚’インスタントã«çµ„ã¿ç«‹ã¦ã‚‹ã“ã¨ãŒã§ãるパイプラインã¯ã€ã¾ã•ã«Unix哲å¦ã®ä½“ç¾ã§ã‚ã‚Šã€CLIã®çœŸé«„ã¯ã“ã“ã«ã‚ã‚‹ã¨è¨€ã£ã¦ã‚‚よã„
Pythonã§Apache beam 入門
2020-12-26 TensorFlowã®å‹‰å¼·ã‚’ã—ã¦ã„ãŸã‚‰ã€Apache beam ã‚’å‰å‡¦ç†ã«æŽ¡ç”¨ã—ã¦ã„ãŸã‚±ãƒ¼ã‚¹ãŒã‚ã‚Šã€èˆˆå‘³ã‚’æŒã£ãŸã®ã§æ·±å €ã‚Šã—ã¦ã¿ã¾ã™ã€‚ 興味ãŒæ¹§ã„ãŸãƒ¢ãƒãƒ™ãƒ¼ã‚·ãƒ§ãƒ³ã¨ã—ã¦ã¯ã€ データé‡ãŒå¢—åŠ ã—ã¦ã‚‚å‰å‡¦ç†éƒ¨åˆ†ã‚’難ãªãスケールã§ããã†(å‰å‡¦ç†éƒ¨åˆ†ã‚’スケールã•ã›ã¦é«˜é€Ÿã«å®Ÿé¨“を回ã—ãŸã„ã€ä¸¦åˆ—化ãªã©ã¯ã™ã¹ã¦è‰¯ã„æ„Ÿã˜ã«beamã«ä»»ã›ã‚Œãã†ãƒãƒƒãƒã¨ã‚¹ãƒˆãƒªãƒ¼ãƒŸãƒ³ã‚°ã®ä¸¡è€…ã«å¯¾å¿œå¯èƒ½ãªã®ã§ã€æŸ”軟ãªæ©Ÿæ¢°å¦ç¿’ã®æŽ¨è«–サービスãŒæä¾›ã§ãã‚‹ã®ã§ã¯? (GCPã®å‚考資料 Data preprocessing for machine learning: options and recommendations)Apache beam を触りã¤ã¤åˆ†æ•£ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚’å¦ã³ãŸã„https://github.com/jhuangtw/xg2xg#services を見ã¦ã¿ã‚‹ã¨Google 内部ã®Flume ã¨ã„ã†ä¸¦åˆ—
AWS Fargateã¨Lambdaã§ã‚µãƒ¼ãƒãƒ¼ãƒ¬ã‚¹ãªã‚¯ãƒãƒ¼ãƒ©ãƒ¼é‹ç”¨ - orangain flavor
ã“ã‚Œã¯Webスクレイピング Advent Calendar 2017ã®7日目ã®è¨˜äº‹ã§ã™ã€‚ã“ã‚“ãªæ„Ÿã˜ã§AWS Fargateã¨AWS Lambdaを使ã£ã¦ã‚µãƒ¼ãƒãƒ¼ãƒ¬ã‚¹ï¼ˆEC2レス）ãªã‚¯ãƒãƒ¼ãƒ©ãƒ¼ã‚’作りã¾ã™ã€‚ ã“ã®è¨˜äº‹ã¯Fargateã§ã®ã‚¯ãƒãƒ¼ãƒªãƒ³ã‚°å‡¦ç†ã«ãƒ•ã‚©ãƒ¼ã‚«ã‚¹ã—ã¦ãŠã‚Šã€ã‚¯ãƒãƒ¼ãƒ«ã—ãŸHTMLã‚’S3ã«ä¿å˜ã™ã‚‹ã¨ã“ã‚ã¾ã§ã‚’主ã«è§£èª¬ã—ã¾ã™ã€‚Lambdaã®æ–¹ã¯ãŠã¾ã‘程度ã®æ‰±ã„ã§ã€ã‚¹ã‚¯ãƒ¬ã‚¤ãƒ”ングã—ãŸãƒ‡ãƒ¼ã‚¿ã®æ‰±ã„（データベースã¸ã®æ ¼ç´ãªã©ï¼‰ã¯ã‚¹ã‚³ãƒ¼ãƒ—外ã§ã™ã€‚ é•·ããªã£ãŸã®ã§ç›®æ¬¡ã§ã™ã€‚ 背景 AWS Fargateã®ç™»å ´ クãƒãƒ¼ãƒ©ãƒ¼ã®æ§‹æˆ ã‚„ã£ã¦ã¿ã‚‹ 1. Scrapyã®ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã§Spiderを作る 2. Scrapy S3 Pipelineをインストールã™ã‚‹ 3. Scrapy S3 Pipelineをプãƒã‚¸ã‚§ã‚¯ãƒˆã«è¿½åŠ ã™ã‚‹ 4. Scrapyã®ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã‚’Dockerizeã™ã‚‹ 5. Amazo
Build pipelines with Pandas using “pdpipeâ€
IntroductionPandas is an amazing library in the Python ecosystem for data analytics and machine learning. They form the perfect bridge between the data world, where Excel/CSV files and SQL tables live, and the modeling world where Scikit-learn or TensorFlow perform their magic. A data science flow is most often a sequence of steps — datasets must be cleaned, scaled, and validated before they can b
Pandasã®ãƒ‘イプラインを作る「pdpipeã€ã‚’使ã£ã¦ã¿ãŸ - u++ã®å‚™å¿˜éŒ²
Pandasã®ãƒ‘イプラインを作る「pdpipeã€ã¨ã„ã†ãƒ©ã‚¤ãƒ–ラリを知ã£ãŸã®ã§ã€å°‘ã—触ã£ã¦ã¿ã¾ã—ãŸã€‚本記事ã§ã¯ã€ç°¡å˜ãªä½¿ã„æ–¹ãŠã‚ˆã³è‰¯ã‹ã£ãŸç‚¹ãƒ»æ‚ªã‹ã£ãŸç‚¹ã‚’ã¾ã¨ã‚ã¾ã™ã€‚ Pandas処ç†ã®ã€Œãƒ‘イプラインã€ã‚’作るライブラリãŒã‚るらã—ã„ Build pipelines with Pandas using ‘pdpipe’ by Tirthajyoti Sarkar in @TDataScience https://t.co/LqbcYByuZb— u++ (@upura0) July 27, 2020 使ã„æ–¹ インストール パイプラインã®æ§‹ç¯‰ å‰å‡¦ç† パイプラインã®å®Ÿè¡Œ before after 良ã‹ã£ãŸç‚¹ 悪ã‹ã£ãŸç‚¹ ãŠã‚り㫠使ã„æ–¹ Kaggleã®Titanicデータセットã§æ¤œè¨¼ã—ã¾ã—ãŸã€‚一連ã®å‡¦ç†ã¯Notebookを公開ã—ã¦ã„ã¾ã™ã€‚ import pandas as pd train = p
A practical introduction to functional programming
Many functional programming articles teach abstract functional techniques. That is, composition, pipelining, higher order functions. This one is different. It shows examples of imperative, unfunctional code that people write every day and translates these examples to a functional style. The first section of the article takes short, data transforming loops and translates them into functional maps a
Python exceptions considered an anti-pattern
What are exceptions? Judging by their name it is an entity representing some exceptional situation that happens inside your program. You might be wondering how do exceptions are an anti-pattern and how does this relate to typing at all? Well, let’s find out! Problems with exceptions First, we have to prove that exceptions have drawbacks. Well, it is usually hard to find “issues†in things you use
Pythonã®Pipelineパッケージ比較：Airflow, Luigi, Gokart, Metaflow, Kedro, PipelineX - Qiita
Pythonã®Pipelineパッケージ比較：Airflow, Luigi, Gokart, Metaflow, Kedro, PipelineXPythonワークフãƒãƒ¼ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ã‚¹PipelineETL ã“ã®è¨˜äº‹ã§ã¯ã€Open-sourceã®Pipeline/Workflow開発用Pythonパッケージã®Airflow, Luigi, Gokart, Metaflow, Kedro, PipelineXを比較ã—ã¾ã™ã€‚ ã“ã®è¨˜äº‹ã§ã¯ã€"Pipeline"ã€"Workflow"ã€"DAG"ã®å˜èªžã¯ã»ã¼åŒã˜æ„味ã§ä½¿ç”¨ã—ã¦ã„ã¾ã™ã€‚ è¦ç´„ ðŸ‘: 良ㄠðŸ‘ðŸ‘: より良ㄠ2015å¹´ã«Airbnb社ã‹ã‚‰ãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¾ã—ãŸã€‚ Airflowã¯ã€Pythonコード（独立ã—ãŸPythonモジュール）ã§DAGを定義ã—ã¾ã™ã€‚ （オプションã¨ã—ã¦ã€éžå…¬å¼ã® dag-factory ç‰ã‚’使用ã—ã¦ã€YAML
gokartを使ã£ã¦ãƒ‡ãƒ¼ã‚¿ãƒ‘イプライン上ã®pandas関連をテストã™ã‚‹ã€‚ - Qiita
書ãã“㨠gokartを使ã£ã¦pandas関連ã®ç¢ºèªã‚’ã™ã‚‹æ–¹æ³• 1ã¤ç›®ã¯inputã®pd.DataframeãŒemptyã ã£ãŸã¨ãã«æ£å¸¸çµ‚了をã™ã‚‹ã‹ã®ç¢ºèª 2ã¤ç›®ã¯dumpã™ã‚‹ã¨ãã«å„columnãŒæƒ³å®šé€šã‚Šã®åž‹ã«ãªã£ã¦ã„ã‚‹ã‹ã®ç¢ºèª gokartã¨ã¯? エムスリーやfringe81ãªã©ãŒé–‹ç™ºã—ã¦ã„ã‚‹OSS SpotifyãŒé–‹ç™ºã—ã¦ã„ã‚‹luigiをラップã—ã¦ä½¿ã„ã‚„ã™ãã—ã¦ã„る。特ã«ã‚³ãƒ¼ãƒ‰ã‚’書ãé‡ãŒæ¸›ã‚‹ã€‚ 対象ãƒãƒ¼ã‚¸ãƒ§ãƒ³ 0.3.11 inputã®pd.DataframeãŒemptyã ã£ãŸã¨ãã«æ£å¸¸çµ‚了をã™ã‚‹ã‹ã®ç¢ºèª 下記ã®ã‚³ãƒ¼ãƒ‰ã¯pd.DataframeãŒemptyã®ã¨ãã«ã‚¨ãƒ©ãƒ¼ãŒç™ºç”Ÿã™ã‚‹ã€‚ å˜ä½“テストを書ãã“ã¨ã¯å‰æã ãŒã€æ‹¾ã„ãã‚Œãªã„ã“ã¨ãŒå¤šã€…ã‚ã£ãŸã€‚ class DataTask(gokart.TaskOnKart): task_namespace = 'sample' def run(s
TypeScriptã§é–¢æ•°åž‹ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ã—ãŸã„å‹¢ã«æŽ¨ã—ãŸã„Pipeline operatorã®è‰¯ã• - タオルケット体æ“
å‰ç½®ã Rubyã§ä¸€çž¬ã ã‘湧ã„ã¦å‡ºã¦ãã¦æ¶ˆãˆãŸPipeline風演算åã«ã¤ã„ã¦ã¯å¿˜ã‚Œã¦ãã ã•ã„。ã‚ã‚Œã¯ãƒ¡ã‚½ãƒƒãƒ‰å‘¼ã³å‡ºã—ã®æ¼”ç®—åã§ã‚ã£ã¦ä»Šå›žå–り上ã’ã‚‹Pipeline operatorã¨ã¯ä¼¼ã¦éžãªã‚‹ã‚‚ã®ã§ã™ã€‚ JavaScripterã§ã‚‚ã€Ramda.jsã‚„RxJSを普段ã‹ã‚‰ä½¿ã£ã¦ã„る人ã«ã¯æ—¢çŸ¥ã®å†…容ã ã¨æ€ã†ã®ã§ã“ã®è¨˜äº‹ã¯èªã¾ãªãã¦å¤§ä¸ˆå¤«ã§ã™ã€‚ ã¡ãªã¿ã«åƒ•ã®ã‚¹ãƒ†ãƒ¼ã‚¿ã‚¹ã¯é–¢æ•°åž‹ã«ã‚ã‹å‹¢ãªã®ã§èªè˜ã«é–“é•ã„ãŒã‚ã£ãŸã‚‰ãªã‚“らã‹ã®è¨‚æ£ã‚‚らãˆã‚‹ã¨ã†ã‚Œã—ã„ã§ã™ã€‚ Pipeline operatorã¨ã¯ å¤ãã¯MLç³»ã®è¨€èªžã§å®šç¾©ã•ã‚Œã¦F#ã‚„Elixirã®æµè¡Œã§ä¸€èˆ¬ã«æœ‰åã«ãªã£ãŸæ¼”ç®—åã“㨠|> ã§ã™ã€‚ f a ã‚’ a |> f ã¨æ›¸ã‘るよã†ã«ãªã‚Šã¾ã™ã€‚ 詳ã—ãã¯ã“ã¡ã‚‰ã§: https://mametter.hatenablog.com/entry/2019/06/15/192311 覚ãˆãŸæ–¹ãŒã„ã„ç†ç”± Ty
Netflix開発ã®MLワークフãƒãƒ¼ãƒ„ール，Metaflowを試ã—ã¦ã¿ãŸã€æ¦‚è¦ç·¨ã€‘ - Qiita
Netflixã‹ã‚‰æ©Ÿæ¢°å¦ç¿’ワークフãƒãƒ¼ç®¡ç†ç”¨ã®Pythonライブラリ，MetaflowãŒãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¾ã—ãŸã€‚ ã“れを使ã†ã¨ï¼Œ データ処ç†ãƒ»ãƒ¢ãƒ‡ãƒ«æ§‹ç¯‰ãƒ—ãƒã‚»ã‚¹ã‚’統一フォーマットã§è¨˜è¿°ã§ã，全体ã®ãƒ•ãƒãƒ¼ã‚’追ã„ã‚„ã™ã„ モデル・å‰å‡¦ç†å·¥ç¨‹ã®ãƒãƒ¼ã‚¸ãƒ§ãƒ³ç®¡ç†ãŒã§ãã‚‹ AWS環境上ã§ã®åˆ†æ•£å‡¦ç†ãŒå¯èƒ½ ã¨ã„ã£ãŸãƒ¡ãƒªãƒƒãƒˆãŒã‚ã‚Šã¾ã™ã€‚ æ°—ã«ãªã‚‹äººã¯ï¼Œtutorialã‚’å‹•ã‹ã—ã¤ã¤å…¬å¼ãƒ‰ã‚ュメントã«ç›®ã‚’通ã—ã¦ã¿ã¾ã—ょã†ã€‚ Tutorialã«ã¤ã„ã¦ã¯ï¼Œpip install metaflowã§ãƒ©ã‚¤ãƒ–ラリを入れãŸå¾Œï¼Œ ã¨ã™ã‚‹ã ã‘ã§ä¸€å¼æƒã„ã¾ã™ã®ã§ï¼Œæ°—軽ã«è©¦ã™ã“ã¨ãŒã§ãã¾ã™ã€‚ 本記事ã§ã¯ï¼Œã–ã£ãã‚Šã¨ã—ãŸæ©Ÿèƒ½æ¦‚è¦ã¨ä½¿ã„方をã¾ã¨ã‚ã¦ã„ããŸã„ã¨æ€ã„ã¾ã™ã€‚ ãƒ©ã‚¤ãƒ–ãƒ©ãƒªæ¦‚è¦ Metaflowã§ã¯ï¼Œãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚„機械å¦ç¿’モデル構築・予測ã®ãƒ¯ãƒ¼ã‚¯ãƒ•ãƒãƒ¼ã‚’Pythonã®ã‚¯ãƒ©ã‚¹ã¨ã—ã¦å®šç¾©ã—，コマンドラインã‹ã‚‰å®Ÿè¡Œã—ã¾ã™ã€‚ ãã®éš›å®Ÿè¡Œã®éƒ½åº¦
How can I use the fields_to_export attribute in BaseItemExporter to order my Scrapy CSV data?
I have made a simple Scrapy spider that I use from the command line to export my data into the CSV format, but the order of the data seem random. How can I order the CSV fields in my output? I use the following command line to get CSV data: scrapy crawl somwehere -o items.csv -t csv According to this Scrapy documentation, I should be able to use the fields_to_export attribute of the BaseItemExport
リリースã€éšœå®³æƒ…å ±ãªã©ã®ã‚µãƒ¼ãƒ“スã®ãŠçŸ¥ã‚‰ã›
最新ã®äººæ°—エントリーã®é…ä¿¡
j次ã®ãƒ–ックマーク
kå‰ã®ãƒ–ックマーク
lã‚ã¨ã§èªã‚€
eコメント一覧を開ã
oページを開ã
GitHub - sdcampbell/nmapurls: Nmapurls parses Nmap xml reports from either piped input or command line arg and outputs a list of http(s) URL's to be used in an automation pipeline.
GitHub - rung/threat-matrix-cicd: Threat matrix for CI/CD Pipeline
Attacking and Securing CI/CD Pipeline
GitHub - apptension/saas-boilerplate: SaaS Boilerplate - Open Source and free SaaS stack that lets you build SaaS products faster in React, Django and AWS. Focus on essential business logic instead of coding repeatable features!
GitHub - orangain/serverless-crawler: Sample of server-less crawler using AWS Fargate and Lambda
GitHub - orangain/scrapy-s3pipeline: Scrapy pipeline to store chunked items into Amazon S3 or Google Cloud Storage bucket.
GitHub - tonyfischetti/assertr: Assertive programming for R analysis pipelines
{{#tags}}- {{label}}
{{/tags}}