FastBuilt from the ground up to support gradual typing and deliver responsive incremental checks. Performant on large codebases with millions of lines of Python. IntegratedDesigned to help improve code quality and development speed by flagging type errors interactively in your terminal or live in your favorite editor. Fully FeaturedFollows the typing standards introduced in PEPs 484, 526, 612, and
Python Developers Survey 2017 ResultsAt the very end of 2017, the Python Software Foundation together with JetBrains conducted an official Python Developers Survey. We set out to identify the latest trends and gather insight into how the Python development world looks today. Over 9,500 developers from almost 150 different countries participated to help us map out an accurate landscape of the Pytho
We are pleased to announce that the April 2018 release of the Python Extension for Visual Studio Code is now available from the marketplace and the gallery. You can download the Python extension from the marketplace, or install it directly from the extension gallery in Visual Studio Code. You can learn more about Python support in Visual Studio Code in the VS Code documentation. In this release we
4/30 公開 5/1 増補改訂: 大幅加筆しました。 この記事では、2018年以降に実現可能になったモダンなPythonプロジェクトのはじめかたを整理して紹介します。 PythonにもPipenvという公式推奨の高機能なパッケージマネージャーが登場し、さらに2018年に入ってからの機能向上で、npmやyarnのような開発体験が得られるようになってきました。 私はここしばらくはフロントエンドやNode.jsに携わっていて、npmやyarnに慣れきっていたせいか、pipenv導入以前はvirtualenvやpipを組み合わせた開発が面倒で仕方なかったですが、Pipenv導入によって一変しました。 これからはPythonのプロジェクトがよりクリーンかつ簡単にはじめられるようになり、開発体験も向上するでしょう。 それでは、まずはPythonのインストールからです。 Pythonのインストール P
>> import time >> import pychromecast >> import zeroconf >> # Create a browser which prints the friendly name of found chromecast devices >> zconf = zeroconf.Zeroconf() >> browser = pychromecast.CastBrowser(pychromecast.SimpleCastListener(lambda uuid, service: print(browser.devices[uuid].friendly_name)), zconf) >> browser.start_discovery() >> # Shut down discovery >> pychromecast.discovery.stop_di
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Additive models for time series modeling Time series are one of the most common data types encountered in daily life. Financial prices, weather, home energy usage, and even weight are all examples of data that can be collected at regular intervals. Almost every data scientist will encounter time series in their daily work and learning how to model them is an important skill in the data science too
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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: Speech Recognition With Python Have you ever wondered how to add speech recognition to your Python project? If so, then keep reading! It’s easier than you might think. Far from a being a fad, the overwhelming success of speech-enabled product
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