A craft cannot be learned by reading alone; practice is also necessary. To learn best, you need a well-defined task at an appropriate level of difficulty. Python Practice Projects is such a collection of problems, each designed to straddle the line between toy example and production system. All too often the answer to, "What is a good project for learning programming?" is "Whatever interests you."
Python 3.4 has reached end-of-life. Python 3.4.10, the final release of the 3.4 series, is available here. Python 3.4.0 was released on March 16th, 2014. Major new features of the 3.4 series, compared to 3.3 Python 3.4 includes a range of improvements of the 3.x series, including hundreds of small improvements and bug fixes. Among the new major new features and changes in the 3.4 release series ar
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In the first post, I took a brief look at the programming languages ATS, C#, Go, Haskell, OCaml, Python and Rust to try to decide which would be the best language in which to write 0install (which is currently implemented in Python). Now it's time to eliminate a few candidates and look in more detail at the others. Last time, I converted 4 lines of Python code from 0install into each language. Thi
Introduction The structure of this tutorial assumes an intermediate level knowledge of Python but not much else. No knowledge of concurrency is expected. The goal is to give you the tools you need to get going with gevent, help you tame your existing concurrency problems and start writing asynchronous applications today. Contributors In chronological order of contribution: Stephen Diehl Jérémy Bet
Django Vanilla Views Beautifully simple class-based views. View --+------------------------- RedirectView | +-- GenericView -------+-- TemplateView | | | +-- FormView | +-- GenericModelView --+-- ListView | +-- DetailView | +-- CreateView | +-- UpdateView | +-- DeleteView Django's generic class-based view implementation is unnecessarily complicated. Django vanilla views gives you exactly the same
Unit testing guidelines The Pylons Project rather rigorously follows a unit testing dogma along the lines described by Tres Seaver in Avoiding Temptation: Notes on using unittest effectively. This document is based upon that article. This document deals almost exclusively with unit tests. We have no particular dogma for integration tests or functional tests, although many of the tips below can be
Playdoh¶ Mozilla’s Playdoh is a web application template based on Django. Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Playdoh is simply a pre-configured Django project that adds some essential modules and middleware to meet the following goals: Enhance the security of your application and its data Achieve optimal performance in the fac
Python is a popular dynamic language with a large part of its appeal coming from powerful libraries and extension modules. These augment the language and make it a productive environment for a wide variety of tasks, ranging from web development (Django) to numerical analysis (NumPy). Unfortunately, Python's performance is quite poor when compared to modern implementations of languages such as Lua
Bottle: Python Web Framework¶ Bottle is a fast, simple and lightweight WSGI micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the Python Standard Library. Routing: Requests to function-call mapping with support for clean and dynamic URLs. Templates: Fast and pythonic built-in template engine and support for mako, jinja2 and cheetah templat
Introduction I had interview for the Flask book recently and some questions were related to realtime functionality - how it works, how to integrate realtime portion with conventional WSGI applications, how to structure application code and so on. We used Google Hangouts and it was supposed to record interview, but it failed. So, I decided to write elaborate blog post instead, in which I will try t
本気でPythonをやりたいならあわせて読みたい「え?君せっかく Python のバージョン管理に pyenv 使ってるのに Vim の補完はシステムライブラリ参照してるの?」 2013-06-23 21:30 おしりに追記しました 2013-06-24 10:00 設定等微修正しました 2013-06-24 15:20 quickrunの設定を修正しました 2013-07-03 14:30 間違い等を修正しました 様々な開発環境を試してきましたが、結局Vimに落ち着いてしまっているAlisueです、どうも。 Vimを最強のPython IDEにするを書いてからかれこれ二年ほどが経ちます。 二年もあると新しいVimプラグインが増えるなどし、先の記事内容では最強ではなくなってしまいました。なのでこの辺でもう一度現在の最強をまとめてみたいと思います。 基本方針 プラグイン関係はすべてNeoBu
scikit-learn(sklearn)の日本語の入門記事があんまりないなーと思って書きました。 どちらかっていうとよく使う機能の紹介的な感じです。 英語が読める方は公式のチュートリアルがおすすめです。 scikit-learnとは? scikit-learnはオープンソースの機械学習ライブラリで、分類や回帰、クラスタリングなどの機能が実装されています。 また様々な評価尺度やクロスバリデーション、パラメータのグリッドサーチなどの痒いところに手が届く機能もあります。 インストール scikit-learnの他にもnumpyとかscipyとかが必要です。 Windows 64 bit版の人は以下のURLに色々なインストーラーがおいてあるのでおすすめ Python Extension Packages for Windows - Christoph Gohlke その他の人は以下のURLを見て
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