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[#]: collector: (lujun9972)
[#]: translator: (wxy)
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (4 essential tools to set up your Python environment for success)
[#]: via: (https://opensource.com/article/20/6/python-tools)
[#]: author: (Moshe Zadka https://opensource.com/users/moshez)
4 essential tools to set up your Python environment for success
======
This selection of tools will streamline your Python environment for
smooth and consistent development practices.
![Python programming language logo with question marks][1]
Python is a wonderful general-purpose programming language, often taught as a first programming language. Twenty years in, multiple books written, and it remains [my language of choice][2]. While the language is often said to be straight-forward, configuring Python for development has not been described as such (as documented by [xkcd][3]).
![xkcd python illustration][4]
A complex Python environment: [xkcd][3]
There are many ways to use Python in your day-to-day life. I will explain how I use the Python ecosystem tools, and I will be honest where I am still looking for alternatives.
### Use pyenv to manage Python versions
The best way I have found to get a Python version working on your machine is `pyenv`. This software will work on Linux, Mac OS X, and WSL2: the three "UNIX-like" environments that I usually care about.
Installing `pyenv` itself can be a little tricky at times. One way is to use the dedicated [pyenv installer][5], which uses a `curl | bash` method to bootstrap (see the instructions for more details).
If you're on a Mac (or another system where you run Homebrew), you can follow instructions on how to install and use pyenv [here][6].
Once you install and set up `pyenv` per the directions, you can use `pyenv global` to set a "default Python" version. In general, you will want to select your "favorite" version. This will usually be the latest stable, but other considerations can change that.
### Make virtual environments simpler with virtualenvwrapper
One advantage of using `pyenv` to install Python is that all subsequent Python interpreter installations you care about are owned by you instead of the operating system you use.
Though installing things inside Python itself is usually not the best option, there is one exception: in your "favorite" Python chosen above, install and configure `virtualenvwrapper`. This gives you the ability to create and switch to virtual environments at a moment's notice.
I walk through exactly how to install and use `virtualenvwrapper` [in this article][7].
Here is where I recommend a unique workflow. There is one virtual environment that you will want to make so that you can reuse it a lot—`runner`. In this environment, install your favorite `runner`; that is, software that you will regularly use to run other software. As of today, my preference is `tox`.
### Use tox as a Python runner
[tox][8] is a great tool to automate your test runs of Python. In each Python environment, I create a `tox.ini` file. Whatever system I use for continuous integration will run it, and I can run the same locally with `virtualenvwrapper`'s workon syntax described in the article above:
```
$ workon runner
$ tox
```
The reason this workflow is important is that I test my code against multiple versions of Python and multiple versions of the library dependencies. That means there are going to be multiple environments in the tox runner. Some will try running against the latest dependencies. Some will try running against frozen dependencies (more on that next), and I might also generate those locally with `pip-compile`.
Side note: I am currently [looking at `nox`][9] as a replacement for `tox`. The reasons are beyond the scope of this article, but it's worth taking a look at.
### Use pip-compile for Python dependency management
Python is a dynamic programming language, which means it loads its dependencies on every execution of the code. Understanding exactly what version of each dependency is running could mean the difference between smoothly running code and an unexpected crash. That means we have to think about dependency management tooling.
For each new project, I include a `requirements.in` file that is (usually) only the following:
```
`.`
```
Yes, that's right. A single line with a single dot. I document "loose" dependencies, such as `Twisted>=17.5` in the `setup.py`file. That is in contrast to exact dependencies like `Twisted==18.1`, which make it harder to upgrade to new versions of the library when you need a feature or a bug fix.
The `.` means "current directory," which uses the current directory's `setup.py` as the source for dependencies.
This means that using `pip-compile requirements.in > requirements.txt` will create a frozen dependencies file. You can use this dependencies file either in a virtual environment created by `virtualenvwrapper` or in `tox.ini`.
Sometimes it is useful to have `requirements-dev.txt`, generated from `requirements-dev.in` (contents: `.[dev]`) or `requirements-test.txt`, generated from `requirements-test.in` (contents: `.[test]`).
I am looking to see if `pip-compile` should be replaced in this flow by [`dephell`][10]. The `dephell` tool has a bunch of interesting things about it, like the use of asynchronous HTTP requests to speak dependency downloads.
### Conclusion
Python is as powerful as it is pleasing on the eyes. In order to write that code, I lean on a particular toolchain that has worked well for me. The tools `pyenv`, `virtualenvwrapper`, `tox`, and `pip-compile` are all separate. However, they each have their own role, with no overlaps, and together, they deliver a powerful Python workflow.
--------------------------------------------------------------------------------
via: https://opensource.com/article/20/6/python-tools
作者：[Moshe Zadka][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://opensource.com/users/moshez
[b]: https://github.com/lujun9972
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/python_programming_question.png?itok=cOeJW-8r (Python programming language logo with question marks)
[2]: https://opensource.com/article/19/10/why-love-python
[3]: https://xkcd.com/1987/
[4]: https://opensource.com/sites/default/files/uploads/python_environment_xkcd_1.png (xkcd python illustration)
[5]: https://github.com/pyenv/pyenv-installer
[6]: https://opensource.com/article/20/4/pyenv
[7]: https://opensource.com/article/19/6/python-virtual-environments-mac
[8]: https://opensource.com/article/19/5/python-tox
[9]: https://nox.thea.codes/en/stable/
[10]: https://github.com/dephell/dephell

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[#]: collector: (lujun9972)
[#]: translator: (wxy)
[#]: reviewer: (wxy)
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (4 essential tools to set up your Python environment for success)
[#]: via: (https://opensource.com/article/20/6/python-tools)
[#]: author: (Moshe Zadka https://opensource.com/users/moshez)
建立成功的 Python 环境的 4 个基本工具
======
> 选择的这些工具将简化你的 Python 环境，以实现顺畅和一致的开发实践。
![Python programming language logo with question marks][1]
Python 是一门出色的通用编程语言，经常作为第一门编程语言来教授。二十年来，我为它撰写了很多本书，而它仍然是[我的首选语言][2]。虽然通常来说这门语言是简洁明了的，但是（正如 [xkcd][3] 所说的），从来没有人说过配置 Python 环境也是一样的简单。
![xkcd python illustration][4]
*一个复杂的Python环境。 [xkcd][3]*
在日常生活中有很多使用 Python 的方法。我将解释我是如何使用这些 Python 生态系统工具的，坦诚的说，我仍在寻找更多替代品。
### 使用 pyenv 来管理 Python 版本
我发现在你的机器上运行一个特定版本的 Python 的最好方法是使用 `pyenv`。这个软件可以在 Linux、Mac OS X 和 WSL2 上工作：这是我通常关心的三个 “类 UNIX” 环境。
安装 `pyenv` 本身有时会有点棘手。一种方法是使用专用的 [pyenv 安装程序][5]，它使用 `curl | bash` 的方法来进行（详见说明）。
如果你是在 Mac 上（或者你运行 Homebrew 的其他系统），你可以按照[这里][6]的说明来安装和使用 `pyenv`。
按照说明安装和设置了 `pyenv` 之后，你可以使用 `pyenv global` 来设置一个 “默认的” Python 版本。一般来说，你会选择你 “最喜欢的” 版本。这通常是最新的稳定版本，但如果有其他考虑因素也可能做不同的选择。
### 使用 virtualenvwrapper 让虚拟环境更简单
使用 `pyenv` 安装 Python 的一个好处是，你后继安装的所有后续 Python 解释器环境都是你自己的，而不属于你的操作系统。
虽然在 Python 本身内部安装东西通常不是最好的选择，但有一个例外：在上面选择的 “最喜欢的” Python 中，安装并配置 `virtualenvwrapper`。这样你就可以在瞬间创建和切换到虚拟环境。
我在[这篇文章中][7]具体介绍了如何安装和使用 `virtualenvwrapper`。
这里我推荐一个独特的工作流程。你可以制作一个虚拟环境，这样你就可以大量重复使用它来运行许多<ruby>运行器<rt>runner</rt></ruby>。在这个环境中，安装你最喜欢的运行器 —— 也就是你会经常用来运行其他软件的软件。就目前而言，我的首选是 `tox`。
### 使用 tox 作为 Python 运行器
[tox][8] 是一个很好的工具，可以让你的 Python 测试自动化。在每个 Python 环境中，我都会创建一个 `tox.ini` 文件。无论我使用什么系统做持续集成，都可以运行它，我可以用上面文章中描述的 `virtualenvwrapper` 的 `workon` 语法在本地运行同样的东西：
```
$ workon runner
$ tox
```
这个工作流程之所以重要，是因为我要在多个版本的 Python 和多个版本的库依赖中测试我的代码。这意味着在 `tox` 运行器中会有多个环境。有些人会尝试在最新的依赖关系中运行，有些人会尝试在冻结的依赖关系中运行（接下来会有更多的介绍），我也可能会用 `pip-compile` 在本地生成这些环境。
附注：我目前正在[研究使用 nox][9] 作为 `tox` 的替代品。原因超出了本文的范围，但值得一试。
### 使用 pip-compile 进行 Python 依赖性管理
Python 是一种动态编程语言，这意味着它在每次执行代码时都会加载其依赖关系。确切了解每个依赖项的具体运行版本可能意味着是平稳运行代码还是意外崩溃。这意味着我们必须考虑依赖管理工具。
对于每个新项目，我都会包含一个 `requirements.in` 文件，（通常）只有以下内容：
```
.
```
是的，没错。只有一个点的单行。我在 `setup.py` 文件中记录了 “松散” 的依赖关系，比如 `Twisted>=17.5`。这与 `Twisted==18.1` 这样的确切依赖关系形成了鲜明对比，后者在需要一个特性或错误修复时升级到新版本的库变得更加困难。
`.` 的意思是 “当前目录”，它使用当前目录下的 `setup.py` 作为依赖关系的来源。
这意味着使用 `pip-compile requirements.in > requirements.txt` 将创建一个冻结的依赖文件。你可以在 `virtualenvwrapper` 创建的虚拟环境中或者 `tox.ini` 中使用这个依赖文件。
有时，从 `requirements-dev.in`（内容：`.[dev]`）生成的 `requirements-dev.txt` 或从 `requirements-test.in`（内容：`.[test]`）生成的 `requirements-test.txt` 很有用。
我正在研究在这个流程中是否应该用 [dephell][10] 代替 `pip-compile`。`dephell` 工具具有许多有趣的功能，比如使用异步 HTTP 请求来下载依赖项。
### 结论
Python 的功能既强大又赏心悦目。为了编写这些代码，我依靠了一个对我来说很有效的特定工具链。工具 `pyenv`、`virtualenvwrapper`、`tox` 和 `pip-compile` 都是独立的。但是，它们各有各的作用，没有重叠，它们一起打造了一个强大的 Python 工作流。
--------------------------------------------------------------------------------
via: https://opensource.com/article/20/6/python-tools
作者：[Moshe Zadka][a]
选题：[lujun9972][b]
译者：[wxy](https://github.com/wxy)
校对：[wxy](https://github.com/wxy)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://opensource.com/users/moshez
[b]: https://github.com/lujun9972
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/python_programming_question.png?itok=cOeJW-8r (Python programming language logo with question marks)
[2]: https://opensource.com/article/19/10/why-love-python
[3]: https://xkcd.com/1987/
[4]: https://opensource.com/sites/default/files/uploads/python_environment_xkcd_1.png (xkcd python illustration)
[5]: https://github.com/pyenv/pyenv-installer
[6]: https://opensource.com/article/20/4/pyenv
[7]: https://opensource.com/article/19/6/python-virtual-environments-mac
[8]: https://opensource.com/article/19/5/python-tox
[9]: https://nox.thea.codes/en/stable/
[10]: https://github.com/dephell/dephell