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[#]: subject: (3 Python 3.2 features that are still relevant today)
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[#]: via: (https://opensource.com/article/21/5/python-32)
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[#]: author: (Moshe Zadka https://opensource.com/users/moshez)
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[#]: collector: (lujun9972)
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[#]: translator: (geekpi)
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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3 Python 3.2 features that are still relevant today
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======
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Explore some of the underutilized but still useful Python features.
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![Business woman on laptop sitting in front of window][1]
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This the third article in a series about features that first appeared in a version of Python 3.x. Some of those Python versions have been out for a while. For example, Python 3.2 was first released in 2011, yet some of the cool and useful features introduced in it are still underused. Here are three of them.
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### argparse subcommands
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The `argparse` module first appeared in Python 3.2. There are many third-party modules for command-line parsing. But the built-in `argparse` module is more powerful than many give it credit for.
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Documenting all the ins and outs of `argparse` would take its own article series. For a small taste, here is an example of how you can do subcommands with `argparse`.
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Imagine a command with two subcommands: `negate`, which takes one argument, and `multiply` which takes two:
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```
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$ computebot negate 5
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-5
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$ computebot multiply 2 3
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6
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[/code] [code]
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import argparse
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parser = argparse.ArgumentParser()
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subparsers = parser.add_subparsers()
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```
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The `add_subparsers()` methods creates an object that you can add subcommands to. The only trick to remember is that you need to add what subcommand was called through a `set_defaults()`:
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```
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negate = subparsers.add_parser("negate")
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negate.set_defaults(subcommand="negate")
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negate.add_argument("number", type=float)
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[/code] [code]
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multiply = subparsers.add_parser("multiply")
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multiply.set_defaults(subcommand="multiply")
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multiply.add_argument("number1", type=float)
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multiply.add_argument("number2", type=float)
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```
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One of my favorite `argparse` features is that, because it separates parsing from running, testing the parsing logic is particularly pleasant.
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```
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`parser.parse_args(["negate", "5"])`[/code] [code]` Namespace(number=5.0, subcommand='negate')`[/code] [code]`parser.parse_args(["multiply", "2", "3"])`[/code] [code]` Namespace(number1=2.0, number2=3.0, subcommand='multiply')`
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```
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### contextlib.contextmanager
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Contexts are a powerful tool in Python. While many _use_ them, writing a new context often seems like a dark art. With the `contextmanager` decorator, all you need is a one-shot generator.
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Writing a context that prints out the time it took to do something is as simple as:
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```
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import contextlib, timeit
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@contextlib.contextmanager
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def timer():
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before = timeit.default_timer()
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try:
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yield
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finally:
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after = timeit.default_timer()
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print("took", after - before)
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```
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And you can use it with just:
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```
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import time
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with timer():
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time.sleep(10.5)
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[/code] [code]` took 10.511025413870811`
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```
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### functools.lru_cache
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Sometimes the caching results from a function in memory make sense. For example, imagine the classical problem: "How many ways can you make change for a dollar with quarters, dimes, nickels, and cents?"
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The code for this can be deceptively simple:
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```
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def change_for_a_dollar():
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def change_for(amount, coins):
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if amount == 0:
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return 1
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if amount < 0 or len(coins) == 0:
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return 0
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some_coin = next(iter(coins))
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return (
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change_for(amount, coins - set([some_coin]))
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+
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change_for(amount - some_coin, coins)
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)
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return change_for(100, frozenset([25, 10, 5, 1]))
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```
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On my computer, this takes around 13ms:
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```
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with timer():
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change_for_a_dollar()
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[/code] [code]` took 0.013737603090703487`
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```
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It turns out that when you calculate how many ways you can do something like making change from 50 cents, you use the same coins repeatedly. You can use `lru_cache` to avoid recalculating this over and over.
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```
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import functools
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def change_for_a_dollar():
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@functools.lru_cache
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def change_for(amount, coins):
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if amount == 0:
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return 1
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if amount < 0 or len(coins) == 0:
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return 0
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some_coin = next(iter(coins))
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return (
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change_for(amount, coins - set([some_coin]))
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+
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change_for(amount - some_coin, coins)
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)
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return change_for(100, frozenset([25, 10, 5, 1]))
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[/code] [code]
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with timer():
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change_for_a_dollar()
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[/code] [code]` took 0.004180959425866604`
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```
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A three-fold improvement for the cost of one line. Not bad.
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### Welcome to 2011
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Although Python 3.2 was released 10 years ago, many of its features are still cool—and underused. Add them to your toolkit if you haven't already.
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/21/5/python-32
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作者：[Moshe Zadka][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/moshez
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[b]: https://github.com/lujun9972
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[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/lenovo-thinkpad-laptop-concentration-focus-windows-office.png?itok=-8E2ihcF (Woman using laptop concentrating)
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@ -0,0 +1,176 @@
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[#]: subject: (3 Python 3.2 features that are still relevant today)
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[#]: via: (https://opensource.com/article/21/5/python-32)
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[#]: author: (Moshe Zadka https://opensource.com/users/moshez)
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[#]: collector: (lujun9972)
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[#]: translator: (geekpi)
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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3 个 Python 3.2 到今天仍然有用的功能
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======
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探索一些未被充分利用但仍然有用的 Python 特性。
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![Business woman on laptop sitting in front of window][1]
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这是关于首次出现在 Python 3.x 版本中的特性的系列文章中的第三篇。其中一些 Python 版本已经推出了一段时间。例如，Python 3.2 是在 2011 年首次发布的，但其中引入的一些很酷、很有用的特性仍然没有被使用。下面是其中的三个。
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### argparse 子命令
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`argparse` 模块首次出现在 Python 3.2 中。有许多用于命令行解析的第三方模块。但是内置的 `argparse` 模块比许多人认为的要强大。
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记录所有的 `argparse` 的特性将需要自己的系列文章。下面是一个例子，说明如何用 `argparse` 做子命令。
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想象一下，一个命令有两个子命令：`negate`，需要一个参数，`multiply`，需要两个参数：
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```
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$ computebot negate 5
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-5
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$ computebot multiply 2 3
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6
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[/code] [code]
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import argparse
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parser = argparse.ArgumentParser()
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subparsers = parser.add_subparsers()
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```
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`add_subparsers()` 方法创建一个对象，你可以向其添加子命令。唯一需要记住的技巧是，你需要添加通过 `set_defaults()` 调用的子命令：
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```
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negate = subparsers.add_parser("negate")
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negate.set_defaults(subcommand="negate")
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negate.add_argument("number", type=float)
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[/code] [code]
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multiply = subparsers.add_parser("multiply")
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multiply.set_defaults(subcommand="multiply")
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multiply.add_argument("number1", type=float)
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multiply.add_argument("number2", type=float)
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```
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我最喜欢的一个 `argparse` 功能是，因为它把解析和运行分开，测试解析逻辑特别令人愉快。
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```
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`parser.parse_args(["negate", "5"])`[/code] [code]` Namespace(number=5.0, subcommand='negate')`[/code] [code]`parser.parse_args(["multiply", "2", "3"])`[/code] [code]` Namespace(number1=2.0, number2=3.0, subcommand='multiply')`
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```
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### contextlib.contextmanager
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上下文是 Python 中一个强大的工具。虽然很多人_使用_它们，但编写一个新的上下文常常看起来像一门黑暗的艺术。有了 `contextmanager` 装饰器，你所需要的只是一个一次性的生成器。
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编写一个打印出做某事所需时间的上下文，就像这样简单：
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```
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import contextlib, timeit
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@contextlib.contextmanager
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def timer():
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before = timeit.default_timer()
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try:
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yield
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finally:
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after = timeit.default_timer()
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print("took", after - before)
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```
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你可以这样使用：
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```
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import time
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with timer():
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time.sleep(10.5)
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[/code] [code]` took 10.511025413870811`
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```
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### functools.lru_cache
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有时，在内存中缓存一个函数的结果是有意义的。例如，想象一下经典的问题：”有多少种方法可以用 25 美分、1 美分、2 美分和 3 美分可以来换取 1 美元？“
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这个问题的代码可以说是非常简单：
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```
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def change_for_a_dollar():
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def change_for(amount, coins):
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if amount == 0:
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return 1
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if amount < 0 or len(coins) == 0:
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return 0
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some_coin = next(iter(coins))
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return (
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change_for(amount, coins - set([some_coin]))
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+
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change_for(amount - some_coin, coins)
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)
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return change_for(100, frozenset([25, 10, 5, 1]))
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```
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在我的电脑上，这需要 13ms 左右：
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```
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with timer():
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change_for_a_dollar()
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[/code] [code]` took 0.013737603090703487`
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```
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事实证明，当你计算有多少种方法可以做一些事情，比如用 50 美分找钱，你会重复使用相同的硬币。你可以使用 `lru_cache` 来避免重复计算。
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```
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import functools
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def change_for_a_dollar():
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@functools.lru_cache
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def change_for(amount, coins):
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if amount == 0:
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return 1
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if amount < 0 or len(coins) == 0:
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return 0
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some_coin = next(iter(coins))
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return (
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change_for(amount, coins - set([some_coin]))
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+
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change_for(amount - some_coin, coins)
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)
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return change_for(100, frozenset([25, 10, 5, 1]))
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[/code] [code]
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with timer():
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change_for_a_dollar()
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[/code] [code]` took 0.004180959425866604`
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```
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一行的代价是三倍的改进。不错。
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### 欢迎来到 2011 年
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尽管 Python 3.2 是在 10 年前发布的，但它的许多特性仍然很酷，而且没有得到充分利用。如果你还没使用，那么将他们添加到你的工具箱中。
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--------------------------------------------------------------------------------
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||||
via: https://opensource.com/article/21/5/python-32
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作者：[Moshe Zadka][a]
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||||
选题：[lujun9972][b]
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译者：[geekpi](https://github.com/geekpi)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
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||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
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[a]: https://opensource.com/users/moshez
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[b]: https://github.com/lujun9972
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[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/lenovo-thinkpad-laptop-concentration-focus-windows-office.png?itok=-8E2ihcF (Woman using laptop concentrating)
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