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[#]: subject: (3 Python 3.2 features that are still relevant today)
[#]: via: (https://opensource.com/article/21/5/python-32)
[#]: author: (Moshe Zadka https://opensource.com/users/moshez)
[#]: collector: (lujun9972)
[#]: translator: (geekpi)
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
3 Python 3.2 features that are still relevant today
======
Explore some of the underutilized but still useful Python features.
![Business woman on laptop sitting in front of window][1]
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.
### argparse subcommands
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.
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`.
Imagine a command with two subcommands: `negate`, which takes one argument, and `multiply` which takes two:
```
$ computebot negate 5
-5
$ computebot multiply 2 3
6
[/code] [code]
import argparse
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers()
```
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()`:
```
negate  = subparsers.add_parser("negate")
negate.set_defaults(subcommand="negate")
negate.add_argument("number", type=float)
[/code] [code]
multiply  = subparsers.add_parser("multiply")
multiply.set_defaults(subcommand="multiply")
multiply.add_argument("number1", type=float)
multiply.add_argument("number2", type=float)
```
One of my favorite `argparse` features is that, because it separates parsing from running, testing the parsing logic is particularly pleasant.
```
`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')`
```
### contextlib.contextmanager
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.
Writing a context that prints out the time it took to do something is as simple as:
```
import contextlib, timeit
@contextlib.contextmanager
def timer():
    before = timeit.default_timer()
    try:
        yield
    finally:
        after = timeit.default_timer()
        print("took", after - before)
```
And you can use it with just:
```
import time
with timer():
    time.sleep(10.5)
[/code] [code]`    took 10.511025413870811`
```
### functools.lru_cache
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?"
The code for this can be deceptively simple:
```
def change_for_a_dollar():
    def change_for(amount, coins):
        if amount == 0:
            return 1
        if amount < 0 or len(coins) == 0:
            return 0
        some_coin = next(iter(coins))
        return (
            change_for(amount, coins - set([some_coin]))
            +
            change_for(amount - some_coin, coins)
        )
    return change_for(100, frozenset([25, 10, 5, 1]))
```
On my computer, this takes around 13ms:
```
with timer():
    change_for_a_dollar()
[/code] [code]`    took 0.013737603090703487`
```
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.
```
import functools
def change_for_a_dollar():
    @functools.lru_cache
    def change_for(amount, coins):
        if amount == 0:
            return 1
        if amount < 0 or len(coins) == 0:
            return 0
        some_coin = next(iter(coins))
        return (
            change_for(amount, coins - set([some_coin]))
            +
            change_for(amount - some_coin, coins)
        )
    return change_for(100, frozenset([25, 10, 5, 1]))
[/code] [code]
with timer():
    change_for_a_dollar()
[/code] [code]`    took 0.004180959425866604`
```
A three-fold improvement for the cost of one line. Not bad.
### Welcome to 2011
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.
--------------------------------------------------------------------------------
via: https://opensource.com/article/21/5/python-32
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/lenovo-thinkpad-laptop-concentration-focus-windows-office.png?itok=-8E2ihcF (Woman using laptop concentrating)

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[#]: subject: (3 Python 3.2 features that are still relevant today)
[#]: via: (https://opensource.com/article/21/5/python-32)
[#]: author: (Moshe Zadka https://opensource.com/users/moshez)
[#]: collector: (lujun9972)
[#]: translator: (geekpi)
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
3 个 Python 3.2 到今天仍然有用的功能
======
探索一些未被充分利用但仍然有用的 Python 特性。
![Business woman on laptop sitting in front of window][1]
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。
### argparse 子命令
`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。
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。
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：
```
$ computebot negate 5
-5
$ computebot multiply 2 3
6
[/code] [code]
import argparse
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers()
```
`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：
```
negate = subparsers.add_parser("negate")
negate.set_defaults(subcommand="negate")
negate.add_argument("number", type=float)
[/code] [code]
multiply = subparsers.add_parser("multiply")
multiply.set_defaults(subcommand="multiply")
multiply.add_argument("number1", type=float)
multiply.add_argument("number2", type=float)
```
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。
```
`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')`
```
### contextlib.contextmanager
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。
编写一个打印出做某事所需时间的上下文，就像这样简单：
```
import contextlib, timeit
@contextlib.contextmanager
def timer():
before = timeit.default_timer()
try:
yield
finally:
after = timeit.default_timer()
print("took", after - before)
```
你可以这样使用：
```
import time
with timer():
time.sleep(10.5)
[/code] [code]` took 10.511025413870811`
```
### functools.lru_cache
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？“
这个问题的代码可以说是非常简单：
```
def change_for_a_dollar():
def change_for(amount, coins):
if amount == 0:
return 1
if amount < 0 or len(coins) == 0:
return 0
some_coin = next(iter(coins))
return (
change_for(amount, coins - set([some_coin]))
+
change_for(amount - some_coin, coins)
)
return change_for(100, frozenset([25, 10, 5, 1]))
```
在我的电脑上，这需要 13ms 左右：
```
with timer():
change_for_a_dollar()
[/code] [code]` took 0.013737603090703487`
```
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。
```
import functools
def change_for_a_dollar():
@functools.lru_cache
def change_for(amount, coins):
if amount == 0:
return 1
if amount < 0 or len(coins) == 0:
return 0
some_coin = next(iter(coins))
return (
change_for(amount, coins - set([some_coin]))
+
change_for(amount - some_coin, coins)
)
return change_for(100, frozenset([25, 10, 5, 1]))
[/code] [code]
with timer():
change_for_a_dollar()
[/code] [code]` took 0.004180959425866604`
```
一行的代价是三倍的改进。不错。
### 欢迎来到 2011 年
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。
--------------------------------------------------------------------------------
via: https://opensource.com/article/21/5/python-32
作者：[Moshe Zadka][a]
选题：[lujun9972][b]
译者：[geekpi](https://github.com/geekpi)
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/lenovo-thinkpad-laptop-concentration-focus-windows-office.png?itok=-8E2ihcF (Woman using laptop concentrating)