In : (lambda f: f(f, int(input('Input: ')), 1, 0, 1))(lambda f, t, i, a, b: print(f'fib({i}) = {b}') or t == i or f
 ...: (f, t, i + 1, b, a + b))
Input: 10
fib(1) = 1
fib(2) = 1
fib(3) = 2
fib(4) = 3
fib(5) = 5
fib(6) = 8
fib(7) = 13
fib(8) = 21
fib(9) = 34
fib(10) = 55
Out: True

In : [(t:=(t[1], sum(t)) if i else (0,1))[1] for i in range(10)]
Out: [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
PEP 570: Python Positional-Only parameters

In : import sys
...: import urllib.request
...:
...:
...: def audit_hook(event, args):
...: if event in ['urllib.Request']:
...: print(f'Network {event=} {args=}')
...:
...: sys.addaudithook(audit_hook)

In : urllib.request.urlopen('https://httpbin.org/get"htmlcode">

In : def audit_hook(event, args):
...: if event in ['make_request']:
...: print(f'Network {event=} {args=}')
...:

In : sys.addaudithook(audit_hook)

In : sys.audit('make_request', 'https://baidu.com')
Network event='make_request' args=('https://baidu.com',)

In : sys.audit('make_request', 'https://douban.com')
Network event='make_request' args=('https://douban.com',)
Multiprocessing shared memory


可以跨进程直接访问同一内存 (共享):


# IPython进程A
In : from multiprocessing import shared_memory

In : a = shared_memory.ShareableList([1, 'a', 0.1])

In : a
Out: ShareableList([1, 'a', 0.1], name='psm_d5d6ba1b') # 注意name
# IPython进程B(另外一个终端进入IPython)
In : from multiprocessing import shared_memory

In : b = shared_memory.ShareableList(name='psm_d5d6ba1b') # 使用name就可以共享内存

In : b
Out: ShareableList([1, 'a', 0.1], name='psm_d5d6ba1b')
New importlib.metadata module


使用新的 importlib.metadata 模块可以直接读取第三方包的元数据:


In : from importlib.metadata import version, files, requires, distribution

In : version('flask')
Out: '1.1.1'

In : requires('requests')
Out:
['chardet (<3.1.0,>=3.0.2)',
 'idna (<2.9,>=2.5)',
 'urllib3 (!=1.25.0,!=1.25.1,<1.26,>=1.21.1)',
 'certifi (>=2017.4.17)',
 "pyOpenSSL (>=0.14) ; extra == 'security'",
 "cryptography (>=1.3.4) ; extra == 'security'",
 "idna (>=2.0.0) ; extra == 'security'",
 "PySocks (!=1.5.7,>=1.5.6) ; extra == 'socks'",
 'win-inet-pton ; (sys_platform == "win32" and python_version == "2.7") and extra == \'socks\'']

In : dist = distribution('celery')

In : dist.version
Out: '4.3.0'

In : dist.metadata['Requires-Python']
Out: '>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*'

In : dist.metadata['License']

In : dist.entry_points
Out:
[EntryPoint(name='celery', value='celery.__main__:main', group='console_scripts'),
 EntryPoint(name='celery', value='celery.contrib.pytest', group='pytest11')]

In : files('celery')[8]
Out: PackagePath('celery/__init__.py')

In : dist.locate_file(files('celery')[8])
Out: PosixPath('/Users/dongweiming/test/venv/lib/python3.8/site-packages/celery/__init__.py')
functools.cached_property


缓存属性 (cached_property) 是一个非常常用的功能，很多知名 Python 项目都自己实现过它，现在终于进入版本库了。
具体的可以看我之前写的文章: functools.cached_property(Python 3.8)
functools.lru_cache 作为装饰器时可以不加参数


lru_cache 装饰器支持 max_size 和 typed 2 个参数，如果对默认参数不敏感，过去只能这么用 (需要空括号):


In : @lru_cache()
...: def add(a, b):
...: return a + b
...:



从 3.8 开始可以直接作为装饰器，而不是作为返回装饰器的函数 (不加括号):


In : @lru_cache
...: def add(a, b):
...: return a + b
...:


就像 dataclasses.dataclass ，绝大部分场景都是这么用:


@dataclass
class InventoryItem:
 ...


其实 dataclass 支持多个参数:


def dataclass(cls=None, /, *, init=True, repr=True, eq=True, order=False,
 unsafe_hash=False, frozen=False):


所以这种使用全部缺省值的装饰器工厂用法中，括号反而显得多余了。
Asyncio REPL


具体的可以看我之前写的文章: asyncio REPL(Python 3.8)
F-strings DEBUG


具体的可以看我之前写的文章: 使用 f-strings 调试 (Python3.8)
Async Mock


单元测试模块 unittest 添加了 mock 异步代码的类:


In : import asyncio

In : from unittest.mock import AsyncMock, MagicMock

In : mock = AsyncMock(return_value={'json': 123})
In : await mock()
Out: {'json': 123}

In : asyncio.run(mock())
Out: {'json': 123}

In : async def main(*args, **kwargs):
...: return await mock(*args, **kwargs)
...:

In : asyncio.run(main())
Out: {'json': 123}

In : mock = MagicMock() # AsyncMock也可以

In : mock.__aiter__.return_value = [1, 2, 3]

In : async def main():
...: return [i async for i in mock]
...:

In : asyncio.run(main())
Out: [1, 2, 3]
Generalized iterable unpacking in yield and return


具体的可以看我之前写的文章: Python3.8 对「可迭代解包」的改进
后记


全部更改详情请查阅 Changelog (延伸阅读链接 1)，另外推荐阅读延伸阅读链接 3 和 4，都是非常好的补充
延伸阅读


https://docs.python.org/3.8/whatsnew/3.8.html


https://www.python.org/dev/peps/pep-0578/#id11


https://speakerdeck.com/matrixise/whats-new-in-python-3-dot-8


https://realpython.com/python38-new-features/
总结
以上所述是小编给大家介绍的Python 3.8正式发布,来尝鲜这些新特性吧,希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对网站的支持！

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