Библиотека Python разработчика | Книги по питону
Погружение в CPython и архитектуру. Разбираем неочевидное поведение (GIL, Memory), Best Practices (SOLID, DDD) и тонкости Django/FastAPI. Решаем задачи с подвохом и оптимизируем алгоритмы. 🐍 По всем вопросам @evgenycarter РКН clck.ru/3Ko7Hq
显示更多📈 Telegram 频道 Библиотека Python разработчика | Книги по питону 的分析概览
频道 Библиотека Python разработчика | Книги по питону (@bookpython) 俄语 语言赛道中的 是活跃参与者。目前社区聚集了 18 312 名订阅者,在 技术与应用 类别中位列第 7 334,并在 俄罗斯 地区排名第 36 889 位。
📊 受众指标与增长动态
自 невідомо 创建以来,项目保持高速增长,吸引了 18 312 名订阅者。
根据 12 六月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 -83,过去 24 小时变化为 -1,整体触达仍然可观。
- 认证状态: 未认证
- 互动率 (ER): 平均受众互动率为 5.49%。内容发布后 24 小时内通常能获得 2.76% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 1 006 次浏览,首日通常累积 505 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 2。
- 主题关注点: 内容集中在 numbers, yield, модуль, none, декоратор 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台:
“Погружение в CPython и архитектуру. Разбираем неочевидное поведение (GIL, Memory), Best Practices (SOLID, DDD) и тонкости Django/FastAPI. Решаем задачи с подвохом и оптимизируем алгоритмы. 🐍
По всем вопросам @evgenycarter
РКН clck.ru/3Ko7Hq”
凭借高频更新(最新数据采集于 13 六月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 技术与应用 类别中的关键影响点。
multiprocessing.Pool is for you. It spawns multiple processes and delegates tasks to them automatically. Simply create a pool with Pool(number_of_processes) and run p.map with the list of inputs.
In : import math
In : from multiprocessing import Pool
In : inputs = [i ** 2 for i in range(100, 130)]
In : def f(x):
...: return len(str(math.factorial(x)))
...:
In : %timeit [f(x) for x in inputs]
1.44 s ± 19.2 ms per loop (...)
In : p = Pool(4)
In : %timeit p.map(f, inputs)
451 ms ± 34 ms per loop (...)
You can also omit the number_of_processes parameter, the default value for it is the number of CPU cores on the current system.1, 2, 3. OK, so far, so good. What about tuple containing only one element? You just add trailing comma to the only value: 1,. Well, that’s somewhat ugly and error prone, but makes sense.
What about empty tuple? Is it a bare ,? No, it’s (). Do parentheses create tuple as well as commas? No, they don’t, (4) is not a tuple, it’s just 4.
In : a = [
...: (1, 2, 3),
...: (1, 2),
...: (1),
...: (),
...: ]
In : [type(x) for x in a]
Out: [tuple, tuple, int, tuple]
To make things more obscure, tuple literals often require additional parentheses. If you want a tuple to be the only argument of a function, that f(1, 2, 3) doesn’t work for an obvious reason, you need f((1, 2, 3)) instead.if, bool, not etc.
False objects are None, False, 0 of any type, and empty collections: "", [], {} etc., including custom collections with the __len__ method as long as __len__ returns 0.
You can also define custom truth value testing for your objects, the __bool__ magic method is there for this:
class Rectangle:
def __init__(self, width, height):
self._w = width
self._h = height
def __bool__(self):
return bool(self._w and self._h)
In : bool(Rectangle(2, 3))
Out: True
In : bool(Rectangle(2, 0))
Out: False
In : bool(Rectangle(0, 2))
Out: False
Mind, that __bool__ is called __nonzero__ in Python 2.__getitem__ method:
In : class Iterable:
...: def __getitem__(self, i):
...: if i > 10:
...: raise IndexError
...: return i
...:
In : list(Iterable())
Out: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
The second way is to define __iter__ method that returns an iterator. An iterator is an object with a __next__ method that returns next value from the original iterable once called:
In : class Iterator:
...: def __init__(self):
...: self._i = 0
...:
...: def __next__(self):
...: i = self._i
...: if i > 10:
...: raise StopIteration
...: self._i += 1
...: return i
...:
...: class Iterable:
...: def __iter__(self):
...: return Iterator()
...:
...:
In : list(Iterable())
Out: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Usually, an iterator also has an __iter__ method that just returns self: it allows iterator to be iterated too, that means that most of the iterators are also iterables.zip() мы можем все немного упростить.
Обратите внимание: функция zip() возвращает zip-объект, но с помощью приведения типов вы можете преобразовать его — например, с помощью list(), tuple() или dict().
Подписывайтесь на канал 👉@pythonofffifs. These are all and any.
any returns True if some of the values are true; all returns True if all of them are. all returns True for an empty iterable while any returns False in that case.
Both functions are usually useful while used together with list comprehensions:
package_broken = any(
part.is_broken() for part package.get_parts()
)
package_ok = all(
part.ok() for part package.get_parts()
)
any and all are usually interchangeable thanks to De Morgan's laws. Choose one that is easier to understand.obj.__class__ = AnyClass
Though it's probably a bad idea to use such tricks as part of your regular architecture, it can be extremely useful during debugging. Here is how you can track all attribute accesses of an object without modifying its original code:
class User:
def __init__(self, name):
self._name = name
def to_str(self):
return '<{}>'.format(self._name)
class LoggedUser(User):
def __getattribute__(self, attr):
print('`{}` accessed'.format(attr))
return super().__getattribute__(attr)
u = User('lol')
u.__class__ = LoggedUser
print(u.to_str())search, которая позволит вам найти подстроку
Если вам нужны сложные сопоставления, например, учет регистра — этот метод подойдет вам лучше всего. Но у него есть и недостатки: сложность и скорость работы. То есть, в простеньких задачах его лучше не использовать.
Подписывайтесь на канал 👉@pythonofffif 'port' not in config:
config['port'] = 80
port = config['port']
Setting default values to dictionaries can be done more elegant:
config = config.setdefault('port', 80)
setdefault sets the new value unless some value is already set. It also returns the new stored value whether it was changed or not:
In : config = {}
In : config.setdefault('port', 80)
Out: 80
In : config.setdefault('port', 443)
Out: 80
现已上线!2025 年 Telegram 研究 — 年度关键洞察 
