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
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Channel Библиотека Python разработчика | Книги по питону (@bookpython) in the Russian language segment is an active participant. Currently, the community unites 18 312 subscribers, ranking 7 332 in the Technologies & Applications category and 36 891 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 18 312 subscribers.
According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -82 over the last 30 days and by 0 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 5.51%. Within the first 24 hours after publication, content typically collects 2.69% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 009 views. Within the first day, a publication typically gains 492 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
- Thematic interests: Content is focused on key topics such as numbers, yield, модуль, none, декоратор.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“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”
Thanks to the high frequency of updates (latest data received on 12 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
Все уроки записаны в 2022 году, материал обновлен и дополнен.
Спикеры:
Марсель Ибраев (Southbridge)
Павел Селиванов (Yandex Cloud).
Форматы обучения:
Поток: открываем доступ к двум новым темам каждую неделю, общаемся в чате с куратором и спикерами, два раза в неделю — AMA-сессии по темам курса со спикерами.
Видеокурс: доступны сразу все темы, можно изучать в своём темпе.
Оба формата включают практику на стендах и итоговую сертификацию.
Подробнее про курс: https://slurm.club/3zPAuGrsuper() function allows referring to the base class. This can be extremely helpful in cases when a derived class wants to add something to the method implementation instead of overriding it completely:
class BaseTestCase(TestCase):
def setUp(self):
self._db = create_db()
class UserTestCase(BaseTestCase):
def setUp(self):
super().setUp()
self._user = create_user()
The function's name doesn't mean excellent or very good. The word super implies above in this context (like in superintendant). Despite what I said earlier, super() doesn't always refer to the base class, it can easily return a sibling. The proper name could be next() since the next class according to MRO is returned.
class Top:
def foo(self):
return 'top'
class Left(Top):
def foo(self):
return super().foo()
class Right(Top):
def foo(self):
return 'right'
class Bottom(Left, Right):
pass
# prints 'right'
print(Bottom().foo())
Mind that super() may produce different results since they depend on the MRO of the original call.
>>> Bottom().foo()
'right'
>>> Left().foo()
'top'gen.throw(e) you may raise an exception at the point where the gen generator is paused, i. e. at some yield. If gen catches the exception, get.throw(e) returns the next value yielded (or StopIteration is raised). If gen doesn't catch the exception, it propagates back to you.
In : def gen():
...: try:
...: yield 1
...: except ValueError:
...: yield 2
...:
...: g = gen()
...:
In : next(g)
Out: 1
In : g.throw(ValueError)
Out: 2
In : g.throw(RuntimeError('TEST'))
...
RuntimeError: TEST
You can use it to control generator behavior more precisely, not only by sending data to it but by notifying about some problems with values yielded for example. But this is rarely required, and you have a little chance to encounter g.throw in the wild.
However, the @contextmanager decorator from contextlib does exactly this to let the code inside the context catch exceptions.
In : from contextlib import contextmanager
...:
...: @contextmanager
...: def atomic():
...: print('BEGIN')
...:
...: try:
...: yield
...: except Exception:
...: print('ROLLBACK')
...: else:
...: print('COMMIT')
...:
In : with atomic():
...: print('ERROR')
...: raise RuntimeError()
...:
BEGIN
ERROR
ROLLBACKö can be just LATIN SMALL LETTER O WITH DIAERESIS (U+00F6) or a combination of o and a diaeresis modifier: LATIN SMALL LETTER O (U+006F) + COMBINING DIAERESIS (U+0308).
Compatible sequences look different but may be treated the same semantically, e. g. ff and ff.
For each of these types of equivalence, you can normalize a Unicode string by compressing or decompressing sequences. In Python, you can use unicodedata for this:
modes = [
# Compress canonically equivalent
'NFC',
# Decompress canonically equivalent
'NFD',
# Compress compatible
'NFKC',
# Decompress compatible
'NFKD',
]
s = 'ff + ö'
for mode in modes:
norm = unicodedata.normalize(mode, s)
print('\t'.join([
mode,
norm,
str(len(norm.encode('utf8'))),
]))
NFC ff + ö 8
NFD ff + ö 9
NFKC ff + ö 7
NFKD ff + ö 8__init__ it may be better to pass them as arguments and have a factory method instead. It separates business logic from technical details on how objects are created.
In this example __init__ accepts host and port to construct a database connection:
class Query:
def __init__(self, host, port):
self._connection = Connection(host, port)
The possible refactoring is:
class Query:
def __init__(self, connection):
self._connection = connection
@classmethod
def create(cls, host, port):
return cls(Connection(host, port))
This approach has at least these advantages:
• It makes dependency injection easy. You can do Query(FakeConnection()) in your tests.
• The class can have as many factory methods as needed; the connection may be constructed not only by host and port but also by cloning another connection, reading a config file or object, using the default, etc.
• Such factory methods can be turned into asynchronous functions; this is completely impossible for __init__.utf-8, which is (usually) a default in Python. When you read from a file, Python automatically decodes utf-8. You can choose any other encoding with encoding= parameter of the open function, or you can read plane bytes by appending b to its mode.
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