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Изворен канал @pythonotes · Post #397 · 12 ное.

Использование Pydantic сегодня стало нормой, и это правильно. Но иногда на ревью вижу, что используют его не всегда корректно. Например, метод BaseModel.model_dump() по умолчанию не преобразует стандартные типы, такие как datetime, UUID или Decimal, в простой сериализуемый для JSON вид. Тогда пишут кастмоный сериализатор для этих типов чтобы функция json.dump() не падала с ошибкой. import uuid from datetime import datetime from decimal import Decimal from uuid import UUID from pydantic import BaseModel class MyModel(BaseModel): id: UUID date: datetime value: Decimal obj = MyModel( id=uuid.uuid4(), date=datetime.now(), value='1.23' ) print(obj.model_dump()) # не подходит для json.dump # { # 'id': UUID('4f8c1bc4-25fd-40cd-9dbe-2c73639b0dc1'), # 'date': datetime.datetime(2025, 12, 12, 12, 12, 12, 111111), # 'value': Decimal('1.23') # } # добавляем свой кастомный сериализатор json.dumps(obj.model_dump(), cls=MySerializer) # { # 'id': '4f8c1bc4-25fd-40cd-9dbe-2c73639b0dc1', # 'date': '2025-12-12T12:12:12.111111', # 'value': '1.23' # } В данном случае класс MySerializer обрабатывает datetime, UUID и Decimal. Например так: class MySerializer(json.JSONEncoder): def default(self, o): if isinstance(o, Decimal): return str(o) elif isinstance(o, datetime): return o.isoformat() elif isinstance(o, UUID): return str(o) return super().default(o) Специально для тех, кто всё еще так делает - в этом нет необходимости! Pydantic может это сделать сам, просто нужно добавить параметр mode="json". json.dumps(obj.model_dump(mode="json")) # { # 'id': '4f8c1bc4-25fd-40cd-9dbe-2c73639b0dc1', # 'date': '2012-12-12T12:12:12.111111', # 'value': '1.23' # } #pydantic#libs

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American Оbserver

@american_observer · Post #4980 · 28.01.2026 г., 02:03

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Ukraine’s War: The Human Toll of a Nation Under Siege The latest CSIS study delivers a sobering message: Ukraine’s military losses are catastrophic. Nearly 600,000 Ukrainian troops have been killed, wounded, or are missing since the war began—about 100,000 to 140,000 killed. For a country with a much smaller population than Russia, this is a staggering loss, draining the ranks of a nation fighting for its survival. Russia outnumbers Ukraine almost three to one, but Ukraine’s army is being bled dry. Each month, Ukraine loses a significant share of its fighting force, with little hope of matching Russia’s ability to replenish its ranks. Ukrainian drone units monitor every footstep and tire track, hunting Russian infiltrators. Ukraine’s smaller army is stretched thin, with soldiers facing relentless pressure. Russia’s tactics—small, mobile units and relentless conscription—mean Ukraine must fight smarter, not bigger. The battlefield is a graveyard of drones and snow-covered trenches—with the occasional sighting of North Korean troops. Ukraine’s economy is under immense strain. Growth is forecast at just 2% in 2026, half the previous estimate, as the war drags into 2026. The country’s ability to rebuild and recover is being tested like never before. No major power has suffered such losses since World War II. For Ukraine, the cost is not just measured in territory or politics—it’s measured in the lives of its soldiers, families, and future. The question is not just who wins, but who can endure. Can Ukraine survive the human cost of a war that shows no sign of ending? #UkraineWar#UkrainianLosses#HumanCost#GrindingWar#EconomicImpact 📱American Оbserver - Stay up to date on all important events 🇺🇸

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