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所在平台: Udemy |
课程主页: https://www.udemy.com/course/pydantic/
课程评论：没有评论
Pydantic V2: 核心要点 这是一个专注于 Pydantic V2 库的高阶课程，**不适合初学者**。课程将分享多年使用 Pydantic (从 v1 开始) 的经验，重点介绍专业、高效地使用 Pydantic 并充分发挥其潜力的核心知识。 Pydantic 是一个灵活强大的 Python 数据建模、验证和解析框架。尽管常与 FastAPI 等框架关联，但其应用远不止 REST API 开发。它可用于数据库 (如 Redis, DynamoDB, Clickhouse)、消息队列 (如 SQS, ElasticMQ, RabbitMQ) 甚至 CSV 文件的数据建模和验证，还能为自定义 Python 函数提供参数验证。 Pydantic 是任何严肃 Python 开发者工具箱中不可或缺的一部分，它灵活、开发快速且易于理解。当你的 Python 项目涉及大量数据验证和模型定义到 Python 类时，Pydantic 都能被有效利用。你可以将 Pydantic 视为 Python `dataclasses` 的增强版，它增加了高级灵活的数据验证层，并能轻松地将 Pydantic 类反序列化 (加载) 和序列化 (输出) 为普通字典和 JSON。与 `dataclasses` 类似，Pydantic 使用 Python 的类型提示定义数据模型，并在此基础上增加了完全可定制的验证和序列化/反序列化功能。
This is an advanced level course on using the Pydantic V2 library. This course is not for beginners!I have worked with Pydantic (starting with v1) for many years, and use that experience to bring you a course that focuses on the essential parts of Pydantic you will need to know to use it professionally, effectively and to leverage it's full potential.Pydantic provides a very flexible framework for modeling, validating and parsing data in Python.Although Pydantic is often associated with frameworks such FastAPI, it has far broader applications well beyond just REST API development. From modeling and validating data in databases (like Redis, DynamoDB, Clickhouse), queues (like SQS, ElasticMQ, RabbitMQ), and even CSV files, to even providing argument validation for your custom Python functions!Pydantic is a very flexible, fast-to-develop, and easy-to-understand data modeling framework that belongs in every serious Python developer's toolkit.Anytime you have a Python project that contains a fair amount of data validation and modeling into Python classes, Pydantic can be leveraged very effectively. You can think of Pydantic as somewhat similar to Python's dataclasses, but with an advanced and flexible data validation layer, as well as the easy ability to deserialize (load) and serialize (output) these Python/Pydantic classes into plain dictionaries and JSON. Just like dataclasses, Pydantic uses Python's type hinting capabilities to define data models, but then adds in validation and serialization/deserialization capabilities, which are all fully customizable.
