61 posts tagged with "python"

Pydantic BaseModel class decorators are a powerful and modern way to customize a model's behavior and validation logic. While a lot of Pydantic's functionality is configured through class attributes or the ConfigDict, decorators offer a more explicit and code-centric approach, especially for complex validation.

Pydantic V2 introduced several new decorators to enhance validation and model configuration. The most important ones are:

• @model_validator
• @field_validator
• @computed_field

These decorators are typically imported from pydantic.

msgspec is an extremely fast serialization and validation library that consistently outperforms Pydantic v2 in benchmarks. This performance advantage comes from its design as a lean, compiled-code-based library focused on a narrow set of data handling tasks, whereas Pydantic v2 is a feature-rich framework.

The performance differences are most pronounced in two key areas: parsing/decoding (converting data like JSON into Python objects) and serialization/encoding (converting Python objects into data like JSON).

Maintaining a clean and consistent codebase is crucial for any successful project. Enforcing standards across your team can be challenging, but with pre-commit, you can automate this process directly into your development workflow. This guide outlines a powerful pre-commit configuration that combines formatting, linting, and static type checking to ensure your code is always in top shape.

Python's PyYAML library is a powerful tool for working with YAML, but it has one common limitation: when you load a YAML file and dump it back, it doesn't preserve the original formatting. This can be a problem if you have multi-line strings formatted as literal blocks (|) and want to keep them that way for readability.

Fortunately, there's a straightforward and effective way to solve this by creating a custom representer for PyYAML. This guide will walk you through the process, using the code you provided, to ensure your multi-line strings are always dumped as literal blocks.

No, it is not possible for standard Python YAML libraries like PyYAML to preserve the original literal block format (e.g., | or >) when you load a YAML file and then dump it back. This is because the parser converts the literal block content into a standard Python string, discarding the original formatting. When you dump the string back to YAML, the dumper uses its own rules to represent the string, which typically defaults to a quoted style or a folded block style, but not necessarily the original one.

For better and more precise measurement of execution time in Django, you can use a custom middleware or a decorator. Middleware is ideal for measuring the total request-response cycle, while a decorator is best for timing a specific function or view.

The best way to precisely measure the execution time of a function or endpoint in Flask is by using a decorator or middleware. This approach allows you to wrap your functions with timing logic without directly altering the original function's code, which keeps your application clean and maintainable.

The best and most precise way to measure the execution time of a function or endpoint in FastAPI is by using a custom decorator or a middleware. This approach allows you to wrap your functions with timing logic without modifying the function's code itself, promoting clean, reusable, and maintainable code.

To annotate JSON schema properties in Python using msgspec, you use msgspec.field to provide metadata and constraints for a struct field. This allows you to define a more detailed schema beyond just the Python type hints, including documentation, default values, and validation rules.

To JSON-encode a Python object using msgspec, you use the msgspec.json.encode() function. This function takes a Python object and returns a bytes object containing the JSON representation. msgspec is known for its high performance and correctness in handling data serialization.

Here's a simple guide with examples.