cerberus: Validate Python Data Against Schemas

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Overview

Cerberus lets Python applications define schemas and validate data against them, returning whether the data conforms. It addresses the common need to check structured input consistently, rather than scattering validation logic throughout application code.

It is designed to be lightweight and extensible: built-in checks cover common validation needs, while custom validation can be added when an application needs domain-specific rules. It is a fit for teams building Python services or tools that want schema-based validation without a dependency-heavy framework.

Key Features

Validates data against declarative schemas.
Provides built-in type checking and other base validation functionality.
Supports custom validation for application-specific rules.
Designed to be non-blocking and extensible.
Has no runtime dependencies, according to the project README.
Supports Python interpreters according to the project's interpreter support policy.

Use Cases

Validate request or configuration data in a Python application before processing it.
Check imported records against expected fields and types, helping data-processing scripts catch malformed input.
Share validation schemas across application components instead of duplicating checks.
Add domain-specific validation rules when built-in checks do not cover an application's requirements.

Project Facts

Language: Python
License: ISC
Stars: 3.3k
Forks: 248
Topics: data-validation, python
Archived: No

Getting Started

Install from PyPI:

pip install cerberus

See the README and documentation for schema syntax, customization, and testing details.

Considerations

Cerberus focuses on validation and does not claim to provide a complete application or data-modeling framework. The README describes interpreter support as a testing policy, so check the documentation and run the test suite on your target Python environment if compatibility is important. Custom requirements may need custom validation logic.