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| Comment | ||||
|---|---|---|---|---|
| pydantic | The most widely used data validation library, using Python type hints to define and validate data models. | 27.8M | 18 | — |
| marshmallow | A library for object serialization, deserialization, and validation via declarative schemas. | 3.7M | 11 | 7,409 |
| msgspec | High-performance serialization and validation library using Python type annotations, with JSON and MessagePack support. | 1.1M | 4 | 3,157 |
| cattrs | Structuring and unstructuring of attrs and dataclass objects, with composable validation hooks. | 2.0M | 3 | 591 |
| jsonschema | Reference implementation of the JSON Schema specification for validating JSON documents. | 14.8M | 1 | 1,323 |
| cerberus | Lightweight and extensible schema validation for Python dictionaries. | 210K | 1 | 2,635 |
| voluptuous | Schema validation library focused on validating data coming into Python from external sources. | 205K | 1 | 2,030 |
| pandera | Statistical data validation for pandas, Polars, and other DataFrame libraries, with type annotations support. | 305K | 18 | 1,450 |
| great-expectations | Data quality framework for defining, documenting, and validating expectations on large datasets in pipelines. | 840K | 35 | 11,184 |
| typeguard | Runtime type checking for Python functions and variables, enforcing type annotations at execution time. | 2.0M | 4 | 1,719 |
| beartype | Fast runtime type checker using O(1) constant-time type checks with no overhead at scale. | 2.1M | 10 | 3,128 |