Add optional row-level validation with Pydantic #39
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- Create pydantic_types.py module for optional Pydantic imports - Add version check requiring Pydantic >= 2.4.0 for TypeAdapter - Include BaseModel, ValidationError, ConfigDict, TypeAdapter exports - Add comprehensive tests for availability detection - Follow same pattern as dataframe_types.py for consistency
Implements efficient row validation using Pydantic's TypeAdapter for batch processing. Adds type detection helpers to dataframe_types for cleaner code.
Adds max_errors and convert_nans config options to pyproject.toml, following the existing config pattern for consistency.
Enables row-level validation using Pydantic models on input DataFrames, with configuration support for max_errors and convert_nans.
Enables row-level validation using Pydantic models on return DataFrames, with configuration support for max_errors and convert_nans.
Maintains focus on lightweight column validation while introducing row validation as an optional feature. Includes clear performance guidance: column validation is fast, row validation has overhead.
Adds detailed documentation for row validation feature including: - Basic usage with Pydantic models - Performance considerations (lightweight column validation vs data-validating row validation) - Error message examples - Combined column and row validation - Return value validation with @df_out - Configuration options in pyproject.toml - Advanced Pydantic features (validators, ConfigDict)
- Remove conditional logic from test_pydantic_types.py since pydantic is always in dev deps - Add pandas-no-pydantic scenario to CI isolation tests - Verifies column validation works without pydantic - Ensures row validation raises appropriate error when pydantic unavailable - Tests are now properly split: regular tests assume all deps, isolation tests verify optional deps
Add optional row-level validation using Pydantic models
Required for row validation tests to run in CI
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- Exclude pydantic_types.py from coverage (optional dependency module) - Add test for unknown DataFrame type error path - Add tests for iterative validation fallback with both Pandas and Polars - Ensures all important code paths are tested, not just hitting coverage numbers
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- Remove verbose/obvious comments - Simplify docstrings to be concise - Make _convert_nan_to_none a one-liner dict comprehension - Remove unnecessary explanatory comments - More pythonic and easier to read
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Adds row_validator parameter to df_in and df_out decorators for validating actual data values using Pydantic models (>= 2.4.0).
Column validation remains lightweight. Row validation uses batch processing for optimal performance.