Refactor: Extract DataFrame type handling to dedicated module (v0.16.1) #38
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This new module centralizes all pandas/polars DataFrame type handling: - Lazy imports with HAS_PANDAS and HAS_POLARS flags - DataFrameType union for type hints - get_dataframe_types() helper for isinstance checks - get_available_library_names() helper for error messages This lays the foundation for eliminating code duplication in utils.py and provides better structure for future lazy dataframe support.
This commit completes the refactoring by updating utils.py to use the centralized helpers from dataframe_types module: - Removed duplicate pandas/polars import blocks - Removed duplicate DataFrameType definition - Replaced 3 instances of "build type tuple dynamically" with get_dataframe_types() calls in: * assert_is_dataframe() * log_dataframe_input() * log_dataframe_output() - Use get_available_library_names() for error messages This reduces utils.py from 146 to 87 lines while improving maintainability and testability.
This module handles optional dependencies (pandas/polars) and cannot be meaningfully tested in normal unit tests where both libraries are present. It is thoroughly tested in the isolation test suite instead.
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Summary
Refactors DataFrame type handling into a dedicated
dataframe_types.pymodule, eliminating code duplication and improving separation of concerns.Changes
New module:
daffy/dataframe_types.pyget_dataframe_types()helper for isinstance checksget_available_library_names()for error messagesDataFrameType,HAS_PANDAS,HAS_POLARSflagsUpdated modules:
validation.py: ImportDataFrameTypefrom new moduledecorators.py: ImportDataFrameTypefrom new moduleutils.py: Remove duplicate type handling code, use centralized helpersResult:
utils.pyfrom 146 to 87 linesdataframe_types.pyhandles pandas/polars,utils.pyhandles general utilitiesMotivation
Prepares codebase for future lazy dataframe support while improving maintainability:
Version
Bumped to 0.16.1 (patch) - purely internal refactoring with no functional changes.