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feature #58141: Consistent naming conventions for string dtype aliases
Key implementation steps:
- Created factory functions (string, datetime, integer, floating,
decimal, boolean, list, categorical, interval, period, sparse, date,
duration, map, struct) to generate pandas dtypes (e.g., StringDtype,
Int64Dtype, ArrowDtype) based on parameters like backend, bits, unit,
and precision.
- Added support for both NumPy and PyArrow backends, enabling seamless
switching (e.g., integer() returns Int64Dtype for NumPy or
ArrowDtype(pa.int64()) for PyArrow).
- Implemented parameter validation to ensure correct usage (e.g.,
validating mode in string() to be "string" or "binary", and unit in
datetime() for NumPy).
- Integrated PyArrow types for advanced dtypes (e.g., pa.float64(),
pa.list_(), pa.map_()), supporting modern data processing frameworks.
- Implemented comprehensive tests in test_factory.py to validate dtype
creation across all functions, ensuring correct behavior for different
backends, verifying string representations (e.g., "double[pyarrow]"
for pa.float64()), and confirming proper error handling (e.g., raising
ValueError for invalid inputs).
- Addressed PyArrow compatibility by implementing correct method calls,
such as using pa.bool_() for boolean dtypes, ensuring proper
integration.
This change simplifies dtype creation, reduces duplication, and ensures
compatibility across backends, making it easier to extend support for
new dtypes in the future.
Co-authored-by: Pedro Santos <[email protected]>
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