Add Support for Unsigned Integer Data Types and Expand Documentation. #17
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The README has been updated to include an example of how to use the pandas-pyarrow library. This provides a specific illustration of handling problematic data types like float16 or db-dtypes that can cause issues in PyArrow, aiding users to fully exploit the library's benefits. Signed-off-by: DanielAvdar <66269169+DanielAvdar@users.noreply.github.com>
The minimum version for the pyarrow dependency in the pyproject.toml file has been updated from "7.0.0" to "10.0.1". Users are now required to use at least version 10.0.1 of pyarrow in order to prevent potential compatibility issues. Signed-off-by: DanielAvdar <66269169+DanielAvdar@users.noreply.github.com>
The numeric_mapper method in the pandas_pyarrow mappers module now includes the 'uint' data type. This enables mapping unsigned integer types with 8, 16, 32, and 64 bit variants. Signed-off-by: DanielAvdar <66269169+DanielAvdar@users.noreply.github.com>
Refactored the pandas-pyarrow tests for better coverage and efficiency. In particular, common and uncommon data types are now handled separately in the testing process. The tests were also updated to use the `convert_to_pyarrow` function, replacing the old `PandasArrowConverter` class. Finally, more comprehensive testing was made possible by expanding the range of test data types. Signed-off-by: DanielAvdar <66269169+DanielAvdar@users.noreply.github.com>
This commit updates the tests for `pandas-pyarrow` to use the `convert_to_pyarrow` function instead of the previously used `SchemArrow`. Additionally, it changes `DateTimeMapper` to `datetime_mapper` for better efficiency. These changes enhance coverage and efficiency in testing, as well as broaden the range of test data types. Signed-off-by: DanielAvdar <66269169+DanielAvdar@users.noreply.github.com>
The conversion process has been refactored for the tests in the pandas-pyarrow package. Instead of creating a PandasArrowConverter object for each test case, the convert_to_pyarrow function is directly used for converting the dataframes. This simplification makes the tests more straightforward and potentially more efficient. Signed-off-by: DanielAvdar <66269169+DanielAvdar@users.noreply.github.com>
Three new test cases for numeric types i.e., float16, uint16 and complex64 have been added in the pandas-pyarrow package testing. These additions will ensure the tested coverage and compatibility of these data types within the pandas-pyarrow integration. Signed-off-by: DanielAvdar <66269169+DanielAvdar@users.noreply.github.com>
Variables `float`, `float32`, and `float64` have been moved from the `COMMON_DTYPES_SAMPLE` to the `UNCOMMON_DTYPES_SAMPLE` list within the pandas-pyarrow testing suite. This rearrangement better illustrates which datatypes can be converted to PyArrow via the pandas API and which cannot, thus improving testing accuracy and clarity. Signed-off-by: DanielAvdar <66269169+DanielAvdar@users.noreply.github.com>
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #17 +/- ##
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Coverage 100.00% 100.00%
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Files 7 7
Lines 57 57
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Hits 57 57 ☔ View full report in Codecov by Sentry. |
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