pandas.testing.assert_extension_array_equal#
- pandas.testing.assert_extension_array_equal(left, right, check_dtype=True, index_values=None, check_exact=<no_default>, rtol=<no_default>, atol=<no_default>, obj='ExtensionArray')[source]#
Check that left and right ExtensionArrays are equal.
- Parameters:
- left, rightExtensionArray
The two arrays to compare.
- check_dtypebool, default True
Whether to check if the ExtensionArray dtypes are identical.
- index_valuesIndex | numpy.ndarray, default None
Optional index (shared by both left and right), used in output.
- check_exactbool, default False
Whether to compare number exactly.
Changed in version 2.2.0: Defaults to True for integer dtypes if none of
check_exact
,rtol
andatol
are specified.- rtolfloat, default 1e-5
Relative tolerance. Only used when check_exact is False.
- atolfloat, default 1e-8
Absolute tolerance. Only used when check_exact is False.
- objstr, default ‘ExtensionArray’
Specify object name being compared, internally used to show appropriate assertion message.
Added in version 2.0.0.
Notes
Missing values are checked separately from valid values. A mask of missing values is computed for each and checked to match. The remaining all-valid values are cast to object dtype and checked.
Examples
>>> from pandas import testing as tm >>> a = pd.Series([1, 2, 3, 4]) >>> b, c = a.array, a.array >>> tm.assert_extension_array_equal(b, c)