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')

Check that left and right ExtensionArrays are equal.

This method compares two ExtensionArray instances for equality, including checks for missing values, the dtype of the arrays, and the exactness of the comparison (or tolerance when comparing floats).

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 and atol 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.

See also

testing.assert_series_equal

Check that left and right Series are equal.

testing.assert_frame_equal

Check that left and right DataFrame are equal.

testing.assert_index_equal

Check that left and right Index are equal.

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)