pandas.testing.assert_extension_array_equal

pandas.testing.assert_extension_array_equal(left, right, check_dtype=True, index_values=None, check_less_precise=NoDefault.no_default, check_exact=False, rtol=1e-05, atol=1e-08)[source]

Check that left and right ExtensionArrays are equal.

Parameters

left, right:ExtensionArray

The two arrays to compare.

check_dtype:bool, default True

Whether to check if the ExtensionArray dtypes are identical.

index_values:numpy.ndarray, default None

Optional index (shared by both left and right), used in output.

check_less_precise:bool or int, default False

Specify comparison precision. Only used when check_exact is False. 5 digits (False) or 3 digits (True) after decimal points are compared. If int, then specify the digits to compare.

Deprecated since version 1.1.0: Use rtol and atol instead to define relative/absolute tolerance, respectively. Similar to math.isclose().

check_exact:bool, default False

Whether to compare number exactly.

rtol:float, default 1e-5

Relative tolerance. Only used when check_exact is False.

New in version 1.1.0.

atol:float, default 1e-8

Absolute tolerance. Only used when check_exact is False.

New in version 1.1.0.

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.testing import assert_extension_array_equal
>>> a = pd.Series([1, 2, 3, 4])
>>> b, c = a.array, a.array
>>> assert_extension_array_equal(b, c)