# 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)

  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)