# pandas.testing.assert_index_equal

pandas.testing.assert_index_equal(left, right, exact='equiv', check_names=True, check_less_precise=NoDefault.no_default, check_exact=True, check_categorical=True, check_order=True, rtol=1e-05, atol=1e-08, obj='Index')

  Check that left and right Index are equal.

  Parameters
    left Index
    right Index
    exact bool or {‘equiv’}, default ‘equiv’

      Whether to check the Index class, dtype and inferred_type are identical. If ‘equiv’, then RangeIndex can be substituted for Int64Index as well.

    check_names bool, default True

      Whether to check the names attribute.

    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 True

      Whether to compare number exactly.

    check_categorical bool, default True

      Whether to compare internal Categorical exactly.

    check_order bool, default True

      Whether to compare the order of index entries as well as their values. If True, both indexes must contain the same elements, in the same order. If False, both indexes must contain the same elements, but in any order.

      New in version 1.2.0.

    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.

    obj str, default ‘Index’

      Specify object name being compared, internally used to show appropriate assertion message.

  Examples

      >>> from pandas.testing import assert_index_equal
      >>> a = pd.Index([1, 2, 3])
      >>> b = pd.Index([1, 2, 3])
      >>> assert_index_equal(a, b)