assert_index_equal(left: 'Index', right: 'Index', exact: 'bool | str' = 'equiv', check_names: 'bool' = True, check_less_precise: 'bool | int | NoDefault' = <no_default>, check_exact: 'bool' = True, check_categorical: 'bool' = True, check_order: 'bool' = True, rtol: 'float' = 1e-05, atol: 'float' = 1e-08, obj: 'str' = 'Index') -> 'None'
Whether to check the Index class, dtype and inferred_type are identical. If 'equiv', then RangeIndex can be substituted for Int64Index as well.
Whether to check the names attribute.
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.
Use :None:None:`rtol` and :None:None:`atol` instead to define relative/absolute tolerance, respectively. Similar to :None:func:`math.isclose`.
Whether to compare number exactly.
Whether to compare internal Categorical exactly.
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.
Relative tolerance. Only used when check_exact is False.
Absolute tolerance. Only used when check_exact is False.
Specify object name being compared, internally used to show appropriate assertion message.
Check that left and right Index are equal.
>>> from pandas import testing as tmSee :
... a = pd.Index([1, 2, 3])
... b = pd.Index([1, 2, 3])
... tm.assert_index_equal(a, b)
The following pages refer to to this document either explicitly or contain code examples using this.
pandas._testing.asserters.assert_index_equal
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them