pandas.Index.join#

final Index.join(other, *, how='left', level=None, return_indexers=False, sort=False)[source]#

Compute join_index and indexers to conform data structures to the new index.

Parameters:

otherIndex: The other index on which join is performed.
how{‘left’, ‘right’, ‘inner’, ‘outer’}
levelint or level name, default None: It is either the integer position or the name of the level.
return_indexersbool, default False: Whether to return the indexers or not for both the index objects.
sortbool, default False: Sort the join keys lexicographically in the result Index. If False, the order of the join keys depends on the join type (how keyword).

Returns:

join_index, (left_indexer, right_indexer): The new index.

See also

DataFrame.join: Join columns with other DataFrame either on index or on a key.
DataFrame.merge: Merge DataFrame or named Series objects with a database-style join.

Examples

>>> idx1 = pd.Index([1, 2, 3])
>>> idx2 = pd.Index([4, 5, 6])
>>> idx1.join(idx2, how="outer")
Index([1, 2, 3, 4, 5, 6], dtype='int64')
>>> idx1.join(other=idx2, how="outer", return_indexers=True)
(Index([1, 2, 3, 4, 5, 6], dtype='int64'),
array([ 0,  1,  2, -1, -1, -1]), array([-1, -1, -1,  0,  1,  2]))