# xarray.CFTimeIndex.join

#### CFTimeIndex.join(other, , how='left', level=None, return_indexers=False, sort=False)[[source]](https://github.com/pydata/xarray/blob/main/xarray/../.pixi/envs/doc/lib/python3.15/site-packages/pandas/core/indexes/base.py#L4579-L4732)

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

This method computes the result of a join between two Index objects
using the specified join type, and optionally returns integer indexers
that map each original index to the new joined index.

* **Parameters:**
  * **other** ([`Index`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index)) – The other index on which join is performed.
  * **how** (`{'left', 'right', 'inner', 'outer'}`)
  * **level** ([`int`](https://docs.python.org/3/builtins/functions.html#int) or `level name`, *default* [`None`](https://docs.python.org/3/builtins/constants.html#None)) – It is either the integer position or the name of the level.
  * **return_indexers** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool), *default* [`False`](https://docs.python.org/3/builtins/constants.html#False)) – Whether to return the indexers or not for both the index objects.
  * **sort** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool), *default* [`False`](https://docs.python.org/3/builtins/constants.html#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

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```pycon
>>> 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]))
```

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