 * right: use only keys from right frame (SQL: right outer join)
        * outer: use union of keys from both frames (SQL: full outer join)
        * inner: use intersection of keys from both frames (SQL: inner join).

    Returns
    -------
    DataFrame
        The merged DataFrame output type will be the same as
        'left', if it is a subclass of DataFrame.

    See Also
    --------
    merge : Merge with a database-style join.
    merge_asof : Merge on nearest keys.

    Examples
    --------
    >>> from pandas import merge_ordered
    >>> df1 = pd.DataFrame(
    ...     {
    ...         "key": ["a", "c", "e", "a", "c", "e"],
    ...         "lvalue": [1, 2, 3, 1, 2, 3],
    ...         "group": ["a", "a", "a", "b", "b", "b"]
    ...     }
    ... )
    >>> df1
          key  lvalue group
    0   a       1     a
    1   c       2     a
    2   e       3     a
    3   a       1     b
    4   c       2     b
    5   e       3     b

    >>> df2 = pd.DataFrame({"key": ["b", "c", "d"], "rvalue": [1, 2, 3]})
    >>> df2
          key  rvalue
    0   b       1
    1   c       2
    2   d       3

    >>> merge_ordered(df1, df2, fill_method="ffill", left_by="group")
      key  lvalue group  rvalue
    0   a       1     a     NaN
    1   b       1     a     1.0
    2   c       2     a     2.0
    3   d       2     a     3.0
    4   e       3     a     3.0
    5   a       1     b     NaN
    6   b       1     b     1.0
    7   c       2     b     2.0
    8   d       2     b     3.0
    9   e       3     b     3.0
    r`