in multiple DataFrame objects by index at once by
        passing a list.

        Parameters
        ----------
        other : DataFrame, Series, or a list containing any combination of them
            Index should be similar to one of the columns in this one. If a
            Series is passed, its name attribute must be set, and that will be
            used as the column name in the resulting joined DataFrame.
        on : str, list of str, or array-like, optional
            Column or index level name(s) in the caller to join on the index
            in `other`, otherwise joins index-on-index. If multiple
            values given, the `other` DataFrame must have a MultiIndex. Can
            pass an array as the join key if it is not already contained in
            the calling DataFrame. Like an Excel VLOOKUP operation.
        how : {'left', 'right', 'outer', 'inner', 'cross'}, default 'left'
            How to handle the operation of the two objects.

            * left: use calling frame's index (or column if on is specified)
            * right: use `other`'s index.
            * outer: form union of calling frame's index (or column if on is
              specified) with `other`'s index, and sort it lexicographically.
            * inner: form intersection of calling frame's index (or column if
              on is specified) with `other`'s index, preserving the order
              of the calling's one.
            * cross: creates the cartesian product from both frames, preserves the order
              of the left keys.

              .. versionadded:: 1.2.0

        lsuffix : str, default ''
            Suffix to use from left frame's overlapping columns.
        rsuffix : str, default ''
            Suffix to use from right frame's overlapping columns.
        sort : bool, default False
            Order result DataFrame lexicographically by the join key. If False,
            the order of the join key depends on the join type (how keyword).
        validate : str, optional
            If specified, checks if join is of specified type.

            * "one_to_one" or "1:1": check if join keys are unique in both left
              and right datasets.
            * "one_to_many" or "1:m": check if join keys are unique in left dataset.
            * "many_to_one" or "m:1": check if join keys are unique in right dataset.
            * "many_to_many" or "m:m": allowed, but does not result in checks.

            .. versionadded:: 1.5.0

        Returns
        -------
        DataFrame
            A dataframe containing columns from both the caller and `other`.

        See Also
        --------
        DataFrame.merge : For column(s)-on-column(s) operations.

        Notes
        -----
        Parameters `on`, `lsuffix`, and `rsuffix` are not supported when
        passing a list of `DataFrame` objects.

        Examples
        --------
        >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
        ...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})

        >>> df
          key   A
        0  K0  A0
        1  K1  A1
        2  K2  A2
        3  K3  A3
        4  K4  A4
        5  K5  A5

        >>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
        ...                       'B': ['B0', 'B1', 'B2']})

        >>> other
          key   B
        0  K0  B0
        1  K1  B1
        2  K2  B2

        Join DataFrames using their indexes.

        >>> df.join(other, lsuffix='_caller', rsuffix='_other')
          key_caller   A key_other    B
        0         K0  A0        K0   B0
        1         K1  A1        K1   B1
        2         K2  A2        K2   B2
        3         K3  A3       NaN  NaN
        4         K4  A4       NaN  NaN
        5         K5  A5       NaN  NaN

        If we want to join using the key columns, we need to set key to be
        the index in both `df` and `other`. The joined DataFrame will have
        key as its index.

        >>> df.set_index('key').join(other.set_index('key'))
              A    B
        key
        K0   A0   B0
        K1   A1   B1
        K2   A2   B2
        K3   A3  NaN
        K4   A4  NaN
        K5   A5  NaN

        Another option to join using the key columns is to use the `on`
        parameter. DataFrame.join always uses `other`'s index but we can use
        any column in `df`. This method preserves the original DataFrame's
        index in the result.

        >>> df.join(other.set_index('key'), on='key')
          key   A    B
        0  K0  A0   B0
        1  K1  A1   B1
        2  K2  A2   B2
        3  K3  A3  NaN
        4  K4  A4  NaN
        5  K5  A5  NaN

        Using non-unique key values shows how they are matched.

        >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],
        ...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})

        >>> df
          key   A
        0  K0  A0
        1  K1  A1
        2  K1  A2
        3  K3  A3
        4  K0  A4
        5  K1  A5

        >>> df.join(other.set_index('key'), on='key', validate='m:1')
          key   A    B
        0  K0  A0   B0
        1  K1  A1   B1
        2  K1  A2   B1
        3  K3  A3  NaN
        4  K0  A4   B0
        5  K1  A5   B1
        r