def join(self, other, on=None, how='<STR_LIT:left>', lsuffix='<STR_LIT>', rsuffix='<STR_LIT>',<EOL>sort=False):
<EOL>return self._join_compat(other, on=on, how=how, lsuffix=lsuffix,<EOL>rsuffix=rsuffix, sort=sort)<EOL>
Join columns of another DataFrame.

Join columns with `other` DataFrame either on index or on a key
column. Efficiently join multiple DataFrame objects by index at once by
passing a list.

Parameters
----------
other : DataFrame, Series, or list of DataFrame
    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'}, 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.
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).

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

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

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

Support for specifying index levels as the `on` parameter was added
in version 0.23.0.

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