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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def join(self, other, on=None, how='left', lsuffix='', rsuffix='', sort=False):
 """ 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 -------- key A 0 K0 A0 1 K1 A1 2 K2 A2 3 K3 A3 4 K4 A4 5 K5 A5 key B 0 K0 B0 1 K1 B1 2 K2 B2 Join DataFrames using their indexes. 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. 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. 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 """

        # For SparseDataFrame's benefit
        return self._join_compat(other, on=on, how=how, lsuffix=lsuffix,
                                 rsuffix=rsuffix, sort=sort)