same data type
            Its row and column indices are used to define the new indices
            of this object.
        method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}
            Method to use for filling holes in reindexed DataFrame.
            Please note: this is only applicable to DataFrames/Series with a
            monotonically increasing/decreasing index.

            * None (default): don't fill gaps
            * pad / ffill: propagate last valid observation forward to next
              valid
            * backfill / bfill: use next valid observation to fill gap
            * nearest: use nearest valid observations to fill gap.

        copy : bool, default True
            Return a new object, even if the passed indexes are the same.
        limit : int, default None
            Maximum number of consecutive labels to fill for inexact matches.
        tolerance : optional
            Maximum distance between original and new labels for inexact
            matches. The values of the index at the matching locations must
            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.

            Tolerance may be a scalar value, which applies the same tolerance
            to all values, or list-like, which applies variable tolerance per
            element. List-like includes list, tuple, array, Series, and must be
            the same size as the index and its dtype must exactly match the
            index's type.

        Returns
        -------
        Series or DataFrame
            Same type as caller, but with changed indices on each axis.

        See Also
        --------
        DataFrame.set_index : Set row labels.
        DataFrame.reset_index : Remove row labels or move them to new columns.
        DataFrame.reindex : Change to new indices or expand indices.

        Notes
        -----
        Same as calling
        ``.reindex(index=other.index, columns=other.columns,...)``.

        Examples
        --------
        >>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],
        ...                     [31, 87.8, 'high'],
        ...                     [22, 71.6, 'medium'],
        ...                     [35, 95, 'medium']],
        ...                    columns=['temp_celsius', 'temp_fahrenheit',
        ...                             'windspeed'],
        ...                    index=pd.date_range(start='2014-02-12',
        ...                                        end='2014-02-15', freq='D'))

        >>> df1
                    temp_celsius  temp_fahrenheit windspeed
        2014-02-12          24.3             75.7      high
        2014-02-13          31.0             87.8      high
        2014-02-14          22.0             71.6    medium
        2014-02-15          35.0             95.0    medium

        >>> df2 = pd.DataFrame([[28, 'low'],
        ...                     [30, 'low'],
        ...                     [35.1, 'medium']],
        ...                    columns=['temp_celsius', 'windspeed'],
        ...                    index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
        ...                                            '2014-02-15']))

        >>> df2
                    temp_celsius windspeed
        2014-02-12          28.0       low
        2014-02-13          30.0       low
        2014-02-15          35.1    medium

        >>> df2.reindex_like(df1)
                    temp_celsius  temp_fahrenheit windspeed
        2014-02-12          28.0              NaN       low
        2014-02-13          30.0              NaN       low
        2014-02-14           NaN              NaN       NaN
        2014-02-15          35.1              NaN    medium
        )