Return an object with matching indices as other object.

        Conform the object to the same index on all axes. Optional
        filling logic, placing NaN in locations having no value
        in the previous index. A new object is produced unless the
        new index is equivalent to the current one and copy=False.

        Parameters
        ----------
        other : Object of the 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 most
            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.

            .. versionadded:: 0.21.0 (list-like tolerance)

        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

def reindex_like(self, other, method=None, copy=True, limit=None,
                     tolerance=None):
        """
        Return an object with matching indices as other object.

        Conform the object to the same index on all axes. Optional
        filling logic, placing NaN in locations having no value
        in the previous index. A new object is produced unless the
        new index is equivalent to the current one and copy=False.

        Parameters
        ----------
        other : Object of the 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 most
            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.

            .. versionadded:: 0.21.0 (list-like tolerance)

        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
        """
        d = other._construct_axes_dict(axes=self._AXIS_ORDERS, method=method,
                                       copy=copy, limit=limit,
                                       tolerance=tolerance)

        return self.reindex(**d)