def reindex_like(self, other, method=None, copy=True, limit=None, tolerance=None):
    "\n        Return an object with matching indices as other object.\n\n        Conform the object to the same index on all axes. Optional\n        filling logic, placing NaN in locations having no value\n        in the previous index. A new object is produced unless the\n        new index is equivalent to the current one and copy=False.\n\n        Parameters\n        ----------\n        other : Object of the same data type\n            Its row and column indices are used to define the new indices\n            of this object.\n        method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}\n            Method to use for filling holes in reindexed DataFrame.\n            Please note: this is only applicable to DataFrames/Series with a\n            monotonically increasing/decreasing index.\n\n            * None (default): don't fill gaps\n            * pad / ffill: propagate last valid observation forward to next\n              valid\n            * backfill / bfill: use next valid observation to fill gap\n            * nearest: use nearest valid observations to fill gap\n\n        copy : bool, default True\n            Return a new object, even if the passed indexes are the same.\n        limit : int, default None\n            Maximum number of consecutive labels to fill for inexact matches.\n        tolerance : optional\n            Maximum distance between original and new labels for inexact\n            matches. The values of the index at the matching locations most\n            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.\n\n            Tolerance may be a scalar value, which applies the same tolerance\n            to all values, or list-like, which applies variable tolerance per\n            element. List-like includes list, tuple, array, Series, and must be\n            the same size as the index and its dtype must exactly match the\n            index's type.\n\n            .. versionadded:: 0.21.0 (list-like tolerance)\n\n        Returns\n        -------\n        Series or DataFrame\n            Same type as caller, but with changed indices on each axis.\n\n        See Also\n        --------\n        DataFrame.set_index : Set row labels.\n        DataFrame.reset_index : Remove row labels or move them to new columns.\n        DataFrame.reindex : Change to new indices or expand indices.\n\n        Notes\n        -----\n        Same as calling\n        ``.reindex(index=other.index, columns=other.columns,...)``.\n\n        Examples\n        --------\n        >>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],\n        ...                     [31, 87.8, 'high'],\n        ...                     [22, 71.6, 'medium'],\n        ...                     [35, 95, 'medium']],\n        ...     columns=['temp_celsius', 'temp_fahrenheit', 'windspeed'],\n        ...     index=pd.date_range(start='2014-02-12',\n        ...                         end='2014-02-15', freq='D'))\n\n        >>> df1\n                    temp_celsius  temp_fahrenheit windspeed\n        2014-02-12          24.3             75.7      high\n        2014-02-13          31.0             87.8      high\n        2014-02-14          22.0             71.6    medium\n        2014-02-15          35.0             95.0    medium\n\n        >>> df2 = pd.DataFrame([[28, 'low'],\n        ...                     [30, 'low'],\n        ...                     [35.1, 'medium']],\n        ...     columns=['temp_celsius', 'windspeed'],\n        ...     index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',\n        ...                             '2014-02-15']))\n\n        >>> df2\n                    temp_celsius windspeed\n        2014-02-12          28.0       low\n        2014-02-13          30.0       low\n        2014-02-15          35.1    medium\n\n        >>> df2.reindex_like(df1)\n                    temp_celsius  temp_fahrenheit windspeed\n        2014-02-12          28.0              NaN       low\n        2014-02-13          30.0              NaN       low\n        2014-02-14           NaN              NaN       NaN\n        2014-02-15          35.1              NaN    medium\n        "
    d = other._construct_axes_dict(axes=self._AXIS_ORDERS, method=method, copy=copy, limit=limit, tolerance=tolerance)
    return self.reindex(**d)
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):
    "\n        Return an object with matching indices as other object.\n\n        Conform the object to the same index on all axes. Optional\n        filling logic, placing NaN in locations having no value\n        in the previous index. A new object is produced unless the\n        new index is equivalent to the current one and copy=False.\n\n        Parameters\n        ----------\n        other : Object of the same data type\n            Its row and column indices are used to define the new indices\n            of this object.\n        method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}\n            Method to use for filling holes in reindexed DataFrame.\n            Please note: this is only applicable to DataFrames/Series with a\n            monotonically increasing/decreasing index.\n\n            * None (default): don't fill gaps\n            * pad / ffill: propagate last valid observation forward to next\n              valid\n            * backfill / bfill: use next valid observation to fill gap\n            * nearest: use nearest valid observations to fill gap\n\n        copy : bool, default True\n            Return a new object, even if the passed indexes are the same.\n        limit : int, default None\n            Maximum number of consecutive labels to fill for inexact matches.\n        tolerance : optional\n            Maximum distance between original and new labels for inexact\n            matches. The values of the index at the matching locations most\n            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.\n\n            Tolerance may be a scalar value, which applies the same tolerance\n            to all values, or list-like, which applies variable tolerance per\n            element. List-like includes list, tuple, array, Series, and must be\n            the same size as the index and its dtype must exactly match the\n            index's type.\n\n            .. versionadded:: 0.21.0 (list-like tolerance)\n\n        Returns\n        -------\n        Series or DataFrame\n            Same type as caller, but with changed indices on each axis.\n\n        See Also\n        --------\n        DataFrame.set_index : Set row labels.\n        DataFrame.reset_index : Remove row labels or move them to new columns.\n        DataFrame.reindex : Change to new indices or expand indices.\n\n        Notes\n        -----\n        Same as calling\n        ``.reindex(index=other.index, columns=other.columns,...)``.\n\n        Examples\n        --------\n        >>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],\n        ...                     [31, 87.8, 'high'],\n        ...                     [22, 71.6, 'medium'],\n        ...                     [35, 95, 'medium']],\n        ...     columns=['temp_celsius', 'temp_fahrenheit', 'windspeed'],\n        ...     index=pd.date_range(start='2014-02-12',\n        ...                         end='2014-02-15', freq='D'))\n\n        >>> df1\n                    temp_celsius  temp_fahrenheit windspeed\n        2014-02-12          24.3             75.7      high\n        2014-02-13          31.0             87.8      high\n        2014-02-14          22.0             71.6    medium\n        2014-02-15          35.0             95.0    medium\n\n        >>> df2 = pd.DataFrame([[28, 'low'],\n        ...                     [30, 'low'],\n        ...                     [35.1, 'medium']],\n        ...     columns=['temp_celsius', 'windspeed'],\n        ...     index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',\n        ...                             '2014-02-15']))\n\n        >>> df2\n                    temp_celsius windspeed\n        2014-02-12          28.0       low\n        2014-02-13          30.0       low\n        2014-02-15          35.1    medium\n\n        >>> df2.reindex_like(df1)\n                    temp_celsius  temp_fahrenheit windspeed\n        2014-02-12          28.0              NaN       low\n        2014-02-13          30.0              NaN       low\n        2014-02-14           NaN              NaN       NaN\n        2014-02-15          35.1              NaN    medium\n        "
    d = other._construct_axes_dict(axes=self._AXIS_ORDERS, method=method, copy=copy, limit=limit, tolerance=tolerance)
    return self.reindex(**d)