def reindex_like(self, other: 'DataFrame', copy: bool=True) -> 'DataFrame':
    "\n        Return a DataFrame with matching indices as other object.\n\n        Conform the object to the same index on all axes. Places NA/NaN in locations\n        having no value 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 : DataFrame\n            Its row and column indices are used to define the new indices\n            of this object.\n        copy : bool, default True\n            Return a new object, even if the passed indexes are the same.\n\n        Returns\n        -------\n        DataFrame\n            DataFrame 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\n        >>> df1 = ps.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',\n        ...                             'windspeed'],\n        ...                    index=pd.date_range(start='2014-02-12',\n        ...                                        end='2014-02-15', freq='D'))\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 = ps.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        >>> 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).sort_index() # doctest: +NORMALIZE_WHITESPACE\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       None\n        2014-02-15          35.1              NaN    medium\n        "
    if isinstance(other, DataFrame):
        return self.reindex(index=other.index, columns=other.columns, copy=copy)
    else:
        raise TypeError('other must be a pandas-on-Spark DataFrame')
Return a DataFrame with matching indices as other object.

Conform the object to the same index on all axes. Places NA/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 : DataFrame
    Its row and column indices are used to define the new indices
    of this object.
copy : bool, default True
    Return a new object, even if the passed indexes are the same.

Returns
-------
DataFrame
    DataFrame 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 = ps.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 = ps.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).sort_index() # doctest: +NORMALIZE_WHITESPACE
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          28.0              NaN       low
2014-02-13          30.0              NaN       low
2014-02-14           NaN              NaN       None
2014-02-15          35.1              NaN    medium
def reindex_like(self, other: 'DataFrame', copy: bool=True) -> 'DataFrame':
    "\n        Return a DataFrame with matching indices as other object.\n\n        Conform the object to the same index on all axes. Places NA/NaN in locations\n        having no value 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 : DataFrame\n            Its row and column indices are used to define the new indices\n            of this object.\n        copy : bool, default True\n            Return a new object, even if the passed indexes are the same.\n\n        Returns\n        -------\n        DataFrame\n            DataFrame 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\n        >>> df1 = ps.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',\n        ...                             'windspeed'],\n        ...                    index=pd.date_range(start='2014-02-12',\n        ...                                        end='2014-02-15', freq='D'))\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 = ps.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        >>> 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).sort_index() # doctest: +NORMALIZE_WHITESPACE\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       None\n        2014-02-15          35.1              NaN    medium\n        "
    if isinstance(other, DataFrame):
        return self.reindex(index=other.index, columns=other.columns, copy=copy)
    else:
        raise TypeError('other must be a pandas-on-Spark DataFrame')