pyspark.pandas.DataFrame.reindex_like#

DataFrame.reindex_like(other, copy=True)[source]#

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

otherDataFrame: Its row and column indices are used to define the new indices of this object.
copybool, 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() 
            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