pyspark.pandas.Series.reindex_like

Series.reindex_like(other: Union[Series, DataFrame]) → pyspark.pandas.series.Series

Return a Series 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.

Parameters

otherSeries or DataFrame

Its row and column indices are used to define the new indices of this object.

Returns

Series

Series 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, ...).

Examples

>>> s1 = ps.Series([24.3, 31.0, 22.0, 35.0],
...                index=pd.date_range(start='2014-02-12',
...                                    end='2014-02-15', freq='D'),
...                name="temp_celsius")
>>> s1
2014-02-12    24.3
2014-02-13    31.0
2014-02-14    22.0
2014-02-15    35.0
Name: temp_celsius, dtype: float64

>>> s2 = ps.Series(["low", "low", "medium"],
...                index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
...                                        '2014-02-15']),
...                name="winspeed")
>>> s2
2014-02-12       low
2014-02-13       low
2014-02-15    medium
Name: winspeed, dtype: object

>>> s2.reindex_like(s1).sort_index()
2014-02-12       low
2014-02-13       low
2014-02-14      None
2014-02-15    medium
Name: winspeed, dtype: object

pyspark.pandas.Series.reindex pyspark.pandas.Series.reset_index