pyspark.pandas.Series.reindex_like#

Series.reindex_like(other)[source]#

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