pyspark.pandas.Series.reindex¶

Series.reindex(index: Optional[Any] = None, fill_value: Optional[Any] = None) → pyspark.pandas.series.Series¶

Conform Series to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced.

Parameters

index: array-like, optional: New labels / index to conform to, should be specified using keywords. Preferably an Index object to avoid duplicating data
fill_valuescalar, default np.NaN: Value to use for missing values. Defaults to NaN, but can be any “compatible” value.

Returns

Series with changed index.

See also

Series.reset_index: Remove row labels or move them to new columns.

Examples

Create a series with some fictional data.

>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
>>> ser = ps.Series([200, 200, 404, 404, 301],
...                 index=index, name='http_status')
>>> ser
Firefox      200
Chrome       200
Safari       404
IE10         404
Konqueror    301
Name: http_status, dtype: int64

Create a new index and reindex the Series. By default values in the new index that do not have corresponding records in the Series are assigned NaN.

>>> new_index= ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
...             'Chrome']
>>> ser.reindex(new_index).sort_index()
Chrome           200.0
Comodo Dragon      NaN
IE10             404.0
Iceweasel          NaN
Safari           404.0
Name: http_status, dtype: float64

We can fill in the missing values by passing a value to the keyword fill_value.

>>> ser.reindex(new_index, fill_value=0).sort_index()
Chrome           200
Comodo Dragon      0
IE10             404
Iceweasel          0
Safari           404
Name: http_status, dtype: int64

To further illustrate the filling functionality in reindex, we will create a Series with a monotonically increasing index (for example, a sequence of dates).

>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
>>> ser2 = ps.Series([100, 101, np.nan, 100, 89, 88],
...                  name='prices', index=date_index)
>>> ser2.sort_index()
2010-01-01    100.0
2010-01-02    101.0
2010-01-03      NaN
2010-01-04    100.0
2010-01-05     89.0
2010-01-06     88.0
Name: prices, dtype: float64

Suppose we decide to expand the series to cover a wider date range.

>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> ser2.reindex(date_index2).sort_index()
2009-12-29      NaN
2009-12-30      NaN
2009-12-31      NaN
2010-01-01    100.0
2010-01-02    101.0
2010-01-03      NaN
2010-01-04    100.0
2010-01-05     89.0
2010-01-06     88.0
2010-01-07      NaN
Name: prices, dtype: float64

pyspark.pandas.Series.rename_axis

pyspark.pandas.Series.reindex_like