pyspark.sql.DataFrame.sort

DataFrame.sort(*cols: Union[str, pyspark.sql.column.Column, List[Union[str, pyspark.sql.column.Column]]], **kwargs: Any) → pyspark.sql.dataframe.DataFrame

Returns a new DataFrame sorted by the specified column(s).

New in version 1.3.0.

Parameters

colsstr, list, or Column, optional

list of Column or column names to sort by.

Other Parameters

ascendingbool or list, optional

boolean or list of boolean (default True). Sort ascending vs. descending. Specify list for multiple sort orders. If a list is specified, length of the list must equal length of the cols.

Examples

>>> df.sort(df.age.desc()).collect()
[Row(age=5, name='Bob'), Row(age=2, name='Alice')]
>>> df.sort("age", ascending=False).collect()
[Row(age=5, name='Bob'), Row(age=2, name='Alice')]
>>> df.orderBy(df.age.desc()).collect()
[Row(age=5, name='Bob'), Row(age=2, name='Alice')]
>>> from pyspark.sql.functions import *
>>> df.sort(asc("age")).collect()
[Row(age=2, name='Alice'), Row(age=5, name='Bob')]
>>> df.orderBy(desc("age"), "name").collect()
[Row(age=5, name='Bob'), Row(age=2, name='Alice')]
>>> df.orderBy(["age", "name"], ascending=[0, 1]).collect()
[Row(age=5, name='Bob'), Row(age=2, name='Alice')]

pyspark.sql.DataFrame.show pyspark.sql.DataFrame.sortWithinPartitions