sort

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

Syntax

sort(*cols: Union[int, str, Column, List[Union[int, str, Column]]], **kwargs: Any)

Parameters

Parameter	Type	Description
cols	int, str, list, or Column, optional	list of Column or column names or column ordinals to sort by.
ascending	bool or list, optional, default True	boolean or list of boolean. Sort ascending vs. descending. Specify list for multiple sort orders. If a list is specified, the length of the list must equal the length of the cols.

Parameter	Type	Description
cols	int, str, list, or Column, optional	list of Column or column names or column ordinals to sort by.
ascending	bool or list, optional, default True	boolean or list of boolean. Sort ascending vs. descending. Specify list for multiple sort orders. If a list is specified, the length of the list must equal the length of the cols.

Returns

DataFrame: Sorted DataFrame.

Notes

A column ordinal starts from 1, which is different from the 0-based __getitem__. If a column ordinal is negative, it means sort descending.

Examples

Python

from pyspark.sql import functions as sf
df = spark.createDataFrame([
    (2, "Alice"), (5, "Bob")], schema=["age", "name"])

df.sort(sf.asc("age")).show()
# +---+-----+
# |age| name|
# +---+-----+
# |  2|Alice|
# |  5|  Bob|
# +---+-----+

df.sort(df.age.desc()).show()
# +---+-----+
# |age| name|
# +---+-----+
# |  5|  Bob|
# |  2|Alice|
# +---+-----+

df.sort("age", ascending=False).show()
# +---+-----+
# |age| name|
# +---+-----+
# |  5|  Bob|
# |  2|Alice|
# +---+-----+

df = spark.createDataFrame([
    (2, "Alice"), (2, "Bob"), (5, "Bob")], schema=["age", "name"])
df.orderBy(sf.desc("age"), "name").show()
# +---+-----+
# |age| name|
# +---+-----+
# |  5|  Bob|
# |  2|Alice|
# |  2|  Bob|
# +---+-----+