pyspark.sql.DataFrame.sort#

DataFrame.sort(*cols, **kwargs)[source]#

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

Added in version 1.3.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters:

colsint, str, list, or Column, optional

list of Column or column names or column ordinals to sort by.

Changed in version 4.0.0: Supports column ordinal.

Returns:

DataFrame

Sorted DataFrame.

Other Parameters:

ascendingbool 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.

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

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

Sort the DataFrame in ascending order.

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

>>> df.sort(1).show()
+---+-----+
|age| name|
+---+-----+
|  2|Alice|
|  5|  Bob|
+---+-----+

Sort the DataFrame in descending order.

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

>>> df.orderBy(df.age.desc()).show()
+---+-----+
|age| name|
+---+-----+
|  5|  Bob|
|  2|Alice|
+---+-----+

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

>>> df.sort(-1).show()
+---+-----+
|age| name|
+---+-----+
|  5|  Bob|
|  2|Alice|
+---+-----+

Specify multiple columns

>>> from pyspark.sql import functions as sf
>>> 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|
+---+-----+

>>> df.orderBy(-1, "name").show()
+---+-----+
|age| name|
+---+-----+
|  5|  Bob|
|  2|Alice|
|  2|  Bob|
+---+-----+

>>> df.orderBy(-1, 2).show()
+---+-----+
|age| name|
+---+-----+
|  5|  Bob|
|  2|Alice|
|  2|  Bob|
+---+-----+

Specify multiple columns for sorting order at ascending.

>>> df.orderBy(["age", "name"], ascending=[False, False]).show()
+---+-----+
|age| name|
+---+-----+
|  5|  Bob|
|  2|  Bob|
|  2|Alice|
+---+-----+

>>> df.orderBy([1, "name"], ascending=[False, False]).show()
+---+-----+
|age| name|
+---+-----+
|  5|  Bob|
|  2|  Bob|
|  2|Alice|
+---+-----+

>>> df.orderBy([1, 2], ascending=[False, False]).show()
+---+-----+
|age| name|
+---+-----+
|  5|  Bob|
|  2|  Bob|
|  2|Alice|
+---+-----+