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[source]¶

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

New in version 1.3.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

colsstr, list, or Column, optional: list of Column or column names to sort by.

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.

Examples

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

Sort the DataFrame in ascending order.

>>> df.sort(asc("age")).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|
+---+-----+

Specify multiple columns

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

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