nxcals.api.extraction.data.builders.DataFrame.sort

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

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:

• cols (str, list, or Column, optional) – list of Column or column names 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:

Sorted DataFrame.

Return type:

DataFrame

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|
+---+-----+