Merk
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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. |
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
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|
# +---+-----+