nxcals.api.extraction.data.builders.DataFrame.orderBy
- DataFrame.orderBy(*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 ofColumn
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:
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| +---+-----+