pyspark.sql.DataFrame.agg¶

DataFrame.agg(*exprs: Union[pyspark.sql.column.Column, Dict[str, str]]) → pyspark.sql.dataframe.DataFrame[source]¶

Aggregate on the entire DataFrame without groups (shorthand for df.groupBy().agg()).

New in version 1.3.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

exprsColumn or dict of key and value strings: Columns or expressions to aggregate DataFrame by.

Returns

DataFrame: Aggregated DataFrame.

Examples

>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
>>> df.agg({"age": "max"}).show()
+--------+
|max(age)|
+--------+
|       5|
+--------+
>>> df.agg(sf.min(df.age)).show()
+--------+
|min(age)|
+--------+
|       2|
+--------+

pyspark.sql.DataFrame.__getitem__

pyspark.sql.DataFrame.alias