pyspark.sql.GroupedData.max¶

GroupedData.max(*cols: str) → pyspark.sql.dataframe.DataFrame[source]¶

Computes the max value for each numeric columns for each group.

New in version 1.3.0.

Changed in version 3.4.0: Supports Spark Connect.

Examples

>>> df = spark.createDataFrame([
...     (2, "Alice", 80), (3, "Alice", 100),
...     (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])
>>> df.show()
+---+-----+------+
|age| name|height|
+---+-----+------+
|  2|Alice|    80|
|  3|Alice|   100|
|  5|  Bob|   120|
| 10|  Bob|   140|
+---+-----+------+

Group-by name, and calculate the max of the age in each group.

>>> df.groupBy("name").max("age").sort("name").show()
+-----+--------+
| name|max(age)|
+-----+--------+
|Alice|       3|
|  Bob|      10|
+-----+--------+

Calculate the max of the age and height in all data.

>>> df.groupBy().max("age", "height").show()
+--------+-----------+
|max(age)|max(height)|
+--------+-----------+
|      10|        140|
+--------+-----------+

pyspark.sql.GroupedData.count pyspark.sql.GroupedData.mean