pyspark.sql.functions.max#
- pyspark.sql.functions.max(col)[source]#
- Aggregate function: returns the maximum value of the expression in a group. - New in version 1.3.0. - Changed in version 3.4.0: Supports Spark Connect. - Parameters
- colColumnor str
- The target column on which the maximum value is computed. 
 
- col
- Returns
- Column
- A column that contains the maximum value computed. 
 
 - Notes - Null values are ignored during the computation. 
- NaN values are larger than any other numeric value. 
 - Examples - Example 1: Compute the maximum value of a numeric column - >>> import pyspark.sql.functions as sf >>> df = spark.range(10) >>> df.select(sf.max(df.id)).show() +-------+ |max(id)| +-------+ | 9| +-------+ - Example 2: Compute the maximum value of a string column - >>> import pyspark.sql.functions as sf >>> df = spark.createDataFrame([("A",), ("B",), ("C",)], ["value"]) >>> df.select(sf.max(df.value)).show() +----------+ |max(value)| +----------+ | C| +----------+ - Example 3: Compute the maximum value of a column in a grouped DataFrame - >>> import pyspark.sql.functions as sf >>> df = spark.createDataFrame([("A", 1), ("A", 2), ("B", 3), ("B", 4)], ["key", "value"]) >>> df.groupBy("key").agg(sf.max(df.value)).show() +---+----------+ |key|max(value)| +---+----------+ | A| 2| | B| 4| +---+----------+ - Example 4: Compute the maximum value of multiple columns in a grouped DataFrame - >>> import pyspark.sql.functions as sf >>> df = spark.createDataFrame( ... [("A", 1, 2), ("A", 2, 3), ("B", 3, 4), ("B", 4, 5)], ["key", "value1", "value2"]) >>> df.groupBy("key").agg(sf.max("value1"), sf.max("value2")).show() +---+-----------+-----------+ |key|max(value1)|max(value2)| +---+-----------+-----------+ | A| 2| 3| | B| 4| 5| +---+-----------+-----------+ - Example 5: Compute the maximum value of a column with null values - >>> import pyspark.sql.functions as sf >>> df = spark.createDataFrame([(1,), (2,), (None,)], ["value"]) >>> df.select(sf.max(df.value)).show() +----------+ |max(value)| +----------+ | 2| +----------+ - Example 6: Compute the maximum value of a column with “NaN” values - >>> import pyspark.sql.functions as sf >>> df = spark.createDataFrame([(1.1,), (float("nan"),), (3.3,)], ["value"]) >>> df.select(sf.max(df.value)).show() +----------+ |max(value)| +----------+ | NaN| +----------+