You can use the following methods to calculate the max value of a column in a PySpark DataFrame:

**Method 1: Calculate Max for One Specific Column**

from pyspark.sql import functions as F #calculate max of column named 'game1' df.agg(F.max('game1')).collect()[0][0]

**Method 2: Calculate Max for Multiple Columns**

from pyspark.sql.functions import max #calculate max for game1, game2 and game3 columns df.select(max(df.game1), max(df.game2), max(df.game3)).show()

The following examples show how to use each method in practice with the following PySpark DataFrame:

**from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
#define data
****data = [['Mavs', 25, 11, 10],
['Nets', 22, 8, 14],
['Hawks', 14, 22, 10],
['Kings', 30, 22, 35],
['Bulls', 15, 14, 12],
['Blazers', 10, 14, 18]]
****#define column names
columns = ['team', 'game1', 'game2', 'game3']
#create dataframe using data and column names
df = spark.createDataFrame(data, columns)
#view dataframe
df.show()
+-------+-----+-----+-----+
| team|game1|game2|game3|
+-------+-----+-----+-----+
| Mavs| 25| 11| 10|
| Nets| 22| 8| 14|
| Hawks| 14| 22| 10|
| Kings| 30| 22| 35|
| Bulls| 15| 14| 12|
|Blazers| 10| 14| 18|
+-------+-----+-----+-----+**

**Example 1: Calculate Max for One Specific Column**

We can use the following syntax to calculate the max of values in the **game1** column of the DataFrame only:

from pyspark.sql import functions as F #calculate max of column named 'game1' df.agg(F.max('game1')).collect()[0][0] 30

The max of values in the **game1** column turns out to be **30**.

We can verify this is correct by manually identifying the max of the values in this column:

All values in game1 column: 10, 14, 15, 22, 25, **30**

We can see that **30** is indeed the max value in the column.

**Example 2: Calculate Max for Multiple Columns**

We can use the following syntax to calculate the max of values for the **game1**, **game2** and **game3** columns of the DataFrame:

from pyspark.sql.functions import max #calculate max for game1, game2 and game3 columns df.select(max(df.game1), max(df.game2), max(df.game3)).show() +----------+----------+----------+ |max(game1)|max(game2)|max(game3)| +----------+----------+----------+ | 30| 22| 35| +----------+----------+----------+

From the output we can see:

- The max of values in the
**game1**column is**30**. - The max of values in the
**game2**column is**22**. - The max of values in the
**game3**column is**35**.

**Additional Resources**

The following tutorials explain how to perform other common tasks in PySpark:

How to Calculate Mean of Multiple Columns in PySpark

How to Calculate the Mean by Group in PySpark

How to Sum Multiple Columns in PySpark