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

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

to sum the values across multiple columns in a PySpark DataFrame:

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

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

from pyspark.sql.functions import sum #calculate sum for game1, game2 and game3 columns df.select(sum(df.game1), sum(df.game2), sum(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 Sum for One Specific Column**

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

from pyspark.sql import functions as F #calculate sum of column named 'game1' df.agg(F.sum('game1')).collect()[0][0] 116

The sum of values in the **game1** column turns out to be **116**.

We can verify this is correct by manually calculating the sum of values in this column:

Sum of values in game1: 25 + 22 + 14 + 30 + 15 + 10 = **116**

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

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

from pyspark.sql.functions import sum #calculate sum for game1, game2 and game3 columns df.select(sum(df.game1), sum(df.game2), sum(df.game3)).show() +----------+----------+----------+ |sum(game1)|sum(game2)|sum(game3)| +----------+----------+----------+ | 116| 91| 99| +----------+----------+----------+

From the output we can see:

- The sum of values in the
**game1**column is**116**. - The sum of values in the
**game2**column is**91**. - The sum of values in the
**game3**column is**99**.

**Note**: If there are null values in the column, the **sum** function will ignore these values by default.

**Additional Resources**

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

How to Sum Multiple Columns in PySpark DataFrame

How to Add Multiple Columns to PySpark DataFrame

How to Add New Rows to PySpark DataFrame