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
