You can use the following methods to calculate the mean of a column in a PySpark DataFrame:
Method 1: Calculate Mean for One Specific Column
from pyspark.sql import functions as F
#calculate mean of column named 'game1'
df.agg(F.mean('game1')).collect()[0][0]
Method 2: Calculate Mean for Multiple Columns
from pyspark.sql.functions import mean
#calculate mean for game1, game2 and game3 columns
df.select(mean(df.game1), mean(df.game2), mean(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 Mean for One Specific Column
We can use the following syntax to calculate the mean of values in the game1 column of the DataFrame only:
from pyspark.sql import functions as F
#calculate mean of column named 'game1'
df.agg(F.mean('game1')).collect()[0][0]
19.333333333333332
The mean of values in the game1 column turns out to be 19.333.
We can verify this is correct by manually calculating the mean of values in this column:
Mean of values in game1: (25 + 22 + 14 + 30 + 15 + 10) / 6 = 19.333.
Example 2: Calculate Mean for Multiple Columns
We can use the following syntax to calculate the mean of values for the game1, game2 and game3 columns of the DataFrame:
from pyspark.sql.functions import mean
#calculate mean for game1, game2 and game3 columns
df.select(mean(df.game1), mean(df.game2), mean(df.game3)).show()
+------------------+------------------+----------+
| avg(game1)| avg(game2)|avg(game3)|
+------------------+------------------+----------+
|19.333333333333332|15.166666666666666| 16.5|
+------------------+------------------+----------+
From the output we can see:
- The mean of values in the game1 column is 19.333.
- The mean of values in the game2 column is 15.167.
- The mean of values in the game3 column is 16.5.
Note: If there are null values in the column, the mean 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
