How to Calculate Conditional Mean in PySpark


You can use the following methods to calculate a conditional mean in a PySpark DataFrame:

Method 1: Calculate Conditional Mean for String Variable

from pyspark.sql import functions as F

df.filter(df.team=='A').agg(F.mean('points').alias('mean_points')).show()

This particular example calculates the mean value of the points column only for the rows where the value in the team column is equal to A.

Method 2: Calculate Conditional Mean for Numeric Variable

from pyspark.sql import functions as F

df.filter(df.points>10).agg(F.mean('assists').alias('mean_assists')).show()

This particular example calculates the mean value of the assists column only for the rows where the value in the points column is greater than 10.

The following examples show how to use each method in practice with the following PySpark DataFrame that contains information about various basketball players:

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()

#define data
data = [['A', 'Guard', 11, 4], 
        ['A', 'Guard', 8, 5], 
        ['A', 'Forward', 22, 5], 
        ['A', 'Forward', 22, 9], 
        ['B', 'Guard', 14, 12], 
        ['B', 'Guard', 14, 3],
        ['B', 'Forward', 13, 5],
        ['B', 'Forward', 7, 2]] 
  
#define column names
columns = ['team', 'position', 'points', 'assists'] 
  
#create dataframe using data and column names
df = spark.createDataFrame(data, columns) 
  
#view dataframe
df.show()

+----+--------+------+-------+
|team|position|points|assists|
+----+--------+------+-------+
|   A|   Guard|    11|      4|
|   A|   Guard|     8|      5|
|   A| Forward|    22|      5|
|   A| Forward|    22|      9|
|   B|   Guard|    14|     12|
|   B|   Guard|    14|      3|
|   B| Forward|    13|      5|
|   B| Forward|     7|      2|
+----+--------+------+-------+

Example 1: Calculate Conditional Mean for String Variable

We can use the following syntax to calculate the mean value in the points column only for the rows where the corresponding value in the team column is equal to A:

from pyspark.sql import functions as F

#calculate mean value in points column for rows where team column is equal to 'A'
df.filter(df.team=='A').agg(F.mean('points').alias('mean_points')).show()

+-----------+
|mean_points|
+-----------+
|      15.75|
+-----------+

We can see that the mean value in the points column among players on team A is 15.75.

Note: We used the alias function to rename the column in the resulting DataFrame to mean_points.

Example 2: Calculate Conditional Mean for Numeric Variable

We can use the following syntax to calculate the mean value in the assists column only for the rows where the corresponding value in the points column is greater than 10:

from pyspark.sql import functions as F

#calculate mean value in assists column for rows where points is greater than 10
df.filter(df.points>10).agg(F.mean('assists').alias('mean_assists')).show() 

+-----------------+
|     mean_assists|
+-----------------+
|6.333333333333333|
+-----------------+

We can see that the mean value in the assists column among players who had more than 10 points is 6.33.

Additional Resources

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

How to Calculate the Mean of a Column in PySpark
How to Calculate Mean of Multiple Columns in PySpark
How to Calculate the Mean by Group in PySpark
How to Calculate a Rolling Mean in PySpark

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