What are some examples of aggregate functions in PySpark?

Aggregate functions in PySpark are essential for summarizing data across distributed datasets. They allow computations like sum, average, count, maximum, and minimum to be performed efficiently in parallel across multiple nodes in a cluster.

These functions are primarily used with large datasets to get single values or smaller result sets, providing insights into the dataset’s characteristics. Aggregate functions operate on a group of rows and calculate a single return value for every group. All these aggregate functions accept input as, Column type or column name as a string and several other arguments based on the function.

PySpark Aggregate Functions

PySpark SQL Aggregate functions are grouped as “agg_funcs” in Pyspark. Below is a list of functions defined under this group. Click on each link to learn with example.

PySpark Aggregate Functions Examples

First, let’s create a DataFrame to work with PySpark aggregate functions. All examples provided here are also available at PySpark Examples GitHub project.

simpleData = [("James", "Sales", 3000),
    ("Michael", "Sales", 4600),
    ("Robert", "Sales", 4100),
    ("Maria", "Finance", 3000),
    ("James", "Sales", 3000),
    ("Scott", "Finance", 3300),
    ("Jen", "Finance", 3900),
    ("Jeff", "Marketing", 3000),
    ("Kumar", "Marketing", 2000),
    ("Saif", "Sales", 4100)
  ]
schema = ["employee_name", "department", "salary"]
df = spark.createDataFrame(data=simpleData, schema = schema)
df.printSchema()
df.show(truncate=False)

Yields below output.

# Output
+-------------+----------+------+
|employee_name|department|salary|
+-------------+----------+------+
|        James|     Sales|  3000|
|      Michael|     Sales|  4600|
|       Robert|     Sales|  4100|
|        Maria|   Finance|  3000|
|        James|     Sales|  3000|
|        Scott|   Finance|  3300|
|          Jen|   Finance|  3900|
|         Jeff| Marketing|  3000|
|        Kumar| Marketing|  2000|
|         Saif|     Sales|  4100|
+-------------+----------+------+

Now let’s see how to aggregate data in PySpark.

approx_count_distinct Aggregate Function

In PySpark approx_count_distinct() function returns the count of distinct items in a group.

# approx_count_distinct()
print("approx_count_distinct: " + 
      str(df.select(approx_count_distinct("salary")).collect()[0][0]))

# Prints approx_count_distinct: 6

avg (average) Aggregate Function

avg() function returns the average of values in the input column.

# avg
print("avg: " + str(df.select(avg("salary")).collect()[0][0]))

# Prints avg: 3400.0

collect_list Aggregate Function

collect_list() function returns all values from an input column with duplicates.

# collect_list
df.select(collect_list("salary")).show(truncate=False)

+------------------------------------------------------------+
|collect_list(salary)                                        |
+------------------------------------------------------------+
|[3000, 4600, 4100, 3000, 3000, 3300, 3900, 3000, 2000, 4100]|
+------------------------------------------------------------+

collect_set Aggregate Function

collect_set() function returns all values from an input column with duplicate values eliminated.

# collect_set
df.select(collect_set("salary")).show(truncate=False)

+------------------------------------+
|collect_set(salary)                 |
+------------------------------------+
|[4600, 3000, 3900, 4100, 3300, 2000]|
+------------------------------------+

countDistinct Aggregate Function

countDistinct() function returns the number of distinct elements in a columns

# countDistinct
df2 = df.select(countDistinct("department", "salary"))
df2.show(truncate=False)
print("Distinct Count of Department & Salary: "+str(df2.collect()[0][0]))

count function

count() function returns number of elements in a column.

print("count: "+str(df.select(count("salary")).collect()[0]))

Prints county: 10

grouping function

grouping() Indicates whether a given input column is aggregated or not. returns 1 for aggregated or 0 for not aggregated in the result. If you try grouping directly on the salary column you will get below error.

Exception in thread "main" org.apache.spark.sql.AnalysisException:
  // grouping() can only be used with GroupingSets/Cube/Rollup

first function

first() function returns the first element in a column when ignoreNulls is set to true, it returns the first non-null element.

# first
df.select(first("salary")).show(truncate=False)

+--------------------+
|first(salary, false)|
+--------------------+
|3000                |
+--------------------+

last function

last() function returns the last element in a column. when ignoreNulls is set to true, it returns the last non-null element.

# last
df.select(last("salary")).show(truncate=False)

+-------------------+
|last(salary, false)|
+-------------------+
|4100               |
+-------------------+

kurtosis function

kurtosis() function returns the kurtosis of the values in a group.

df.select(kurtosis("salary")).show(truncate=False)

+-------------------+
|kurtosis(salary)   |
+-------------------+
|-0.6467803030303032|
+-------------------+

max function

max() function returns the maximum value in a column.

df.select(max("salary")).show(truncate=False)

+-----------+
|max(salary)|
+-----------+
|4600       |
+-----------+

min function

min() function

df.select(min("salary")).show(truncate=False)

+-----------+
|min(salary)|
+-----------+
|2000       |
+-----------+

mean function

mean() function returns the average of the values in a column. Alias for Avg

df.select(mean("salary")).show(truncate=False)

+-----------+
|avg(salary)|
+-----------+
|3400.0     |
+-----------+

skewness function

skewness() function returns the skewness of the values in a group.

df.select(skewness("salary")).show(truncate=False)

+--------------------+
|skewness(salary)    |
+--------------------+
|-0.12041791181069571|
+--------------------+

stddev(), stddev_samp() and stddev_pop()

stddev() alias for stddev_samp.

stddev_samp() function returns the sample standard deviation of values in a column.

stddev_pop() function returns the population standard deviation of the values in a column.

df.select(stddev("salary"), stddev_samp("salary"), 
    stddev_pop("salary")).show(truncate=False)

+-------------------+-------------------+------------------+
|stddev_samp(salary)|stddev_samp(salary)|stddev_pop(salary)|
+-------------------+-------------------+------------------+
|765.9416862050705  |765.9416862050705  |726.636084983398  |
+-------------------+-------------------+------------------+

sum function

sum() function Returns the sum of all values in a column.

df.select(sum("salary")).show(truncate=False)

+-----------+
|sum(salary)|
+-----------+
|34000      |
+-----------+

sumDistinct function

sumDistinct() function returns the sum of all distinct values in a column.

df.select(sumDistinct("salary")).show(truncate=False)

+--------------------+
|sum(DISTINCT salary)|
+--------------------+
|20900               |
+--------------------+

variance(), var_samp(), var_pop()

variance() alias for var_samp

var_samp() function returns the unbiased variance of the values in a column.

var_pop() function returns the population variance of the values in a column.

df.select(variance("salary"),var_samp("salary"),var_pop("salary")) 
  .show(truncate=False)

+-----------------+-----------------+---------------+
|var_samp(salary) |var_samp(salary) |var_pop(salary)|
+-----------------+-----------------+---------------+
|586666.6666666666|586666.6666666666|528000.0       |
+-----------------+-----------------+---------------+

Source code of PySpark Aggregate examples

# Imports
import pyspark
from pyspark.sql import SparkSession
from pyspark.sql.functions import approx_count_distinct,collect_list
from pyspark.sql.functions import collect_set,sum,avg,max,countDistinct,count
from pyspark.sql.functions import first, last, kurtosis, min, mean, skewness 
from pyspark.sql.functions import stddev, stddev_samp, stddev_pop, sumDistinct
from pyspark.sql.functions import variance,var_samp,  var_pop

spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate()

simpleData = [("James", "Sales", 3000),
    ("Michael", "Sales", 4600),
    ("Robert", "Sales", 4100),
    ("Maria", "Finance", 3000),
    ("James", "Sales", 3000),
    ("Scott", "Finance", 3300),
    ("Jen", "Finance", 3900),
    ("Jeff", "Marketing", 3000),
    ("Kumar", "Marketing", 2000),
    ("Saif", "Sales", 4100)
  ]
schema = ["employee_name", "department", "salary"]
  
df = spark.createDataFrame(data=simpleData, schema = schema)
df.printSchema()
df.show(truncate=False)

print("approx_count_distinct: " + 
      str(df.select(approx_count_distinct("salary")).collect()[0][0]))

print("avg: " + str(df.select(avg("salary")).collect()[0][0]))

df.select(collect_list("salary")).show(truncate=False)

df.select(collect_set("salary")).show(truncate=False)

df2 = df.select(countDistinct("department", "salary"))
df2.show(truncate=False)
print("Distinct Count of Department & Salary: "+str(df2.collect()[0][0]))

print("count: "+str(df.select(count("salary")).collect()[0]))
df.select(first("salary")).show(truncate=False)
df.select(last("salary")).show(truncate=False)
df.select(kurtosis("salary")).show(truncate=False)
df.select(max("salary")).show(truncate=False)
df.select(min("salary")).show(truncate=False)
df.select(mean("salary")).show(truncate=False)
df.select(skewness("salary")).show(truncate=False)
df.select(stddev("salary"), stddev_samp("salary"), 
    stddev_pop("salary")).show(truncate=False)
df.select(sum("salary")).show(truncate=False)
df.select(sumDistinct("salary")).show(truncate=False)
df.select(variance("salary"),var_samp("salary"),var_pop("salary")) 
  .show(truncate=False)

Conclusion

In this article, I’ve consolidated and listed all PySpark Aggregate functions with Python examples and also learned the benefits of using PySpark SQL functions. Additionally, aggregate functions are often used in conjunction with group-by operations to perform calculations on grouped data. Overall, aggregate functions facilitate data analysis tasks, enabling users to derive meaningful insights and make informed decisions based on the summarized information.

Happy Learning !!

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