How to Filter Rows After a Group-By Operation in a PySpark DataFrame: The Ultimate Guide

Diving Straight into Filtering Rows After Group-By in a PySpark DataFrame

Grouping data and then filtering rows based on aggregated results is a common task for data engineers working with Apache Spark in ETL pipelines, data analysis, or reporting. For example, you might want to identify departments with an average salary above a threshold or customers with more than a certain number of orders. PySpark’s group-by operations, combined with filtering, make this straightforward and scalable. This guide is crafted for data engineers with intermediate PySpark knowledge, building on your interest in PySpark filtering techniques [Timestamp: March 16, 2025]. If you’re new to PySpark, start with our PySpark Fundamentals.

We’ll cover the basics of filtering after group-by, advanced filtering with multiple aggregations, handling nested data, using SQL expressions, and optimizing performance. Each section includes practical code examples, outputs, and common pitfalls, explained in a clear, conversational tone to keep things actionable and relevant.

Understanding Filtering After Group-By in PySpark

A group-by operation in PySpark aggregates data by one or more columns, producing metrics like counts, sums, or averages. Filtering after group-by involves selecting groups that meet specific conditions based on these aggregates, such as keeping groups with a minimum number of rows or an average value above a threshold. In PySpark, this is typically done using groupBy() followed by agg() for aggregations, and then filter() to apply conditions on the aggregated results.

Basic Group-By and Filtering Example

Let’s group employees by department and filter departments with an average salary above $50,000.

Copy codefrom pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg

# Initialize Spark session
spark = SparkSession.builder.appName("GroupByFilterExample").getOrCreate()

# Create employees DataFrame
employees_data = [
    (1, "Alice", 30, 50000, 101),
    (2, "Bob", 25, 45000, 102),
    (3, "Charlie", 35, 60000, 103),
    (4, "David", 28, 40000, 101),
    (5, "Eve", 32, 55000, 103)
]
employees = spark.createDataFrame(employees_data, ["employee_id", "name", "age", "salary", "dept_id"])

# Group by dept_id and calculate average salary
grouped_df = employees.groupBy("dept_id").agg(avg("salary").alias("avg_salary"))

# Filter departments with avg_salary > 50000
filtered_df = grouped_df.filter(col("avg_salary") > 50000)

# Show results
filtered_df.show()

# Output:
# +-------+----------+
# |dept_id|avg_salary|
# +-------+----------+
# |    103|   57500.0|
# +-------+----------+

# Validate row count
assert filtered_df.count() == 1, "Expected 1 department with avg_salary > 50000"

What’s Happening Here? We group the employees DataFrame by dept_id and compute the average salary per department using avg("salary"). The result is a DataFrame with dept_id and avg_salary columns. We then filter for departments where avg_salary exceeds 50,000, keeping only department 103 (average salary 57,500). This is a simple way to identify groups meeting aggregated conditions.

Key Methods:

• groupBy(columns): Groups rows by specified columns.
• agg(functions): Applies aggregation functions (e.g., avg, count) to grouped data.
• filter(condition): Filters groups based on aggregated values.

Common Mistake: Filtering before aggregation.

Copy code# Incorrect: Filtering before group-by
filtered_df = employees.filter(col("salary") > 50000).groupBy("dept_id").agg(avg("salary").alias("avg_salary"))

# Fix: Filter after aggregation
grouped_df = employees.groupBy("dept_id").agg(avg("salary").alias("avg_salary"))
filtered_df = grouped_df.filter(col("avg_salary") > 50000)

Error Output: Incorrect results, as pre-filtering removes rows needed for accurate aggregation.

Fix: Apply filter() after groupBy() and agg() to filter based on aggregated values.

Advanced Group-By Filtering with Multiple Aggregations

Real-world scenarios often require filtering based on multiple aggregated metrics, such as departments with high average salaries and a minimum number of employees. You can combine multiple aggregations in agg() and use complex conditions in filter().

Example: Filtering by Average Salary and Employee Count

Let’s filter departments with an average salary above $45,000 and at least two employees.

Copy codefrom pyspark.sql.functions import count

# Group by dept_id and calculate average salary and count
grouped_df = employees.groupBy("dept_id").agg(
    avg("salary").alias("avg_salary"),
    count("employee_id").alias("emp_count")
)

# Filter departments with avg_salary > 45000 and emp_count >= 2
filtered_df = grouped_df.filter(
    (col("avg_salary") > 45000) & (col("emp_count") >= 2)
)

# Show results
filtered_df.show()

# Output:
# +-------+----------+---------+
# |dept_id|avg_salary|emp_count|
# +-------+----------+---------+
# |    101|   45000.0|        2|
# |    103|   57500.0|        2|
# +-------+----------+---------+

# Validate
assert filtered_df.count() == 2, "Expected 2 departments"

What’s Going On? We group by dept_id and compute two aggregates: avg("salary") for the average salary and count("employee_id") for the number of employees. We then filter for departments where the average salary exceeds 45,000 and the employee count is at least 2, keeping departments 101 and 103. This is ideal for scenarios requiring multiple criteria, like identifying well-staffed, high-paying departments.

Common Mistake: Incorrect aggregation alias.

Copy code# Incorrect: Missing alias or typo
grouped_df = employees.groupBy("dept_id").agg(avg("salary"))  # No alias, column named avg(salary)

# Fix: Use alias
grouped_df = employees.groupBy("dept_id").agg(avg("salary").alias("avg_salary"))
filtered_df = grouped_df.filter(col("avg_salary") > 45000)

Error Output: AnalysisException: cannot resolve 'avg_salary' if alias is missing.

Fix: Always use alias() to name aggregated columns for clear filtering.

Filtering Nested Data After Group-By

Nested data, like structs, is common in semi-structured datasets. You can group by nested fields or apply group-by operations to nested data, then filter based on aggregates, using dot notation to access fields.

Example: Filtering by Nested Salary Aggregation

Suppose employees has a details struct with salary and bonus. We want to filter departments with an average nested salary above $50,000.

Copy codefrom pyspark.sql.types import StructType, StructField, StringType, IntegerType

# Define schema with nested struct
schema = StructType([
    StructField("employee_id", IntegerType()),
    StructField("name", StringType()),
    StructField("details", StructType([
        StructField("salary", IntegerType()),
        StructField("bonus", IntegerType())
    ])),
    StructField("dept_id", IntegerType())
])

# Create employees DataFrame
employees_data = [
    (1, "Alice", {"salary": 50000, "bonus": 5000}, 101),
    (2, "Bob", {"salary": 45000, "bonus": 3000}, 102),
    (3, "Charlie", {"salary": 60000, "bonus": 7000}, 103),
    (4, "David", {"salary": 40000, "bonus": 2000}, 101),
    (5, "Eve", {"salary": 55000, "bonus": 6000}, 103)
]
employees = spark.createDataFrame(employees_data, schema)

# Group by dept_id and calculate average nested salary
grouped_df = employees.groupBy("dept_id").agg(
    avg("details.salary").alias("avg_salary")
)

# Filter departments with avg_salary > 50000
filtered_df = grouped_df.filter(col("avg_salary") > 50000)

# Show results
filtered_df.show()

# Output:
# +-------+----------+
# |dept_id|avg_salary|
# +-------+----------+
# |    103|   57500.0|
# +-------+----------+

# Validate
assert filtered_df.count() == 1

What’s Going On? We group by dept_id and compute the average of the details.salary field using avg("details.salary"). We filter for departments where this average exceeds 50,000, keeping department 103. This is useful for JSON-like data where numerical fields are nested, aligning with your interest in complex data structures [Timestamp: March 27, 2025].

Common Mistake: Incorrect nested field access.

Copy code# Incorrect: Non-existent field
grouped_df = employees.groupBy("dept_id").agg(avg("details.wage").alias("avg_salary"))  # Raises AnalysisException

# Fix: Verify schema
employees.printSchema()
grouped_df = employees.groupBy("dept_id").agg(avg("details.salary").alias("avg_salary"))

Error Output: AnalysisException: cannot resolve 'details.wage'.

Fix: Use printSchema() to confirm nested field names.

Group-By Filtering with SQL Expressions

PySpark’s SQL module is a natural fit for group-by filtering, using GROUP BY and HAVING clauses to filter groups based on aggregates. This is intuitive for SQL users and integrates well with SQL-based workflows.

Example: SQL-Based Group-By Filtering

Let’s filter departments with an average salary above $50,000 using SQL.

Copy code# Register DataFrame as a temporary view
employees = spark.createDataFrame(employees_data[:5], ["employee_id", "name", "age", "salary", "dept_id"])
employees.createOrReplaceTempView("employees")

# SQL query with GROUP BY and HAVING
filtered_df = spark.sql("""
    SELECT dept_id, AVG(salary) AS avg_salary
    FROM employees
    GROUP BY dept_id
    HAVING AVG(salary) > 50000
""")

# Show results
filtered_df.show()

# Output:
# +-------+----------+
# |dept_id|avg_salary|
# +-------+----------+
# |    103|   57500.0|
# +-------+----------+

# Validate
assert filtered_df.count() == 1

What’s Going On? The SQL query groups employees by dept_id, calculates the average salary, and uses HAVING AVG(salary) > 50000 to filter groups, keeping department 103. The HAVING clause is the SQL equivalent of filtering after aggregation, making it a direct way to apply group-based conditions.

Common Mistake: Using WHERE instead of HAVING.

Copy code# Incorrect: WHERE for group condition
spark.sql("""
    SELECT dept_id, AVG(salary) AS avg_salary
    FROM employees
    GROUP BY dept_id
    WHERE AVG(salary) > 50000
""")  # Raises SyntaxError

# Fix: Use HAVING
spark.sql("""
    SELECT dept_id, AVG(salary) AS avg_salary
    FROM employees
    GROUP BY dept_id
    HAVING AVG(salary) > 50000
""")

Error Output: SyntaxError: cannot use WHERE after GROUP BY; use HAVING.

Fix: Use HAVING to filter groups after aggregation in SQL.

Optimizing Group-By Filtering Performance

Group-by operations and filtering on large datasets can be resource-intensive due to shuffling and aggregation. Here are four strategies to optimize performance, leveraging your interest in Spark optimization [Timestamp: March 19, 2025].

1. Select Relevant Columns: Include only necessary columns before grouping to reduce shuffling.
2. Filter Early: Apply row-level filters before group-by to minimize the dataset.
3. Partition Data: Partition by the group-by column (e.g., dept_id) for faster aggregation.
4. Cache Results: Cache grouped or filtered DataFrames for reuse in multi-step pipelines.

Example: Optimized Group-By Filtering

Copy code# Filter early and select relevant columns
optimized_df = employees.select("dept_id", "salary") \
                       .filter(col("salary").isNotNull())

# Group and filter
grouped_df = optimized_df.groupBy("dept_id").agg(
    avg("salary").alias("avg_salary")
)
filtered_df = grouped_df.filter(col("avg_salary") > 50000).cache()

# Show results
filtered_df.show()

# Output:
# +-------+----------+
# |dept_id|avg_salary|
# +-------+----------+
# |    103|   57500.0|
# +-------+----------+

# Validate
assert filtered_df.count() == 1

What’s Going On? We filter out null salary values and select only dept_id and salary to reduce data processed. We group by dept_id, compute the average salary, filter for averages above 50,000, and cache the result. This minimizes shuffling and speeds up downstream operations, aligning with your focus on efficient ETL pipelines [Timestamp: March 15, 2025].

Wrapping Up Your Group-By Filtering Mastery

Filtering rows after a group-by operation in PySpark is a powerful technique for analyzing aggregated data. From basic average-based filtering to multiple aggregations, nested data, SQL expressions, and performance optimizations, you’ve got a robust toolkit for group-based tasks. Try these methods in your next Spark project and share your insights on X. For more DataFrame operations, check out DataFrame Transformations.

More Spark Resources to Keep You Going

• Apache Spark Documentation
• Databricks Spark Guide
• PySpark DataFrame Basics
• PySpark Performance Tuning

Published: April 17, 2025