Returning the last value in a group during aggregation in PySpark

PySpark @


PySpark’s last() function is part of the PySpark SQL module, and it’s used to return the last value in a group during aggregation. Whether you are working with streaming data and need to get the most recent value or managing static datasets where the last entry is critical, the last() function is a vital tool.

Advantages of using PySpark’s last()

  • Simplicity: With just a single function, you can retrieve the last element of a dataset without needing complex operations.
  • Flexibility: It can be used with any data type and is not limited to numerical values alone.
  • Efficiency: As part of the Spark SQL function library, it leverages Spark’s optimized execution engine to process data quickly.
  • Versatility: It’s useful in a variety of scenarios, from financial analyses to real-time data processing.

Use cases for PySpark’s last()

  • Financial Data Analysis: Determine the most recent transaction or stock price in a time series dataset.
  • Event Logging: Capture the last event or status update in log data.
  • Sensor Data Monitoring: Obtain the latest reading from a batch of sensor data.
  • Data Synchronization: Resolve conflicts by keeping the last update in data replication scenarios.


from pyspark.sql import SparkSession
# Create a Spark session
spark = SparkSession.builder \
    .appName("PySpark Last Function @") \
from pyspark.sql import Row
# Define the data
data = [
    Row(category="A", value="apple"),
    Row(category="B", value="banana"),
    Row(category="A", value="avocado"),
    Row(category="B", value="blueberry"),
    Row(category="A", value="almond")
# Create a DataFrame
df = spark.createDataFrame(data)


|category|    value|
|       A|    apple|
|       B|   banana|
|       A|  avocado|
|       B|blueberry|
|       A|   almond|

Using the last() function to get the last value in each category

from pyspark.sql.functions import last
from pyspark.sql import Window

# Define the window specification
windowSpec = Window.partitionBy(df['category']).orderBy(df['value']).rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)

# Apply the last() function
df_with_last = df.withColumn("last_value", last(df['value']).over(windowSpec))

# Show the result'category', 'last_value').distinct().show()

The resulting DataFrame will show the last value for each category when ordered by the ‘value’ column:


|       A|   avocado|
|       B| blueberry|

Spark important urls to refer

  1. Spark Examples
  2. PySpark Blogs
  3. Bigdata Blogs
  4. Spark Interview Questions
  5. Official Page
Author: user