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PySparkSnowflakeDatabricks

Window Functions in PySpark DataFrames

At NeoMart, analysts often need more than simple aggregations.
They want rankings, running totals, moving averages, and session-level analytics. For example:

  • Rank products by revenue per category
  • Calculate each customer’s cumulative spend
  • Determine the last purchase date for loyalty scoring

This is where window functions come in.

Why Window Functions Matter​

  • Allow calculations over partitions of data
  • Keep row-level detail while performing aggregations
  • Essential for ranking, cumulative metrics, and analytics features
  • Perfect for dashboards, reporting, and feature engineering in ML

Unlike groupBy, which reduces rows, window functions retain all rows.

1. Import Window Functions​

from pyspark.sql.window import Window
from pyspark.sql.functions import row_number, sum, avg, rank, desc

2. Ranking Rows: row_number()​

window_spec = Window.partitionBy("category").orderBy(desc("revenue"))

df.withColumn("rank", row_number().over(window_spec)).show()

Story Example​

NeoMart wants top 3 products per category for promotion campaigns.

3. Cumulative / Running Totals​

window_spec = Window.partitionBy("customer_id").orderBy("date")

df.withColumn("running_total", sum("amount").over(window_spec)).show()

This tracks each customer’s cumulative spend over time, essential for loyalty programs.

4. Moving Averages​

window_spec = Window.orderBy("date").rowsBetween(-3, 0)

df.withColumn("moving_avg", avg("amount").over(window_spec)).show()

Use Case​

  • Smooth out daily sales volatility
  • Detect trends or anomalies

5. Ranking With Ties: rank() vs dense_rank()​

window_spec = Window.partitionBy("category").orderBy(desc("revenue"))

df.withColumn("rank", rank().over(window_spec)).show()
df.withColumn("dense_rank", dense_rank().over(window_spec)).show()
  • rank() → leaves gaps for ties
  • dense_rank() → no gaps

Useful for leaderboards or top-n product ranking.

Summary​

Window functions in PySpark:

  • Operate over partitions without reducing rows
  • Enable rankings, cumulative sums, moving averages
  • Are essential for BI, ML features, and advanced analytics
  • Retain the flexibility of DataFrame operations

Next, we’ll cover Handling Missing Data — Drop, Fill, Replace, to clean and prepare datasets for analysis and modeling.