--- title: A Practical Introduction to PySpark Window Functions description: This tutorial explains PySpark window functions as an alternative to groupBy() when you need aggregate calculations without collapsing rows. It covers... canonical: https://daily.dev/posts/a-practical-introduction-to-pyspark-window-functions-u4fudx8gk twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: A Practical Introduction to PySpark Window Functions | daily.dev og:description: This tutorial explains PySpark window functions as an alternative to groupBy() when you need aggregate calculations without collapsing rows. It covers... og:url: https://daily.dev/posts/a-practical-introduction-to-pyspark-window-functions-u4fudx8gk og:image: https://api.daily.dev/og/posts/U4fUdX8Gk.png og:image:alt: A Practical Introduction to PySpark Window Functions og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # A Practical Introduction to PySpark Window Functions **[Towards Data Science](https://daily.dev/sources/tds)** · 18 min read · 0 upvotes · 0 comments ## Summary This tutorial explains PySpark window functions as an alternative to groupBy() when you need aggregate calculations without collapsing rows. It covers partitionBy(), orderBy(), and window frames (rowsBetween/rangeBetween), and demonstrates ranking rows, running totals, lag() comparisons, moving averages, and top-N-per-group selection using a sample sales dataset. It also discusses performance considerations like data shuffling, uneven partition keys, caching reused windowed DataFrames, and common mistakes like confusing row-based and time-based windows. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How do I calculate a running total per group in PySpark using window functions? Define a window partitioned by the grouping column (e.g., store), ordered by date and a tie-breaker like transaction ID, and framed from the first row of the partition to the current row. Applying a sum aggregate over this window produces a cumulative total that resets whenever the partition key changes, without collapsing rows like groupBy does. _See how daily.dev surfaces practical PySpark patterns for engineers building running-total pipelines._ ### What is the difference between rowsBetween and rangeBetween in PySpark window functions? rowsBetween() defines a window frame using fixed row positions, such as the current row plus the previous three rows regardless of their values, while rangeBetween() uses the values in the ordering column, including rows whose values fall within a specified numeric range, such as timestamps within the last seven days. Mixing them up causes row-based windows to be mistaken for time-based ones. _daily.dev helps data engineers compare window-framing techniques before they hit production bugs._ ### Why would I use a PySpark window function instead of groupBy? Window functions let you calculate aggregates like rankings, running totals, or group shares while keeping every original row intact, whereas groupBy() collapses all rows in a group into a single summary row. This is useful for tasks like ranking transactions within each store or comparing a row to previous rows without losing transaction-level detail. _daily.dev keeps data engineers current on techniques for combining aggregation with row-level detail._ ## Similar posts on daily.dev - [PySpark for Beginners: Building Intermediate-Level Skills](https://daily.dev/posts/pyspark-for-beginners-building-intermediate-level-skills-ekfbvwpto) · Towards Data Science · 0 upvotes · 0 comments - [Expert in SQL Server Windows Function\( 5 min\)](https://daily.dev/posts/expert-in-sql-server-windows-function-5-min--9vrbtx6uj) · Medium · 0 upvotes · 0 comments - [PySpark for Beginners: Beyond the Basics](https://daily.dev/posts/pyspark-for-beginners-beyond-the-basics-b9sknjojt) · Towards Data Science · 1 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#data-engineering](https://daily.dev/tags/data-engineering), [#apache-spark](https://daily.dev/tags/apache-spark), [#pyspark](https://daily.dev/tags/pyspark) [View this post on daily.dev](https://daily.dev/posts/a-practical-introduction-to-pyspark-window-functions-u4fudx8gk) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"A Practical Introduction to PySpark Window Functions","url":"https://daily.dev/posts/a-practical-introduction-to-pyspark-window-functions-u4fudx8gk","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/a-practical-introduction-to-pyspark-window-functions-u4fudx8gk"},"datePublished":"2026-09-02T11:50:42.673Z","dateModified":"2026-09-02T12:22:00.771Z","description":"This tutorial explains PySpark window functions as an alternative to groupBy() when you need aggregate calculations without collapsing rows. 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