<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DataFrames on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/dataframes/</link><description>Recent content in DataFrames on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 19 Dec 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/dataframes/index.xml" rel="self" type="application/rss+xml"/><item><title>Data Transformation with PySpark DataFrames</title><link>https://ai-blog.noorshomelab.dev/databricks-mastery-2025/data-transformation-pyspark/</link><pubDate>Fri, 19 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/databricks-mastery-2025/data-transformation-pyspark/</guid><description>&lt;h2 id="introduction-to-data-transformation-with-pyspark-dataframes"&gt;Introduction to Data Transformation with PySpark DataFrames&lt;/h2&gt;
&lt;p&gt;Welcome back, data adventurers! In our previous chapters, we learned how to get around Databricks, set up our environment, and even load some data. But what good is raw data if we can&amp;rsquo;t make sense of it, clean it up, or reshape it to answer critical questions? This is where the magic of data transformation comes comes in, and PySpark DataFrames are our trusty wands!&lt;/p&gt;</description></item></channel></rss>