<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data-Cleaning on DATATWEETS</title><link>https://datatweets.com/tags/data-cleaning/</link><description>Recent content in Data-Cleaning on DATATWEETS</description><generator>Hugo</generator><language>en</language><copyright>Copyright (c) 2025 Datatweets</copyright><lastBuildDate>Fri, 03 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://datatweets.com/tags/data-cleaning/index.xml" rel="self" type="application/rss+xml"/><item><title>Cleaning Messy Data with Pandas: A Practical Guide</title><link>https://datatweets.com/blog/cleaning-messy-data-with-pandas/</link><pubDate>Fri, 03 Jul 2026 00:00:00 +0000</pubDate><guid>https://datatweets.com/blog/cleaning-messy-data-with-pandas/</guid><description>Real datasets are never as clean as tutorial datasets. This guide builds a detect-decide-fix workflow for pandas, then applies it to a real, freely-licensed museum collection dataset — missing values, disguised placeholders, inconsistent text, duplicates, and messy dates included.</description></item><item><title>Python Regex: A Practical Guide to Extracting and Cleaning Messy Text</title><link>https://datatweets.com/blog/python-regex-for-data-analysis/</link><pubDate>Fri, 03 Jul 2026 00:00:00 +0000</pubDate><guid>https://datatweets.com/blog/python-regex-for-data-analysis/</guid><description>Messy ticket subjects, log lines, and free-text fields all hide structured data. This guide builds a pattern-then-question mental model for Python&amp;rsquo;s re module, then works through groups, findall, sub, and re.compile on a support-ticket inbox you can reproduce yourself.</description></item></channel></rss>