<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Numpy on DATATWEETS</title><link>https://datatweets.com/tags/numpy/</link><description>Recent content in Numpy 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/numpy/index.xml" rel="self" type="application/rss+xml"/><item><title>Descriptive Statistics in Python: A Practical Guide to Center and Spread</title><link>https://datatweets.com/blog/python-descriptive-statistics/</link><pubDate>Fri, 03 Jul 2026 00:00:00 +0000</pubDate><guid>https://datatweets.com/blog/python-descriptive-statistics/</guid><description>A practical walkthrough of descriptive statistics in Python: measures of center, measures of spread, and percentiles, computed with pandas and NumPy on a real restaurant tipping dataset.</description></item><item><title>Python Libraries for Data Cleaning: What pandas Alone Won't Cover</title><link>https://datatweets.com/blog/python-libraries-for-data-cleaning/</link><pubDate>Fri, 03 Jul 2026 00:00:00 +0000</pubDate><guid>https://datatweets.com/blog/python-libraries-for-data-cleaning/</guid><description>pandas covers most data-cleaning jobs, but not all of them. This guide surveys three libraries worth keeping in your toolbox — numpy for vectorized numeric fixes, re for pattern-based text cleaning, and rapidfuzz for catching near-duplicate rows exact matching can&amp;rsquo;t see — each demonstrated on one small, reproducible dataset.</description></item></channel></rss>