<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Dataframe on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/dataframe/</link><description>Recent content in Dataframe on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Fri, 10 Jul 2026 08:20:25 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/dataframe/index.xml" rel="self" type="application/rss+xml"/><item><title>Pandas Library</title><link>https://abaj.ai/wiki/ccs/programming/libraries/pandas/</link><pubDate>Fri, 10 Jul 2026 01:43:39 +1000</pubDate><guid>https://abaj.ai/wiki/ccs/programming/libraries/pandas/</guid><description>&lt;p>pandas is &lt;a
 href="https://abaj.ai/wiki/ccs/programming/libraries/numpy/"
 
 
>numpy&lt;/a> with &lt;em>labels&lt;/em>. a &lt;code>Series&lt;/code> is a 1-d array married to an &lt;strong>index&lt;/strong>; a &lt;code>DataFrame&lt;/code> is a dict of such columns sharing one row index. the single organising idea — the one that explains both the magic and the bugs — is that &lt;strong>every operation aligns on labels first&lt;/strong> and computes second. everything on this page runs against pandas 3.0 (outputs are real).&lt;span class="margin-note" data-note="official docs: https://pandas.pydata.org/docs/ — the user guide&amp;#39;s &amp;#39;10 minutes to pandas&amp;#39; undersells itself by about three hours">
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