<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Python on Srikanth Cherla</title><link>https://cherla.org/tags/python/</link><description>Recent content in Python on Srikanth Cherla</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Wed, 27 May 2026 11:22:30 +0200</lastBuildDate><atom:link href="https://cherla.org/tags/python/index.xml" rel="self" type="application/rss+xml"/><item><title>Getting Started with Python Pandas</title><link>https://cherla.org/posts/2019/06/getting-started-with-python-pandas/</link><pubDate>Sun, 09 Jun 2019 00:00:00 +0000</pubDate><guid>https://cherla.org/posts/2019/06/getting-started-with-python-pandas/</guid><description>&lt;p&gt;I resisted pandas for a long time, then found myself spending way too much time writing data processing routines from scratch on a recommender systems project. It reminded me of when I used to manually compute neural network gradients before discovering Theano.&lt;/p&gt;
&lt;p&gt;Two resources that worked well for getting up to speed quickly:&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;&lt;a href="https://www.kaggle.com/learn"&gt;Kaggle Learn micro-courses&lt;/a&gt;&lt;/strong&gt; — well-organised, beginner-friendly, and the pandas course pairs nicely with their visualisation and embedding courses.&lt;/p&gt;</description></item></channel></rss>