<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Python Capabilities on DataThrillz</title><link>https://datathrillz.com/tags/python-capabilities/</link><description>Recent content in Python Capabilities on DataThrillz</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 26 Oct 2020 21:13:35 +0000</lastBuildDate><atom:link href="https://datathrillz.com/tags/python-capabilities/index.xml" rel="self" type="application/rss+xml"/><item><title>R vs Python</title><link>https://datathrillz.com/posts/2020-10-26-r-vs-python/</link><pubDate>Mon, 26 Oct 2020 21:13:35 +0000</pubDate><guid>https://datathrillz.com/posts/2020-10-26-r-vs-python/</guid><description>&lt;p&gt;I generally reach for Python when building data science pipelines, however I discovered R before I decided to invest in learning Python. R has saved me lots of time when it came to quickly and easily preparing nice-looking plots for research. It begs the question of where is R better than Python for certain purposes?  We will discuss the benefits and downsides of Python and R so that we can reach for the appropriate tool when needed, instead of treating all problems like nails when a screwdriver is required. &lt;/p&gt;</description></item></channel></rss>