<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>compilers on Micah Yong</title><link>https://www.micahyong.com/tags/compilers/</link><description>Recent content in compilers on Micah Yong</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><copyright>Copyright © 2023, Micah Yong; all rights reserved.</copyright><lastBuildDate>Sun, 20 Jun 2021 23:30:42 -0700</lastBuildDate><atom:link href="https://www.micahyong.com/tags/compilers/index.xml" rel="self" type="application/rss+xml"/><item><title>Type inference for data frames</title><link>https://www.micahyong.com/post/exploring-geographic-data/</link><pubDate>Sun, 20 Jun 2021 23:30:42 -0700</pubDate><guid>https://www.micahyong.com/post/exploring-geographic-data/</guid><description>In this essay, I&amp;rsquo;ll talk about a powerful visualization called choropleths, why they&amp;rsquo;re horrendous to reproduce, and how we can empower data scientists to build and use them more intelligently.
Update: My professor and advisor recently launched a company, Ponder, to make Pandas both scalable and intelligent. See my work on geographic types embodied here.
It&amp;rsquo;s hot It&amp;rsquo;s hot in California. No, like really hot. If you&amp;rsquo;re from the West Coast, this isn&amp;rsquo;t news to you—you were born and raised in the ever-increasing heat.</description></item></channel></rss>