<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Robert Sare on The Carpentries</title><link>https://deploy-preview-705--carpentries-website.netlify.app/blog/author/robert-sare/</link><description>Recent content in Robert Sare on The Carpentries</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 20 Nov 2024 10:51:28 -0500</lastBuildDate><atom:link href="https://deploy-preview-705--carpentries-website.netlify.app/blog/author/robert-sare/index.xml" rel="self" type="application/rss+xml"/><item><title>My Favorite Tool: Rasterio</title><link>https://deploy-preview-705--carpentries-website.netlify.app/blog/2017/11/sare-favorite/</link><pubDate>Tue, 28 Nov 2017 00:00:00 +0000</pubDate><guid>https://deploy-preview-705--carpentries-website.netlify.app/blog/2017/11/sare-favorite/</guid><description>&lt;p>&lt;strong>This post originally appeared on the &lt;a href="https://datacarpentry.org">Data Carpentry website&lt;/a>&lt;/strong>&lt;/p>
&lt;p>&lt;a href="https://mapbox.github.io/rasterio/">Rasterio&lt;/a> is a Python spatial data library that has changed the way I work with large
spatial datasets.&lt;/p>
&lt;p>Ever struggled to do calculations with big datasets in proprietary GIS software? Re-project your results to analyze relationships
with other data?&lt;/p>
&lt;p>Rasterio makes manipulating gridded spatial data (rasters) simple and brings these data into the Python ecosystem.&lt;/p>
&lt;p>Want to do some preliminary analysis on a low-memory machine?&lt;/p></description></item></channel></rss>