<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Processing Large Numeric Arrays in Python]]></title><description><![CDATA[<p dir="auto">In these articles <a href="https://dgolovin-dev.github.io/article-processing-big-numeric-arrays-in-python/" rel="nofollow ugc">Dima</a> explains how he worked with numpy, pandas, xarray, cython and numba to optimally implement operations on large numeric arrays on the Quantiacs platform.</p>
<p dir="auto"><a href="https://quantiacs.medium.com/processing-large-numeric-arrays-in-python-part-i-94b5fd46390f" rel="nofollow ugc">Part I</a> deals with data loading issues.</p>
<p dir="auto"><a href="https://medium.com/geekculture/processing-large-numeric-arrays-in-python-part-ii-1a15e54b8c60" rel="nofollow ugc">Part II</a> shows different implementations of an exponential moving averages.</p>
<p dir="auto">Both articles illustrate how to improve speed and reduce memory consumption.</p>
]]></description><link>http://quantiacs.com/community/topic/182/processing-large-numeric-arrays-in-python</link><generator>RSS for Node</generator><lastBuildDate>Mon, 10 Aug 2026 14:26:31 GMT</lastBuildDate><atom:link href="http://quantiacs.com/community/topic/182.rss" rel="self" type="application/rss+xml"/><pubDate>Tue, 15 Mar 2022 14:07:38 GMT</pubDate><ttl>60</ttl></channel></rss>