<?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>Timing Functions on DataThrillz</title><link>https://datathrillz.com/tags/timing-functions/</link><description>Recent content in Timing Functions on DataThrillz</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 20 Feb 2021 17:07:51 +0000</lastBuildDate><atom:link href="https://datathrillz.com/tags/timing-functions/index.xml" rel="self" type="application/rss+xml"/><item><title>Using Decorators in Python</title><link>https://datathrillz.com/posts/2021-02-20-using-decorators-in-python/</link><pubDate>Sat, 20 Feb 2021 17:07:51 +0000</pubDate><guid>https://datathrillz.com/posts/2021-02-20-using-decorators-in-python/</guid><description>&lt;p&gt;In Python, decorators allow Data Scientists to extend and modify callables, such as functions, methods and classes, without explicitly changing the callable. Using decorators can improve the readability of your code as well code flexibility and modularity. In this article, we’ll discuss why we would use decorators, how to implement decorators and give a few examples.&lt;/p&gt;
&lt;h1 id="use-cases-for-decorators-in-python"&gt;&lt;strong&gt;Use Cases for Decorators in Python&lt;/strong&gt;&lt;/h1&gt;
&lt;p&gt;Decorators are powerful because they enable “wrapping” of functions with a master function. Some common use cases for decorators in Python include:&lt;/p&gt;</description></item></channel></rss>