<?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>Petrou, Theodore on Wijnand Baretta</title><link>https://wijnandbaretta.com/authors/petrou-theodore/</link><description>Recent content in Petrou, Theodore on Wijnand Baretta</description><image><title>Wijnand Baretta</title><url>https://wijnandbaretta.com/images/og-default.png</url><link>https://wijnandbaretta.com/images/og-default.png</link></image><generator>Hugo -- 0.152.2</generator><language>en</language><lastBuildDate>Fri, 19 Jan 2018 00:00:00 +0000</lastBuildDate><atom:link href="https://wijnandbaretta.com/authors/petrou-theodore/index.xml" rel="self" type="application/rss+xml"/><item><title>Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python</title><link>https://wijnandbaretta.com/books/pandas-cookbook-recipes-for-scientific-computing-time-series-analysis-and-data-visualization-using-python/</link><pubDate>Fri, 19 Jan 2018 00:00:00 +0000</pubDate><guid>https://wijnandbaretta.com/books/pandas-cookbook-recipes-for-scientific-computing-time-series-analysis-and-data-visualization-using-python/</guid><description>&lt;h1 id="pandas-cookbook-recipes-for-scientific-computing-time-series-analysis-and-data-visualization-using-python"&gt;Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python&lt;/h1&gt;
&lt;h2 id="summary"&gt;Summary&lt;/h2&gt;
&lt;p&gt;&amp;ldquo;Pandas Cookbook&amp;rdquo; by Theodore Petrou is a comprehensive guide designed for data analysts and enthusiasts who aim to leverage the power of the pandas library in Python. The book offers a collection of practical recipes structured around real-world scenarios that demonstrate how to utilize pandas for scientific computing, time series analysis, and data visualization. It emphasizes transforming raw data into insightful analysis through the effective use of pandas&amp;rsquo; functionalities, covering essential aspects like data cleaning, manipulation, aggregation, and visualization.&lt;/p&gt;</description></item></channel></rss>