Michael Heydt - Learning pandas

BIRMINGHAM - MUMBAI

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First published: April 2015

Second edition: June 2017

Production reference: 1300617

ISBN 978-1-78712-313-7
www.packtpub.com

Michael Heydt is a technologist, entrepreneur, and educator with decades of professional software development and financial and commodities trading experience. He has worked extensively on Wall Street specializing in the development of distributed, actor-based, high-performance, and high-availability trading systems. He is currently founder of Micro Trading Services, a company that focuses on creating cloud and micro service-based software solutions for finance and commodities trading. He holds a master's in science in mathematics and computer science from Drexel University, and an executive master's of technology management from the University of Pennsylvania School of Applied Science and the Wharton School of Business.

Sonali Dayal is a freelance data scientist in the San Francisco Bay Area. Her work on building analytical models and data pipelines influences major product and financial decisions for clients. Previously, she has worked as a freelance software and data science engineer for early stage startups, where she built supervised and unsupervised machine learning models, as well as interactive data analytics dashboards. She received her BS in biochemistry from Virginia Tech in 2011.

Nicola Rainiero is a civil geotechnical engineer with a background in the construction industry as a self-employed designer engineer. He is also specialized in renewable energy and has collaborated with the Sant Anna University of Pisa for two European projects, REGEOCITIES and PRISCA, using qualitative and quantitative data analysis techniques.

He has the ambition to simplifying his work with open software, using and developing new ones. Sometimes obtaining good results, other less good.

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Pandas is a popular Python package used for practical, real-world data analysis. It provides efficient, fast, and high-performance data structures that make data exploration and analysis very easy. This learner's guide will help you through a comprehensive set of features provided by the pandas library to perform efficient data manipulation and analysis.

, pandas and Data Analysis, is a hands-on introduction to the key features of pandas. The idea of this chapter is to provide some context for using pandas in the context of statistics and data science. The chapter will get into several concepts in data science and show how they are supported by pandas. This will set a context for each of the subsequent chapters, mentioning each chapter relates to both data science and data science processes.