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Learning pandas - Python Data Discovery and Analysis Made Easy Kindle Edition
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|Length: 506 pages||Enhanced Typesetting: Enabled||Page Flip: Enabled|
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About the Author
Michael Heydt is an independent consultant, educator, and trainer with nearly 30 years of professional software development experience, during which he focused on agile software design and implementation using advanced technologies in multiple verticals, including media, finance, energy, and healthcare. He holds an MS degree in mathematics and computer science from Drexel University and an executive master's of technology management degree from the University of Pennsylvania's School of Engineering and Wharton Business School. His studies and research have focused on technology management, software engineering, entrepreneurship, information retrieval, data sciences, and computational finance. Since 2005, he has been specializing in building energy and financial trading systems for major investment banks on Wall Street and for several global energy trading companies, utilizing .NET, C#, WPF, TPL, DataFlow, Python, R, Mono, iOS, and Android. His current interests include creating seamless applications using desktop, mobile, and wearable technologies, which utilize high concurrency, high availability, real-time data analytics, augmented and virtual reality, cloud services, messaging, computer vision, natural user interfaces, and software-defined networks. He is the author of numerous technology articles, papers, and books (Instant Lucene.NET, Learning pandas). He is a frequent speaker at .NET users' groups and various mobile and cloud conferences, and he regularly delivers webinars on advanced technologies.--This text refers to the paperback edition.
- File size : 16074 KB
- Print length : 506 pages
- Publisher : Packt Publishing (April 16, 2015)
- Publication date : April 16, 2015
- Word Wise : Not Enabled
- Enhanced typesetting : Enabled
- Language: : English
- X-Ray : Not Enabled
- ASIN : B00W9Q7VPA
- Text-to-Speech : Enabled
- Screen Reader : Supported
- Lending : Not Enabled
- Best Sellers Rank: #1,550,899 in Kindle Store (See Top 100 in Kindle Store)
- Customer Reviews:
Top reviews from the United States
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I did check the Table of Contents before purchasing, but what threw me off were all the 5-star reviews from people who claim it's their job using scientific libraries, or they've been using pandas for awhile. Because of these reviews and the length of the chapters, I though there would be some "comprehensive insights" or "powerful data manipulations" as some reviewer say... some real meat that would be conceptual, comprehensive and/or practical in these pages. But nope, you learn a useless function called "twiceprice" which takes a column of stock prices and multiplies it by 2. What a useless, un-insightful, un-practical example. Most of the examples don't use real life data, he just uses series of a,b,c and 1,2,3.
"Learning the Pandas Library" by Matt Harrison, 212 pages, (self-)published in 2016, £18 for a hardcopy
"Learning pandas" by Michael Heydt, 504 pages, Packt, 2015, £38
"Mastering pandas" by Anthony Fermi, 364 pages, Packt, 2015, £33
"Python Data Analytics" by Fabio Nelli, 364 pages, Apress, 2015, £23
pretty much for the sake of due diligence, not expecting any of the titles to beat "Python for Data Analysis", a definite keeper.
I started with "Learning the Pandas library", the thinnest of the bunch, and quickly decided to send it back to Amazon: the book could not add to, or replace, "Python for Data Analysis".
I reached the same conclusion on "Mastering pandas": the book could not compete with "Python for Data Analysis" on Pandas coverage, and sought to differentiate itself with statistics and machine-learning content, but the latter did not impress.
"Python Data Analytics" made a good impression, but its Pandas coverage, packed in less than 50 pages, did not really cut it.
"Learning pandas" was last on the list, and similarly made a good impression, but only as a competent "cover version" of Wes McKinney's book. An incomplete cover version, I should say: the two books have similar page nominal counts, but Packt-standard large font size and generous white space mean that "Learning pandas" is maybe 30% thinner than "Python for Data Analysis". The writing is decent but unspectacular - in contrast, Wes McKinney seems to be as good at teaching as he is at coding. Why would you buy "Learning pandas" if you can buy "Python for Data Analysis"? (And actually spend less! Somehow, the cover band charges more than the original). I have no answer, so I record my appreciation - this isn't garbage that I came to expect from Packt - but move on.
I skipped the first few chapters, but if you are new to Python and using Python packages, do be sure to go through the content.
The next couple of chapters discuss the inner workings of pandas DataFrame and Series. Worth going through as it provides a foundation for the remainder of the book's examples.
Around chapter 6 is where the application examples dig in and they are quite useful. I've referred to many of these examples. They include reading and writing data with different data sources, slicing and dicing data and running stats on your data.
Examples towards the end of the book get progressively sophisticated with shaping data. I didn't read everything in those chapters, but towards the end of the book are some chapters on data visualization and working with time series data. Definitely a "must" if you are looking to make use of pandas in your data analysis work.
I keep this ebook in my reference collection and refer to it when in need to figure out how to solve a data issue where pandas might be a good fit. A helpful book in the Python + data space.