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Learning Pandas - Python Data Discovery and Analysis Made Easy, by Michael Heydt
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Key Features
- Employ the use of pandas for data analysis closely to focus more on analysis and less on programming
- Get programmers comfortable in performing data exploration and analysis on Python using pandas
- Step-by-step demonstration of using Python and pandas with interactive and incremental examples to facilitate learning
This learner's guide will help you understand how to use the features of pandas for interactive data manipulation and analysis.
This book is your ideal guide to learning about pandas, all the way from installing it to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights. You start with an overview of pandas and NumPy and then dive into the details of pandas, covering pandas' Series and DataFrame objects, before ending with a quick review of using pandas for several problems in finance.
With the knowledge you gain from this book, you will be able to quickly begin your journey into the exciting world of data science and analysis.
What You Will Learn- Install pandas on Windows, Mac, and Linux using the Anaconda Python distribution
- Learn how pandas builds on NumPy to implement flexible indexed data
- Adopt pandas' Series and DataFrame objects to represent one- and two-dimensional data constructs
- Index, slice, and transform data to derive meaning from information
- Load data from files, databases, and web services
- Manipulate dates, times, and time series data
- Group, aggregate, and summarize data
- Visualize techniques for pandas and statistical data
Michael Heydt is an independent consultant, educator, and trainer with nearly 30 years of professional software development experience, during which time, he focused on Agile software design and implementation using advanced technologies in multiple verticals, including media, finance, energy, and healthcare. Since 2005, he has specialized 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, and 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. He is a frequent speaker at .NET user groups and various mobile and cloud conferences, and he regularly delivers webinars and conducts training courses on emerging and advanced technologies.
Table of Content- Sales Rank: #254026 in Books
- Published on: 2015-03-24
- Released on: 2015-04-16
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.14" w x 7.50" l, 1.89 pounds
- Binding: Paperback
- 372 pages
About the Author
Michael Heydt
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.
Most helpful customer reviews
11 of 12 people found the following review helpful.
I don't know why there are so many 5-star reviews.
By FeFiFoFu
I purchased Learning Pandas based on the steady stream of 5-star reviews here on Amazon, but the reviews completely let me down on this one. I've only started learning Pandas in the past week or so and I find this book way too basic, with little detail. Honestly, at around the page 250 of 480, all you've done is imported data from a CSV file, print rows or columns to screen, deleted and added a column or row. At this point, you still haven't any aggregated or summarized any data yet. In addition, the items covered to that point were not covered in depth.
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.
4 of 4 people found the following review helpful.
Practical examples and useful reference content
By Natester
I've been working with the pandas library for a while but had been looking for a text to help navigate the rich feature set of the pandas library. I purchased this book as soon as it became available and I'm quite satisfied with the content.
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.
3 of 3 people found the following review helpful.
getting to know indexes and look-ups
By Ken B. Pierce Jr.
I've been learning pandas for a few months and really looked forward to a new reference. While I've read most of Kinney's book (he started the package) I've found the concepts around indexing and the multiple ways to access data a bit difficult, especially coming from an R/dataframe background. The series and dataframe chapters in Heydt's book clarified a number of things for me about the use of .loc, .loc[] and .ix. I especially got confused with Panda's auto-indexing feature and how if you have an integer index and pass myData[2] you might get the row with the index label 2 or you might get the third row if Pandas added an auto-incrementing index (or your integer index is sorted and complete). I liked the explanation in this book. Pandas is really powerful and while I'll continue to use R/dplyr/etc for many tasks, I'm increasingly finding Pandas and python super useful. Note for GIS applications using Arc, it is relatively easy to move from a filegeodatabase to a numpy array to Pandas and back. This has been great for doing a lot of calculations with really big feature sets.
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