Learning pandas

by Michael Heydt (Author)

20 ratings

ISBN-13: 978-1783985128

ISBN-10: 1783985127

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Learning pandas - Second Edition: High performance data manipulation and analysis using Python
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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

Book Description

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

About the Author

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

A Tour of pandas
Installing pandas
Numpy for pandas
The pandas Series Object
The pandas Dataframe Object
Accessing Data
Tidying up Your Data
Combining and Reshaping Data
Grouping and Aggregating Data
Time-series Data
Visualization
Applications to Finance

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Editorial Reviews

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.

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Product details

Publisher ‏ : ‎ Packt Publishing (April 16, 2015)
Language ‏ : ‎ English
Paperback ‏ : ‎ 504 pages
ISBN-10 ‏ : ‎ 1783985127
ISBN-13 ‏ : ‎ 978-1783985128
Item Weight ‏ : ‎ 1.89 pounds
Dimensions ‏ : ‎ 7.5 x 1.14 x 9.25 inches

Best Sellers Rank: #2,375,840 in Books (See Top 100 in Books)
- #2,161 in Data Processing
- #2,584 in Python Programming
- #6,142 in Computer Programming Languages

Customer Reviews:
20 ratings

Customer reviews

5 star		63%
4 star		29%
3 star 0% (0%)		0%
2 star 0% (0%)		0%
1 star		8%

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Top reviews from the United States

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MarketsExplorer

It is a good comprehensive overview of pandas

Reviewed in the United States on January 30, 2016

Verified Purchase

It is a good comprehensive overview of pandas. But if you try to read it through, it feels like reading a reference book. The examples are dry and mostly not practical and you probably won't remember much after reading the book. The examples show you how things work but they are rarely practical examples. Many of the examples are based on data typed in by hand, which rarely reflects the real world situation where data are read in from a file. If the book had provided some real world examples, such as reading in data from a file with date string as index, and manipulate the date and other data as one would in a real world financial application (which is mainly what panda was created for), then I would have given it a 5 star. I don't want to sound picky, but I just feel reading some kind of dry reference on pandas probably can achieve similar result. I don't dislike the book, I just wish it provided more solutions to real world problems.

One person found this helpful

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C. Nyberg

A good way to get up and running with Pandas.

Reviewed in the United States on March 14, 2016

Verified Purchase

An excellent book for those who are completely new to Pandas. I wish it would get more in depth with aggregations and provide some more examples, but overall the book was quite good and I would recommend as a friendlier, albeit more basic, version of Wes' Python for data analysis.

One person found this helpful

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Harmon L.

Five Stars

Reviewed in the United States on July 26, 2016

Verified Purchase

Excellent

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Dimitri Shvorob

A very decent copycat

Reviewed in the United States on July 18, 2016

Wishing to learn Pandas, I started by buying and reading "Python for Data Analysis" by Wes McKinney, the author of Pandas. I then went ahead and bought the other Pandas-related titles available on Amazon:

"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.

10 people found this helpful

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FeFiFoFu

I don't know why there are so many 5-star reviews.

Reviewed in the United States on April 2, 2016

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.

34 people found this helpful

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Ken B. Pierce Jr.

getting to know indexes and look-ups

Reviewed in the United States on June 10, 2015

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.

3 people found this helpful

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