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The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python

Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.

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Book ID Asin: B0789WKTKJ
Book Title: Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series)
Book Author: Chen Daniel Y.
Book Format and Price:
Book Format Name: Kindle
Book Format Price: $17.27
Book Format Name: Paperback
Book Format Price: $31.97-$39.59
Book Price: $24.96
Book Category: Kindle Store, Kindle eBooks, Computers & Technology and unknown
Book Rating: 288 ratings

Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series) by Chen Daniel Y. Book Review

Name: Frank InMichigan
Rating: 3.0 out of 5 stars
Title: .0 out of 5 stars Ok book but could be better. Lots of Obscure Information and other Filler Material.
Date: Reviewed in the United States 🇺🇸 on May 5, 2020
Review: Book is ok, I somewhat prefer having a physical book rather than chase down internet pages especially when I'm new at a subject. Having said that, I think the material could have been covered in more detail, in half the pages.

I feel that you could look at the book table of contents, and find the material yourself for each chapter.

Some complaints
- book gives a lot of information (e.g. about the parameters of a function) in a long paragraph instead of a bullet form.
- lot's of space (pages) filled with pointless tables, some longer than they need to be to make the point, all with normal size font.
- lot of things are explained using some pretty big tables with hundreds or more rows. Why ? Author couldn't be bothered to make some smaller CSV files ?
- Feels like the author has struggled to fill the pages in the book. There is a chapter dedicated to STRING FORMATTING and REGEX .. nothing really to do with a Pandas book. Other chapters give examples of things are that fairly obscure - such as all kinds of plots such as violin plots, instead of actually explaining plots in a concise manner - or obscure cases that you would rarely encounter, and if you did, this book would not be a good reference either way.
- Another example of filler material, Chapter 5 is about "missing data", and there's a section that shows you where it could come from, and chapter 5.3.2 ( 2 pages), is really an exact repeat of the tables from the section on merging .. because those examples tables contain missing data. A simple paragraph pointing you to that previous section could have sufficed.

Name: I. Luv Sushi
Rating: 5.0 out of 5 stars
Title: .0 out of 5 stars Best Python/Data Science book I've read in years...
Date: Reviewed in the United States 🇺🇸 on December 17, 2019
Review: I'm a Data Scientist for a large bank, also currently pursuing a Masters in DS. I've tried to slog through any number of Python/R/Data Science books and usually can barely crack the first 100 pages. First of all, they all seem to be compelled to drag me through numpy. I get it. Everything is based on numpy. But I rarely use numpy iRL! In short, most of the authors take 600 pages to say what they can easily get to in 200 and change.

Daniel's book is a breath of fresh air. He still manages to educate without getting bogged down in the minutia or the overly verbose theory, math, etc. etc. The chapters cut right to the chase of what you need to know to become expert at pandas (as well as seaborn and other nifty libraries) while also providing Data Science-applicable foundations (tidy data, visualizations, etc.).

Also worth mentioning is the companion information available on his github repo. I made great use of the Jupyter notebooks, adding copious examples from the book. These will be my go-to references going forward.

I can honestly say I've never referred to a technical book as a "page-turner" but this one definitely fits that description. I averaged three chapters a day and will re-read it in about a month. For those in the Data Science field, or wanting to become experts in pandas, I highly, highly, highly recommend this book.

Name: Ian Jones
Rating: 5.0 out of 5 stars
Title: .0 out of 5 stars Excellent introduction to Pandas - highly rated!
Date: Reviewed in the United States 🇺🇸 on October 27, 2019
Review: Getting into the world of data analytics and data science can daunting. I’ve bought and partly read several books on Pandas, and read a lot of the usual blogs and stack overflow articles, however this book stands out in terms of having clear explanations and up to date examples, and a graded level of introduction where the author goes wide before he goes deep. He makes early use of data visualization which is a wonderful way to teach, as seeing the results of the analysis in a graphical way complements the tabular results from Pandas (which is a wonderful tool).

I like this book so much it's becoming a little worn around the edges, as I take it with me while travelling on business (my day job is cybersecurity) and I can see many applications for pandas to summarize, present and search for outliers in a large amount of security data.

Several topics which really confused me for a long time became clear in minutes with this book, which is a testament to a good writer - there are not just examples; there are the right examples that illustrate how to do something and the kind of trap you can fall into when you do it wrong.

I've just bought the kindle version to complement my paper copy, something I rarely do, but this book is worth it!

Name: Jolene B.
Rating: 5.0 out of 5 stars
Title: .0 out of 5 stars Sometimes it just helps to have the physical reference book
Date: Reviewed in the United States 🇺🇸 on August 7, 2020
Review: I am a struggling beginner, and I reach for this book a lot. I like the Appendixes. The explanations of dictionaries, slices, loops, and comprehensions are simple and give enough example that it makes sense. Throughout the book, there is an intro paragraph as each topic is introduced. The context helps with understanding.

Name: Pat
Rating: 5.0 out of 5 stars
Title: .0 out of 5 stars Very clearly written out
Date: Reviewed in the United States 🇺🇸 on April 29, 2021
Review: This is one of the most concise books on different aspects of Pandas I have skimmed through so far. I recommend what the author recommends in the beginning - Read through the appendix first.

Name: Bill Hayes
Rating: 5.0 out of 5 stars
Title: .0 out of 5 stars IMHO, best intro of the three major Pandas books.
Date: Reviewed in the United States 🇺🇸 on June 19, 2018
Review: I'm more than half way through this book and found it much better as an intro to Pandas than the two other books I began reading: "Pandas Cookbook" by Petrou and "Python for Data Analysis" by Wes McKinney (the creator of Pandas). Both of the latter are fine books but Chen's book is more concise: he explains a method, shows one or two good examples, then moves on. Perfect for the way I learn. (I have 14 years of Python experience.)

Name: Rachel Gelman
Rating: 4.0 out of 5 stars
Title: .0 out of 5 stars Great for data analysis not for in depth pandas
Date: Reviewed in the United States 🇺🇸 on May 7, 2020
Review: A lot of methods not covered. But it does give you a good basis for data analysis.

Name: Shinn-Cherng Liu
Rating: 5.0 out of 5 stars
Title: .0 out of 5 stars like the book's name "for every one"
Date: Reviewed in the United States 🇺🇸 on April 3, 2018
Review: I buy other books seam to more deeply and professional ,and need more times to study that isn't my intent.
For my purpose is lean Pandas quickly and use to resolution my problem.
The books's arrange and deepness is properly for novice.
The book Let me quickly master Pandas but not need lean every detail or scientist's relation matter ,like the book's name "for every one".
Thanks Daniel Y.