Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series)

This book was required material for a college level Management Information Systems (MIS) Course. I used it to work through Intro to Python and Pandas material. This book has some typos and outdated information (in 2024; see new edition for latest information). If you can brush off the surface imperfections, it's actually very practical and useful teaching. You will need to practice what's in this book on data sets. I used the concepts in this book to write a program at my full-time job that accomplishes 30 hours of work in 2 minutes using Pandas. This book was pure gold for my needs! A big thanks to the author for writing this book! I bought the new edition too.

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!

Definitely gives me good overview of Pandas and how to use it but it is far away from complete reference.

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.

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.

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.

I rarely buy paper books anymore but this was worth the purchase as a reference book.My only beef was that the figures are not in color. Sigh. But I guess it dropped the price; I would have paid more for the color version.

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.)

Its nice introduction to pandas library of python. Basic concepts are explained in good presentation.

Dieses Buch ist sehr gut aufgebaut und auch die verwendeten Beispieltabellen sind einfach gehalten. Top.

Parfait booking.

This is one of best books on the subjects. The author has done an excellent job of introducing Pandas to the first time users. The speed is not overwhelming, Highly recommended.

Es muy común y frustrante tener que saltarse los dos primeros capítulos de un libro sobre Análisis de datos y Ciencia de Datos, debido a que los autores siempre quieren dar un curso introductorio de Python.En este caso, Daniel Y. Chen evita dar el curso introductorio de Python enfocándose al tema desde el primer capitulo (Pandas con apoyo de Matplotlib y Seaborn). Sin embargo, siempre deja muy claro al principio de cada capítulo los conocimientos previos que debe de tener el lector para poder entender el tema.El conocimiento compartido en cada capítulo es incremental y ordenado, por lo que el libro se puede leer en una secuencia lineal y sirve como un gran apoyo para cursos especializados.Si el interés del lector es aprender sobre Pandas a fondo, este es el libro que debe de adquirir.