Python for Data Analysis, 2e: Data Wrangling with Pandas, NumPy, and IPython

I have purchased other books for jumping into machine learning using Python but they always somewhat gloss over the basics, and you have to accept a bit of magic around Pandas, Matplotlib etc to follow along. I'm so glad I went back to build a solid foundation with this book, so I'm no longer fumbling around with magic commands or spending a huge proportion of time trawling Stack Overflow. Probably my favourite aspect of this book is that you can just read it- every single concept is demonstrated in code, on the paper, with the full input and outputs. The only time I've opened my editor is to play around with concepts I wanted to clarify- the rest has been just a good solid read with everything clearly demonstrated. It's well structured and builds concepts as you progress but is also an excellent reference book I can see myself dipping back into time and again. I think this is essential foundational material for starting your journey into data analysis and/or machine learning with Python.

A good technical book explaining how to use Pandas with strong supporting tutorials and data. Well laid out and passed well. No complaints.

A great and very useful book that really helps.

Awesome book if you need/want to go deeper with Pandas.

Wes is a great writer and teacher, I feel I am learning more about data analysis with python by tracing out the code in the book (Wes refers to this as strengthening one's "muscle memory") in my Juptyer notebooks on my laptop than I had from trying moocs on data analysis.

I'm not the kind of person that gives 5 stars, but every once in a while i just have to. If your looking to get into data science with python this and hands-on machine learning with scikit-learn and tensorflow are solid buys.

Superb. Covering numpy, pandas and matplotlib. Extensive coverage for cleaning data, also comparing different ways of doing it.

This is a great book written by the author of pandas For a bit more background and explanations then the Chen book works well with this.

The quality of the book is awesome (as described) quality of the packaging is awesome and book. Nice book, covers all the topics gradually and thoroughly. Just started and liking it already. Will post another review after having read couple of chapters.

Acquisition pour un perfectionnement en tant que Data Analyst

This is the best reference I use for dealing with python, numpy and mainly pandas. Must have for anyone learning or using pandas. The author (who actually wrote pandas)style is into the point, clear and with simple examples that demonstrate the usage in real world. Also this book has all the info to help you prepare data for sci-kit learn and tf .

This book has been my foundation of using python as a data analyst. This book primarily focuses on the pandas Python library, which is awesome at processing and organizing data (Python pandas is like MS Excel times 100. This is not an exaggeration). It also introduces the reader into numpy (lower level number crunching and arrays), matplotlib (data visualizations), scikitlearn (machine learning), and other useful data science libraries. The book contains other book recommendations for continuing education. Although this would be a challenging book for a brand new Python user, I would still recommend it, especially if you are currently doing a lot of work in MS Excel and/ or exporting data from databases. I had a few false starts learning Python, and my biggest stumbling block was lack of application in what I was learning. This book puts practical tools in the reader's hands very quickly. I personally don't have time to make goofy games etc. that other books have used as practice examples. Despite other reviews criticizing the use of random data throughout the book, I found the examples easy to follow and useful. I would also argue that learning how to generate random data is useful in itself (thus the purpose of the numpy random library), and that there are practical examples throughout the book. Chapter 14 devoted to real-world data analysis examples. I am almost finished with my second time through the book, this time working through every example. This book has been well worth the hours spent in it. For context, I previously relied on Excel, SQL, and some AutoHotKey. This book has significantly improved how I work. Thanks, Wes and team.

You must have this book if you want to learn Pandas and Data Science.