Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython Paperback – 24 Oct. 2017

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.

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

A great and very useful book that really helps.

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.

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

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.

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

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