Python Data Science Handbook Paperback – 6 Dec. 2016

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(Purchased through O'Reilly directly). I'm halfway through the book and so far I have to admit it's one of the best books I've read on the subjects covered. Mostly small snippets of code which are easy to digest and reason about. Coverage touches IPython, Numpy, Pandas, Matplotlib and finally Scikit-Learn. Frankly, no need to look anywhere else and purchase dated books on these topics individually. Pandas and Numpy's coverage is well worth its weight in gold.

Most data science books assume that you know how to program using NumPy, matplotlib and pandas. This book does not. This book spends a lot of time teaching how to actually program the bits in Python to achieve ML models.That said, some pieces are inconsistent with each other and the order of the material may not be the best for a novice programmer. If you need to learn how to program this isn't the book. If you are a good programmer and want to understand the ML ecosystem in Python then get this.

There's some great Python books out there for data analysis(Wes McKinneys (slightly dated) "Python for Data Analysis"and Allen Downeys "Think Stats" etc.) but its hard to beatthis one.What sets it apart is the way this book explains the*reasoning* behind different commands and also some of thecommon "gotchas" that can mess things up. Also VanderPlashas a knack for explaining the *concepts* rather than justtrotting out various commands. (Read the dicussion aboutPython variables for example - I realise now I had the wrongmental model for years!)I'm working in Statistics in the UK and our team istransitioning from SPSS/SAS to Python. Most of us in theoffice are using this book to get a foothold in the Pythondata analysis world. It's excellent overall.(Also the new high level plotting library "Altair" fromVanderPlas and Granger looks very interesting: for anyonestruggling with the plotting syntax of matplotlib, seaborn,Pandas, yhats ggplot etc...)

Good. But needs better interpretation of the codes and too kernel inclined

Comprehensive Data Science reference

lots of important topics are missing.i did not return it because amazon cheatted my last time when i returned books - never returned my money.and i paid post expenses and books of course.waste...

Rather too much comparison with C. I found this irritating and unnecessary. My many years of scientific programming experience has not been via C, and I suspect this is true of many long-in-the-tooth scientists deciding to get to grips with Python. Otherwise a good grounding but a little expensive. Disappointing to find no signal or image processing chapters, ie digital filtering, FFTs, data, smoothing, sharpening etc. I guess it assumes you know enough MATLAB to work it our for yourself.