Python Data Science Handbook: Essential Tools for Working with Data

Covers all the essentials very clearly.

Through Chapter 11 so far and the Figures are frequently very wrong. There seem to be a couple other minor errors, but this desperately needs an update for the Kindle version. Content otherwise seems good so far, so hope this gets fixed.Update: Just after I entered this, the figures seem to have been updated. Checked 4 and all are fixed, so moved the rating up one and will update when through all chapters.

Contents are great, but most figures do not correspond to the code for the Kindle edition.

I bought a kindle version of this book.The explanation is good.However, graphs that are not related to the content come up so often, so I have to run all the code in the book or go to the author's github page to see the graphs.It's quite inconvenient.I think the kindle version should be updated for this issue.

Here we have a book with a bunch of color-coded plots, but the whole thing is printed in black and white. The content is good, but on the whole 2/10 would not recommend.

I averaged the rating over the two editions, as the first went far beyond my expectations. The first could be categorized as a 'must have' for beginners, and till today represents the the best Python book I would recommend to all newbies.The first ed of this book was a game changer for an experienced data scientist with non-CS/data structures training/orientation, transitioning from other languages to Python. Having advanced ML programming experience from training in Stats, Econ and Math I tried many authors to switch, finally found traction for the first time with Python by using the first edition this book.Numpy, multi-dimensional arrays, Pandas are beautifully explained, clear conceptualization. As is scikit-learn.So why the three stars?Tensor Flow was gaining traction when the first ed and I eagerly waited for 2nd ed from this brilliant author. And after almost a decade of advancement I expected many new features and Python modules would be added.I was shocked and seriously disappointed to find no difference between the two editions. The main difference is cosmetic - chapters in the first are broken down and referred to as sections in the second, each header in the first are now separate chapters. No tensor flow or NLP or even locational analytics added. The content is shockingly the same. What is telling is there is no explanation on the difference between the two editions. Contrast this with Geron's second edition which came out three years earlier, and added Tensor Flow.Save your $. I am adding this review as the first edition was hugely discounted in Amazon, I find that today its priced the same as the second. I would wait for the price of the second ed to reduce. I own a copy of the first, thankfully, got a preview of the second from a friend who is sore about this.

How do you have a data science book, with an ENTIRE SECTION on visualizations with lines like “note the color difference” AND THE WHOLE THING IS BLACK AND WHITE

Not sure whose idea was it to publish and sell a book that is heavy on color graphs as black and white. Returning it!