Python Data Science Handbook: Essential Tools for Working with Data 1st Edition, Kindle Edition

The figures were generated in color, but printed black and white, so they are often unintelligible. It's hard to tell the red dots from the blue when they are both grey.Apart from that major oversight, the book is ok. If you want to learn data science, this is not for you; it doesn't get into the fundamentals much at all. If you are an experienced R user looking for how to translate into python, this will get you started. The rest of my review comes from this perspective.The book spends far too much time on low-level ipython, numpy, and matplotlib functionality (chapters 1, 2, and 4). You are rarely going to use this stuff.The pandas section (chapter 3) is fine, but I was a little disappointed in the treatment of the grouping/aggregation functions. The book mentions the split-apply-combine paradigm of Hadley Wickham, but doesn't cover the topic in nearly as much detail as the paper of the same name. I was hoping to learn how to translate the dplyr verbs (group_by, filter, select, mutate, summarize, arrange) into pandas, but this book doesn't provide that. You will learn the basics of grouping and aggregation, but your code is going to be a lot more verbose than it was in R.The machine learning case studies in chapter 5 are pretty nice - probably the only reason I would recommend this book. The chapter provides a good overview of the scikit-learn API and effective patterns for machine learning problems.

I am currently taking a Machine Learning course from Udacity and this book has proven to be a great reference guide for several projects and quizes. Although it does not go in depth in regards to machine learning (although almost half of the book is dedicated to it), it does give an understanding of essential concepts. For those interested in machine learning I would recommend bying "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Geron as well as this book.There is no one book for data science, and this one is no exception. Just keep that in mind before buying it.Other than that, I am really happy with my purchase.P.S. For those complaining about black and white graphs and diagrams - check the author's GitHub.

This is an excellent reference book for people working with data science. Remember, 80% of the effort in machine learning, data analysis or data science in general is about processing data and understanding data. This book is for that purpose and I think it's the best book out there about data processing, analysis and visualization using python. If you look for hardcore machine learning, go for other books. Highly recommended!

I have used R for a few years and this was my first book that covered Python for data science. Even though it does not go into super great depth in any area, it is definitely a super book. It covers everything from Pandas, Matplotlib, and scikit-learn. I would highly recommend it for anyone that is new to Python and/or data science. The book is written with Jupyter Notebooks so it is easy to follow along and try code from the book in your own notebook.

When I first received this book, I was surprised that it didn't get to scikit-learn until the last third of the book. The first third is about numpy and pandas, and the middle third is about matplotlib. Now that I've been applying it at work, however, I've found that the items covered in the first two thirds were really essential. I wouldn't be nearly as productive if I had just jumped straight to the sections on scikit-learn. The author does an excellent job covering broad terrain with enough detail that you are able to apply it to your problems. You will find yourself going back to use this book as a reference.

I really enjoyed this book. I had not much experience with python prior to reading the book however I was able to pick it up quickly. Before long I was plotting distributions of real time statistics and prototyped a predictive modeling micro service. I consider this a must have book for any aspiring data scientist.

This book is well written and easy to follow. It's saved me from spending hours searching the internet to get acquainted with the standard libraries.I have used it extensively for the intro to ML at Berkeley and for now the book belongs to my short list of desk reference books.

I love the presentation style and the treatment of the subject in this book. This is a must have for experienced programmers breaking into the Data Science/ Machine Learning in Python. The book could have been organized better into more chapters instead of five.

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.

Ich erkläre zunächst meinen eigenen Background und darauf aufbauend, was ich an anderen Python-Büchern/Tutorials vermisst habe:Ich bin promovierter Statistiker mit langjähriger Erfahrung in R und arbeite seit etwas mehr als 2 Jahren mit Linux. Shell-Skills (bash) sind zwar vorhanden, aber definitiv noch ausbaufähig. Ich stehe am Anfang einer Data Science-Karriere in der Industrie. Da Data Science nach meinem Verständnis aus Computer Science + Statistik + epsilon besteht und da ich einen starken Mathematik/Statistik-Background habe, möchte ich meine Programmier-Skills verbessern. Dazu gehört das Erlernen weiterer Programmiersprachen wie Python und C++.Mein Ziel: Lerne Datenanalyse in Python. Insbesondere NumPy, SciPy, Pandas und Matplotlib.Dies ist nicht mein erstes Python-Buch. Was mir an anderen Büchern/Onlinetutorien aufgefallen ist, dass diese oft auf Computer Scientists (Informatiker) zugeschnitten sind. Es war regelmäßig frustrierend, wenn kleine Details nicht erklärt wurden, die für Informatiker selbstverständlich sind.Das Buch "Python Data Science Handbook" ist anders. Es erklärt vieles, was für einen Nicht-Informatiker nicht selbstverständlich ist. Insbesondere ist das erste Kapitel wertvoll für einen Statistiker wie mich. Es erklärt detailliert, wie man mit ipython in einer Shell arbeitet.Fazit: Für Informatiker, die tiefes Verständnis für Python aufbauen wollen, sind andere Bücher empfehlenswert. Wenn man dagegen Grundkenntnisse in Python mitbringt und hauptsächlich an der Datenanalyse in Python interessiert ist, kann ich dieses Buch herzlichst empfehlen.

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

Excelent book that provides lot of basic tools for essential libraries for performing data analysis in Python.

Comprehensive Data Science reference