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

I got this book when I was transitioning to doing data science with Python and was struggling to become familiar with standard tools. It's written by the creator of Pandas, and follows the style of the Pandas documentation: dense, telegraphic, peppered with examples.It's hard work because Wes McKinney often does not articulate why you would need to do something (assuming you are already knowledgeable on the underlying process), and writes like an impatient person who would rather be doing something else. Additionally examples often suffer from being both too long and too short - too long in that almost every example is on a toy dataset created from scratch, too short in that most of those datasets have only 5 or 10 elements and do not always showcase complex operations. Other examples (particularly involving time series) have an overabundance of data that make the critical results hard to spot. Frankly, my first month with Pandas was a miserable one.But I give the book 5 stars both because I came to love Pandas as I got more familiar with it, and because while McKinney is not fun to read, he does pack the book with useful information and it is (mostly) well organized. If anything it would benefit from being longer and with a more patient treatment of larger and more concrete datasets (eg the Titanic passenger dataset used in the Pandas documentation). The initial chapter on the basics of using Python could go - if you need this book, then you don't want to be trying to learn the rudiments of Python from it. If you can accept that you'll need a lot of bookmarks or margin notes to get through a rather steep learning curve, it will reward your persistence.

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.

This book covers all of the basics that you would want to know to get started in programming in Python for data analysis, as the title implies, but it doesn't really offer compelling real-world examples. The data seem to be made up and the analyses don't go into enough detail to help you really learn how pandas and numpy work. Overall this is a decent starter book but you will have to bookmark the python and pandas documentation online if you want to have a reference to all of the functionality those tools have, and there are many places online where you can get better examples to learn from. If you haven't made your mind up about which tool to use for data analysis, I highly recommend checking out dplyr in R, which has an excellent free book online (R for data science, hadley wickham). I find it very easy to learn and it is much easier to set up R and RStudio than it is to set up Python, even though I love Python and Pandas.

This product arrived fast. The book was in great shape. Couldn't have asked for a better buying experience

So far, this book has been an inspiring reading. It contains a huge number of data cleansing, transformation, analysis & etc. code snippets. The code is very clean and - for the most part - self-explaining (at least, for a seasoned software developer). The book step by step displays the motivations behind the design and functionality of center-piece Python modules - and you would not expect anything less from the original designer of Pandas. I feel this wonderful book being a natural extension of ageless Practical CS classics by Niklaus Wirth, Kernighan-Ritchie, and B. Stroustrup for Data Science Age.

I was looking for a book that could help me to learn python. I gave this book a try and I realized that the data analysis that I learnt from this book is pretty good from a pandas viewpoint (mostly).It does explain about numpy, matplotlib and seaborn libraries, but most of the time is oriented from the pandas perspective.Nevertheless, if you want to learn machine learning, numpy and other libraries, consider buying another book.All in all, I liked the book because it teaches you and really well how to wrangle data. I only had wish it had more numpy and other libraries.