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Python for Data Analysis, 2e: Data Wrangling with Pandas, Numpy, and Ipython Paperback – 3 November 2017

by Wes Mckinney (Author)

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Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

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Product details

Language : English
Paperback : 522 pages
ISBN-10 : 1491957662
ISBN-13 : 978-1491957660

Best Sellers Rank: 1,012 in Books (See Top 100 in Books)
- 6 in Programming Languages

Customer reviews:
1 rating

Product description

About the Author

Wes McKinney is the main author of pandas, the popular open sourcePython library for data analysis. Wes is an active speaker andparticipant in the Python and open source communities. He worked as a quantitative analyst at AQR Capital Management and Python consultant before founding DataPad, a data analytics company, in 2013. He graduated from MIT with an S.B. in Mathematics.

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

What Is This Book About?

This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. My goal is to offer a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools that will equip you to become an effective data analyst. While 'data analysis' is in the title of the book, the focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis.

New for the Second Edition

The first edition of this book was published in 2012, during a time when open source data analysis libraries for Python (such as pandas) were very new and developing rapidly. In this updated and expanded second edition, I have overhauled the chapters to account both for incompatible changes and deprecations as well as new features that have occurred in the last five years. I’ve also added fresh content to introduce tools that either did not exist in 2012 or had not matured enough to make the first cut. Finally, I have tried to avoid writing about new or cutting-edge open source projects that may not have had a chance to mature. I would like readers of this edition to find that the content is still almost as relevant in 2020 or 2021 as it is in 2017.

The major updates in this second edition include:

All code, including the Python tutorial, updated for Python 3.6 (the first edition used Python 2.7)
Updated Python installation instructions for the Anaconda Python Distribution and other needed Python packages
Updates for the latest versions of the pandas library in 2017
A new chapter on some more advanced pandas tools, and some other usage tips
A brief introduction to using statsmodels and scikit-learn
I also reorganized a significant portion of the content from the first edition to make the book more accessible to newcomers.

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Akbar Azad

A go-to reference book

18 December 2020

Verified Purchase

I never regret purchasing this book. It is my Swiss army knife for analysis in Python. I used R extensively in previous role and in university. Thus, despite understanding some techniques like aggregation, pivots, I needed to know how to their Python syntax. This book is sharp, straightforward, in helping you answer questions like "How do I do this (data wrangling technique) in Python?"

Helpful

Most helpful customer reviews on Amazon.com

Amazon.com: 104 reviews

Ocean State Prime

less than what I had hoped

31 July 2018 - Published on Amazon.com

Verified Purchase

This book falls somewhere between a manual page providing one example per function and a cookbook, tending more toward the former. Examples are dry and most are constructed using random data. There is very little in the way of practical use cases. I bought the book hoping to get some inspiration for using numpy and/or pandas for some types of analyses I find myself doing, but that didn't happen. Probably I've gathered enough overview that I now can put together useful queries that will provide useful hits on Stack Exchange. I wish I had better to say.

46 people found this helpful

Saurabh

poor explanations and uses random numbers for examples

17 April 2018 - Published on Amazon.com

Verified Purchase

Wes is the creator of Pandas but he is not an effective writer. This has left a bad taste of pandas in my mind. A lot of examples created in this book are using random numbers and this is a poor way of teaching someone as it's too abstract. Random number generated examples rarely have anything to do with data encountered in real life.

This book's problem is the classic curse of knowledge. The author does not know what it's like to get started with pandas and what are the difficulties users will have.

59 people found this helpful

Ocean Horizon

Provides the Python tools for implementing machine learning algorithms...

8 January 2018 - Published on Amazon.com

Verified Purchase

This is the Python book for the data scientist: already knows Python or at least OOP programming, but wants to be able to utilize the native and NumPy structures for writing machine learning algorithms. Slicing, broadcasting, tuples, pandas data frames -- all useful for applying Python's tools to data science. This is not a beginner book, but it's exactly what I needed to learn the details for translating equations to code.

36 people found this helpful

Archibold

A good resource, but not a good learning tool

22 March 2019 - Published on Amazon.com

Verified Purchase

As others have said, this book provides a good manual. If you have a project in mind and some programming background, you can adapt the examples in the book to complete the task. That said, a lot of the book reads more as documentation than instruction, and the documentation is more sparse than the official pandas documentation. Furthermore, some of the examples are rather opaque in understanding the main point, and the use of random number generators for example data manipulation sometimes makes it difficult to understand what a specific block of code is doing.

Overall, this book provides a jumping off point in understanding the capabilities of pandas as well as its strengths, but it wasn't terribly useful in even basic data science workflow and concepts. For that, I highly recommend something like Hadley Wickham's "R for Data Science," which is much more approachable and rewarding in its use of example datasets, its more personable writing style, and its outlining of good practices for data science.

21 people found this helpful

Christopher

Awesome Book to Gain Practical Data Skills with Python

5 April 2019 - Published on Amazon.com

Verified Purchase

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.

20 people found this helpful

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