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Wes McKinney

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

by Wes McKinney (Author) Format: Paperback

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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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ISBN-10

1491957662
ISBN-13

978-1491957660
Edition

2nd
Publisher

O'Reilly Media
Publication date

November 14, 2017
Language

English
Dimensions

7.25 x 1 x 9.5 inches
Print length

550 pages

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From the Publisher

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 install instructions for the Anaconda Python Distribution & other 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
Reorganized since from the first edition to make the book more accessible to newcomers.

Editorial Reviews

About the Author

Wes McKinney is a New York?based software developer and entrepreneur. After finishing his undergraduate degree in mathematics at MIT in 2007, he went on to do quantitative finance work at AQR Capital Management in Greenwich, CT. Frustrated by cumbersome data analysis tools, he learned Python and started building what would later become the pandas project. He's now an active member of the Python data community and is an advocate for the use of Python in data analysis, finance, and statistical computing applications.

Wes was later the co-founder and CEO of DataPad, whose technology assets and team were acquired by Cloudera in 2014. He has since become involved in big data technology, joining the Project Management Committees for the Apache Arrow and Apache Parquet projects in the Apache Software Foundation. In 2016, he joined Two Sigma Investments in New York City, where he continues working to make data analysis faster and easier through open source software.

Product details

Publisher ‏ : ‎ O'Reilly Media
Publication date ‏ : ‎ November 14, 2017
Edition ‏ : ‎ 2nd
Language ‏ : ‎ English
Print length ‏ : ‎ 550 pages
ISBN-10 ‏ : ‎ 1491957662
ISBN-13 ‏ : ‎ 978-1491957660
Item Weight ‏ : ‎ 1.85 pounds
Dimensions ‏ : ‎ 7.25 x 1 x 9.5 inches
Best Sellers Rank: #147,631 in Books (See Top 100 in Books)
- #39 in Data Modeling & Design (Books)
- #57 in Data Processing
- #105 in Python Programming
Customer Reviews:
(1,823)

About the author

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Wes McKinney

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Since 2007, I have been creating fast, easy-to-use data wrangling and statistical computing tools, mostly in the Python programming language. I am best known for creating the pandas project and writing the book Python for Data Analysis. I am also a contributor to the Apache Arrow, Kudu, and Parquet projects within the Apache Software Foundation. I am currently the CTO and Co-founder of Voltron Data, which builds accelerated computing technologies powered by Apache Arrow. I previously worked for Ursa Labs (within RStudio / Posit), Two Sigma, Cloudera, DataPad, and AQR Capital Management.

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1,823 global ratings

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Customers say

Customers find the book excellent for data analysis, particularly as an introduction to Python, and appreciate its comprehensive coverage of complex topics. The writing style receives positive feedback, and customers consider it useful. However, the detailed instructions and readability receive mixed reviews, with several customers finding the explanations poor and the content difficult to follow.

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73 customers mention content, 62 positive, 11 negative

Customers find the book comprehensive and instructive, particularly as an introduction to Python and data science, with clear explanations of basics.

Great book for someone familiar with other programming language and getting into Python....Read more

Excellent book, helps to understand better the logic of Pandas.Read more

Great content. Five star content. But, pages started coming off the binding one day after I got this in the mail....Read more

...This is every bit as detailed as I hoped it would be with a great introduction, great examples, and great coverage of fundamental, basic, and...Read more

18 customers mention difficulty level, 13 positive, 5 negative

Customers find the book suitable for beginners, with one mentioning it serves as a great introduction to pandas.

This is a great book for starting out. If you have a basic knowledge of python, I would highly recommend this....Read more

...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.Read more

...This is not a beginner book, but it's exactly what I needed to learn the details for translating equations to code.Read more

This book gave me my first job. And I am still learning it. It is simple, talks some general idea why functions design like this, and introduces...Read more

9 customers mention writing style, 7 positive, 2 negative

Customers appreciate the writing style of the book, with one mentioning it helps develop Python scripting skills.

Well written by the creator of Pandas. The author's copious use of code snippets to illustrate his points makes the material very usable....Read more

Great writing.Read more

...We’ll written and generally doesn’t get into minutiae. Very useful.Read more

Wes is the creator of Pandas but he is not an effective writer. This has left a bad taste of pandas in my mind....Read more

7 customers mention comprehensiveness, 5 positive, 2 negative

Customers appreciate the book's comprehensive approach, covering complex topics and focusing on the pandas Python library.

Good, relevant, practical content. Bad book quality. Pages started coming loose after a few days....Read more

...The topics are very well explained and it covers most topics which is essential for analyzing the data using Python.Read more

...The topics are just very random.Read more

...This book primarily focuses on the pandas Python library, which is awesome at processing and organizing data...Read more

7 customers mention usefulness, 5 positive, 2 negative

Customers find the book useful.

Useful, especially in terms of getting data into and out of python from other data sources (databases, spreadsheets, etc.)Read more

...We’ll written and generally doesn’t get into minutiae. Very useful.Read more

...the capabilities of pandas as well as its strengths, but it wasn't terribly useful in even basic data science workflow and concepts....Read more

...some general idea why functions design like this, and introduces some practical functions....Read more

10 customers mention detailed instructions, 5 positive, 5 negative

Customers have mixed opinions about the book's instructions, with some finding them easy to follow while others report poor explanations and note that the book reads more like documentation than instructional material.

...the use of random data throughout the book, I found the examples easy to follow and useful....Read more

...random numbers and this is a poor way of teaching someone as it's too abstract....Read more

Excellent step-by-step instructions. Interesting examples.Read more

...Operations/syntax/methodology are presented without a logical hierachical structure, e.g. when introducing a certain object type, only the trivial...Read more

8 customers mention readability, 3 positive, 5 negative

Customers have mixed opinions about the book's readability, with several finding it difficult to follow.

Reads like a manual. Difficult to learn unless you’re looking for something specific.Read more

...Everything is easy to understand. Able to develop your python scripting skills from beginner to proRead more

Hard to follow, problematic explanations, and one needs simply a manual for this book. What a waste of time!Read more

...The small pictures and poor resolution make it difficult to read. The topics are just very random.Read more

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