• Complain

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

Here you can read online McKinney - Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2011, publisher: OReilly Media, genre: Home and family. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

No cover
  • Book:
    Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition
  • Author:
  • Publisher:
    OReilly Media
  • Genre:
  • Year:
    2011
  • Rating:
    4 / 5
  • Favourites:
    Add to favourites
  • Your mark:
    • 80
    • 1
    • 2
    • 3
    • 4
    • 5

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

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

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. Youll 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. Its 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

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition — read online for free the complete book (whole text) full work

Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

Light

Font size:

Reset

Interval:

Bookmark:

Make
Python for Data Analysis

Second Edition

Data Wrangling with Pandas, NumPy, and IPython

Wes McKinney

Python for Data Analysis

by Wes McKinney

Copyright 2018 William McKinney. All rights reserved.

Printed in the United States of America.

Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.

OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .

  • Editor: Marie Beaugureau
  • Production Editor: Kristen Brown
  • Copyeditor: Jasmine Kwityn
  • Proofreader: Rachel Monaghan
  • Indexer: Lucie Haskins
  • Interior Designer: David Futato
  • Cover Designer: Karen Montgomery
  • Illustrator: Rebecca Demarest
  • October 2012: First Edition
  • October 2017: Second Edition
Revision History for the Second Edition
  • 2017-09-25: First Release

See http://oreilly.com/catalog/errata.csp?isbn=9781491957660 for release details.

The OReilly logo is a registered trademark of OReilly Media, Inc. Python for Data Analysis, the cover image, and related trade dress are trademarks of OReilly Media, Inc.

While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

978-1-491-95766-0

[LSI]

Preface
Section 1. 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. Ive 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.

Section 2. Conventions Used in This Book

The following typographical conventions are used in this book:

Italic
Indicates new terms, URLs, email addresses, filenames, and file extensions.
Constant width
Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.
Constant width bold
Shows commands or other text that should be typed literally by the user.
Constant width italic
Shows text that should be replaced with user-supplied values or by values determined by context.

Tip

This element signifies a tip or suggestion.



Note

This element signifies a general note.



Caution

This element indicates a warning or caution.


Section 3. Using Code Examples

You can find data files and related material for each chapter is available in this books GitHub repository at http://github.com/wesm/pydata-book.

This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless youre reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from OReilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your products documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: Python for Data Analysis by Wes McKinney (OReilly). Copyright 2017 Wes McKinney, 978-1-491-95766-0.

If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at .

Section 4. OReilly Safari

Note

Safari (formerly Safari Books Online) is a membership-based training and reference platform for enterprise, government, educators, and individuals.


Members have access to thousands of books, training videos, Learning Paths, interactive tutorials, and curated playlists from over 250 publishers, including OReilly Media, Harvard Business Review, Prentice Hall Professional, Addison-Wesley Professional, Microsoft Press, Sams, Que, Peachpit Press, Adobe, Focal Press, Cisco Press, John Wiley & Sons, Syngress, Morgan Kaufmann, IBM Redbooks, Packt, Adobe Press, FT Press, Apress, Manning, New Riders, McGraw-Hill, Jones & Bartlett, and Course Technology, among others.

For more information, please visit http://oreilly.com/safari.

Section 5. How to Contact Us

Please address comments and questions concerning this book to the publisher:

  • OReilly Media, Inc.
  • 1005 Gravenstein Highway North
  • Sebastopol, CA 95472
  • 800-998-9938 (in the United States or Canada)
  • 707-829-0515 (international or local)
  • 707-829-0104 (fax)

We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at http://bit.ly/python_data_analysis_2e.

To comment or ask technical questions about this book, send email to .

For more information about our books, courses, conferences, and news, see our website at http://www.oreilly.com.

Find us on Facebook: http://facebook.com/oreilly

Follow us on Twitter: http://twitter.com/oreillymedia

Watch us on YouTube: http://www.youtube.com/oreillymedia

Section 6. Acknowledgments
Next page
Light

Font size:

Reset

Interval:

Bookmark:

Make

Similar books «Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition»

Look at similar books to Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.


Reviews about «Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition»

Discussion, reviews of the book Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.