Susan E. McGregor - Practical Python Data Wrangling and Data Quality
Here you can read online Susan E. McGregor - Practical Python Data Wrangling and Data Quality full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2022, 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.
- Book:Practical Python Data Wrangling and Data Quality
- Author:
- Publisher:OReilly Media
- Genre:
- Year:2022
- Rating:5 / 5
- Favourites:Add to favourites
- Your mark:
Practical Python Data Wrangling and Data Quality: summary, description and annotation
We offer to read an annotation, description, summary or preface (depends on what the author of the book "Practical Python Data Wrangling and Data Quality" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.
There are awesome discoveries to be made and valuable stories to be told in datasets--and this book will help you uncover them. Whether you already work with data or just want to understand its possibilities, the techniques and advice in this practical book will help you learn how to better clean, evaluate, and analyze data to generate meaningful insights and compelling visualizations.
Through foundational concepts and worked examples, author Susan McGregor provides the tools you need to evaluate and analyze all kinds of data and communicate your findings effectively. This book provides a methodical, jargon-free way for practitioners of all levels to harness the power of data.
- Use Python 3.8+ to read, write, and transform data from a variety of sources
- Understand and use programming basics in Python to wrangle data at scale
- Organize, document, and structure your code using best practices
- Complete exercises either on your own machine or on the web
- Collect data from structured data files, web pages, and APIs
- Perform basic statistical analysis to make meaning from data sets
- Visualize and present data in clear and compelling ways
Susan E. McGregor: author's other books
Who wrote Practical Python Data Wrangling and Data Quality? Find out the surname, the name of the author of the book and a list of all author's works by series.
Practical Python Data Wrangling and Data Quality — 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 "Practical Python Data Wrangling and Data Quality" 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.
Font size:
Interval:
Bookmark:
by Susan E. McGregor
Copyright 2022 Susan McGregor. 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). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .
- Acquitisions Editor: Jessica Haberman
- Development Editor: Jeff Bleiel
- Production Editor: Daniel Elfanbaum
- Interior Designer: David Futato
- Cover Designer: Karen Montgomery
- Illustrator: Kate Dullea
- February 2022: First Edition
- 2020-12-08: First Release
- 2021-02-01: Second Release
- 2021-03-02: Third Release
- 2021-04-05: Fourth Release
- 2021-05-12: Fifth Release
- 2021-06-15: Sixth Release
- 2021-07-21: Seventh Release
- 2021-09-08: Eighth Release
See http://oreilly.com/catalog/errata.csp?isbn=9781492091509 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Practical Python Data Wrangling and Data Quality, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
The views expressed in this work are those of the author, and do not represent the publishers views. 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.
978-1-492-09143-1
[LSI]
With Early Release ebooks, you get books in their earliest formthe authors raw and unedited content as they writeso you can take advantage of these technologies long before the official release of these titles.
If you have comments about how we might improve the content and/or examples in this book, or if you notice missing material within this chapter, please reach out to the author at .
Welcome! If youve picked up this book, youre likely one of the many millions of people who is intrigued by the processes and possibilities surrounding datathat incredible, elusive new currency thats transforming the way we live, work, and even connect with one another. Most of us are vaguely aware of the fact that datacollected by from our electronic devices and other activitiesis being used to shape what advertisements we see, what media is recommended to us and which search results populate first when we look for something online.
But data is not just something that is availableor usefulto big companies or governmental number-crunchers. Being able to access, understand and gather insight from data is a valuable skill whether youre a data scientist or a daycare worker. And fortunately, the tools needed to use data effectively are more freely accessible than ever before. Not only can you do significant data work using only free software and programming languages, you dont even need an expensive computerall of the exercises in this book, for example, were designed and run on a Chromebook that cost less than $500.
The goal of this book is to provide you with the guidance and confidence you need to begin exploring the world of data, from wrangling it (in other words, getting it into a state where it can be assessed and analyzed), to evaluating its quality (which is often both more nuanced and more difficult). With those foundations in place, well move on to some of the basic methods of analyzing and presenting data to generate meaningful insight. While these latter sections will be far from comprehensive (both data analysis and visualization are robust fields unto themselves), they will give you the core skills needed to generate accurate, informative analyses and visualizations using your newly cleaned and acquired data.
This book is intended for true beginners; all you need are a basic understanding of how to use computers (e.g. how to download a file, open a program, copy and paste etc.), an open mind, and a willingness to experiment. I especially encourage you to take a chance on this book if you are someone who feels intimidated by data or programming, if youre bad at math, or imagine that working with data or learning to program will be too hard for you. I have spent nearly a decade teaching hundreds of people who didnt think of themselves as technical the exact skills contained in this book, and I have never once had a student who was truly unable to complete this work. In my experience, the biggest barrier to programming and work with data is not the difficulty of the material, but the quality of the instruction. I am grateful to the many students over the years whose questions have, I think, made my ability to convey this material immeasurably better---and that I now have the opportunity to pass that insight on to so many others through this book. And while I wont pretend that a book can truly replace having access to a human teacher, I am confident that it will give you enough information to master the basics, while pointing the way towards more in-depth (and interactive) resources when necessary.
Folks who have some experience with data wrangling but have reached the limits of spreadsheet tools or want to expand the range of data formats they can easily access and manipulate will also find this book useful, as will those with front-end programming skills (in JavaScript or PHP, for example) who are looking for a way to get started with Python.
In the preface to media theorist Douglas Rushkkoffs 2010 book Program or be Programmed he compares the act of programming to that of driving a car. Unless you learn to program, Rushkoff writes, you are a perpetual passenger in the digital world, one who is getting driven from place to place. Only the car has no windows and if the driver tells you there is only one supermarket in the county, you have to believe him.
You can relegate your programming to others, Rushkoff continues,but then you have to trust them that their programs are really doing what youre asking, and in a way that is in your best interests. More and more these days, the latter assertion is being thrown into question.
Yet while most of us would agree that almost anyone can learn to drive I have met few peopleapart from myselfwho truly believe that anyone can program. This is despite the fact that, from a cognitive perspective, driving a motor vehicle is vastly more complex than programming a computer. Why, then, do so many of us imagine that programming will be too hard for us?
Here, for me, is the real strength of Rushkoffs analogy, because the windowless car he describes doesnt just hide the outside world from the passenger, it also hides the driver from passersby. Part of the reason why it is easy for so many of us to believe that anyone can drive a car is because we have evidence of it: we quite literally see all kinds of people driving cars, every day.
Font size:
Interval:
Bookmark:
Similar books «Practical Python Data Wrangling and Data Quality»
Look at similar books to Practical Python Data Wrangling and Data Quality. 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.
Discussion, reviews of the book Practical Python Data Wrangling and Data Quality 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.