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Jacqueline Kazil - Data Wrangling with Python: Tips and Tools to Make Your Life Easier

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Jacqueline Kazil Data Wrangling with Python: Tips and Tools to Make Your Life Easier
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Praise for Data Wrangling with Python

This should be required reading for any new data scientist, data engineer or other technical data professional. This hands-on, step-by-step guide is exactly what the field needs and what I wish I had when I first starting manipulating data in Python. If you are a data geek that likes to get their hands dirty and that needs a good definitive source, this is your book.

Dr. Tyrone Grandison, CEO, Proficiency Labs Intl.

Theres a lot more to data wrangling than just writing code, and this well-written book tells you everything you need to know. This will be an invaluable step-by-step resource at a time when journalism needs more data experts.

Randy Picht, Executive Director of the Donald W. Reynolds Journalism Institute at the Missouri School of Journalism

Few resources are as comprehensive and as approachable as this book. It not only explains what you need to know, but why and how. Whether you are new to data journalism, or looking to expand your capabilities, Katharine and Jacquelines book is a must-have resource.

Joshua Hatch, Senior Editor, Data and Interactives, The Chronicle of Higher Education and The Chronicle of Philanthropy

A great survey course on everythingliterally everythingthat we do to tell stories with data, covering the basics and the state of the art. Highly recommended.

Brian Boyer, Visuals Editor, NPR

Data Wrangling with Python is a practical, approachable guide to learning some of the most common tasks youll ever have to do with code: find, extract, tidy and examine data.

Chrys Wu, technologist

This book is a useful response to a question I often get from journalists: Im pretty good using spreadsheets, but what should I learn next? Although not aimed solely at a journalism readership, Data Wrangling with Python provides a clear path for anyone who is using spreadsheets and wondering how to improve her skills to obtain, clean, and analyze data. It covers everything from how to load and examine text files to automated screen-scraping to new command-line tools for performing data analysis and visualizing the results.

I followed a well-worn path to analyzing data and finding meaning in it: I started with spreadsheets, followed by relational databases and mapping programs. They are still useful tools, but they dont take full advantage of automation, which enables users to process more data and to replicate their work. Nor do they connect seamlessly to the wide range of data available on the Internet. Next to these pillars we need to add another: a programming language. While Ive been working with Python and other languages for a while now, that use has been haphazard rather than methodical.

Both the case for working with data and the sophistication of tools has advanced during the past 20 years, which makes it more important to think about a common set of techniques. The increased availability of data (both structured and unstructured) and the sheer volume of it that can be stored and analyzed has changed the possibilities for data analysis: many difficult questions are now easier to answer, and some previously impossible ones are within reach. We need a glue that helps to tie together the various parts of the data ecosystem, from JSON APIs to filtering and cleaning data to creating charts to help tell a story.

In this book, that glue is Python and its robust suite of tools and libraries for working with data. If youve been feeling like spreadsheets (and even relational databases) arent up to answering the kinds of questions youd like to ask, or if youre ready to grow beyond these tools, this is a book for you. I know Ive been waiting for it.

Derek Willis, News Applications Developer at ProPublica and Cofounder of OpenElections

Data Wrangling with Python

by Jacqueline Kazil and Katharine Jarmul

Copyright 2016 Jacqueline Kazil and Kjamistan, Inc. 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://safaribooksonline.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .

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  • February 2016: First Edition
Revision History for the First Edition
  • 2016-02-02 First Release

See http://oreilly.com/catalog/errata.csp?isbn=9781491948811 for release details.

The OReilly logo is a registered trademark of OReilly Media, Inc. Data Wrangling with Python, the cover image of a blue-lipped tree lizard, and related trade dress are trademarks of OReilly Media, Inc.

While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors 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.

978-1-4919-4881-1

[LSI]

Preface

Welcome to Data Wrangling with Python! In this book, we will help you take your data skills from a spreadsheet to the next level: leveraging the Python programming language to easily and quickly turn noisy data into usable reports. The easy syntax and quick startup for Python make programming accessible to everyone.

Imagine a manual process you execute weekly, such as copying and pasting data from multiple sources into one spreadsheet for processing. This might take you an hour or two every week. But after youve automated and scripted this task, it may take only 30 seconds to process! This frees up your time to do other things or automate more processes. Or imagine you are able to transform your data in such a way that you can execute tasks you never could before because you simply did not have the ability to process the information in itscurrent form. But after working through Python exercises with this book, you should be able to more effectively gather information from data you previously deemed inaccessible, too messy, or too vast.

We will guide you through the process of data acquisition, cleaning, presentation, scaling, and automation. Our goal is to teach you how to easily wrangle your data, so you can spend more time focused on the content and analysis. We will overcome the limitations of your current tools and replace manual processing with clean, easy-to-read Python code. By the time you finish working through this book, you will have automated your data processing, scheduled file editing and cleanup tasks, acquired and parsed data from locations youmay not have been able to access before, and processed larger datasets.

Using a project-based approach, each chapter will grow in complexity. We encourage you to follow along and apply the methods using your own datasets. If you dont have a particular project or investigation in mind, sample datasets will be available online for your use.

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