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 you’ve 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 its current 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 you may 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 don’t have a particular project or investigation in mind, sample datasets will be available online for your use.

Who Should Read This Book

This book is for folks who want to explore data wrangling beyond desktop tools. If you are great at Excel and want to take your data analysis to the next level, this book will help! Additionally, if you are coming from another language and want to get started with Python for the purpose of data wrangling, you will find this book useful.

If you come across something you do not understand, we encourage you to reach out so that we can improve the content of the book, but you should also be prepared to supplement your learning by searching the Internet or inquiring online. We’ve included a few tips on debugging in Appendix E, so you can take a look there as well!

Who Should Not Read This Book

This book is definitely not meant for experienced Python programmers who already know which libraries and techniques to use for their data wrangling needs (for those folks, we recommend Wes McKinney’s Python for Data Analysis, also from O’Reilly). If you are an experienced Python developer or a developer in another language with data analysis capabilities (Scala, R), this book is probably not for you. However, if you are an experienced developer in a web language that lacks data analysis capabilities (PHP, JavaScript), this book can teach you about Python via data wrangling.

How This Book Is Organized

The structure of the book follows the life span of an average data analysis project or story. It starts with formulating a question, then moves on to acquiring the data, cleaning the data, exploring the data, communicating the data findings, scaling with larger datasets, and finally automating the process. This approach allows you to move from simple questions to more complex problems and investigations. We will cover basic means of communicating your findings before we get into advanced data-gathering techniques.

If the material in some of these chapters is not new to you, it is possible to use the book as a reference or skip sections with which you are already familiar. However, we recommend you take a cursory view of each section’s contents, to ensure you don’t miss possible new resources and techniques.

What Is Data Wrangling?

Data wrangling is about taking a messy or unrefined source of data and turning it into something useful. You begin by seeking out raw data sources and determining their value: How good are they as datasets? How relevant are they to your goal? Is there a better source? Once you’ve parsed and cleaned the data so that the datasets are usable, you can utilize tools and methods (like Python scripts) to help you analyze them and present your findings in a report. This allows you to take data no one would bother looking at and make it both clear and actionable.

What to Do If You Get Stuck

Don’t fret—it happens to everyone! Consider the process of programming a series of events where you get stuck over and over again. When you are stuck and you work through the problem, you gain knowledge that allows you to grow and learn as a developer and data analyst. Most people do not master programming; instead, they master the process of getting unstuck.

What are some “unsticking” techniques? First, you can use a search engine to try to find the answer. Often, you will find many people have already run into the same problem. If you don’t find a helpful solution, you can ask your question online. We cover a few great online and real-life resources in Appendix B.

Asking questions is hard. But no matter where you are in your learning, do not feel intimidated about asking the greater coding community for help. One of the earliest questions one of this book’s authors (Jackie) asked about programming in a public forum ended up being one that was referenced by many people afterward. It is a great feeling to know that a new programmer like yourself can help those that come after you because you took a chance and asked a question you thought might be stupid.

We also recommend you read “How to Ask Questions”, before posting your questions online. It covers ways to help frame your questions so others can best help you.

Lastly, there are times when you will need an extra helping hand in real life. Maybe the question you have is multifaceted and not easily asked or answered on a website or mailing list. Maybe your question is philosophical or requires a debate or re-hashing of different approaches. Whatever it may be, you can find folks who can likely answer your question at local Python groups. To find a local meetup, try Meetup. In Chapter 1, you will find more detailed information on how to find helpful and supportive communities.

Conventions Used in This Book

The following typographical conventions are used in this book:


Indicates new terms, URLs, email addresses, filenames, directory names and paths, 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 italic

Shows text that should be replaced with user-supplied values or by values determined by context.


This element signifies a tip or suggestion.


This element signifies a general note.


This element indicates a warning or caution.

Using Code Examples

We’ve set up a data repository on GitHub at In this repository, you will find the data we used along with some code samples to help you follow along. If you find any issues in the repository or have any questions, please file an issue.

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 you’re 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 O’Reilly 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 product’s documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Data Wrangling with Python by Jacqueline Kazil and Katharine Jarmul (O’Reilly). Copyright 2016 Jacqueline Kazil and Kjamistan, Inc., 978-1-4919-4881-1.”

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

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The authors would like to thank their editors, Dawn Schanafelt and Meghan Blanchette, for their tremendous help, work, and effort—this wouldn’t have been possible without you. They would also like to thank their tech editors, Ryan Balfanz, Sarah Boslaugh, Kat Calvin, and Ruchi Parekh, for their help in working through code examples and thinking about the book’s audience.

Jackie Kazil would like to thank Josh, her husband, for the support on this adventure—everything from encouragement to cupcakes. The house would have fallen apart at times if he hadn’t been there to hold it up. She would also like to thank Katharine (Kjam) for partnering. This book would not exist without Kjam, and she’s delighted to have had a chance to work together again after years of being separated. Lastly, she would also like to thank her mom, Lydie, who provided her with so many of the skills, except for English, that were needed to finish this book.

Katharine Jarmul would like to send a beary special thanks to her partner, Aaron Glenn, for countless hours of thinking out loud, rereading, debating whether Unix should be capitalized, and making delicious pasta while she wrote. She would like to thank all four of her parents for their patience with endless book updates and dong bells. Sie möchte auch Frau Hoffmann für ihre endlose Geduld bei zahllosen Gesprächen auf Deutsch über dieses Buch bedanken.

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