Python数据清洗实战：从入门到精通

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Conventions Used in This Book

The following typographical conventions are used in this book:

Italic

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 ele‐

ments 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 deter‐

mined 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 https://github.com/jackiekazil/data-

wrangling. 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

xiv | Preface

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 signifi‐

cant 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 Jac‐

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 permissions@oreilly.com.

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How to Contact Us

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

Preface | xv

O’Reilly Media, Inc.

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Acknowledgments

The authors would like to thank their editors, Dawn Schanafelt and Meghan Blanch‐

ette, 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 Gesprä‐

chen auf Deutsch über dieses Buch bedanken.

xvi | Preface

Public high schools in the United States are government-run schools funded largely by taxes from the local

community, meaning children can attend and be educated at little to no cost to their parents.

While writing this book, one of the authors read an article about her public high

school,

which had reportedly begun charging a $20 fee to graduating seniors and

$200 a row for prime seating at the graduation ceremony.

According to the local news report, “the new fees are a part of an effort to cover an

estimated $12,000 in graduation costs for Manatee High School after the financially

strapped school district pulled its $3,400 contribution this year.”

The article explains the reason why the graduation costs are so high in comparison to

the school district’s budget. However, it does not explain why the school district was

unable to make its usual contribution. The question remained: Why is the Manatee

County School District so financially strapped that it cannot make its regular contri‐

bution to the graduating class?

The initial questions you have in your investigation will often lead to deeper ques‐

tions that define a problem. For example: What has the district been spending money

on? How have the district’s spending patterns changed over time?

Identifying our specific topic area and the questions we want to anwer allows us to

identify the data we will need to find. After formulating these questions, the first

dataset we need to look for is the spending and budget data for the Manatee County

School District.

Before we continue, let’s look at a brief overview of the entire process, from initial

identification of a problem all the way to the final story (see Figure 1-1).

Once you have identified your questions, you can begin to ask questions about your

data, such as: Which datasets best tell the story I want to communicate? Which data‐

sets explore the subject in depth? What is the overall theme? What are some datasets

associated with those themes? Who might be tracking or keeping this data? Are these

datasets publicly available?

When you begin the storytelling process, you should focus on

researching the questions you want to answer. Then you can figure

out which datasets are most valuable to you. In this initial stage,

don’t get too caught up in the tools you’ll use to analyze the data or

the data wrangling process.

2 | Chapter 1: Introduction to Python

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