Python数据科学入门：全面剖析数据处理之旅

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"《数据科学基础Python指南》是一本备受赞誉的入门级人工智能教材，全面介绍了使用Python进行数据分析的全流程。该书以实践为导向，每个练习都像一个有趣挑战，旨在提升读者的数据处理能力。对于初学者和有志于成为数据科学家的人来说，这无疑是一本必读之作。作者彼得·哈蒙顿（Peter Hampton）来自乌尔斯特大学，他称赞这本书能够快速让读者掌握数据科学领域的常见任务和工具，包括数据抓取、清洗、分析和存储。它不仅提供了技术基础，还帮助读者将更多时间用于实际研究，而不是不断探索技术细节。杰森·蒙托霍（Jason Montojo）作为《使用Python 3进行实用编程：计算机科学入门》的合著者，对这本书给出了高度评价。他指出，《数据科学基础Python》特别适合那些对解决问题充满好奇、热衷于数据发现的人，书中深入浅出地介绍了技术和工具，并通过精心设计的示例和习题，确保了其实践性和易读性。洛克什·库马尔·马卡尼（Lokesh Kumar Makani），作为一家名为Skyhigh Networks的CASB专家，虽然在评论中没有提供具体内容，但他的认可表明这本书在专业领域内也受到了肯定。《数据科学基础Python》是数据科学学习者和从业人员的理想起点，无论是新手还是进阶者，都能从中获得扎实的基础知识和实用技能。"

to this differentiation, in this book I, too, use single quotes for single characters

and double quotes for character strings.

The Book Forum

The community forum for this book can be found online at the Pragmatic

Programmers web page for this book.

There you can ask questions, post

comments, and submit errata.

Another great resource for questions and answers (not specific to this book)

is the newly created Data Science Stack Exchange forum.

Your Turn

The end of each chapter features a unit called “Your Turn.” This unit has

descriptions of several projects that you may want to accomplish on your own

(or with someone you trust) to strengthen your understanding of the material.

The projects marked with a single star

are the simplest. All you need to work

on them is solid knowledge of the functions mentioned in the preceding

chapters. Expect to complete single-star projects in no more than thirty

minutes. You’ll find solutions to them in Appendix 2, Solutions to Single-Star

Projects, on page 175.

The projects marked with two stars

are hard(er). They may take you an hour

or more, depending on your programming skills and habits. Two-star projects

involve the use of intermediate data structures and well thought-out algorithms.

Finally, the three-star

***

projects are the hardest. Some of the three-star projects

may not even have a perfect solution, so don’t get desperate if you cannot find

one! Just by working on these projects, you certainly make yourself a better

programmer and a better data scientist. And if you’re an educator, think of

the three-star projects as potential mid-semester assignments.

Now, let’s get started!

Dmitry Zinoviev

dzinoviev@gmail.com

August 2016

pragprog.com/book/dzpyds

datascience.stackexchange.com

Preface • xvi

report erratum • discuss

Unit 1

Data Analysis Sequence

The steps of a typical data analysis study are generally consistent with a

general scientific discovery sequence.

Your data science discovery starts with the question to be answered and the

type of analysis to be applied. The simplest analysis type is descriptive, where

the data set is described by reporting its aggregate measures, often in a

visual form. No matter what you do next, you have to at least describe the

data! During exploratory data analysis, you try to find new relationships

between existing variables. If you have a small data sample and would like

to describe a bigger population, statistics-based inferential analysis is right

for you. A predictive analyst learns from the past to predict the future. Causal

analysis identifies variables that affect each other. Finally, mechanistic data

analysis explores exactly how one variable affects another variable.

However, your analysis is only as good as the data you use. What is the ideal

data set? What data has the answer to your question in an ideal world? By

the way, the ideal data set may not exist at all or be hard or infeasible to

obtain. Things happen, but perhaps a smaller or not so feature-rich data set

would still work?

Fortunately, getting the raw data from the web or from a database is not that

hard, and there are plenty of Python tools that assist with downloading and

deciphering it. You’ll take a closer look in Unit 2, Data Acquisition Pipeline,

on page 5.

In this imperfect world, there is no perfect data. “Dirty” data has missing

values, outliers, and other “non-standard” items. Some examples of “dirty”

data are birth dates in the future, negative ages and weights, and email

addresses not intended for use (

noreply@

). Once you obtain the raw data, the

next step is to use data-cleaning tools and your knowledge of statistics to

regularize the data set.

With clean data in your files, you then perform descriptive and exploratory

analysis. The output of this step often includes scatter plots (mentioned on

page 143), histograms, and statistical summaries (explained on page 150). They

give you a smell and sense of data—an intuition that is indispensable for

further research, especially if the data set has many dimensions.

report erratum • discuss

Data Analysis Sequence • 3

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