开源工具引领数据分析探索：数据的魅力实践

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《数据之魅：基于开源工具的数据分析》是一本由Philipp K. Janert撰写的专业书籍，专为那些对数据分析感兴趣，尤其是想要利用开源工具进行实践的人提供深入指导。本书的核心内容围绕如何在当今的信息时代中，有效地利用开源工具进行数据探索、清洗、建模和可视化，从而揭示数据背后的潜在价值。作者以其丰富的经验，向读者介绍了诸如Python（Pandas, NumPy, and SciPy等）、R语言、SQL、Apache Hadoop、Apache Spark等广泛的开源工具和技术。书中不仅涵盖了理论知识，还提供了实用的示例和案例，帮助读者掌握如何在实际项目中应用这些工具，提高数据分析的效率和准确性。在版权方面，本书享有2011年Philipp K. Janert的版权，强调所有权利保留，并且在美国出版。O'Reilly Media公司是发行方，地址位于加利福尼亚州塞巴斯托波尔。此书的版本更新日期为2010年11月，适合教育、商业或销售推广用途，同时也提供在线版本供读者访问。编辑Mike Loukides参与了本书的编辑工作，确保内容的连贯性和专业性。生产编辑Sumita Mukherji负责项目的生产流程，Copyeditor Matt Darnell则负责校对和文字润色。生产服务由MPS Limited、Macmillan Company以及Newgen North America, Inc.提供支持。Indexer Fred Brown负责索引编排，而Cover Designer Karen Montgomery和Interior Designer Edie Freedman及Ron Bilodeau则共同负责设计封面和内部版式，确保视觉效果的吸引力。此外，本书的插图由作者Philipp K. Janert亲手绘制，突显了其对细节的关注和专业精神。通过阅读这本书，读者不仅能获得技术技能提升，还能了解到开源数据分析工具在现代数据分析实践中的核心地位及其深远影响。无论你是初学者还是经验丰富的分析师，都能从中受益匪浅，进一步领略数据之魅。

O’Reilly-5980006 master October 28, 2010 22:0

Data analysis, as I understand it, is not a ﬁxed set of techniques. It is a way of life, and it

has a name: curiosity. There is always something else to ﬁnd out and something more to

learn. This book is not the last word on the matter; it is merely a snapshot in time: things I

knew about and found useful today.

“Works are of value only if they give rise to better ones.”

(Alexander von Humboldt, writing to Charles Darwin, 18 September 1839)

Before We Begin

More data analysis efforts seem to go bad because of an excess of sophistication rather

than a lack of it.

This may come as a surprise, but it has been my experience again and again. As a

consultant, I am often called in when the initial project team has already gotten stuck.

Rarely (if ever) does the problem turn out to be that the team did not have the required

skills. On the contrary, I usually ﬁnd that they tried to do something unnecessarily

complicated and are now struggling with the consequences of their own invention!

Based on what I have seen, two particular risk areas stand out:

•

The use of “statistical” concepts that are only partially understood (and given the

relative obscurity of most of statistics, this includes virtually all statistical concepts)

•

Complicated (and expensive) black-box solutions when a simple and transparent

approach would have worked at least as well or better

I strongly recommend that you make it a habit to avoid all statistical language. Keep it

simple and stick to what you know for sure. There is absolutely nothing wrong with

speaking of the “range over which points spread,” because this phrase means exactly what

it says: the range over which points spread, and only that! Once we start talking about

“standard deviations,” this clarity is gone. Are we still talking about the observed width of

the distribution? Or are we talking about one speciﬁc measure for this width? (The

standard deviation is only one of several that are available.) Are we already making an

implicit assumption about the nature of the distribution? (The standard deviation is only

suitable under certain conditions, which are often not fulﬁlled in practice.) Or are we even

confusing the predictions we could make if these assumptions were true with the actual

data? (The moment someone talks about “95 percent anything” we know it’s the latter!)

I’d also like to remind you not to discard simple methods until they have been proven

insufﬁcient. Simple solutions are frequently rather effective: the marginal beneﬁt that

more complicated methods can deliver is often quite small (and may be in no reasonable

relation to the increased cost). More importantly, simple methods have fewer

opportunities to go wrong or to obscure the obvious.

xiv PREFACE

O’Reilly-5980006 master October 28, 2010 22:0

True story: a company was tracking the occurrence of defects over time. Of course, the

actual number of defects varied quite a bit from one day to the next, and they were

looking for a way to obtain an estimate for the typical number of expected defects. The

solution proposed by their IT department involved a compute cluster running a neural

network! (I am not making this up.) In fact, a one-line calculation (involving a moving

average or single exponential smoothing) is all that was needed.

I think the primary reason for this tendency to make data analysis projects more

complicated than they are is discomfort: discomfort with an unfamiliar problem space and

uncertainty about how to proceed. This discomfort and uncertainty creates a desire to

bring in the “big guns”: fancy terminology, heavy machinery, large projects. In reality, of

course, the opposite is true: the complexities of the “solution” overwhelm the original

problem, and nothing gets accomplished.

Data analysis does not have to be all that hard. Although there are situations when

elementary methods will no longer be sufﬁcient, they are much less prevalent than you

might expect. In the vast majority of cases, curiosity and a healthy dose of common sense

will serve you well.

The attitude that I am trying to convey can be summarized in a few points:

Simple is better than complex.

Cheap is better than expensive.

Explicit is better than opaque.

Purpose is more important than process.

Insight is more important than precision.

Understanding is more important than technique.

Think more, work less.

Although I do acknowledge that the items on the right are necessary at times, I will give

preference to those on the left whenever possible.

It is in this spirit that I am offering the concepts and techniques that make up the rest of

this book.

Conventions Used in This Book

The following typographical conventions are used in this book:

Italic

Indicates new terms, URLs, and email addresses

Constant width

Used to refer to language and script elements

PREFACE xv

O’Reilly-5980006 master October 28, 2010 22:0

Using Code Examples

This book is here to help you get your job done. In general, you may use the code in this

book in your programs and documentation. You do not need to contact us for permission

unless youre 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 OReilly 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 products documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title,

author, publisher, and ISBN. For example: “Data Analysis with Open Source Tools, by Philipp

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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xvi PREFACE

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