Python数据可视化大师指南：创建迷人图表

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"《Mastering Python Data Visualization》是一本由Kirthi Raman编著的专业书籍，旨在帮助读者掌握Python编程在数据可视化方面的深入技巧。这本书适合对Python数据分析感兴趣的开发者和专业人士，特别是那些希望利用Python丰富的绘图库如Matplotlib、Seaborn、Plotly等，生成各种视觉效果吸引人的图表的人。该书详细介绍了如何通过Python中的这些工具，高效地创建各类图表，包括但不限于折线图、柱状图、散点图、箱线图和热力图等，以清晰、直观的方式呈现数据。作者不仅讲解理论概念，还提供了实际操作示例和最佳实践，让读者能够迅速上手并提升技能。书中涵盖的主题涵盖了数据清洗、预处理、探索性数据分析（EDA），以及如何使用matplotlib、seaborn等库进行高级定制，使图表更具吸引力。此外，书中还会探讨如何在大数据集和复杂数据结构上实现高效可视化，以及如何利用现代Web技术（如Bokeh和Plotly）制作交互式图表，以便用户能够动态探索数据。版权方面，该书受到Packt Publishing的保护，未经许可，不得以任何形式复制、存储或传输。尽管作者和出版商努力确保信息的准确性，但书中提供的内容并不保证完全无误，且不承担因本书导致的直接或间接损失的责任。《Mastering Python Data Visualization》是一本实用的教程，不仅适合初学者系统学习Python数据可视化，也适合有一定经验的开发人员深化理解并扩展他们的可视化工具箱。通过阅读这本书，读者将能够提升自己的数据分析和沟通能力，为项目决策提供有力的数据支持。"

Preface

[ ix ]

What you need for this book

For this book, you need Python 2.7.6 or a later version installed on your operating

system. For the examples in this book, Mac OS X 10.10.5's Python default version

(2.7.6) has been used. Other software packages used in this book are IPython, which

is an interactive Python environment. The new version of IPython is called Jupyter,

which now has kernels for 50 different languages.

Install the prepackaged scientic Python distributions, such as Anaconda from

Continuum or Enthought Python Distribution if possible. Anaconda typically comes

with over 300 Python packages. For the Python packages that are not included in the

prepackaged list, you may either use pip or conda to install them. Some examples are

provided in Appendix, Go Forth and Explore Visualization.

Who this book is for

There are many books on Python and data visualization. However, there are very

few that can be recommended to somebody who wants to build on the existing

knowledge about Python, and there are even fewer that discuss niche techniques to

make your code easier to work with and reusable. If you know a few things about

Python programming but have an insatiable drive to learn more, this book will show

you ways to obtain analytical results and produce amazing visual displays.

This book covers methods to produce analytical results using real-world problems.

It is not written for beginners, but if you need clarication, you can follow the

suggested reading hints in the book. If this book is your rst exposure to Python or

data visualization, you will do well to study some introductory texts. My favorite

is Introduction to Computer Science and Programming by Professor John Guttag, which

is freely available at MIT OpenCourseWare, and Visualize This by Nathan Yau

from UCLA.

Conventions

In this book, you will nd a number of text styles that distinguish between different

kinds of information. Here are some examples of these styles and an explanation of

their meaning.

Code words in text, database table names, folder names, lenames, le extensions,

pathnames, dummy URLs, user input, and Twitter handles are shown as follows:

"First we use norm() from SciPy to create normal distribution samples and later,

use hstack() from NumPy to stack them horizontally and apply gaussian_kde()

from SciPy."

www.it-ebooks.info

Preface

[ xii ]

Downloading the color images of this book

We also provide you with a PDF le that has color images of the screenshots/

diagrams used in this book. The color images will help you better understand the

changes in the output. You can download this le from: https://www.packtpub.

com/sites/default/files/downloads/8327OS_Graphics.pdf.

Errata

Although we have taken every care to ensure the accuracy of our content, mistakes

do happen. If you nd a mistake in one of our books—maybe a mistake in the text or

the code—we would be grateful if you could report this to us. By doing so, you can

save other readers from frustration and help us improve subsequent versions of this

book. If you nd any errata, please report them by visiting http://www.packtpub.

com/submit-errata, selecting your book, clicking on the Errata Submission Form

link, and entering the details of your errata. Once your errata are veried, your

submission will be accepted and the errata will be uploaded to our website or added

to any list of existing errata under the Errata section of that title.

To view the previously submitted errata, go to https://www.packtpub.com/books/

content/support and enter the name of the book in the search eld. The required

information will appear under the Errata section.

Piracy

Piracy of copyrighted material on the Internet is an ongoing problem across all

media. At Packt, we take the protection of our copyright and licenses very seriously.

If you come across any illegal copies of our works in any form on the Internet, please

provide us with the location address or website name immediately so that we can

pursue a remedy.

Please contact us at copyright@packtpub.com with a link to the suspected pirated

material.

We appreciate your help in protecting our authors and our ability to bring you

valuable content.

Questions

If you have a problem with any aspect of this book, you can contact us at

questions@packtpub.com, and we will do our best to address the problem.

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