Python Data Visualization Cookbook (Anglais) Broché – 25 novembre 2013
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Description du produit
Présentation de l'éditeur
As a developer with knowledge of Python you are already in a great position to start using data visualization. This superb cookbook shows you how in plain language and practical recipes, culminating with 3D animations.
- Learn how to set up an optimal Python environment for data visualization
- Understand the topics such as importing data for visualization and formatting data for visualization
- Understand the underlying data and how to use the right visualizations
Today, data visualization is a hot topic as a direct result of the vast amount of data created every second. Transforming that data into information is a complex task for data visualization professionals, who, at the same time, try to understand the data and objectively transfer that understanding to others. This book is a set of practical recipes that strive to help the reader get a firm grasp of the area of data visualization using Python and its popular visualization and data libraries.
Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts.
Python Data Visualization Cookbook starts by showing you how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. During the book, we go from simple plots and charts to more advanced ones, thoroughly explaining why we used them and how not to use them. As we go through the book, we will also discuss 3D diagrams. We will peep into animations just to show you what it takes to go into that area. Maps are irreplaceable for displaying geo-spatial data, so we also show you how to build them. In the last chapter, we show you how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.
This book will help those who already know how to program in Python to explore a new field one of data visualization. As this book is all about recipes that explain how to do something, code samples are abundant, and they are followed by visual diagrams and charts to help you understand the logic and compare your own results with what is explained in the book.
What you will learn from this book
- Install and use iPython
- Use Python's virtual environments
- Install and customize NumPy and matplotlib
- Draw common and advanced plots
- Visualize data using maps
- Create 3D animated data visualizations
- Import data from various formats
- Export data from various formats
This book is written in a Cookbook style targeted towards an advanced audience. It covers the advanced topics of data visualization in Python.
Biographie de l'auteur
Igor Milovanovic is an experienced developer with a strong background in Linux system knowledge and software engineering. He is skilled at building scalable, data-driven, distributed-software-rich systems.
He is an Evangelist for high-quality systems design who holds strong interests in software architecture and development methodologies. He is always persistent on advocating methodologies that promote high-quality software, such as test-driven development, one-step builds, and continuous integration.
He also possesses a solid knowledge of product development. Having field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa.
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Détails sur le produit
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Commentaires client les plus utiles sur Amazon.com
First and foremost, it depends crucially on data sets that are supposedly available from packt publishing. But I haven't been able to get them, because my purchase was from Amazon and not from Packt. I've sent them an email and we'll see if they ever respond. There is nothing more frustrating than getting a book about something you need to know, and finding that the book is useless until you complete some additional cycle to get the required data.
Second, there are lots of typos. And I mean *lots*. On the top of p.16, for example, I'm told to type in
plt.rcParams['lines.linewidth'] = '3
Of course, you have to set it to 3, not '3. On the previous page, I'm told that
plt.rcParams['lines.color'] = 'r'
will make the plot red. I have no idea why, but the plot came out blue.
There are multiple places where it is clear that English is not the author's first language. (He typically leaves out "the", which has no direct analog in Russian.)
At the end of Chapter 1, we are told to "see the back of this book for useful online resources." The only thing at the back of the book is a bunch of ads for other Packt Publishing titles. Shame on you!
Finally, the book is just poorly thought out. As an example, matplotlib has something alled a subplot. This is "introduced" on p.42, where you simply see plt.subplot(211) and plt.subplot(212) in code, without explanation. If you look in the index, you'll see that there is a section called "using subplots" on p.118-120. So I went and looked at that, where I found:
"If you are reading this book from the start, you are probably familiar with the subplot class ..."
Well, I was reading from the start, but it was never explained! And it's not eplained in this section, either.
In fact, *nothing* is explained. If you want to learn matplotlib, just go to the documentation at matplotlib.org. It's great.
Note added the next day: the packtpublishing web site now appears to be working, so I've bumped the review to 3 stars
Author uses lots of examples to demonstrate different visualization terminology, which really helps people to understand the abstract image processing technology. This book also shows you how to setup the virtual env to isolate development environment. Although the main purpose of this book is to teach how to visualize data, many of the example programs also show the best python development practice. Majority of the code is runnable without touch-up. Some typos are pretty easy to be spotted. I would recommend it to people who already have python experience and would like to extend their experience to data visualization area.
While this is not a rigorous tutorial, the author goes into exactly the right depth to allow you to make a decision on methodology and begin implementing right away.
If, rather than becoming a NumPy scholar, you expect to have to deliver results from varied species of data, having this in your back pocket will help you accomplish that.