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Introduction
What’s New in the Second Edition
Aside from using the most up-to-date version of Python that supports all the visualization packages (version 3.3 at the time of the writing the second edition), I’ve also introduced the following additional content:
+ 3-D plots and graphs
+ Non-rectangular contour plots
+ Matplotlib’s basemap toolkit
+ Reading and writing MATLAB binary les
+ Reading and writing data to NumPy arrays
+ Reading and writing images to NumPy arrays
+ Making movies
+ IPython, IPython Notebook, and Spyder development environments
How This Book Is Structured
the book is designed so that you can easily skip back and forth as you engage various topics.
Chapter 1 is a case study that introduces the topics discussed throughout the book: data analysis, data
management, and, of course, data visualization. the case study involves reading GPS data, analyzing it, and plotting it along with relevant annotations (direction of travel, speed, etc.). A fully functional Python script will be built from the ground up, complemented with lots of explanations. the fruit of our work will be an eye-catching GPS route.
If you’re new to data analysis and visualization, consider reading Chapter 2 first. the chapter describes how to set up a development environment to perform the tasks associated with data analysis and visualization in Python, including the selection of an OS, installing Python, and installing third-party packages.
If you’re new to Python, your next stop should be Chapter 3. In this chapter, I swiftly discuss the Python programming language. I won’t be overly rehashing basic programming paradigms; instead I’ll provide a quick overview of the building blocks for the Python programming.
Regardless of your Python programming experience, I highly encourage you to read Chapter 4 before proceeding to the next chapters. Organization is the key to successful data analysis and visualization. this chapter covers organizing data files, pros and cons of different file formats, file naming conventions, finding data files, and automating file creation. the ideas in Chapter 4 are used throughout the book
From there on out, you have several options. If you intend to process text and data files, proceed to Chapter 5. Chapter 5 covers text files from all aspects: I/O operations, string processing, the csv module, regular expressions, and localization and internationalization. If Chapter 5 leaves you wanting to know more about file processing, proceed to Chapter 10. Chapter 10 includes advanced file processing topics: binary files, command-line arguments, file and directory manipulation, and more. Both Chapters 5 and 10 are augmented with numerous examples.
If graphs and plots are your heart’s desire, skip directly to Chapter 6. In Chapter 6 I examine matplotlib and explore its capabilities.
If you’re interested in the numerical aspects of data, it is advised you read Chapter 7 first. Chapter 7 discusses the basic building blocks for scientific computing. Chapter 8 builds on Chapter 7 and includes more advanced topics such as numerical analysis and signal processing.
Image processing is an important aspect of data processing. Chapter 9 deals with tools available as part of the Python Imaging Library (Pillow) package and shows how to further expand the package and perform more complex image processing tasks.
Chapter 10 includes advanced file processing topics including binary files and random access, object serialization, command line parameters, file compression and more.
Finally, the Appendix provides additional source code listings used in the book
