The best data is the data that we can see and understand. As developers, we want to create and build the most comprehensive and understandable visualizations. It is not always simple; we need to find the data, read it, clean it, massage it, and then use the right tool to visualize it. This book explains the process of how to read, clean, and visualize the data into information with straight and simple (and not so simple) recipes.
How to read local data, remote data, CSV, JSON, and data from relational databases are all explained in this book.
Some simple plots can be plotted with a simple one-liner in Python using matplotlib, but doing more advanced charting requires knowledge of more than just Python. We need to understand the information theory and human perception aesthetics to produce the most appealing visualizations.
This book will explain some practices behind plotting with matplotlib in Python, statistics used, and usage examples for different charting features we should use in an optimal way.
This book is written and the code is developed on Ubuntu 12.03 using Python 2.7, IPython 0.13.2, virtualenv 1.9.1, matplotlib 1.2.1, NumPy 1.7.1, and SciPy 0.11.0.
Chapter 1, Preparing Your Working Environment, covers a set of installation recipes and advices on how to install the required Python packages and libraries on your platform.
Chapter 2, Knowing Your Data, introduces you to common data formats and how to read and write them, be it CSV, JSON, XSL, or relational databases.
Chapter 3, Drawing Your First Plots and Customizing Them, starts with drawing simple plots and covers some of the customization.
Chapter 4, More Plots and Customizations, follows up from previous chapter and covers more advanced charts and grid customization.
Chapter 5, Making 3D Visualizations, covers three-dimensional data visualizations such as 3D bars, 3D histograms, and also matplotlib animations.
Chapter 6, Plotting Charts with Images and Maps, covers image processing, projecting data onto maps, and creating CAPTCHA test images.
Chapter 7, Using Right Plots to Understand Data, covers explanations and recipes on some more advanced plotting techniques such as spectrograms and correlations.
Chapter 8, More on matplotlib Gems, covers a set of charts such as Gantt charts, box plots, and whisker plots, and also explains how to use LaTeX for rendering text in matplotlib.
For this book, you will need Python 2.7.3 or a later version installed on your operating system. This book was written using Ubuntu 12.03's Python default version (2.7.3).
Other software packages used in this book are IPython, which is an interactive Python environment that is very powerful, and flexible. This can be installed using package managers for Linux-based OSes or prepared installers for Windows and Mac OSes.
If you are new to Python installation and software installation in general, it is very much recommended to use prepackaged scientific Python distributions such as Anaconda, Enthought Python Distribution, or Python(X,Y).
Other required software mainly comprises of Python packages that are all installed using the Python installation manager, pip, which itself is installed using Python's easy_install setup tool.
Python Data Visualization Cookbook is for developers who already know about Python programming in general. If you have heard about data visualization but don't know where to start, this book will guide you from the start and help you understand data, data formats, data visualization, and how to use Python to visualize data.
You will need to know some general programming concepts, and any kind of programming experience will be helpful. However, the code in this book is explained almost line by line. You don't need math for this book; every concept that is introduced is thoroughly explained in plain English, and references are available for further interest in the topic.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text are shown as follows: "We packed our little demo in class DemoPIL, so that we can extend it easily, while sharing the common code around the demo function, run_fixed_filters_demo."
A block of code is set as follows:
def _load_image(self, imfile):
self.im = mplimage.imread(imfile)When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
# tidy up tick labels size
all_axes = plt.gcf().axes
for ax in all_axes:
for ticklabel in ax.get_xticklabels() + ax.get_yticklabels():
ticklabel.set_fontsize(10)
Any command-line input or output is written as follows:
$ sudo python setup.py install
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "We then set up a label for the stem plot and the position of baseline, which defaults to 0."
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