The best data is the data that we can see and understand. As developers and data scientists, 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, filter 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 sometimes 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 one simple line in Python using matplotlib, but performing more advanced charting requires knowledge of more than just Python. We need to understand 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 that we should use in an optimal way.
Chapter 1, Preparing Your Working Environment, covers a set of installation recipes and advice 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 customization.
Chapter 4, More Plots and Customizations, follows up from the 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, deals with 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 it also explains how to use LaTeX for rendering text in matplotlib.
Chapter 9, Visualizations on the Clouds with Plot.ly, introduces how to use Plot.ly to create and share your visualizations on its cloud environment.
For this book, you will need Python 2.7.3 or a later version installed on your operating system.
Another software package used in this book is 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 OS X.
If you are new to Python installation and software installation in general, it is highly recommended to use prepackaged scientific Python distributions such as Anaconda, Enthought Python Distribution or Python(x, y).
Other required software mainly comprises 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, Second Edition is for developers and data scientists who already use Python and want to learn how to create visualizations of their data in a practical way. 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 several headings that appear frequently (Getting ready, How to do it, How it works, There's more, and See also).
To give clear instructions on how to complete a recipe, we use these sections as follows:
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
This section usually consists of a detailed explanation of what happened in the previous section.
This section consists of additional information about the recipe in order to make the reader more knowledgeable about the recipe.
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, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We packed our little demo in the DemoPIL class, 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 my_function(x):
return x*xWhen we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
for a in range(10):
print a
Any command-line input or output is written as follows:
$ sudo python setup.py install
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