Preface

Data visualization is intended to provide information clearly and help the viewer understand them qualitatively. The well-known expression that a picture is worth a thousand words may be rephrased as "a picture tells a story as well as a large collection of words". Visualization is, therefore, a very precious tool that helps the viewer understand a concept quickly. However, data visualization is more of an art than a skill because if you try to overdo it, it could have a reverse effect.

We are currently faced with a plethora of data containing many insights that hold the key to success in the modern day. It is important to find the data, clean it, and use the right tool to visualize it. This book explains several different ways to visualize data using Python packages, along with very useful examples in many different areas such as numerical computing, financial models, statistical and machine learning, and genetics and networks.

This book presents an example code developed on Mac OS X 10.10.5 using Python 2.7, IPython 0.13.2, matplotlib 1.4.3, NumPy 1.9.2, SciPy 0.16.0, and conda build version 1.14.1.

What this book covers

Chapter 1, A Conceptual Framework for Data Visualization, expounds that data visualization should actually be referred to as "the visualization of information for knowledge inference". This chapter covers the framework, explaining the transition from data/information to knowledge and how meaningful representations (through logarithms, colormaps, scatterplots, correlations, and others) can make knowledge much easier to grasp.

Chapter 2, Data Analysis and Visualization, explains the importance of visualization and shows several steps in the visualization process, including several options of tools to choose from. Visualization methods have existed for a long time, and we are exposed to them very early; for instance, even young children can interpret bar charts. Interactive visualization has many strengths, and this chapter explains them with examples.

Chapter 3, Getting Started with the Python IDE, explains how you can use Anaconda from Continuum Analytics without worrying about installing each Python library individually. Anaconda has simplified packaging and deployment methods that make it easier to run the IPython notebook alongside other libraries.

Chapter 4, Numerical Computing and Interactive Plotting, covers interactive plotting methods with working examples in computational physics and applied mathematics. Some notable examples are interpolation methods, approximation, clustering, sampling, correlation, and convex optimization using SciPy.

Chapter 5, Financial and Statistical Models, explores financial engineering, which has many numerical and graphical methods that make an interesting use case to explore Python. This chapter covers stock quotes, regression analysis, the Monte Carlo algorithm, and simulation methods with examples.

Chapter 6, Statistical and Machine Learning, covers statistical methods such as linear and nonlinear regression and clustering and classification methods using numpy, scipy, matplotlib, and scikit-learn.

Chapter 7, Bioinformatics, Genetics, and Network Models, covers interesting examples such as social network and instances of directed graphs in real life, data structures that are appropriate for these problems, and network analysis. This chapter uses specific libraries such as graph-tool, NetworkX, matplotlib, scipy, and numpy.

Chapter 8, Advanced Visualization, covers simulation methods and examples of signal processing to show several visualization methods. Here, we also have a comparison of other advanced tools out there, such as Julia and D3.js.

Appendix, Go Forth and Explore Visualization, gives an overview of conda and lists out various Python libraries.

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 scientific 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 clarification, you can follow the suggested reading hints in the book. If this book is your first 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 find 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, filenames, file 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."

A block of code is set as follows:

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
students = pd.read_csv("/Users/Macbook/python/data/ucdavis.csv")
g = sns.FacetGrid(students, palette="Set1", size=7)
g.map(plt.scatter, "momheight", "height", s=140, linewidth=.7, edgecolor="#ffad40", color="#ff8000")
g.set_axis_labels("Mothers Height", "Students Height")

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

import blockspring 
import json  

print blockspring.runParsed("stock-price-comparison", 
   { "tickers": "FB, LNKD, TWTR", 
   "start_date": "2014-01-01", "end_date": "2015-01-01" }).params

Any command-line input or output is written as follows:

conda install jsonschema

Fetching package metadata: ....
Solving package specifications: .
Package plan for installation in environment /Users/MacBook/anaconda:

The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    jsonschema-2.4.0           |           py27_0          51 KB

The following NEW packages will be INSTALLED:

    jsonschema: 2.4.0-py27_0

Proceed ([y]/n)?

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Further, you can select the Copy code option to copy the contents of the code block into Canopy's copy-and-paste buffer to be used in an editor."

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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