Chapter 2. Creating Attractive Data Visualizations
In this chapter, we will cover:
- Graphing Anscombe's quartet
- Choosing seaborn color palettes
- Choosing matplotlib color maps
- Interacting with IPython notebook widgets
- Viewing a matrix of scatterplots
- Visualizing with d3.js via mpld3
- Creating heatmaps
- Combining box plots and kernel density plots with violin plots
- Visualizing network graphs with hive plots
- Displaying geographical maps
- Using ggplot2-like plots
- Highlighting data points with influence plots
Introduction
Data analysis is more of an art than a science. Creating attractive visualizations is an integral part of this art. Obviously, what one person finds attractive, other people may find completely unacceptable. Just as in art, in the rapidly evolving world of data analysis, opinions, and taste change over time; however, in principle, nobody is absolutely right or wrong. As data artists and Pythonistas, we can choose from among several libraries of which I will cover matplotlib, seaborn...
