This chapter illustrates the examples of networks and bioinformatics and the choice of Python packages to be able to plot the results. We looked at a brief introduction to graphs and multigraphs and used the sparse matrix and distance graphs to illustrate how you can store and display graphs with several different packages, such as NetworkX, igraph (from igraph.org), and graph-tool.

The clustering coefficient and centrality of graphs demonstrates how you can compute clustering coefficients so that they are able to know how significant a node or vertex is in the graph. We also looked at the analysis of social network data with an illustration of Twitter friends and followers visually, using the Python-Twitter package and the NetworkX library.

You also learned about genetic programming samples with a demonstration of how you can see codons in a DNA sequence and how to compute GC ratio with the bio package. In addition to this, we demonstrated how to display the structures of DNA, RNA, or protein.

The planar graph test, the acyclic graph test, and maximum flow using the NetworkX package, along with a very few lines of code of how to test all of these was discussed. In addition, you can plot stochastic block models with several choices, such as PyMC or StochPy. In the next chapter, we will conclude with advanced visualization methods that you can choose from.