Creating statistical graphs
Most people interpret data visually. They prefer to view colorful, meaningful graphs to make sense of the data. As a data science practitioner, it’s your job to create and interpret these graphs for others.
In Chapter 4, Extending Python, Files, Errors, and Graphs, you were introduced to matplotlib and many different kinds of graphs. In this section, you will expand upon your knowledge by learning about new techniques to enhance the outputs and information displayed in your histograms and scatterplots. Additionally, you will see how box plots can be used to visualize statistical distributions, and how heat maps can provide nice visual representations of correlations.
In this section, you will use Python – in particular, matplotlib and seaborn – to create these graphs. Although software packages such as Tableau are rather popular, they are essentially drag-and-drop. Since Python is an all-purpose programming language, the limitations...
