From Sankey diagrams and spider plots to ridge plots, this article spans various visualizations, each with its unique use case and insight.
“Let the dataset change your mindset.” — Hans Rosling
Every data scientist knows that graphs are crucial to their data stories. Python developers are fortunate to work with a language offering a rich plot collection. This article will demonstrate this richness by discussing use cases involving lesser-known visualizations like Sankey diagrams, ridge plots, insets, spider plots, and wordcloud plots. We will also discuss uses for more familiar graphical representations, such as scatter and bar plots. Most plots will utilize the Matplotlib, Seaborn, and Plotly Python libraries.
We will use attributes such as shape, size, color, direction, area size, and marker-symbol area to create plots for ten diverse use cases. In every use case, our goal is to create effective, efficient, and aesthetic visualizations. Let us describe what we mean by these words in the context of plots: (a) Effective: All information that needs to be communicated is contained in the graph (b) Efficient. No redundant data is contained in the graph. (c ) Aesthetic…