Using Seaborn to Visualize Financial Distributions and Categories
Summary
This tutorial introduces Seaborn, a visualization library built on Matplotlib, using Chinese stock financial statement data. It demonstrates distribution and rug plots for return on equity, joint and pair plots for examining relationships among financial measures, and categorical plots for comparing revenue or return on equity across stocks, years, and quarters. It explains how hue adds a grouping variable and how bar plots can show aggregates such as means or standard deviations.
The examples also cover box plots, violin plots, strip plots, and swarm plots. A winsorization step clips values by a multiple of the standard deviation before plotting, while the plots themselves help compare central tendency, spread, and individual observations. The tutorial notes that swarm plots can be cumbersome for large datasets and that violin plots may take more effort to interpret than box plots. Its illustrations are exploratory, based on selected financial data and dates; they do not establish predictive relationships or investment performance.
Key ideas
- Seaborn provides distribution and categorical visualizations on top of Matplotlib.
- Joint plots compare two financial variables, while pair plots show relationships across several numeric columns.
- Bar plots can display category-level aggregates, and count plots show category frequencies.
- Box, violin, strip, and swarm plots offer different views of grouped distributions and individual observations.
- Winsorization can reduce the influence of extreme values, but the examples are descriptive rather than evidence of trading edges.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.