Visualizing distributions of data

An early step in any effort to analyze or model data should be to understand how the variables are distributed. Techniques for distribution visualization can provide quick answers to many important questions. What range do the observations cover? What is their central tendency? Are they heavily skewed in one direction? Is there evidence for bimodality? Are there significant outliers? Do the answers to these questions vary across subsets defined by other variables?

The distributions module contains several functions designed to answer questions such as these. The axes-level functions are histplot(), kdeplot(), ecdfplot(), and rugplot(). They are grouped together within the figure-level displot(), jointplot(), and pairplot() functions.

There are several different approaches to visualizing a distribution, and each has its relative advantages and drawbacks. It is important to understand these factors so that you can choose the best approach for your particular aim.

Plotting univariate histograms

Perhaps the most common approach to visualizing a distribution is the histogram. This is the default approach in