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 …

A kernel density estimate (KDE) plot is a method for visualizing the distribution of observations in a dataset, analogous to a histogram. KDE represents the data using a continuous probability …

Figure-level interface for drawing distribution plots onto a FacetGrid. This function provides access to several approaches for visualizing the univariate or bivariate distribution of data, …

Plot empirical cumulative distribution functions. An ECDF represents the proportion or count of observations falling below each unique value in a dataset. Compared to a histogram or density …

A box plot (or box-and-whisker plot) shows the distribution of quantitative data in a way that facilitates comparisons between variables or across levels of a categorical variable.

An object managing multiple subplots that correspond to joint and marginal axes for plotting a bivariate relationship or distribution.

It depicts the joint distribution of two variables using a cloud of points, where each point represents an observation in the dataset. This depiction allows the eye to infer a substantial …

The diagonal plots are treated differently: a univariate distribution plot is drawn to show the marginal distribution of the data in each column. It is also possible to show a subset of …

A histogram is a classic visualization tool that represents the distribution of one or more variables by counting the number of observations that fall within discrete bins. This function can …

Structure in the residual plot can reveal a violation of linear regression assumptions: