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 ...

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, including subsets of data defined by semantic mapping and faceting across multiple subplots.

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper.

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 plot, it has the advantage that each observation is visualized directly, meaning that there are no binning or smoothing parameters that need to be adjusted.

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 density curve in one or more dimensions.

It is also important to keep in mind that a bar plot shows only the mean (or other aggregate) value, but it is often more informative to show the distribution of values at each level of the categorical variables. In those cases, approaches such as a boxplot() or …

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.

As the size of the dataset grows, categorical scatter plots become limited in the information they can provide about the distribution of values within each category. When this happens, there are several approaches for summarizing the distributional information in ways that facilitate easy comparisons across the category levels.

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

jointplot() plots the relationship or joint distribution of two variables while adding marginal axes that show the univariate distribution of each one separately: sns . jointplot ( data = penguins , x = "flipper_length_mm" , y = "bill_length_mm" , hue = "species" )