Bar plots can tell us what the mean of our dataset is, but they don’t give us any hints as to the distribution of the dataset values. For all we know, the data could be clustered around the mean or spread out evenly across the entire range.

To find out more about each of these datasets, we’ll need to examine their distributions. A common way of doing so is by plotting the data as a histogram, but histograms have their drawback as well.

Seaborn offers another option for graphing distributions: KDE Plots.

KDE stands for Kernel Density Estimator. A KDE plot gives us the sense of a univariate as a curve. A univariate dataset only has one variable and is also referred to as being one-dimensional, as opposed to bivariate or two-dimensional datasets which have two variables.

KDE plots are preferable to histograms because depending on how you group the data into bins and the width of the bins, you can draw wildly different conclusions about the shape of the data. Using a KDE plot can mitigate these issues, because they smooth the datasets, allow us to generalize over the shape of our data, and aren’t beholden to specific data points.