kdeplot

Kdeplot is a term used for a kernel density estimate (KDE) plot, a visualization that represents the estimated probability density function of a continuous variable, or the joint density of two variables. KDE plots provide a nonparametric alternative to histograms, producing a smooth curve (univariate) or a smooth density surface or contours (bivariate) based on the data. The estimate is constructed by placing a kernel function at each data point and summing the contributions; the Gaussian kernel is the default in many plotting libraries, though other kernels are available.

Bandwidth controls the degree of smoothing: larger bandwidth yields smoother curves, smaller bandwidth preserves detail but can introduce noise. Common bandwidth selection rules include Scott's rule and Silverman's rule, while many tools also offer data-driven optimization or cross-validation. In two dimensions, kdeplots can show contour lines or colored heatmaps representing density, and can be shaded to fill under the surface.

In practice, kdeplot is widely used in data analysis and visualization libraries, such as Python's seaborn, where the function can plot univariate densities with x or y, or bivariate densities with x and y, optionally grouped by a categorical hue, and can be combined with rug plots or overlaid with histograms. Other libraries provide similar KDE plotting capabilities.

Interpretation notes: the KDE is an estimate, not a guaranteed true density; results depend on the sample size, bandwidth, and kernel choice. KDEs can blur multimodal features if smoothing is excessive and may be less reliable at distribution tails.