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‘Kernel Density Estimation’ or ‘KDE’ (Parzen, 1962; Rosenblatt, 1956) is a type of nonparametric density estimation (David W. Scott, 2015) that improves upon the traditional ‘histogram’ approach by, for example, i) utilizing the exact location of each data point (instead of ‘binning’), ii) being able to produce smooth distributions with continuous and meaningful derivatives, and iii) removing the arbitrary offset of an initial bin edge. The kalepy package presents a Python KDE implementation designed for broad applicability by including numerous features absent in other packages. kalepy provides optional weightings, reflecting boundary conditions, support for an arbitrary number of dimensions, numerous kernel (i.e., window) functions, built-in plotting, and built-in resampling.

kalepy: a Python package for kernel density estimation, sampling and plotting