Generate Kernel Density Estimate plot using Gaussian kernels.

In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function (PDF) of a random variable. This function uses Gaussian kernels and includes automatic bandwidth determination.

param bw_method
str, scalar or callable, optional

The method used to calculate the estimator bandwidth. This can be ‘scott’, ‘silverman’, a scalar constant or a callable. If None (default), ‘scott’ is used. See scipy.stats.gaussian_kde for more information.

param ind
NumPy array or integer, optional

Evaluation points for the estimated PDF. If None (default), 1000 equally spaced points are used. If ind is a NumPy array, the KDE is evaluated at the points passed. If ind is an integer, ind number of equally spaced points are used. **kwds : optional Additional keyword arguments are documented in pandas.%(this-datatype)s.plot().


matplotlib.axes.Axes or numpy.ndarray of them


This feature is currently unsupported by Intel Scalable Dataframe Compiler