xorbits.pandas.DataFrame.plot.density#

DataFrame.plot.density(*args, **kwargs)#

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.

Parameters
• bw_method (str, scalar or callable, optional (Not supported yet)) – 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.

• ind (NumPy array or int, optional (Not supported yet)) – 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.

• **kwargs – Additional keyword arguments are documented in `DataFrame.plot()`.

Return type

matplotlib.axes.Axes or numpy.ndarray of them

`scipy.stats.gaussian_kde`

Representation of a kernel-density estimate using Gaussian kernels. This is the function used internally to estimate the PDF.

Examples

Given a Series of points randomly sampled from an unknown distribution, estimate its PDF using KDE with automatic bandwidth determination and plot the results, evaluating them at 1000 equally spaced points (default):

A scalar bandwidth can be specified. Using a small bandwidth value can lead to over-fitting, while using a large bandwidth value may result in under-fitting:

Finally, the ind parameter determines the evaluation points for the plot of the estimated PDF:

For DataFrame, it works in the same way:

A scalar bandwidth can be specified. Using a small bandwidth value can lead to over-fitting, while using a large bandwidth value may result in under-fitting:

Finally, the ind parameter determines the evaluation points for the plot of the estimated PDF:

This docstring was copied from pandas.plotting._core.PlotAccessor.