Pandas DataFrame: plot.kde() function

Last update on August 19 2022 21:50:51 (UTC/GMT +8 hours)

DataFrame.plot.kde() function

The plot.kde() function is used to 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.

Syntax:

DataFrame.plot.kde(self, bw_method=None, ind=None, **kwargs)

Parameters:

Name	Description	Type/Default Value	Required / Optional
bw_method	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.	str, scalar or callable	Optional
ind	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.	NumPy array or integer	Optional
**kwds	Additional keyword arguments are documented in pandas.%(this-datatype)s.plot().		Optional

Returns: matplotlib.axes.Axes or numpy.ndarray of them.

Example:

Download the Pandas DataFrame Notebooks from here.

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