Pandas DataFrame: plot.kde() function
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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