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Series.plot.kde(bw_method=None, ind=None, **kwds)[source]

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 bandwith determination.


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.

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.Series.plot().


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

See also

Representation of a kernel-density estimate using Gaussian kernels. This is the function used internally to estimate the PDF.
Generate a KDE plot for a DataFrame.


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):

>>> s = pd.Series([1, 2, 2.5, 3, 3.5, 4, 5])
>>> ax = s.plot.kde()

A scalar bandwidth can be specified. Using a small bandwidth value can lead to overfitting, while using a large bandwidth value may result in underfitting:

>>> ax = s.plot.kde(bw_method=0.3)
>>> ax = s.plot.kde(bw_method=3)

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

>>> ax = s.plot.kde(ind=[1, 2, 3, 4, 5])
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