pandas.DataFrame.plot.density#

DataFrame.plot.density(bw_method=None,ind=None,**kwargs)[source]#

Generate Kernel Density Estimate plot using Gaussian kernels.

In statistics,kernel density estimation (KDE) is a non-parametricway to estimate the probability density function (PDF) of a randomvariable. This function uses Gaussian kernels and includes automaticbandwidth determination.

Parameters:

bw_methodstr, 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.Seescipy.stats.gaussian_kde for more information.

indNumPy array or int, optional

Evaluation points for the estimated PDF. If None (default),1000 equally spaced points are used. Ifind is a NumPy array, theKDE is evaluated at the points passed. Ifind is an integer,ind number of equally spaced points are used.

**kwargs

Additional keyword arguments are documented inDataFrame.plot().

Returns:

matplotlib.axes.Axes or numpy.ndarray of them

See also

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 unknowndistribution, estimate its PDF using KDE with automaticbandwidth determination and plot the results, evaluating them at1000 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 canlead to over-fitting, while using a large bandwidth value may resultin under-fitting:

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

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

Finally, theind parameter determines the evaluation points for theplot of the estimated PDF:

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

For DataFrame, it works in the same way:

>>>df=pd.DataFrame({...'x':[1,2,2.5,3,3.5,4,5],...'y':[4,4,4.5,5,5.5,6,6],...})>>>ax=df.plot.kde()

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

>>>ax=df.plot.kde(bw_method=0.3)

>>>ax=df.plot.kde(bw_method=3)

Finally, theind parameter determines the evaluation points for theplot of the estimated PDF:

>>>ax=df.plot.kde(ind=[1,2,3,4,5,6])