pandas.DataFrame.plot.kde#

DataFrame.plot.kde(bw_method=None, ind=None, weights=None, **kwargs)[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 bandwidth 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. See scipy.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. 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.
weightsNumPy array, optional: Weights of datapoints. This must be the same shape as datapoints. If None (default), the samples are assumed to be equally weighted.
**kwargs: Additional keyword arguments are documented in DataFrame.plot().

Returns:

matplotlib.axes.Axes or numpy.ndarray of them: The matplotlib axes containing the KDE plot.

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 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()

../../_images/pandas-DataFrame-plot-kde-1.png

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:

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

../../_images/pandas-DataFrame-plot-kde-2.png

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

../../_images/pandas-DataFrame-plot-kde-3.png

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])

../../_images/pandas-DataFrame-plot-kde-4.png

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()

../../_images/pandas-DataFrame-plot-kde-5.png

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:

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

../../_images/pandas-DataFrame-plot-kde-6.png

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

../../_images/pandas-DataFrame-plot-kde-7.png

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

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

../../_images/pandas-DataFrame-plot-kde-8.png

pandas.DataFrame.plot.kde#

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