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# pandas.Series.plot.kde

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

  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.

    **kwargs

      Additional keyword arguments are documented in `DataFrame.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 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 over-fitting, while using a large bandwidth value may result in under-fitting:

      >>> 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])

  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 can lead to over-fitting, while using a large bandwidth value may result in under-fitting:

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

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

  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])