DOC: update the pandas.DataFrame.plot.density docstring
This docstring is quite similar to the one of #20041.

Unfortunately, I was not able to compute a kernel estimate of a
two-dimensional random variable. Hence, the example is more of an
analysis of some independent data series.

Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

- [x] PR title is "DOC: update the <your-function-or-method> docstring"
- [ ] The validation script passes: `scripts/validate_docstrings.py <your-function-or-method>`
- [x] The PEP8 style check passes: `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] The html version looks good: `python doc/make.py --single <your-function-or-method>`
- [ ] It has been proofread on language by another sprint participant (sort of; it's based on PR20041)

Please include the output of the validation script below between the "```" ticks:

```
################################################################################
################## Docstring (pandas.DataFrame.plot.density)  ##################
################################################################################

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.

.. _kernel density estimation:
    https://en.wikipedia.org/wiki/Kernel_density_estimation

Parameters
----------
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 :class:`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
    :meth:`pandas.DataFrame.plot`.

Returns
-------
axes : matplotlib.AxesSubplot or np.array 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.
Series.plot.kde : Generate a KDE plot for a Series.

Examples
--------
Given several Series of points randomly sampled from unknown
distributions, estimate their distribution using KDE with automatic
bandwidth determination and plot the results, evaluating them at
1000 equally spaced points (default):

.. plot::
    :context: close-figs

    >>> 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 overfitting, while using a large bandwidth value may result
in underfitting:

.. plot::
    :context: close-figs

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

.. plot::
    :context: close-figs

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

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

.. plot::
    :context: close-figs

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

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	Errors in parameters section
		Parameters {'kwds'} not documented
		Unknown parameters {'**kwds'}
```

I left the error because David and Israel mentioned doing it either way on Gitter ..