Use a different KDE implementation

[tpvasconcelos]

We have experienced some issues with statsmodels' KDE implementation (see in-line comments in ridgeplot._kde.estimate_density_trace().

• statsmodels uses scipy under the hood. This could be a good alternative to investigate. pandas.Series.plot.kde also uses scipy.stats.gaussian_kde
• Alternatively, https://github.com/LBL-EESA/fastkde could also be an option
• Python 3.13 will ship a kde() utility as part of the built-in statistics module:
  • https://docs.python.org/3.13/library/statistics.html#statistics.kde
  • https://docs.python.org/3.13/library/statistics.html#sampling-from-kernel-density-estimation
  • gh-115532: Add kernel density estimation to the statistics module python/cpython#115863
  • Add kernel density estimation to the statistics module python/cpython#115532

Things to keep in mind:

• Backwards compatibility with the existing ridgeplot() arguments that are passed to statsmodels' KDEUnivariate
• Performance. e.g., statsmodels provides a faster FFT implementation when using the gaussian kernel.
• ...more?

[fkiraly]

Design-wise, would it not be cleanest to:

• separate plotting from density estimation a-priori
• leave some configurability which method to use?

I wanted to write some abstract density estimation intefaces anyway, for skpro (though ofc no need to use them - just saying that I have done some thinking around that topic).