In this course, you’ve been working with samples, statistically speaking. Whether the data is discrete or continuous, it’s assumed to be derived from a population that has a true, exact distribution described by just a few parameters.
A kernel density estimation (KDE) is a way to estimate the probability density function (PDF) of the random variable that underlies our sample. KDE is a means of data smoothing.
Sticking with the Pandas library, you can create and overlay density plots using
plot.kde(), which is available for both
Pygator on Sept. 16, 2019
what does density=True do? and rwidth kwarg from before, don’t know what these do.