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 `Series`

and `DataFrame`

objects.

Pygatoron Sept. 16, 2019what does density=True do? and rwidth kwarg from before, don’t know what these do.