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