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Kernel Density Estimates

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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.

Comments & Discussion

Pygator on Sept. 16, 2019

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

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