Kernel density estimation (KDE) plot is a plot used to visualize the probability density function estimation. It is in a way similar to a histogram which we discussed in the previous section, however, the KDE plot is a continuous curve, not a set of bars, and is based on all of the data points rather than the intervals. Let’s have a look at an example of a KDE plot:

As you can see, here we have a histogram combined with a KDE plot (orange curve). This combination gives us a much clearer probability density function approximation than a single histogram.

With seaborn creating a KDE plot is as simple as it gets, since there is a special kdeplot() function. Its most important parameters data, x and y work the same way as in the countplot() function.

First Option

We can simply set only one of these parameters via passing a sequence of values. Here is an example to clarify everything:

We only set the value for the data parameter passing a Series object and use the fill parameter to fill in the area under the curve (it is not filled in by default).

Second Option

It is also possible to set a 2D object like a DataFrame for data and a column name (or a key if the data is a dictionary) for x (vertical orientation) or y (horizontal orientation):

We achieved the same results passing the whole DataFrame as the data parameter and the column name for the x parameter.

By the way, the KDE plot we created has a characteristic bell curve and closely resembles the normal distribution with the mean of approximately 52°F.

In case you want to explore more about the kdeplot() function, feel free to refer to its documentation.