To plot a KDE in Seaborn, we use the method sns.kdeplot().

A KDE plot takes the following arguments:

• data - the univariate dataset being visualized, like a Pandas DataFrame, Python list, or NumPy array
• shade - a boolean that determines whether or not the space underneath the curve is shaded

Let’s examine the KDE plots of our three datasets:

sns.kdeplot(dataset1, shade=True)
sns.kdeplot(dataset2, shade=True)
sns.kdeplot(dataset3, shade=True)
plt.legend()
plt.show()

Notice that when using a KDE we need to plot each of the original datasets separately, rather than using a combined dataframe like we did with the bar plot.

It looks like there are some big differences between the three datasets:

• Dataset 1 is skewed left
• Dataset 2 is normally distributed
• Dataset 3 is bimodal (it has two peaks)

So although all three datasets have roughly the same mean, the shapes of the KDE plots demonstrate the differences in how the values are distributed.