Let’s bring one more Python package into the mix. Seaborn has a
displot() function that plots the histogram and KDE for a univariate distribution in one step. Let’s use the NumPy array
d from ealier:
import seaborn as sns
The call above produces a KDE. There is also optionality to fit a specific distribution to the data. This is different than a KDE and consists of parameter estimation for generic data and a specified distribution name:
sns.distplot(d, fit=stats.laplace, kde=False)
Again, note the slight difference. In the first case, you’re estimating some unknown PDF. In the second, you’re taking a known distribution and finding what parameters best describe it given the empirical data.