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Plotting With Seaborn

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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

sns.set_style('darkgrid')
sns.distplot(d)

Seaborn's distplot

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)

Histogram with fitted laplace distribution

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.

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