Advanced Plots in Seaborn

In the previous chapter, we discussed various plots in Matplotlib, but there are still a few visualizations left that we want to discuss. First, we will revise bar plots since Seaborn offers some neat additional features for them. Moreover, we will cover kernel density estimation, correlograms, and violin plots.

Bar Plots

In the last chapter, we already explained how to create bar plots with Matplotlib. Creating bar plots with subgroups was quite tedious, but Seaborn offers a very convenient way to create various bar plots. They can also be used in Seaborn to represent estimates of central tendency with the height of each bar, while uncertainty is indicated by error bars at the top of the bar.

The following example gives you a good idea of how this works:

import pandas as pd
import seaborn as sns
data = pd.read_csv("../Datasets/salary.csv")
sns.set(style="whitegrid")
sns.barplot(x="Education", y="Salary"...