Intermediate Data Visualization with Seaborn
Chris Moffitt
Instructor

matplotlib provides the raw building blocks for Seaborn's visualizationsimport matplotlib.pyplot as plt import pandas as pddf = pd.read_csv("wines.csv")fig, ax = plt.subplots() ax.hist(df['alcohol'])

pandas is a foundational library for analyzing dataimport pandas as pddf = pd.read_csv("wines.csv") df['alcohol'].plot.hist()

histplot is similar to the histogram shown in previous examplesimport seaborn as snssns.histplot(df['alcohol'])

displot leverages the histplot and other functions for distribution plotsimport seaborn as snssns.displot(df['alcohol'], kind='kde')

df['alcohol'].plot.hist()

sns.displot(df['alcohol'])

Intermediate Data Visualization with Seaborn