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 pd
df = pd.read_csv("wines.csv")
fig, ax = plt.subplots() ax.hist(df['alcohol'])
pandas
is a foundational library for analyzing dataimport pandas as pd
df = pd.read_csv("wines.csv") df['alcohol'].plot.hist()
histplot
is similar to the histogram shown in previous examplesimport seaborn as sns
sns.histplot(df['alcohol'])
displot
leverages the histplot
and other functions for distribution plotsimport seaborn as sns
sns.displot(df['alcohol'], kind='kde')
df['alcohol'].plot.hist()
sns.displot(df['alcohol'])
Intermediate Data Visualization with Seaborn