What are the key differences between Matplotlib, Seaborn, and Bokeh libraries in terms of their features and use cases? Provide an example of a specific visualization that is more suitable for each library.
In the Seaborn library, what are the main functions to create relational, categorical, and distribution plots? Briefly explain the purpose of each type of plot and provide an example use case.
Relational Plots: Visualizes the relationship between multiple statistical relationships. Has multiple options for different plot types and details to best visualize different types of data.
sns.relplot(
data=dots, kind="line",
x="time", y="firing_rate", col="align",
hue="choice", size="coherence", style="choice",
facet_kws=dict(sharex=False),
)
Categorical Plots: Visualizes data grouped by different categories associated with the data. Also has multiple different options.
sns.catplot(data=tips, kind="swarm", x="day", y="total_bill", hue="smoker")
Distribution Plots: Visualizes the distribution of values of variables in a dataset.
sns.displot(data=tips, x="total_bill", col="time", kde=True)
Code examples from Seaborn Docs
Discuss the role of the Seaborn Cheat Sheet in a Python developer’s workflow. What are some key sections or elements featured in the cheat sheet that can help a developer quickly reference Seaborn functionalities?
The Seaborn cheat sheets is a quick reference for loading datasets appropriately and creating plots using Seaborn. The sections I will be using the most are in “Plotting With Seaborn” so I can get an idea of how to implement a particular plot and what its different options are.