More Data Week: Intro to Data Visualization Packages
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.
I have a seismologist buddy who uses Matplotlib for presenting research. It seems to be valuable for static plots of large amounts of data. Seaborn is built on top of Matplotlib and takes visualization up a notch with aesthetically appealing flourishes. Maybe stick with Matplotlib if your office budget doesn’t allow for color printers. Bokeh is intended for interactive and web-based data visualization. Use this for attracting customers to your website or impressing executives.
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:
sns.replot()used to visualize the relationship between two variables. categorical:sns.catplot()*used to visualize the relationship between categories of data and (an)other variable(s). *distribution:sns.displot()*used to visualize a single variable across some metric/constant such as time.
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?
Looks like a general instruction sheet including common methods and brief explanations of use/implementation.
Bokeh looks like it could be fun