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.
Matplotlib - Basic visualizations. Charts and graphs. Versatile. Pandas uses matplotlib.
Seaborn - Heatmaps, improved data distributions visualization. Add-on to Matplotlib.
Bokeh - Interactive visualization. Zoom in, pan, or toggle the display.
(https://machinelearningmastery.com/data-visualization-in-python-with-matplotlib-seaborn-and-bokeh/)[https://machinelearningmastery.com/data-visualization-in-python-with-matplotlib-seaborn-and-bokeh/]
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.
Visualization Statistical Relationship
seaborn.relplot - Figure-level interface for drawing relational plots. Provides access to other axes-level functions.
seaborn.scatterplot()
seaborn.lineplot()
Syntax :
seaborn.relplot(x=None, y=None, data=None, **kwargs)
By default is uses mean.
Syntax:
barplot([x, y, hue, data, order, hue_order, …])
Basic bar graph, by count.
Syntax:
countplot([x, y, hue, data, order, …])
Also known as the “box and whisker” plot. Shows a distribution of data representing comparison between two variables. The whiskers represent data that lays outside the core confidence interval.
Syntax:
boxplot([x, y, hue, data, order, hue_order, …])
Similar to boxplot but more visual using kernel density.
Syntax:
violinplot([x, y, hue, data, order, …])
Syntax:
boxplot([x, y, hue, data, order, hue_order, …])
A histogram.
Syntax:
distplot(a[, bins, hist, kde, rug, fit, …])
Combines two graphs into one. For example, scatter and histogram.
Another two in one. This time, overlaying the data.
Shows ditribution of individual plots as ticks along the axis. A good visualization for clusters.
Syntax:
rugplot(data=None, *, x=None, y=None, hue=None, height=0.025, expand_margins=True, palette=None, hue_order=None, hue_norm=None, legend=True, ax=None, **kwargs)
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 cheatsheet makes it much faster and easier to find the code to enter at any given stage. It is a excellent reference for any stage of the development in Seaborn. This turns changes into a menu, reducing the need to remember all the names and syntax.
What use cases are Seaborn and Bokeh better? Or is it all preference?