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Graphics and Visualization with Jupyter Lab
A great many problems are simplified through visualization of the data. The human eye is particularly suited to identifying relationships and trends. Given a display of a potential relationship (or trend), it makes sense to turn to more formal statistical methods to quantify the relationship.
Python offers a number of graphical tools. For data analytics purposes, one of the most popular is matplotlib. This package offers numerous graphic capablities. It integrates well with Jupyter Lab, providing us an interactive environment to visualize and analyze data.
It’s possible to do a great deal of Python development in Jupyter Lab. While wonderful, this is not a perfect Integrated Development Environment (IDE). The one minor drawback is the interactive notebook relies on global variables, something that isn’t ideal for writing modules or applications. The use of global variables can lead to confusion when transforming...
