13.1 Description

In the previous chapters, the sequence of projects created a pipeline to acquire and then clean the raw data. The intent is to build automated data gathering as Python applications.

We noted that ad hoc data inspection is best done with a notebook, not an automated CLI tool. Similarly, creating command-line applications for analysis and presentation can be challenging. Analytical work seems to be essentially exploratory, making it helpful to have immediate feedback from looking at results.

Additionally, analytical work transforms raw data into information, and possibly even insight. Analytical results need to be shared to create significant value. A Jupyter notebook is an exploratory environment that can create readable, helpful presentations.

One of the first things to do with raw data is to create diagrams to illustrate the distribution of univariate data and the relationships among variables in multivariate data. We’ll emphasize the following common kinds of...