A Robust Dev-to-Production Workflow for Home Use, Using Jupyter Notebooks and PyTest By: Leon Shernoff
Date: April 12, 2018, 6 p.m.
Working on a substantial Python project at home can be confusing and frustrating. A work environment can suddenly impact the direction of a project in unexpected ways, because of the many stakeholders; but they usually have a robust process in place for actually doing the coding (otherwise nothing gets done). Implementing a solid and productive workflow routine at home can be a challenge, but it is of great benefit for complex projects. This talk uses a sample text-processing project to demonstrate a home workflow design featuring sandboxing in Jupyter notebooks, migration of working routines to project-specific modules and straight-ahead Python files, and writing unit tests for these in PyTest.
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