Python has been adopted by many disciplinary communities, showing its adaptability to many problems. Scientific computing and web development are two examples of such communities. These might, at first glance, seem to share few common interests, especially at the level of algorithms and libraries. However, at the level of integrated practice in time-constrained academic environments, where framework development is less valued than research and teaching productivity, ease of adoption of tools from each of these communities can be tremendously valuable.
Using examples from the recently-deployed West Texas Lightning Mapping Array, which is processed and visualized in real-time, this paper will argue that a shared sense, among disciplinary communities, of the essence of how one deploys Python for specific problems is beneficial for continuation and growth of Python's status as a go-to language for practitioners in academic settings.