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This project is the development of an API to support an application to manage the process of evaluating grant applications for corporate social responsibility programs. Many companies perceive there to be significant benefit from having corporate social respsonsibility programs that provide financial and or in-kind products or services to non-profit organizations. Those benefits include genuinely helping do some good in the world and helping with recruiting and retention as most people like to feel like they are working for an organization that serves a higher purpose in addition to maximizing shareholder value.
In order to derive those benefits companies have to not just make sure that they are providing grants to worthy organizations, but also engage employees in the process and share information with them so that they understand the good that is being done. One way that works really well to achieve both of those purposes is to engage employees in the process of evaluating grant applications.
However, this can be administratively burdensome as most often corporate social responsiblity programs are managed by a small number of non-technical people, considered to be non-core and operate with modest financial resources. Keeping the information straight across thousands of applications and hundreds of employees, each of whom are evaluating multiple applications can quickly become unwieldy.
This project aims to solve this problem and make it possible for a small number of people to effectively engage employees to make high quality evaluations of grant applications. This project is phase one of the completed application and includes the data models and a REST API built with django and django-rest-framework.
Below are a few Advanced Python for Data Science highlights:
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Data model utilizes abstract base classes to propagate common features
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Custom management command with composed luigi tasks to load sample data from Random Data API.
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Test suite has been implemented with an abstract base class that streamlines testing and reduces boilerplate
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Full self generating openAPI Specification API documentation
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Documentation includes:
- PyData Spinx Theme
- Github actions deployment
- Data model graph created with django graph_models and rendered with sphinx.ext.graphviz
- Google style docstrings parsed by autodoc with the help of napoleon
- Django model parameters parsed by autodoc with the help of sphinxcontrib-django
- Links to source code with sphinx.ext.viewcode
