Here's a numpy example from a numpy tutorial on doing AQI

[skrawcz]

See https://github.com/numpy/numpy-tutorials/tree/main/content
for where this is pulled from.

The names of the functions could be better I think. Otherwise this
doesn't seem like a bad pipeline. Versus doing this analysis in a notebook,
the strength of Hamilton here is in forcing concise definitions and language
around steps in the analysis -- and then magically the analysis is pretty
reusable / very easy to augment.

E.g. add some @config.when or split things into python modules to be swapped out.

Additions

• example showing using pure numpy code.

Removals

• N/A

Changes

• N/A

Testing

1. Script runs with python run_analysis.py

Notes

• This is based on https://github.com/numpy/numpy-tutorials/tree/main/content

Checklist

• PR has an informative and human-readable title
• Changes are limited to a single goal (no scope creep)
• Code can be automatically merged (no conflicts)
• Code follows the standards laid out in the TODO link to standards
• Passes all existing automated tests
• Any change in functionality is tested
• New functions are documented (with a description, list of inputs, and expected output)
• Placeholder code is flagged / future todos are captured in comments
• Project documentation has been updated (including the "Unreleased" section of the CHANGELOG)
• Reviewers requested with the Reviewers tool ➡️

Testing checklist

Python

- [ ] python 3.6

• python 3.7

[elijahbenizzy]

Nice. I think this is actually a great use for validators on the shape of the data, given that typing is just np.ndarray

[elijahbenizzy]

Adding some end-to-end tests could be nice -- this could be an integration test

[elijahbenizzy]

LGTM!