data-analyses

Place for sharing quick reports, and works in progress

This repository is for quick sharing of works in progress and simple analyses. For collaborative short-term tasks, create a new folder and work off a separate branch. For longer-term projects, consider making a new repository!

Using this Repo

• Use this link to get started in JupyterHub, set up SSH, and start commiting to the repo!

Contributing

Pre-commit

This repository uses pre-commit hooks to format code, including Black. This ensures baseline consistency in code formatting.

[!IMPORTANT] Before contributing to this project, please install pre-commit locally by running pip install pre-commit and pre-commit install in the root of the repo.

Once installed, pre-commit checks will run before you can make commits locally. If a pre-commit check fails, it will need to be addressed before you can make your commit. Many formatting issues are fixed automatically within the pre-commit actions, so check the changes made by pre-commit on failure – they may have automatically addressed the issues that caused the failure, in which case you can simply re-add the files, re-attempt the commit, and the checks will then succeed.

Installing pre-commit locally saves time dealing with formatting issues on pull requests. There is a GitHub Action that runs pre-commit on all files, not just changed ones, as part of our continuous integration.

Quick Links - Get Started in Data Analysis

Data Analytics Documentation - Welcome

https://docs.calitp.org/data-infra/analytics_welcome/overview.html

Data Analytics Documentation - Introduction to Analytics Tools

https://docs.calitp.org/data-infra/analytics_tools/overview.html

Publishing Reports

The sites folder contains the YAML files that drive sites deployed to https://analysis.calitp.org/; the existing sites can be used as examples/templates for deploying additional sites. Also, the Data Services Documentation has a specific chapter dedicated to various ways to publish data.

Caveats (when using the portfolio site)

Jupyter Book/Sphinx do not play nicely with Markdown headers written out in display() calls. Therefore, portfolio.py uses a custom Papermill engine to template Markdown cells directly, following Python formatted-string syntax. For example, your Markdown cell could contain # {district_name} and it will be templated by the underlying engine.