apethani21 / weight-data-analysis

Analysing weight data.

Weight data analysis

• Downloading weight data stored in a Google Sheet

• Storing the data in, and regularly updating a Postgres (originally SQLite3) database by checking the Google Sheet for updates, with a cron job.

• Originally regularly updated a Jupyter Notebook with a cron job, and then tried Dash and Streamlit.

• create.sql initialises the WEIGHT table.

• migration.py performs a full migration of the data in the Google Sheet to the postgres database, avoiding duplication.

• update_db.py checks the number of rows in the database to figure out how many of the tail-end rows to add from the Google Sheet, and adds them.

• api_handler.py handles authentication for querying the Google sheet, and postgres_utils.py handles postgres authentication, connection and query execution.

• weight-loss-analysis.ipynb contains the analysis, which was then replicated with a Dash dashboard with dashboarder.py and a Streamlit dashboard with streamer_app.py