Streamlit application utilising DuckDB for querying in-memory data (load ed from encrypted parquet files) alongside heavy caching due to the reactive architecture of Streamlit.

For local setup you should just poetry install the project.

Consider setting up pyenv and poetry as per this article.

After installing dependencies above, you could run locally with:
python -m streamlit run app.py .

With PyCharm (or IntelliJ Idea) you can also utilise the debugger. Just make sure that inside your Run configuration module is set to streamlit and script parameters are set to run app.py.

Dataset is easily generated from this repo.

The committed parquet files are encrypted, so you won't be able to re-use the full dataset locally. Instead us the *_unencrypted.parquet files to get a high level sense of the data.

Or consider simply running the script from above mentioned repo.

General performance tab:

Performance plotting tab: