Track D BYOD: Bring Your Own Data

Track D is built around a realistic accounting case study (NSO), but the skills are meant to transfer.

This BYOD (Bring Your Own Data) pipeline lets you take real exports (from a bookkeeping/accounting system) and convert them into the same Track D dataset contract used in the workbook.

What “BYOD” means in Track D

Think of BYOD as a boring, reliable 3-step pipeline:

  1. Export data from a real system (or a CSV you already have).

  2. Normalize it into Track D’s canonical tables (normalized/).

  3. Validate + analyze the normalized tables using the Track D workflow.

The core idea is: separate the messy export from the clean analysis tables.

Quick start (template)

Create a BYOD project folder (this writes header-only templates under tables/):

pystatsv1 trackd byod init --dest byod/my_project --profile core_gl

Edit byod/my_project/config.toml to choose an adapter (examples):

  • adapter = "passthrough" — your tables/ files are already in Track D’s canonical format

  • adapter = "core_gl" — generic GL export adapter (varies by source)

  • adapter = "gnucash_gl" — GnuCash “Export Transactions to CSV” (complex layout)

Then normalize:

pystatsv1 trackd byod normalize --project byod/my_project

You should now have:

  • byod/my_project/normalized/chart_of_accounts.csv

  • byod/my_project/normalized/gl_journal.csv

Validate the normalized tables:

pystatsv1 trackd validate --datadir byod/my_project/normalized --profile core_gl

PyPI-only setup (no Git required)

If you just want to use Track D tools (you don’t need to clone the repo):

  1. Create a virtual environment.

  2. Install PyStatsV1 from PyPI.

  3. Use the CLI + the workbook downloads in this section.

python -m venv .venv

# Windows (Git Bash)
source .venv/Scripts/activate

# macOS/Linux
# source .venv/bin/activate

python -m pip install -U pip
pip install "pystatsv1[workbook]"

(Optional) sanity check:

pystatsv1 doctor

Next: choose a tutorial

Where this fits in the workbook