Release v1.0.0 for templates/template_eda_notebook.
Publication
- Version: 1.0.0
- GitHub release: https://github.com/docxology/template_eda_notebook/releases/tag/v1.0.0
- DOI: https://doi.org/10.5281/zenodo.21086293
- Zenodo: https://zenodo.org/records/21086293
- PDF SHA-256:
0b10852bda89361cd71063867b55d9aed942881476867813facd549a961b0c1d
Abstract
Exploratory data analysis (EDA) is the most common entry point in applied
research, yet it is also where reproducibility most often breaks down: logic
accumulates in notebook cells that are never tested and quietly drift from the
prose describing them. This paper presents the computational-notebook
exemplar of the Research Project Template (https://github.com/docxology/template):
an interactive walkthrough notebook
(projects/templates/template_eda_notebook/notebooks/eda_walkthrough.ipynb)
that imports a small, fully-tested EDA library rather than carrying logic in its
cells.
We ship a deterministic dataset (data/measurements.csv) with a designed
correlation structure and a handful of missing values, then load, clean,
summarize, correlate, and visualize it entirely through tested functions in
src/eda/. The library is side-effect-free — no plotting and no file I/O — and
standalone (numpy and pandas only), so it is covered above the 90% project gate
and reused identically from the notebook, the thin analysis script
(scripts/eda_analysis.py), and this manuscript.
Contributions are methodological and architectural. On the methods side,
we walk the canonical first EDA pass: surface missingness explicitly rather than
imputing it, compute per-column descriptive statistics and per-group means, and
rank features by Pearson correlation. On the architecture side, we demonstrate
the notebook-to-tested-source extraction workflow — explore fast in a cell, and
the moment a computation matters, move it into the library behind a failing
test — verified by a zero-mock suite and a structural notebook-binding check
().