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 (). --- Associated artifacts GitHub release: v1.0.0 (https://github.com/docxology/template_eda_notebook/releases/tag/v1.0.0) DOI: https://doi.org/10.5281/zenodo.21086292 Zenodo: https://zenodo.org/records/21086292 PDF SHA-256: 0b10852bda89361cd71063867b55d9aed942881476867813facd549a961b0c1d