UofT-DSI | DSI | - Assignment 2

[zaf-tech]

What changes are you trying to make? (e.g. Adding or removing code, refactoring existing code, adding reports)

Completed the three required parts of assignment_2.ipynb:

1. Read inflammation_01.csv with readlines() and printed each row.
2. Implemented patient_summary(file_path, operation) using np.loadtxt + np.mean/np.max/np.min with axis=1 to summarize each of the 60 patients' 40-day flare-up counts.
3. Implemented detect_problems(file_path), which calls patient_summary(file_path, 'mean') and passes the result to the provided check_zeros() helper.

What did you learn from the changes you have made?

How NumPy's axis parameter controls whether a reduction (mean/max/min) is applied per-row vs per-column — axis=1 was needed here to get one summary value per patient (row) rather than per day (column). Also reinforced composing small functions (patient_summary → detect_problems) instead of duplicating logic.

Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?

Could have used pandas.read_csv + DataFrame.mean(axis=1) instead of np.loadtxt. Stuck with NumPy since that's what the assignment hints and course slides (10_numpy) pointed to, and it avoids adding a pandas dependency for a one-off summary.

Were there any challenges? If so, what issue(s) did you face? How did you overcome it?

My local assignment-2 branch had drifted from a stale remote branch with an older solution attempt; I reset it to start clean from main. Separately, this repo's root pyproject.toml pulls in a large, unrelated dependency set (deploying-ai-env) that fails to build on this machine's Python version, so uv run at the project root couldn't execute the notebook. I worked around it by running jupyter nbconvert --execute in an isolated environment (uv run --no-project --with numpy --with nbconvert --with ipykernel) so I could verify the notebook end-to-end without touching the project's dependency file.

How were these changes tested?

Executed the full notebook non-interactively via jupyter nbconvert --to notebook --execute and confirmed the real outputs:

• Cell 1 prints all 60 rows of inflammation_01.csv.
• len(patient_summary(all_paths[0], 'min')) → 60, as required.
• detect_problems(all_paths[0]) → False, matching the expected result.

A reference to a related issue in your repository (if applicable)

Checklist

• I can confirm that my changes are working as intended