Assignment_2_Submission

[Yutong2002]

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

I created two new functions: patient_summary() and detect_problems() to enhance the data analysis workflow for the inflammation dataset. patient_summary function helped to calculates the per-patient summary statistics (mean, max, or min) using NumPy; and detect_problems function helped use patient_summary() to calculate each patient’s mean inflammation score. Together, these functions allow automated summarization and basic data validation for all inflammation data files.

What did you learn from the changes you have made?

I learned how to 1) Use NumPy’s operations (np.mean, np.max, np.min) with the axis argument to efficiently compute statistics across rows or columns. 2) Build modular code by separating functionality into reusable helper functions (patient_summary, check_zeros, and detect_problems). 3) Combine data summarization and validation logic in a structured and readable way.

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

N/A

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

At first, it was slightly confusing how NumPy applies functions like np.mean() or np.max() across different axes. I initially got unexpected results (single values instead of arrays) because I forgot to specify axis=1. To overcome this, I printed the shape of the data and experimented with small arrays to visualize how axis=0 (per day) and axis=1 (per patient) affect the results.

How were these changes tested?

I tested the updated function with csv dataset provided.

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

N/A

Checklist

• I can confirm that my changes are working as intended

[Dmytro-Bonislavskyi]

Well done!