Assignment 2

[BukkyShonibare]

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

Adding the completed assignment_2.ipynb notebook which includes: reading and displaying the first inflammation CSV file; creating the patient_summary function to calculate mean, max, and min flare-ups; and creating the detect_problems function to identify patients with zero mean inflammation scores.

What did you learn from the changes you have made?

I practiced reading CSV data, using NumPy for statistical computations, writing reusable functions, and implementing simple data validation logic. I also reinforced understanding of how to handle arrays row-wise for patient-level analysis.

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

I considered using Python’s built-in lists and loops instead of NumPy for calculations, but NumPy is more efficient and concise for this type of data analysis.

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

I initially struggled with ensuring the operations were performed across rows (patients) instead of columns (days). I resolved it by using axis=1 in NumPy functions and confirming the output array had 60 elements.

How were these changes tested?

Each function was tested with the provided CSV file by printing the first file to inspect the data; running patient_summary with "mean", "max", and "min" to confirm correct outputs; and running detect_problems to confirm it returned False for the first file (no zero mean values).

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

Checklist

• I can confirm that my changes are working as intended

[tianyi21]

Hi Adebukola,

✅ You implemented the function to read and display a file.
✅ You completed the patient_summary() function to summarize a file.
✅ You implemented the detect_problems() function to check the file.

🎉 Your A2 is complete!
🎊 All your Python assessments are now complete. Thank you for your participation, and I wish you all the best for your future!

Thanks,
Tianyi [LS]