Assignment 2

[HromovaAnna]

What changes are you trying to make?

I implemented three main functions to analyze patient inflammation data for a clinical trial. These functions help read and summarize the data, detect errors, and compute statistical operations across multiple CSV files.

What did you learn from the changes you have made?

I learned how to handle large sets of patient data stored in CSV format and how to apply various statistical operations to summarize this data effectively. The challenge of detecting errors in the dataset, particularly focusing on mean values of inflammation scores, provided valuable insights into error-checking strategies.

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

Another possible approach was to utilize pandas instead of numpy to handle the CSV data. Pandas offers more flexibility for reading, manipulating, and analyzing large datasets with intuitive DataFrame structures, though numpy is more efficient for mathematical operations.

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

A major challenge was ensuring that the functions were properly connected—especially between reading data, computing summaries, and detecting errors. I resolved this by defining clear input/output structures and ensuring consistency in how the functions interact. Additionally, the zero detection mechanism had to be precise, which was managed using numpy’s where() function.

How were these changes tested?

I tested the changes by reading and printing sample data from the provided CSV files, confirming that the operations return arrays of the expected length (60). Additionally, I verified that the error detection function returned False when appropriate by checking sample datasets.

[SidraBushra]

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