Three weeks ago you formatted one field. Last week you parsed a whole CSV and grouped it. Your advisor now wants the complete analysis — cleaned, grouped, ranked by satisfaction, missing-value safe — every time they send new data. What does that take?
String the whole chain together. Parse the CSV, filter the incomplete rows, group by demographic, rank the groups, flag anything with too few responses. That's the whole methodology section.
That's exactly the Week 4 arc. You already have most of the functions. This week you add the last pieces: list comprehensions for one-line transforms, regex for extracting patterns from free text, proper sorting by field, error handling for messy data, and a capstone function that chains everything together. By the end you have a single function call that takes a raw Qualtrics CSV and produces the ranked summary your advisor needs.
How is the capstone different from just calling all the functions in sequence?
The capstone wraps the sequence in one named function with a docstring. It handles errors gracefully instead of crashing on bad data. It produces a structured output dict your advisor can read or you can serialise to JSON. The pipeline is not the sum of the functions — it's the contract that connects them.
top_groups_by_score: list comprehension — filter valid responses in one expression, return top N groupsextract_free_text_themes: string processing + regex — extract theme labels from open-text responsesrank_groups_by_satisfaction: sorting — sorted(key=...) to rank demographic groupssafe_compute_avg: error handling — try/except for missing keys and non-numeric valuesthesis_pipeline: capstone — parse → clean → group → rank → flag low-sample groupsGoal: one function call, any Qualtrics CSV, reproducible methodology table output.
7 lessons this week
Three weeks ago you formatted one field. Last week you parsed a whole CSV and grouped it. Your advisor now wants the complete analysis — cleaned, grouped, ranked by satisfaction, missing-value safe — every time they send new data. What does that take?
String the whole chain together. Parse the CSV, filter the incomplete rows, group by demographic, rank the groups, flag anything with too few responses. That's the whole methodology section.
That's exactly the Week 4 arc. You already have most of the functions. This week you add the last pieces: list comprehensions for one-line transforms, regex for extracting patterns from free text, proper sorting by field, error handling for messy data, and a capstone function that chains everything together. By the end you have a single function call that takes a raw Qualtrics CSV and produces the ranked summary your advisor needs.
How is the capstone different from just calling all the functions in sequence?
The capstone wraps the sequence in one named function with a docstring. It handles errors gracefully instead of crashing on bad data. It produces a structured output dict your advisor can read or you can serialise to JSON. The pipeline is not the sum of the functions — it's the contract that connects them.
top_groups_by_score: list comprehension — filter valid responses in one expression, return top N groupsextract_free_text_themes: string processing + regex — extract theme labels from open-text responsesrank_groups_by_satisfaction: sorting — sorted(key=...) to rank demographic groupssafe_compute_avg: error handling — try/except for missing keys and non-numeric valuesthesis_pipeline: capstone — parse → clean → group → rank → flag low-sample groupsGoal: one function call, any Qualtrics CSV, reproducible methodology table output.