Three weeks of functions. You can format, clean, filter, loop, group, read CSV, and serialise JSON. But wave 4 just arrived with 400 respondents, some missing outcome fields, some with malformed IDs. What breaks first?
treatment_summary divides by N — if a group has zero eligible respondents after filtering, it crashes. And if outcome is blank in a row, float("") raises ValueError.
Those are exactly the two failure modes that stop a pipeline from running unattended. This week you handle them. List comprehensions replace multi-line loops. Regex extracts IDs from messy text. Sorting and grouping rank your results. And error handling wraps the unsafe operations so the pipeline recovers gracefully.
By the end of the week, is the capstone a single function I can hand to my co-author?
A single function: analysis_pipeline(respondents). It filters, groups, summarises, ranks, and flags outlier groups. The output is a plain Python dict — serialisable to JSON, auditable, ready for the supplementary materials. That's the reproducible pipeline that replaces the weekend of SPSS re-clicking.
top_respondents_by_outcome: list comprehension to filter and sort the top Nextract_respondent_ids: regex extraction from messy JSON-like textrank_groups_by_outcome: sorting with sorted(key=...) on grouped statssafe_compute_outcome: try/except around the unsafe outcome extractionanalysis_pipeline: the capstone — filter → group → summarise → rank → flag outliersGoal: a single function that regenerates every descriptive table in your paper from one input.
7 lessons this week
Three weeks of functions. You can format, clean, filter, loop, group, read CSV, and serialise JSON. But wave 4 just arrived with 400 respondents, some missing outcome fields, some with malformed IDs. What breaks first?
treatment_summary divides by N — if a group has zero eligible respondents after filtering, it crashes. And if outcome is blank in a row, float("") raises ValueError.
Those are exactly the two failure modes that stop a pipeline from running unattended. This week you handle them. List comprehensions replace multi-line loops. Regex extracts IDs from messy text. Sorting and grouping rank your results. And error handling wraps the unsafe operations so the pipeline recovers gracefully.
By the end of the week, is the capstone a single function I can hand to my co-author?
A single function: analysis_pipeline(respondents). It filters, groups, summarises, ranks, and flags outlier groups. The output is a plain Python dict — serialisable to JSON, auditable, ready for the supplementary materials. That's the reproducible pipeline that replaces the weekend of SPSS re-clicking.
top_respondents_by_outcome: list comprehension to filter and sort the top Nextract_respondent_ids: regex extraction from messy JSON-like textrank_groups_by_outcome: sorting with sorted(key=...) on grouped statssafe_compute_outcome: try/except around the unsafe outcome extractionanalysis_pipeline: the capstone — filter → group → summarise → rank → flag outliersGoal: a single function that regenerates every descriptive table in your paper from one input.