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Week 2 Checkpoint: Collections, Loops, and Grouping — Python For Researchers

Day 15 · ~11 min●

Five days, five functions. filter_eligible, compute_outcome_per_respondent, find_first_outlier, group_by_treatment, treatment_summary. Which one changed how you think about your data?

Honestly group_by_treatment. I've been running SPSS pivot tables for three years and never thought of the operation as "accumulate into a dict keyed by treatment group." Now I can't un-see it.

That mental model carries through the entire Week 4 capstone. The dict is the shape of the summary table before it's formatted. What about the while loop from Day 12?

find_first_outlier was the one that felt most like real research work — stopping early when you find what you need, preserving the original ordering of the dataset.

A quality-control check that respects temporal sequence. That's a methods-section footnote.

Ready to consolidate. Week 2 was the heaviest conceptually.

Six questions — four from this week, two reviewing Week 1 concepts. The Week 1 review questions are marked [REVIEW] in the explanations.

Quiz

0/6

6 questions80% to passretry anytime

1.What does filter_eligible(respondents, 18.0) return when respondents = [{"id": "R_001", "age": 22.0}, {"id": "R_002", "age": 15.0}]?

def filter_eligible(respondents, min_age):
    eligible = []
    for r in respondents:
        if r["age"] >= min_age:
            eligible.append(r)
    return eligible

[{"id": "R_001", "age": 22.0}][{"id": "R_002", "age": 15.0}][{"id": "R_001", "age": 22.0}, {"id": "R_002", "age": 15.0}][]

2.You call group_by_treatment(respondents) on a list where one respondent has treatment_group: "control" and another has treatment_group: "Control" (capital C). What happens?

Python normalises case automatically — both land in the "control" bucketThe second respondent raises a KeyError because the key already existsThey land in separate buckets: "control" and "Control" are different dict keysThe function returns an error because dict keys must be lowercase

3.find_first_outlier uses a while loop with break. What is the value of idx after the loop if no respondent in the list exceeds the cutoff?

0-1len(respondents)len(respondents) - 1

4.What does treatment_summary return for a single-group list of two respondents with outcomes 4.0 and 5.0 and ages 25.0 and 35.0?

respondents = [
    {"treatment_group": "control", "outcome": 4.0, "age": 25.0},
    {"treatment_group": "control", "outcome": 5.0, "age": 35.0}
]

{"control": {"n": 2, "mean_outcome": 4.5, "mean_age": 30.0}}{"control": {"n": 2, "mean_outcome": 9.0, "mean_age": 60.0}}{"control": [4.0, 5.0]}{"control": {"n": 1, "mean_outcome": 4.5, "mean_age": 30.0}}

5.[REVIEW] What does is_eligible(18.0, 18.0) return?

def is_eligible(age, min_age):
    return age >= min_age

TrueFalse018.0

6.[REVIEW] Which string operation normalises " Treatment A " to "treatment_a"?

".lower().replace(' ', '_')"".strip().lower().replace(' ', '_')"".lower().strip().replace(' ', '_')"".replace(' ', '_').strip().lower()"