Implementing Phase 3 for `summary()`

[kengggg]

@notskynet-bot

Phase 3: summary() — Requirements

• Add epydem.summary(df, by=None, date_cols=None, numeric_cols=None, categorical_cols=None, top_k=3, output="long") -> pd.DataFrame

• Conservative defaults (strict):

  • If date_cols, numeric_cols, and categorical_cols are all None/empty => return only n (respecting by)
  • Do not infer columns automatically

• Metrics:

  • n per group
  • Missingness per specified column: missing_n, missing_pct
  • Date cols: min, max after pd.to_datetime(..., errors="coerce") (invalid -> missing)
  • Numeric cols: count, mean, std, min, p25, median, p75, max
  • Categorical cols: top_1..top_k and top_#_n
    • deterministic tie-break: count desc, then string(value) asc
    • missing token: <NA>

• Output:

  • default output="long" schema: by..., column, metric, value
  • optional output="wide" pivots metrics into columns (index includes by... + column)

• Tests:

  • by vs no-by
  • default-only-n behavior
  • date coercion behavior
  • numeric quartiles
  • categorical tie-breaking

• Docs: add README example(s) for summary()

[mewincaka]

acknowledged @notkeng

@kengggg — here is the ACK/PLAN for Phase 3 summary().

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Debate

Roles

Role	Position
Minimalist (M)	Keep summary() as a thin wrapper — delegate to pandas describe() internally, minimise custom logic.
Explicitist (E)	Follow the spec to the letter — no auto-inference, conservative defaults, custom metric assembly for each dtype category.
Pragmatist (P)	Build a single function but split internal helpers (_date_stats, _numeric_stats, _categorical_stats) so each concern is testable in isolation.

Divergence

• M argues we could just wrap df.groupby().describe() and reshape. E counters that the spec demands specific metric names (p25, missing_pct, top_k with deterministic tie-break) that describe() does not produce. P agrees with E — a describe() wrapper cannot satisfy the categorical tie-break rule or the <NA> missing token.
• M suggests inferring column types when *_cols are all None. E rejects — the spec explicitly says "do not infer columns automatically"; if all col lists are empty, return only n.
• P proposes splitting into private helpers per dtype. M worries about over-engineering. E supports it — each helper maps 1-to-1 with the spec's metric groups and is independently testable.

Consensus

All three agree on:

1. No auto-inference — conservative default returns only n.
2. Custom metric assembly — pandas describe() alone is insufficient.
3. Internal helpers per dtype category — keeps the main function readable and each concern testable.
4. Long-form default — build long first, pivot to wide when requested.

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Decision

Implement summary() as a single public function in epydem/summary.py backed by three private helpers (_date_stats, _numeric_stats, _categorical_stats). Build long-form output first, then pivot for output="wide". Conservative defaults: all-None column lists yield only n. Deterministic categorical tie-break via (count desc, str(value) asc). Missing token is <NA>.

---

Plan

#	Step	Files	Detail
1	Create epydem/summary.py	epydem/summary.py (new)	Public summary() + private helpers _date_stats, _numeric_stats, _categorical_stats, _count_n. Signature: summary(df, by=None, date_cols=None, numeric_cols=None, categorical_cols=None, top_k=3, output="long") -> pd.DataFrame.
2	Wire into public API	epydem/__init__.py	Import and re-export summary.
3	Tests	tests/test_summary.py (new)	Coverage: by vs no-by, default-only-n, date coercion + invalid→missing, numeric quartiles, categorical tie-breaking, output="wide", edge cases (empty df, all-missing column).
4	Docs	README.md	Add summary() usage examples (long and wide output).
5	PR	—	Open PR phase3-summary → main.

ETA: ~4 hours

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Checklist

• epydem/summary.py — summary() + _count_n, _date_stats, _numeric_stats, _categorical_stats
• epydem/__init__.py — export summary
• Conservative default: all-None cols → only n
• n per group (respecting by)
• Missingness: missing_n, missing_pct per specified column
• Date cols: min, max via pd.to_datetime(..., errors="coerce"); invalid → missing
• Numeric cols: count, mean, std, min, p25, median, p75, max
• Categorical cols: top_1..top_k, top_#_n; tie-break count desc, str(value) asc; missing token <NA>
• output="long" schema: by..., column, metric, value
• output="wide" pivot: index by... + column, metrics as columns
• Test: by vs no-by
• Test: default-only-n behavior
• Test: date coercion behavior
• Test: numeric quartiles
• Test: categorical tie-breaking
• Docs: README example(s) for summary()
• PR: phase3-summary → main

---

Plan spec (JSON)

{"branch":"phase3-summary","eta_hours":4,"files":{"create":["epydem/summary.py","tests/test_summary.py"],"modify":["epydem/__init__.py","README.md"]},"issue":8,"phase":3,"plan_version":1,"steps":["Create epydem/summary.py with summary() function","Wire summary into epydem/__init__.py","Create tests/test_summary.py with full coverage","Update README.md with summary() examples","Open PR against main"],"title":"Implement epydem.summary()"}

Plan ID: 32533f4c

---

To approve, comment: @notskynet-bot ok 32533f4c

[kengggg]

@notskynet-bot ok 32533f4c

[mewincaka]

OK mismatch: @kengggg your approval referenced plan_id 32533f4c but the expected plan_id is d4ff330f. Please re-approve with the correct ID:

ok d4ff330f

[kengggg]

@notskynet-bot ok d4ff330f

[mewincaka]

OK received (d4ff330f). Starting execution.

[mewincaka]

OK received (d4ff330f). Starting execution.

[mewincaka]

OK received (d4ff330f). Starting execution.