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Phase 3: strict summary() defaults + deterministic tie-break #9

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21 changes: 17 additions & 4 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -41,8 +41,10 @@ weekly_cum = epydem.transform_incidence(weekly, cumulative=True)

## Descriptive summary

`summary()` returns a DataFrame (raw stats). `summary_markdown()` returns a pretty Markdown table
string for quick sharing.
`summary()` returns a tidy (long-format) DataFrame of descriptive statistics.
`summary_markdown()` wraps it into a pretty Markdown table for quick sharing.

**Conservative defaults** — if you omit all column lists, `summary()` returns only `n` per group (no auto-inference):

```python
import pandas as pd
Expand All @@ -56,16 +58,27 @@ df = pd.DataFrame(
}
)

# Raw DataFrame summary (long/tidy by default)
# Only n per group (no columns specified)
epydem.summary(df, by=["sex"])

# Full summary: explicitly specify columns you care about
out = epydem.summary(
df,
by=["sex"],
date_cols=["onset_date"],
numeric_cols=["age"],
categorical_cols=["sex"],
top_k=3, # default; top-k categories per categorical column
)

# Wide output (metrics as columns)
wide = epydem.summary(
df,
numeric_cols=["age"],
output="wide",
)

# Pretty report (Markdown table)
# Pretty Markdown report
md = epydem.summary_markdown(
df,
by=["sex"],
Expand Down
38 changes: 17 additions & 21 deletions epydem/summary.py
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Expand Up @@ -29,7 +29,7 @@ def summary(
date_cols: Sequence[str] | None = None,
numeric_cols: Sequence[str] | None = None,
categorical_cols: Sequence[str] | None = None,
top_k: int = 5,
top_k: int = 3,
output: OutputFormat = "long",
) -> pd.DataFrame:
"""Descriptive summary statistics for an epidemiological line list.
Expand Down Expand Up @@ -61,20 +61,8 @@ def summary(
numeric_cols = list(numeric_cols) if numeric_cols is not None else []
categorical_cols = list(categorical_cols) if categorical_cols is not None else []

# Default heuristic: if user doesn't specify, infer a little.
if not date_cols:
for c in df.columns:
if c.endswith("_date") or c in {"date", "onset_date", "report_date"}:
date_cols.append(c)

if not numeric_cols:
numeric_cols = [c for c in df.select_dtypes(include="number").columns]

# treat remaining non-numeric, non-by, non-date as categorical if user didn't specify
if not categorical_cols:
candidates = [c for c in df.columns if c not in set(by_cols + date_cols + numeric_cols)]
# avoid very wide free-text columns by default
categorical_cols = candidates
# Conservative: do NOT infer columns automatically.
# If no column lists are specified, summary returns only n per group.

work = df.copy()

Expand Down Expand Up @@ -144,16 +132,24 @@ def _add(group_key: tuple, metric: str, value, col: str | None = None):
_add(key, "p75", float(desc["75%"]), col=c)
_add(key, "max", float(desc["max"]), col=c)

# Categorical top-k
# Categorical top-k (deterministic tie-break: count desc, then str(value) asc)
for c in categorical_cols:
if c not in g.columns:
continue
s = g[c].astype("object")
s = g[c].copy()
vc = s.value_counts(dropna=False)
for rank, (val, cnt) in enumerate(vc.head(top_k).items(), start=1):
label = "<NA>" if pd.isna(val) else str(val)
_add(key, f"top_{rank}", label, col=c)
_add(key, f"top_{rank}_n", int(cnt), col=c)
# Build sortable frame: count desc, then label asc for ties
vc_df = vc.reset_index()
vc_df.columns = ["raw_value", "cnt"]
vc_df["label"] = vc_df["raw_value"].apply(
lambda v: "<NA>" if pd.isna(v) else str(v)
)
vc_df = vc_df.sort_values(
["cnt", "label"], ascending=[False, True]
).reset_index(drop=True)
for rank, row in enumerate(vc_df.head(top_k).itertuples(), start=1):
_add(key, f"top_{rank}", row.label, col=c)
_add(key, f"top_{rank}_n", int(row.cnt), col=c)

out = pd.DataFrame(rows)

Expand Down
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