from __future__ import annotations
from typing import Iterable, Dict, Any
import pandas as pd
import numpy as np
def profile(df: pd.DataFrame, columns: Iterable[str] | None = None, percentiles: Iterable[float] | None = None) -> Dict[str, Any]:
if columns is None or len(columns) == 0:
# default numeric columns
columns = [c for c in df.columns if pd.api.types.is_numeric_dtype(df[c])]
percentiles = list(percentiles or [0.25, 0.5, 0.75])
stats = {}
for c in columns:
s = pd.to_numeric(df[c], errors="coerce")
if s.dropna().empty:
stats[str(c)] = {}
continue
desc = s.describe(percentiles=percentiles)
# unify key names
out = {
"count": int(desc.get("count", 0)),
"mean": _num(desc.get("mean")),
"std": _num(desc.get("std")),
"min": _num(desc.get("min")),
"max": _num(desc.get("max")),
}
for p in percentiles:
label = f"{int(p*100)}%" # pandas usa '25%'/'50%'/'75%'
key = f"p{int(p*100)}"
out[key] = _num(desc.get(label))
stats[str(c)] = out
return {"stats": stats}
def _num(v):
if v is None or (isinstance(v, float) and np.isnan(v)):
return None
if isinstance(v, (int, float)):
return float(v)
try:
return float(v)
except Exception:
return None
