DataFrame Analyst MCP

from __future__ import annotations from typing import Dict, Any import pandas as pd import numpy as np def detect_outliers(df: pd.DataFrame, column: str, method: str = "iqr", factor: float = 1.5, z: float = 3.0) -> Dict[str, Any]: s = pd.to_numeric(df[column], errors="coerce") idx = [] if method == "iqr": q1 = s.quantile(0.25) q3 = s.quantile(0.75) iqr = q3 - q1 lower = q1 - factor * iqr upper = q3 + factor * iqr mask = (s < lower) | (s > upper) idx = list(s[mask].index) elif method == "zscore": mu = s.mean() sd = s.std(ddof=0) if sd == 0 or np.isnan(sd): idx = [] else: mask = (np.abs((s - mu) / sd) > z) idx = list(s[mask].index) else: raise ValueError("method must be 'iqr' or 'zscore'") outliers = [{"row": int(i), "value": _num(s.loc[i])} for i in idx] return {"outliers": outliers, "count": len(outliers)} def _num(v): if v is None or (isinstance(v, float) and np.isnan(v)): return None return float(v)