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| import pandas as pd | |
| ALLOWED_OPS = {"mean", "median", "count"} | |
| def execute_plan(df: pd.DataFrame, plan: dict) -> pd.DataFrame: | |
| q = df.copy() | |
| # filters | |
| for col, rule in (plan.get("filters") or {}).items(): | |
| if col not in q.columns: | |
| raise ValueError("Unknown column: %s" % col) | |
| if not isinstance(rule, dict): | |
| raise ValueError("Bad filter rule for %s" % col) | |
| if "eq" in rule: | |
| q = q[q[col] == rule["eq"]] | |
| if "in" in rule: | |
| q = q[q[col].isin(rule["in"])] | |
| if "not_in" in rule: | |
| q = q[~q[col].isin(rule["not_in"])] | |
| if "gte" in rule: | |
| q = q[q[col] >= rule["gte"]] | |
| if "lte" in rule: | |
| q = q[q[col] <= rule["lte"]] | |
| groupby = plan.get("groupby") or [] | |
| metrics = plan.get("metrics") or [] | |
| if groupby: | |
| gb = q.groupby(groupby, dropna=False) | |
| agg_dict = {} | |
| for m in metrics: | |
| col, op = m.get("col"), m.get("op") | |
| label = m.get("label", "%s_%s" % (op, col)) | |
| if op not in ALLOWED_OPS: | |
| raise ValueError("Unsupported op: %s" % op) | |
| if op == "count": | |
| agg_dict[label] = (col, "count") | |
| else: | |
| agg_dict[label] = (col, op) | |
| res = gb.agg(**agg_dict).reset_index() if agg_dict else gb.size().reset_index(name="count") | |
| else: | |
| # global summary | |
| rows = {} | |
| for m in metrics: | |
| col, op = m.get("col"), m.get("op") | |
| label = m.get("label", "%s_%s" % (op, col)) | |
| if op not in ALLOWED_OPS: | |
| raise ValueError("Unsupported op: %s" % op) | |
| if op == "count": | |
| rows[label] = int(q[col].count()) | |
| else: | |
| rows[label] = float(getattr(q[col], op)()) | |
| res = pd.DataFrame([rows]) if rows else q.head(20) | |
| for s in (plan.get("sort_by") or []): | |
| res = res.sort_values(s.get("col"), ascending=bool(s.get("asc", True))) | |
| limit = min(int(plan.get("limit", 20)), 50) | |
| return res.head(limit) | |