#!/usr/bin/env python3 """比对「加氢记录」与「氢费台账-加氢订单」，输出标黄/标红 Excel。""" from __future__ import annotations import re from collections import defaultdict from pathlib import Path import pandas as pd from openpyxl import load_workbook from openpyxl.styles import Font, PatternFill FILE_RECORD = Path( "/Users/sylvawong/Library/Containers/com.tencent.xinWeChat/Data/Documents/" "xwechat_files/wxid_l80gh7d3x7u012_385c/msg/file/2026-05/" "加氢记录-（2026.1月-4月）.xlsx" ) FILE_LEDGER = Path( "/Users/sylvawong/Library/Containers/com.tencent.xinWeChat/Data/Documents/" "xwechat_files/wxid_l80gh7d3x7u012_385c/msg/file/2026-05/" "氢费台账总表2026.5.21(1).xlsx" ) OUT_DIR = Path("/Users/sylvawong/Desktop/CURSOR/ONE-OS") OUT_FILE = OUT_DIR / "加氢记录与台账比对结果.xlsx" COMPARE_FIELDS = [ ("加氢站", "加氢站名称", "加氢站"), ("成本单价", "成本单价(元/kg)", "成本单价"), ("成本金额", "成本费用(元)", "成本金额"), ("加氢单价", "加氢单价(元/kg)", "加氢单价"), ("加氢金额", "加氢金额（元）", "加氢金额"), ("客户名", "客户名称", "客户名"), ("氢费计算方式", "结算类别", "氢费计算方式"), ] YELLOW = PatternFill(start_color="FFFF00", end_color="FFFF00", fill_type="solid") RED = PatternFill(start_color="FF9999", end_color="FF9999", fill_type="solid") RED_FONT = Font(color="9C0006", bold=True) def read_record_sheet(path: Path) -> pd.DataFrame: raw = pd.read_excel(path, sheet_name=0, header=None) # 第 0 行为汇总行，第 1 行为表头 raw.columns = [ str(c).strip() if pd.notna(c) else f"_col{i}" for i, c in enumerate(raw.iloc[1]) ] df = raw.iloc[2:].copy().reset_index(drop=True) rename = { "序号": "序号", "日期": "日期", "车牌": "车牌", "加气量（kg)": "加气量（kg)", "加气量（kg）": "加气量（kg)", "加氢站": "加氢站", "成本单价": "成本单价", "成本金额": "成本金额", "加氢单价": "加氢单价", "加氢金额": "加氢金额", "客户名": "客户名", "氢费计算方式": "氢费计算方式", } for old, new in rename.items(): if old in df.columns: df = df.rename(columns={old: new}) return df def parse_date(val) -> str: if pd.isna(val): return "" if isinstance(val, pd.Timestamp): return val.strftime("%Y-%m-%d") if isinstance(val, (int, float)) and val > 40000: try: return (pd.Timestamp("1899-12-30") + pd.Timedelta(days=int(val))).strftime("%Y-%m-%d") except Exception: pass s = str(val).strip() for fmt in ("%Y-%m-%d", "%Y/%m/%d", "%Y-%m-%d %H:%M:%S"): try: return pd.to_datetime(s).strftime("%Y-%m-%d") except Exception: continue try: return pd.to_datetime(val).strftime("%Y-%m-%d") except Exception: return s[:10] if len(s) >= 10 else s def norm_plate(val) -> str: if pd.isna(val): return "" return re.sub(r"[\s\-·]", "", str(val).strip().upper()) def norm_kg(val, tol_decimals=3) -> str: if pd.isna(val) or str(val).strip() in ("", "-", "nan"): return "" try: return f"{round(float(val), tol_decimals):.{tol_decimals}f}" except Exception: return str(val).strip() def norm_str(val) -> str: if pd.isna(val): return "" return str(val).strip() def norm_num(val, tol=0.02) -> float | None: if pd.isna(val) or str(val).strip() in ("", "-", "nan"): return None try: return float(val) except Exception: return None def values_equal(a, b, numeric: bool) -> bool: if numeric: na, nb = norm_num(a), norm_num(b) if na is None and nb is None: return True if na is None or nb is None: return False return abs(na - nb) <= 0.02 return norm_str(a) == norm_str(b) def make_key(row, date_col, plate_col, kg_col) -> str: return f"{parse_date(row[date_col])}|{norm_plate(row[plate_col])}|{norm_kg(row[kg_col])}" def build_index(df, date_col, plate_col, kg_col): """同一 key 可能多行，用列表保存行号。""" idx: dict[str, list[int]] = defaultdict(list) for i, row in df.iterrows(): k = make_key(row, date_col, plate_col, kg_col) parts = k.split("|") if parts[0] and parts[1] and parts[2]: idx[k].append(i) return idx def pair_rows(keys_a: list[int], keys_b: list[int]): """按顺序一对一配对，多余行不配。""" n = min(len(keys_a), len(keys_b)) return list(zip(keys_a[:n], keys_b[:n])) def compare_pair(row_a: pd.Series, row_b: pd.Series) -> tuple[list[str], list[str]]: """返回 (差异字段名列表, 备注片段列表)。""" diffs = [] notes = [] for col_a, col_b, label in COMPARE_FIELDS: numeric = label in ("成本单价", "成本金额", "加氢单价", "加氢金额") va = row_a.get(col_a) vb = row_b.get(col_b) if not values_equal(va, vb, numeric): diffs.append(label) notes.append(f"{label}:记录[{norm_str(va)}]≠台账[{norm_str(vb)}]") return diffs, notes def add_ledger_cols(df: pd.DataFrame, row_b: pd.Series | None) -> dict: if row_b is None: return {f"台账_{label}": "" for _, _, label in COMPARE_FIELDS} out = {} for col_a, col_b, label in COMPARE_FIELDS: out[f"台账_{label}"] = row_b.get(col_b, "") return out def run(): df_rec = read_record_sheet(FILE_RECORD) df_led = pd.read_excel(FILE_LEDGER, sheet_name="加氢订单") # 台账限定 2026-01 ~ 2026-04，与加氢记录范围一致 df_led["_date_parsed"] = df_led["加氢时间"].apply(parse_date) df_led_14 = df_led[ (df_led["_date_parsed"] >= "2026-01-01") & (df_led["_date_parsed"] < "2026-05-01") ].copy() idx_rec = build_index(df_rec, "日期", "车牌", "加气量（kg)") idx_led = build_index(df_led_14, "加氢时间", "车牌号", "加氢量(kg)") all_keys = set(idx_rec) | set(idx_led) pairs: list[tuple[int | None, int | None, str]] = [] for k in sorted(all_keys): ra = idx_rec.get(k, []) rb = idx_led.get(k, []) paired = pair_rows(ra, rb) for ia, ib in paired: pairs.append((ia, ib, k)) for ia in ra[len(paired) :]: pairs.append((ia, None, k)) for ib in rb[len(paired) :]: pairs.append((None, ib, k)) # --- 加氢记录侧输出 --- rows_rec_out = [] rec_diff_cells: dict[int, set[str]] = defaultdict(set) # excel row -> col names rec_red_rows: set[int] = set() for ia, ib, k in pairs: if ia is None: continue row_a = df_rec.loc[ia] row_b = df_led_14.loc[ib] if ib is not None else None extra = add_ledger_cols(df_rec, row_b) remark_parts = [] if ib is None: if k in idx_led: remark_parts.append("同键重复：加氢记录行数多于台账，本条未配对") else: remark_parts.append("台账缺失：加氢订单(1-4月)中无匹配记录") rec_red_rows.add(len(rows_rec_out) + 2) # +2: header + 1-based else: diffs, notes = compare_pair(row_a, row_b) if diffs: remark_parts.append("字段差异：" + "；".join(notes)) for label in diffs: rec_diff_cells[len(rows_rec_out) + 2].add(label) rec_diff_cells[len(rows_rec_out) + 2].add(f"台账_{label}") out_row = row_a.to_dict() out_row.update(extra) out_row["比对备注"] = "；".join(remark_parts) if remark_parts else "一致" rows_rec_out.append(out_row) df_rec_out = pd.DataFrame(rows_rec_out) # --- 加氢订单侧输出（含仅台账有、加氢记录无）--- rows_led_out = [] led_diff_cells: dict[int, set[str]] = defaultdict(set) led_red_rows: set[int] = set() for ia, ib, k in pairs: if ib is None: continue row_b = df_led_14.loc[ib] row_a = df_rec.loc[ia] if ia is not None else None out_row = row_b.drop(labels=["_date_parsed"], errors="ignore").to_dict() remark_parts = [] if ia is None: if k in idx_rec: remark_parts.append("同键重复：台账行数多于加氢记录，本条未配对") else: remark_parts.append("加氢记录缺失：加氢记录表中无匹配记录") led_red_rows.add(len(rows_led_out) + 2) else: diffs, notes = compare_pair(row_a, row_b) if diffs: remark_parts.append("字段差异：" + "；".join(notes)) col_map = {label: col_b for _, col_b, label in COMPARE_FIELDS} for label in diffs: led_diff_cells[len(rows_led_out) + 2].add(col_map[label]) out_row["比对备注"] = "；".join(remark_parts) if remark_parts else "一致" rows_led_out.append(out_row) df_led_out = pd.DataFrame(rows_led_out) # 汇总 matched = sum(1 for ia, ib, _ in pairs if ia is not None and ib is not None) only_rec = sum(1 for ia, ib, _ in pairs if ia is not None and ib is None) only_led = sum(1 for ia, ib, _ in pairs if ia is None and ib is not None) only_rec_true = sum( 1 for ia, ib, k in pairs if ia is not None and ib is None and k not in idx_led ) only_rec_dup = only_rec - only_rec_true only_led_true = sum( 1 for ia, ib, k in pairs if ia is None and ib is not None and k not in idx_rec ) only_led_dup = only_led - only_led_true diff_count = sum( 1 for ia, ib, _ in pairs if ia is not None and ib is not None and compare_pair(df_rec.loc[ia], df_led_14.loc[ib])[0] ) summary = pd.DataFrame( [ ["加氢记录行数", len(df_rec)], ["台账加氢订单(1-4月)行数", len(df_led_14)], ["匹配键(日期+车牌+加氢量)对数", matched], ["仅加氢记录有-合计", only_rec], [" 其中台账真缺失", only_rec_true], [" 其中同键重复多出", only_rec_dup], ["仅台账有-合计", only_led], [" 其中加氢记录真缺失", only_led_true], [" 其中同键重复多出", only_led_dup], ["匹配但字段有差异", diff_count], ], columns=["项目", "数量"], ) OUT_DIR.mkdir(parents=True, exist_ok=True) with pd.ExcelWriter(OUT_FILE, engine="openpyxl") as writer: summary.to_excel(writer, sheet_name="比对汇总", index=False) df_rec_out.to_excel(writer, sheet_name="加氢记录-比对", index=False) df_led_out.to_excel(writer, sheet_name="加氢订单-比对", index=False) apply_styles_rec(df_rec_out, rec_diff_cells, rec_red_rows) apply_styles_led(df_led_out, led_diff_cells, led_red_rows) print(f"已输出: {OUT_FILE}") print(summary.to_string(index=False)) def col_letter_map(columns: list) -> dict[str, int]: return {str(c): i + 1 for i, c in enumerate(columns)} def apply_styles_rec(df: pd.DataFrame, diff_cells: dict, red_rows: set): wb = load_workbook(OUT_FILE) ws = wb["加氢记录-比对"] cmap = col_letter_map(list(df.columns)) label_to_col = {label: label for _, _, label in COMPARE_FIELDS} label_to_col.update({label: f"台账_{label}" for _, _, label in COMPARE_FIELDS}) for row_idx, labels in diff_cells.items(): for label in labels: col = label_to_col.get(label, label) if col in cmap: ws.cell(row=row_idx, column=cmap[col]).fill = YELLOW for row_idx in red_rows: for c in range(1, ws.max_column + 1): ws.cell(row=row_idx, column=c).fill = RED if "比对备注" in cmap: ws.cell(row=row_idx, column=cmap["比对备注"]).font = RED_FONT wb.save(OUT_FILE) def apply_styles_led(df: pd.DataFrame, diff_cells: dict, red_rows: set): wb = load_workbook(OUT_FILE) ws = wb["加氢订单-比对"] cmap = col_letter_map(list(df.columns)) for row_idx, labels in diff_cells.items(): for col_name in labels: if col_name in cmap: ws.cell(row=row_idx, column=cmap[col_name]).fill = YELLOW for row_idx in red_rows: for c in range(1, ws.max_column + 1): ws.cell(row=row_idx, column=c).fill = RED if "比对备注" in cmap: ws.cell(row=row_idx, column=cmap["比对备注"]).font = RED_FONT wb.save(OUT_FILE) if __name__ == "__main__": run()