a27e3b8e43
Optimized the root .gitignore to exclude virtual environments, node modules, and temp folders to ensure clean and lightweight version tracking. Co-authored-by: Cursor <cursoragent@cursor.com>
362 lines
13 KiB
Python
362 lines
13 KiB
Python
#!/usr/bin/env python3
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"""比对「加氢记录」与「氢费台账-加氢订单」，输出标黄/标红 Excel。"""
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from __future__ import annotations
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import re
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from collections import defaultdict
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from pathlib import Path
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import pandas as pd
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from openpyxl import load_workbook
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from openpyxl.styles import Font, PatternFill
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FILE_RECORD = Path(
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"/Users/sylvawong/Library/Containers/com.tencent.xinWeChat/Data/Documents/"
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"xwechat_files/wxid_l80gh7d3x7u012_385c/msg/file/2026-05/"
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"加氢记录-（2026.1月-4月）.xlsx"
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)
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FILE_LEDGER = Path(
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"/Users/sylvawong/Library/Containers/com.tencent.xinWeChat/Data/Documents/"
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"xwechat_files/wxid_l80gh7d3x7u012_385c/msg/file/2026-05/"
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"氢费台账总表2026.5.21(1).xlsx"
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)
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OUT_DIR = Path("/Users/sylvawong/Desktop/CURSOR/ONE-OS")
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OUT_FILE = OUT_DIR / "加氢记录与台账比对结果.xlsx"
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COMPARE_FIELDS = [
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("加氢站", "加氢站名称", "加氢站"),
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("成本单价", "成本单价(元/kg)", "成本单价"),
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("成本金额", "成本费用(元)", "成本金额"),
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("加氢单价", "加氢单价(元/kg)", "加氢单价"),
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("加氢金额", "加氢金额（元）", "加氢金额"),
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("客户名", "客户名称", "客户名"),
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("氢费计算方式", "结算类别", "氢费计算方式"),
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]
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YELLOW = PatternFill(start_color="FFFF00", end_color="FFFF00", fill_type="solid")
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RED = PatternFill(start_color="FF9999", end_color="FF9999", fill_type="solid")
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RED_FONT = Font(color="9C0006", bold=True)
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def read_record_sheet(path: Path) -> pd.DataFrame:
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raw = pd.read_excel(path, sheet_name=0, header=None)
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# 第 0 行为汇总行，第 1 行为表头
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raw.columns = [
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str(c).strip() if pd.notna(c) else f"_col{i}" for i, c in enumerate(raw.iloc[1])
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]
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df = raw.iloc[2:].copy().reset_index(drop=True)
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rename = {
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"序号": "序号",
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"日期": "日期",
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"车牌": "车牌",
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"加气量（kg)": "加气量（kg)",
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"加气量（kg）": "加气量（kg)",
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"加氢站": "加氢站",
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"成本单价": "成本单价",
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"成本金额": "成本金额",
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"加氢单价": "加氢单价",
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"加氢金额": "加氢金额",
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"客户名": "客户名",
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"氢费计算方式": "氢费计算方式",
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}
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for old, new in rename.items():
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if old in df.columns:
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df = df.rename(columns={old: new})
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return df
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def parse_date(val) -> str:
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if pd.isna(val):
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return ""
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if isinstance(val, pd.Timestamp):
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return val.strftime("%Y-%m-%d")
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if isinstance(val, (int, float)) and val > 40000:
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try:
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return (pd.Timestamp("1899-12-30") + pd.Timedelta(days=int(val))).strftime("%Y-%m-%d")
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except Exception:
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pass
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s = str(val).strip()
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for fmt in ("%Y-%m-%d", "%Y/%m/%d", "%Y-%m-%d %H:%M:%S"):
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try:
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return pd.to_datetime(s).strftime("%Y-%m-%d")
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except Exception:
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continue
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try:
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return pd.to_datetime(val).strftime("%Y-%m-%d")
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except Exception:
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return s[:10] if len(s) >= 10 else s
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def norm_plate(val) -> str:
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if pd.isna(val):
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return ""
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return re.sub(r"[\s\-·]", "", str(val).strip().upper())
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def norm_kg(val, tol_decimals=3) -> str:
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if pd.isna(val) or str(val).strip() in ("", "-", "nan"):
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return ""
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try:
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return f"{round(float(val), tol_decimals):.{tol_decimals}f}"
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except Exception:
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return str(val).strip()
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def norm_str(val) -> str:
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if pd.isna(val):
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return ""
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return str(val).strip()
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def norm_num(val, tol=0.02) -> float | None:
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if pd.isna(val) or str(val).strip() in ("", "-", "nan"):
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return None
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try:
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return float(val)
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except Exception:
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return None
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def values_equal(a, b, numeric: bool) -> bool:
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if numeric:
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na, nb = norm_num(a), norm_num(b)
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if na is None and nb is None:
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return True
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if na is None or nb is None:
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return False
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return abs(na - nb) <= 0.02
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return norm_str(a) == norm_str(b)
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def make_key(row, date_col, plate_col, kg_col) -> str:
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return f"{parse_date(row[date_col])}|{norm_plate(row[plate_col])}|{norm_kg(row[kg_col])}"
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def build_index(df, date_col, plate_col, kg_col):
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"""同一 key 可能多行，用列表保存行号。"""
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idx: dict[str, list[int]] = defaultdict(list)
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for i, row in df.iterrows():
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k = make_key(row, date_col, plate_col, kg_col)
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parts = k.split("|")
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if parts[0] and parts[1] and parts[2]:
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idx[k].append(i)
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return idx
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def pair_rows(keys_a: list[int], keys_b: list[int]):
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"""按顺序一对一配对，多余行不配。"""
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n = min(len(keys_a), len(keys_b))
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return list(zip(keys_a[:n], keys_b[:n]))
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def compare_pair(row_a: pd.Series, row_b: pd.Series) -> tuple[list[str], list[str]]:
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"""返回 (差异字段名列表, 备注片段列表)。"""
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diffs = []
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notes = []
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for col_a, col_b, label in COMPARE_FIELDS:
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numeric = label in ("成本单价", "成本金额", "加氢单价", "加氢金额")
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va = row_a.get(col_a)
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vb = row_b.get(col_b)
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if not values_equal(va, vb, numeric):
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diffs.append(label)
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notes.append(f"{label}:记录[{norm_str(va)}]≠台账[{norm_str(vb)}]")
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return diffs, notes
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def add_ledger_cols(df: pd.DataFrame, row_b: pd.Series | None) -> dict:
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if row_b is None:
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return {f"台账_{label}": "" for _, _, label in COMPARE_FIELDS}
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out = {}
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for col_a, col_b, label in COMPARE_FIELDS:
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out[f"台账_{label}"] = row_b.get(col_b, "")
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return out
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def run():
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df_rec = read_record_sheet(FILE_RECORD)
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df_led = pd.read_excel(FILE_LEDGER, sheet_name="加氢订单")
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# 台账限定 2026-01 ~ 2026-04，与加氢记录范围一致
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df_led["_date_parsed"] = df_led["加氢时间"].apply(parse_date)
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df_led_14 = df_led[
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(df_led["_date_parsed"] >= "2026-01-01") & (df_led["_date_parsed"] < "2026-05-01")
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].copy()
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idx_rec = build_index(df_rec, "日期", "车牌", "加气量（kg)")
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idx_led = build_index(df_led_14, "加氢时间", "车牌号", "加氢量(kg)")
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all_keys = set(idx_rec) | set(idx_led)
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pairs: list[tuple[int | None, int | None, str]] = []
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for k in sorted(all_keys):
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ra = idx_rec.get(k, [])
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rb = idx_led.get(k, [])
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paired = pair_rows(ra, rb)
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for ia, ib in paired:
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pairs.append((ia, ib, k))
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for ia in ra[len(paired) :]:
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pairs.append((ia, None, k))
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for ib in rb[len(paired) :]:
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pairs.append((None, ib, k))
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# --- 加氢记录侧输出 ---
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rows_rec_out = []
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rec_diff_cells: dict[int, set[str]] = defaultdict(set) # excel row -> col names
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rec_red_rows: set[int] = set()
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for ia, ib, k in pairs:
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if ia is None:
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continue
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row_a = df_rec.loc[ia]
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row_b = df_led_14.loc[ib] if ib is not None else None
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extra = add_ledger_cols(df_rec, row_b)
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remark_parts = []
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if ib is None:
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if k in idx_led:
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remark_parts.append("同键重复：加氢记录行数多于台账，本条未配对")
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else:
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remark_parts.append("台账缺失：加氢订单(1-4月)中无匹配记录")
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rec_red_rows.add(len(rows_rec_out) + 2) # +2: header + 1-based
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else:
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diffs, notes = compare_pair(row_a, row_b)
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if diffs:
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remark_parts.append("字段差异：" + "；".join(notes))
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for label in diffs:
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rec_diff_cells[len(rows_rec_out) + 2].add(label)
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rec_diff_cells[len(rows_rec_out) + 2].add(f"台账_{label}")
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out_row = row_a.to_dict()
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out_row.update(extra)
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out_row["比对备注"] = "；".join(remark_parts) if remark_parts else "一致"
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rows_rec_out.append(out_row)
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df_rec_out = pd.DataFrame(rows_rec_out)
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# --- 加氢订单侧输出（含仅台账有、加氢记录无）---
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rows_led_out = []
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led_diff_cells: dict[int, set[str]] = defaultdict(set)
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led_red_rows: set[int] = set()
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for ia, ib, k in pairs:
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if ib is None:
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continue
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row_b = df_led_14.loc[ib]
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row_a = df_rec.loc[ia] if ia is not None else None
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out_row = row_b.drop(labels=["_date_parsed"], errors="ignore").to_dict()
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remark_parts = []
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if ia is None:
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if k in idx_rec:
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remark_parts.append("同键重复：台账行数多于加氢记录，本条未配对")
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else:
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remark_parts.append("加氢记录缺失：加氢记录表中无匹配记录")
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led_red_rows.add(len(rows_led_out) + 2)
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else:
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diffs, notes = compare_pair(row_a, row_b)
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if diffs:
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remark_parts.append("字段差异：" + "；".join(notes))
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col_map = {label: col_b for _, col_b, label in COMPARE_FIELDS}
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for label in diffs:
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led_diff_cells[len(rows_led_out) + 2].add(col_map[label])
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out_row["比对备注"] = "；".join(remark_parts) if remark_parts else "一致"
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rows_led_out.append(out_row)
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df_led_out = pd.DataFrame(rows_led_out)
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# 汇总
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matched = sum(1 for ia, ib, _ in pairs if ia is not None and ib is not None)
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only_rec = sum(1 for ia, ib, _ in pairs if ia is not None and ib is None)
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only_led = sum(1 for ia, ib, _ in pairs if ia is None and ib is not None)
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only_rec_true = sum(
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1 for ia, ib, k in pairs if ia is not None and ib is None and k not in idx_led
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)
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only_rec_dup = only_rec - only_rec_true
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only_led_true = sum(
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1 for ia, ib, k in pairs if ia is None and ib is not None and k not in idx_rec
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)
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only_led_dup = only_led - only_led_true
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diff_count = sum(
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1
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for ia, ib, _ in pairs
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if ia is not None and ib is not None and compare_pair(df_rec.loc[ia], df_led_14.loc[ib])[0]
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)
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summary = pd.DataFrame(
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[
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["加氢记录行数", len(df_rec)],
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["台账加氢订单(1-4月)行数", len(df_led_14)],
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["匹配键(日期+车牌+加氢量)对数", matched],
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["仅加氢记录有-合计", only_rec],
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[" 其中台账真缺失", only_rec_true],
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[" 其中同键重复多出", only_rec_dup],
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["仅台账有-合计", only_led],
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[" 其中加氢记录真缺失", only_led_true],
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[" 其中同键重复多出", only_led_dup],
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["匹配但字段有差异", diff_count],
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],
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columns=["项目", "数量"],
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)
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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with pd.ExcelWriter(OUT_FILE, engine="openpyxl") as writer:
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summary.to_excel(writer, sheet_name="比对汇总", index=False)
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df_rec_out.to_excel(writer, sheet_name="加氢记录-比对", index=False)
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df_led_out.to_excel(writer, sheet_name="加氢订单-比对", index=False)
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apply_styles_rec(df_rec_out, rec_diff_cells, rec_red_rows)
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apply_styles_led(df_led_out, led_diff_cells, led_red_rows)
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print(f"已输出: {OUT_FILE}")
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print(summary.to_string(index=False))
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def col_letter_map(columns: list) -> dict[str, int]:
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return {str(c): i + 1 for i, c in enumerate(columns)}
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def apply_styles_rec(df: pd.DataFrame, diff_cells: dict, red_rows: set):
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wb = load_workbook(OUT_FILE)
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ws = wb["加氢记录-比对"]
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cmap = col_letter_map(list(df.columns))
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label_to_col = {label: label for _, _, label in COMPARE_FIELDS}
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label_to_col.update({label: f"台账_{label}" for _, _, label in COMPARE_FIELDS})
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for row_idx, labels in diff_cells.items():
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for label in labels:
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col = label_to_col.get(label, label)
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if col in cmap:
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ws.cell(row=row_idx, column=cmap[col]).fill = YELLOW
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for row_idx in red_rows:
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for c in range(1, ws.max_column + 1):
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ws.cell(row=row_idx, column=c).fill = RED
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if "比对备注" in cmap:
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ws.cell(row=row_idx, column=cmap["比对备注"]).font = RED_FONT
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wb.save(OUT_FILE)
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def apply_styles_led(df: pd.DataFrame, diff_cells: dict, red_rows: set):
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wb = load_workbook(OUT_FILE)
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ws = wb["加氢订单-比对"]
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cmap = col_letter_map(list(df.columns))
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for row_idx, labels in diff_cells.items():
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for col_name in labels:
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if col_name in cmap:
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ws.cell(row=row_idx, column=cmap[col_name]).fill = YELLOW
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for row_idx in red_rows:
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for c in range(1, ws.max_column + 1):
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ws.cell(row=row_idx, column=c).fill = RED
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if "比对备注" in cmap:
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ws.cell(row=row_idx, column=cmap["比对备注"]).font = RED_FONT
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wb.save(OUT_FILE)
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if __name__ == "__main__":
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run()
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