371 lines
14 KiB
Python
371 lines
14 KiB
Python
"""
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京东白条账单清理模块
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"""
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import csv
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import re
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from decimal import Decimal
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from pathlib import Path
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import yaml
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from .base import (
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BaseCleaner, parse_amount, format_amount,
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is_in_date_range, create_arg_parser
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)
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from category import infer_category
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# 加载京东专属分类配置
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JD_CONFIG_FILE = Path(__file__).parent.parent / "config" / "category_jd.yaml"
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def load_jd_config():
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"""加载京东分类配置"""
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with open(JD_CONFIG_FILE, "r", encoding="utf-8") as f:
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return yaml.safe_load(f)
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_jd_config = load_jd_config()
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def infer_jd_category(merchant: str, product: str, original_category: str) -> tuple[str, bool, int]:
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"""
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根据京东账单的商户名称、商品说明和原分类推断统一分类
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Args:
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merchant: 商户名称（如"京东外卖"、"京东平台商户"）
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product: 交易说明/商品说明
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original_category: 京东原始分类（如"食品酒饮"、"数码电器"）
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Returns:
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(分类名称, 是否确定, 复核等级)
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复核等级:
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0 = 无需复核（商户映射或原分类映射成功，高置信度）
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1 = 低优先级复核（通用关键词匹配成功，需确认）
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2 = 高优先级复核（全部匹配失败或未知分类，需人工分类）
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"""
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# 1. 先检查商户名称直接映射（如"京东外卖" -> "餐饮美食"）
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merchant_mapping = _jd_config.get("商户映射", {})
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for merchant_key, category in merchant_mapping.items():
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if merchant_key in merchant:
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return category, True, 0 # 商户映射，无需复核
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# 2. 尝试直接映射京东原分类
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category_mapping = _jd_config.get("分类映射", {})
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# 处理多分类情况（如"食品酒饮 其他网购"）
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original_cats = original_category.split() if original_category else []
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for orig_cat in original_cats:
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if orig_cat in category_mapping:
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mapped = category_mapping[orig_cat]
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if mapped: # 非空映射 → 使用映射结果
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return mapped, True, 0 # 原分类映射，无需复核
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# 空映射（如"其他"→""）→ 继续检查下一个原分类或进入关键词匹配
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else:
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# 未知分类（不在映射表中）→ 保留原分类，HIGH 复核
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return orig_cat, True, 2
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# 3. 使用通用分类推断（已包含京东平台商户关键词）
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category, is_certain = infer_category(merchant, product, "支出")
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if is_certain:
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return category, True, 1 # 关键词匹配，低优先级复核
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# 4. 返回默认分类
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return _jd_config.get("默认分类", "其他支出"), False, 2 # 全部失败，高优先级复核
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# 与支付宝/微信对齐的表头（包含"复核等级"字段）
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ALIGNED_HEADER = [
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"交易时间", "交易分类", "交易对方", "对方账号", "商品说明",
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"收/支", "金额", "收/付款方式", "交易状态", "交易订单号", "商家订单号", "备注", "复核等级"
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]
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class JDCleaner(BaseCleaner):
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"""京东白条账单清理器"""
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def clean(self) -> None:
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"""执行清理"""
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self.print_header()
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# 读取数据，跳过京东导出文件的头部信息
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with open(self.input_file, "r", encoding="utf-8") as f:
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reader = csv.reader(f)
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header = None
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rows = []
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for row in reader:
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# 跳过空行
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if not row or not row[0].strip():
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continue
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# 清理每个字段的 tab 字符
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row = [cell.strip().replace('\t', '') for cell in row]
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# 查找实际的CSV头部行（包含"交易时间"和"商户名称"）
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if header is None:
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if len(row) >= 2 and "交易时间" in row[0] and "商户名称" in row[1]:
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header = row
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continue
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# 跳过头部信息行
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continue
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# 收集数据行
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rows.append(row)
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# 确保找到了有效的头部
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if header is None:
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raise ValueError("无法找到有效的京东账单表头（需包含'交易时间'和'商户名称'列）")
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self.stats["original_count"] = len(rows)
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print(f"原始数据行数: {len(rows)}")
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# 第一步：按日期范围筛选
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rows_filtered = [
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row for row in rows
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if row and is_in_date_range(row[0], self.start_date, self.end_date)
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]
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self.stats["filtered_count"] = len(rows_filtered)
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date_desc = f"{self.start_date} ~ {self.end_date}" if self.start_date or self.end_date else "全部"
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print(f"筛选后数据行数: {len(rows_filtered)} ({date_desc})")
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# 第二步：分离退款和支出条目（过滤掉"不计收支"）
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refund_rows = []
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expense_rows = []
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skipped_count = 0 # 不计收支（还款、冻结等）
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for row in rows_filtered:
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if len(row) < 7:
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continue
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income_expense = row[6].strip() # 收/支 列
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transaction_desc = row[2].strip() # 交易说明
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status = row[5].strip() # 交易状态
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# 过滤掉"不计收支"记录（还款、冻结、预授权等）
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if income_expense == "不计收支":
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skipped_count += 1
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continue
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# 退款判断：交易说明以"退款-"开头 或 状态包含"退款成功"
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if transaction_desc.startswith("退款-") or "退款" in status:
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refund_rows.append(row)
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elif income_expense == "支出":
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expense_rows.append(row)
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print(f"退款条目数: {len(refund_rows)}")
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print(f"支出条目数: {len(expense_rows)}")
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print(f"不计收支过滤: {skipped_count} 条（还款/冻结等）")
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# 第三步：处理退款
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# 京东账单特点：已全额退款的记录金额会显示为 "179.00(已全额退款)"
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final_expense_rows = self._process_expenses(expense_rows, refund_rows)
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print(f"\n处理结果:")
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print(f" 全额退款删除: {self.stats['fully_refunded']} 条")
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print(f" 部分退款调整: {self.stats['partially_refunded']} 条")
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if self.stats.get("zero_amount", 0) > 0:
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print(f" 0元记录过滤: {self.stats['zero_amount']} 条")
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print(f" 最终保留行数: {len(final_expense_rows)}")
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# 第四步：转换为对齐格式并重新分类
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aligned_rows = [self._convert_and_reclassify(row_data) for row_data in final_expense_rows]
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# 按时间排序（最新在前）
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aligned_rows.sort(key=lambda x: x[0], reverse=True)
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# 统计复核数量
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review_high_count = sum(1 for row in aligned_rows if row[-1] == "HIGH")
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self.stats["final_count"] = len(aligned_rows)
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if review_high_count > 0:
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print(f" 高优先级复核: {review_high_count} 条（无法判断）")
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# 写入文件
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self.write_output(ALIGNED_HEADER, aligned_rows)
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print(f"\n清理后的数据已保存到: {self.output_file}")
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# 统计支出
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self._print_expense_summary(aligned_rows)
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def _parse_jd_amount(self, amount_str: str) -> tuple[Decimal, bool]:
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"""
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解析京东账单金额
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京东金额格式特点：
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- 普通金额: "179.00"
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- 全额退款: "179.00(已全额退款)"
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Returns:
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(金额, 是否已全额退款)
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"""
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amount_str = amount_str.strip()
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# 检查是否包含"已全额退款"
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if "(已全额退款)" in amount_str or "（已全额退款）" in amount_str:
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# 提取金额部分
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amount_part = re.sub(r'[（(]已全额退款[)）]', '', amount_str)
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return parse_amount(amount_part), True
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return parse_amount(amount_str), False
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def _process_expenses(self, expense_rows: list, refund_rows: list) -> list:
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"""
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处理支出记录
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京东账单特点：
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1. 已全额退款的记录金额显示为 "金额(已全额退款)"
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2. 部分退款可能有单独的退款记录
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"""
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# 构建退款索引（按订单号）
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order_refunds = {}
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for row in refund_rows:
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if len(row) >= 9:
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order_no = row[8].strip() # 交易订单号
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amount = parse_amount(row[3]) # 金额
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if order_no:
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if order_no not in order_refunds:
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order_refunds[order_no] = Decimal("0")
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order_refunds[order_no] += amount
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print(f" 退款记录: {row[0]} | {row[1]} | {amount}元")
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final_rows = []
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for row in expense_rows:
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if len(row) < 9:
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continue
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order_no = row[8].strip() # 交易订单号
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amount, is_fully_refunded = self._parse_jd_amount(row[3])
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# 情况1：金额已标注"已全额退款"
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if is_fully_refunded:
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self.stats["fully_refunded"] += 1
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desc = row[2][:25] if len(row[2]) > 25 else row[2]
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print(f" 全额退款删除: {row[0]} | {row[1]} | {desc}... | {row[3]}")
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continue
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# 情况2：检查是否有对应的退款记录
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refund_amount = order_refunds.get(order_no, Decimal("0"))
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if refund_amount > 0:
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if refund_amount >= amount:
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# 全额退款
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self.stats["fully_refunded"] += 1
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desc = row[2][:25] if len(row[2]) > 25 else row[2]
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print(f" 全额退款删除: {row[0]} | {row[1]} | {desc}... | 原{amount}元")
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else:
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# 部分退款
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remaining = amount - refund_amount
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new_row = row.copy()
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new_row[3] = format_amount(remaining)
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remark = f"原金额{amount}元,退款{refund_amount}元"
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final_rows.append((new_row, remark))
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self.stats["partially_refunded"] += 1
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print(f" 部分退款: {row[0]} | {row[1]} | 原{amount}元 -> {format_amount(remaining)}元")
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else:
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# 无退款，正常记录
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if amount > 0:
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final_rows.append((row, None))
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else:
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self.stats["zero_amount"] = self.stats.get("zero_amount", 0) + 1
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return final_rows
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def _convert_and_reclassify(self, row_tuple: tuple) -> list:
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"""
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转换为对齐格式并重新分类
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京东原始字段:
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0: 交易时间, 1: 商户名称, 2: 交易说明, 3: 金额,
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4: 收/付款方式, 5: 交易状态, 6: 收/支, 7: 交易分类,
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8: 交易订单号, 9: 商家订单号, 10: 备注
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对齐后字段:
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交易时间, 交易分类, 交易对方, 对方账号, 商品说明,
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收/支, 金额, 收/付款方式, 交易状态, 交易订单号, 商家订单号, 备注, 复核等级
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"""
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if isinstance(row_tuple, tuple):
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row, remark = row_tuple
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else:
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row, remark = row_tuple, None
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transaction_time = row[0]
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merchant = row[1] # 商户名称
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product = row[2] # 交易说明
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amount, _ = self._parse_jd_amount(row[3])
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payment_method = row[4] if len(row) > 4 else ""
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status = row[5] if len(row) > 5 else ""
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income_expense = row[6] if len(row) > 6 else "支出"
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original_category = row[7] if len(row) > 7 else ""
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order_no = row[8] if len(row) > 8 else ""
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merchant_order_no = row[9] if len(row) > 9 else ""
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final_remark = remark if remark else (row[10] if len(row) > 10 else "/")
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# 使用京东专属分类推断
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category, is_certain, review_level = infer_jd_category(merchant, product, original_category)
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# 复核等级映射: 0=空, 1=LOW, 2=HIGH
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review_marks = {0: "", 1: "LOW", 2: "HIGH"}
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review_mark = review_marks.get(review_level, "")
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return [
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transaction_time,
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category,
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merchant,
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"/", # 对方账号（京东无此字段）
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product,
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income_expense,
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format_amount(amount),
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payment_method,
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status,
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order_no,
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merchant_order_no,
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final_remark,
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review_mark
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]
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def reclassify(self, rows: list) -> list:
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"""
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重新分类京东账单
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京东账单在 _convert_and_reclassify 中已完成分类
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此方法为接口兼容保留
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"""
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return rows
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def _print_expense_summary(self, expense_rows: list):
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"""打印支出统计"""
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total = Decimal("0")
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categories = {}
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for row in expense_rows:
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if row[5] == "支出":
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amt = Decimal(row[6])
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total += amt
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cat = row[1]
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categories[cat] = categories.get(cat, Decimal("0")) + amt
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print(f"清理后支出总额: ￥{total}")
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print("\n=== 按分类统计 ===")
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for cat, amt in sorted(categories.items(), key=lambda x: -x[1]):
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print(f" {cat}: ￥{amt}")
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def main():
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"""命令行入口"""
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parser = create_arg_parser("清理京东白条账单数据")
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args = parser.parse_args()
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from .base import compute_date_range
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cleaner = JDCleaner(args.input_file, args.output_file)
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start_date, end_date = compute_date_range(args)
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cleaner.set_date_range(start_date, end_date)
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cleaner.clean()
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if __name__ == "__main__":
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main()
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