mirror of
https://github.com/primedigitaltech/market-assistant.git
synced 2026-07-22 08:01:34 +08:00
ca6e71eb4b
- 新增 pipeline.csv 与 pipeline.jd.matrix_group_label，根目录模块保留兼容转发 - 竞品报告主实现迁至 competitor_report.jd_report，兼容入口补全下划线辅助函数再导出 - 增加 pipeline/__init__.py 避免 Django 将 pipeline 识别为多路径命名空间包 Made-with: Cursor
269 lines
7.8 KiB
Python
269 lines
7.8 KiB
Python
"""
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与 ``jd_pc_search`` 导出 CSV 列对齐的字段名映射（入库 / API / 导出共用）。
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内部键与爬虫侧 ``JD_ITEM_CSV_FIELDS`` / ``WARE_PARSED_CSV_FIELDNAMES`` 一致，便于对照源码。
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"""
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from __future__ import annotations
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import re
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def strip_buyer_ranking_line_prefix(value: str) -> str:
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"""去掉榜单列上的 ``榜单/曝光：`` 历史前缀，展示与入库一致。"""
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s = (value or "").strip()
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prefix = "榜单/曝光："
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if s.startswith(prefix):
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return s[len(prefix) :].strip()
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if s.startswith("榜单/曝光"):
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return s[len("榜单/曝光") :].lstrip("：").strip()
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return s
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# --- 搜索导出 pc_search_export.csv（纯中文表头，与 jd_h5_search_requests.JD_EXPORT_COLUMN_HEADERS 一致）---
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JD_SEARCH_INTERNAL_KEYS: tuple[str, ...] = (
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"item_id",
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"sku_id",
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"title",
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"price",
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"coupon_price",
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"original_price",
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"selling_point",
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"comment_sales_floor",
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"total_sales",
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"hot_list_rank",
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"comment_count",
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"shop_name",
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"shop_url",
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"shop_info_url",
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"location",
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"detail_url",
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"image",
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"seckill_info",
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"attributes",
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"leaf_category",
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"platform",
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"keyword",
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"page",
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)
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JD_SEARCH_CSV_HEADERS: dict[str, str] = {
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"item_id": "主商品ID",
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"sku_id": "SKU",
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"title": "标题",
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"price": "标价",
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"coupon_price": "券后到手价",
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"original_price": "原价",
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"selling_point": "卖点",
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"comment_sales_floor": "销量楼层",
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"total_sales": "销量展示",
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"hot_list_rank": "榜单类文案",
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"comment_count": "评价量",
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"shop_name": "店铺名",
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"shop_url": "店铺链接",
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"shop_info_url": "店铺信息链接",
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"location": "地域",
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"detail_url": "商品链接",
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"image": "主图",
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"seckill_info": "秒杀",
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"attributes": "规格属性",
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"leaf_category": "类目",
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"platform": "平台",
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"keyword": "搜索词",
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"page": "页码",
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}
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# CSV 表头 -> 模型属性名
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SEARCH_CSV_HEADER_TO_FIELD: dict[str, str] = {
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h: k for k, h in JD_SEARCH_CSV_HEADERS.items()
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}
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# lean 商详：ORM 内部键（英文 snake_case）；CSV 表头为中文（见 DETAIL_CSV_COLUMNS）
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MERGED_LEAN_DETAIL_INTERNAL_KEYS: tuple[str, ...] = (
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"detail_brand",
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"detail_price_final",
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"detail_shop_name",
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"detail_category_path",
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"detail_product_attributes",
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"detail_body_ingredients",
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"buyer_ranking_line",
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"buyer_promo_text",
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)
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LEAN_DETAIL_EXPORT_FIELDNAMES: tuple[str, ...] = MERGED_LEAN_DETAIL_INTERNAL_KEYS
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LEAN_DETAIL_CSV_HEADERS: tuple[str, ...] = (
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"品牌",
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"到手价",
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"店铺名称",
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"类目路径",
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"商品参数",
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"配料表",
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"榜单排名",
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"促销摘要",
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)
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DETAIL_CSV_HEADER_TO_FIELD: dict[str, str] = dict(
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zip(LEAN_DETAIL_CSV_HEADERS, MERGED_LEAN_DETAIL_INTERNAL_KEYS)
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)
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# --- 商详 detail_ware_export.csv（lean：SKU + 上列；full 模式爬虫仍可能多列，入库只认 DETAIL_CSV_COLUMNS）---
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JD_DETAIL_MERGE_KEYS: tuple[str, ...] = MERGED_LEAN_DETAIL_INTERNAL_KEYS
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DETAIL_CSV_COLUMNS: tuple[str, ...] = ("SKU", *LEAN_DETAIL_CSV_HEADERS)
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DETAIL_CSV_TO_FIELD: dict[str, str] = {
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"SKU": "sku_id",
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**DETAIL_CSV_HEADER_TO_FIELD,
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}
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# --- 评价 comments_flat.csv（表头中文；爬虫行字典仍用英文 API 键，写出时映射）---
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COMMENT_CSV_COLUMNS: tuple[str, ...] = (
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"SKU",
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"评价ID",
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"用户昵称",
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"评价内容",
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"评价时间",
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"购买次数",
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"晒图链接",
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"评分",
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)
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COMMENT_CSV_TO_FIELD: dict[str, str] = {
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"SKU": "sku_id",
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"评价ID": "comment_id",
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"用户昵称": "user_nick_name",
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"评价内容": "tag_comment_content",
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"评价时间": "comment_date",
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"购买次数": "buy_count_text",
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"晒图链接": "large_pic_urls",
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"评分": "comment_score",
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}
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# 爬虫评价行 dict 键（与京东接口字段一致）→ CSV 中文表头
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COMMENT_ROW_DICT_KEYS: tuple[str, ...] = (
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"sku",
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"commentId",
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"userNickName",
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"tagCommentContent",
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"commentDate",
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"buyCountText",
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"largePicURLs",
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"commentScore",
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)
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# --- 合并宽表 keyword_pipeline_merged.csv（lean = 搜索块 + 商详块 + 评论块；改列请改对应块，勿在尾部堆列）---
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MERGED_SEARCH_INTERNAL_KEYS: tuple[str, ...] = (
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"pipeline_keyword",
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"sku_id",
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"ware_id",
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"title",
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"price",
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"coupon_price",
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"original_price",
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"selling_point",
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"hot_list_rank",
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"comment_fuzzy",
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"comment_sales_floor",
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"total_sales",
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"shop_name",
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"detail_url",
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"image",
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"attributes",
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"leaf_category",
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"keyword",
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"page",
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)
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def _merged_search_csv_header_for_internal(k: str) -> str:
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"""整合表搜索块：内部键 → 中文 CSV 表头（与 PC 搜索导出列名对齐，合并表专有列单独处理）。"""
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if k == "ware_id":
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return JD_SEARCH_CSV_HEADERS["item_id"]
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if k == "comment_fuzzy":
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return "评价量"
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return JD_SEARCH_CSV_HEADERS[k]
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MERGED_SEARCH_CSV_COLUMNS: tuple[str, ...] = (
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"流水线关键词",
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*(
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_merged_search_csv_header_for_internal(k)
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for k in MERGED_SEARCH_INTERNAL_KEYS[1:]
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),
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)
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# 商详块：CSV 为中文表头；内部键见 MERGED_LEAN_DETAIL_INTERNAL_KEYS
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MERGED_LEAN_DETAIL_KEYS: tuple[str, ...] = LEAN_DETAIL_CSV_HEADERS
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MERGED_COMMENT_CSV_COLUMNS: tuple[str, ...] = (
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"评论条数",
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"评价摘要",
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)
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MERGED_COMMENT_INTERNAL_KEYS: tuple[str, ...] = (
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"pipeline_comment_count",
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"comment_preview",
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)
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MERGED_CSV_COLUMNS: tuple[str, ...] = (
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*MERGED_SEARCH_CSV_COLUMNS,
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*MERGED_LEAN_DETAIL_KEYS,
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*MERGED_COMMENT_CSV_COLUMNS,
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)
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MERGED_INTERNAL_KEYS: tuple[str, ...] = (
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*MERGED_SEARCH_INTERNAL_KEYS,
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*MERGED_LEAN_DETAIL_INTERNAL_KEYS,
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*MERGED_COMMENT_INTERNAL_KEYS,
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)
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assert len(MERGED_CSV_COLUMNS) == len(MERGED_INTERNAL_KEYS)
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MERGED_CSV_TO_FIELD: dict[str, str] = dict(zip(MERGED_CSV_COLUMNS, MERGED_INTERNAL_KEYS))
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MERGED_FIELD_TO_CSV_HEADER: dict[str, str] = {
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internal: csv_h for csv_h, internal in MERGED_CSV_TO_FIELD.items()
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}
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def remap_merged_row_english_detail_keys_to_csv_headers(merged: dict[str, str]) -> None:
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"""整合表写入前：将 ``ware_flat`` / 抽取逻辑产生的英文商详与购买者内部键改为中文 CSV 列名（原地修改）。"""
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for ik, zh in zip(MERGED_LEAN_DETAIL_INTERNAL_KEYS, LEAN_DETAIL_CSV_HEADERS):
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if ik in merged:
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merged[zh] = str(merged.get(ik) or "")
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del merged[ik]
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def infer_total_sales_from_sales_floor(cell: str) -> str:
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"""
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从「销量楼层(commentSalesFloor)」列文案截取可作 ``销量展示(totalSales)`` 的片段（与列表接口未单独落 totalSales 列时的兜底一致）。
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"""
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t = (cell or "").strip()
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if not t:
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return ""
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m = re.search(r"已售\s*[\d,，.+]*\s*[万亿]?\s*\+?", t)
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if m:
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return m.group(0).strip()
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m2 = re.search(r"已售\s*[\d,，.+\s万千亿]+", t)
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return m2.group(0).strip() if m2 else ""
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def merged_csv_effective_total_sales(row: dict[str, str]) -> str:
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"""合并表一行：优先已有 ``销量展示(totalSales)`` 列，否则从销量楼层推断。"""
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h_ts = MERGED_FIELD_TO_CSV_HEADER["total_sales"]
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h_fl = MERGED_FIELD_TO_CSV_HEADER["comment_sales_floor"]
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direct = str(row.get(h_ts) or "").strip()
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if direct:
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return direct
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return infer_total_sales_from_sales_floor(str(row.get(h_fl) or ""))
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def search_csv_effective_total_sales(row: dict[str, str]) -> str:
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"""PC 搜索导出表一行：与 ``merged_csv_effective_total_sales`` 解析规则一致（中文表头）。"""
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h_ts = JD_SEARCH_CSV_HEADERS["total_sales"]
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h_fl = JD_SEARCH_CSV_HEADERS["comment_sales_floor"]
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direct = str(row.get(h_ts) or "").strip()
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if direct:
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return direct
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return infer_total_sales_from_sales_floor(str(row.get(h_fl) or ""))
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