#!/usr/bin/env python3 # 从 xlsx 重算 data.json。 # 平台消耗统计: B=Date, C=Adpingid, D=包名, E=地区, I=花费 # 投手: B=Adpingid, D=pkg, L=Media Buyer投放人 # 规则：@某某 == 某某；未填投放人的 Adpingid 丢弃； # exclude.json 的 packages（按包名）与 adpings（按 Adpingid）永久剔除，均为精确匹配。 # 每个 package 明细：Geo 后缀 = 该 package 花费最高的地区；Adpingid = 该地区内花费最高的 Adping。 import openpyxl, collections, json, datetime, os, sys, glob HERE = os.path.dirname(os.path.abspath(__file__)) # 源文件按月滚动（2608 -> 2609 -> ...）。全部读进来合并： # 同一天出现在多个文件时，以文件名较新的为准（源表会修订历史数据）。 SRC_FILES = sorted(glob.glob(os.path.join(HERE, '*每日数据-武汉萌奇熊.xlsx'))) # 花名册 投手.xlsx 单独处理，不参与消耗数据合并 if not SRC_FILES: sys.exit('ERROR: 仓库里找不到 *每日数据-武汉萌奇熊.xlsx') _cfg = json.load(open(os.path.join(HERE, 'exclude.json'))) EXCL = set(_cfg['packages']) # 按包名剔除 EXCL_ADP = set(_cfg.get('adpings', {})) # 按 Adpingid 剔除 NON_BUYER = {'GP下架'} WINDOW_DAYS = 3 def key(v): if isinstance(v, float) and v.is_integer(): return str(int(v)) return str(v).strip() books = [(f, openpyxl.load_workbook(f, data_only=True)) for f in SRC_FILES] # --- 投手花名册 --- # 优先用独立的 投手.xlsx（月度数据文件已不再带「投手」sheet）； # 没有它时退回到最新一个仍带该 sheet 的月度文件。 # 花名册候选，按优先级从高到低；命中第一个就用它。 # 花名册统一放在 roster.xlsx —— 用户每次发来的新表（文件名各异）都覆盖到这个名字， # 这样不必每换一个文件名就改一次代码。 ROSTER_PATTERNS = ['roster.xlsx'] roster_file, roster = None, None for pat in ROSTER_PATTERNS: hits = sorted(glob.glob(os.path.join(HERE, pat))) if hits: roster_file = hits[-1] _rwb = openpyxl.load_workbook(roster_file, data_only=True) roster = _rwb['投手'] if '投手' in _rwb.sheetnames else _rwb[_rwb.sheetnames[0]] break if roster is None: for f, wb_ in reversed(books): if '投手' in wb_.sheetnames: roster_file, roster = f, wb_['投手'] break if roster is None: sys.exit('ERROR: 既没有 投手.xlsx，月度文件里也没有「投手」sheet，无法归属投放人') amap = {} dropped_ids = set(EXCL_ADP) # 先纳入按 Adpingid 剔除的，再补上按包名命中的 for r in roster.iter_rows(min_row=2, values_only=True): adp, pkg, buyer, status = r[1], r[3], r[11], r[0] if adp is None or str(status).strip() == '示例': continue k = key(adp) if k in EXCL_ADP: continue if pkg is not None and str(pkg).strip() in EXCL: # 精确匹配，避免误伤 in.swiggy.android dropped_ids.add(k) continue b = str(buyer).strip() if buyer is not None else '' if b in ('', 'None') or b.lstrip('@').strip() in NON_BUYER: continue amap[k] = b.lstrip('@').strip() # 花名册补充：只填花名册里查不到的 Adpingid（花名册优先） EXTRA_PATH = os.path.join(HERE, 'roster_extra.json') extra_used = [] if os.path.exists(EXTRA_PATH): for a, b in json.load(open(EXTRA_PATH)).get('adpings', {}).items(): a = str(a).strip() if a in EXCL_ADP or a in dropped_ids: continue if a not in amap: amap[a] = str(b).strip().lstrip('@').strip() extra_used.append(a) owned = collections.Counter(amap.values()) # 每位投手名下的 Adpingid 数（已扣除排除包名） # --- 平台消耗统计：逐文件读取，按日期归组，新文件覆盖同一天的旧数据 --- by_date = {} for f, wb_ in books: if '平台消耗统计' not in wb_.sheetnames: continue day = collections.defaultdict(list) for r in wb_['平台消耗统计'].iter_rows(min_row=2, values_only=True): d, adp, pkg, geo, spend = r[1], r[2], r[3], r[4], r[8] if d is None or adp is None or not isinstance(d, datetime.datetime): continue try: s = float(spend) if spend is not None else 0.0 except (TypeError, ValueError): s = 0.0 day[d.date()].append((d.date(), key(adp), (str(pkg).strip() if pkg else '(未知)'), (str(geo).strip() if geo else ''), s)) by_date.update(day) # books 已按文件名升序，后者覆盖前者 rows = [r for day_rows in by_date.values() for r in day_rows] if not rows: sys.exit('ERROR: 平台消耗统计 无有效数据行') end = max(r[0] for r in rows) start = end - datetime.timedelta(days=WINDOW_DAYS - 1) pkg_spend = collections.defaultdict(float) # (buyer, pkg) -> spend geo_spend = collections.defaultdict(float) # (buyer, pkg, geo) -> spend ga_spend = collections.defaultdict(float) # (buyer, pkg, geo, adping) -> spend ids = collections.defaultdict(set) unmatched = collections.defaultdict(float) excluded_spend, excluded_rows = 0.0, 0 for d, adp, pkg, geo, s in rows: if not (start <= d <= end): continue if pkg in EXCL or adp in dropped_ids: excluded_spend += s; excluded_rows += 1 continue b = amap.get(adp) if b is None: unmatched[(adp, pkg)] += s continue pkg_spend[(b, pkg)] += s geo_spend[(b, pkg, geo)] += s ga_spend[(b, pkg, geo, adp)] += s ids[b].add(adp) by_buyer = collections.defaultdict(dict) for (b, pkg), v in pkg_spend.items(): by_buyer[b][pkg] = v # --- 上一周期：紧邻本窗口之前的同样天数，口径完全一致 --- prev_end = start - datetime.timedelta(days=1) prev_start = prev_end - datetime.timedelta(days=WINDOW_DAYS - 1) prev = collections.defaultdict(float) prev_has_data = any(prev_start <= d <= prev_end for d in by_date) for d, adp, pkg, geo, s_ in rows: if not (prev_start <= d <= prev_end): continue if pkg in EXCL or adp in dropped_ids: continue b = amap.get(adp) if b is not None: prev[b] += s_ out = [] for b, pk in by_buyer.items(): pkgs = [] for pkg, v in sorted(pk.items(), key=lambda x: -x[1])[:5]: # 花费最高的地区，再取该地区内花费最高的 Adpingid top_geo = max((g for (bb, pp, g) in geo_spend if bb == b and pp == pkg), key=lambda g: geo_spend[(b, pkg, g)]) top_adp = max((a for (bb, pp, g, a) in ga_spend if bb == b and pp == pkg and g == top_geo), key=lambda a: ga_spend[(b, pkg, top_geo, a)]) pkgs.append({ "pkg": pkg, "geo": top_geo, "label": f"{pkg}_{top_geo}" if top_geo else pkg, "adping": top_adp, "spend": round(v, 4), }) out.append({ "name": b, "@name": b, "total": round(sum(pk.values()), 4), "prev_total": round(prev.get(b, 0.0), 4), "active_ids": len(ids[b]), "owned_ids": owned[b], "pkgs": pkgs, }) out.sort(key=lambda x: -x['total']) lbl = f"{start.month}/{start.day}-{end.month}/{end.day}" data = { "meta": { "week": f"Last {WINDOW_DAYS} Days: {lbl}", "source": "萌奇熊日报", "generated_at": datetime.date.today().isoformat(), "date_range": f"{start:%Y/%m/%d}-{end:%Y/%m/%d}", "prev_range": (f"{prev_start:%Y/%m/%d}-{prev_end:%Y/%m/%d}" if prev_has_data else ""), "note": "投放人以「投手」表 Media Buyer 列为准，@某某 已与某某合并计算；" "未填写投放人的 Adpingid 不计入；exclude.json 所列包名已排除。" "Package 后缀为该 Package 花费最高的投放地区，Adpingid 为该地区内花费最高的 Adping。", }, "grand_total": round(sum(b['total'] for b in out), 4), "week34_id_count": sum(len(v) for v in ids.values()), "buyers": out, "unmatched": [{"adping": k[0], "pkg": k[1], "spend": round(v, 4)} for k, v in sorted(unmatched.items(), key=lambda x: -x[1])], } json.dump(data, open(os.path.join(HERE, 'data.json'), 'w'), ensure_ascii=False, indent=2) print('源文件:', ', '.join(os.path.basename(f) for f in SRC_FILES)) print('投手表取自:', os.path.basename(roster_file)) if extra_used: print('roster_extra 补充生效:', ', '.join(f'{a}->{amap[a]}' for a in extra_used)) print(f"窗口 {start} -> {end}") print(f"排除包名命中 {excluded_rows} 行，剔除花费 {excluded_spend:.2f}") print(f"合计 {data['grand_total']:.2f} / Adpingid {data['week34_id_count']}") print(f"上一周期 {prev_start} -> {prev_end} 合计 {sum(prev.values()):.2f}" + ('' if prev_has_data else ' (无数据)')) for b in out: print(f" {b['name']:6s} {b['total']:10.2f} 在投{b['active_ids']:3d}/负责{b['owned_ids']:3d}") for p in b['pkgs']: print(f" {p['label']:52s} {p['adping']:>10s} {p['spend']:9.2f}")