FANGKKandClaude Opus 5
fcf9a5c01b
Package now renders as pkg_GEO, where GEO is the region with the highest total spend for that package. The new Adpingid column shows the top-spending Adping within that region, so label and id stay consistent. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
136 lines
5.5 KiB
Python
136 lines
5.5 KiB
Python
#!/usr/bin/env python3
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# 从 xlsx 重算 data.json。
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# 平台消耗统计: B=Date, C=Adpingid, D=包名, E=地区, I=花费
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# 投手: B=Adpingid, D=pkg, L=Media Buyer投放人
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# 规则：@某某 == 某某；未填投放人的 Adpingid 丢弃；exclude.json 里的包名永久剔除（精确匹配）。
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# 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。
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import openpyxl, collections, json, datetime, os, sys
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HERE = os.path.dirname(os.path.abspath(__file__))
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SRC = os.path.join(HERE, '2608每日数据-武汉萌奇熊.xlsx')
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EXCL = set(json.load(open(os.path.join(HERE, 'exclude.json')))['packages'])
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NON_BUYER = {'GP下架'}
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WINDOW_DAYS = 3
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def key(v):
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if isinstance(v, float) and v.is_integer():
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return str(int(v))
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return str(v).strip()
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wb = openpyxl.load_workbook(SRC, data_only=True)
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# --- 投手表：Adpingid -> 投放人 ---
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amap, dropped_ids = {}, set()
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for r in wb['投手'].iter_rows(min_row=2, values_only=True):
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adp, pkg, buyer, status = r[1], r[3], r[11], r[0]
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if adp is None or str(status).strip() == '示例':
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continue
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k = key(adp)
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if pkg is not None and str(pkg).strip() in EXCL: # 精确匹配，避免误伤 in.swiggy.android
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dropped_ids.add(k)
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continue
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b = str(buyer).strip() if buyer is not None else ''
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if b in ('', 'None') or b.lstrip('@').strip() in NON_BUYER:
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continue
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amap[k] = b.lstrip('@').strip()
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owned = collections.Counter(amap.values()) # 每位投手名下的 Adpingid 数（已扣除排除包名）
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# --- 平台消耗统计 ---
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rows = []
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for r in wb['平台消耗统计'].iter_rows(min_row=2, values_only=True):
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d, adp, pkg, geo, spend = r[1], r[2], r[3], r[4], r[8]
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if d is None or adp is None or not isinstance(d, datetime.datetime):
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continue
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try:
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s = float(spend) if spend is not None else 0.0
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except (TypeError, ValueError):
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s = 0.0
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rows.append((d.date(), key(adp), (str(pkg).strip() if pkg else '(未知)'),
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(str(geo).strip() if geo else ''), s))
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if not rows:
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sys.exit('ERROR: 平台消耗统计 无有效数据行')
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end = max(r[0] for r in rows)
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start = end - datetime.timedelta(days=WINDOW_DAYS - 1)
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pkg_spend = collections.defaultdict(float) # (buyer, pkg) -> spend
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geo_spend = collections.defaultdict(float) # (buyer, pkg, geo) -> spend
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ga_spend = collections.defaultdict(float) # (buyer, pkg, geo, adping) -> spend
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ids = collections.defaultdict(set)
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unmatched = collections.defaultdict(float)
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excluded_spend, excluded_rows = 0.0, 0
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for d, adp, pkg, geo, s in rows:
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if not (start <= d <= end):
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continue
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if pkg in EXCL or adp in dropped_ids:
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excluded_spend += s; excluded_rows += 1
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continue
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b = amap.get(adp)
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if b is None:
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unmatched[(adp, pkg)] += s
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continue
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pkg_spend[(b, pkg)] += s
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geo_spend[(b, pkg, geo)] += s
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ga_spend[(b, pkg, geo, adp)] += s
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ids[b].add(adp)
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by_buyer = collections.defaultdict(dict)
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for (b, pkg), v in pkg_spend.items():
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by_buyer[b][pkg] = v
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out = []
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for b, pk in by_buyer.items():
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pkgs = []
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for pkg, v in sorted(pk.items(), key=lambda x: -x[1])[:5]:
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# 花费最高的地区，再取该地区内花费最高的 Adpingid
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top_geo = max((g for (bb, pp, g) in geo_spend if bb == b and pp == pkg),
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key=lambda g: geo_spend[(b, pkg, g)])
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top_adp = max((a for (bb, pp, g, a) in ga_spend
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if bb == b and pp == pkg and g == top_geo),
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key=lambda a: ga_spend[(b, pkg, top_geo, a)])
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pkgs.append({
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"pkg": pkg,
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"geo": top_geo,
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"label": f"{pkg}_{top_geo}" if top_geo else pkg,
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"adping": top_adp,
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"spend": round(v, 4),
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})
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out.append({
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"name": b, "@name": b,
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"total": round(sum(pk.values()), 4),
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"active_ids": len(ids[b]),
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"owned_ids": owned[b],
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"pkgs": pkgs,
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})
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out.sort(key=lambda x: -x['total'])
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lbl = f"{start.month}/{start.day}-{end.month}/{end.day}"
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data = {
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"meta": {
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"week": f"Last {WINDOW_DAYS} Days: {lbl}",
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"source": "数据已处理（平台消耗统计 × 投手表匹配，已剔除未填写投放人的 Adpingid 及排除包名）",
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"generated_at": datetime.date.today().isoformat(),
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"date_range": f"{start:%Y/%m/%d}-{end:%Y/%m/%d}",
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"note": "投放人以「投手」表 Media Buyer 列为准，@某某 已与某某合并计算；"
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"未填写投放人的 Adpingid 不计入；exclude.json 所列包名已排除。"
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"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。",
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},
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"grand_total": round(sum(b['total'] for b in out), 4),
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"week34_id_count": sum(len(v) for v in ids.values()),
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"buyers": out,
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"unmatched": [{"adping": k[0], "pkg": k[1], "spend": round(v, 4)}
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for k, v in sorted(unmatched.items(), key=lambda x: -x[1])],
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}
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json.dump(data, open(os.path.join(HERE, 'data.json'), 'w'), ensure_ascii=False, indent=2)
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print(f"窗口 {start} -> {end}")
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print(f"排除包名命中 {excluded_rows} 行，剔除花费 {excluded_spend:.2f}")
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print(f"合计 {data['grand_total']:.2f} / Adpingid {data['week34_id_count']}")
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for b in out:
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print(f" {b['name']:6s} {b['total']:10.2f} 在投{b['active_ids']:3d}/负责{b['owned_ids']:3d}")
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for p in b['pkgs']:
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print(f" {p['label']:52s} {p['adping']:>10s} {p['spend']:9.2f}")
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