FANGKKandClaude Opus 5
06f701699e
build_data.py now emits active_ids (Adpingids with spend in the window) and owned_ids (Adpingids assigned to the buyer, exclusions removed). active_ids sums to the 覆盖 Adpingid 数 card. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
108 lines
4.2 KiB
Python
108 lines
4.2 KiB
Python
#!/usr/bin/env python3
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# 从 xlsx 重算 data.json。取数规则见 README 注释：
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# 平台消耗统计: B=Date, C=Adpingid, D=包名, I=花费
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# 投手: B=Adpingid, L=Media Buyer投放人
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# @某某 == 某某；未填投放人的 Adpingid 丢弃；exclude.json 里的包名永久剔除。
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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；同时收集需按 Adping 剔除的 id ---
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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, spend = r[1], r[2], r[3], 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)'), 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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buyers = collections.defaultdict(lambda: collections.defaultdict(float))
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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, 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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if adp in amap:
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buyers[amap[adp]][pkg] += s
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ids[amap[adp]].add(adp)
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else:
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unmatched[(adp, pkg)] += s
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out = []
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for b, pk in buyers.items():
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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]), # 本周期内有消耗的 Adpingid 数
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"owned_ids": owned[b], # 名下负责的 Adpingid 总数
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"pkgs": [{"pkg": k, "spend": round(v, 4)}
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for k, v in sorted(pk.items(), key=lambda x: -x[1])[:5]],
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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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},
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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}")
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