207 lines
8.0 KiB
Python
207 lines
8.0 KiB
Python
#!/usr/bin/env python3
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"""
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各渠道付费用户近14天完课频次差异分析。
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限定：2026年3月后订单、剔除退费。
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"""
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import psycopg2
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import psycopg2.extras
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from collections import defaultdict
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import numpy as np
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conn = psycopg2.connect(
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host="bj-postgres-16pob4sg.sql.tencentcdb.com",
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port=28591,
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user="ai_member",
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password="LdfjdjL83h3h3^$&**YGG*",
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dbname="vala_bi"
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)
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conn.set_session(autocommit=True)
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cur = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
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START = "2026-05-09"
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END = "2026-05-22"
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print("=" * 80)
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print("📊 各渠道付费用户近14天完课频次差异")
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print(" 限定：3月后订单 + 剔除退费")
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print("=" * 80)
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# ── 渠道分类 ─────────────────────────────────────────────────
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ENDPOINT_INTERNAL = {'app-active-h5-0-0', 'app-sales-bj-qhm-0'}
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def classify(kf):
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if kf in ENDPOINT_INTERNAL: return "端内"
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if kf.startswith("sales-adp"): return "销售渠道"
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if kf == "newmedia-dianpu-xhs-0-0": return "小红书店铺"
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if kf.startswith("newmedia-daren"): return "达人直播"
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if kf == "newmedia-dianpu-wwxx-0-0": return "万物"
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return "其他端外"
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# ═══════════════════════════════════════════════════════════
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# Step 1: 付费用户 + 渠道 + 等级
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# ═══════════════════════════════════════════════════════════
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print("\n[1/4] 获取付费用户...")
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cur.execute("""
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SELECT o.account_id, o.goods_id, o.key_from
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FROM bi_vala_order o
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INNER JOIN bi_vala_app_account a ON o.account_id = a.id AND a.status = 1
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WHERE o.order_status = 3 AND o.pay_success_date >= '2026-03-01' AND o.deleted_at IS NULL
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""")
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rows = cur.fetchall()
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user_goods = defaultdict(set)
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user_channels = defaultdict(set)
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for r in rows:
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user_goods[r[0]].add(r[1])
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user_channels[r[0]].add(classify(r[2]))
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L1_G = {57,60,63}; L2_G = {31,32,33,54}; L12_G = {61}
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def level(aid):
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g = user_goods.get(aid, set())
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h1, h2, h12 = bool(g & L1_G), bool(g & L2_G), bool(g & L12_G)
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if h12 or (h1 and h2): return "L1+L2"
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if h1: return "仅L1"
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if h2: return "仅L2"
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return "其他"
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# ═══════════════════════════════════════════════════════════
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# Step 2: 角色映射
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# ═══════════════════════════════════════════════════════════
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print("[2/4] 获取角色映射...")
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all_aids = list(user_goods.keys())
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cur.execute("SELECT id, account_id FROM bi_vala_app_character WHERE account_id = ANY(%s) AND deleted_at IS NULL", (all_aids,))
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char_to_account = {}
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for r in cur.fetchall():
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char_to_account[r[0]] = r[1]
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all_char_ids = set(char_to_account.keys())
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# ═══════════════════════════════════════════════════════════
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# Step 3: 各角色近14天完课次数
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# ═══════════════════════════════════════════════════════════
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print("[3/4] 统计各角色近14天完课次数（8张分表）...")
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char_completion_count = defaultdict(int)
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for shard in range(8):
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cur.execute(f"""
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SELECT user_id, COUNT(*) as cnt
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FROM bi_user_chapter_play_record_{shard}
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WHERE play_status = 1
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AND updated_at >= %s AND updated_at < %s::date + interval '1 day'
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GROUP BY user_id
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""", (START, END))
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for r in cur.fetchall():
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cid = r[0]
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if cid in all_char_ids:
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char_completion_count[cid] += r[1]
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# 汇总到 account
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account_completions = defaultdict(int)
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for cid, cnt in char_completion_count.items():
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account_completions[char_to_account[cid]] += cnt
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# ═══════════════════════════════════════════════════════════
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# Step 4: 按渠道聚合统计
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# ═══════════════════════════════════════════════════════════
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print("[4/4] 按渠道聚合...")
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CHANNELS = ["端内", "销售渠道", "小红书店铺", "达人直播", "万物", "其他端外"]
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channel_user_set = defaultdict(set)
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for aid, chs in user_channels.items():
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for ch in chs:
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channel_user_set[ch].add(aid)
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def stats(aid_set):
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"""返回 (总人数, 有完课人数, 总完课次数, 完课次数列表)"""
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comps = []
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active = 0
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for aid in aid_set:
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c = account_completions.get(aid, 0)
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if c > 0:
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active += 1
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comps.append(c)
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total_comps = sum(comps)
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return len(aid_set), active, total_comps, comps
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# ── 表格输出 ──
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header = f"{'渠道':<12s} {'等级':<8s} {'总付费':>6s} {'活跃人数':>7s} {'活跃率':>7s} {'总完课次':>7s} {'人均(全)':>8s} {'人均(活跃)':>9s} {'中位数':>6s} {'P75':>5s} {'P90':>5s}"
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print(f"\n{header}")
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print("-" * 105)
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for ch in CHANNELS:
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users = channel_user_set.get(ch, set())
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if not users:
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continue
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# 渠道总计
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tot, act, total_c, comps = stats(users)
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avg_all = total_c / tot if tot else 0
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avg_act = total_c / act if act else 0
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sorted_c = sorted(comps)
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med = np.median(sorted_c) if sorted_c else 0
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p75 = np.percentile(sorted_c, 75) if sorted_c else 0
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p90 = np.percentile(sorted_c, 90) if sorted_c else 0
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print(f"{ch:<12s} {'合计':<8s} {tot:>6d} {act:>7d} {act/tot*100:>6.1f}% {total_c:>7d} {avg_all:>8.1f} {avg_act:>9.1f} {med:>6.0f} {p75:>5.0f} {p90:>5.0f}")
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# 按等级拆分
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for lv in ["仅L1", "仅L2", "L1+L2"]:
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subset = {a for a in users if level(a) == lv}
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if not subset:
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continue
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t, a, tc, cs = stats(subset)
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aa = tc / t if t else 0
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ag = tc / a if a else 0
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sc = sorted(cs)
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md = np.median(sc) if sc else 0
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p7 = np.percentile(sc, 75) if sc else 0
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p9 = np.percentile(sc, 90) if sc else 0
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print(f"{'':<12s} {lv:<8s} {t:>6d} {a:>7d} {a/t*100:>6.1f}% {tc:>7d} {aa:>8.1f} {ag:>9.1f} {md:>6.0f} {p7:>5.0f} {p9:>5.0f}")
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print("-" * 105)
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# ── 频次分布 ──
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print(f"\n{'═' * 70}")
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print("📋 完课频次分布（活跃用户）")
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print(f"{'═' * 70}")
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BINS = [(1,1), (2,2), (3,4), (5,7), (8,14), (15, 30), (31, 999)]
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def dist(aid_set):
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comps = [account_completions.get(a, 0) for a in aid_set if account_completions.get(a, 0) > 0]
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total = len(comps)
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d = []
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for lo, hi in BINS:
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cnt = sum(1 for c in comps if lo <= c <= hi)
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pct = cnt / total * 100 if total else 0
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d.append((f"{lo}-{hi}" if hi < 999 else f"{lo}+", cnt, pct))
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return d, total
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print(f"\n{'渠道':<12s} {'活跃':>5s}", end="")
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for lo, hi in BINS:
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label = f"{lo}-{hi}" if hi < 999 else f"{lo}+"
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print(f" {label:>7s}", end="")
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print()
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for ch in CHANNELS:
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users = channel_user_set.get(ch, set())
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if not users:
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continue
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d, tot = dist(users)
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if tot == 0:
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continue
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print(f"{ch:<12s} {tot:>5d}", end="")
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for label, cnt, pct in d:
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print(f" {pct:>6.1f}%", end="")
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print()
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# ── 频道对比柱状图 ──
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print(f"\n{'═' * 70}")
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print("📊 人均完课次数对比（活跃用户）")
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print(f"{'═' * 70}")
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for ch in CHANNELS:
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users = channel_user_set.get(ch, set())
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if not users:
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continue
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_, act, tc, _ = stats(users)
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avg = tc / act if act else 0
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bar = "█" * max(1, int(avg * 2))
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print(f" {ch:<12s} 人均 {avg:5.1f} 次 {bar}")
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cur.close()
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conn.close()
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print("\n✅ 完成")
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