maogeigei
11fe8d42e2
- V1.0/subskill/ → V1.0/subskills/（browser-harness/mcn-dou-analysis/mcn-script-review/mcn-video-prompt，git 识别 R100 纯重命名保留历史） - 路径说明同步：V1.0/SKILL.md（96/105行）、Lite1.0/SKILL.md（66行）、mcn-dou-analysis/SKILL.md 红线行、操作规范.md 159/175行、mcn-work-shop app.js SKILL_HINT_ACCOUNT - 用户环境 ~/.workbuddy/skills/短视频脚本创作/ 与源仓库 V1.0 同 inode（junction），自动同步无需单独改 - mcn-dou-analysis 内部 subskills/nuwa-skill-main 为正常结构不受影响
314 lines
12 KiB
Python
314 lines
12 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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excel_tool.py — 统一Excel工具（筛选/查询/导出）
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三种模式:
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1. select-top6 筛选TOP视频（最近三个月 + ≤15分钟 + 点赞分享降序，默认取6条，可用 --count 指定；默认数量为 SKILL.md「视频筛选标准」的执行镜像，调整该参数时须同步本处）
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2. query 按标题关键词查询视频数据（含三比率计算）
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3. export-selected 批量导出选中视频数据（含三比率计算）
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Excel列结构（13列标准格式）:
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0:序号 1:视频ID 2:视频标题 3:视频地址 4:点赞数 5:点赞(显示)
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6:评论数 7:分享数 8:收藏数 9:播放量 10:视频时长 11:发布时间 12:标签
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三比率定义（账号基准均值参考，来源于历史账号分析实测均值；仅作横向对比参考，不代表当前分析账号）:
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享赞比 = 分享数 / 点赞数 × 100% （基准 19.6%）
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评赞比 = 评论数 / 点赞数 × 100% （基准 1.85%）
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藏赞比 = 收藏数 / 点赞数 × 100% （基准 7.5%）
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用法示例:
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# 筛选TOP视频（默认6条，数量与 SKILL.md「视频筛选标准」同步）
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python excel_tool.py select-top6 "C:/path/to/短视频表格.xlsx" [--output top6.json]
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# 指定数量
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python excel_tool.py select-top6 "C:/path/to/短视频表格.xlsx" --count 6 [--output top6.json]
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# 按关键词查询（逗号分隔多关键词，OR逻辑）
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python excel_tool.py query "C:/path/to/短视频表格.xlsx" --keywords "咬下苹果,露水,划船上岸"
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# 查询所有视频并按点赞降序
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python excel_tool.py query "C:/path/to/短视频表格.xlsx" --all
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# 批量导出选中视频（从文件读取标题关键词，每行一个）
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python excel_tool.py export-selected "C:/path/to/短视频表格.xlsx" --titles-file selected.txt --output result.json
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"""
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import sys
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import os
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import json
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import datetime
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import argparse
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try:
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import openpyxl
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except ImportError:
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print("ERROR: openpyxl not installed. Run: pip install openpyxl", file=sys.stderr)
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sys.exit(1)
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# ============================================================
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# 通用工具函数
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# ============================================================
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HEADERS = ['序号', '视频ID', '视频标题', '视频地址', '点赞数', '点赞(显示)',
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'评论数', '分享数', '收藏数', '播放量', '视频时长', '发布时间', '标签']
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# 账号基准均值
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BENCHMARK = {'享赞比': 19.6, '评赞比': 1.85, '藏赞比': 7.5}
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def parse_duration(val):
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"""解析视频时长字段，返回秒数。支持 HH:MM:SS / MM:SS / 秒 / 毫秒 / 中文单位（N秒 / N分 / N分M秒）格式。"""
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if isinstance(val, (int, float)):
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return float(val) / 1000 if val > 1000 else float(val)
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if isinstance(val, str):
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val = val.strip()
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# 中文单位
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if '秒' in val or '分' in val:
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total = 0.0
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if '分' in val:
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m_part = val.split('分')[0]
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total += int(m_part) * 60 if m_part.strip().isdigit() else 0
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s_part = val.split('分')[1].replace('秒', '').strip()
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total += float(s_part) if s_part else 0
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else:
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total = float(val.replace('秒', ''))
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return total
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parts = val.split(':')
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if len(parts) == 3:
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return int(parts[0]) * 3600 + int(parts[1]) * 60 + int(parts[2])
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elif len(parts) == 2:
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return int(parts[0]) * 60 + int(parts[1])
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try:
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return float(val)
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except ValueError:
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return 99999
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return 99999
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def compute_ratios(likes, comments, shares, saves):
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"""计算三比率，返回字典。"""
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likes = likes or 0
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return {
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'享赞比': round((shares or 0) / likes * 100, 1) if likes else 0,
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'评赞比': round((comments or 0) / likes * 100, 2) if likes else 0,
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'藏赞比': round((saves or 0) / likes * 100, 1) if likes else 0,
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}
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def format_ratio_comparison(ratio_name, value):
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"""格式化比率与基准的比较标记。"""
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bench = BENCHMARK.get(ratio_name, 0)
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if value > bench * 1.3:
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return f'{ratio_name}{value}%^^'
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elif value > bench * 1.1:
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return f'{ratio_name}{value}%^'
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elif value < bench * 0.7:
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return f'{ratio_name}{value}%vv'
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elif value < bench * 0.9:
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return f'{ratio_name}{value}%v'
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else:
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return f'{ratio_name}{value}%~'
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def load_excel_rows(excel_path):
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"""加载Excel，返回数据行列表（跳过表头）。"""
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if not os.path.exists(excel_path):
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print(f'ERROR: file not found: {excel_path}', file=sys.stderr)
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sys.exit(1)
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wb = openpyxl.load_workbook(excel_path, read_only=True)
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ws = wb.active
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rows = list(ws.iter_rows(min_row=2, values_only=True))
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wb.close()
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return rows
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def row_to_dict(row):
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"""将Excel行转为字典。"""
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return dict(zip(HEADERS, [str(v) if v is not None else '' for v in row]))
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def row_to_summary(row):
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"""将Excel行转为含三比率的摘要字典。"""
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likes = row[4] or 0
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comments = row[6] or 0
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shares = row[7] or 0
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saves = row[8] or 0
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return {
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'标题': str(row[2]) if row[2] else '',
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'点赞': likes,
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'评论': comments,
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'分享': shares,
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'收藏': saves,
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'播放': row[9] or 0,
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'时长': f'{parse_duration(row[10]):.0f}s',
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'发布': str(row[11])[:10] if row[11] else '',
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'标签': str(row[12]) if row[12] else '',
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**compute_ratios(likes, comments, shares, saves),
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}
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# ============================================================
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# 1. select-top6
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# ============================================================
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def select_top6(excel_path, count=6):
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"""筛选TOP视频（默认取6条，可用 count 指定数量）。
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默认数量为 SKILL.md「视频筛选标准」的执行镜像：调整 SKILL.md 该参数时必须同步本默认值。
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"""
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rows = load_excel_rows(excel_path)
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# 时间过滤：最近三个月
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three_months_ago = datetime.datetime.now() - datetime.timedelta(days=90)
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recent = [
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r for r in rows
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if r[11] and datetime.datetime.strptime(str(r[11])[:10], '%Y-%m-%d') >= three_months_ago
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]
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# 时长过滤：≤15分钟（900秒）
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recent = [r for r in recent if parse_duration(r[10]) <= 900]
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# 排序：点赞数 + 分享数 降序
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recent.sort(key=lambda r: (r[4] or 0) + (r[7] or 0), reverse=True)
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# 取前 count 条
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top = recent[:count]
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return [row_to_dict(r) for r in top]
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def cmd_select_top6(args):
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"""select-top6 命令处理。"""
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excel_path = os.path.expanduser(args.excel_path)
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top6 = select_top6(excel_path, count=args.count)
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print(f'筛选完成，共 {len(top6)} 条视频：')
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for i, v in enumerate(top6):
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print(f' {i+1}. {v["视频标题"][:30]}... | 赞:{v["点赞数"]} 转:{v["分享数"]} | {v["视频时长"]} | {v["发布时间"]}')
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if args.output:
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with open(args.output, 'w', encoding='utf-8') as f:
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json.dump(top6, f, ensure_ascii=False, indent=2)
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print(f'\n已保存到: {args.output}')
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# ============================================================
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# 2. query
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# ============================================================
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def cmd_query(args):
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"""query 命令处理：按关键词查询或全量查询。"""
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excel_path = os.path.expanduser(args.excel_path)
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rows = load_excel_rows(excel_path)
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if args.all:
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# 全量查询，按点赞降序
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matched = sorted(rows, key=lambda r: r[4] or 0, reverse=True)
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else:
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# 关键词过滤（OR逻辑）
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keywords = [k.strip() for k in args.keywords.split(',')]
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matched = [r for r in rows if r[2] and any(k in str(r[2]) for k in keywords)]
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print(f'查询到 {len(matched)} 条视频：')
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print(f'{"序号":<4} | {"标题":<25} | {"点赞":>8} | {"评论":>6} | {"分享":>6} | {"收藏":>6} | {"时长":>6} | {"发布":>12} | 三比率')
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print('-' * 120)
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for row in matched:
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summary = row_to_summary(row)
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ratios = f'{format_ratio_comparison("享赞比", summary["享赞比"])} / {format_ratio_comparison("评赞比", summary["评赞比"])} / {format_ratio_comparison("藏赞比", summary["藏赞比"])}'
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print(f'{row[0]:<4} | {summary["标题"][:25]:<25} | {summary["点赞"]:>8} | {summary["评论"]:>6} | {summary["分享"]:>6} | {summary["收藏"]:>6} | {summary["时长"]:>6} | {summary["发布"]:>12} | {ratios}')
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if args.output:
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result = [row_to_summary(r) for r in matched]
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with open(args.output, 'w', encoding='utf-8') as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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print(f'\n已保存到: {args.output}')
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# ============================================================
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# 3. export-selected
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# ============================================================
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def cmd_export_selected(args):
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"""export-selected 命令处理：批量导出选中视频。"""
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excel_path = os.path.expanduser(args.excel_path)
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rows = load_excel_rows(excel_path)
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# 从文件读取标题关键词
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with open(args.titles_file, 'r', encoding='utf-8') as f:
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keywords = [line.strip() for line in f if line.strip()]
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# 匹配
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matched = []
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for row in rows:
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if row[2]:
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for k in keywords:
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if k in str(row[2]):
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matched.append(row)
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break
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# 按点赞降序
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matched.sort(key=lambda r: r[4] or 0, reverse=True)
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result = [row_to_summary(r) for r in matched]
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print(f'导出 {len(result)} 条视频：')
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for i, s in enumerate(result):
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print(f' {i+1}. {s["标题"][:25]}... | 赞:{s["点赞"]} | {s["时长"]} | {s["发布"]}')
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if args.output:
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with open(args.output, 'w', encoding='utf-8') as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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print(f'\n已保存到: {args.output}')
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else:
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# 默认输出到终端
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print(json.dumps(result, ensure_ascii=False, indent=2))
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# ============================================================
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# Main
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# ============================================================
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def main():
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parser = argparse.ArgumentParser(
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description='统一Excel工具（筛选/查询/导出）',
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formatter_class=argparse.RawDescriptionHelpFormatter
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)
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subparsers = parser.add_subparsers(dest='action', help='操作模式')
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# select-top6
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p_top6 = subparsers.add_parser('select-top6', help='筛选TOP视频（默认取6条，与 SKILL.md「视频筛选标准」同步）')
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p_top6.add_argument('excel_path', help='短视频表格.xlsx 路径')
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p_top6.add_argument('--count', '-n', type=int, default=6, help='筛选数量（默认6条）')
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p_top6.add_argument('--output', '-o', default=None, help='输出JSON路径')
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# query
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p_query = subparsers.add_parser('query', help='按标题关键词查询视频数据')
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p_query.add_argument('excel_path', help='短视频表格.xlsx 路径')
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p_query.add_argument('--keywords', '-k', default=None, help='标题关键词（逗号分隔，OR逻辑）')
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p_query.add_argument('--all', action='store_true', help='查询全部视频')
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p_query.add_argument('--output', '-o', default=None, help='输出JSON路径')
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# export-selected
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p_export = subparsers.add_parser('export-selected', help='批量导出选中视频数据')
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p_export.add_argument('excel_path', help='短视频表格.xlsx 路径')
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p_export.add_argument('--titles-file', required=True, help='标题关键词文件（每行一个）')
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p_export.add_argument('--output', '-o', default=None, help='输出JSON路径')
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args = parser.parse_args()
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if args.action == 'select-top6':
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cmd_select_top6(args)
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elif args.action == 'query':
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if not args.all and not args.keywords:
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print('ERROR: query 需要 --keywords 或 --all', file=sys.stderr)
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sys.exit(1)
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cmd_query(args)
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elif args.action == 'export-selected':
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cmd_export_selected(args)
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else:
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parser.print_help()
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if __name__ == '__main__':
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main()
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