101 lines
4.5 KiB
Python
101 lines
4.5 KiB
Python
#!/usr/bin/env python3
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"""B2: 动态跟踪表 → 中东项目启动跟踪看板 (2026-06-08)"""
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import pandas as pd, warnings, json
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from pathlib import Path
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warnings.filterwarnings('ignore')
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import sys
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DATA_DATE = sys.argv[1] if len(sys.argv) > 1 else '2026-06-08'
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RAW_DIR = Path(f'/mnt/y/Openclaw_Hub/03.资源/实施项目 wiki/raw/月报数据/{DATA_DATE}')
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OUT_DIR = Path(f'/mnt/y/Openclaw_Hub/03.资源/实施项目 wiki/dashboard/data/{DATA_DATE}/cleaned')
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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INPUT_FILE = RAW_DIR / '动态跟踪表_REPORT3.xlsx'
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df = pd.read_excel(INPUT_FILE, sheet_name='REPORT3', header=None)
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# Row 0 = title, Row 1 = 驻外机构, Row 2 = project names, Row 3+ = tasks
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# Find Middle East columns
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me_cols = [] # (start_col, region_name, project_name)
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current_region = None
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current_project = None
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for c in range(0, df.shape[1], 3):
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region = str(df.iloc[1, c]) if c < df.shape[1] and pd.notna(df.iloc[1, c]) else ''
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project = str(df.iloc[2, c]) if c < df.shape[1] and pd.notna(df.iloc[2, c]) else ''
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if region and 'nan' not in region:
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current_region = region
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if project and project != 'nan' and '项目名称' not in project:
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current_project = project
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if current_region and '中东' in str(current_region):
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if current_project and current_project != 'nan':
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me_cols.append((c, current_region, current_project))
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print(f'Found {len(me_cols)} Middle East project columns')
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# Build task rows for each project
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all_tasks = []
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TASK_ROWS_START = 3 # Row index where task data starts (0-based)
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for start_col, region, project in me_cols:
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for row_idx in range(TASK_ROWS_START, df.shape[0]):
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task = str(df.iloc[row_idx, start_col + 0]) if pd.notna(df.iloc[row_idx, start_col + 0]) else ''
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plan_date = df.iloc[row_idx, start_col + 1] if start_col + 1 < df.shape[1] else None
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gap = df.iloc[row_idx, start_col + 2] if start_col + 2 < df.shape[1] else None
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if task and task != 'nan' and task.strip():
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all_tasks.append({
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'项目名称': project,
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'区域': region,
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'工作任务': task.strip(),
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'计划完成日期': plan_date if pd.notna(plan_date) else '',
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'差距天数': gap if pd.notna(gap) else '',
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})
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df_tasks = pd.DataFrame(all_tasks)
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print(f'Extracted {len(df_tasks)} task rows across {df_tasks["项目名称"].nunique()} projects')
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# Analyze completion status
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df_tasks['差距数值'] = pd.to_numeric(df_tasks['差距天数'], errors='coerce')
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df_tasks['计划日期_parsed'] = pd.to_datetime(df_tasks['计划完成日期'], errors='coerce')
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# Project-level summary
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proj_summary = df_tasks.groupby('项目名称').agg(
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总任务数=('工作任务', 'count'),
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已完成=('差距天数', lambda x: (x.astype(str).str.strip() == '0').sum()),
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逾期=('差距数值', lambda x: (x > 0).sum()),
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未开始=('差距天数', lambda x: (x.astype(str).str.strip().isin(['', 'nan', '不涉及', '/', 'None'])).sum()),
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不涉及=('差距天数', lambda x: (x.astype(str).str.contains('不涉及')).sum()),
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).reset_index()
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proj_summary['完成率'] = (proj_summary['已完成'] / proj_summary['总任务数'] * 100).round(0).astype(int).astype(str) + '%'
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proj_summary = proj_summary.sort_values('逾期', ascending=False)
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print(f'\n📊 项目启动跟踪汇总:')
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print(f' 项目数: {len(proj_summary)}')
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print(f' 总任务数: {proj_summary["总任务数"].sum()}')
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print(f' 已完成: {proj_summary["已完成"].sum()}')
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print(f' 逾期: {proj_summary["逾期"].sum()}')
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print(f'\n逾期项目:')
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print(proj_summary[proj_summary['逾期'] > 0].head(15).to_string())
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# Output
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df_tasks.to_parquet(OUT_DIR / 'tracking_tasks.parquet', index=False)
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df_tasks.to_csv(OUT_DIR / 'tracking_tasks.csv', index=False, encoding='utf-8-sig')
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proj_summary.to_csv(OUT_DIR / 'tracking_project_summary.csv', index=False, encoding='utf-8-sig')
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report = {
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'source': str(INPUT_FILE),
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'me_projects': len(proj_summary),
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'total_tasks': int(proj_summary['总任务数'].sum()),
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'completed': int(proj_summary['已完成'].sum()),
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'overdue': int(proj_summary['逾期'].sum()),
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}
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with open(OUT_DIR / 'tracking_validation.json', 'w', encoding='utf-8') as f:
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json.dump(report, f, ensure_ascii=False, indent=2)
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print(f'\n✅ tracking_tasks.parquet + project_summary')
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print(f'\n完整项目列表:')
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print(proj_summary[['项目名称','总任务数','已完成','逾期','完成率']].to_string())
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