[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KFJC4CF43772FEVM6K438X":3,"skill-same:01M6KFJC4CF43772FEVM6K438X":67},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":39,"commitSha":40,"skillDirectory":41,"forksCount":14,"repositoryHtmlUrl":42,"repositoryHomepageUrl":6,"ownerHtmlUrl":43,"ownerBlogUrl":44,"ownerDescription":45,"updatedAt":46,"install":47,"resources":60},"01M6KFJC4CF43772FEVM6K438X","incremental-excel-build",null,"Build complex Excel files through staged, verifiable steps with intermediate CSV outputs for debugging","xyliu-cs\u002FOpenSpace-GDPval","xyliu-cs","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F89236536?v=4","01M6KF8Z1V4TXS86CKSH48BY8T","xy","01M6KF8Z67443W0SHXM85JEF0B",0,"MIT","https:\u002F\u002Fgithub.com\u002Fxyliu-cs\u002FOpenSpace-GDPval\u002Fblob\u002Fmain\u002Fgdpval_bench\u002Fskills\u002Fincremental-excel-build\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000004","电子表格","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33],"xlsx","build","incremental excel build","debugging","csv","files","with","intermediate","for","staged","2026-11-10 10:12:30",false,"---\nname: incremental-excel-build\ndescription: Build complex Excel files through staged, verifiable steps with intermediate CSV outputs for debugging\n---\n\n# Incremental Excel Build Pattern\n\nWhen creating complex Excel files with calculations, forecasts, or data transformations, use an **incremental build-and-verify** approach instead of monolithic scripts. This pattern breaks the workflow into discrete, testable stages with intermediate CSV outputs that can be inspected at each step.\n\n## When to Use\n\n- Creating Excel files with multiple data sources\n- Complex calculations or forecasts that need validation\n- Tasks where debugging intermediate results is important\n- Workflows that may need to be re-run from a specific stage\n\n## The Four-Stage Pattern\n\n### Stage 1: Data Extraction\n\nExtract raw data from source systems and save to CSV.\n\n```python\n# extract_data.py\nimport pandas as pd\n\ndef extract_store_data():\n    # Query database, API, or read source files\n    stores = pd.read_csv('source_stores.csv')\n    sales_history = pd.read_csv('source_sales.csv')\n    \n    # Save intermediate output for verification\n    stores.to_csv('intermediate_stores.csv', index=False)\n    sales_history.to_csv('intermediate_sales.csv', index=False)\n    \n    print(f\"Extracted {len(stores)} stores, {len(sales_history)} sales records\")\n    return stores, sales_history\n\nif __name__ == '__main__':\n    extract_store_data()\n```\n\n**Verification checkpoint:** Open `intermediate_stores.csv` and `intermediate_sales.csv` to verify data completeness and format before proceeding.\n\n### Stage 2: Data Preparation\u002FTransformation\n\nClean, filter, and transform data for calculations.\n\n```python\n# prepare_data.py\nimport pandas as pd\n\ndef prepare_data():\n    # Load intermediate files from Stage 1\n    stores = pd.read_csv('intermediate_stores.csv')\n    sales = pd.read_csv('intermediate_sales.csv')\n    \n    # Filter active stores, clean data\n    active_stores = stores[stores['status'] == 'active']\n    \n    # Merge and prepare for calculations\n    prepared = pd.merge(active_stores, sales, on='store_id', how='left')\n    prepared = prepared.fillna(0)  # Handle missing values\n    \n    # Save for verification\n    prepared.to_csv('intermediate_prepared.csv', index=False)\n    \n    print(f\"Prepared data for {len(prepared)} store-week combinations\")\n    return prepared\n\nif __name__ == '__main__':\n    prepare_data()\n```\n\n**Verification checkpoint:** Review `intermediate_prepared.csv` to confirm filtering logic and data integrity.\n\n### Stage 3: Calculations\u002FForecasts\n\nPerform business logic, forecasts, or complex calculations.\n\n```python\n# calculate_forecast.py\nimport pandas as pd\nimport numpy as np\n\ndef calculate_forecast():\n    # Load prepared data from Stage 2\n    data = pd.read_csv('intermediate_prepared.csv')\n    \n    # Apply forecast logic\n    data['forecast_week1'] = data['avg_sales'] * 1.05  # 5% growth\n    data['forecast_week2'] = data['avg_sales'] * 1.08\n    data['forecast_week3'] = data['avg_sales'] * 1.10\n    data['forecast_week4'] = data['avg_sales'] * 1.12\n    \n    # Calculate totals and metrics\n    data['total_forecast'] = data[['forecast_week1', 'forecast_week2', \n                                    'forecast_week3', 'forecast_week4']].sum(axis=1)\n    \n    # Save calculations for verification\n    data.to_csv('intermediate_calculated.csv', index=False)\n    \n    print(f\"Calculated forecasts with avg total: ${data['total_forecast'].mean():.2f}\")\n    return data\n\nif __name__ == '__main__':\n    calculate_forecast()\n```\n\n**Verification checkpoint:** Validate `intermediate_calculated.csv` for calculation accuracy and reasonableness of forecast values.\n\n### Stage 4: Excel Output\n\nFormat and write final Excel file with proper styling.\n\n```python\n# create_excel.py\nimport pandas as pd\nfrom openpyxl import load_workbook\nfrom openpyxl.styles import Font, PatternFill, Alignment\n\ndef create_excel_output():\n    # Load calculated data from Stage 3\n    data = pd.read_csv('intermediate_calculated.csv')\n    \n    # Create Excel writer\n    writer = pd.ExcelWriter('final_output.xlsx', engine='openpyxl')\n    data.to_excel(writer, sheet_name='Forecast', index=False)\n    \n    # Apply formatting\n    workbook = writer.book\n    worksheet = writer.sheets['Forecast']\n    \n    # Header styling\n    header_fill = PatternFill(start_color='4472C4', end_color='4472C4', fill_type='solid')\n    header_font = Font(bold=True, color='FFFFFF')\n    \n    for cell in worksheet[1]:\n        cell.fill = header_fill\n        cell.font = header_font\n        cell.alignment = Alignment(horizontal='center')\n    \n    # Format currency columns\n    for col in ['forecast_week1', 'forecast_week2', 'forecast_week3', \n                'forecast_week4', 'total_forecast']:\n        col_letter = list(data.columns).index(col) + 1\n        for row in range(2, len(data) + 2):\n            worksheet.cell(row=row, column=col_letter).number_format = '$#,##0.00'\n    \n    # Auto-adjust column widths\n    for column in worksheet.columns:\n        max_length = max(len(str(cell.value)) for cell in column)\n        worksheet.column_dimensions[column[0].column_letter].width = min(max_length + 2, 20)\n    \n    writer.close()\n    print(\"Created final_output.xlsx with formatting\")\n\nif __name__ == '__main__':\n    create_excel_output()\n```\n\n**Verification checkpoint:** Open `final_output.xlsx` to verify formatting, data accuracy, and completeness.\n\n## Runner Script\n\nCreate a main runner that orchestrates all stages:\n\n```python\n# run_pipeline.py\nimport subprocess\nimport sys\n\ndef run_stage(script_name, stage_name):\n    print(f\"\\n=== Running {stage_name} ===\")\n    result = subprocess.run(['python', script_name], capture_output=True, text=True)\n    print(result.stdout)\n    if result.returncode != 0:\n        print(f\"ERROR in {stage_name}: {result.stderr}\")\n        sys.exit(1)\n    return True\n\ndef main():\n    stages = [\n        ('extract_data.py', 'Data Extraction'),\n        ('prepare_data.py', 'Data Preparation'),\n        ('calculate_forecast.py', 'Forecast Calculation'),\n        ('create_excel.py', 'Excel Output')\n    ]\n    \n    for script, name in stages:\n        run_stage(script, name)\n    \n    print(\"\\n=== Pipeline Complete ===\")\n\nif __name__ == '__main__':\n    main()\n```\n\n## Benefits\n\n1. **Debugging**: If Stage 3 fails, you can inspect `intermediate_prepared.csv` without re-running extraction\n2. **Verification**: Each stage produces inspectable output before proceeding\n3. **Reusability**: Individual stages can be modified independently\n4. **Transparency**: Stakeholders can review intermediate data\n5. **Recovery**: Failed runs can resume from the last successful stage\n\n## File Organization\n\n```\nproject\u002F\n├── run_pipeline.py          # Main orchestrator\n├── extract_data.py          # Stage 1\n├── prepare_data.py          # Stage 2\n├── calculate_forecast.py    # Stage 3\n├── create_excel.py          # Stage 4\n├── intermediate_stores.csv  # Stage 1 output (gitignore in production)\n├── intermediate_sales.csv   # Stage 1 output\n├── intermediate_prepared.csv # Stage 2 output\n├── intermediate_calculated.csv # Stage 3 output\n└── final_output.xlsx        # Stage 4 output (deliverable)\n```\n\n## Tips\n\n- Add `.gitignore` entries for intermediate CSV files in production\n- Include timestamp logging in each stage for audit trails\n- Consider adding stage-specific unit tests\n- For large datasets, add memory-efficient streaming in extraction stage","# Incremental Excel Build Pattern\n\nWhen creating complex Excel files with calculations, forecasts, or data transformations, use an **incremental build-and-verify** approach instead of monolithic scripts. This pattern breaks the workflow into discrete, testable stages with intermediate CSV outputs that can be inspected at each step.\n\n## When to Use\n\n- Creating Excel files with multiple data sources\n- Complex calculations or forecasts that need validation\n- Tasks where debugging intermediate results is important\n- Workflows that may need to be re-run from a specific stage\n\n## The Four-Stage Pattern\n\n### Stage 1: Data Extraction\n\nExtract raw data from source systems and save to CSV.\n\n```python\n# extract_data.py\nimport pandas as pd\n\ndef extract_store_data():\n    # Query database, API, or read source files\n    stores = pd.read_csv('source_stores.csv')\n    sales_history = pd.read_csv('source_sales.csv')\n    \n    # Save intermediate output for verification\n    stores.to_csv('intermediate_stores.csv', index=False)\n    sales_history.to_csv('intermediate_sales.csv', index=False)\n    \n    print(f\"Extracted {len(stores)} stores, {len(sales_history)} sales records\")\n    return stores, sales_history\n\nif __name__ == '__main__':\n    extract_store_data()\n```\n\n**Verification checkpoint:** Open `intermediate_stores.csv` and `intermediate_sales.csv` to verify data completeness and format before proceeding.\n\n### Stage 2: Data Preparation\u002FTransformation\n\nClean, filter, and transform data for calculations.\n\n```python\n# prepare_data.py\nimport pandas as pd\n\ndef prepare_data():\n    # Load intermediate files from Stage 1\n    stores = pd.read_csv('intermediate_stores.csv')\n    sales = pd.read_csv('intermediate_sales.csv')\n    \n    # Filter active stores, clean data\n    active_stores = stores[stores['status'] == 'active']\n    \n    # Merge and prepare for calculations\n    prepared = pd.merge(active_stores, sales, on='store_id', how='left')\n    prepared = prepared.fillna(0)  # Handle missing values\n    \n    # Save for verification\n    prepared.to_csv('intermediate_prepared.csv', index=False)\n    \n    print(f\"Prepared data for {len(prepared)} store-week combinations\")\n    return prepared\n\nif __name__ == '__main__':\n    prepare_data()\n```\n\n**Verification checkpoint:** Review `intermediate_prepared.csv` to confirm filtering logic and data integrity.\n\n### Stage 3: Calculations\u002FForecasts\n\nPerform business logic, forecasts, or complex calculations.\n\n```python\n# calculate_forecast.py\nimport pandas as pd\nimport numpy as np\n\ndef calculate_forecast():\n    # Load prepared data from Stage 2\n    data = pd.read_csv('intermediate_prepared.csv')\n    \n    # Apply forecast logic\n    data['forecast_week1'] = data['avg_sales'] * 1.05  # 5% growth\n    data['forecast_week2'] = data['avg_sales'] * 1.08\n    data['forecast_week3'] = data['avg_sales'] * 1.10\n    data['forecast_week4'] = data['avg_sales'] * 1.12\n    \n    # Calculate totals and metrics\n    data['total_forecast'] = data[['forecast_week1', 'forecast_week2', \n                                    'forecast_week3', 'forecast_week4']].sum(axis=1)\n    \n    # Save calculations for verification\n    data.to_csv('intermediate_calculated.csv', index=False)\n    \n    print(f\"Calculated forecasts with avg total: ${data['total_forecast'].mean():.2f}\")\n    return data\n\nif __name__ == '__main__':\n    calculate_forecast()\n```\n\n**Verification checkpoint:** Validate `intermediate_calculated.csv` for calculation accuracy and reasonableness of forecast values.\n\n### Stage 4: Excel Output\n\nFormat and write final Excel file with proper styling.\n\n```python\n# create_excel.py\nimport pandas as pd\nfrom openpyxl import load_workbook\nfrom openpyxl.styles import Font, PatternFill, Alignment\n\ndef create_excel_output():\n    # Load calculated data from Stage 3\n    data = pd.read_csv('intermediate_calculated.csv')\n    \n    # Create Excel writer\n    writer = pd.ExcelWriter('final_output.xlsx', engine='openpyxl')\n    data.to_excel(writer, sheet_name='Forecast', index=False)\n    \n    # Apply formatting\n    workbook = writer.book\n    worksheet = writer.sheets['Forecast']\n    \n    # Header styling\n    header_fill = PatternFill(start_color='4472C4', end_color='4472C4', fill_type='solid')\n    header_font = Font(bold=True, color='FFFFFF')\n    \n    for cell in worksheet[1]:\n        cell.fill = header_fill\n        cell.font = header_font\n        cell.alignment = Alignment(horizontal='center')\n    \n    # Format currency columns\n    for col in ['forecast_week1', 'forecast_week2', 'forecast_week3', \n                'forecast_week4', 'total_forecast']:\n        col_letter = list(data.columns).index(col) + 1\n        for row in range(2, len(data) + 2):\n            worksheet.cell(row=row, column=col_letter).number_format = '$#,##0.00'\n    \n    # Auto-adjust column widths\n    for column in worksheet.columns:\n        max_length = max(len(str(cell.value)) for cell in column)\n        worksheet.column_dimensions[column[0].column_letter].width = min(max_length + 2, 20)\n    \n    writer.close()\n    print(\"Created final_output.xlsx with formatting\")\n\nif __name__ == '__main__':\n    create_excel_output()\n```\n\n**Verification checkpoint:** Open `final_output.xlsx` to verify formatting, data accuracy, and completeness.\n\n## Runner Script\n\nCreate a main runner that orchestrates all stages:\n\n```python\n# run_pipeline.py\nimport subprocess\nimport sys\n\ndef run_stage(script_name, stage_name):\n    print(f\"\\n=== Running {stage_name} ===\")\n    result = subprocess.run(['python', script_name], capture_output=True, text=True)\n    print(result.stdout)\n    if result.returncode != 0:\n        print(f\"ERROR in {stage_name}: {result.stderr}\")\n        sys.exit(1)\n    return True\n\ndef main():\n    stages = [\n        ('extract_data.py', 'Data Extraction'),\n        ('prepare_data.py', 'Data Preparation'),\n        ('calculate_forecast.py', 'Forecast Calculation'),\n        ('create_excel.py', 'Excel Output')\n    ]\n    \n    for script, name in 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