incremental-excel-build

name	incremental-excel-build
description	Build complex Excel files through staged, verifiable steps with intermediate CSV outputs for debugging

Incremental Excel Build Pattern

When 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.

When to Use

• Creating Excel files with multiple data sources
• Complex calculations or forecasts that need validation
• Tasks where debugging intermediate results is important
• Workflows that may need to be re-run from a specific stage

The Four-Stage Pattern

Stage 1: Data Extraction

Extract raw data from source systems and save to CSV.

# extract_data.py
import pandas as pd

def extract_store_data():
    # Query database, API, or read source files
    stores = pd.read_csv('source_stores.csv')
    sales_history = pd.read_csv('source_sales.csv')
    
    # Save intermediate output for verification
    stores.to_csv('intermediate_stores.csv', index=False)
    sales_history.to_csv('intermediate_sales.csv', index=False)
    
    print(f"Extracted {len(stores)} stores, {len(sales_history)} sales records")
    return stores, sales_history

if __name__ == '__main__':
    extract_store_data()

Verification checkpoint: Open intermediate_stores.csv and intermediate_sales.csv to verify data completeness and format before proceeding.

Stage 2: Data Preparation/Transformation

Clean, filter, and transform data for calculations.

# prepare_data.py
import pandas as pd

def prepare_data():
    # Load intermediate files from Stage 1
    stores = pd.read_csv('intermediate_stores.csv')
    sales = pd.read_csv('intermediate_sales.csv')
    
    # Filter active stores, clean data
    active_stores = stores[stores['status'] == 'active']
    
    # Merge and prepare for calculations
    prepared = pd.merge(active_stores, sales, on='store_id', how='left')
    prepared = prepared.fillna(0)  # Handle missing values
    
    # Save for verification
    prepared.to_csv('intermediate_prepared.csv', index=False)
    
    print(f"Prepared data for {len(prepared)} store-week combinations")
    return prepared

if __name__ == '__main__':
    prepare_data()

Verification checkpoint: Review intermediate_prepared.csv to confirm filtering logic and data integrity.

Stage 3: Calculations/Forecasts

Perform business logic, forecasts, or complex calculations.

# calculate_forecast.py
import pandas as pd
import numpy as np

def calculate_forecast():
    # Load prepared data from Stage 2
    data = pd.read_csv('intermediate_prepared.csv')
    
    # Apply forecast logic
    data['forecast_week1'] = data['avg_sales'] * 1.05  # 5% growth
    data['forecast_week2'] = data['avg_sales'] * 1.08
    data['forecast_week3'] = data['avg_sales'] * 1.10
    data['forecast_week4'] = data['avg_sales'] * 1.12
    
    # Calculate totals and metrics
    data['total_forecast'] = data[['forecast_week1', 'forecast_week2', 
                                    'forecast_week3', 'forecast_week4']].sum(axis=1)
    
    # Save calculations for verification
    data.to_csv('intermediate_calculated.csv', index=False)
    
    print(f"Calculated forecasts with avg total: ${data['total_forecast'].mean():.2f}")
    return data

if __name__ == '__main__':
    calculate_forecast()

Verification checkpoint: Validate intermediate_calculated.csv for calculation accuracy and reasonableness of forecast values.

Stage 4: Excel Output

Format and write final Excel file with proper styling.

# create_excel.py
import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import Font, PatternFill, Alignment

def create_excel_output():
    # Load calculated data from Stage 3
    data = pd.read_csv('intermediate_calculated.csv')
    
    # Create Excel writer
    writer = pd.ExcelWriter('final_output.xlsx', engine='openpyxl')
    data.to_excel(writer, sheet_name='Forecast', index=False)
    
    # Apply formatting
    workbook = writer.book
    worksheet = writer.sheets['Forecast']
    
    # Header styling
    header_fill = PatternFill(start_color='4472C4', end_color='4472C4', fill_type='solid')
    header_font = Font(bold=True, color='FFFFFF')
    
    for cell in worksheet[1]:
        cell.fill = header_fill
        cell.font = header_font
        cell.alignment = Alignment(horizontal='center')
    
    # Format currency columns
    for col in ['forecast_week1', 'forecast_week2', 'forecast_week3', 
                'forecast_week4', 'total_forecast']:
        col_letter = list(data.columns).index(col) + 1
        for row in range(2, len(data) + 2):
            worksheet.cell(row=row, column=col_letter).number_format = 
    
    
     column  worksheet.columns:
        max_length = (((cell.value))  cell  column)
        worksheet.column_dimensions[column[].column_letter].width = (max_length + , )
    
    writer.close()
    ()

 __name__ == :
    create_excel_output()

Verification checkpoint: Open final_output.xlsx to verify formatting, data accuracy, and completeness.

Runner Script

Create a main runner that orchestrates all stages:

# run_pipeline.py
import subprocess
import sys

def run_stage(script_name, stage_name):
    print(f"\n=== Running {stage_name} ===")
    result = subprocess.run(['python', script_name], capture_output=True, text=True)
    print(result.stdout)
    if result.returncode != 0:
        print(f"ERROR in {stage_name}: {result.stderr}")
        sys.exit(1)
    return True

def main():
    stages = [
        ('extract_data.py', 'Data Extraction'),
        ('prepare_data.py', 'Data Preparation'),
        ('calculate_forecast.py', 'Forecast Calculation'),
        ('create_excel.py', 'Excel Output')
    ]
    
    for script, name in stages:
        run_stage(script, name)
    
    print("\n=== Pipeline Complete ===")

if __name__ == '__main__':
    main()

Benefits

1. Debugging: If Stage 3 fails, you can inspect intermediate_prepared.csv without re-running extraction
2. Verification: Each stage produces inspectable output before proceeding
3. Reusability: Individual stages can be modified independently
4. Transparency: Stakeholders can review intermediate data
5. Recovery: Failed runs can resume from the last successful stage

File Organization

project/
├── run_pipeline.py          # Main orchestrator
├── extract_data.py          # Stage 1
├── prepare_data.py          # Stage 2
├── calculate_forecast.py    # Stage 3
├── create_excel.py          # Stage 4
├── intermediate_stores.csv  # Stage 1 output (gitignore in production)
├── intermediate_sales.csv   # Stage 1 output
├── intermediate_prepared.csv # Stage 2 output
├── intermediate_calculated.csv # Stage 3 output
└── final_output.xlsx        # Stage 4 output (deliverable)

Tips

• Add .gitignore entries for intermediate CSV files in production
• Include timestamp logging in each stage for audit trails
• Consider adding stage-specific unit tests
• For large datasets, add memory-efficient streaming in extraction stage