incremental-excel-build

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()