Automating pandas code generation and execution for Excel reports

Asking an LLM to generate the pandas code for a recurring Excel cleanup task — rather than having it try to do the data manipulation itself in conversation — is the reliable pattern: the model writes deterministic code once, and that code runs the same way every time you execute it on a new file. This keeps the actual data transformation in real, auditable Python rather than in a language model's per-run judgment.

Step-by-Step Implementation

1. Load Raw Spreadsheets: Import target tables into memory using pandas data loaders.

2. Apply Cleanup Pipelines: Drop duplicates, format date columns, and fill missing numeric values.

3. Export Analytical Reports: Save results to structured excel formats or render tables as PDF summaries.

# Excel data cleansing and pandas aggregation pipeline
import pandas as pd

def clean_spreadsheet_report(file_path: str, output_path: str):
    # Load raw sheet data
    df = pd.read_excel(file_path)
    
    # Clean duplicate entries and fill empty cost rows
    df.drop_duplicates(subset=["TransactionID"], keep="first", inplace=True)
    df["Amount"].fillna(0.0, inplace=True)
    
    # Run analytical summaries
    summary = df.groupby("Category")["Amount"].sum().reset_index()
    
    # Save clean output
    summary.to_excel(output_path, index=False)
    print(f"Cleaned spreadsheet saved to {output_path}")

Once the assistant has generated the cleaning script, treat it like any other code you'd review before running against real data: check the column names it assumed match your actual spreadsheet, and verify the aggregation logic against a known-correct manual calculation on a small sample before trusting it on the full report.