spreadsheet-direct-python

name	spreadsheet-direct-python
description	Use direct Python execution for reliable spreadsheet operations instead of shell_agent

Direct Python Execution for Spreadsheet Tasks

When to Use This Skill

Use direct run_shell with embedded Python scripts for spreadsheet operations when:

• Reading or writing complex Excel files with multiple sheets
• Applying formulas, formatting, or data transformations
• Working with openpyxl, pandas, or similar libraries
• The operation involves multiple steps that could exceed agent step limits
• You need precise control over error handling and debugging

Why Direct Execution?

The shell_agent tool can:

• Hit maximum step limits on complex multi-step operations
• Produce unexplained errors on formatting operations
• Fail on intricate spreadsheet reads/writes due to iterative parsing

Direct run_shell with Python is more reliable because it:

• Executes in a single step with no iteration limits
• Provides clearer, immediate error messages
• Handles complex operations without step constraints
• Gives full control over library imports and execution flow

How to Use

Basic Pattern

python3 << 'EOF'
import openpyxl
from openpyxl import Workbook

# Your spreadsheet code here
wb = openpyxl.load_workbook('file.xlsx')
# ... operations ...
wb.save('output.xlsx')
print('Success')
EOF

Example 1: Read and Transform Excel Data

import pandas as pd

# Load data from specific sheet
df = pd.read_excel('input.xlsx', sheet_name='Revenue')

# Apply transformations
df['Net_Revenue'] = df['Gross_Revenue'] * (1 - df['Tax_Rate'])

# Save results
df.to_excel('output.xlsx', index=False, sheet_name='Processed')

Example 2: Multi-Sheet Operations with openpyxl

from openpyxl import load_workbook

wb = load_workbook('tour_data.xlsx')

# Iterate through sheets
for sheet_name in wb.sheetnames:
    ws = wb[sheet_name]
    # Apply formatting or calculations
    for row in ws.iter_rows(min_row=2, max_col=5):
        # Process cells
        pass

wb.save('tour_data_processed.xlsx')

Example 3: Complex Formatting Operations

from openpyxl import load_workbook
from openpyxl.styles import Font, PatternFill, Alignment

wb = load_workbook('report.xlsx')
ws = wb.active

# Apply header styling
header_fill = PatternFill(start_color='4472C4', fill_type='solid')
header_font = Font(bold=True, color='FFFFFF')

for cell in ws[1]:
    cell.fill = header_fill
    cell.font = header_font
    cell.alignment = Alignment(horizontal='center')

wb.save('report_formatted.xlsx')

Example 4: Error Handling Pattern

import sys
from openpyxl import load_workbook

try:
    wb = load_workbook('data.xlsx')
    ws = wb.active
    
    # Your operations here
    value = ws['A1'].value
    
    wb.save('output.xlsx')
    print(f"Success: Processed {ws.max_row} rows")
    
except Exception as e:
    print(f"Error: {str(e)}", file=sys.stderr)
    sys.exit(1)

Best Practices

1. Use heredoc syntax (<< 'EOF') for multi-line Python scripts in shell commands
2. Import only needed libraries to reduce execution time
3. Print clear success/error messages for debugging
4. Use pandas-compatible DataFrame constructors — prefer pd.DataFrame.from_dict() over direct dict-to-DataFrame conversion for cross-version compatibility
5. Save intermediate results for complex multi-step transformations
6. Test with small data before scaling to large spreadsheets
7. Use pandas for data manipulation and openpyxl for formatting when both are needed

When NOT to Use This Skill

• Simple single-cell reads/writes (use shell_agent or basic commands)
• Operations that require interactive user input
• Tasks where you need the agent to iteratively refine the approach

Common Libraries

Library	Best For
openpyxl	Reading/writing .xlsx files, formatting, formulas
pandas	Data manipulation, analysis, merging datasets
xlrd	Reading older .xls files (read-only)
xlsxwriter	Creating new .xlsx files with advanced formatting

Troubleshooting

Issue: FileNotFoundError

• Solution: Verify the file path is absolute or relative to the working directory

Issue: PermissionError

• Solution: Ensure the file is not open in another application

Issue: MemoryError on large files

• Solution: Process data in chunks using pandas chunksize parameter

Issue: Formatting not applying

• Solution: Ensure you're modifying cell styles before saving, and use .copy() for style objects