---
name: spreadsheet-direct-python-8ae6a4ddce04
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

```bash
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

```python
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

```python
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

```python
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

```python
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
4. **Save intermediate results** for complex multi-step transformations
5. **Test with small data** before scaling to large spreadsheets
6. **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


---
Source attribution: zhanghong3721
Original project: https://github.com/zhanghong3721/kimiclaw/blob/a56eb2c0cba8a30e497cee4f4a4c79aa1f76aff0/openspace/gdpval_bench/skills/spreadsheet-direct-python/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2026 ✨Data Intelligence Lab@HKU✨

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: zhanghong3721
Original project: https://github.com/zhanghong3721/kimiclaw/blob/a56eb2c0cba8a30e497cee4f4a4c79aa1f76aff0/openspace/gdpval_bench/skills/spreadsheet-direct-python/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Marketing, Product Management
Roles: Backend Developer, Data Analyst, Frontend Developer, Growth Marketer, Product Manager
Specialties: API, Backend
Tools: Openpyxl, Pandas
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: d9522998112b8f2c2bcdafc91bef6e8a8e04ec76c71b87ede135f3d837b0a63a