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python-spreadsheet-debug

Diagnose Python spreadsheet failures through staged checks of the interpreter, library imports, file access, and basic operations. Minimal test scripts help separate environment problems from errors in the full processing script.

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Systematic Python debugging workflow for spreadsheet tasks to isolate environment issues from script logic

集成工具Excel
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适用场景

  • Investigating unclear errors returned by run_shell
  • Diagnosing spreadsheet scripts that suddenly stop working
  • Checking Python setup in a new environment or container
  • Isolating Excel reading and aggregation failures

包含的文件(2)

  • SKILL.md3.4 KB
  • .skill_id38 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Python Spreadsheet Debugging Workflow

When executing Python scripts for spreadsheet/data processing tasks, use this systematic debugging approach to efficiently isolate environment configuration issues from script logic errors, especially when run_shell returns opaque errors.

Step 1: Verify Python Environment

First, confirm the Python interpreter path and version to ensure you're working in the expected environment:

which python python3
python --version
python3 --version

This identifies whether Python is available and which version is being used.

Step 2: Test Library Imports in Isolation

Before running your full script, verify that required libraries can be imported successfully. Test each critical import individually:

python -c "import pandas; print('pandas:', pandas.__version__)"
python -c "import openpyxl; print('openpyxl:', openpyxl.__version__)"
python -c "import xlrd; print('xlrd:', xlrd.__version__)"

Replace library names based on your script's requirements. This identifies missing dependencies or version conflicts early.

Step 3: Run Minimal Test Script

Create and execute a minimal script that exercises only the core functionality without full business logic:

# test_minimal.py
import pandas as pd

# Test 1: Can we read a file?
try:
    df = pd.read_excel('sample.xlsx')
    print(f"✓ File read successful: {len(df)} rows")
except Exception as e:
    print(f"✗ File read failed: {e}")

# Test 2: Can we perform basic operations?
try:
    result = df.groupby('category')['amount'].sum()
    print(f"✓ GroupBy operation successful")
except Exception as e:
    print(f"✗ Operation failed: {e}")

Run this with: python test_minimal.py

Purpose: This isolates whether the issue is with file access, library functionality, or specific script logic.

Step 4: Execute Full Script

Once the minimal test passes, run the complete script:

python your_script.py

If errors occur now, you know the environment is correctly configured and can focus on debugging the specific logic.

Quick Diagnostic Command

For rapid diagnosis, run all checks in sequence:

echo "=== Python Version ===" && python --version && \
echo "=== Key Imports ===" && python -c "import pandas, openpyxl; print('OK')" && \
echo "=== Ready for full script ==="

Common Issues and Resolutions

SymptomLikely CauseResolution
ModuleNotFoundErrorMissing packagepip install <package-name>
ImportError with versionVersion conflictCheck pip list, reinstall specific version
FileNotFoundErrorWrong path/contextVerify working directory with pwd
PermissionErrorFile accessCheck file permissions or path

When to Use This Pattern

  • run_shell returns cryptic or truncated error messages
  • Script worked previously but suddenly fails
  • Deploying to a new environment or container
  • Debugging spreadsheet processing with pandas, openpyxl, xlrd, etc.

Benefits

  • Fast isolation: Identifies environment vs. logic issues within seconds
  • Incremental confidence: Each passing step narrows the problem space
  • Reusable pattern: Apply consistently across all Python spreadsheet tasks

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