Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs.
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Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs.
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Stars38,174
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LanguagePython
LicenseMIT
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</> SKILL.md
python-error-handling.SKILL.md
---name: python-error-handling
description: Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs.
---# Python Error Handling
Build robust Python applications with proper input validation, meaningful exceptions, and graceful failure handling. Good error handling makes debugging easier and systems more reliable.
## When to Use This Skill
- Validating user input and API parameters
- Designing exception hierarchies for applications
- Handling partial failures in batch operations
- Converting external data to domain types
- Building user-friendly error messages
- Implementing fail-fast validation patterns
## Core Concepts
### 1. Fail Fast
Validate inputs early, before expensive operations. Report all validation errors at once when possible.
### 2. Meaningful Exceptions
Use appropriate exception types with context. Messages should explain what failed, why, and how to fix it.
### 3. Partial Failures
In batch operations, don't let one failure abort everything. Track successes and failures separately.
raise ValueError(f"'page_size' must be 1-100, got {page_size}")
# Avoid: Generic exception, no context
raise Exception("Invalid parameter")
```
## Detailed worked examples and patterns
Detailed sections (starting with `## Advanced Patterns`) live in `references/details.md`. Read that file when the navigation summary above is insufficient.
## Best Practices Summary
1. **Validate early** - Check inputs before expensive operations
2. **Use specific exceptions** - `ValueError`, `TypeError`, not generic `Exception`
3. **Include context** - Messages should explain what, why, and how to fix
4. **Convert types at boundaries** - Parse strings to enums/domain types early
5. **Chain exceptions** - Use `raise ... from e` to preserve debug info
6. **Handle partial failures** - Don't abort batches on single item errors
7. **Use Pydantic** - For complex input validation with structured errors
8. **Document failure modes** - Docstrings should list possible exceptions
9. **Log with context** - Include IDs, counts, and other debugging info
10. **Test error paths** - Verify exceptions are raised correctly
