사용 방법
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- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/wshobson/agents --skill python-error-handling시작 전 확인
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원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
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.
4. Preserve Context
Chain exceptions to maintain the full error trail for debugging.
Quick Start
python
def fetch_page(url: str, page_size: int) -> Page:
if not url:
raise ValueError("'url' is required")
if not 1 <= page_size <= 100:
raise ValueError(f"'page_size' must be 1-100, got {page_size}")
# Now safe to proceed...Fundamental Patterns
Pattern 1: Early Input Validation
Validate all inputs at API boundaries before any processing begins.
python
def process_order(
order_id: str,
quantity: int,
discount_percent: float,
) -> OrderResult:
"""Process an order with validation."""
# Validate required fields
if not order_id:
raise ValueError("'order_id' is required")
# Validate ranges
if quantity <= 0:
raise ValueError(f"'quantity' must be positive, got {quantity}")
if not 0 <= discount_percent <= 100:
raise ValueError(
f"'discount_percent' must be 0-100, got {discount_percent}"
)
# Validation passed, proceed with processing
return _process_validated_order(order_id, quantity, discount_percent)Pattern 2: Convert to Domain Types Early
Parse strings and external data into typed domain objects at system boundaries.
python
from enum import Enum
class OutputFormat(Enum):
JSON = "json"
CSV = "csv"
PARQUET = "parquet"
def parse_output_format(value: str) -> OutputFormat:
"""Parse string to OutputFormat enum.
Args:
value: Format string from user input.
Returns:
Validated OutputFormat enum member.
Raises:
ValueError: If format is not recognized.
"""
try:
return OutputFormat(value.lower())
except ValueError:
valid_formats = [f.value for f in OutputFormat]
raise ValueError(
f"Invalid format '{value}'. "
f"Valid options: {', '.join(valid_formats)}"
)
# Usage at API boundary
def export_data(data: list[dict], format_str: str) -> bytes:
output_format = parse_output_format(format_str) # Fail fast
# Rest of function uses typed OutputFormat
...Pattern 3: Pydantic for Complex Validation
Use Pydantic models for structured input validation with automatic error messages.
python
from pydantic import BaseModel, Field, field_validator
class CreateUserInput(BaseModel):
"""Input model for user creation."""
email: str = Field(..., min_length=5, max_length=255)
name: str = Field(..., min_length=1, max_length=100)
age: int = Field(ge=0, le=150)
@field_validator("email")
@classmethod
def validate_email_format(cls, v: str) -> str:
if "@" not in v or "." not in v.split("@")[-1]:
raise ValueError("Invalid email format")
return v.lower()
@field_validator("name")
@classmethod
def normalize_name(cls, v: str) -> str:
return v.strip().title()
# Usage
try:
user_input = CreateUserInput(
email="user@example.com",
name="john doe",
age=25,
)
except ValidationError as e:
# Pydantic provides detailed error information
print(e.errors())Pattern 4: Map Errors to Standard Exceptions
Use Python's built-in exception types appropriately, adding context as needed.
| Failure Type | Exception | Example |
|---|---|---|
| Invalid input | ValueError | Bad parameter values |
| Wrong type | TypeError | Expected string, got int |
| Missing item | KeyError | Dict key not found |
| Operational failure | RuntimeError | Service unavailable |
| Timeout | TimeoutError | Operation took too long |
| File not found | FileNotFoundError | Path doesn't exist |
| Permission denied | PermissionError | Access forbidden |
python
# Good: Specific exception with context
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
- Validate early - Check inputs before expensive operations
- Use specific exceptions -
ValueError,TypeError, not genericException - Include context - Messages should explain what, why, and how to fix
- Convert types at boundaries - Parse strings to enums/domain types early
- Chain exceptions - Use
raise ... from eto preserve debug info - Handle partial failures - Don't abort batches on single item errors
- Use Pydantic - For complex input validation with structured errors
- Document failure modes - Docstrings should list possible exceptions
- Log with context - Include IDs, counts, and other debugging info
- Test error paths - Verify exceptions are raised correctly
