chore: import upstream snapshot with attribution
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wehub-resource-sync
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---
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name: python-error-handling
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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.
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---
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# Python Error Handling
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Build robust Python applications with proper input validation, meaningful exceptions, and graceful failure handling. Good error handling makes debugging easier and systems more reliable.
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## When to Use This Skill
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- Validating user input and API parameters
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- Designing exception hierarchies for applications
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- Handling partial failures in batch operations
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- Converting external data to domain types
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- Building user-friendly error messages
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- Implementing fail-fast validation patterns
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## Core Concepts
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### 1. Fail Fast
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Validate inputs early, before expensive operations. Report all validation errors at once when possible.
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### 2. Meaningful Exceptions
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Use appropriate exception types with context. Messages should explain what failed, why, and how to fix it.
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### 3. Partial Failures
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In batch operations, don't let one failure abort everything. Track successes and failures separately.
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### 4. Preserve Context
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Chain exceptions to maintain the full error trail for debugging.
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## Quick Start
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```python
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def fetch_page(url: str, page_size: int) -> Page:
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if not url:
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raise ValueError("'url' is required")
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if not 1 <= page_size <= 100:
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raise ValueError(f"'page_size' must be 1-100, got {page_size}")
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# Now safe to proceed...
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```
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## Fundamental Patterns
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### Pattern 1: Early Input Validation
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Validate all inputs at API boundaries before any processing begins.
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```python
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def process_order(
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order_id: str,
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quantity: int,
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discount_percent: float,
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) -> OrderResult:
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"""Process an order with validation."""
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# Validate required fields
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if not order_id:
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raise ValueError("'order_id' is required")
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# Validate ranges
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if quantity <= 0:
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raise ValueError(f"'quantity' must be positive, got {quantity}")
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if not 0 <= discount_percent <= 100:
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raise ValueError(
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f"'discount_percent' must be 0-100, got {discount_percent}"
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)
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# Validation passed, proceed with processing
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return _process_validated_order(order_id, quantity, discount_percent)
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```
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### Pattern 2: Convert to Domain Types Early
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Parse strings and external data into typed domain objects at system boundaries.
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```python
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from enum import Enum
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class OutputFormat(Enum):
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JSON = "json"
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CSV = "csv"
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PARQUET = "parquet"
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def parse_output_format(value: str) -> OutputFormat:
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"""Parse string to OutputFormat enum.
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Args:
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value: Format string from user input.
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Returns:
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Validated OutputFormat enum member.
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Raises:
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ValueError: If format is not recognized.
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"""
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try:
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return OutputFormat(value.lower())
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except ValueError:
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valid_formats = [f.value for f in OutputFormat]
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raise ValueError(
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f"Invalid format '{value}'. "
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f"Valid options: {', '.join(valid_formats)}"
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)
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# Usage at API boundary
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def export_data(data: list[dict], format_str: str) -> bytes:
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output_format = parse_output_format(format_str) # Fail fast
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# Rest of function uses typed OutputFormat
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...
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```
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### Pattern 3: Pydantic for Complex Validation
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Use Pydantic models for structured input validation with automatic error messages.
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```python
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from pydantic import BaseModel, Field, field_validator
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class CreateUserInput(BaseModel):
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"""Input model for user creation."""
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email: str = Field(..., min_length=5, max_length=255)
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name: str = Field(..., min_length=1, max_length=100)
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age: int = Field(ge=0, le=150)
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@field_validator("email")
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@classmethod
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def validate_email_format(cls, v: str) -> str:
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if "@" not in v or "." not in v.split("@")[-1]:
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raise ValueError("Invalid email format")
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return v.lower()
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@field_validator("name")
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@classmethod
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def normalize_name(cls, v: str) -> str:
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return v.strip().title()
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# Usage
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try:
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user_input = CreateUserInput(
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email="user@example.com",
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name="john doe",
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age=25,
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)
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except ValidationError as e:
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# Pydantic provides detailed error information
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print(e.errors())
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```
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### Pattern 4: Map Errors to Standard Exceptions
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Use Python's built-in exception types appropriately, adding context as needed.
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| Failure Type | Exception | Example |
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|--------------|-----------|---------|
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| Invalid input | `ValueError` | Bad parameter values |
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| Wrong type | `TypeError` | Expected string, got int |
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| Missing item | `KeyError` | Dict key not found |
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| Operational failure | `RuntimeError` | Service unavailable |
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| Timeout | `TimeoutError` | Operation took too long |
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| File not found | `FileNotFoundError` | Path doesn't exist |
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| Permission denied | `PermissionError` | Access forbidden |
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```python
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# Good: Specific exception with context
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raise ValueError(f"'page_size' must be 1-100, got {page_size}")
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# Avoid: Generic exception, no context
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raise Exception("Invalid parameter")
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```
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## Detailed worked examples and patterns
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Detailed sections (starting with `## Advanced Patterns`) live in `references/details.md`. Read that file when the navigation summary above is insufficient.
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## Best Practices Summary
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1. **Validate early** - Check inputs before expensive operations
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2. **Use specific exceptions** - `ValueError`, `TypeError`, not generic `Exception`
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3. **Include context** - Messages should explain what, why, and how to fix
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4. **Convert types at boundaries** - Parse strings to enums/domain types early
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5. **Chain exceptions** - Use `raise ... from e` to preserve debug info
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6. **Handle partial failures** - Don't abort batches on single item errors
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7. **Use Pydantic** - For complex input validation with structured errors
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8. **Document failure modes** - Docstrings should list possible exceptions
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9. **Log with context** - Include IDs, counts, and other debugging info
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10. **Test error paths** - Verify exceptions are raised correctly
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