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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.

4. Preserve Context

Chain exceptions to maintain the full error trail for debugging.

Quick Start

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.

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.

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.

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 |

# 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

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