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")

Advanced Patterns

Pattern 5: Custom Exceptions with Context

Create domain-specific exceptions that carry structured information.

python

class ApiError(Exception):
    """Base exception for API errors."""

    def __init__(
        self,
        message: str,
        status_code: int,
        response_body: str | None = None,
    ) -> None:
        self.status_code = status_code
        self.response_body = response_body
        super().__init__(message)

class RateLimitError(ApiError):
    """Raised when rate limit is exceeded."""

    def __init__(self, retry_after: int) -> None:
        self.retry_after = retry_after
        super().__init__(
            f"Rate limit exceeded. Retry after {retry_after}s",
            status_code=429,
        )

# Usage
def handle_response(response: Response) -> dict:
    match response.status_code:
        case 200:
            return response.json()
        case 401:
            raise ApiError("Invalid credentials", 401)
        case 404:
            raise ApiError(f"Resource not found: {response.url}", 404)
        case 429:
            retry_after = int(response.headers.get("Retry-After", 60))
            raise RateLimitError(retry_after)
        case code if 400 <= code < 500:
            raise ApiError(f"Client error: {response.text}", code)
        case code if code >= 500:
            raise ApiError(f"Server error: {response.text}", code)

Pattern 6: Exception Chaining

Preserve the original exception when re-raising to maintain the debug trail.

python

import httpx

class ServiceError(Exception):
    """High-level service operation failed."""
    pass

def upload_file(path: str) -> str:
    """Upload file and return URL."""
    try:
        with open(path, "rb") as f:
            response = httpx.post("https://upload.example.com", files={"file": f})
            response.raise_for_status()
            return response.json()["url"]
    except FileNotFoundError as e:
        raise ServiceError(f"Upload failed: file not found at '{path}'") from e
    except httpx.HTTPStatusError as e:
        raise ServiceError(
            f"Upload failed: server returned {e.response.status_code}"
        ) from e
    except httpx.RequestError as e:
        raise ServiceError(f"Upload failed: network error") from e

Pattern 7: Batch Processing with Partial Failures

Never let one bad item abort an entire batch. Track results per item.

python

from dataclasses import dataclass

@dataclass
class BatchResult[T]:
    """Results from batch processing."""

    succeeded: dict[int, T]  # index -> result
    failed: dict[int, Exception]  # index -> error

    @property
    def success_count(self) -> int:
        return len(self.succeeded)

    @property
    def failure_count(self) -> int:
        return len(self.failed)

    @property
    def all_succeeded(self) -> bool:
        return len(self.failed) == 0

def process_batch(items: list[Item]) -> BatchResult[ProcessedItem]:
    """Process items, capturing individual failures.

    Args:
        items: Items to process.

    Returns:
        BatchResult with succeeded and failed items by index.
    """
    succeeded: dict[int, ProcessedItem] = {}
    failed: dict[int, Exception] = {}

    for idx, item in enumerate(items):
        try:
            result = process_single_item(item)
            succeeded[idx] = result
        except Exception as e:
            failed[idx] = e

    return BatchResult(succeeded=succeeded, failed=failed)

# Caller handles partial results
result = process_batch(items)
if not result.all_succeeded:
    logger.warning(
        f"Batch completed with {result.failure_count} failures",
        failed_indices=list(result.failed.keys()),
    )

Pattern 8: Progress Reporting for Long Operations

Provide visibility into batch progress without coupling business logic to UI.

python

from collections.abc import Callable

ProgressCallback = Callable[[int, int, str], None]  # current, total, status

def process_large_batch(
    items: list[Item],
    on_progress: ProgressCallback | None = None,
) -> BatchResult:
    """Process batch with optional progress reporting.

    Args:
        items: Items to process.
        on_progress: Optional callback receiving (current, total, status).
    """
    total = len(items)
    succeeded = {}
    failed = {}

    for idx, item in enumerate(items):
        if on_progress:
            on_progress(idx, total, f"Processing {item.id}")

        try:
            succeeded[idx] = process_single_item(item)
        except Exception as e:
            failed[idx] = e

    if on_progress:
        on_progress(total, total, "Complete")

    return BatchResult(succeeded=succeeded, failed=failed)

Best Practices Summary

Validate early - Check inputs before expensive operations
Use specific exceptions -
```
ValueError
```
,
```
TypeError
```
, not generic
```
Exception
```
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 e
```
to 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

python-error-handling

NPX Install

Tags

SKILL.md Content

Python Error Handling

When to Use This Skill

Core Concepts

1. Fail Fast

2. Meaningful Exceptions

3. Partial Failures

4. Preserve Context

Quick Start

Fundamental Patterns

Pattern 1: Early Input Validation

Pattern 2: Convert to Domain Types Early

Pattern 3: Pydantic for Complex Validation

Pattern 4: Map Errors to Standard Exceptions

Advanced Patterns

Pattern 5: Custom Exceptions with Context

Pattern 6: Exception Chaining

Pattern 7: Batch Processing with Partial Failures

Pattern 8: Progress Reporting for Long Operations

Best Practices Summary