Python Error Handling
Seth HobsonCommunityCreated by Seth Hobson
- Skill
- Guidelines
- Software development
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a comprehensive guide to building resilient Python applications. It focuses on defensive programming techniques, such as failing fast, using meaningful exception types, and implementing structured validation. By adopting these patterns, developers can create systems that are easier to debug, maintain, and operate in production environments, ensuring that errors are caught early and handled with appropriate context.
What it does
- Defines patterns for early input validation at system boundaries.
- Provides strategies for creating custom exception hierarchies.
- Demonstrates how to handle partial failures in batch processing operations.
- Shows how to use Pydantic for structured data validation.
- Explains best practices for exception chaining to preserve debug trails.
- Maps common failure types to standard Python built-in exceptions.
When to use it
- When designing robust APIs that require strict input validation.
- When building batch processing pipelines that must handle individual item failures.
- When standardizing error reporting across a large application.
- When you need to convert external data into typed domain objects.
- When debugging complex systems where error context is frequently lost.
Advantages
- Improves system reliability by catching errors before expensive operations.
- Enhances debuggability through preserved exception context and clear error messages.
- Reduces code duplication by standardizing validation and error handling logic.
- Ensures consistent API behavior by enforcing strict input contracts.
Usage examples
- “How do I validate a user input dictionary using Pydantic?”
- The agent provides a Pydantic model example with field validators to ensure data integrity and structured error reporting.
- “How can I process a list of items without stopping on the first error?”
- The agent demonstrates a batch processing pattern that tracks successes and failures in a dictionary, allowing the caller to inspect individual results.
- “What is the best way to re-raise an exception in Python?”
- The agent explains the use of 'raise ... from e' to chain exceptions, preserving the original traceback for easier debugging.
- “How should I handle API errors in Python?”
- The agent provides a pattern for defining custom exception classes that carry status codes and response bodies for better error handling.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/wshobson/python-error-handling/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip python-error-handling.zip -d .agents/skills
Documentation by Seth Hobson
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="[email protected]",
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
Validate early - Check inputs before expensive operations
Use specific exceptions -
ValueError,TypeError, not genericExceptionInclude 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 infoHandle 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
Files
- ATTRIBUTION.mdAdded by AutoGineer999 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md6.0 KiB
- references/details.md4.7 KiB
references/details.md
python-error-handling — detailed worked examples
Advanced Patterns
Pattern 5: Custom Exceptions with Context
Create domain-specific exceptions that carry structured information.
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.
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 ePattern 7: Batch Processing with Partial Failures
Never let one bad item abort an entire batch. Track results per item.
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.
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)Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
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Attribution and licence
- Created by
- Seth Hobson
- Publisher
- Seth Hobson (Community)
- Source
- wshobson/agents · plugins/python-development/skills/python-error-handling · commit 4bde316
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Seth Hobson or Seth Hobson.
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