Error Handling Patterns
Fundamental Error Handling Strategies
Basic Exception Handling
def validate_list_length(input_list, expected_length):
try:
if len(input_list) != expected_length:
raise ValueError(f"List length must be {expected_length}")
except ValueError as e:
print(f"Validation Error: {e}")
return False
return True
Comprehensive Error Management
Custom Exception Classes
class ListLengthError(Exception):
def __init__(self, message, actual_length, expected_length):
self.message = message
self.actual_length = actual_length
self.expected_length = expected_length
super().__init__(self.message)
def advanced_list_validation(data_list, min_length, max_length):
if len(data_list) < min_length:
raise ListLengthError(
"List too short",
len(data_list),
min_length
)
if len(data_list) > max_length:
raise ListLengthError(
"List too long",
len(data_list),
max_length
)
Error Handling Patterns
Pattern |
Description |
Use Case |
Try-Except |
Basic error catching |
Simple validations |
Custom Exceptions |
Detailed error information |
Complex validations |
Logging |
Persistent error tracking |
Production environments |
Error Handling Flow
graph TD
A[Input List] --> B{Length Validation}
B --> |Valid| C[Process List]
B --> |Invalid| D[Capture Error]
D --> E{Log Error}
E --> F[Handle/Recover]
E --> G[Notify Administrator]
Logging Error Patterns
import logging
logging.basicConfig(level=logging.ERROR)
def robust_list_processor(input_list, max_length=10):
try:
if len(input_list) > max_length:
logging.error(f"List exceeds maximum length: {len(input_list)}")
return None
return sum(input_list)
except Exception as e:
logging.exception("Unexpected error in list processing")
return None
LabEx Insight
Effective error handling transforms potential failures into manageable, informative events that enhance code reliability and debugging.
Best Practices
- Use specific exception types
- Provide meaningful error messages
- Log errors for future analysis
- Implement graceful error recovery mechanisms