Practical Argument Handling
Argument Validation Techniques
Effective argument handling involves robust validation and flexible processing strategies. This section explores practical approaches to managing function arguments.
Argument Validation Strategies
Validation Type |
Description |
Technique |
Type Checking |
Ensure correct argument types |
Type hints, isinstance() |
Value Validation |
Validate argument ranges/conditions |
Custom validation functions |
Default Handling |
Manage missing or optional arguments |
Default values, optional parameters |
Type Validation Methods
def validate_arguments(func):
def wrapper(*args, **kwargs):
## Type checking decorator
signature = inspect.signature(func)
bound_arguments = signature.bind(*args, **kwargs)
for name, value in bound_arguments.arguments.items():
param = signature.parameters[name]
## Check type annotations
if param.annotation != param.empty:
if not isinstance(value, param.annotation):
raise TypeError(f"Argument {name} must be {param.annotation}")
return func(*args, **kwargs)
return wrapper
@validate_arguments
def process_user(name: str, age: int):
print(f"Name: {name}, Age: {age}")
## Usage examples
process_user("Alice", 30) ## Valid
## process_user(123, "Invalid") ## Raises TypeError
Flexible Argument Processing
def flexible_function(*args, **kwargs):
## Handle variable arguments dynamically
print("Positional Arguments:", args)
print("Keyword Arguments:", kwargs)
## Optional argument handling
config = kwargs.get('config', {})
debug = config.get('debug', False)
if debug:
print("Debug mode enabled")
## Demonstration
flexible_function(1, 2, 3, config={'debug': True})
Argument Handling Workflow
graph TD
A[Function Call] --> B{Argument Validation}
B --> |Type Check| C[Validate Types]
B --> |Value Check| D[Validate Values]
B --> |Transform| E[Process Arguments]
E --> F[Execute Function]
Advanced Argument Manipulation
from functools import wraps
def sanitize_arguments(func):
@wraps(func)
def wrapper(*args, **kwargs):
## Sanitize and transform arguments
sanitized_args = [str(arg).strip() for arg in args]
sanitized_kwargs = {k: str(v).strip() for k, v in kwargs.items()}
return func(*sanitized_args, **sanitized_kwargs)
return wrapper
@sanitize_arguments
def create_user(username, email):
print(f"Username: {username}, Email: {email}")
## Usage
create_user(" john_doe ", "john@example.com")
Error Handling Strategies
def safe_argument_processing(func):
def wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
except TypeError as e:
print(f"Argument Error: {e}")
## Provide default or fallback behavior
except ValueError as e:
print(f"Invalid Argument: {e}")
return wrapper
@safe_argument_processing
def divide_numbers(a: int, b: int):
return a / b
## Demonstration
divide_numbers(10, 0) ## Handles division error
LabEx Recommendations
LabEx emphasizes the importance of robust argument handling to create more reliable and maintainable Python code. Practice these techniques to improve your programming skills.
Conclusion
Practical argument handling involves validation, transformation, and flexible processing techniques that make Python functions more robust and adaptable to various input scenarios.