Best Practices
Designing Effective Boolean Flags
1. Clear and Descriptive Naming
## Bad example
def process(data, mode=False):
pass
## Good example
def process_data(data, skip_validation=False):
pass
2. Minimal Flag Usage
## Avoid excessive flags
def complex_function(
data,
validate=False,
clean=False,
transform=False,
normalize=False
):
## Too many flags reduce readability
pass
## Preferred: Use configuration object
def process_data(data, config=None):
config = config or {}
## More flexible and maintainable
Flag Design Principles
flowchart TD
A[Boolean Flag Design] --> B[Clarity]
A --> C[Simplicity]
A --> D[Predictability]
A --> E[Minimal Complexity]
Type Hinting and Validation
from typing import Optional, Dict, Any
def data_processor(
data: list,
options: Optional[Dict[str, bool]] = None
) -> Any:
## Explicit type hints
options = options or {}
## Validate flag types
for key, value in options.items():
if not isinstance(value, bool):
raise TypeError(f"Flag {key} must be boolean")
Configuration Management
Enum-Based Configuration
from enum import Enum, auto
class ProcessingMode(Enum):
STRICT = auto()
LENIENT = auto()
DEBUG = auto()
def process_data(
data,
mode: ProcessingMode = ProcessingMode.STRICT
):
if mode == ProcessingMode.DEBUG:
## Detailed logging
pass
Practice |
Impact |
Recommendation |
Default Arguments |
Low Overhead |
Preferred |
Keyword Arguments |
Moderate Overhead |
Use Sparingly |
Configuration Objects |
Higher Overhead |
Complex Scenarios |
Error Handling Strategies
def robust_function(
data,
fail_silently: bool = False
):
try:
## Complex processing
result = process(data)
except Exception as e:
if not fail_silently:
raise
return None
Advanced Flag Techniques
Decorator-Based Flags
def debug_flag(func):
def wrapper(*args, **kwargs):
debug = kwargs.pop('debug', False)
if debug:
print(f"Calling {func.__name__}")
return func(*args, **kwargs)
return wrapper
@debug_flag
def complex_operation(data):
## Function implementation
pass
LabEx Recommendations
- Keep flags simple and meaningful
- Use type hints
- Provide sensible defaults
- Consider alternative patterns for complex scenarios
- Document flag behaviors clearly
Common Antipatterns to Avoid
- Flag explosion
- Cryptic flag names
- Inconsistent flag behaviors
- Overloading function responsibilities
By following these best practices, you can create more maintainable, readable, and flexible Python functions with boolean flags.