Function Parameters in Python – Best Practices for Data Science 2026
Understanding how to define and use function parameters effectively is crucial for writing clean, flexible, and reusable data science code. In 2026, modern Python function parameter patterns help you create more maintainable and user-friendly functions.
TL;DR — Key Parameter Types
- Positional parameters – Required, order matters
- Default parameters – Optional with sensible defaults
- Keyword-only parameters – Force usage by name
- *args and **kwargs – For flexible arguments
1. Modern Function Parameters Example
from typing import List, Optional, Dict, Any
from datetime import datetime
def analyze_sales_data(
transactions: List[Dict[str, Any]],
min_amount: float = 0.0, # Default parameter
region: Optional[str] = None, # Optional parameter
*,
include_outliers: bool = True, # Keyword-only parameter
date_range: Optional[tuple] = None # Keyword-only
) -> Dict[str, float]:
"""
Analyze sales data with flexible parameters.
Args:
transactions: List of transaction dictionaries
min_amount: Minimum amount to include (default: 0.0)
region: Filter by specific region (optional)
include_outliers: Whether to include outlier values
date_range: Optional (start_date, end_date) tuple
Returns:
Dictionary with summary statistics
"""
# Filter logic here
filtered = [t for t in transactions if t.get("amount", 0) >= min_amount]
if region:
filtered = [t for t in filtered if t.get("region") == region]
if date_range:
start, end = date_range
filtered = [t for t in filtered if start <= t.get("date") <= end]
amounts = [t["amount"] for t in filtered]
return {
"total_sales": sum(amounts),
"average_sale": sum(amounts) / len(amounts) if amounts else 0,
"transaction_count": len(amounts),
"max_sale": max(amounts) if amounts else 0
}
2. Best Practices for Function Parameters in Data Science 2026
- Use **type hints** for all parameters and return values
- Provide sensible **default values** for optional parameters
- Use **keyword-only parameters** (after
*) for important flags and options - Avoid using mutable defaults (like lists or dicts) — use
Noneinstead - Keep the number of parameters reasonable (ideally ≤ 5–6)
- Use
**kwargssparingly and only when truly needed for flexibility
3. Common Data Science Function Pattern
def preprocess_data(
df,
target_column: str,
*,
drop_na: bool = True,
scale_features: bool = False,
test_size: float = 0.2
):
"""Clean and prepare data for modeling."""
if drop_na:
df = df.dropna()
# Further preprocessing logic...
return df
Conclusion
Well-designed function parameters make your data science code more flexible, readable, and maintainable. In 2026, the standard is to use type hints, sensible defaults, and keyword-only parameters for important options. Following these practices will help you write professional, reusable functions that other data scientists can easily understand and use.
Next steps:
- Review your existing data science functions and improve them by adding type hints, better defaults, and keyword-only parameters where appropriate