Passing Valid Arguments to Functions – Best Practices for Data Science 2026
Passing the correct arguments to functions is fundamental to writing reliable data science code. In 2026, professional data scientists focus not only on handling incorrect arguments gracefully, but also on designing functions that make it easy and safe to pass valid arguments.
TL;DR — Key Principles for Valid Arguments
- Use clear, descriptive parameter names
- Provide sensible default values
- Use type hints to communicate expected types
- Validate important arguments early
- Make dangerous parameters keyword-only
1. Well-Designed Function with Valid Argument Patterns
from typing import List, Optional, Literal
import pandas as pd
def train_classification_model(
df: pd.DataFrame,
target_column: str,
feature_columns: Optional[List[str]] = None,
*,
model_type: Literal["random_forest", "xgboost", "lightgbm"] = "random_forest",
n_estimators: int = 100,
max_depth: Optional[int] = None,
random_state: int = 42,
test_size: float = 0.2
) -> dict:
"""
Train a classification model with clear and safe argument design.
Args:
df: Input DataFrame containing features and target
target_column: Name of the target column
feature_columns: List of feature columns (if None, uses all except target)
model_type: Type of model to train (keyword-only)
n_estimators: Number of trees (for tree-based models)
max_depth: Maximum depth of trees
random_state: Random seed for reproducibility
test_size: Proportion of data for testing
Returns:
Dictionary with model performance metrics and metadata
"""
# Early validation of critical arguments
if target_column not in df.columns:
raise ValueError(f"Target column '{target_column}' not found in DataFrame.")
if feature_columns is None:
feature_columns = [col for col in df.columns if col != target_column]
# Main training logic...
print(f"Training {model_type} model with {len(feature_columns)} features...")
return {
"model_type": model_type,
"n_estimators": n_estimators,
"accuracy": 0.885,
"feature_count": len(feature_columns),
"random_state": random_state
}
2. Best Practices for Passing Valid Arguments in 2026
- Use **descriptive parameter names** that clearly communicate purpose
- Provide **sensible defaults** for optional parameters
- Make configuration or dangerous parameters **keyword-only** (using `*`)
- Use **type hints** (including `Literal` for limited choices)
- Validate critical inputs early in the function with clear error messages
- Document expected formats and constraints in the docstring
Conclusion
Passing valid arguments is not just about avoiding errors — it's about designing functions that are intuitive and safe to use. In 2026, the best data science code uses clear parameter names, sensible defaults, keyword-only arguments for configuration, and early validation with informative error messages. These practices make your functions easier to use, less error-prone, and more maintainable across team projects.
Next steps:
- Review your most frequently used data science functions and improve their parameter design using descriptive names, sensible defaults, and keyword-only arguments