Passing Valid Arguments to Functions

Passing Valid Arguments to Functions

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

What Makes an Argument Valid

A valid argument has the type the function expects, a value in the allowed range, and the structure the code relies on, such as a DataFrame that contains the required columns. Documenting these rules in the signature and docstring makes correct calls easy.

Designing the signature well also prevents mistakes. Keyword-only options, sensible defaults and a small number of parameters reduce the chance that callers pass values in the wrong order.

In data science, the most common invalid input is a DataFrame with a missing or misspelled column. A short check that lists the missing columns saves a lot of debugging time.

Worked Example: Checking a DataFrame Argument

The function below accepts valid input, rejects a frame with missing columns, and uses keyword-only options.

import pandas as pd

REQUIRED = {"region", "amount"}

def region_totals(df: pd.DataFrame, *, min_amount: float = 0.0) -> pd.Series:
    missing = REQUIRED - set(df.columns)
    if missing:
        raise ValueError(f"df is missing columns: {sorted(missing)}")
    if min_amount < 0:
        raise ValueError("min_amount must be >= 0")
    return df[df["amount"] >= min_amount].groupby("region")["amount"].sum()

good = pd.DataFrame({"region": ["N", "S", "N"], "amount": [100.0, 40.0, 60.0]})
print(region_totals(good, min_amount=50))

bad = good.rename(columns={"amount": "amt"})
try:
    region_totals(bad)
except ValueError as err:
    print(err)

With min_amount=50 the South order of 40 is excluded, so only region N appears with a total of 160. The renamed frame fails immediately with a message listing the missing column.

Common Mistakes

• Assuming every DataFrame has the expected columns.
• Accepting negative or out-of-range numbers silently.
• Using many positional parameters that are easy to swap.
• Validating after expensive work instead of before.

Quick Summary

• Valid means right type, right range and right structure.
• Check required DataFrame columns up front.
• Use keyword-only options with safe defaults.
• Validate before doing expensive work.

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

Related Tutorials

• Passing Incorrect Arguments to Functions – Error Handling & Debugging in Data Science 2026
• Passing Invalid Arguments to Functions – Robust Error Handling in Data Science 2026