Passing Incorrect Arguments to Functions – Error Handling & Debugging in Data Science 2026
Passing the wrong type, number, or value of arguments to a function is one of the most common sources of errors in data science code. In 2026, writing functions that gracefully handle incorrect arguments and provide clear error messages is a key professional skill.
TL;DR — Best Practices
- Use type hints to catch errors early
- Validate arguments inside the function
- Raise clear, informative exceptions
- Provide helpful error messages with context
1. Common Mistakes & Proper Handling
def calculate_average(numbers):
"""Calculate average of a list of numbers."""
if not isinstance(numbers, (list, tuple)):
raise TypeError(f"Expected list or tuple, got {type(numbers).__name__} instead.")
if len(numbers) == 0:
raise ValueError("Cannot calculate average of an empty list.")
try:
return sum(numbers) / len(numbers)
except TypeError:
raise TypeError("List must contain only numeric values (int or float).") from None
2. Real-World Data Science Example
import pandas as pd
from typing import Optional
def train_model(
df: pd.DataFrame,
target_column: str,
model_type: str = "random_forest"
) -> dict:
"""
Train a machine learning model with proper argument validation.
"""
# Validate DataFrame
if not isinstance(df, pd.DataFrame):
raise TypeError("`df` must be a pandas DataFrame.")
# Validate target column
if target_column not in df.columns:
raise ValueError(f"Target column '{target_column}' not found. "
f"Available columns: {list(df.columns)}")
# Validate model_type
valid_models = ["random_forest", "xgboost", "lightgbm"]
if model_type not in valid_models:
raise ValueError(f"model_type must be one of {valid_models}. Got '{model_type}'.")
# Main logic here...
print(f"Training {model_type} model with target '{target_column}'...")
return {"accuracy": 0.89, "model_type": model_type}
3. Best Practices in 2026
- Always use **type hints** to make expected argument types clear
- Validate critical arguments at the beginning of the function
- Raise specific exceptions (`TypeError`, `ValueError`, `KeyError`) with helpful messages
- Include available options or column names in error messages when relevant
- Use `from None` when re-raising to avoid long traceback chains
- Consider using libraries like `pydantic` for complex argument validation in production pipelines
Conclusion
Passing incorrect arguments is inevitable during development and usage. In 2026, professional data science code anticipates these mistakes and responds with clear, actionable error messages. Good argument validation and informative exceptions save hours of debugging and make your functions much more user-friendly for teammates and future-you.
Next steps:
- Review your important data science functions and add proper argument validation with clear error messages