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

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

Why Incorrect Arguments Cause Errors

When a function receives the wrong number of arguments, Python raises a TypeError before the body runs. When it receives the right number but an unsuitable value, such as a string where a number is expected, the error usually appears later, deep inside the function, with a message that is harder to understand.

Good data functions check their important inputs at the start and raise a clear error that names the parameter and the problem. This turns a confusing traceback into a message the caller can fix in seconds.

Type hints document what is expected, but Python does not enforce them at run time. They help editors and linters catch mistakes early, while explicit checks protect the function when it is called from notebooks, scripts or other people's code.

Worked Example: Turning Confusing Errors into Clear Ones

We compare an unchecked function with a checked version when the same bad inputs are passed.

def average_unchecked(values):
    return sum(values) / len(values)

def average(values: list[float]) -> float:
    if not isinstance(values, (list, tuple)):
        raise TypeError(f"values must be a list or tuple, got {type(values).__name__}")
    if not values:
        raise ValueError("values must not be empty")
    return sum(values) / len(values)

for bad in ("123", []):
    for func in (average_unchecked, average):
        try:
            func(bad)
        except (TypeError, ValueError, ZeroDivisionError) as err:
            print(f"{func.__name__}({bad!r}): {type(err).__name__}: {err}")

try:
    average([1, 2], [3])
except TypeError as err:
    print("wrong number of arguments:", err)

The unchecked function fails with messages about adding an int and a str or dividing by zero, which say nothing about the real cause. The checked version explains exactly what was wrong with the input, and Python itself reports the call with too many arguments.

Common Mistakes

• Letting errors surface deep inside the function with unclear messages.
• Catching every exception and hiding bad input.
• Relying on type hints as if Python enforced them.
• Writing error messages that do not name the parameter.

Quick Summary

• Python raises TypeError for a wrong number of arguments.
• Validate key inputs at the top of the function.
• Raise TypeError for wrong types and ValueError for bad values.
• Make error messages name the parameter and the problem.

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

Related Tutorials

• Passing Valid Arguments to Functions
• Passing Invalid Arguments to Functions – Robust Error Handling in Data Science 2026