Working with Modules and Handling Exceptions

Build reusable modules and write Python code that fails gracefully, not silently.

Two skills separate a beginner Python script from production-ready code: organizing logic into modules, and handling errors through proper exception handling. Together, they make your data pipelines reusable and resilient.

Creating Your Own Module

Any Python file can become a module. Save reusable functions in utils.py, then import them elsewhere in your project.

# utils.py
def clean_text(value):
    return value.strip().lower()

# main.py
from utils import clean_text
print(clean_text("  Hello World  "))

Why Exceptions Happen

Errors — missing files, bad data types, division by zero — are inevitable when working with real datasets. Python raises an exception whenever it encounters something it can't process, and unhandled exceptions crash your program.

Try, Except, Else, Finally

try:
    result = 10 / int(user_input)
except ZeroDivisionError:
    print("You cannot divide by zero.")
except ValueError:
    print("Please enter a valid number.")
else:
    print("Result:", result)
finally:
    print("Execution completed.")

Best Practices

• Only wrap the code that can actually fail inside try
• Use finally to close files or database connections
• Raise custom exceptions with raise ValueError("message") when validating data

Mastering this combination means your data scripts keep running — and tell you exactly what went wrong — instead of crashing without explanation.