Error Handling

There are scenarios where you need to include code that might throw errors but cannot avoid it because it is critical to the program. That is where error handling comes to picture.

Error handling in Python involves using try, except, else, and finally blocks to manage exceptions effectively and ensure the program continues to run smoothly. Here's a breakdown of how error handling works:

Basic Syntax

try:
    # Code that may raise an exception
    risky_operation()
except SomeError as e:     
    # Handle the exception
    print(f"An error occurred: {e}")

Common Exception Handling Blocks

• try: The block of code you want to execute.
• except: The block that runs if an exception occurs.
• else: (Optional) Runs if no exceptions are raised.
• finally: (Optional) Always runs, regardless of exceptions.

Example with All Blocks

try:
    num = int(input("Enter a number: "))
    print(10 / num)
except ValueError:
    print("Please enter a valid integer.")
except ZeroDivisionError:
    print("Division by zero is not allowed.")
else:
    print("The operation was successful.")
finally:
    print("Execution complete.")

Catching Multiple Exceptions

You can catch multiple exceptions in one except block:

try:
    file = open("nonexistent.txt", "r")
    content = file.read()
except (FileNotFoundError, IOError) as e:
    print(f"An error occurred: {e}")

Using raise to Re-raise Exceptions

You can raise exceptions deliberately or propagate them after handling:

try:
    raise ValueError("Custom error message")
except ValueError as e:
    print(e)
    raise  # Re-raise the exception

Defining Custom Exceptions

You can define custom exceptions by subclassing the Exception class:

class CustomError(Exception):
    pass

try:
    raise CustomError("This is a custom error.")
except CustomError as e:
    print(e)

Best Practices

• Always catch specific exceptions rather than a generic Exception unless absolutely necessary.
• Use finally for cleanup tasks, such as closing files or releasing resources.
• Log errors for better debugging and monitoring in production systems.