The assert statement in Python is a debugging tool that tests whether a condition in your code is True. If the condition is False, Python raises an AssertionError exception and stops execution. While useful for development and testing, assert statements require careful consideration regarding their proper use cases.

The Basic Syntax

The official syntax for the assert statement is:

assert_stmt ::= "assert" expression ["," expression]

This means you can write either:

• assert condition - Raises AssertionError if the condition is False
• assert condition, "error message" - Raises AssertionError with a custom message

Simple Example: Sum Calculator

Let's examine a practical example with a sum calculator function that validates its output:

def sum_calc(*args):
    assert 10 <= sum(args) < 20
    return sum(args)

result = sum_calc(1, 2, 3, 10)
print(result)  # Output: 16

The assert statement checks that the sum falls within the range of 10 to 20 (inclusive of 10, exclusive of 20).

When the Assert Fails

If we exceed the boundaries:

result = sum_calc(1, 2, 3, 10, 5)  # Sum = 21

Python throws:

Traceback (most recent call last):
  File "assert_1.py", line 18, in <module>
    result = sum_calc(1, 2, 3, 10, 5)
  File "assert_1.py", line 15, in sum_calc
    assert 10 <= sum(args) < 20
AssertionError

Similarly, if the sum is too low: 

result = sum_calc(1, 2, 3)  # Sum = 6

You'll get the same error message.

What Happens Behind the Scenes

Assert statements are equivalent to this conditional code: 

def sum_calc(*args):
    if __debug__:
        if not (10 <= sum(args) < 20):
            raise AssertionError("Error - Beyond the boundaries")
    return sum(args)

The __debug__ flag is True by default. You can check its value in any Python interpreter:

print(__debug__)  # Output: True

Disabling Assertions

You can disable assertions entirely by running Python with the -O (optimize) flag:

python -O your_script.py

After this, checking __debug__ returns:

print(__debug__)  # Output: False

Custom Error Messages

Adding meaningful error messages helps with debugging:

print(__debug__)  # Output: False

When the assertion fails, you'll see:

AssertionError: Error - Beyond the boundaries

Best Practices and Recommendations

✅ When to Use Assert

• During development to catch bugs early
• For testing internal invariants that should never be False
• When debugging complex logic
• In unit tests (though testing frameworks like pytest are preferred)

❌ When to Avoid Assert

• For user input validation
• For production data validation
• For handling business logic errors
• For conditions that might naturally occur during runtime

Instead of assertions, use explicit error handling:

# Bad: Using assert for data validation
def process_age(age):
    assert age >= 0, "Age cannot be negative"
    return age * 2

# Good: Using proper error handling
def process_age(age):
    if age < 0:
        raise ValueError("Age cannot be negative")
    return age * 2

Final Takeaways

1. Assert statements are debugging tools intended for developers, not end-users
2. Never use assertions for data validation or business logic
3. Assertions can be disabled, making them unreliable for production
4. Use testing frameworks like pytest for comprehensive testing
5. Keep custom error messages informative but concise

Understanding when and how to use assertions properly will improve your code quality while avoiding common pitfalls that can lead to unexpected behavior in production environments.