Of course! The phrase python assertisnone is a common misspelling or a search query for the correct way to assert that a value is None in Python.

The correct and standard way to do this is by using the is operator with the assert statement.

The Correct Way: assert value is None

The most Pythonic and recommended way to check if a variable is None is to use the is operator.

Why is and not ?

• is checks for identity. It asks, "Are these two variables pointing to the exact same object in memory?". Since None is a singleton object in Python (there is only ever one None), is is the most direct and efficient way to check for it.
• checks for equality. It asks, "Do these two objects have the same value?". While None == None is True, using is is more explicit about your intent and is the standard convention.

Examples

Let's look at some practical examples.

Basic Assertion

This is the simplest case. If my_variable is None, the assertion passes. If it's anything else, the program will stop and raise an AssertionError.

def process_data(data):
    # Some processing might result in no data
    if not data:
        return None
    # ... process data ...
    return "processed_data"
# --- Test Case 1: Assertion should PASS ---
result = process_data([])
print(f"Test 1: Result is {result}")
assert result is None, f"Expected None, but got {result}"
print("Test 1: Assertion passed successfully!\n")
# --- Test Case 2: Assertion should FAIL ---
result = process_data([1, 2, 3])
print(f"Test 2: Result is {result}")
try:
    # This will raise an AssertionError because result is a string
    assert result is None, f"Expected None, but got {result}"
except AssertionError as e:
    print(f"Test 2: Caught expected AssertionError: {e}")

Output:

Test 1: Result is None
Test 1: Assertion passed successfully!
Test 2: Result is processed_data
Test 2: Caught expected AssertionError: Expected None, but got processed_data

Asserting that a Value is NOT None

If you want to check that a variable is not None, you can use the is not operator.

def get_user(user_id):
    # In a real app, you'd fetch from a database
    if user_id == 123:
        return {"id": 123, "name": "Alice"}
    return None
# --- Test Case 1: Asserting it is NOT None (should PASS) ---
user = get_user(123)
print(f"User data: {user}")
assert user is not None, "Failed to retrieve user data. User was None."
print("Test 1: Assertion passed successfully!\n")
# --- Test Case 2: Asserting it is NOT None (should FAIL) ---
user = get_user(999) # This user doesn't exist
print(f"User data: {user}")
try:
    assert user is not None, "Failed to retrieve user data. User was None."
except AssertionError as e:
    print(f"Test 2: Caught expected AssertionError: {e}")

Output:

User data: {'id': 123, 'name': 'Alice'}
Test 1: Assertion passed successfully!
User data: None
Test 2: Caught expected AssertionError: Failed to retrieve user data. User was None.

Advanced: Custom Assertion Messages

The assert statement can take an optional second argument, which is a message to display if the assertion fails. This is extremely useful for debugging.

def find_item(items, item_id):
    # Simulates a search that might fail
    for item in items:
        if item['id'] == item_id:
            return item
    return None
inventory = [
    {"id": 1, "name": "Apple"},
    {"id": 2, "name": "Banana"},
]
# --- This will fail, and the custom message will be shown ---
try:
    item = find_item(inventory, 3)
    # The custom message makes it much clearer what went wrong
    assert item is not None, f"Could not find an item with ID 3. Inventory: {inventory}"
except AssertionError as e:
    print(f"Error: {e}")

Output:

Error: Could not find an item with ID 3. Inventory: [{'id': 1, 'name': 'Apple'}, {'id': 2, 'name': 'Banana'}]

Important Note: assert in Production Code

It's crucial to understand that assert statements can be disabled. When you run Python with the -O (optimize) flag (python -O your_script.py), all assert statements are ignored.

Therefore, assert should never be used for validating data that comes from an external source (like user input or API calls). For that, you should use proper error handling with try...except blocks or raise a specific exception like ValueError or TypeError.

Bad Practice (for external data):

def set_user_age(age):
    # This can be bypassed with python -O
    assert isinstance(age, int), "Age must be an integer"
    assert age > 0, "Age must be positive"
    # ...

Good Practice (for external data):

def set_user_age(age):
    if not isinstance(age, int):
        raise TypeError("Age must be an integer")
    if age <= 0:
        raise ValueError("Age must be positive")
    # ...

Summary