Typeerror not supported between instances of nonetype and int

Are you stuck with this “typeerror not supported between instances of nonetype and int”?

In this article, we’ll show you how to resolve this “not supported between instances of ‘nonetype’ and ‘int’” type error.

Keep reading, as you will get the solution and have a better understanding of this typeerror.

What is “typeerror not supported between instances of nonetype and int”?

The error message “typeerror: not supported between instances of nonetype and int” usually occurs when you are trying to perform an operation between a NoneType variable and an integer.

NoneType is a built-in data type in Python that represents the absence of a value or a null value.

So that’s why, when you try to perform an operation between a variable of the NoneType and an integer.

Python raises a TypeError because it does not support operations between these two data types.

It is not possible to perform mathematical operations between None and an integer.

Why does this “not supported between instances of nonetype and int” occur?

This error occur due to several reason such as:

→ If you try to perform an operation on a variable that has not been initialized or has been initialized to None.

→ If you are trying to perform an operation on the return value of a function that returns None.

→ If you pass a None value as an argument to a function that expects a different data type, such as an integer.

→ If you accidentally assign None to a variable that is supposed to hold an integer value, and then try to perform an operation on it.

How to fix “typeerror not supported between instances of nonetype and int”?

After that, since we already know the causes of the “typeerror: ” not supported between instances of ‘nonetype’ and ‘int’” error, let’s explore the different ways to fix it.

Solution 1: Initializing the variable

Ensure that the variable is initialized with an integer value before it is used in an operation.

x = 15
y = x + 5
print(y)

Output:

20

Solution 2: Checking for None value

You have to check for None before performing the operation.

You can add a conditional statement to check if the variable is None before performing the operation.

x = None
if x is not None:
    y = x + 1
    print(y)
else:
    print("x is None")

Output:

x is None

Solution 3: Checking function return value

If the variable is assigned a value returned by a function, you have to ensure that the function returns an integer value and not None.

def my_function():
    return 15

x = my_function()
y = x + 5
print(y)

Output:

20

Solution 4: Converting None to integer

Check if x is None and assign 0 to x if it is. then convert x to an integer using the int() function before performing the addition operation with y.

This avoids the error since x now has an appropriate data type for the operation.

x = None
y = 15

if x is None:
    x = 0

z = int(x) + y
print(z)

Output:

15

Solution 5: Use a try/except block

Handle the error with a try-except block. You can use a try-except block to catch the TypeError and handle it appropriately.

x = None
try:
    y = x + 1
    print(y)
except TypeError:
    print("TypeError: x is None")

Output:

TypeError: x is None

Conclusion

That’s the end of our discussion for today about the “typeerror not supported between instances of nonetype and int” error message.

This article already provides different solutions that you may use to fix this “not supported between instances of nonetype and int” type error.

We are hoping that this article provided you with sufficient solutions to get rid of the error.

You could also check out other “typeerror” articles that may help you in the future if you encounter them.

• Typeerror object of type series is not json serializable
• Typeerror numpy.ndarray object is not callable
• Typeerror fit missing 1 required positional argument y

Thank you very much for reading to the end of this article.

Why NoneType TypeErrors happen so often

Python returns None from many functions when there is nothing meaningful to return: from methods that mutate in place (list.append, dict.update), from lookups that miss (dict.get with no default), and from any function that ends without an explicit return. When you use that None as if it were the expected value, you get a TypeError.

Common triggers of NoneType TypeError

• Method chaining on mutating operations. sorted(list) returns a new sorted list, but list.sort() sorts in place and returns None. Never write x = my_list.sort().
• Print statements accidentally assigned. x = print(value) assigns None, not value.
• Missing return in a function. A function that only has if branches without returns falls through to an implicit return None.
• Dictionary lookups with missing key. value = my_dict.get(key) returns None if key is missing. Provide a default: my_dict.get(key, default_value).
• Regex match returning None. re.search returns None if no match. Guard with an if before .group().

Working diagnostic pattern

# BAD — silent None propagation
def get_config(env):
    if env == "prod":
        return {"db": "prod-cluster"}
    # No else — implicit return None

config = get_config("staging")
print(config["db"])  # TypeError: 'NoneType' object is not subscriptable

# GOOD — explicit branch handling
def get_config(env):
    if env == "prod":
        return {"db": "prod-cluster"}
    if env == "staging":
        return {"db": "staging-cluster"}
    raise ValueError(f"Unknown env: {env}")

Best practices

• Use type hints (Python 3.6+). Type-checkers like mypy or Pyright catch these before runtime.
• Prefer Optional[T] when a function may return None. Callers must handle the None case.
• Fail fast at boundaries. Raise an explicit exception in helper functions rather than returning None silently.

Caren Bautista

Technical Writer at PIES IT Solution

Responsible for crafting clear, well-structured, and beginner-friendly content across the platform. Handles the writing, proofreading, and editorial review of tutorials, guides, and documentation to ensure every article is accurate, readable, and easy to follow.

Expertise: Technical Writing · Content Creation · Documentation · Editorial Writing · JavaScript · TypeScript · Python · Python Errors · HTTP Errors · MS Excel
 · View all posts by Caren Bautista →