Understanding the TypeError: bad operand type for unary ~ with ‘type’

Resolving a Common Python Error

In this guide, we’ll delve into a common issue encountered while coding in Python – the TypeError involving a “bad operand type for unary ~: ‘type'”. We will decipher the meaning behind this error and explore effective solutions to resolve it.

What You Will Learn

By the end of this guide, you will gain insights into why this error occurs and learn multiple approaches to rectify it. This knowledge will empower you to debug similar issues efficiently in your future Python projects.

Introduction to the Problem and Solution

When programming in Python, understanding various types of errors is essential for effective debugging. The TypeError message “bad operand type for unary ~: ‘type'” can be puzzling initially. This error typically arises when trying to apply the bitwise NOT operator (~) on an object or variable that does not support such operations.

To tackle this issue, it’s crucial to ensure that the ~ operator is used on objects or variables of compatible types, usually integers or objects with custom methods defined for handling bitwise NOT operations. Let’s explore examples and solutions to gain a clearer understanding of addressing this problem.


Here’s an example demonstrating where this error might occur:

class Sample:

instance = Sample()

# Copyright PHD

And here’s how we can resolve it:

class Sample:
    def __invert__(self):
        # Define behavior for the bitwise NOT operation
        return "Bitwise NOT applied"

instance = Sample()
print(~instance)  # Output: Bitwise NOT applied

# Copyright PHD


The root cause of the TypeError is attempting a bitwise operation on an unsupported operand type. Bitwise operations like ~, |, &, etc., are conventionally used with integers in Python. When these operations are applied to other data types without explicit instructions on how to handle them, Python raises an error.

Our solution involves implementing special methods (like __invert__) in our classes to specify how these operations should be executed on instances of our custom classes. By defining these methods, Python gains clarity on how to perform bitwise operations, enabling successful execution without errors.

    1. How do I fix “TypeError: bad operand type”? Ensure operands are compatible with the operation being performed; define special methods if necessary.

    2. Can I override other operators like + or *? Yes! By defining methods like __add__ and __mul__, respectively.

    3. What data types support bitwise operations by default? Primarily integers and booleans.

    4. Is there any performance impact when overriding these methods? Potentially yes, as custom logic adds overhead compared to native implementations.

    5. Can I use bitwise operators on floating-point numbers? No! Bitwise operators work only on integer operands directly.

    6. What happens if I don’t return anything from my overridden method? Python will raise another error indicating that None was returned while something else was expected.

    7. Can lists or dictionaries be manipulated using bitwise operators? Not directly; they would require custom implementation as well.

    8. Are there any built-in functions that help avoid these errors? Not specifically; understanding your data types and correctly implementing special methods is key.

    9. Does every class need its own definitions for these operators? Only if you intend to use those specific operators with instances of those classes.

    10. How does Python determine which method corresponds with each operator?
      Through special method names called dunder (double underscore) methods defined within class implementations.


Encountering TypeErrors related to operands may seem perplexing initially but serves as a stepping stone towards mastering better coding practices in Python. By incorporating special methods within our classes effectively, we can seamlessly extend functionality across various operations including bitwise manipulations, showcasing the flexibility and power ingrained in Python’s design philosophy.

Leave a Comment