5 Best Ways to Test If Different Sized Data Types Are Not Subtypes of Each Other in Python

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💡 Problem Formulation: When working with Python, it’s not uncommon to encounter data types of varying sizes, such as int32 versus int64. We sometimes need to assert whether these varying sizes indeed represent distinct types and not subtypes of one another. This article explores various methods to confirm that similar-looking data types with different sizes are actually distinct in Python, ensuring more robust code and type checking.

Method 1: Using type() and Comparison

This method involves getting the type of each variable using the type() function and then comparing the types directly to see if they are the same. This will work for built-in types where different sizes still mean different types.

Here’s an example:

a = 100
b = 10000000000
type_a = type(a)
type_b = type(b)
are_same_subtype = type_a == type_b
print(are_same_subtype)

Output:

True

This code snippet creates two integer variables of potentially different sizes, retrieves their types using the type() function, and compares those types directly. This approach is simple but effective when working with built-in types where size does not imply a subtype relationship.

Method 2: Using the isinstance() Function

The isinstance() function is useful for checking if an object is an instance of a class or a subclass thereof. While similar data types of different sizes might be instances of a base class, they are not necessarily subtypes of each other.

Here’s an example:

import numpy as np

array_int32 = np.array([1], dtype=np.int32)
array_int64 = np.array([1], dtype=np.int64)

is_subtype = isinstance(array_int32, np.int64)

print(is_subtype)

Output:

False

This snippet uses the NumPy library, which includes its own data types, to illustrate checking subtypes with the isinstance() function. The example shows that an int32 array is not a subtype of an int64 even though they are both integers.

Method 3: Using issubclass()

This method relies on the issubclass() function to check if one type is a subclass of another. It’s especially useful when dealing with user-defined classes.

Here’s an example:

class BaseClass:
    pass

class DerivedClass(BaseClass):
    pass

print(issubclass(DerivedClass, BaseClass))
print(issubclass(BaseClass, DerivedClass))

Output:

True
False

In this example, we defined a base class and a derived class. Using issubclass(), we can see that the derived class is a subclass of the base class, but not vice versa. Though this isn’t directly applicable to built-in data types, it demonstrates subtype relationships clearly.

Method 4: Using Custom Comparison Functions

For more fine-tuned type checking, one can write custom comparison functions that can consider size or any other property that indicates distinct types.

Here’s an example:

def are_same_type(obj1, obj2):
    return isinstance(obj1, type(obj2)) and isinstance(obj2, type(obj1))

print(are_same_type(10, 10.0))
print(are_same_type(10, 10))

Output:

False
True

The custom function are_same_type() checks if two objects are instances of each other’s type. This example illustrates that an integer and a float are recognized as different types, despite both representing numbers.

Bonus One-Liner Method 5: Using np.issubdtype() for NumPy Types

For users of NumPy, the library provides a convenient function np.issubdtype() that checks if one data type is a subtype of another within the NumPy type system.

Here’s an example:

import numpy as np

print(np.issubdtype(np.int32, np.integer))
print(np.issubdtype(np.int32, np.int64))

Output:

True
False

This one-liner demonstrates using NumPy’s own subtype checking utility. While np.int32 is a subtype of the general np.integer class, it is not a subtype of np.int64, despite both being integer types.

Summary/Discussion

  • Method 1: Type Comparison. Simple and direct. May not handle complex or custom data types well.
  • Method 2: Using isinstance(). More accurate for subtype checking. Requires a class or type to check against.
  • Method 3: Using issubclass(). Best suited for user-defined classes. Not applicable for built-in types without hierarchy.
  • Method 4: Custom Comparison Functions. Highly adaptable. Requires additional implementation effort.
  • Bonus Method 5: np.issubdtype(). Excellent for NumPy types. Specific to NumPy, not applicable outside this context.