**π‘ 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.