Test whether similar float type of different sizes are subdtypes of floating class in Python

To test whether similar float types of different sizes are subtypes of the floating class, use the numpy.issubdtype() method in Python NumPy. The method accepts a dtype or object coercible to one as parameters.

Syntax

numpy.issubdtype(arg1, arg2)

Parameters:

• arg1 ? First data type to check
• arg2 ? Second data type to check against

Returns: Boolean value indicating if arg1 is a subtype of arg2

Testing Float Types

First, import the required library ?

import numpy as np

Now, use the issubdtype() method to check if different floating point data types are subtypes of np.floating ?

import numpy as np

# Test different float types against np.floating
print("Testing float16:", np.issubdtype(np.float16, np.floating))
print("Testing float32:", np.issubdtype(np.float32, np.floating))
print("Testing float64:", np.issubdtype(np.float64, np.floating))

Testing float16: True
Testing float32: True
Testing float64: True

Comparison with Other Data Types

Let's compare floating types with integer and complex types to see the difference ?

import numpy as np

# Compare with integer types
print("Testing int32:", np.issubdtype(np.int32, np.floating))
print("Testing int64:", np.issubdtype(np.int64, np.floating))

# Compare with complex types  
print("Testing complex64:", np.issubdtype(np.complex64, np.floating))
print("Testing complex128:", np.issubdtype(np.complex128, np.floating))

Testing int32: False
Testing int64: False
Testing complex64: False
Testing complex128: False

Summary

Conclusion

The numpy.issubdtype() method confirms that all NumPy floating point types (float16, float32, float64) are subtypes of np.floating, while integer and complex types are not. This is useful for type checking in numerical computations.