That’s true! But sometimes, when you’re working with more complex data types, especially in scientific computing, type()
won’t give you that granular level of detail. I often use NumPy for that. For example, checking for a specific numeric type like np.uint32
:
import numpy as np
a = np.uint32(5)
print(type(a)) # Output: <class 'numpy.uint32'>
This is especially useful if you’re dealing with binary data or need to ensure specific data types for performance reasons in data-heavy applications. Python’s native types don’t always cover that, but libraries like NumPy give you that flexibility