What are the differences between NumPy ufuncs and Python functions?

What are the differences between NumPy ufuncs and Python functions?

NumPy ufuncs (universal functions) are optimized for performance and can operate on arrays element-wise, while Python functions are more general and can handle any data type but are slower for large datasets.

Can you give an example of a NumPy ufunc?

Sure! An example of a NumPy ufunc is np.add(), which adds two arrays element-wise. For instance, np.add([1, 2], [3, 4]) results in [4, 6].

Why should I use ufuncs instead of regular Python functions?

Ufuncs are faster and more efficient for large datasets because they are implemented in C and optimized for performance, reducing the overhead of Python's function calls.

Are there any limitations to using ufuncs?

Yes, ufuncs are primarily designed for numerical operations and may not support all data types or complex operations that regular Python functions can handle.