What are some examples comparing Numba and Cython?

Can you give me examples of how Numba and Cython compare in terms of performance?

Sure! Numba is a just-in-time compiler for Python that can speed up numerical functions, while Cython is a static compiler that translates Python code to C for performance. For example, a simple loop that sums numbers can be significantly faster with Numba when using the @jit decorator.

What about more complex examples? How do they handle larger data sets?

For larger data sets, Numba excels with its ability to compile functions on-the-fly, making it great for dynamic data. Cython, on the other hand, requires more upfront work to define types but can yield very efficient C code for static data structures. An example would be using Numba for a large array operation versus Cython for a fixed-size array.

Are there any specific scenarios where one is better than the other?

Yes, if you need rapid development and flexibility, Numba is often better. However, if you need maximum performance and have a stable codebase, Cython might be the way to go.

Can you show me a code snippet for both?

Absolutely! Here's a simple Numba example:

from numba import jit

@jit
def sum_array(arr):
    total = 0
    for i in arr:
        total += i
    return total

And for Cython:

def sum_array(double[:] arr):
    cdef double total = 0
    for i in range(arr.shape[0]):
        total += arr[i]
    return total