How can I speed up my NumPy code using Numba?

How can I speed up my NumPy code using Numba?

You can use Numba to compile your NumPy functions to machine code, which can significantly speed up execution. Just decorate your function with @jit.

Can you show me a simple example of using Numba with NumPy?

Sure! Here's a basic example:

import numpy as np
from numba import jit

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

arr = np.random.rand(1000000)
print(sum_array(arr))

What are the limitations of using Numba?

Numba works best with NumPy arrays and functions that can be compiled. It may not support all Python features, like complex data types or certain libraries.

Is there a way to profile the performance of my code before and after using Numba?

Yes, you can use the time module or the timeit library to measure execution time before and after applying Numba. This will help you see the performance improvements.