In python, whats the most efficient way to apply function to a list

my actual data is huge and quite heavy. But if I simplify and say if I have a list of numbers x = [1,3,45,45,56,545,67]

and I have a function that performs some action on these numbers?

def sumnum(x):
    return(x=np.sqrt(x)+1)

whats the best way apply this function to the list? I dont want to apply for loop. Would 'map' be the best option or anything faster/ efficient than that?

thanks, Prasad

In standard Python, the map function is probably the easiest way to apply a function to an array (not sure about efficiency though). However, if your array is huge, as you mentioned, you may want to look into using numpy.vectorize, which is very similar to Python's built-in map function.

Edit: A possible code sample:

vsumnum = np.vectorize(sumnum)
x = vsumnum(x)

The first function call returns a function which is vectorized, meaning that numpy has prepared it to be mapped to your array, and the second function call actually applies the function to your array and returns the resulting array. Taken from the docs, this method is provided for convenience, not efficiency and is basically the same as a for loop

Edit 2: As @Ch3steR mentioned, numpy also allows for elementwise operations to arrays, so in this case because you are doing simple operations, you can just do np.sqrt(x) + 1, which will add 1 to the square root of each element. Functions like map and numpy.vectorize are better for when you have more complicated operations to apply to an array