Manipulating arrays with NumPy – computation, aggregations, comparisons

As we said, NumPy is all about manipulating large arrays with great performance and controlled memory consumption. Let's say, for example, that we want to compute the double of each element in a large array. In the following example, you can see an implementation of such a function with a standard Python loop:

chapter11_compare_operations.py

import numpy as np
np.random.seed(0)  # Set the random seed to make examples reproducible
m = np.random.randint(10, size=1000000)  # An array with a million of elements
def standard_double(array):
    output = np.empty(array.size)
    for i in range(array.size):
        output[i] = array[i] * 2
    return output

https://github.com/PacktPublishing/Building-Data-Science-Applications-with-FastAPI/blob/main/chapter11/chapter11_compare_operations...