Python supports lists, so why would you use numpy?

Technically they're both Python lists, but creating them may be different in times of performance. Let's put it to the test.

First we test how long it takes to add a lot of numbers to a list c[] and measure that.

Because we use Python lists we don't need any modules other than the time module.

import time
start = time.clock()
a = range(10000000) 
b = range(10000000)
c = []
for i in range(len(a)):
    c.append(a[i] + b[i])
elapsed = (time.clock() - start)
print("List Time used:",elapsed)

List Time used: 4.712518

Then we measure how long that takes with numpy.

The module numpy should be installed on your computer or virtual environment.

You can install it with pip if you don't have it installed.

import time
start = time.clock()
import numpy as np
a = np.arange(10000000)
b = np.arange(10000000)
c = a + b
elapsed = (time.clock() - start)
print("Time used:",elapsed)

Time used: 0.693733

These measurements are on my pc, so on your pc they may be different. So it can be a lot faster to use numpy when creating lists.

In this case it was almost 10 times faster, so if you want to increase performance it may be as simple as switching to numpy.