At least you can become familiar with what the most used Python's modules are used for and what they are capable. That way when you start a project you will know that there are alternative/different way to do something and you can do deeper research about it. And eventually, use it. Numpy is fast. Really fast.

I don't know NumPy and I have never used it but because of this topic, I google it just a bit to make an example. I just know that it's fast. So here is how much.

In [1]: import math
In [2]: nums = list(range(2, 100001))
In [3]: def power_it(numbers):
...: return [math.pow(num, 5) for num in numbers]
...:
In [4]: %timeit power_it(nums)
22.6 ms ± 2.65 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [5]: import numpy as np
In [6]: def np_pow(numbers):
...: return np.power(numbers, 5)
...:
In [7]: %timeit np_pow(nums)
4.89 ms ± 14.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [8]: l = np.array(nums)
In [9]: %timeit np_pow(l)
258 µs ± 1.39 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

I have to thank you for that because I'm planning to use it constantly now. I am surpriced

Consider I have opened two browsers Chrome and Firefox with 118 and 499 tabs. ;)