Time Complexity

Time Complexity is computational complexity that describes the amount of computer time it takes to run an algorithm

Here are some examples of time complexity

nums = [1, 2, 3, 4, 5]
for n in nums:
    print(n)

Answer

O(N)
The code runtime depends on length of nums. As the nums gets bigger, the run time only gets bigger in linear time O(N)

nums = [1, 2, 3, 4, 5]

for n1 in nums:
    for n2 in nums:
        print(n1, n2)

Answer

O(N^2)
The code runtime depends on length of nums. As the nums gets bigger, the run time gets bigger by N^2 because of the nested loop

Averaged is often used when the case is random(unpredictable).
Amortized is often used when the case is predictable but is too good to just diminish all the runtime performance just because of some slow cases(predictable).
From: Amortized vs Averaged runtime https://gist.github.com/jconnolly/5acf05f279a7e9e40371

Basics