Python: Time complexity of min() function inside for loop-CodePudding

I'm calculating time complexities of algorithms and I assumed both code below to have time complexities of O(n^2) However my books says first code is O(n^2) and second one is O(n). I don't understand why. Both are using min/max, so whats the difference? Code 1:

def sum(l, n):

for i in range(1, n - 1):
    x = min(l[0:i])
    y = min(l[i:num])
return x y

Code 2:

def sum(a, n):
r = [0] * n
l = [0] * n
min_el = a[0]

for i in range(n):
    min_el = min(min_el, a[i])
    l[i] = min_el


print(min_el)

CodePudding user response：

In the first block of code the block of code runs the min function over the whole array, which takes O(n) time. Now considering it is in a loop of length n then the total time is O(n^2)

Looking at the 2nd block of code. Note that the min function is only comparing 2 values, which is arguably O(1). Now considering that it is in a loop of length n. The total time is simply the summation of O(n n n), which is equal to O(n)

CodePudding user response：

In the first code, it gives an array to the min() function, and this O(n) time complexity because it checks all elements in the array, in the second code, min() functions only compare two values and it takes O(1)