BigO notation can help you understand how long an algorithm will take to process, and also its' memory requirements.
def do_something():
print("Hey there!")
def do_something(array: List):
for element in array:
print(element)
O(log n)  LOGARITHMIC TIME**: at each iteration, you exponentially reduce the number of elements to be processed (so it takes less than “n”). E.g.: binary search on an ordered array of numbers.
O(n log n): Combines Linear time with Logarithmic time. It happens when you must iterate on all elements, but you will not do an action for each one of them.
O(n²)  QUADRATIC TIME: It happens when you have 2 arrays, and you must do a linear time operation on each combination of both arrays. E.g.:
def find_intersection(fruits_i_like: List, fruits_i_have: List):
can_eat = []
for liked in fruits_i_like:
for have in fruits_i_have:
if have == like:
can_eat.append(have)
return can_eat
Notice that you have a for inside a for  that is why it is called quadratic time (n²). If you have more than 2 loops, it may also me named as “POLINOMIAL TIME”.
O(2ⁿ)  EXPONENTIAL TIME: You have an algorithm that the execution time doubles for each item added on the array. This is a “brute force” algorithm. E.g. getting all subsets from a set, getting all possible combinations of “4 digit pins” on a smartphone lock screen  in that case, 10⁴.
O(n!)  FACTORIAL TIME: The time grows like a mathematical factorial each time you add a new element on the array. E.g.
def factorial_algorithm(number):
for iteration in range(number):
print(iteration)
factorial(number1)
O(1)  \o/ winner!
O(log n)
O(n)
O(n log n)  border line of the acceptable
O(n²)  here things start to get ugly o.O
O(2ⁿ)
O(n!)  :'( noooooo
IMPORTANT: below we always take into account the worst scenario.
n: the number of elements on an array
definitions, variable returns: O(1)
for loops: O(n)
The cost formula is:
* T = an + b
 > coeficient: pretty fast to run (insignificant time)

> total elements on the array

time spent on one iteration (it is so insignificant that can be taken out of the total time)
T = n