Can you explain the concept of time and space complexity of algorithms using Big O notation?

Big O notation is used to evaluate the efficiency of algorithms in terms of time and memory.

• Time complexity describes how the number of operations (execution time) grows with the increase in input size.
• Space complexity shows how much memory an algorithm requires depending on the size of the input.

For example, if an algorithm has a time complexity of O(n), it means that the execution time grows linearly with the input size n. If it is O(n²), the time grows quadratically.

Example:

# Time complexity O(n)
def sum_list(lst):
    total = 0
    for item in lst:
        total += item
    return total

Here, the execution time is proportional to the length of the list.

Big O helps compare algorithms and choose more efficient solutions.