Severus Snape

*The professor's grip tightens even more, as he leans in closer. His voice is low and menacing, as he speaks through clenched teeth.* "You will regret the day you ever crossed me. I will make sure of it."

*The professor smiles cruelly, his eyes gleaming with satisfaction as he sees your fear. He pulls you even closer, his face only inches from yours.* "Good. You should be afraid. I'm going to make you suffer for what you've done."

To solve this problem, we need to create a function called `list_stats` that computes the sum, maximum, minimum, and average of a list of numbers without using any built-in functions except `len()`. The solution must be efficient and handle all valid inputs as specified. ### Approach 1. **Initialize Variables**: Start by initializing the sum, maximum, and minimum values. The sum is initialized to 0, while both maximum and minimum are initialized to the first element of the list. 2. **Iterate Through the List**: Loop through each number in the list to accumulate the sum, check and update the maximum and minimum values. 3. **Compute the Average**: After computing the sum, divide it by the length of the list to get the average. 4. **Return Results**: Return the computed values as a tuple in the specified order (sum, maximum, minimum, average). ### Solution Code ```python def list_stats(numbers): sum_total = 0 max_val = numbers[0] min_val = numbers[0] for num in numbers: sum_total += num if num > max_val: max_val = num if num < min_val: min_val = num average = sum_total / len(numbers) return (sum_total, max_val, min_val, average) ``` ### Explanation - **Sum Calculation**: The sum is calculated by iterating through each element in the list and accumulating the total in `sum_total`. - **Max and Min Values**: As we iterate through the list, we compare each element with the current maximum and minimum values. If a larger or smaller value is found, we update the respective variables. - **Average Calculation**: The average is obtained by dividing the total sum by the number of elements in the list using `len()`, which is allowed. - **Efficiency**: The algorithm runs in O(n) time complexity, where n is the number of elements in the list, making it efficient for large lists as well. This approach ensures that we meet the problem constraints and efficiently compute the required statistics using basic iteration and arithmetic operations.