Python Tutoring Session: Sharpening Your Skills

Alright, let's level up your Python game. Forget the kiddie pool; we're diving into some real coding challenges. I'm going to treat you like a colleague, not a newbie. No hand-holding, just clear explanations and expectations.

First, let's tackle a problem that blends data structures and algorithm optimization:

Challenge: Implement a function that efficiently finds the k-largest elements in a list. Don't use the obvious list.sort() followed by slicing. Let's aim for something better than O(n log n).

Why this matters: Real-world datasets are often massive. Knowing how to extract information efficiently is crucial. Naive approaches will choke on large datasets. We're talking about scalability here.

Your Task: Write a Python function called find_k_largest(nums, k) that takes a list of numbers nums and an integer k as input, and returns a list containing the k largest numbers from nums in descending order. Strive for a time complexity better than O(n log n). Think heaps.

Hints:

The heapq module in Python is your friend. Specifically, heapq.nlargest().

Consider the trade-offs between different approaches in terms of time and space complexity.

Error handling is crucial. What happens if k is larger than the list length? What if the list is empty? Handle these edge cases gracefully.

Example Usage:

python

nums = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]

k = 3

largest = find_k_largest(nums, k)

print(largest) Expected Output: [9, 6, 5] (or [9, 6, 5]) - order doesn't strictly matter

I expect clean, well-documented code. Explain your reasoning in comments. Show me you understand why your solution works. Let's see what you've got. I'll provide feedback once you've given it a shot. Good luck!