👀 Views: 0 💬 Answers: 1 📅 Created: 2025-06-14

python sorting data-types performance Python

I've searched everywhere and can't find a clear answer. I've been struggling with this for a few days now and could really use some help... I'm testing a new approach and I've been researching this but I tried several approaches but none seem to work. I'm trying to sort a list of numbers that are represented in different formats, specifically as strings and floats. The list contains elements like '10', '2.5', 3, and '20.1', but when I attempt to sort them, I'm getting unexpected results due to the mixed types. Here's the code I've written: ```python mixed_numbers = ['10', 2.5, 3, '20.1'] mixed_numbers.sort() print(mixed_numbers) ``` When I run this, I see that the output is `['10', '20.1', 2.5, 3]`, which is not what I expected. The strings are sorted lexicographically rather than numerically, causing '10' to come before 2.5 and 3. I need to sort this list in a way that treats all elements as numbers. I've tried using a custom sorting key: ```python mixed_numbers.sort(key=lambda x: float(x)) ``` This works, but I'm concerned about performance since the list could become quite large. Is there a more efficient way to handle sorting like this, especially if I need to maintain the original types of elements or handle potential exceptions? What would be the best practice for sorting such mixed data types in a scalable manner? I'm using Python 3.9 and would appreciate any insights or alternative approaches. My development environment is CentOS. I'm coming from a different tech stack and learning Python. I'm open to any suggestions. I recently upgraded to Python latest. Thanks for any help you can provide! What's the best practice here?