Python Random Sampling Implementation

This challenge asks you to implement a function that performs random sampling from a given list. Random sampling is a fundamental technique used in various fields, including statistics, machine learning, and data analysis, to select a subset of data points without bias.

Problem Description

Your task is to create a Python function called random_sample that takes two arguments:

1. data_list: A list of elements from which to sample.
2. sample_size: An integer representing the number of elements to randomly select from data_list.

The function should return a new list containing sample_size elements randomly chosen from data_list. The sampling should be done without replacement, meaning an element cannot be selected more than once.

Key Requirements:

• The function must return a new list; it should not modify the original data_list.
• The order of elements in the returned sample does not matter.
• If sample_size is greater than the length of data_list, the function should return a shuffled version of the entire data_list.
• If sample_size is zero or negative, an empty list should be returned.

Expected Behavior:

The output should be a list of distinct elements from the input data_list, with the number of elements equal to sample_size (or the length of data_list if sample_size exceeds it).

Edge Cases to Consider:

• data_list is empty.
• sample_size is 0 or negative.
• sample_size is equal to the length of data_list.
• sample_size is greater than the length of data_list.

Examples

Example 1:

Input:
data_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
sample_size = 3

Output:
[7, 2, 5]  # (or any other combination of 3 unique elements)

Explanation:
We are asked to select 3 random elements from the list [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] without replacement. The output shows a valid random sample.

Example 2:

Input:
data_list = ['apple', 'banana', 'cherry', 'date']
sample_size = 5

Output:
['cherry', 'apple', 'date', 'banana'] # (or any other shuffled permutation)

Explanation:
Since sample_size (5) is greater than the length of data_list (4), the function should return a shuffled version of the entire list.

Example 3:

Input:
data_list = [100, 200, 300]
sample_size = 0

Output:
[]

Explanation:
When sample_size is 0, an empty list is returned.

Constraints

• data_list can contain any type of Python objects (integers, strings, etc.).
• The length of data_list can range from 0 to 1000.
• sample_size can range from -5 to 1000.
• The implementation should aim for reasonable efficiency, especially for larger lists. A naive approach of repeatedly picking elements one by one and checking for duplicates might be too slow if not optimized.

Notes

This challenge is designed to test your understanding of list manipulation and random number generation in Python. While Python's standard random module has built-in functions for sampling, this exercise encourages you to think about how such a function might be implemented from more basic principles (though you are free to use random module functions in your solution). Consider how you can ensure randomness and avoid duplicates efficiently.