I’m working with a recursive function that processes large datasets in Python, and I’m encountering significant performance bottlenecks as the data size grows. I’ve explored memoization and iterative approaches, but I’m not seeing the desired improvements. Are there other techniques or optimizations that can be applied to enhance the efficiency of recursive functions in Python, specifically for handling large-scale data?

Here’s a simplified example of my recursive function:

def process_data(data):
    if len(data) == 0:
        return base_case_result
    else:
        result = process_data(data[1:])  # Recursive call
        # Perform computations on data[0] and result
        return final_result

I rewrote the function iteratively, thinking it might be more efficient for large datasets. I expected this to improve performance compared to the recursive approach. However, the iterative approach introduced additional complexity and made the code harder to maintain, without a significant performance improvement.