How is the time and space efficiency of algorithms determined?

The temporal efficiency of an algorithm is measured by the number of operations or steps it performs, usually depending on the size of the input data. This is often expressed in terms of asymptotic complexity, such as O(n), O(log n), O(n²), where n is the size of the input.

Space efficiency reflects the amount of memory the algorithm uses during execution, also depending on the size of the input data.

For example, if you have a sorting algorithm for a list of n elements, the time complexity might be O(n log n), and the space complexity could be O(n) or O(1), depending on the implementation.

In Python, you can estimate temporal efficiency using the timeit module, and space efficiency using sys.getsizeof or specialized profilers.