Using memoization in Python

The video tutorial explains memoization in Python, focusing on the functools LRU cache. It describes how memoization can optimize function calls by caching return values for predictable inputs. The tutorial provides an example of a deterministic function and demonstrates the use of LRU cache to reduce function call overhead. It also covers cache management techniques, including cache info and cache clear methods, and introduces the Cache variant in Python 3.9, which offers unbounded caching.

What is the primary benefit of using memoization in Python?

• It reduces the size of the code.

• It allows for parallel processing.

• It speeds up program execution by caching results.

• It makes the code more readable.

What does the LRU in LRU cache stand for?

• Latest Reused Utility

• Least Recently Used

• Last Recently Updated

• Longest Running Unit

How does the LRU cache improve function performance?

• By changing the function's return type.

• By increasing the memory usage.

• By rewriting the function code.

• By reducing the number of function calls.

Why must function arguments be hashable when using LRU cache?

• Because they are stored in a set.

• Because they are stored in a dictionary.

• Because they are stored in a list.

• Because they are stored in a tuple.

What is a key difference between LRU cache and the new Cache introduced in Python 3.9?

• Cache is only available in Python 2.

• Cache requires more memory than LRU cache.

• Cache is unbounded and does not expire entries.

• Cache is slower than LRU cache.