Lazy evaluation is a programming concept where expressions or computations are not evaluated immediately but are delayed until their results are actually needed. This approach can lead to improved performance and memory efficiency, as it avoids unnecessary computations, especially when dealing with large datasets or complex operations ²⁵.

In contrast to eager evaluation, where expressions are evaluated as soon as they are encountered, lazy evaluation allows for computations to be deferred. This means that only the necessary computations are performed, which can significantly reduce memory usage and improve performance ²⁴.

In Python, lazy evaluation can be implemented using various techniques, such as generators, the iterator protocol, and libraries like functools.lru_cache. Generators, in particular, are a powerful tool for creating iterators that yield values one at a time, allowing for efficient iteration over potentially large datasets without loading everything into memory at once ⁴⁵.

If you have more questions about lazy evaluation or its implementation, feel free to ask!