Generators provide lazy evaluation from "summary" of Fluent Python by Luciano Ramalho
Generators provide lazy evaluation, which means that they yield items one at a time using the yield keyword, rather than returning a whole list of items at once. This is a powerful concept because it allows us to work with large sequences of data without having to store them all in memory at once. By using lazy evaluation, we can create generators that only generate the next item in the sequence when it is actually needed. This can be much more memory-efficient than eagerly generating all the items upfront. Lazy evaluation is particularly useful when working with very large datasets that would not fit into memory all at once. Generators can be thought of as a way to generate items on-the-fly as they are requested, rather than generating everything upfront. This can lead to significant performance improvements in certain situations, especially when dealing with large datasets or complex calculations. Lazy evaluation allows us to iterate over a potentially infinite sequence of items without having to worry about running out of memory. Generators can be used to represent infinite sequences, such as the Fibonacci sequence, where each item depends on the previous two items. In Python, generators are a powerful tool for creating iterators. They can be used in a wide variety of situations where lazy evaluation is beneficial, such as processing large files line by line, generating permutations or combinations of items, or implementing algorithms that require backtracking. By using generators and lazy evaluation, we can write more efficient and elegant code that is easier to understand and maintain. This is a key feature of Python that sets it apart from other programming languages and makes it a popular choice for data processing and scientific computing.
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