Generator Expressions: A Memory-Efficient Alternative to List Comprehensions in Python

This paper addresses the issue of high memory usage when list comprehensions handle large datasets, introducing the solution of Python generator expressions. Generator expressions are created using parentheses `()`, with syntax similar to list comprehensions but employing lazy evaluation (deferred computation). **They do not generate all results at once; instead, they produce elements one by one as needed**, significantly saving memory. Generator expressions are generator objects, which can be iterated through with a `for` loop or manually accessed using the `next()` function. Importantly, they can only be iterated once (becoming empty after use). Compared to list comprehensions (which store all elements at once, requiring substantial memory), generator expressions have extremely low memory footprints, retaining only the current element being processed. Applicable scenarios include: processing large datasets (e.g., log statistics), requiring only a single iteration result (e.g., calculating the sum of even numbers), and simulating infinite sequences (e.g., the Fibonacci sequence). In summary, generator expressions are an efficient tool for optimizing memory. By using lazy evaluation to avoid excessive data storage, they are suitable for large data processing or single-iteration needs. It is recommended to replace list comprehensions with generator expressions as needed.