Difference between generator and normal function
The generator function in Python is normally defined like a normal function, using the @’ keyword. If we need to generate value Generator functions make use of the yield keyword rather than using return. The major difference between the generator and normal function may be that the Generator function runs when the next() function is called and not by its name as in the case of normal functions.
| Scope | Generator | Normal |
|---|---|---|
| Execution | They can be paused in the middle of execution and resumed | They runs to completion and returns a value |
| Return value | They can return multiple values through multiple iterations. | They returns a single value (or none) |
| Memory usage | They keeps the current value in memory, | They create a large amount of memory overhead |
| Usage | They are used generate values that can be iterated over. | They are used when to perform a task and return a result. |
Difference between Generator and Normal Function
Normal functions in Python are used for traditional computation tasks, with execution proceeding from start to finish, typically returning a single result. On the other hand, generator functions employ the `yield` statement to produce values lazily, preserving their state across multiple calls. This allows generators to efficiently handle large datasets or infinite sequences by yielding values one at a time and pausing execution when necessary, making them a valuable tool for memory-efficient and iterative tasks. In this article, we’ll look into Python generator functions and normal function differences i.e. how different are their syntax, how is data handled, and practical applications.