In Python, there are several differences between a normal function and a generator function:
- Normal Function: A normal function is defined using the def keyword followed by the function name and parentheses containing optional parameters. It uses the return statement to return a value.
- Generator Function: A generator function is also defined using the def keyword, but instead of using return, it uses the yield keyword to yield a value. Generator functions can have one or more yield statements.
- Normal Function: A normal function executes until it encounters a return statement, which terminates the function and returns a value. Each time the function is called, it starts execution from the beginning.
- Generator Function: A generator function executes until it encounters a yield statement, at which point it yields a value and pauses its execution state. When the generator is iterated, it resumes execution from where it left off until it encounters the next yield statement or the function ends.
- Return Value:
- Normal Function: A normal function can return a single value using the return statement. Once the function returns a value, it cannot continue execution.
- Generator Function: A generator function can yield multiple values using the yield statement. It can yield a value multiple times and can also have multiple yield statements. Each time a value is yielded, the function's execution state is paused, allowing it to be resumed later.
- Normal Function: A normal function is not directly iterable. To obtain multiple values from a normal function, you need to store the values in a data structure like a list and return the entire structure.
- Generator Function: A generator function returns a generator object when called, which is an iterable. The generator object can be iterated using a loop or other iterable-consuming constructs, retrieving values one at a time as they are generated.
- Memory Usage:
- Normal Function: A normal function can consume more memory if it creates and stores all the values in memory, such as when returning a large list or tuple.
- Generator Function: A generator function conserves memory as it generates values on-the-fly, yielding them one at a time. It does not store all the values in memory simultaneously.
- Use Cases:
- Normal Function: Normal functions are used for general-purpose computations, calculations, and reusable code blocks that return a result.
- Generator Function: Generator functions are used when you need to generate a sequence of values on-the-fly, especially for large or infinite sequences. They are useful when you want to conserve memory, generate values lazily, or work with data streams.
Overall, generator functions provide a way to generate values lazily and conserve memory by producing values on-demand, whereas normal functions execute fully and return a value. The choice between them depends on the specific use case and whether you need to generate values iteratively or return a single result.