What is the true purpose of the \'send\' function in Python generators, and how does it differ from the \'yield\' keyword?

Shielding Light on the Purpose of the Generator's "send" Function

In the realm of Python generators, the yield keyword stands as a cornerstone, allowing for the creation of iterable sequences. However, alongside yield, another enigmatic function lurks in the shadows: send.

The documentation provides a cryptic description, stating that send "resumes the execution and “sends” a value into the generator function." This raises questions both about its purpose and its relationship with yield.

Value is Input and Output?

The first perplexity arises from the notion that value serves as an input to the generator function. However, the documentation also suggests that send returns the next value yielded by the generator. Isn't this the same function performed by yield?

Unveiling the True Purpose

The key to resolving this enigma lies in understanding that send enables the injection of values while the generator is yielding. Consider the following example:

<code class="python">def double_inputs():
    while True:
        x = yield
        yield x * 2</code>

Imagine this generator as a black box with two holes: one for receiving values (yield) and one for returning them (yield). If you were to call next(generator) to start the generator, it would pause at the first yield statement, waiting for an input.

Now, you can use send to feed a value into the generator. The value is temporarily stored in the x variable. Upon resuming the generator, the code beyond the first yield statement executes, effectively doubling the input value and returning it through yield.

A Non-Yieldworthy Example

To demonstrate the unique capabilities of send that cannot be achieved with yield, consider the following:

<code class="python">gen = double_inputs()
next(gen)       # run up to the first yield
gen.send(10)    # goes into 'x' variable</code>

This code effectively injects a value of 10 into the generator. It then resumes execution and returns 20, the doubled value. This sequence of actions is impossible to achieve solely with yield.

Twisted's Magic with send

One practical application of send is exemplified by Twisted's @defer.inlineCallbacks decorator. It allows you to write functions that yield Deferred objects, which represent future values. The underlying framework intercepts these Deferred objects, executing the necessary computations in the background.

When the computation completes, the framework sends the result back to the generator, simulating the resumption of execution and allowing the generator to proceed with subsequent operations.

Conclusion

The send function on Python generators empowers you to inject values into generators that are paused at yield statements. This capability enables sophisticated control flow and can simplify asynchronous programming, as demonstrated by Twisted's @defer.inlineCallbacks decorator. By understanding the unique purpose of send alongside yield, you can unleash the full potential of generators in your Python code.

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