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1. If the compiler detects the yield keyword anywhere inside a function, that function no longer returns via the return statement. Instead, it immediately returns a lazy "pending list" object called a generator
2. A generator is iterable. What is an iterable? It's anything like a list or set or range or dict-view, with a built-in protocol for visiting each element in a certain order.

Comparing example to "just returning a list"

The above example can be thought of as merely creating a list which you append to and return:

Please note: generators can actually be used for many more things, such as implementing coroutines or non-deterministic programming or other elegant things. However, the "lazy lists" viewpoint I present here is the most common use you will find.

A coroutine (generators which generally accept input via the yield keyword e.g. nextInput = yield nextOutput, as a form of two-way communication) is basically a computation which is allowed to pause itself and request input (e.g. to what it should do next). When the coroutine pauses itself (when the running coroutine eventually hits a yield keyword), the computation is paused and control is inverted (yielded) back to the 'calling' function (the frame which requested the next value of the computation). The paused generator/coroutine remains paused until another invoking function (possibly a different function/context) requests the next value to unpause it (usually passing input data to direct the paused logic interior to the coroutine's code).

You can think of python coroutines as lazy incrementally-pending lists, where the next element doesn't just depend on the previous computation, but also on input you may opt to inject during the generation process.

1. If the compiler detects the yield keyword anywhere inside a function, that function no longer returns via the return statement. Instead, it immediately returns a lazy "pending list" object called a generator
2. A generator is iterable. What is an iterable? It's anything like a list, set, range, dictionary view, or any other object with a built-in protocol for visiting each element in a certain order.

Comparing the example to "just returning a list"

The above example can be thought of as merely creating a list that you append to and return:

Please note: generators can actually be used for many more things, such as implementing coroutines, non-deterministic programming, and other elegant things. However, the "lazy lists" viewpoint I present here is the most common use you will find.

A coroutine (generators that generally accept input via the yield keyword e.g. nextInput = yield nextOutput, as a form of two-way communication) is basically a computation that is allowed to pause itself and request input (e.g. to what it should do next). When the coroutine pauses itself (when the running coroutine eventually hits a yield keyword), the computation is paused and control is inverted (yielded) back to the 'calling' function (the frame which requested the next value of the computation). The paused generator/coroutine remains paused until another invoking function (possibly a different function/context) requests the next value to unpause it (usually passing input data to direct the paused logic interior to the coroutine's code).

You can think of Python coroutines as lazy incrementally-pending lists, where the next element doesn't just depend on the previous computation but also on input that you may opt to inject during the generation process.

A coroutine (generators which generally accept input via the yield keyword e.g. nextInput = yield nextOutput, as a form of two-way communication) is basically a computation which is allowed to pause itself and request input (e.g. to what it should do next). When the coroutine pauses itself (when the running coroutine's eventually hits a yield keyword), the computation is paused and control is inverted (yielded) back to the 'calling' function (the frame which requested the next value of the computation). The paused generator/coroutine remains paused until another invoking function (possibly a different function/context) requests the next value to unpause it (usually passing input data to direct the paused logic interior to the coroutine's code).

A coroutine (generators which generally accept input via the yield keyword e.g. nextInput = yield nextOutput, as a form of two-way communication) is basically a computation which is allowed to pause itself and request input (e.g. to what it should do next). When the coroutine pauses itself (when the running coroutine eventually hits a yield keyword), the computation is paused and control is inverted (yielded) back to the 'calling' function (the frame which requested the next value of the computation). The paused generator/coroutine remains paused until another invoking function (possibly a different function/context) requests the next value to unpause it (usually passing input data to direct the paused logic interior to the coroutine's code).

In a nutshell: a generator is a lazy, incrementally-pending list, and yield statements allow you to use function notation to program the list values the generator should incrementally spit out.

In a nutshell: Most commonly, a generator is a lazy, incrementally-pending list, and yield statements allow you to use function notation to program the list values the generator should incrementally spit out. Furthermore, advanced usage lets you use generators as coroutines (see below).

A coroutine (generators which generally accept input via the yield keyword e.g. nextInput = yield nextOutput, as a form of two-way communication) is basically a computation which is allowed to pause itself and request input (e.g. to what it should do next). When the coroutine pauses itself (when the running coroutine's eventually hits a yield keyword), the computation is paused and control is inverted (yielded) back to the 'calling' function (the frame which requested the next value of the computation). The paused generator/coroutine remains paused until another invoking function (possibly a different function/context) requests the next value to unpause it (usually passing input data to direct the paused logic interior to the coroutine's code).

You can think of python coroutines as lazy incrementally-pending lists, where the next element doesn't just depend on the previous computation, but also on input you may opt to inject during the generation process.