__iter__()
and __next__()
methods, maintain internal states, and raise Stop Iteration can make the process complex. Enter generators in Python—a simpler and more efficient way to create iterators.In this guide, we’ll explore what generators are, how to create them, and why they are so powerful. Let’s dive in!return
statement to send back a value and terminate, generators use the yield
statement. This allows the function to “pause” its execution and resume later, preserving its state.yield
and return
:yield | return |
---|---|
Pauses the function and saves state. | Terminates the function entirely. |
Allows resumption from the last state. | Does not allow resumption. |
Generates values one at a time. | Returns a single value immediately. |
yield
statement instead of return
. Below are examples to illustrate how they work.1 2 3
next()
function.next()
multiple times, you can use a for
loop to access all values from the generator object.1 2 3
1 2 3 4 5
itertools
for advanced operations like chaining or filtering.(x for x in range())
) for quick and concise generator creation.yield
Functionality: Show how the function pauses and resumes.