Python Tutorials

Python Generator Functions

Generator Functions

1Python provides a generator to create your iterator function.

2The generator is a different type of function that does not return a single value, instead it returns an iterator object with a sequence of values.

3In generators, the yield keyword statement is used instead of the return keyword statement.

Example

def my_generator():
    print("First value.")
    yield 1

    print("Second value.")
    yield 2

    print("Third value.")
    yield 3

obj = my_generator()
print(next(obj))
print(next(obj))
print(next(obj))

'First value.'
1
'Second value.'
2
'Third value.'
3

In the above example, we created a function and included the yield keyword statement. Because of the yield keyword statement, this function is considered a generator function.

Here, we also created one reference variable and assigned that generator function to that variable, when we pass that variable to the next() function as an input argument, then we get the yield statement return value.

Generator Stop Iteraration Error

The stop iteration (StopIteraration) error appears when you call the next() function more than the yield keyword statement; in other words, your generator function has no value for an iteration.

Example

def my_generator():
    print("First value.")
    yield 1

    print("Second value.")
    yield 2

    print("Third value.")
    yield 3

obj = my_generator()
print(next(obj))
print(next(obj))
print(next(obj))
print(next(obj))

'First value.'
1
'Second value.'
2
'Third value.'
3
StopIteration

Here, we call the next() function more than the yield keyword statement present in our generator function, so we get a StopIteraration error message.

Generator Stop Iteration Error Handling

It is possible to handle the StopIteration error by using the try-except block.

Example

def my_generator():
    print("First value.")
    yield 1

    print("Second value.")
    yield 2

    print("Third value.")
    yield 3

obj = my_generator()
try:
    print(next(obj))
    print(next(obj))
    print(next(obj))
    print(next(obj))
except StopIteration:
    print("StopIteration handled successfully.")

'First value.'
1
'Second value.'
2
'Third value.'
3
'StopIteration handled successfully.'

The try-except block successfully handled the StopIteration error in the example above.

Python Generator With Loop Statement

It is also possible to call the next() function multiple times using the looping statements.

Example

def my_generator():
    print("First value.")
    yield 1

    print("Second value.")
    yield 2

    print("Third value.")
    yield 3

obj = my_generator()
for number in obj:
    print(number)

'First value.'
1
'Second value.'
2
'Third value.'
3

Here, our generator function object is iterated using a for statement.

The generator function calls like any other function; however, they pause when encountering the yield keyword statement.

The first value from the iterator stream is sent to the calling environment; however, local variables and their states are saved internally.

Example

def my_generator(number):
    for i in range(number):
        yield i

obj = my_generator(5)
for number in obj:
    print(number)

0
1
2
3
4

Using the for loop statement in the generator function, we created the yield keyword statement, and our generator function also looped using a for loop statement.

Generator With a return Keyword Statement.

In the generator function, you cannot include the return keyword. If you include it, then it will terminate the function.

The return keyword vs. yield keyword:
The return keyword statement returns a value and terminates the execution of a function.
A yield keyword statement returns a value and pauses execution while maintaining internal states.

Example

def my_generator():
    print("First value.")
    yield 1

    print("Second value.")
    yield 2

    return

    print("Third value.")
    yield 3

obj = my_generator()
print(next(obj))
print(next(obj))
print(next(obj))

'First value.'
1
'Second value.'
2
StopIteration

The generator function terminates execution after two yield keyword statements due to the inclusion of the return keyword in the generator function.

Python Generator Expression

Similarly to the lambda function, we also create anonymous generator functions.

The generator expression is similar to the list comprehension expression, except that it uses round brackets (()) instead of square brackets ([]).

Generator expressions generate one item at a time, while list comprehensions generate the entire list.

A generator executes slowly, fetching items only when the next function is called. In this way, generator expressions are more memory efficient than equivalent list comprehensions.

Example

colors_list = ["Red", "Blue", "Green"]

colors_filter_list = [i for i in colors_list]
colors_list_generator = (i for i in colors_list)

print(colors_filter_list)
print(colors_list_generator)

['Red', 'Blue', 'Green']
'<'generator object '<'genexpr'>' at 0x107adb350'>'

You can see that the list expression prints all elements in the output, while the generator expression returns an object.

In order to get the generator expression items, you need to call the next() function.

Example

colors_list = ["Red", "Blue", "Green"]

colors_list_generator = (i for i in colors_list)

print(next(colors_list_generator))
print(next(colors_list_generator))
print(next(colors_list_generator))

'Red'
'Blue'
'Green'

As a result, we get the items of the generator expression after passing the generator expression object to the next() function.