Python Tutorials · Python Functions

Python Generators

Learn all about Python Generators in this comprehensive tutorial.

5 min read beginner

Python Recursion

Python Range

•Generators are functions that can pause and resume their execution.
•The yield keyword is what makes a function a generator.
•Generators are memory-efficient because they generate values on-the-fly instead of storing everything in memory.
•You can manually iterate through a generator using the next() function:
•Similar to list comprehensions, you can create generators using generator expressions with parentheses instead of square brackets:
•Generators can be used to create the Fibonacci sequence.
•Generators have special methods for advanced control:

Generators

Generators are functions that can pause and resume their execution.

When a generator function is called, it returns a generator object, which is an iterator.

The code inside the function is not executed yet, it is only compiled. The function only executes when you iterate over the generator.

python

def my_generator():  yield 1  yield 2  yield 3
for value in my_generator():  print(value)

The yield Keyword

The yield keyword is what makes a function a generator.

When yield is encountered, the function's state is saved, and the value is returned. The next time the generator is called, it continues from where it left off.

python

def count_up_to(n):  count = 1  while count <= n:    yield count    count += 1
for num in count_up_to(5):  print(num)

Note: Unlike return, which terminates the function, yield pauses it and can be called multiple times.

Generators Saves Memory

Generators are memory-efficient because they generate values on-the-fly instead of storing everything in memory.

For large datasets, generators save memory:

python

def large_sequence(n):  for i in range(n):    yield i
# This doesn't create a million numbers in memory
gen = large_sequence(1000000)
print(next(gen))
print(next(gen))
print(next(gen))

Using next() with Generators

You can manually iterate through a generator using the next() function:

python

def simple_gen():  yield "Emil"  yield "Tobias"  yield "Linus"
gen = simple_gen()
print(next(gen))
print(next(gen))
print(next(gen))

When there are no more values to yield, the generator raises a StopIteration exception:

python

def simple_gen():  yield 1  yield 2
gen = simple_gen()
print(next(gen))
print(next(gen))
print(next(gen))  # This will raise StopIteration

Generator Expressions

Similar to list comprehensions, you can create generators using generator expressions with parentheses instead of square brackets:

python

# List comprehension - creates a list
list_comp = [val_1 * val_1 for val_1 in range(5)]
print(list_comp)
# Generator expression - creates a generator
gen_exp = (val_1 * val_1 for val_1 in range(5))
print(gen_exp)
print(list(gen_exp))

python

# Calculate sum of squares without creating a list
total = sum(val_1 * val_1 for val_1 in range(10))
print(total)

Fibonacci Sequence Generator

Generators can be used to create the Fibonacci sequence.

It can continue generating values indefinitely, without running out of memory:

python

def fibonacci():  a, b = 0, 1  while True:    yield a    a, b = b, a + b
# Get first 100 Fibonacci numbers
gen = fibonacci()
for _ in range(100):  print(next(gen))

Generator Methods

Generators have special methods for advanced control:

The send() method allows you to send a value to the generator:

python

def echo_generator():  while True:    received = yield    print("Received:", received)
gen = echo_generator()
next(gen)  # Prime the generator
gen.send("Hello")
gen.send("World")

The close() method stops the generator:

python

def my_gen():  try:    yield 1    yield 2    yield 3  finally:    print("Generator closed")
gen = my_gen()
print(next(gen))
gen.close()

Module quiz

2 questions

Which of the following is true about Python Generators?

It is strictly typedIt is evaluated at runtimeIt requires explicit memory managementIt is deprecated

What is the most common pitfall when working with Python Generators?

SyntaxErrorIndentationErrorNameErrorTypeError

Answer all questions to submit.

Python Recursion

Python Range

Python Generators

AI Summary

Generators

The yield Keyword

Generators Saves Memory

Using next() with Generators

Generator Expressions

Fibonacci Sequence Generator

Generator Methods

Module quiz