Python Generators

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.

Example

def my_generator():
  yield 1
  yield 2
  yield 3

for value in my_generator():
  print(value)

Note: Generators allow you to iterate over data without storing the entire dataset in memory. Instead of using return, generators use the yield keyword.

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.

Example

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:

Example

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:

Example

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:

Example

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:

Example

# List comprehension - creates a list
list_comp = [x * x for x in range(5)]
print(list_comp)

# Generator expression - creates a generator
gen_exp = (x * x for x in range(5))
print(gen_exp)
print(list(gen_exp))

Example

# Calculate sum of squares without creating a list
total = sum(x * x for x 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:

Example

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:

send() Method

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

Example

def echo_generator():
  while True:
    received = yield
    print("Received:", received)

gen = echo_generator()
next(gen)  # Prime the generator
gen.send("Hello")
gen.send("World")

close() Method

The close() method stops the generator:

Example

def my_gen():
  try:
    yield 1
    yield 2
    yield 3
  finally:
    print("Generator closed")

gen = my_gen()
print(next(gen))
gen.close()