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
A simple generator function:
def my_generator():
yield 1
yield 2
yield 3
for value in my_generator():
print(value)
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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
Generator that yields numbers:
def count_up_to(n):
count = 1
while count <= n:
yield count
count += 1
for num in count_up_to(5):
print(num)
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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
Generator for large sequences:
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))
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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))
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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
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Generator Expressions
Similar to list comprehensions, you can create generators using generator expressions with parentheses instead of square brackets:
Example
List comprehension vs generator expression:
# 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))
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Example
Using a generator expression with sum:
# Calculate sum of squares without creating a list
total = sum(x * x for x in range(10))
print(total)
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Fibonacci Sequence Generator
Generators can be used to create the Fibonacci sequence.
It can continue generating values indefinitely, without running out of memory:
Example
Generate 100 Fibonacci numbers:
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))
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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")
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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()
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