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Python Generator(s) : How to Produce large sequences

by WhereWhatHow
Python Generators

A Python Generator is a function that returns an iterator that produces a sequence of values when iterated over.

Generators are useful when we want to produce a large sequence of values, but we don’t want to store all of them in memory at once.

Create Python Generator

In Python, similar to defining a normal function, we can define a generator function using the def keyword, but instead of the return statement we use the yield statement.

def generator_name(arg):
    # statements
    yield something

Here, the yield keyword is used to produce a value from the generator.

When the generator function is called, it does not execute the function body immediately. Instead, it returns a generator object that can be iterated over to produce the values.

Example: Python Generator

Here’s an example of a generator function that produces a sequence of numbers,

def my_generator(n):

    # initialize counter
    value = 0

    # loop until counter is less than n
    while value < n:

        # produce the current value of the counter
        yield value

        # increment the counter
        value += 1

# iterate over the generator object produced by my_generator
for value in my_generator(3):

    # print each value produced by generator



In the above example, the my_generator() generator function takes an integer n as an argument and produces a sequence of numbers from 0 to n-1.

The yield keyword is used to produce a value from the generator and pause the generator function’s execution until the next value is requested.

The for loop iterates over the generator object produced by my_generator(), and the print statement prints each value produced by the generator.

We can also create a generator object from the generator function by calling the function like we would any other function as,

generator = my_range(3)
# 0
# 1
# 2

Python Generator Expression

In Python, a generator expression is a concise way to create a generator object.

It is similar to a list comprehension, but instead of creating a list, it creates a generator object that can be iterated over to produce the values in the generator.

Generator Expression Syntax

A generator expression has the following syntax,

(expression for item in iterable)

Here, expression is a value that will be returned for each item in the iterable.

The generator expression creates a generator object that produces the values of expression for each item in the iterable, one at a time, when iterated over.

Example 2: Python Generator Expression

# create the generator object
squares_generator = (i * i for i in range(5))

# iterate over the generator and print the values
for i in squares_generator:



Here, we have created the generator object that will produce the squares of the numbers 0 through 4 when iterated over.

And then, to iterate over the generator and get the values, we have used the for loop.

Advantages of Generators Over Other Iterators

Generators have some advantages over other iterators in Python:

  1. Easy to implement and can be more memory-efficient than storing an entire sequence in memory.
  2. Can be used to produce an “infinite” sequence of values, by using a while True loop and yielding values indefinitely.
  3. They can be combined with other iterators using the itertools module to produce complex sequences of values.
Python Generators

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