Generator expressions in Python

One way in which Python works with large data sets and efficiently manage memory resources is through the use of generator expressions. In this tutorial, we will explore what a generator expression is and how to use it in Python. We will examine the syntax and functionality of generator expressions and show how they can be useful in different programming scenarios. By the end of this tutorial, you will have a solid understanding of generator expressions and the benefits they offer for Python developers.

Python Generator expressions :

• Generator expressions are similar to list comprehensions, but create a generator instead of a list.
• They are a memory-efficient way to iterate over large data sets, as they generate only one value at a time.
• They can be used to generate, filter, and transform data on the fly.

Generator expressions vs list comprehensions :

• While list comprehensions generate a list, making it faster to iterate over multiple times, generator expressions generate a generator which is lighter on memory, making them suitable for large data sets.
• If you only need to iterate over the data once, generators can be preferred over lists due to their memory efficiency.
• However, if you need random access to the items, or if you need to iterate over the data multiple times, then list comprehensions can be more useful.

Syntax :

• Generator expressions have a similar syntax to list comprehensions, but use parentheses instead of square brackets.
• A simple example of generator function is :   g = (x * 2 for x in range(10))  

Memory utilization in case of generator expressions :

• Generator expressions are more memory efficient compared to list comprehensions, as they produce one value at a time instead of creating a whole list.
• They can be used to work with large data sets that can be generated on the fly, without having to store them in memory.

Iterable vs iterator :

• An iterable is an object that can be looped over using a  for  loop.
• An iterator is an object that generates each value on the fly while being looped over, using the  next()  method.
• Generator expressions are iterable and produce an iterator when called.
• Code Sample : 

g = (x * 2 for x in range(3))
# Using the for loop
for i in g:
   print(i)
# Using the next() method
g = (x * 2 for x in range(3))
print(next(g))
print(next(g))
print(next(g))