Python Generator – Python Generator Expressions (Best Lesson)

In our last Python Tutorial, we studied Python functions. Today, in this Python Generator tutorial, we will study what is a generator in Python Programming.

Along with this, we will discuss Python Generator Expressions, Python list vs generator, and Python Function vs Generators.

So, let’s start the Python Generator Tutorial.

Python Generator – Python Generator Expressions (Best Lesson)

Define Python Generator?

A Python generator is a kind of an iterable, like a Python list or a python tuple. It generates for us a sequence of values that we can iterate on.

You can use it to iterate on a for-loop in python, but you can’t index it. Let’s take a look at how to create one with python generator example.

The Syntax of Generator in Python 3

To create a python generator, we use the yield statement, inside a function, instead of the return statement. Let’s take a quick example.

>>> def counter():
     i=1
     while(i<=10):
          yield i
          i+=1

With this, we defined a Python generator called counter() and assigned 1 to the local variable i. As long as i is less than or equal to 10, the loop will execute.

Inside the loop, we yield the value of I, and then increment it.

Then, we iterate on this generator using the for-loop.

>>> for i in counter():
         print(i)

Output

Working of Python Generator

To understand how this code works, we’ll start with the for-loop. For each item in the Python generator (each item that it yields), it prints it, here.

We begin with i=1. So, the first item that it yields is 1. The for-loop prints this because of our print statement. Then, we increment I to 2.

And the process follows until i is incremented to 11. Then, the while loop’s condition becomes False.

However, if you forget the statement to increment I, it results in an infinite generator. This is because a Python generator needs to hold only one value at a time.

So, there are no memory restrictions.

>>> def even(x):
  while x%2==0:
    yield 'Even'
>>> for i in even(2):
  print(i)

Output

Here, since 2 is even, 2%2 is always 0. Hence, the condition for while is always true.

Because of this, the Python3 generator even() keeps yielding the value True until we hit Ctrl+C on the keyboard to interrupt the execution.

Note that a generator may contain more than one Python yield statement. This is comparable to how a Python generator function may contain more than one return statement.

>>> def my_gen(x):
  while(x>0):
    if x%2==0:
      yield 'Even'
    else:
      yield 'Odd'
    x-=1
>>> for i in my_gen(7):
  print(i)

Output

Yielding into a Python List

This one’s a no-brainer. If you apply the list() function to the call to the Python generator, it will return a list of the yielded values, in the order in which they are yielded.

Here, we take an example that creates a list of squares of numbers, on the condition that the squares are even.

>>> def even_squares(x):
  for i in range(x):
    if i**2%2==0:
      yield i**2

To create a list, we just apply the list() function to the call to this Python generator. We do not iterate on it using a for-loop.

>>> print(list(even_squares(10)))

Output

As you can see, in numbers 1 to 9 (not 10, because range(10) gives us 0 to 9), the even squares are 0, 4, 16, 36, and 64. The others, that are 1, 9, 25, 49, 81 are odd. So, they’re not yielded.

Python List vs Generator in Python

This is a very simple difference. A list holds a number of values at once. But a Python generator holds only one value at a time, the value to yield.

This is why it needs much less space compared to a list. With a generator, we also don’t need to wait until all the values are rendered.

Python Generator vs Function

Now, to compare a generator to a function, we first talk about return and Python yield.

When the interpreter reaches the return statement in a function, it stops executing the Python Generator function and executes the statement after the function call.

However, when it reaches the Python yield statement in a generator, it yields the value to the iterable. Finally, it gets back to the generator to continue for the next value.

Also, when a function stops executing, its local variables are destroyed. This is not the same with a Python generator. Take a look.

>>> def mygen():
  i=7
  while i>0:
    yield i
    i-=1
>>> for i in mygen():
  print(i)

Output

Python Generator Expressions

Just like a list comprehension, we can use expressions to create python generators shorthand. Let’s take a list for this.

>>> mylist=[1,3,6,10]
>>> (x**2 for x in mylist)

Output

As is visible, this gave us a Python generator object. But to access the values, we need to store this into a variable, and then apply the next() function to it.

>>> a=(x**2 for x in mylist)
>>> next(a)

Output

>>> next(a)

Output

>>> next(a)

Output

>>> next(a)

Output

>>> next(a)

Output

So, this was all about Python Generator Tutorial. Hope you like our explanation.

Python Interview Questions on Generators

1. What are generators in Python?
2. Why generators are used in Python?
3. What is the difference between generators and iterators in Python?
4. Which keyword is used for generator in Python?
5. How is a generator function recognized in Python?

Conclusion

Now that you know the benefits of python generator over a list or over a function, you will understand its importance.

Some things, we can do with a generator, with a function, or even with a list comprehension. But using a Python generator is the most efficient.