Generator expressions allow for a clean, single-line definition and creation of an iterator. By using a generator expression, there is no need to define a full generator function as we covered in the previous exercises.

Generator expressions resemble the syntax of list comprehensions. However, they do differ in the following ways:

Generator Expressions List Comprehensions
Returns a newly defined iterator Returns a new list
Uses parentheses Uses brackets

Let’s look at an example of how the two compare:

# List comprehension a_list = [i*i for i in range(4)] # Generator comprehension a_generator = (i*i for i in range(4))

In this code above, a_list will be a list object containing the values [0, 1, 4, 9]. The object a_generator will be a generator object that cannot be accessed directly like a_list. It will need to be traversed to retrieve the values it contains. To show this further, we can print out a_list and a_generator and see what is returned:

print(a_list) print(a_generator)

Running this code will produce the following output:

[0, 1, 4, 9] <generator object <genexpr> at 0x7f82e0e4d4c0>

Since our generator expression returns an iterator object, we can loop through to obtain the values within it:

for i in a_generator: print(i)

Which produces the following output:

0 1 4 9

We can practice more with generator expressions by using them to create some new college courses!



Given the defined generator function cs_generator(), retrieve a generator object by calling cs_generator() and set it to a variable called cs_courses. Print out the values within the iterator using a for loop.


After the for loop, create an iterator using a generator expression and put it in a variable called cs_generator_exp. The iterator should produce the same output as cs_generator().


Print out the values of the cs_generator_exp iterator object using a for loop. The output should match the for loop print output of iterating over cs_courses.

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