Generator expressions are very similar to list comprehensions. The main difference is that it does not create a full set of results at once; it creates a generator object which can then be iterated over.

For instance, see the difference in the following code:

# list comprehension
[x**2 for x in range(10)]
# Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

# generator comprehension
(x**2 for x in xrange(10))
# Output: <generator object <genexpr> at 0x11b4b7c80>

These are two very different objects:

• the list comprehension returns a list object whereas the generator comprehension returns a generator.
• generator objects cannot be indexed and makes use of the next function to get items in order.

Note: We use xrange since it too creates a generator object. If we would use range, a list would be created. Also, xrange exists only in later version of python 2. In python 3, range just returns a generator. For more information, see the *Differences between range and xrange functions* example.

—

g = (x**2 for x in xrange(10))
print(g[0])

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: 'generator' object has no attribute '__getitem__'

g.next()  # 0
g.next()  # 1
g.next()  # 4
...
g.next()  # 81

g.next()  # Throws StopIteration Exception

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration

NOTE: The function g.next() should be substituted by next(g) and xrange with range since Iterator.next() and xrange() do not exist in Python 3.

Although both of these can be iterated in a similar way:

for i in [x**2 for x in range(10)]:
    print(i)

"""
Out:
0
1
4
...
81
"""

for i in (x**2 for x in xrange(10)):
    print(i)

"""
Out:
0
1
4
.
.
.
81
"""

Use cases