13.2. Generator Expression

Comprehensions executes instantly
Comprehensions are stored in the memory until end of a program
Comprehensions should be used when accessing values more than one
Generator Expressions are lazy evaluated
Generator Expressions are cleared once they are executed
Generator Expressions should be used when accessing value once (for example in the loop)

13.2.1. List Comprehension

Comprehensions executes instantly
Comprehensions will be in the memory until end of a program
Comprehensions - Using values more than one

Definition:

>>> data = [x for x in range(0,5)]

Usage:

>>> list(data)
[0, 1, 2, 3, 4]
>>>
>>> list(data)
[0, 1, 2, 3, 4]
>>>
>>> list(data)
[0, 1, 2, 3, 4]

Rationale:

>>> print(data)
[0, 1, 2, 3, 4]

13.2.2. Generator Expression

Generators are lazy evaluated
Creates generator object and assign reference
Code is not executed instantly
Sometimes code is not executed at all!
Are cleared once they are executed
Generator will calculate next number for every loop iteration
Generator forgets previous number
Generator doesn't know the next number
It is used for one-time access to values (for example in the loop iterator)

Definition:

>>> data = (x for x in range(0,5))

Usage:

>>> list(data)
[0, 1, 2, 3, 4]
>>>
>>> list(data)
[]
>>>
>>> list(data)
[]

Rationale:

>>> print(data)  
<generator object <genexpr> at 0x...>

13.2.3. Comprehensions or Generator Expression

If you need values evaluated instantly, there is no point in using generators

Comprehensions vs. Generator Expression:

>>> data = [x for x in range(0,10)]
>>> print(data)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

>>> data = (x for x in range(0,10))
>>> print(data)  
<generator object <genexpr> at 0x...>

Comprehension:

>>> data = [x for x in range(0,10)]
>>>
>>> for x in data:  
...     print(x, end=' ')
...     if x == 3:
...         break
0 1 2 3
>>>
>>> for x in data:  
...     print(x, end=' ')
...     if x == 6:
...         break
0 1 2 3 4 5 6
>>>
>>> print(list(data))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>>
>>> print(list(data))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Generator Expressions:

>>> data = (x for x in range(0,10))
>>>
>>> for x in data:  
...     print(x, end=' ')
...     if x == 3:
...         break
0 1 2 3
>>>
>>> for x in data:  
...     print(x, end=' ')
...     if x == 6:
...         break
4 5 6
>>>
>>> print(list(data))
[7, 8, 9]
>>>
>>> print(list(data))
[]

13.2.4. Why Round Brackets?

Round brackets does not produce tuples (commas does)
Round brackets bounds context

>>> data = [x for x in range(0,5)]  # list comprehension
>>> data = (x for x in range(0,5))  # generator expression

Adding round brackets will not change the data type. They are just a boundary for some context. The same in generator expressions:

>>> data = 1
>>> type(data)
<class 'int'>
>>>
>>> data = (1)
>>> type(data)
<class 'int'>

In tuples, the comma does that:

>>> data = (1,)
>>> type(data)
<class 'tuple'>
>>>
>>> data = 1,
>>> type(data)
<class 'tuple'>

13.2.5. Case Study

>>> data = (x for x in range(0,5))          # generator expression
>>> data = [x for x in range(0,5)]          # list comprehension
>>> data = {x for x in range(0,5)}          # set comprehension
>>> data = {x:x for x in range(0,5)}        # dict comprehension

>>> data = tuple(x for x in range(0,5))     # tuple comprehension
>>> data = list(x for x in range(0,5))      # list comprehension
>>> data = set(x for x in range(0,5))       # set comprehension
>>> data = dict((x,x) for x in range(0,5))  # dict comprehension

>>> data = bool(x for x in range(0,5))
>>> data = str(x for x in range(0,5))

>>> data = all(x for x in range(0,5))
>>> data = any(x for x in range(0,5))
>>> data = sum(x for x in range(0,5))
>>> data = min(x for x in range(0,5))
>>> data = max(x for x in range(0,5))
>>> data = hash(x for x in range(0,5))
>>> data = callable(x for x in range(0,5))

>>> data = len(x for x in range(0,5))
Traceback (most recent call last):
TypeError: object of type 'generator' has no len()