Dictionary comprehensions are a quick and easy way of assembling dictionaries in Python. They work just like list comprehensions, and look almost the same. They use curly braces instead of square brackets, and they contain two variables (for key and value), separated by a colon.
For example, to assemble a dict in which the keys are numbers between 0 and 10, and the values are the same number squared, we could do:
>>> squares = {num:num * num for num in range(10)}
>>> print(squares)
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}
Or we could use f-strings to assemble a dict to keep game scores:
>>> scores = {f"player-{num}":0 for num in range(0, 5)}
>>> print(scores)
{'player-0': 0, 'player-1': 0, 'player-2': 0, 'player-3': 0, 'player-4': 0}
In the above example, the f-string gets turned into the dict keys (player-0, etc.) and each value is set to 0. You can also operate on tuples for setting keys and values. For example, we’ll use a list comprehension to create a list of tuples, then turn the tuples into dict keys and values:
>>> my_list = [(f"player-{num}", num * 2) for num in range(0, 5)]
>>> print(my_list)
[('player-0', 0), ('player-1', 2), ('player-2', 4), ('player-3', 6), ('player-4', 8)]
>>> scores = {key:value for (key, value) in my_list}
>>> print(scores)
{'player-0': 0, 'player-1': 2, 'player-2': 4, 'player-3': 6, 'player-4': 8}
Set comprehensions are another great operation in Python - they look like a cross between list and dict comprehensions, and they create set objects.
>>> my_set = {num for num in [1, 2, 1, 0, 3]}
>>> print(my_set)
{0, 1, 2, 3}
Notice that instead of returning the same list of numbers (as num for num would have done in a list comprehension), you instead get a set (note the curly braces) of unique numbers from the list (you only get one 1).
Generator expressions are a more advanced concept. A generator is a type of iterable object - like a list, you can iterate through each element - however, unlike a list, generators evaluate elements on demand, instead of assembling them all at once.
A generator comprehension looks just like a list comprehension, except we use parentheses instead of brackets. For example, to get a list of the square of every even number between 0 and 10, we could do:
# List comprehension
>>> list_comp = [x ** 2 for x in range(10) if x % 2 == 0]
>>> print(list_comp)
[0, 4, 16, 36, 64]
# Generator expression
>>> gen_exp = (x ** 2 for x in range(10) if x % 2 == 0)
>>> print(gen_exp)
<generator object <genexpr> at 0x10d48cc00>
>>> for num in gen_exp:
... print(num)
...
0
4
16
36
64
Generator expressions offer improvements in memory use as execution speed, but are functionally different than lists.
Generators are exhausted, meaning you can only loop over them once, and you can’t reference items in a generator without first converting it to a list.
If we try to loop over the generator created in the previous example a second time, we won’t see any results.
>>> gen_exp = (x ** 2 for x in range(10) if x % 2 == 0)
>>>
>>> for num in gen_exp:
... print(num)
...
0
4
16
36
64
>>> for num in gen_exp:
... print(num)
...
Pay attention to this concept, since it bites a lot of beginners!