List comprehension

List comprehension is a syntactic shorthand for applying a function to each element in a list without explicitly using loop syntax.

Since its introduction to the language, the same functionality has become achievable by using functional methods like map and filter, utilising lambdas however list comprehension is often more straightforward and easier to read.

Syntax

Here is a basic example which applies + 1 to each integer in a list:

values = [1, 2, 4, 6, 8, 9]

new_values = [i + 1 for i in values]
print('new_values', new_values)
# new_values [2, 3, 5, 7, 9, 10]

The basic syntax is as follows:

new_list  = [expression for each member in an iterable]

  • The expression can be the member itself, a call to a method, or any other valid expression that returns a value. In the example above, the expression i + i adds one to each member value.

  • The member is the object or value in the list or iterable. In the example above, the member value is i.

  • The iterable is a list, set, dictionary or any other object that can return its elements one at a time. In the example above, the iterable is each value in values.

This is a much more condensed way of achieving the same outcome with a traditional loop:

values = [1, 2, 4, 6, 8, 9]
new_list = []
for i in values:
  values.append(i+1)

Another example

In the following example, we apply list comprehension with a in range loop structure:

new_list = [i * i for i in range(10) ]
print(new_list)
# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Adding a condition

We can apply a conditional to a comprehension:

new_list = [item + 1 for item in values if item % 2 == 0]

print('new_list:', new_list)
# new_list: [3, 5, 7, 9]

Filters

By applying a condition (and no execution to each element) we effectively create a filter:

numbers = [1, 2, 3, 4, 4, 4]
numbers_filtered = [i for i in numbers if i > 2]
print(numbers_filtered)
# [3, 4, 5]

For comparison, the same outcome could be achieved with a filter and lambda:

with_filter = list(filter(lambda x: x > 2, numbers))
print(with_filter)
# [3, 4, 5]

Set comprehension

We can also apply comprehension to sets. The syntax is practically identical but the difference is the resultant data structure will not contain duplicates.

numbers = [1, 2, 3, 4, 4, 4]
unique =  {i for i in numbers}
print(unique)
# {1,2,3,4}

Dictionary comprehension

squares = {i: i * i for i in range(5)}
print(squares)
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}
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