Using Data Aggregation

List comprehensions provide a concise way to create new lists based on existing iterables. You can integrate data aggregation functions like sum(), min(), and max() directly within list comprehensions to perform calculations on the elements of the new list as it's being created.

Calculating the Sum of Squares:

numbers = [1, 2, 3, 4, 5]
sum_of_squares = sum([n * n for n in numbers])
print(sum_of_squares)
# Output: 55

In this example, the list comprehension [n * n for n in numbers] creates a list of squares, and the sum() function calculates the sum of these squares.

Finding the Minimum of Transformed Values:

numbers = [-3, -1, 0, 1, 3]
min_absolute = min([abs(n) for n in numbers])
print(min_absolute)
# Output: 0

Here, the list comprehension [abs(n) for n in numbers] creates a list of absolute values, and the min() function finds the minimum value in this list.

Finding the Maximum of Filtered Values:

numbers = [1, 2, 3, 4, 5, 6]
max_even = max([n for n in numbers if n % 2 == 0])
print(max_even)
# Output: 6

In this example, the list comprehension [n for n in numbers if n % 2 == 0] creates a list of even numbers, and the max() function finds the maximum value in this list.