Comprehensions

Imagine we wanted a list comprising even squares such as [0, 4, 16, 36, 64]. One natural way to tackle this problem is the following:

even_squares = []
for n in range(10):
    if n % 2 == 0:
        even_squares.append(n**2)

But here again, Python offers a convenient and concise feature to create lists whose syntax resembles the mathematical definition of sets:

even_squares = [n**2 for n in range(10) if n % 2 == 0]

Multiple levels of nesting are also possible:

>>> [(x, y) for x in range(3) for y in range(3) if x != y]
[(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]

We can define sets by comprehension as well. For instance, given a list we might want to generate a set of its duplicate elements.

>>> input_list = ['a', 'b', 'c', 'a', 'd', 'd', 'g']
>>> duplicates = {c for c in input_list if input_list.count(c) > 1}
>>> duplicates
{'a', 'd'}

Another illustrative example is the generation of the set of prime numbers up to 100.

>>> {x for x in range(2, 101) if all(x%y for y in range(2, min(x, 11)))}
{2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97}

We are taking advantage of the all() function which returns True if all the elements in an iterable are true. In this case, if all the modulos of x%y are different from 0.

Finally, in dictionaries:

>>> {x: 2*x for x in range(10)}
{0: 0, 1: 2, 2: 4, 3: 6, 4: 8, 5: 10, 6: 12, 7: 14, 8: 16, 9: 18}

All in all, comprehensions are the quintessential example of Python principles.

The Zen of Python, by Tim Peters

Explicit is better than implicit.

Flat is better than nested.

Readability counts.







Lliçons.jutge.org
Víctor Adell
Universitat Politècnica de Catalunya, 2019

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