Tutorial: Comprehensions, Iterators, and Iterables#

Author: Florent Hivert <florent.hivert@univ-rouen.fr> and Nicolas M. Thiéry <nthiery at users.sf.net>

List comprehensions#

List comprehensions are a very handy way to construct lists in Python. You can use either of the following idioms:

[ <expr> for <name> in <iterable> ]
[ <expr> for <name> in <iterable> if <condition> ]


For example, here are some lists of squares:

sage: [ i^2 for i in [1, 3, 7] ]
[1, 9, 49]
sage: [ i^2 for i in range(1,10) ]
[1, 4, 9, 16, 25, 36, 49, 64, 81]
sage: [ i^2 for i in range(1,10) if i % 2 == 1]
[1, 9, 25, 49, 81]


And a variant on the latter:

sage: [i^2 if i % 2 == 1 else 2 for i in range(10)]
[2, 1, 2, 9, 2, 25, 2, 49, 2, 81]


One can use more than one iterable in a list comprehension:

sage: [ (i,j) for i in range(1,6) for j in range(1,i) ]
[(2, 1), (3, 1), (3, 2), (4, 1), (4, 2), (4, 3), (5, 1), (5, 2), (5, 3), (5, 4)]


Warning

Mind the order of the nested loop in the previous expression.

If instead one wants to build a list of lists, one can use nested lists as in:

sage: [ [ binomial(n, i) for i in range(n+1) ] for n in range(10) ]
[[1],
[1, 1],
[1, 2, 1],
[1, 3, 3, 1],
[1, 4, 6, 4, 1],
[1, 5, 10, 10, 5, 1],
[1, 6, 15, 20, 15, 6, 1],
[1, 7, 21, 35, 35, 21, 7, 1],
[1, 8, 28, 56, 70, 56, 28, 8, 1],
[1, 9, 36, 84, 126, 126, 84, 36, 9, 1]]


Iterators#

Definition#

To build a comprehension, Python actually uses an iterator. This is a device which runs through a bunch of objects, returning one at each call to the next method. Iterators are built using parentheses:

sage: it = (binomial(8, i) for i in range(9))
sage: next(it)
1

sage: next(it)
8
sage: next(it)
28
sage: next(it)
56


You can get the list of the results that are not yet consumed:

sage: list(it)
[70, 56, 28, 8, 1]


Asking for more elements triggers a StopIteration exception:

sage: next(it)
Traceback (most recent call last):
...
StopIteration


An iterator can be used as argument for a function. The two following idioms give the same results; however, the second idiom is much more memory efficient (for large examples) as it does not expand any list in memory:

sage: sum([binomial(8, i) for i in range(9)])
256
sage: sum(binomial(8, i) for i in range(9))
256


Typical usage of iterators#

Iterators are very handy with the functions all(), any(), and exists():

sage: all([True, True, True, True])
True
sage: all([True, False, True, True])
False

sage: any([False, False, False, False])
False
sage: any([False, False, True, False])
True


Let’s check that all the prime numbers larger than 2 are odd:

sage: all( is_odd(p) for p in range(1,100) if is_prime(p) and p>2 )
True


It is well know that if 2^p-1 is prime then p is prime:

sage: def mersenne(p): return 2^p -1
sage: [ is_prime(p) for p in range(20) if is_prime(mersenne(p)) ]
[True, True, True, True, True, True, True]


The converse is not true:

sage: all( is_prime(mersenne(p)) for p in range(1000) if is_prime(p) )
False


Using a list would be much slower here:

sage: %time all( is_prime(mersenne(p)) for p in range(1000) if is_prime(p) )    # not tested
CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s
Wall time: 0.00 s
False
sage: %time all( [ is_prime(mersenne(p)) for p in range(1000) if is_prime(p)] ) # not tested
CPU times: user 0.72 s, sys: 0.00 s, total: 0.73 s
Wall time: 0.73 s
False


You can get the counterexample using exists(). It takes two arguments: an iterator and a function which tests the property that should hold:

sage: exists( (p for p in range(1000) if is_prime(p)), lambda p: not is_prime(mersenne(p)) )
(True, 11)


An alternative way to achieve this is:

sage: counter_examples = (p for p in range(1000) if is_prime(p) and not is_prime(mersenne(p)))
sage: next(counter_examples)
11


itertools#

At its name suggests itertools is a module which defines several handy tools for manipulating iterators:

sage: l = [3, 234, 12, 53, 23]
sage: [(i, l[i]) for i in range(len(l))]
[(0, 3), (1, 234), (2, 12), (3, 53), (4, 23)]


The same results can be obtained using enumerate():

sage: list(enumerate(l))
[(0, 3), (1, 234), (2, 12), (3, 53), (4, 23)]


Here is the analogue of list slicing:

sage: list(Permutations(3))
[[1, 2, 3], [1, 3, 2], [2, 1, 3], [2, 3, 1], [3, 1, 2], [3, 2, 1]]
sage: list(Permutations(3))[1:4]
[[1, 3, 2], [2, 1, 3], [2, 3, 1]]

sage: import itertools
sage: list(itertools.islice(Permutations(3), 1r, 4r))
[[1, 3, 2], [2, 1, 3], [2, 3, 1]]


Note that all calls to islice must have arguments of type int and not Sage integers.

The behaviour of the functions map() and filter() has changed between Python 2 and Python 3. In Python 3, they return an iterator. If you want to return a list like in Python 2 you need to explicitly wrap them in list():

sage: list(map(lambda z: z.cycle_type(), Permutations(3)))
[[1, 1, 1], [2, 1], [2, 1], [3], [3], [2, 1]]

sage: list(filter(lambda z: z.has_pattern([1,2]), Permutations(3)))
[[1, 2, 3], [1, 3, 2], [2, 1, 3], [2, 3, 1], [3, 1, 2]]


Defining new iterators#

One can very easily write new iterators using the keyword yield. The following function does nothing interesting beyond demonstrating the use of yield:

sage: def f(n):
....:   for i in range(n):
....:       yield i
sage: [ u for u in f(5) ]
[0, 1, 2, 3, 4]


Iterators can be recursive:

sage: def words(alphabet,l):
....:    if l == 0:
....:        yield []
....:    else:
....:        for word in words(alphabet, l-1):
....:            for a in alphabet:
....:                yield word + [a]

sage: [ w for w in words(['a','b','c'], 3) ]
[['a', 'a', 'a'], ['a', 'a', 'b'], ['a', 'a', 'c'], ['a', 'b', 'a'], ['a', 'b', 'b'], ['a', 'b', 'c'], ['a', 'c', 'a'], ['a', 'c', 'b'], ['a', 'c', 'c'], ['b', 'a', 'a'], ['b', 'a', 'b'], ['b', 'a', 'c'], ['b', 'b', 'a'], ['b', 'b', 'b'], ['b', 'b', 'c'], ['b', 'c', 'a'], ['b', 'c', 'b'], ['b', 'c', 'c'], ['c', 'a', 'a'], ['c', 'a', 'b'], ['c', 'a', 'c'], ['c', 'b', 'a'], ['c', 'b', 'b'], ['c', 'b', 'c'], ['c', 'c', 'a'], ['c', 'c', 'b'], ['c', 'c', 'c']]
sage: sum(1 for w in words(['a','b','c'], 3))
27


Here is another recursive iterator:

sage: def dyck_words(l):
....:     if l==0:
....:         yield ''
....:     else:
....:         for k in range(l):
....:             for w1 in dyck_words(k):
....:                 for w2 in dyck_words(l-k-1):
....:                     yield '('+w1+')'+w2

sage: list(dyck_words(4))
['()()()()',
'()()(())',
'()(())()',
'()(()())',
'()((()))',
'(())()()',
'(())(())',
'(()())()',
'((()))()',
'(()()())',
'(()(()))',
'((())())',
'((()()))',
'(((())))']

sage: sum(1 for w in dyck_words(5))
42


Standard Iterables#

Finally, many standard Python and Sage objects are iterable; that is one may iterate through their elements:

sage: sum( x^len(s) for s in Subsets(8) )
x^8 + 8*x^7 + 28*x^6 + 56*x^5 + 70*x^4 + 56*x^3 + 28*x^2 + 8*x + 1

sage: sum( x^p.length() for p in Permutations(3) )
x^3 + 2*x^2 + 2*x + 1

sage: factor(sum( x^p.length() for p in Permutations(3) ))
(x^2 + x + 1)*(x + 1)

sage: P = Permutations(5)
sage: all( p in P for p in P )
True

sage: for p in GL(2, 2): print(p); print("")
[1 0]
[0 1]

[0 1]
[1 0]

[0 1]
[1 1]

[1 1]
[0 1]

[1 1]
[1 0]

[1 0]
[1 1]

sage: for p in Partitions(3): print(p)
[3]
[2, 1]
[1, 1, 1]


Beware of infinite loops:

sage: for p in Partitions(): print(p)          # not tested

sage: for p in Primes(): print(p)              # not tested


Infinite loops can nevertheless be very useful:

sage: exists( Primes(), lambda p: not is_prime(mersenne(p)) )
(True, 11)

sage: counter_examples = (p for p in Primes() if not is_prime(mersenne(p)))
sage: next(counter_examples)
11