# 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]


Exercises

1. Construct the list of the squares of the prime integers between 1 and 10:

sage: # edit here

2. Construct the list of the perfect squares less than 100 (hint: use srange() to get a list of Sage integers together with the method i.sqrtrem()):

sage: # edit here


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]]


Exercises

1. Compute the list of pairs $$(i,j)$$ of non negative integers such that i is at most $$5$$, j is at most 8, and i and j are co-prime:

sage: # edit here

2. Compute the same list for $$i < j < 10$$:

sage: # edit here


## 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 xrange(9))  # py2
256
sage: sum(binomial(8, i) for i in range(9))  # py3
256


Exercises

1. Compute the sum of $$\binom{10}{i}$$ for all even $$i$$:

sage: # edit here

2. Compute the sum of the gcd’s of all co-prime numbers $$i, j$$ for $$i<j<10$$:

sage: # edit here


### 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


Exercises

1. Build the list $$\{i^3 \mid -10<i<10\}$$. Can you find two of those cubes $$u$$ and $$v$$ such that $$u + v = 218$$?

sage: # edit here


### 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 use this new behaviour in Python 2, and keep your code compatible with Python3, you can use the compatibility library six as follows:

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

sage: from six.moves import filter
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]]


Exercises

1. Define an iterator for the $$i$$-th prime for $$5<i<10$$:

sage: # edit here


### 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


Exercises

1. Write an iterator with two parameters $$n$$, $$l$$ iterating through the set of nondecreasing lists of integers smaller than $$n$$ of length $$l$$:

sage: # edit here


## 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