.. _tutorial-comprehensions: ================================================== Tutorial: Comprehensions, Iterators, and Iterables ================================================== .. MODULEAUTHOR:: Florent Hivert <florent.hivert@univ-rouen.fr> and Nicolas M. ThiƩry <nthiery at users.sf.net> .. linkall List comprehensions =================== *List comprehensions* are a very handy way to construct lists in Python. You can use either of the following idioms: .. CODE-BLOCK:: python [ <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] .. TOPIC:: Exercises #. Construct the list of the squares of the prime integers between 1 and 10:: sage: # edit here #. Construct the list of the perfect squares less than 100 (hint: use :func:`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]] .. TOPIC:: Exercises #. 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 #. 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 .. TOPIC:: Exercises #. Compute the sum of `\binom{10}{i}` for all even `i`:: sage: # edit here #. Compute the sum of the products of all pairs of co-prime numbers `i, j` for `i<j<10`:: sage: # edit here Typical usage of iterators -------------------------- Iterators are very handy with the functions :func:`all`, :func:`any`, and :func:`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 :func:`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 .. TOPIC:: Exercises #. 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 :mod:`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 :func:`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 :func:`map` and :func:`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]] .. TOPIC:: Exercises #. 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 .. TOPIC:: Exercises #. 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] <BLANKLINE> [0 1] [1 0] <BLANKLINE> [0 1] [1 1] <BLANKLINE> [1 1] [0 1] <BLANKLINE> [1 1] [1 0] <BLANKLINE> [1 0] [1 1] <BLANKLINE> sage: for p in Partitions(3): print(p) [3] [2, 1] [1, 1, 1] .. skip Beware of infinite loops:: sage: for p in Partitions(): print(p) # not tested .. skip :: 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