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itertools — Functions creating iterators for efficient looping — Python v3.0 documentation

itertools — Functions creating iterators for efficient looping¶

This module implements a number of iterator building blocks inspired by constructs from the Haskell and SML programming languages. Each has been recast in a form suitable for Python.

The module standardizes a core set of fast, memory efficient tools that are useful by themselves or in combination. Standardization helps avoid the readability and reliability problems which arise when many different individuals create their own slightly varying implementations, each with their own quirks and naming conventions.

The tools are designed to combine readily with one another. This makes it easy to construct more specialized tools succinctly and efficiently in pure Python.

For instance, SML provides a tabulation tool: tabulate(f) which produces a sequence f(0), f(1), .... But, this effect can be achieved in Python by combining map() and count() to form map(f, count()).

Likewise, the functional tools are designed to work well with the high-speed functions provided by the operator module.

Whether cast in pure python form or compiled code, tools that use iterators are more memory efficient (and often faster) than their list based counterparts. Adopting the principles of just-in-time manufacturing, they create data when and where needed instead of consuming memory with the computer equivalent of “inventory”.

See also

The Standard ML Basis Library, The Standard ML Basis Library.

Haskell, A Purely Functional Language, Definition of Haskell and the Standard Libraries.

Itertool functions¶

The following module functions all construct and return iterators. Some provide streams of infinite length, so they should only be accessed by functions or loops that truncate the stream.


Make an iterator that returns elements from the first iterable until it is exhausted, then proceeds to the next iterable, until all of the iterables are exhausted. Used for treating consecutive sequences as a single sequence. Equivalent to:

def chain(*iterables):
    # chain('ABC', 'DEF') --> A B C D E F
    for it in iterables:
        for element in it:
            yield element

Alternate constructor for chain(). Gets chained inputs from a single iterable argument that is evaluated lazily. Equivalent to:

def from_iterable(iterables):
    # chain.from_iterable(['ABC', 'DEF']) --> A B C D E F
    for it in iterables:
        for element in it:
            yield element
itertools.combinations(iterable, r)¶

Return r length subsequences of elements from the input iterable.

Combinations are emitted in lexicographic sort order. So, if the input iterable is sorted, the combination tuples will be produced in sorted order.

Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeat values in each combination.

Equivalent to:

def combinations(iterable, r):
    # combinations('ABCD', 2) --> AB AC AD BC BD CD
    # combinations(range(4), 3) --> 012 013 023 123
    pool = tuple(iterable)
    n = len(pool)
    indices = range(r)
    yield tuple(pool[i] for i in indices)
    while 1:
        for i in reversed(range(r)):
            if indices[i] != i + n - r:
        indices[i] += 1
        for j in range(i+1, r):
            indices[j] = indices[j-1] + 1
        yield tuple(pool[i] for i in indices)

The code for combinations() can be also expressed as a subsequence of permutations() after filtering entries where the elements are not in sorted order (according to their position in the input pool):

def combinations(iterable, r):
    pool = tuple(iterable)
    n = len(pool)
    for indices in permutations(range(n), r):
        if sorted(indices) == list(indices):
            yield tuple(pool[i] for i in indices)

Make an iterator that returns consecutive integers starting with n. If not specified n defaults to zero. Often used as an argument to map() to generate consecutive data points. Also, used with zip() to add sequence numbers. Equivalent to:

def count(n=0):
    # count(10) --> 10 11 12 13 14 ...
    while True:
        yield n
        n += 1

Make an iterator returning elements from the iterable and saving a copy of each. When the iterable is exhausted, return elements from the saved copy. Repeats indefinitely. Equivalent to:

def cycle(iterable):
    # cycle('ABCD') --> A B C D A B C D A B C D ...
    saved = []
    for element in iterable:
        yield element
    while saved:
        for element in saved:
              yield element

Note, this member of the toolkit may require significant auxiliary storage (depending on the length of the iterable).

itertools.dropwhile(predicate, iterable)¶

Make an iterator that drops elements from the iterable as long as the predicate is true; afterwards, returns every element. Note, the iterator does not produce any output until the predicate first becomes false, so it may have a lengthy start-up time. Equivalent to:

def dropwhile(predicate, iterable):
    # dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1
    iterable = iter(iterable)
    for x in iterable:
        if not predicate(x):
            yield x
    for x in iterable:
        yield x
itertools.groupby(iterable[, key])¶

Make an iterator that returns consecutive keys and groups from the iterable. The key is a function computing a key value for each element. If not specified or is None, key defaults to an identity function and returns the element unchanged. Generally, the iterable needs to already be sorted on the same key function.

The operation of groupby() is similar to the uniq filter in Unix. It generates a break or new group every time the value of the key function changes (which is why it is usually necessary to have sorted the data using the same key function). That behavior differs from SQL’s GROUP BY which aggregates common elements regardless of their input order.

The returned group is itself an iterator that shares the underlying iterable with groupby(). Because the source is shared, when the groupby() object is advanced, the previous group is no longer visible. So, if that data is needed later, it should be stored as a list:

groups = []
uniquekeys = []
data = sorted(data, key=keyfunc)
for k, g in groupby(data, keyfunc):
    groups.append(list(g))      # Store group iterator as a list

groupby() is equivalent to:

class groupby(object):
    # [k for k, g in groupby('AAAABBBCCDAABBB')] --> A B C D A B
    # [(list(g)) for k, g in groupby('AAAABBBCCD')] --> AAAA BBB CC D
    def __init__(self, iterable, key=None):
        if key is None:
            key = lambda x: x
        self.keyfunc = key
        self.it = iter(iterable)
        self.tgtkey = self.currkey = self.currvalue = object()
    def __iter__(self):
        return self
    def __next__(self):
        while self.currkey == self.tgtkey:
            self.currvalue = next(self.it) # Exit on StopIteration
            self.currkey = self.keyfunc(self.currvalue)
        self.tgtkey = self.currkey
        return (self.currkey, self._grouper(self.tgtkey))
    def _grouper(self, tgtkey):
        while self.currkey == tgtkey:
            yield self.currvalue
            self.currvalue = next(self.it) # Exit on StopIteration
            self.currkey = self.keyfunc(self.currvalue)
itertools.filterfalse(predicate, iterable)¶

Make an iterator that filters elements from iterable returning only those for which the predicate is False. If predicate is None, return the items that are false. Equivalent to:

def filterfalse(predicate, iterable):
    # filterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8
    if predicate is None:
        predicate = bool
    for x in iterable:
        if not predicate(x):
            yield x
itertools.islice(iterable[, start], stop[, step])¶

Make an iterator that returns selected elements from the iterable. If start is non-zero, then elements from the iterable are skipped until start is reached. Afterward, elements are returned consecutively unless step is set higher than one which results in items being skipped. If stop is None, then iteration continues until the iterator is exhausted, if at all; otherwise, it stops at the specified position. Unlike regular slicing, islice() does not support negative values for start, stop, or step. Can be used to extract related fields from data where the internal structure has been flattened (for example, a multi-line report may list a name field on every third line). Equivalent to:

def islice(iterable, *args):
    # islice('ABCDEFG', 2) --> A B
    # islice('ABCDEFG', 2, 4) --> C D
    # islice('ABCDEFG', 2, None) --> C D E F G
    # islice('ABCDEFG', 0, None, 2) --> A C E G
    s = slice(*args)
    it = range(s.start or 0, s.stop or sys.maxsize, s.step or 1)
    nexti = next(it)
    for i, element in enumerate(iterable):
        if i == nexti:
            yield element
            nexti = next(it)

If start is None, then iteration starts at zero. If step is None, then the step defaults to one.

itertools.zip_longest(*iterables[, fillvalue])¶

Make an iterator that aggregates elements from each of the iterables. If the iterables are of uneven length, missing values are filled-in with fillvalue. Iteration continues until the longest iterable is exhausted. Equivalent to:

def zip_longest(*args, fillvalue=None):
    # zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-
    def sentinel(counter = ([fillvalue]*(len(args)-1)).pop):
        yield counter()         # yields the fillvalue, or raises IndexError
    fillers = repeat(fillvalue)
    iters = [chain(it, sentinel(), fillers) for it in args]
        for tup in zip(*iters):
            yield tup
    except IndexError:

If one of the iterables is potentially infinite, then the zip_longest() function should be wrapped with something that limits the number of calls (for example islice() or takewhile()). If not specified, fillvalue defaults to None.

itertools.permutations(iterable[, r])¶

Return successive r length permutations of elements in the iterable.

If r is not specified or is None, then r defaults to the length of the iterable and all possible full-length permutations are generated.

Permutations are emitted in lexicographic sort order. So, if the input iterable is sorted, the permutation tuples will be produced in sorted order.

Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeat values in each permutation.

Equivalent to:

def permutations(iterable, r=None):
    # permutations('ABCD', 2) --> AB AC AD BA BC BD CA CB CD DA DB DC
    # permutations(range(3)) --> 012 021 102 120 201 210
    pool = tuple(iterable)
    n = len(pool)
    r = n if r is None else r
    indices = range(n)
    cycles = range(n, n-r, -1)
    yield tuple(pool[i] for i in indices[:r])
    while n:
        for i in reversed(range(r)):
            cycles[i] -= 1
            if cycles[i] == 0:
                indices[i:] = indices[i+1:] + indices[i:i+1]
                cycles[i] = n - i
                j = cycles[i]
                indices[i], indices[-j] = indices[-j], indices[i]
                yield tuple(pool[i] for i in indices[:r])

The code for permutations() can be also expressed as a subsequence of product(), filtered to exclude entries with repeated elements (those from the same position in the input pool):

def permutations(iterable, r=None):
    pool = tuple(iterable)
    n = len(pool)
    r = n if r is None else r
    for indices in product(range(n), repeat=r):
        if len(set(indices)) == r:
            yield tuple(pool[i] for i in indices)
itertools.product(*iterables[, repeat])¶

Cartesian product of input iterables.

Equivalent to nested for-loops in a generator expression. For example, product(A, B) returns the same as ((x,y) for x in A for y in B).

The nested loops cycle like an odometer with the rightmost element advancing on every iteration. This pattern creates a lexicographic ordering so that if the input’s iterables are sorted, the product tuples are emitted in sorted order.

To compute the product of an iterable with itself, specify the number of repetitions with the optional repeat keyword argument. For example, product(A, repeat=4) means the same as product(A, A, A, A).

This function is equivalent to the following code, except that the actual implementation does not build up intermediate results in memory:

def product(*args, repeat=1):
    # product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy
    # product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111
    pools = map(tuple, args) * repeat
    result = [[]]
    for pool in pools:
        result = [x+[y] for x in result for y in pool]
    for prod in result:
        yield tuple(prod)
itertools.repeat(object[, times])¶

Make an iterator that returns object over and over again. Runs indefinitely unless the times argument is specified. Used as argument to map() for invariant parameters to the called function. Also used with zip() to create an invariant part of a tuple record. Equivalent to:

def repeat(object, times=None):
    # repeat(10, 3) --> 10 10 10
    if times is None:
        while True:
            yield object
        for i in range(times):
            yield object
itertools.starmap(function, iterable)¶

Make an iterator that computes the function using arguments obtained from the iterable. Used instead of map() when argument parameters are already grouped in tuples from a single iterable (the data has been “pre-zipped”). The difference between map() and starmap() parallels the distinction between function(a,b) and function(*c). Equivalent to:

def starmap(function, iterable):
    # starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000
    for args in iterable:
        yield function(*args)
itertools.takewhile(predicate, iterable)¶

Make an iterator that returns elements from the iterable as long as the predicate is true. Equivalent to:

def takewhile(predicate, iterable):
    # takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
    for x in iterable:
        if predicate(x):
            yield x
itertools.tee(iterable[, n=2])¶

Return n independent iterators from a single iterable. The case where n==2 is equivalent to:

def tee(iterable):
    def gen(next, data={}):
        for i in count():
            if i in data:
                yield data.pop(i)
                data[i] = next()
                yield data[i]
    it = iter(iterable)
    return (gen(it.__next__), gen(it.__next__))

Note, once tee() has made a split, the original iterable should not be used anywhere else; otherwise, the iterable could get advanced without the tee objects being informed.

Note, this member of the toolkit may require significant auxiliary storage (depending on how much temporary data needs to be stored). In general, if one iterator is going to use most or all of the data before the other iterator, it is faster to use list() instead of tee().


The following examples show common uses for each tool and demonstrate ways they can be combined.

# Show a dictionary sorted and grouped by value
>>> from operator import itemgetter
>>> d = dict(a=1, b=2, c=1, d=2, e=1, f=2, g=3)
>>> di = sorted(d.items(), key=itemgetter(1))
>>> for k, g in groupby(di, key=itemgetter(1)):
...     print(k, map(itemgetter(0), g))
1 ['a', 'c', 'e']
2 ['b', 'd', 'f']
3 ['g']

# Find runs of consecutive numbers using groupby.  The key to the solution
# is differencing with a range so that consecutive numbers all appear in
# same group.
>>> data = [ 1,  4,5,6, 10, 15,16,17,18, 22, 25,26,27,28]
>>> for k, g in groupby(enumerate(data), lambda t:t[0]-t[1]):
...     print(map(operator.itemgetter(1), g))
[4, 5, 6]
[15, 16, 17, 18]
[25, 26, 27, 28]


This section shows recipes for creating an extended toolset using the existing itertools as building blocks.

The extended tools offer the same high performance as the underlying toolset. The superior memory performance is kept by processing elements one at a time rather than bringing the whole iterable into memory all at once. Code volume is kept small by linking the tools together in a functional style which helps eliminate temporary variables. High speed is retained by preferring “vectorized” building blocks over the use of for-loops and generators which incur interpreter overhead.

def take(n, iterable):
    "Return first n items of the iterable as a list"
    return list(islice(iterable, n))

def enumerate(iterable, start=0):
    return zip(count(start), iterable)

def tabulate(function, start=0):
    "Return function(0), function(1), ..."
    return map(function, count(start))

def nth(iterable, n):
    "Returns the nth item or empty list"
    return list(islice(iterable, n, n+1))

def quantify(iterable, pred=bool):
    "Count how many times the predicate is true"
    return sum(map(pred, iterable))

def padnone(iterable):
    """Returns the sequence elements and then returns None indefinitely.

    Useful for emulating the behavior of the built-in map() function.
    return chain(iterable, repeat(None))

def ncycles(iterable, n):
    "Returns the sequence elements n times"
    return chain.from_iterable(repeat(iterable, n))

def dotproduct(vec1, vec2):
    return sum(map(operator.mul, vec1, vec2))

def flatten(listOfLists):
    return list(chain.from_iterable(listOfLists))

def repeatfunc(func, times=None, *args):
    """Repeat calls to func with specified arguments.

    Example:  repeatfunc(random.random)
    if times is None:
        return starmap(func, repeat(args))
    return starmap(func, repeat(args, times))

def pairwise(iterable):
    "s -> (s0,s1), (s1,s2), (s2, s3), ..."
    a, b = tee(iterable)
    for elem in b:
    return zip(a, b)

def grouper(n, iterable, fillvalue=None):
    "grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx"
    args = [iter(iterable)] * n
    return zip_longest(fillvalue=fillvalue, *args)

def roundrobin(*iterables):
    "roundrobin('ABC', 'D', 'EF') --> A D E B F C"
    # Recipe credited to George Sakkis
    pending = len(iterables)
    nexts = cycle(iter(it).__next__ for it in iterables)
    while pending:
            for next in nexts:
                yield next()
        except StopIteration:
            pending -= 1
            nexts = cycle(islice(nexts, pending))

def powerset(iterable):
    "powerset('ab') --> set([]), set(['a']), set(['b']), set(['a', 'b'])"
    # Recipe credited to Eric Raymond
    pairs = [(2**i, x) for i, x in enumerate(iterable)]
    for n in xrange(2**len(pairs)):
        yield set(x for m, x in pairs if m&n)

def compress(data, selectors):
    "compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F"
    return (d for d, s in zip(data, selectors) if s)

def combinations_with_replacement(iterable, r):
    "combinations_with_replacement('ABC', 3) --> AA AB AC BB BC CC"
    pool = tuple(iterable)
    n = len(pool)
    indices = [0] * r
    yield tuple(pool[i] for i in indices)
    while True:
        for i in reversed(range(r)):
            if indices[i] != n - 1:
        indices[i:] = [indices[i] + 1] * (r - i)
        yield tuple(pool[i] for i in indices)
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