Source code for more_itertools.recipes

"""Imported from the recipes section of the itertools documentation.

All functions taken from the recipes section of the itertools library docs
[1]_.
Some backward-compatible usability improvements have been made.

.. [1] http://docs.python.org/library/itertools.html#recipes

"""
import warnings
from collections import deque
from itertools import (
    chain,
    combinations,
    count,
    cycle,
    groupby,
    islice,
    repeat,
    starmap,
    tee,
    zip_longest,
)
import operator
from random import randrange, sample, choice

__all__ = [
    'all_equal',
    'before_and_after',
    'consume',
    'convolve',
    'dotproduct',
    'first_true',
    'flatten',
    'grouper',
    'iter_except',
    'ncycles',
    'nth',
    'nth_combination',
    'padnone',
    'pad_none',
    'pairwise',
    'partition',
    'powerset',
    'prepend',
    'quantify',
    'random_combination_with_replacement',
    'random_combination',
    'random_permutation',
    'random_product',
    'repeatfunc',
    'roundrobin',
    'sliding_window',
    'tabulate',
    'tail',
    'take',
    'triplewise',
    'unique_everseen',
    'unique_justseen',
]


[docs]def take(n, iterable): """Return first *n* items of the iterable as a list. >>> take(3, range(10)) [0, 1, 2] If there are fewer than *n* items in the iterable, all of them are returned. >>> take(10, range(3)) [0, 1, 2] """ return list(islice(iterable, n))
[docs]def tabulate(function, start=0): """Return an iterator over the results of ``func(start)``, ``func(start + 1)``, ``func(start + 2)``... *func* should be a function that accepts one integer argument. If *start* is not specified it defaults to 0. It will be incremented each time the iterator is advanced. >>> square = lambda x: x ** 2 >>> iterator = tabulate(square, -3) >>> take(4, iterator) [9, 4, 1, 0] """ return map(function, count(start))
[docs]def tail(n, iterable): """Return an iterator over the last *n* items of *iterable*. >>> t = tail(3, 'ABCDEFG') >>> list(t) ['E', 'F', 'G'] """ return iter(deque(iterable, maxlen=n))
[docs]def consume(iterator, n=None): """Advance *iterable* by *n* steps. If *n* is ``None``, consume it entirely. Efficiently exhausts an iterator without returning values. Defaults to consuming the whole iterator, but an optional second argument may be provided to limit consumption. >>> i = (x for x in range(10)) >>> next(i) 0 >>> consume(i, 3) >>> next(i) 4 >>> consume(i) >>> next(i) Traceback (most recent call last): File "<stdin>", line 1, in <module> StopIteration If the iterator has fewer items remaining than the provided limit, the whole iterator will be consumed. >>> i = (x for x in range(3)) >>> consume(i, 5) >>> next(i) Traceback (most recent call last): File "<stdin>", line 1, in <module> StopIteration """ # Use functions that consume iterators at C speed. if n is None: # feed the entire iterator into a zero-length deque deque(iterator, maxlen=0) else: # advance to the empty slice starting at position n next(islice(iterator, n, n), None)
[docs]def nth(iterable, n, default=None): """Returns the nth item or a default value. >>> l = range(10) >>> nth(l, 3) 3 >>> nth(l, 20, "zebra") 'zebra' """ return next(islice(iterable, n, None), default)
[docs]def all_equal(iterable): """ Returns ``True`` if all the elements are equal to each other. >>> all_equal('aaaa') True >>> all_equal('aaab') False """ g = groupby(iterable) return next(g, True) and not next(g, False)
[docs]def quantify(iterable, pred=bool): """Return the how many times the predicate is true. >>> quantify([True, False, True]) 2 """ return sum(map(pred, iterable))
[docs]def pad_none(iterable): """Returns the sequence of elements and then returns ``None`` indefinitely. >>> take(5, pad_none(range(3))) [0, 1, 2, None, None] Useful for emulating the behavior of the built-in :func:`map` function. See also :func:`padded`. """ return chain(iterable, repeat(None))
padnone = pad_none
[docs]def ncycles(iterable, n): """Returns the sequence elements *n* times >>> list(ncycles(["a", "b"], 3)) ['a', 'b', 'a', 'b', 'a', 'b'] """ return chain.from_iterable(repeat(tuple(iterable), n))
[docs]def dotproduct(vec1, vec2): """Returns the dot product of the two iterables. >>> dotproduct([10, 10], [20, 20]) 400 """ return sum(map(operator.mul, vec1, vec2))
[docs]def flatten(listOfLists): """Return an iterator flattening one level of nesting in a list of lists. >>> list(flatten([[0, 1], [2, 3]])) [0, 1, 2, 3] See also :func:`collapse`, which can flatten multiple levels of nesting. """ return chain.from_iterable(listOfLists)
[docs]def repeatfunc(func, times=None, *args): """Call *func* with *args* repeatedly, returning an iterable over the results. If *times* is specified, the iterable will terminate after that many repetitions: >>> from operator import add >>> times = 4 >>> args = 3, 5 >>> list(repeatfunc(add, times, *args)) [8, 8, 8, 8] If *times* is ``None`` the iterable will not terminate: >>> from random import randrange >>> times = None >>> args = 1, 11 >>> take(6, repeatfunc(randrange, times, *args)) # doctest:+SKIP [2, 4, 8, 1, 8, 4] """ if times is None: return starmap(func, repeat(args)) return starmap(func, repeat(args, times))
def _pairwise(iterable): """Returns an iterator of paired items, overlapping, from the original >>> take(4, pairwise(count())) [(0, 1), (1, 2), (2, 3), (3, 4)] On Python 3.10 and above, this is an alias for :func:`itertools.pairwise`. """ a, b = tee(iterable) next(b, None) yield from zip(a, b) try: from itertools import pairwise as itertools_pairwise except ImportError: pairwise = _pairwise else:
[docs] def pairwise(iterable): yield from itertools_pairwise(iterable)
pairwise.__doc__ = _pairwise.__doc__
[docs]def grouper(iterable, n, fillvalue=None): """Collect data into fixed-length chunks or blocks. >>> list(grouper('ABCDEFG', 3, 'x')) [('A', 'B', 'C'), ('D', 'E', 'F'), ('G', 'x', 'x')] """ if isinstance(iterable, int): warnings.warn( "grouper expects iterable as first parameter", DeprecationWarning ) n, iterable = iterable, n args = [iter(iterable)] * n return zip_longest(fillvalue=fillvalue, *args)
[docs]def roundrobin(*iterables): """Yields an item from each iterable, alternating between them. >>> list(roundrobin('ABC', 'D', 'EF')) ['A', 'D', 'E', 'B', 'F', 'C'] This function produces the same output as :func:`interleave_longest`, but may perform better for some inputs (in particular when the number of iterables is small). """ # Recipe credited to George Sakkis pending = len(iterables) nexts = cycle(iter(it).__next__ for it in iterables) while pending: try: for next in nexts: yield next() except StopIteration: pending -= 1 nexts = cycle(islice(nexts, pending))
[docs]def partition(pred, iterable): """ Returns a 2-tuple of iterables derived from the input iterable. The first yields the items that have ``pred(item) == False``. The second yields the items that have ``pred(item) == True``. >>> is_odd = lambda x: x % 2 != 0 >>> iterable = range(10) >>> even_items, odd_items = partition(is_odd, iterable) >>> list(even_items), list(odd_items) ([0, 2, 4, 6, 8], [1, 3, 5, 7, 9]) If *pred* is None, :func:`bool` is used. >>> iterable = [0, 1, False, True, '', ' '] >>> false_items, true_items = partition(None, iterable) >>> list(false_items), list(true_items) ([0, False, ''], [1, True, ' ']) """ if pred is None: pred = bool evaluations = ((pred(x), x) for x in iterable) t1, t2 = tee(evaluations) return ( (x for (cond, x) in t1 if not cond), (x for (cond, x) in t2 if cond), )
[docs]def powerset(iterable): """Yields all possible subsets of the iterable. >>> list(powerset([1, 2, 3])) [(), (1,), (2,), (3,), (1, 2), (1, 3), (2, 3), (1, 2, 3)] :func:`powerset` will operate on iterables that aren't :class:`set` instances, so repeated elements in the input will produce repeated elements in the output. Use :func:`unique_everseen` on the input to avoid generating duplicates: >>> seq = [1, 1, 0] >>> list(powerset(seq)) [(), (1,), (1,), (0,), (1, 1), (1, 0), (1, 0), (1, 1, 0)] >>> from more_itertools import unique_everseen >>> list(powerset(unique_everseen(seq))) [(), (1,), (0,), (1, 0)] """ s = list(iterable) return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))
[docs]def unique_everseen(iterable, key=None): """ Yield unique elements, preserving order. >>> list(unique_everseen('AAAABBBCCDAABBB')) ['A', 'B', 'C', 'D'] >>> list(unique_everseen('ABBCcAD', str.lower)) ['A', 'B', 'C', 'D'] Sequences with a mix of hashable and unhashable items can be used. The function will be slower (i.e., `O(n^2)`) for unhashable items. Remember that ``list`` objects are unhashable - you can use the *key* parameter to transform the list to a tuple (which is hashable) to avoid a slowdown. >>> iterable = ([1, 2], [2, 3], [1, 2]) >>> list(unique_everseen(iterable)) # Slow [[1, 2], [2, 3]] >>> list(unique_everseen(iterable, key=tuple)) # Faster [[1, 2], [2, 3]] Similary, you may want to convert unhashable ``set`` objects with ``key=frozenset``. For ``dict`` objects, ``key=lambda x: frozenset(x.items())`` can be used. """ seenset = set() seenset_add = seenset.add seenlist = [] seenlist_add = seenlist.append use_key = key is not None for element in iterable: k = key(element) if use_key else element try: if k not in seenset: seenset_add(k) yield element except TypeError: if k not in seenlist: seenlist_add(k) yield element
[docs]def unique_justseen(iterable, key=None): """Yields elements in order, ignoring serial duplicates >>> list(unique_justseen('AAAABBBCCDAABBB')) ['A', 'B', 'C', 'D', 'A', 'B'] >>> list(unique_justseen('ABBCcAD', str.lower)) ['A', 'B', 'C', 'A', 'D'] """ return map(next, map(operator.itemgetter(1), groupby(iterable, key)))
[docs]def iter_except(func, exception, first=None): """Yields results from a function repeatedly until an exception is raised. Converts a call-until-exception interface to an iterator interface. Like ``iter(func, sentinel)``, but uses an exception instead of a sentinel to end the loop. >>> l = [0, 1, 2] >>> list(iter_except(l.pop, IndexError)) [2, 1, 0] Multiple exceptions can be specified as a stopping condition: >>> l = [1, 2, 3, '...', 4, 5, 6] >>> list(iter_except(lambda: 1 + l.pop(), (IndexError, TypeError))) [7, 6, 5] >>> list(iter_except(lambda: 1 + l.pop(), (IndexError, TypeError))) [4, 3, 2] >>> list(iter_except(lambda: 1 + l.pop(), (IndexError, TypeError))) [] """ try: if first is not None: yield first() while 1: yield func() except exception: pass
[docs]def first_true(iterable, default=None, pred=None): """ Returns the first true value in the iterable. If no true value is found, returns *default* If *pred* is not None, returns the first item for which ``pred(item) == True`` . >>> first_true(range(10)) 1 >>> first_true(range(10), pred=lambda x: x > 5) 6 >>> first_true(range(10), default='missing', pred=lambda x: x > 9) 'missing' """ return next(filter(pred, iterable), default)
[docs]def random_product(*args, repeat=1): """Draw an item at random from each of the input iterables. >>> random_product('abc', range(4), 'XYZ') # doctest:+SKIP ('c', 3, 'Z') If *repeat* is provided as a keyword argument, that many items will be drawn from each iterable. >>> random_product('abcd', range(4), repeat=2) # doctest:+SKIP ('a', 2, 'd', 3) This equivalent to taking a random selection from ``itertools.product(*args, **kwarg)``. """ pools = [tuple(pool) for pool in args] * repeat return tuple(choice(pool) for pool in pools)
[docs]def random_permutation(iterable, r=None): """Return a random *r* length permutation of the elements in *iterable*. If *r* is not specified or is ``None``, then *r* defaults to the length of *iterable*. >>> random_permutation(range(5)) # doctest:+SKIP (3, 4, 0, 1, 2) This equivalent to taking a random selection from ``itertools.permutations(iterable, r)``. """ pool = tuple(iterable) r = len(pool) if r is None else r return tuple(sample(pool, r))
[docs]def random_combination(iterable, r): """Return a random *r* length subsequence of the elements in *iterable*. >>> random_combination(range(5), 3) # doctest:+SKIP (2, 3, 4) This equivalent to taking a random selection from ``itertools.combinations(iterable, r)``. """ pool = tuple(iterable) n = len(pool) indices = sorted(sample(range(n), r)) return tuple(pool[i] for i in indices)
[docs]def random_combination_with_replacement(iterable, r): """Return a random *r* length subsequence of elements in *iterable*, allowing individual elements to be repeated. >>> random_combination_with_replacement(range(3), 5) # doctest:+SKIP (0, 0, 1, 2, 2) This equivalent to taking a random selection from ``itertools.combinations_with_replacement(iterable, r)``. """ pool = tuple(iterable) n = len(pool) indices = sorted(randrange(n) for i in range(r)) return tuple(pool[i] for i in indices)
[docs]def nth_combination(iterable, r, index): """Equivalent to ``list(combinations(iterable, r))[index]``. The subsequences of *iterable* that are of length *r* can be ordered lexicographically. :func:`nth_combination` computes the subsequence at sort position *index* directly, without computing the previous subsequences. >>> nth_combination(range(5), 3, 5) (0, 3, 4) ``ValueError`` will be raised If *r* is negative or greater than the length of *iterable*. ``IndexError`` will be raised if the given *index* is invalid. """ pool = tuple(iterable) n = len(pool) if (r < 0) or (r > n): raise ValueError c = 1 k = min(r, n - r) for i in range(1, k + 1): c = c * (n - k + i) // i if index < 0: index += c if (index < 0) or (index >= c): raise IndexError result = [] while r: c, n, r = c * r // n, n - 1, r - 1 while index >= c: index -= c c, n = c * (n - r) // n, n - 1 result.append(pool[-1 - n]) return tuple(result)
[docs]def prepend(value, iterator): """Yield *value*, followed by the elements in *iterator*. >>> value = '0' >>> iterator = ['1', '2', '3'] >>> list(prepend(value, iterator)) ['0', '1', '2', '3'] To prepend multiple values, see :func:`itertools.chain` or :func:`value_chain`. """ return chain([value], iterator)
[docs]def convolve(signal, kernel): """Convolve the iterable *signal* with the iterable *kernel*. >>> signal = (1, 2, 3, 4, 5) >>> kernel = [3, 2, 1] >>> list(convolve(signal, kernel)) [3, 8, 14, 20, 26, 14, 5] Note: the input arguments are not interchangeable, as the *kernel* is immediately consumed and stored. """ kernel = tuple(kernel)[::-1] n = len(kernel) window = deque([0], maxlen=n) * n for x in chain(signal, repeat(0, n - 1)): window.append(x) yield sum(map(operator.mul, kernel, window))
[docs]def before_and_after(predicate, it): """A variant of :func:`takewhile` that allows complete access to the remainder of the iterator. >>> it = iter('ABCdEfGhI') >>> all_upper, remainder = before_and_after(str.isupper, it) >>> ''.join(all_upper) 'ABC' >>> ''.join(remainder) # takewhile() would lose the 'd' 'dEfGhI' Note that the first iterator must be fully consumed before the second iterator can generate valid results. """ it = iter(it) transition = [] def true_iterator(): for elem in it: if predicate(elem): yield elem else: transition.append(elem) return def remainder_iterator(): yield from transition yield from it return true_iterator(), remainder_iterator()
[docs]def triplewise(iterable): """Return overlapping triplets from *iterable*. >>> list(triplewise('ABCDE')) [('A', 'B', 'C'), ('B', 'C', 'D'), ('C', 'D', 'E')] """ for (a, _), (b, c) in pairwise(pairwise(iterable)): yield a, b, c
[docs]def sliding_window(iterable, n): """Return a sliding window of width *n* over *iterable*. >>> list(sliding_window(range(6), 4)) [(0, 1, 2, 3), (1, 2, 3, 4), (2, 3, 4, 5)] If *iterable* has fewer than *n* items, then nothing is yielded: >>> list(sliding_window(range(3), 4)) [] For a variant with more features, see :func:`windowed`. """ it = iter(iterable) window = deque(islice(it, n), maxlen=n) if len(window) == n: yield tuple(window) for x in it: window.append(x) yield tuple(window)