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# `itertools` — Functions creating iterators for efficient looping

Цей модуль реалізує кілька будівельних блоків iterator, натхненних конструкціями з APL, Haskell і SML. Кожен був перероблений у формі, придатній для Python.

Модуль стандартизує основний набір швидких, ефективних інструментів пам’яті, які корисні окремо або в комбінації. Разом вони утворюють «алгебру ітераторів», що дає змогу створювати спеціалізовані інструменти лаконічно та ефективно на чистому Python.

Наприклад, SML надає інструмент табуляції: `tabulate(f)`, який створює послідовність `f(0), f(1), ...`. Такого ж ефекту можна досягти в Python, об’єднавши `map()` і `count()` для формування `map(f, count())`.

These tools and their built-in counterparts also work well with the high-speed functions in the `operator` module. For example, the multiplication operator can be mapped across two vectors to form an efficient dot-product: `sum(map(operator.mul, vector1, vector2))`.

Нескінченні ітератори:

Ітератор | Аргументи | Результати | приклад
--- | --- | --- | ---
`count()` | start, [step] | початок, початок+крок, початок+2*крок, … | `count(10) --> 10 11 12 13 14 ...`
`cycle()` | p | p0, p1, … plast, p0, p1, … | `cycle('ABCD') --> A B C D A B C D ...`
`repeat()` | елемент [,n] | елем, елем, елем, … нескінченно або до n разів | `repeat(10, 3) --> 10 10 10`

Ітератори, що завершуються на найкоротшій вхідній послідовності:

Ітератор | Аргументи | Результати | приклад
--- | --- | --- | ---
`accumulate()` | p [,func] | p0, p0+p1, p0+p1+p2, … | `accumulate([1,2,3,4,5]) --> 1 3 6 10 15`
`chain()` | p, q, … | p0, p1, … plast, q0, q1, … | `chain('ABC', 'DEF') --> A B C D E F`
`chain.from_iterable()` | ітерований | p0, p1, … plast, q0, q1, … | `chain.from_iterable(['ABC', 'DEF']) --> A B C D E F`
`compress()` | дані, селектори | (d[0], якщо s[0]), (d[1], якщо s[1]), … | `compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F`
`dropwhile()` | pred, seq | seq[n], seq[n+1], starting when pred fails | `dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1`
`filterfalse()` | pred, seq | elements of seq where pred(elem) is false | `filterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8`
`groupby()` | ітерований [, ключ] | субітератори, згруповані за значенням ключа (v) |
`islice()` | seq, [початок,] зупинка [, крок] | елементи з seq[start:stop:step] | `islice('ABCDEFG', 2, None) --> C D E F G`
`starmap()` | функція, посл | func(*seq[0]), func(*seq[1]), … | `starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000`
`takewhile()` | pred, seq | seq[0], seq[1], until pred fails | `takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4`
`tee()` | воно, п | it1, it2, … itn розділяє один ітератор на n |
`zip_longest()` | p, q, … | (p[0], q[0]), (p[1], q[1]), … | `zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-`

Комбінаторні ітератори:

Ітератор | Аргументи | Результати
--- | --- | ---
`product()` | p, q, … [repeat=1] | декартовий добуток, еквівалентний вкладеному циклу for
`permutations()` | p[, r] | кортежі довжини r, усі можливі впорядкування, відсутність повторюваних елементів
`combinations()` | p, r | кортежі довжини r, у відсортованому порядку, без повторюваних елементів
`combinations_with_replacement()` | p, r | кортежі довжини r, у відсортованому порядку, з повторюваними елементами

Приклади | Результати
--- | ---
`product('ABCD', repeat=2)` | `AA AB AC AD BA BB BC BD CA CB CC CD DA DB DC DD`
`permutations('ABCD', 2)` | `AB AC AD BA BC BD CA CB CD DA DB DC`
`combinations('ABCD', 2)` | `AB AC AD BC BD CD`
`combinations_with_replacement('ABCD', 2)` | `AA AB AC AD BB BC BD CC CD DD`

## 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.

`itertools.``accumulate`(iterable[, func, *, initial=None])

  Make an iterator that returns accumulated sums, or accumulated results of other binary functions (specified via the optional func argument).

  If func is supplied, it should be a function of two arguments. Elements of the input iterable may be any type that can be accepted as arguments to func. (For example, with the default operation of addition, elements may be any addable type including `Decimal` or `Fraction`.)

  Usually, the number of elements output matches the input iterable. However, if the keyword argument initial is provided, the accumulation leads off with the initial value so that the output has one more element than the input iterable.

  Приблизно еквівалентно:

      def accumulate(iterable, func=operator.add, *, initial=None):
          'Return running totals'
          # accumulate([1,2,3,4,5]) --> 1 3 6 10 15
          # accumulate([1,2,3,4,5], initial=100) --> 100 101 103 106 110 115
          # accumulate([1,2,3,4,5], operator.mul) --> 1 2 6 24 120
          it = iter(iterable)
          total = initial
          if initial is None:
              try:
                  total = next(it)
              except StopIteration:
                  return
          yield total
          for element in it:
              total = func(total, element)
              yield total

  There are a number of uses for the func argument. It can be set to `min()` for a running minimum, `max()` for a running maximum, or `operator.mul()` for a running product. Amortization tables can be built by accumulating interest and applying payments. First-order recurrence relations can be modeled by supplying the initial value in the iterable and using only the accumulated total in func argument:

      >>> data = [3, 4, 6, 2, 1, 9, 0, 7, 5, 8]
      >>> list(accumulate(data, operator.mul))     # running product
      [3, 12, 72, 144, 144, 1296, 0, 0, 0, 0]
      >>> list(accumulate(data, max))              # running maximum
      [3, 4, 6, 6, 6, 9, 9, 9, 9, 9]

      # Amortize a 5% loan of 1000 with 4 annual payments of 90
      >>> cashflows = [1000, -90, -90, -90, -90]
      >>> list(accumulate(cashflows, lambda bal, pmt: bal*1.05 + pmt))
      [1000, 960.0, 918.0, 873.9000000000001, 827.5950000000001]

      # Chaotic recurrence relation https://en.wikipedia.org/wiki/Logistic_map
      >>> logistic_map = lambda x, _:  r * x * (1 - x)
      >>> r = 3.8
      >>> x0 = 0.4
      >>> inputs = repeat(x0, 36)     # only the initial value is used
      >>> [format(x, '.2f') for x in accumulate(inputs, logistic_map)]
      ['0.40', '0.91', '0.30', '0.81', '0.60', '0.92', '0.29', '0.79', '0.63',
       '0.88', '0.39', '0.90', '0.33', '0.84', '0.52', '0.95', '0.18', '0.57',
       '0.93', '0.25', '0.71', '0.79', '0.63', '0.88', '0.39', '0.91', '0.32',
       '0.83', '0.54', '0.95', '0.20', '0.60', '0.91', '0.30', '0.80', '0.60']

  Перегляньте `functools.reduce()` подібну функцію, яка повертає лише остаточне накопичене значення.

  Нове в версії 3.2.

  Змінено в версії 3.3: Added the optional func parameter.

  Змінено в версії 3.8: Додано необов’язковий початковий параметр.

`itertools.``chain`(*iterables)

  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. Roughly equivalent to:

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

classmethod `chain.``from_iterable`(iterable)

  Альтернативний конструктор для `chain()`. Отримує ланцюгові вхідні дані з одного ітерованого аргументу, який обчислюється ліниво. Приблизно еквівалентно:

      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)

  Повертає r довжину підпослідовностей елементів із вхідного iterable.

  The combination tuples are emitted in lexicographic ordering according to the order of the input iterable. 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.

  Приблизно еквівалентно:

      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)
          if r > n:
              return
          indices = list(range(r))
          yield tuple(pool[i] for i in indices)
          while True:
              for i in reversed(range(r)):
                  if indices[i] != i + n - r:
                      break
              else:
                  return
              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)

  The number of items returned is `n! / r! / (n-r)!` when `0 <= r <= n` or zero when `r > n`.

`itertools.``combinations_with_replacement`(iterable, r)

  Повертає r довжину підпослідовностей елементів із вхідного iterable, що дозволяє повторювати окремі елементи більше одного разу.

  The combination tuples are emitted in lexicographic ordering according to the order of the input iterable. 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, the generated combinations will also be unique.

  Приблизно еквівалентно:

      def combinations_with_replacement(iterable, r):
          # combinations_with_replacement('ABC', 2) --> AA AB AC BB BC CC
          pool = tuple(iterable)
          n = len(pool)
          if not n and r:
              return
          indices = [0] * r
          yield tuple(pool[i] for i in indices)
          while True:
              for i in reversed(range(r)):
                  if indices[i] != n - 1:
                      break
              else:
                  return
              indices[i:] = [indices[i] + 1] * (r - i)
              yield tuple(pool[i] for i in indices)

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

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

  The number of items returned is `(n+r-1)! / r! / (n-1)!` when `n > 0`.

  Нове в версії 3.1.

`itertools.``compress`(data, selectors)

  Make an iterator that filters elements from data returning only those that have a corresponding element in selectors that evaluates to `True`. Stops when either the data or selectors iterables has been exhausted. Roughly equivalent to:

      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)

  Нове в версії 3.1.

`itertools.``count`(start=0, step=1)

  Make an iterator that returns evenly spaced values starting with number start. Often used as an argument to `map()` to generate consecutive data points. Also, used with `zip()` to add sequence numbers. Roughly equivalent to:

      def count(start=0, step=1):
          # count(10) --> 10 11 12 13 14 ...
          # count(2.5, 0.5) -> 2.5 3.0 3.5 ...
          n = start
          while True:
              yield n
              n += step

  When counting with floating point numbers, better accuracy can sometimes be achieved by substituting multiplicative code such as: `(start + step * i for i in count())`.

  Змінено в версії 3.1: Додано аргумент step і дозволено нецілі аргументи.

`itertools.``cycle`(iterable)

  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. Roughly 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
              saved.append(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. Roughly 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
                  break
          for x in iterable:
              yield x

`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. Roughly 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.``groupby`(iterable, key=None)

  Створіть ітератор, який повертає послідовні ключі та групи з iterable. Ключ — це функція, яка обчислює значення ключа для кожного елемента. Якщо не вказано або має значення `None`, key за замовчуванням використовується як функція ідентифікації та повертає елемент без змін. Як правило, iterable вже має бути відсортований за тією самою ключовою функцією.

  Робота `groupby()` подібна до фільтра `uniq` в Unix. Він генерує розрив або нову групу щоразу, коли змінюється значення ключової функції (саме тому зазвичай необхідно відсортувати дані за допомогою тієї самої ключової функції). Така поведінка відрізняється від GROUP BY SQL, яка агрегує загальні елементи незалежно від порядку введення.

  Повернена група сама є ітератором, який ділиться основним ітератором із `groupby()`. Оскільки джерело є спільним, коли об’єкт `groupby()` розширено, попередня група більше не відображається. Отже, якщо ці дані знадобляться пізніше, їх слід зберегти як список:

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

  `groupby()` приблизно еквівалентно:

      class groupby:
          # [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):
              self.id = object()
              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, self.id))
          def _grouper(self, tgtkey, id):
              while self.id is id and self.currkey == tgtkey:
                  yield self.currvalue
                  try:
                      self.currvalue = next(self.it)
                  except StopIteration:
                      return
                  self.currkey = self.keyfunc(self.currvalue)

`itertools.``islice`(iterable, stop)
`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). Roughly 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)
          start, stop, step = s.start or 0, s.stop or sys.maxsize, s.step or 1
          it = iter(range(start, stop, step))
          try:
              nexti = next(it)
          except StopIteration:
              # Consume *iterable* up to the *start* position.
              for i, element in zip(range(start), iterable):
                  pass
              return
          try:
              for i, element in enumerate(iterable):
                  if i == nexti:
                      yield element
                      nexti = next(it)
          except StopIteration:
              # Consume to *stop*.
              for i, element in zip(range(i + 1, stop), iterable):
                  pass

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

`itertools.``permutations`(iterable, r=None)

  Return successive r length permutations of elements in the iterable.

  Якщо r не вказано або має значення `None`, тоді r за замовчуванням відповідає довжині iterable і генеруються всі можливі перестановки повної довжини.

  The permutation tuples are emitted in lexicographic ordering according to the order of the input iterable. 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 permutation.

  Приблизно еквівалентно:

      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
          if r > n:
              return
          indices = list(range(n))
          cycles = list(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
                  else:
                      j = cycles[i]
                      indices[i], indices[-j] = indices[-j], indices[i]
                      yield tuple(pool[i] for i in indices[:r])
                      break
              else:
                  return

  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)

  The number of items returned is `n! / (n-r)!` when `0 <= r <= n` or zero when `r > n`.

`itertools.``product`(*iterables, repeat=1)

  Cartesian product of input iterables.

  Приблизно еквівалентно вкладеним циклам for у виразі генератора. Наприклад, `product(A, B)` повертає те саме, що `((x,y) для x в A для y у B)`.

  Вкладені цикли обертаються як одометр із крайнім правим елементом, що просувається на кожній ітерації. Цей шаблон створює лексикографічне впорядкування, так що якщо ітеровані вхідні елементи відсортовані, кортежі продукту видаються в відсортованому порядку.

  Щоб обчислити добуток iterable із самим собою, вкажіть кількість повторень за допомогою необов’язкового аргументу repeat. Наприклад, `product(A, repeat=4)` означає те саме, що `product(A, A, A, A)`.

  Ця функція приблизно еквівалентна наступному коду, за винятком того, що фактична реалізація не накопичує проміжні результати в пам’яті:

      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 = [tuple(pool) for pool in 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)

  Перед запуском `product()` він повністю споживає вхідні ітерації, зберігаючи пули значень у пам’яті для генерації продуктів. Відповідно, це корисно лише з обмеженими вхідними даними.

`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.

  Приблизно еквівалентно:

      def repeat(object, times=None):
          # repeat(10, 3) --> 10 10 10
          if times is None:
              while True:
                  yield object
          else:
              for i in range(times):
                  yield object

  A common use for repeat is to supply a stream of constant values to map or zip:

      >>> list(map(pow, range(10), repeat(2)))
      [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

`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)`. Roughly 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. Roughly 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
              else:
                  break

`itertools.``tee`(iterable, n=2)

  Повертає n незалежних ітераторів з одного ітератора.

  The following Python code helps explain what tee does (although the actual implementation is more complex and uses only a single underlying FIFO queue).

  Приблизно еквівалентно:

      def tee(iterable, n=2):
          it = iter(iterable)
          deques = [collections.deque() for i in range(n)]
          def gen(mydeque):
              while True:
                  if not mydeque:             # when the local deque is empty
                      try:
                          newval = next(it)   # fetch a new value and
                      except StopIteration:
                          return
                      for d in deques:        # load it to all the deques
                          d.append(newval)
                  yield mydeque.popleft()
          return tuple(gen(d) for d in deques)

  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.

  `tee` iterators are not threadsafe. A `RuntimeError` may be raised when using simultaneously iterators returned by the same `tee()` call, even if the original iterable is threadsafe.

  Цей інструмент itertool може потребувати значного допоміжного сховища (залежно від того, скільки тимчасових даних потрібно зберегти). Загалом, якщо один ітератор використовує більшість або всі дані перед запуском іншого ітератора, швидше використовувати `list()` замість `tee()`.

`itertools.``zip_longest`(*iterables, fillvalue=None)

  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. Roughly equivalent to:

      def zip_longest(*args, fillvalue=None):
          # zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-
          iterators = [iter(it) for it in args]
          num_active = len(iterators)
          if not num_active:
              return
          while True:
              values = []
              for i, it in enumerate(iterators):
                  try:
                      value = next(it)
                  except StopIteration:
                      num_active -= 1
                      if not num_active:
                          return
                      iterators[i] = repeat(fillvalue)
                      value = fillvalue
                  values.append(value)
              yield tuple(values)

  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`.

## Рецепти Itertools

У цьому розділі наведено рецепти для створення розширеного набору інструментів з використанням існуючих itertools як будівельних блоків.

Substantially all of these recipes and many, many others can be installed from the more-itertools project found on the Python Package Index:

    pip install more-itertools

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 prepend(value, iterator):
        "Prepend a single value in front of an iterator"
        # prepend(1, [2, 3, 4]) -> 1 2 3 4
        return chain([value], iterator)

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

    def tail(n, iterable):
        "Return an iterator over the last n items"
        # tail(3, 'ABCDEFG') --> E F G
        return iter(collections.deque(iterable, maxlen=n))

    def consume(iterator, n=None):
        "Advance the iterator n-steps ahead. If n is None, consume entirely."
        # Use functions that consume iterators at C speed.
        if n is None:
            # feed the entire iterator into a zero-length deque
            collections.deque(iterator, maxlen=0)
        else:
            # advance to the empty slice starting at position n
            next(islice(iterator, n, n), None)

    def nth(iterable, n, default=None):
        "Returns the nth item or a default value"
        return next(islice(iterable, n, None), default)

    def all_equal(iterable):
        "Returns True if all the elements are equal to each other"
        g = groupby(iterable)
        return next(g, True) and not next(g, False)

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

    def pad_none(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(tuple(iterable), n))

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

    def convolve(signal, kernel):
        # See:  https://betterexplained.com/articles/intuitive-convolution/
        # convolve(data, [0.25, 0.25, 0.25, 0.25]) --> Moving average (blur)
        # convolve(data, [1, -1]) --> 1st finite difference (1st derivative)
        # convolve(data, [1, -2, 1]) --> 2nd finite difference (2nd derivative)
        kernel = tuple(kernel)[::-1]
        n = len(kernel)
        window = collections.deque([0], maxlen=n) * n
        for x in chain(signal, repeat(0, n-1)):
            window.append(x)
            yield sum(map(operator.mul, kernel, window))

    def flatten(list_of_lists):
        "Flatten one level of nesting"
        return chain.from_iterable(list_of_lists)

    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)
        next(b, None)
        return zip(a, b)

    def grouper(iterable, n, fillvalue=None):
        "Collect data into fixed-length chunks or blocks"
        # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
        args = [iter(iterable)] * n
        return zip_longest(*args, fillvalue=fillvalue)

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

    def partition(pred, iterable):
        "Use a predicate to partition entries into false entries and true entries"
        # partition(is_odd, range(10)) --> 0 2 4 6 8   and  1 3 5 7 9
        t1, t2 = tee(iterable)
        return filterfalse(pred, t1), filter(pred, t2)

    def powerset(iterable):
        "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
        s = list(iterable)
        return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))

    def unique_everseen(iterable, key=None):
        "List unique elements, preserving order. Remember all elements ever seen."
        # unique_everseen('AAAABBBCCDAABBB') --> A B C D
        # unique_everseen('ABBCcAD', str.lower) --> A B C D
        seen = set()
        seen_add = seen.add
        if key is None:
            for element in filterfalse(seen.__contains__, iterable):
                seen_add(element)
                yield element
        else:
            for element in iterable:
                k = key(element)
                if k not in seen:
                    seen_add(k)
                    yield element

    def unique_justseen(iterable, key=None):
        "List unique elements, preserving order. Remember only the element just seen."
        # unique_justseen('AAAABBBCCDAABBB') --> A B C D A B
        # unique_justseen('ABBCcAD', str.lower) --> A B C A D
        return map(next, map(operator.itemgetter(1), groupby(iterable, key)))

    def iter_except(func, exception, first=None):
        """ Call a function repeatedly until an exception is raised.

        Converts a call-until-exception interface to an iterator interface.
        Like builtins.iter(func, sentinel) but uses an exception instead
        of a sentinel to end the loop.

        Examples:
            iter_except(functools.partial(heappop, h), IndexError)   # priority queue iterator
            iter_except(d.popitem, KeyError)                         # non-blocking dict iterator
            iter_except(d.popleft, IndexError)                       # non-blocking deque iterator
            iter_except(q.get_nowait, Queue.Empty)                   # loop over a producer Queue
            iter_except(s.pop, KeyError)                             # non-blocking set iterator

        """
        try:
            if first is not None:
                yield first()            # For database APIs needing an initial cast to db.first()
            while True:
                yield func()
        except exception:
            pass

    def first_true(iterable, default=False, 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) is true.

        """
        # first_true([a,b,c], x) --> a or b or c or x
        # first_true([a,b], x, f) --> a if f(a) else b if f(b) else x
        return next(filter(pred, iterable), default)

    def random_product(*args, repeat=1):
        "Random selection from itertools.product(*args, **kwds)"
        pools = [tuple(pool) for pool in args] * repeat
        return tuple(map(random.choice, pools))

    def random_permutation(iterable, r=None):
        "Random selection from itertools.permutations(iterable, r)"
        pool = tuple(iterable)
        r = len(pool) if r is None else r
        return tuple(random.sample(pool, r))

    def random_combination(iterable, r):
        "Random selection from itertools.combinations(iterable, r)"
        pool = tuple(iterable)
        n = len(pool)
        indices = sorted(random.sample(range(n), r))
        return tuple(pool[i] for i in indices)

    def random_combination_with_replacement(iterable, r):
        "Random selection from itertools.combinations_with_replacement(iterable, r)"
        pool = tuple(iterable)
        n = len(pool)
        indices = sorted(random.choices(range(n), k=r))
        return tuple(pool[i] for i in indices)

    def nth_combination(iterable, r, index):
        "Equivalent to list(combinations(iterable, r))[index]"
        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)