# `itertools` — Funções que criam iteradores para laços eficientes

Esse módulo implementa diversos blocos de instruções com iteradores, inspirados por construções de APL, Haskell, e SML. Cada uma foi adequadamente reformulada para Python.

Esse módulo padroniza um conjunto central de ferramentas rápidas e de uso eficiente da memória, que podem ser utilizadas sozinhas ou combinadas. Juntas, eles formam uma “álgebra de iteradores” tornando possível construir ferramentas sucintas e eficientes em Python puro.

Por exemplo, SML fornece uma ferramenta para tabulação: `tabulate(f)` que produz uma sequência `f(0), f(1), ...`. O mesmo efeito pode ser obtido em Python combinando `map()` e `count()` para formar `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(starmap(operator.mul, zip(vec1, vec2, strict=True)))`.

Iteradores infinitos:

Iterador | Argumentos | Resultado | Exemplo
--- | --- | --- | ---
`count()` | [start[, step]] | start, start+step, start+2*step, … | `count(10) → 10 11 12 13 14 ...`
`cycle()` | p | p0, p1, … ultimo elemento de p, p0, p1, … | `cycle('ABCD') → A B C D A B C D ...`
`repeat()` | elem [,n] | elem, elem, elem, … repete infinitamente ou até n vezes | `repeat(10, 3) → 10 10 10`

Iteradores terminando na sequencia de entrada mais curta:

Iterador | Argumentos | Resultado | Exemplo
--- | --- | --- | ---
`accumulate()` | p [,func] | p0, p0+p1, p0+p1+p2, … | `accumulate([1,2,3,4,5]) → 1 3 6 10 15`
`batched()` | p, n | (p0, p1, …, p_n-1), … | `batched('ABCDEFG', n=3) → ABC DEF G`
`chain()` | p, q, … | p0, p1, … último elemento de p, q0, q1, … | `chain('ABC', 'DEF') → A B C D E F`
`chain.from_iterable()` | iterable | p0, p1, … último elemento de p, q0, q1, … | `chain.from_iterable(['ABC', 'DEF']) → A B C D E F`
`compress()` | data, selectors | (d[0] if s[0]), (d[1] if s[1]), … | `compress('ABCDEF', [1,0,1,0,1,1]) → A C E F`
`dropwhile()` | predicate, seq | seq[n], seq[n+1], starting when predicate fails | `dropwhile(lambda x: x<5, [1,4,6,4,1]) → 6 4 1`
`filterfalse()` | predicate, seq | elements of seq where predicate(elem) fails | `filterfalse(lambda x: x%2, range(10)) → 0 2 4 6 8`
`groupby()` | iterable[, key] | sub-iteradores agrupados pelo valor de key(v) |
`islice()` | seq, [start,] stop [, step] | elementos de seq[start:stop:step] | `islice('ABCDEFG', 2, None) → C D E F G`
`pairwise()` | iterable | (p[0], p[1]), (p[1], p[2]) | `pairwise('ABCDEFG') → AB BC CD DE EF FG`
`starmap()` | func, seq | func(*seq[0]), func(*seq[1]), … | `starmap(pow, [(2,5), (3,2), (10,3)]) → 32 9 1000`
`takewhile()` | predicate, seq | seq[0], seq[1], until predicate fails | `takewhile(lambda x: x<5, [1,4,6,4,1]) → 1 4`
`tee()` | it, n | n iteradores it independentes |
`zip_longest()` | p, q, … | (p[0], q[0]), (p[1], q[1]), … | `zip_longest('ABCD', 'xy', fillvalue='-') → Ax By C- D-`

Iteradores combinatórios:

Iterador | Argumentos | Resultado
--- | --- | ---
`product()` | p, q, … [repeat=1] | produto cartesiano, equivalente a laços `for` aninhados
`permutations()` | p[, r] | tuplas de tamanho r, com todas ordenações possíveis, sem elementos repetidos
`combinations()` | p, r | tuplas de tamanho r, ordenadas, sem elementos repetidos
`combinations_with_replacement()` | p, r | tuplas de tamanho r, ordenadas, com elementos repetidos

Exemplos | Resultado
--- | ---
`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

Todas as funções a seguir constroem e retorna iteradores. Algumas fornecem fluxos de tamanhos infinitos, assim elas devem ser acessados somente por funções ou laços que interrompem o fluxo.

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

  Constrói um iterador que devolve somas acumuladas, ou resultados acumulados de outras funções (especificada pelo argumento opcional func).

  Se func é fornecido, deve ser uma função de dois argumentos. Elementos da entrada iterable pode ser qualquer tipo que pode ser aceito como argumento para func. (Por exemplo, com o operador padrão de adição, esses elementos pode ser qualquer tipo que possa ser somado, incluindo `Decimal` ou `Fraction`.)

  Usualmente, o número de elementos da saída coincide com o número de elementos do iterável da entrada. Contudo, se o argumento nomeado initial é fornecido, a acumulação começa com este valor inicial, assim a saída terá um elemento a mais que o iterável da entrada.

  Aproximadamente equivalente a:

      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

  Há uma serie de usos para o argumento func. Ele pode ser definido como `min()` para um mínimo de execução, `max()` para um máximo de execução, ou `operator.mul()` para um produto de execução. Tabelas de amortização podem ser construídas pela acumulação de juros e aplicação de pagamentos:

      >>> 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 10 annual payments of 90
      >>> account_update = lambda bal, pmt: round(bal * 1.05) + pmt
      >>> list(accumulate(repeat(-90, 10), account_update, initial=1_000))
      [1000, 960, 918, 874, 828, 779, 728, 674, 618, 559, 497]

  Veja `functools.reduce()` para uma função similar que devolve apenas o valor acumulado final.

  Adicionado na versão 3.2.

  Alterado na versão 3.3: Adicionado o parâmetro opcional func.

  Alterado na versão 3.8: Adicionado o parâmetro opcional initial.

itertools.batched(iterable, n)

  Batch data from the iterable into tuples of length n. The last batch may be shorter than n.

  Loops over the input iterable and accumulates data into tuples up to size n. The input is consumed lazily, just enough to fill a batch. The result is yielded as soon as the batch is full or when the input iterable is exhausted:

      >>> flattened_data = ['roses', 'red', 'violets', 'blue', 'sugar', 'sweet']
      >>> unflattened = list(batched(flattened_data, 2))
      >>> unflattened
      [('roses', 'red'), ('violets', 'blue'), ('sugar', 'sweet')]

      >>> for batch in batched('ABCDEFG', 3):
      ...     print(batch)
      ...
      ('A', 'B', 'C')
      ('D', 'E', 'F')
      ('G',)

  Aproximadamente equivalente a:

      def batched(iterable, n):
          # batched('ABCDEFG', 3) → ABC DEF G
          if n < 1:
              raise ValueError('n must be at least one')
          it = iter(iterable)
          while batch := tuple(islice(it, n)):
              yield batch

  Adicionado na versão 3.12.

itertools.chain(*iterables)

  Cria um iterador que devolve elementos do primeiro iterável até o esgotamento, então continua com o próximo iterável, até que todos os iteráveis sejam esgotados. Usando para tratar sequências consecutivas como uma única sequencia. aproximadamente equivalente a:

      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)

  Construtor alternativo para `chain()`. Obtém entradas encadeadas a partir de um único argumento iterável que avaliado preguiçosamente. Aproximadamente equivalente a:

      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)

  Devolve subsequências de elementos com comprimento r a partir da entrada iterável

  As tuplas das combinações são emitidas em ordem lexicográfica de acordo com a ordem do iterável de entrada. Portanto, se o iterável estiver ordenado, as tuplas de combinação serão produzidas em sequência ordenada.

  Os elementos são tratados como únicos baseado em suas posições, não em seus valores. Portanto se os elementos de entrada são únicos, não haverá repetição de valores nas sucessivas combinações.

  Aproximadamente equivalente a:

      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)

  O código para `combinations()` também pode ser expresso como uma subsequência de `permutations()` depois de filtradas as entradas onde os elementos não estão ordenandos (de acordo com a sua posição na entrada):

      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)

  O número de itens devolvidos é `n! / r! / (n-r)!` quando `0 <= r <= n` ou zero quando `r > n`.

itertools.combinations_with_replacement(iterable, r)

  Devolve subsequências de comprimento r de elementos do iterável de entrada permitindo que elementos individuais sejam repetidos mais de uma vez.

  As tuplas das combinações são emitidas em ordem lexicográfica de acordo com a ordem do iterável de entrada. Portanto, se o iterável estiver ordenado, as tuplas de combinação serão produzidas em sequência ordenada.

  Os elementos são tratados como únicos baseado em suas posições, não em seus valores. Portanto se os elementos de entrada forem únicos, não haverá repetição de valores nas combinações geradas.

  Aproximadamente equivalente a:

      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)

  O código para `combinations_with_replacement()` também pode ser expresso como uma subsequência de `product()` depois de filtradas as entradas onde os elementos não estão ordenados (de acordo com a sua posição na entrada):

      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)

  O número de itens devolvidos é `(n+r-1)! / r! / (n-1)!` quando `n > 0`.

  Adicionado na versão 3.1.

itertools.compress(data, selectors)

  Crie um iterador que filtra elementos de data devolvendo apenas aqueles que tem um elemento correspondente em selectors que seja avaliado `True`. Interrompe quando os iteráveis data ou selectors tiveram sido esgotados. Aproximadamente equivalente a:

      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)

  Adicionado na versão 3.1.

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

  Crie um iterador que devolve valores igualmente espaçados começando pelo número start. Frequentemente usado com um argumento da função `map()` para gerar pontos de dados consecutivos. Também usado com `zip()` para adicionar números sequenciais. Aproximadamente equivalente a:

      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

  Quando é feita uma contagem usando números de ponto flutuante, é possível ter melhor precisão substituindo código multiplicativo como `(start + step * i for i in count())`.

  Alterado na versão 3.1: Adicionou argumento step e permitiu argumentos não-inteiros.

itertools.cycle(iterable)

  Crie um iterador que devolve elementos do iterável assim como salva uma cópia de cada um. Quando o iterável é esgotado, devolve elementos da cópia salva. Repete indefinidamente. Aproximadamente equivalente a:

      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)

  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
          uniquekeys.append(k)

  `groupby()` é aproximadamente equivalente a:

      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.

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

  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).

  Aproximadamente equivalente a:

      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

itertools.pairwise(iterable)

  Return successive overlapping pairs taken from the input iterable.

  The number of 2-tuples in the output iterator will be one fewer than the number of inputs. It will be empty if the input iterable has fewer than two values.

  Aproximadamente equivalente a:

      def pairwise(iterable):
          # pairwise('ABCDEFG') → AB BC CD DE EF FG
          iterator = iter(iterable)
          a = next(iterator, None)
          for b in iterator:
              yield a, b
              a = b

  Adicionado na versão 3.10.

itertools.permutations(iterable, r=None)

  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.

  The permutation tuples are emitted in lexicographic order according to the order of the input iterable. So, if the input iterable is sorted, the output 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 repeated values within a permutation.

  Aproximadamente equivalente a:

      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

  O código para `permutations()` também pode ser expresso como uma subsequência de `product()` depois de filtradas as entradas com elementos repetidos (os de mesma posição no conjunto de entrada):

      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)

  O número de itens retornado é `n! / (n-r)!` quando `0 <= r <= n` ou zero quando `r > n`.

itertools.product(*iterables, repeat=1)

  Produto cartesiano de iteráveis de entrada

  Aproximadamente equivalente a laços for aninhados em uma expressão geradora. Por exemplo, `product(A, B)` devolve o mesmo que `((x,y) for x in A for y in B)`.

  Os laços aninhados circulam como um hodômetro com o elemento mais à direita avançando a cada iteração. Este padrão cria uma ordenação lexicográfica de maneira que se os iteráveis de entrada estiverem ordenados, as tuplas produzidas são emitidas de maneira ordenada.

  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 roughly 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 = [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)

  Before `product()` runs, it completely consumes the input iterables, keeping pools of values in memory to generate the products. Accordingly, it is only useful with finite inputs.

itertools.repeat(object[, times])

  Make an iterator that returns object over and over again. Runs indefinitely unless the times argument is specified.

  Aproximadamente equivalente a:

      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

  Um uso comum de repeat é para fornecer um fluxo de valores constantes para map ou 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 (when 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

  Note, the element that first fails the predicate condition is consumed from the input iterator and there is no way to access it. This could be an issue if an application wants to further consume the input iterator after takewhile has been run to exhaustion. To work around this problem, consider using more-iterools before_and_after() instead.

itertools.tee(iterable, n=2)

  Return n independent iterators from a single iterable.

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

      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 a `tee()` has been created, 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 simultaneously using iterators returned by the same `tee()` call, even if the original iterable is threadsafe.

  This itertool may require significant auxiliary storage (depending on how much temporary data needs to be stored). In general, if one iterator uses most or all of the data before another iterator starts, it is faster to use `list()` instead of `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)

  Se um dos iteráveis é potencialmente infinito, então a função `zip_longest()` deve ser embrulhada por algo que limite o número de chamadas (por exemplo `islice()` ou `takewhile()`). Se não especificado, fillvalue tem o valor padrão `None`.

## Receitas com itertools

Esta seção mostra receitas para criação de um ferramental ampliado usando as ferramentas existentes de itertools como elementos construtivos.

The primary purpose of the itertools recipes is educational. The recipes show various ways of thinking about individual tools — for example, that `chain.from_iterable` is related to the concept of flattening. The recipes also give ideas about ways that the tools can be combined — for example, how `starmap()` and `repeat()` can work together. The recipes also show patterns for using itertools with the `operator` and `collections` modules as well as with the built-in itertools such as `map()`, `filter()`, `reversed()`, and `enumerate()`.

A secondary purpose of the recipes is to serve as an incubator. The `accumulate()`, `compress()`, and `pairwise()` itertools started out as recipes. Currently, the `sliding_window()`, `iter_index()`, and `sieve()` recipes are being tested to see whether they prove their worth.

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

    python -m pip install more-itertools

Many of the recipes offer the same high performance as the underlying toolset. 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. High speed is retained by preferring “vectorized” building blocks over the use of for-loops and generators which incur interpreter overhead.

    import collections
    import functools
    import math
    import operator
    import random

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

    def prepend(value, iterable):
        "Prepend a single value in front of an iterable."
        # prepend(1, [2, 3, 4]) → 1 2 3 4
        return chain([value], iterable)

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

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

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

    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 quantify(iterable, predicate=bool):
        "Given a predicate that returns True or False, count the True results."
        return sum(map(predicate, iterable))

    def first_true(iterable, default=False, predicate=None):
        "Returns the first true value or the *default* if there is no true value."
        # 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(predicate, iterable), default)

    def all_equal(iterable, key=None):
        "Returns True if all the elements are equal to each other."
        # all_equal('4٤໔４৪', key=int) → True
        return len(take(2, groupby(iterable, key))) <= 1

    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.casefold) → A B c A D
        if key is None:
            return map(operator.itemgetter(0), groupby(iterable))
        return map(next, map(operator.itemgetter(1), groupby(iterable, key)))

    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.casefold) → A B c D
        seen = set()
        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 sliding_window(iterable, n):
        "Collect data into overlapping fixed-length chunks or blocks."
        # sliding_window('ABCDEFG', 4) → ABCD BCDE CDEF DEFG
        it = iter(iterable)
        window = collections.deque(islice(it, n-1), maxlen=n)
        for x in it:
            window.append(x)
            yield tuple(window)

    def grouper(iterable, n, *, incomplete='fill', fillvalue=None):
        "Collect data into non-overlapping fixed-length chunks or blocks."
        # grouper('ABCDEFG', 3, fillvalue='x') → ABC DEF Gxx
        # grouper('ABCDEFG', 3, incomplete='strict') → ABC DEF ValueError
        # grouper('ABCDEFG', 3, incomplete='ignore') → ABC DEF
        iterators = [iter(iterable)] * n
        match incomplete:
            case 'fill':
                return zip_longest(*iterators, fillvalue=fillvalue)
            case 'strict':
                return zip(*iterators, strict=True)
            case 'ignore':
                return zip(*iterators)
            case _:
                raise ValueError('Expected fill, strict, or ignore')

    def roundrobin(*iterables):
        "Visit input iterables in a cycle until each is exhausted."
        # roundrobin('ABC', 'D', 'EF') → A D E B F C
        # Algorithm credited to George Sakkis
        iterators = map(iter, iterables)
        for num_active in range(len(iterables), 0, -1):
            iterators = cycle(islice(iterators, num_active))
            yield from map(next, iterators)

    def partition(predicate, iterable):
        """Partition entries into false entries and true entries.

        If *predicate* is slow, consider wrapping it with functools.lru_cache().
        """
        # partition(is_odd, range(10)) → 0 2 4 6 8   and  1 3 5 7 9
        t1, t2 = tee(iterable)
        return filterfalse(predicate, t1), filter(predicate, t2)

    def subslices(seq):
        "Return all contiguous non-empty subslices of a sequence."
        # subslices('ABCD') → A AB ABC ABCD B BC BCD C CD D
        slices = starmap(slice, combinations(range(len(seq) + 1), 2))
        return map(operator.getitem, repeat(seq), slices)

    def iter_index(iterable, value, start=0, stop=None):
        "Return indices where a value occurs in a sequence or iterable."
        # iter_index('AABCADEAF', 'A') → 0 1 4 7
        seq_index = getattr(iterable, 'index', None)
        if seq_index is None:
            # Path for general iterables
            it = islice(iterable, start, stop)
            for i, element in enumerate(it, start):
                if element is value or element == value:
                    yield i
        else:
            # Path for sequences with an index() method
            stop = len(iterable) if stop is None else stop
            i = start
            try:
                while True:
                    yield (i := seq_index(value, i, stop))
                    i += 1
            except ValueError:
                pass

    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.
        """
        # iter_except(d.popitem, KeyError) → non-blocking dictionary iterator
        try:
            if first is not None:
                yield first()
            while True:
                yield func()
        except exception:
            pass

The following recipes have a more mathematical flavor:

    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 sum_of_squares(iterable):
        "Add up the squares of the input values."
        # sum_of_squares([10, 20, 30]) → 1400
        return math.sumprod(*tee(iterable))

    def reshape(matrix, cols):
        "Reshape a 2-D matrix to have a given number of columns."
        # reshape([(0, 1), (2, 3), (4, 5)], 3) →  (0, 1, 2), (3, 4, 5)
        return batched(chain.from_iterable(matrix), cols)

    def transpose(matrix):
        "Swap the rows and columns of a 2-D matrix."
        # transpose([(1, 2, 3), (11, 22, 33)]) → (1, 11) (2, 22) (3, 33)
        return zip(*matrix, strict=True)

    def matmul(m1, m2):
        "Multiply two matrices."
        # matmul([(7, 5), (3, 5)], [(2, 5), (7, 9)]) → (49, 80), (41, 60)
        n = len(m2[0])
        return batched(starmap(math.sumprod, product(m1, transpose(m2))), n)

    def convolve(signal, kernel):
        """Discrete linear convolution of two iterables.
        Equivalent to polynomial multiplication.

        Convolutions are mathematically commutative; however, the inputs are
        evaluated differently.  The signal is consumed lazily and can be
        infinite. The kernel is fully consumed before the calculations begin.

        Article:  https://betterexplained.com/articles/intuitive-convolution/
        Video:    https://www.youtube.com/watch?v=KuXjwB4LzSA
        """
        # convolve([1, -1, -20], [1, -3]) → 1 -4 -17 60
        # convolve(data, [0.25, 0.25, 0.25, 0.25]) → Moving average (blur)
        # convolve(data, [1/2, 0, -1/2]) → 1st derivative estimate
        # convolve(data, [1, -2, 1]) → 2nd derivative estimate
        kernel = tuple(kernel)[::-1]
        n = len(kernel)
        padded_signal = chain(repeat(0, n-1), signal, repeat(0, n-1))
        windowed_signal = sliding_window(padded_signal, n)
        return map(math.sumprod, repeat(kernel), windowed_signal)

    def polynomial_from_roots(roots):
        """Compute a polynomial's coefficients from its roots.

           (x - 5) (x + 4) (x - 3)  expands to:   x³ -4x² -17x + 60
        """
        # polynomial_from_roots([5, -4, 3]) → [1, -4, -17, 60]
        factors = zip(repeat(1), map(operator.neg, roots))
        return list(functools.reduce(convolve, factors, [1]))

    def polynomial_eval(coefficients, x):
        """Evaluate a polynomial at a specific value.

        Computes with better numeric stability than Horner's method.
        """
        # Evaluate x³ -4x² -17x + 60 at x = 5
        # polynomial_eval([1, -4, -17, 60], x=5) → 0
        n = len(coefficients)
        if not n:
            return type(x)(0)
        powers = map(pow, repeat(x), reversed(range(n)))
        return math.sumprod(coefficients, powers)

    def polynomial_derivative(coefficients):
        """Compute the first derivative of a polynomial.

           f(x)  =  x³ -4x² -17x + 60
           f'(x) = 3x² -8x  -17
        """
        # polynomial_derivative([1, -4, -17, 60]) → [3, -8, -17]
        n = len(coefficients)
        powers = reversed(range(1, n))
        return list(map(operator.mul, coefficients, powers))

    def sieve(n):
        "Primes less than n."
        # sieve(30) → 2 3 5 7 11 13 17 19 23 29
        if n > 2:
            yield 2
        start = 3
        data = bytearray((0, 1)) * (n // 2)
        limit = math.isqrt(n) + 1
        for p in iter_index(data, 1, start, limit):
            yield from iter_index(data, 1, start, p*p)
            data[p*p : n : p+p] = bytes(len(range(p*p, n, p+p)))
            start = p*p
        yield from iter_index(data, 1, start)

    def factor(n):
        "Prime factors of n."
        # factor(99) → 3 3 11
        # factor(1_000_000_000_000_007) → 47 59 360620266859
        # factor(1_000_000_000_000_403) → 1000000000000403
        for prime in sieve(math.isqrt(n) + 1):
            while not n % prime:
                yield prime
                n //= prime
                if n == 1:
                    return
        if n > 1:
            yield n

    def totient(n):
        "Count of natural numbers up to n that are coprime to n."
        # https://mathworld.wolfram.com/TotientFunction.html
        # totient(12) → 4 because len([1, 5, 7, 11]) == 4
        for p in unique_justseen(factor(n)):
            n -= n // p
        return n