# `itertools` — Functions creating iterators for efficient looping

이 모듈은 APL, Haskell 및 SML의 구성물들에서 영감을 얻은 여러 이터레이터 빌딩 블록을 구현합니다. 각각을 파이썬에 적합한 형태로 개선했습니다.

이 모듈은 자체적으로 혹은 조합하여 유용한 빠르고 메모리 효율적인 도구의 핵심 집합을 표준화합니다. 함께 모여, 순수 파이썬에서 간결하고 효율적으로 특수화된 도구를 구성할 수 있도록 하는 “이터레이터 대수(iterator algebra)”를 형성합니다.

예를 들어, SML은 테이블 화 도구를 제공합니다: 시퀀스 `f(0), f(1), ...`를 생성하는 `tabulate(f)`. `map()`과 `count()`를 결합하여 `map(f, count())`를 형성해서 파이썬에서도 같은 효과를 얻을 수 있습니다.

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)))`.

무한 이터레이터:

이터레이터 | 인자 | 결과 | 예
--- | --- | --- | ---
`count()` | start, [step] | start, start+step, start+2*step, … | `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()` | elem [,n] | elem, elem, elem, … 끝없이 또는 최대 n 번 | `repeat(10, 3) --> 10 10 10`

가장 짧은 입력 시퀀스에서 종료되는 이터레이터:

이터레이터 | 인자 | 결과 | 예
--- | --- | --- | ---
`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()` | iterable | p0, p1, … plast, 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()` | 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()` | iterable[, key] | key(v)의 값으로 그룹화된 서브 이터레이터들 |
`islice()` | seq, [start,] stop [, step] | seq[start:stop:step]의 요소들 | `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()` | pred, seq | seq[0], seq[1], until pred fails | `takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4`
`tee()` | it, n | it1, it2, … itn 하나의 이터레이터를 n개의 이터레이터로 나눕니다 |
`zip_longest()` | p, q, … | (p[0], q[0]), (p[1], q[1]), … | `zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-`

조합형 이터레이터:

이터레이터 | 인자 | 결과
--- | --- | ---
`product()` | p, q, … [repeat=1] | 데카르트 곱(cartesian product), 중첩된 for 루프와 동등합니다
`permutations()` | p[, r] | r-길이 튜플들, 모든 가능한 순서, 반복되는 요소 없음
`combinations()` | p, r | r-길이 튜플들, 정렬된 순서, 반복되는 요소 없음
`combinations_with_replacement()` | p, r | r-길이 튜플들, 정렬된 순서, 반복되는 요소 있음

예 | 결과
--- | ---
`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.

  대략 다음과 동등합니다:

      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:

      >>> 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]

  최종 누적값만 반환하는 유사한 함수에 대해서는 `functools.reduce()`를 참조하십시오.

  버전 3.2에 추가.

  버전 3.3에서 변경: Added the optional func parameter.

  버전 3.8에서 변경: 선택적 initial 매개 변수를 추가했습니다.

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)

  `chain()`의 대체 생성자. 게으르게 평가되는 단일 이터러블 인자에서 연쇄 입력을 가져옵니다. 대략 다음과 동등합니다:

      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)

  입력 iterable에서 요소의 길이 r 서브 시퀀스들을 반환합니다.

  The combination tuples are emitted in lexicographic ordering 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 in each combination.

  대략 다음과 동등합니다:

      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)

  입력 iterable에서 요소의 길이 r 서브 시퀀스들을 반환하는데, 개별 요소를 두 번 이상 반복할 수 있습니다.

  The combination tuples are emitted in lexicographic ordering 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, the generated combinations will also be unique.

  대략 다음과 동등합니다:

      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`.

  버전 3.1에 추가.

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)

  버전 3.1에 추가.

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())`.

  버전 3.1에서 변경: step 인자를 추가하고 정수가 아닌 인자를 허용했습니다.

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)

  iterable에서 연속적인 키와 그룹을 반환하는 이터레이터를 만듭니다. key는 각 요소의 키값을 계산하는 함수입니다. 지정되지 않거나 `None`이면, key의 기본값은 항등함수(identity function)이고 요소를 변경하지 않고 반환합니다. 일반적으로, iterable은 같은 키 함수로 이미 정렬되어 있어야 합니다.

  `groupby()`의 작동은 유닉스의 `uniq` 필터와 유사합니다. 키 함수의 값이 변경될 때마다 중단(break)이나 새 그룹을 생성합니다 (이것이 일반적으로 같은 키 함수를 사용하여 데이터를 정렬해야 하는 이유입니다). 이 동작은 입력 순서와 관계없이 공통 요소를 집계하는 SQL의 GROUP BY와 다릅니다.

  반환되는 그룹 자체는 `groupby()`와 하부 이터러블(iterable)을 공유하는 이터레이터입니다. 소스가 공유되므로, `groupby()` 객체가 진행하면, 이전 그룹은 이 더는 보이지 않게 됩니다. 따라서, 나중에 데이터가 필요하면, 리스트로 저장해야 합니다:

      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()`는 대략 다음과 동등합니다:

      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).

  대략 다음과 동등합니다:

      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.

  대략 다음과 동등합니다:

      def pairwise(iterable):
          # pairwise('ABCDEFG') --> AB BC CD DE EF FG
          a, b = tee(iterable)
          next(b, None)
          return zip(a, b)

  버전 3.10에 추가.

itertools.permutations(iterable, r=None)

  Return successive r length permutations of elements in the iterable.

  r이 지정되지 않았거나 `None`이면, r의 기본값은 iterable의 길이이며 가능한 모든 최대 길이 순열이 생성됩니다.

  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.

  대략 다음과 동등합니다:

      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.

  대략 제너레이터 표현식에서의 중첩된 for-루프와 동등합니다. 예를 들어, `product(A, B)`는 `((x,y) for x in A for y in B)`와 같은 것을 반환합니다.

  중첩된 루프는 매 이터레이션마다 가장 오른쪽 요소가 진행되는 주행 거리계처럼 순환합니다. 이 패턴은 사전식 순서를 만들어서 입력의 이터러블들이 정렬되어 있다면, 곱(product) 튜플이 정렬된 순서로 방출됩니다.

  이터러블의 자신과의 곱을 계산하려면, 선택적 repeat 키워드 인자를 사용하여 반복 횟수를 지정하십시오. 예를 들어, `product(A, repeat=4)`는 `product(A, A, A, A)`와 같은 것을 뜻합니다.

  이 함수는 실제 구현이 메모리에 중간 결과를 쌓지 않는다는 점을 제외하고 다음 코드와 대략 동등합니다:

      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)

  `product()`가 실행되기 전에, 입력 이터러블을 완전히 소비하여, 곱을 생성하기 위해 값의 풀(pool)을 메모리에 유지합니다. 따라서, 유한 입력에만 유용합니다.

itertools.repeat(object[, times])

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

  대략 다음과 동등합니다:

      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 (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

itertools.tee(iterable, n=2)

  단일 iterable에서 n 개의 독립 이터레이터를 반환합니다.

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

  이 이터레이터 도구에는 상당한 보조 기억 장치가 필요할 수 있습니다 (일시적으로 저장해야 하는 데이터양에 따라 다릅니다). 일반적으로, 다른 이터레이터가 시작하기 전에 하나의 이터레이터가 대부분이나 모든 데이터를 사용하면, `tee()` 대신 `list()`를 사용하는 것이 더 빠릅니다.

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`.

## Itertools 조리법

이 섹션에서는 기존 itertools를 빌딩 블록으로 사용하여 확장 도구 집합을 만드는 방법을 보여줍니다.

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 `compress()` and `range()` 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 `iter_index()` recipe is being tested to see whether it proves its 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 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.

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

    def batched(iterable, n):
        "Batch data into tuples of length n. The last batch may be shorter."
        # 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

    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
        args = [iter(iterable)] * n
        if incomplete == 'fill':
            return zip_longest(*args, fillvalue=fillvalue)
        if incomplete == 'strict':
            return zip(*args, strict=True)
        if incomplete == 'ignore':
            return zip(*args)
        else:
            raise ValueError('Expected fill, strict, or ignore')

    def sumprod(vec1, vec2):
        "Compute a sum of products."
        return sum(starmap(operator.mul, zip(vec1, vec2, strict=True)))

    def sum_of_squares(it):
        "Add up the squares of the input values."
        # sum_of_squares([10, 20, 30]) -> 1400
        return sumprod(*tee(it))

    def transpose(it):
        "Swap the rows and columns of the input."
        # transpose([(1, 2, 3), (11, 22, 33)]) --> (1, 11) (2, 22) (3, 33)
        return zip(*it, 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(sumprod, product(m1, transpose(m2))), n)

    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 sumprod(kernel, window)

    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]
        expansion = [1]
        for r in roots:
            expansion = convolve(expansion, (1, -r))
        return list(expansion)

    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 = 2.5
        # polynomial_eval([1, -4, -17, 60], x=2.5) --> 8.125
        n = len(coefficients)
        if n == 0:
            return x * 0  # coerce zero to the type of x
        powers = map(pow, repeat(x), reversed(range(n)))
        return sumprod(coefficients, powers)

    def iter_index(iterable, value, start=0):
        "Return indices where a value occurs in a sequence or iterable."
        # iter_index('AABCADEAF', 'A') --> 0 1 4 7
        try:
            seq_index = iterable.index
        except AttributeError:
            # Slow path for general iterables
            it = islice(iterable, start, None)
            i = start - 1
            try:
                while True:
                    yield (i := i + operator.indexOf(it, value) + 1)
            except ValueError:
                pass
        else:
            # Fast path for sequences
            i = start - 1
            try:
                while True:
                    yield (i := seq_index(value, i+1))
            except ValueError:
                pass

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

    def factor(n):
        "Prime factors of n."
        # factor(99) --> 3 3 11
        for prime in sieve(math.isqrt(n) + 1):
            while True:
                quotient, remainder = divmod(n, prime)
                if remainder:
                    break
                yield prime
                n = quotient
                if n == 1:
                    return
        if n > 1:
            yield n

    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 triplewise(iterable):
        "Return overlapping triplets from an iterable"
        # triplewise('ABCDEFG') --> ABC BCD CDE DEF EFG
        for (a, _), (b, c) in pairwise(pairwise(iterable)):
            yield a, b, c

    def sliding_window(iterable, n):
        # sliding_window('ABCDEFG', 4) --> ABCD BCDE CDEF DEFG
        it = iter(iterable)
        window = collections.deque(islice(it, n), maxlen=n)
        if len(window) == n:
            yield tuple(window)
        for x in it:
            window.append(x)
            yield tuple(window)

    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 before_and_after(predicate, it):
        """ Variant of 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()

    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 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()
        if key is None:
            for element in filterfalse(seen.__contains__, iterable):
                seen.add(element)
                yield element
            # For order preserving deduplication,
            # a faster but non-lazy solution is:
            #     yield from dict.fromkeys(iterable)
        else:
            for element in iterable:
                k = key(element)
                if k not in seen:
                    seen.add(k)
                    yield element
            # For use cases that allow the last matching element to be returned,
            # a faster but non-lazy solution is:
            #      t1, t2 = tee(iterable)
            #      yield from dict(zip(map(key, t1), t2)).values()

    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 nth_combination(iterable, r, index):
        "Equivalent to list(combinations(iterable, r))[index]"
        pool = tuple(iterable)
        n = len(pool)
        c = math.comb(n, r)
        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)