##
6.5.1 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.

`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. Equivalent to:

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

`count`( | [n])

  Make an iterator that returns consecutive integers starting with n. If not specified n defaults to zero. Does not currently support python long integers. Often used as an argument to `imap()` to generate consecutive data points. Also, used with `izip()` to add sequence numbers. Equivalent to:

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

  Note, `count()` does not check for overflow and will return negative numbers after exceeding `sys.maxint`. This behavior may change in the future.

`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. 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).

`dropwhile`( | predicate, iterable)

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

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

`groupby`( | iterable[, key])

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

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

  `groupby()` is equivalent to:

          class groupby(object):
              # [k for k, g in groupby('AAAABBBCCDAABBB')] --> A B C D A B
              # [(list(g)) for k, g in groupby('AAAABBBCCD')] --> AAAA BBB CC D
              def __init__(self, iterable, key=None):
                  if key is None:
                      key = lambda x: x
                  self.keyfunc = key
                  self.it = iter(iterable)
                  self.tgtkey = self.currkey = self.currvalue = xrange(0)
              def __iter__(self):
                  return self
              def next(self):
                  while self.currkey == self.tgtkey:
                      self.currvalue = self.it.next() # Exit on StopIteration
                      self.currkey = self.keyfunc(self.currvalue)
                  self.tgtkey = self.currkey
                  return (self.currkey, self._grouper(self.tgtkey))
              def _grouper(self, tgtkey):
                  while self.currkey == tgtkey:
                      yield self.currvalue
                      self.currvalue = self.it.next() # Exit on StopIteration
                      self.currkey = self.keyfunc(self.currvalue)

  New in version 2.4.

`ifilter`( | predicate, iterable)

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

           def ifilter(predicate, iterable):
               # ifilter(lambda x: x%2, range(10)) --> 1 3 5 7 9
               if predicate is None:
                   predicate = bool
               for x in iterable:
                   if predicate(x):
                       yield x

`ifilterfalse`( | predicate, iterable)

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

           def ifilterfalse(predicate, iterable):
               # ifilterfalse(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

`imap`( | function, *iterables)

  Make an iterator that computes the function using arguments from each of the iterables. If function is set to `None`, then `imap()` returns the arguments as a tuple. Like `map()` but stops when the shortest iterable is exhausted instead of filling in `None` for shorter iterables. The reason for the difference is that infinite iterator arguments are typically an error for `map()` (because the output is fully evaluated) but represent a common and useful way of supplying arguments to `imap()`. Equivalent to:

           def imap(function, *iterables):
               # imap(pow, (2,3,10), (5,2,3)) --> 32 9 1000
               iterables = map(iter, iterables)
               while True:
                   args = [i.next() for i in iterables]
                   if function is None:
                       yield tuple(args)
                   else:
                       yield function(*args)

`islice`( | iterable, [start,] stop [, step])

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

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

  If start is `None`, then iteration starts at zero. If step is `None`, then the step defaults to one. Changed in version 2.5: accept `None` values for default start and step.

`izip`( | *iterables)

  Make an iterator that aggregates elements from each of the iterables. Like `zip()` except that it returns an iterator instead of a list. Used for lock-step iteration over several iterables at a time. Equivalent to:

           def izip(*iterables):
               # izip('ABCD', 'xy') --> Ax By
               iterables = map(iter, iterables)
               while iterables:
                   result = [it.next() for it in iterables]
                   yield tuple(result)

  Changed in version 2.4: When no iterables are specified, returns a zero length iterator instead of raising a `TypeError` exception.

  Note, the left-to-right evaluation order of the iterables is guaranteed. This makes possible an idiom for clustering a data series into n-length groups using "`izip(*[iter(s)]*n)`". For data that doesn't fit n-length groups exactly, the last tuple can be pre-padded with fill values using "`izip(*[chain(s, [None]*(n-1))]*n)`".

  Note, when `izip()` is used with unequal length inputs, subsequent iteration over the longer iterables cannot reliably be continued after `izip()` terminates. Potentially, up to one entry will be missing from each of the left-over iterables. This occurs because a value is fetched from each iterator in-turn, but the process ends when one of the iterators terminates. This leaves the last fetched values in limbo (they cannot be returned in a final, incomplete tuple and they are cannot be pushed back into the iterator for retrieval with `it.next()`). In general, `izip()` should only be used with unequal length inputs when you don't care about trailing, unmatched values from the longer iterables.

`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 `imap()` for invariant parameters to the called function. Also used with `izip()` to create an invariant part of a tuple record. Equivalent to:

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

`starmap`( | function, iterable)

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

           def starmap(function, iterable):
               # starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000
               iterable = iter(iterable)
               while True:
                   yield function(*iterable.next())

`takewhile`( | predicate, iterable)

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

           def takewhile(predicate, iterable):
               # takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
               for x in iterable:
                   if predicate(x):
                       yield x
                   else:
                       break

`tee`( | iterable[, n=2])

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

           def tee(iterable):
               def gen(next, data={}, cnt=[0]):
                   for i in count():
                       if i == cnt[0]:
                           item = data[i] = next()
                           cnt[0] += 1
                       else:
                           item = data.pop(i)
                       yield item
               it = iter(iterable)
               return (gen(it.next), gen(it.next))

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

  Note, this member of the toolkit may require significant auxiliary storage (depending on how much temporary data needs to be stored). In general, if one iterator is going to use most or all of the data before the other iterator, it is faster to use `list()` instead of `tee()`. New in version 2.4.