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528 lines
18 KiB
ReStructuredText
528 lines
18 KiB
ReStructuredText
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:mod:`itertools` --- Functions creating iterators for efficient looping
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=======================================================================
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.. module:: itertools
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:synopsis: Functions creating iterators for efficient looping.
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.. moduleauthor:: Raymond Hettinger <python@rcn.com>
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.. sectionauthor:: Raymond Hettinger <python@rcn.com>
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This module implements a number of :term:`iterator` building blocks inspired by
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constructs from the Haskell and SML programming languages. Each has been recast
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in a form suitable for Python.
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The module standardizes a core set of fast, memory efficient tools that are
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useful by themselves or in combination. Standardization helps avoid the
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readability and reliability problems which arise when many different individuals
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create their own slightly varying implementations, each with their own quirks
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and naming conventions.
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The tools are designed to combine readily with one another. This makes it easy
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to construct more specialized tools succinctly and efficiently in pure Python.
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For instance, SML provides a tabulation tool: ``tabulate(f)`` which produces a
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sequence ``f(0), f(1), ...``. This toolbox provides :func:`imap` and
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:func:`count` which can be combined to form ``imap(f, count())`` and produce an
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equivalent result.
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Likewise, the functional tools are designed to work well with the high-speed
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functions provided by the :mod:`operator` module.
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The module author welcomes suggestions for other basic building blocks to be
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added to future versions of the module.
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Whether cast in pure python form or compiled code, tools that use iterators are
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more memory efficient (and faster) than their list based counterparts. Adopting
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the principles of just-in-time manufacturing, they create data when and where
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needed instead of consuming memory with the computer equivalent of "inventory".
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The performance advantage of iterators becomes more acute as the number of
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elements increases -- at some point, lists grow large enough to severely impact
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memory cache performance and start running slowly.
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.. seealso::
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The Standard ML Basis Library, `The Standard ML Basis Library
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<http://www.standardml.org/Basis/>`_.
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Haskell, A Purely Functional Language, `Definition of Haskell and the Standard
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Libraries <http://www.haskell.org/definition/>`_.
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.. _itertools-functions:
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Itertool functions
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------------------
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The following module functions all construct and return iterators. Some provide
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streams of infinite length, so they should only be accessed by functions or
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loops that truncate the stream.
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.. function:: chain(*iterables)
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Make an iterator that returns elements from the first iterable until it is
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exhausted, then proceeds to the next iterable, until all of the iterables are
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exhausted. Used for treating consecutive sequences as a single sequence.
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Equivalent to::
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def chain(*iterables):
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for it in iterables:
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for element in it:
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yield element
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.. function:: count([n])
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Make an iterator that returns consecutive integers starting with *n*. If not
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specified *n* defaults to zero. Often used as an argument to :func:`imap` to
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generate consecutive data points. Also, used with :func:`izip` to add sequence
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numbers. Equivalent to::
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def count(n=0):
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while True:
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yield n
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n += 1
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.. function:: cycle(iterable)
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Make an iterator returning elements from the iterable and saving a copy of each.
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When the iterable is exhausted, return elements from the saved copy. Repeats
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indefinitely. Equivalent to::
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def cycle(iterable):
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saved = []
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for element in iterable:
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yield element
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saved.append(element)
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while saved:
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for element in saved:
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yield element
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Note, this member of the toolkit may require significant auxiliary storage
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(depending on the length of the iterable).
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.. function:: dropwhile(predicate, iterable)
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Make an iterator that drops elements from the iterable as long as the predicate
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is true; afterwards, returns every element. Note, the iterator does not produce
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*any* output until the predicate first becomes false, so it may have a lengthy
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start-up time. Equivalent to::
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def dropwhile(predicate, iterable):
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iterable = iter(iterable)
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for x in iterable:
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if not predicate(x):
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yield x
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break
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for x in iterable:
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yield x
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.. function:: groupby(iterable[, key])
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Make an iterator that returns consecutive keys and groups from the *iterable*.
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The *key* is a function computing a key value for each element. If not
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specified or is ``None``, *key* defaults to an identity function and returns
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the element unchanged. Generally, the iterable needs to already be sorted on
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the same key function.
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The operation of :func:`groupby` is similar to the ``uniq`` filter in Unix. It
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generates a break or new group every time the value of the key function changes
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(which is why it is usually necessary to have sorted the data using the same key
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function). That behavior differs from SQL's GROUP BY which aggregates common
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elements regardless of their input order.
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The returned group is itself an iterator that shares the underlying iterable
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with :func:`groupby`. Because the source is shared, when the :func:`groupby`
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object is advanced, the previous group is no longer visible. So, if that data
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is needed later, it should be stored as a list::
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groups = []
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uniquekeys = []
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data = sorted(data, key=keyfunc)
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for k, g in groupby(data, keyfunc):
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groups.append(list(g)) # Store group iterator as a list
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uniquekeys.append(k)
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:func:`groupby` is equivalent to::
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class groupby(object):
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def __init__(self, iterable, key=None):
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if key is None:
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key = lambda x: x
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self.keyfunc = key
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self.it = iter(iterable)
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self.tgtkey = self.currkey = self.currvalue = object()
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def __iter__(self):
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return self
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def __next__(self):
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while self.currkey == self.tgtkey:
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self.currvalue = next(self.it) # Exit on StopIteration
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self.currkey = self.keyfunc(self.currvalue)
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self.tgtkey = self.currkey
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return (self.currkey, self._grouper(self.tgtkey))
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def _grouper(self, tgtkey):
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while self.currkey == tgtkey:
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yield self.currvalue
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self.currvalue = next(self.it) # Exit on StopIteration
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self.currkey = self.keyfunc(self.currvalue)
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.. function:: ifilter(predicate, iterable)
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Make an iterator that filters elements from iterable returning only those for
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which the predicate is ``True``. If *predicate* is ``None``, return the items
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that are true. This function is the same as the built-in :func:`filter`
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function. Equivalent to::
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def ifilter(predicate, iterable):
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if predicate is None:
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predicate = bool
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for x in iterable:
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if predicate(x):
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yield x
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.. function:: ifilterfalse(predicate, iterable)
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Make an iterator that filters elements from iterable returning only those for
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which the predicate is ``False``. If *predicate* is ``None``, return the items
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that are false. Equivalent to::
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def ifilterfalse(predicate, iterable):
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if predicate is None:
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predicate = bool
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for x in iterable:
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if not predicate(x):
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yield x
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.. function:: imap(function, *iterables)
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Make an iterator that computes the function using arguments from each of the
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iterables. This function is the same as the built-in :func:`map` function.
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Equivalent to::
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def imap(function, *iterables):
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iterables = [iter(it) for it in iterables)
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while True:
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args = [next(it) for it in iterables]
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if function is None:
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yield tuple(args)
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else:
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yield function(*args)
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.. function:: islice(iterable, [start,] stop [, step])
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Make an iterator that returns selected elements from the iterable. If *start* is
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non-zero, then elements from the iterable are skipped until start is reached.
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Afterward, elements are returned consecutively unless *step* is set higher than
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one which results in items being skipped. If *stop* is ``None``, then iteration
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continues until the iterator is exhausted, if at all; otherwise, it stops at the
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specified position. Unlike regular slicing, :func:`islice` does not support
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negative values for *start*, *stop*, or *step*. Can be used to extract related
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fields from data where the internal structure has been flattened (for example, a
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multi-line report may list a name field on every third line). Equivalent to::
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def islice(iterable, *args):
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s = slice(*args)
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it = range(s.start or 0, s.stop or sys.maxsize, s.step or 1)
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nexti = next(it)
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for i, element in enumerate(iterable):
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if i == nexti:
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yield element
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nexti = next(it)
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If *start* is ``None``, then iteration starts at zero. If *step* is ``None``,
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then the step defaults to one.
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.. function:: izip(*iterables)
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Make an iterator that aggregates elements from each of the iterables. Like
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:func:`zip` except that it returns an iterator instead of a list. Used for
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lock-step iteration over several iterables at a time. Equivalent to::
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def izip(*iterables):
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iterables = map(iter, iterables)
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while iterables:
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result = [next(it) for it in iterables]
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yield tuple(result)
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When no iterables are specified, return a zero length iterator.
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The left-to-right evaluation order of the iterables is guaranteed. This
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makes possible an idiom for clustering a data series into n-length groups
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using ``izip(*[iter(s)]*n)``.
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:func:`izip` should only be used with unequal length inputs when you don't
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care about trailing, unmatched values from the longer iterables. If those
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values are important, use :func:`izip_longest` instead.
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.. function:: izip_longest(*iterables[, fillvalue])
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Make an iterator that aggregates elements from each of the iterables. If the
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iterables are of uneven length, missing values are filled-in with *fillvalue*.
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Iteration continues until the longest iterable is exhausted. Equivalent to::
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def izip_longest(*args, **kwds):
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fillvalue = kwds.get('fillvalue')
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def sentinel(counter = ([fillvalue]*(len(args)-1)).pop):
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yield counter() # yields the fillvalue, or raises IndexError
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fillers = repeat(fillvalue)
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iters = [chain(it, sentinel(), fillers) for it in args]
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try:
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for tup in izip(*iters):
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yield tup
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except IndexError:
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pass
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If one of the iterables is potentially infinite, then the :func:`izip_longest`
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function should be wrapped with something that limits the number of calls (for
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example :func:`islice` or :func:`takewhile`).
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.. function:: repeat(object[, times])
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Make an iterator that returns *object* over and over again. Runs indefinitely
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unless the *times* argument is specified. Used as argument to :func:`imap` for
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invariant parameters to the called function. Also used with :func:`izip` to
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create an invariant part of a tuple record. Equivalent to::
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def repeat(object, times=None):
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if times is None:
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while True:
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yield object
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else:
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for i in range(times):
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yield object
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.. function:: starmap(function, iterable)
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Make an iterator that computes the function using arguments obtained from
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the iterable. Used instead of :func:`imap` when argument parameters are already
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grouped in tuples from a single iterable (the data has been "pre-zipped"). The
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difference between :func:`imap` and :func:`starmap` parallels the distinction
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between ``function(a,b)`` and ``function(*c)``. Equivalent to::
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def starmap(function, iterable):
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for args in iterable:
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yield function(*args)
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.. versionchanged:: 2.6
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Previously, :func:`starmap` required the function arguments to be tuples.
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Now, any iterable is allowed.
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.. function:: takewhile(predicate, iterable)
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Make an iterator that returns elements from the iterable as long as the
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predicate is true. Equivalent to::
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def takewhile(predicate, iterable):
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for x in iterable:
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if predicate(x):
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yield x
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else:
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break
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.. function:: tee(iterable[, n=2])
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Return *n* independent iterators from a single iterable. The case where ``n==2``
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is equivalent to::
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def tee(iterable):
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def gen(next, data={}):
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for i in count():
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if i in data:
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yield data.pop(i)
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else:
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data[i] = next()
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yield data[i]
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it = iter(iterable)
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return (gen(it.__next__), gen(it.__next__))
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Note, once :func:`tee` has made a split, the original *iterable* should not be
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used anywhere else; otherwise, the *iterable* could get advanced without the tee
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objects being informed.
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Note, this member of the toolkit may require significant auxiliary storage
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(depending on how much temporary data needs to be stored). In general, if one
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iterator is going to use most or all of the data before the other iterator, it
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is faster to use :func:`list` instead of :func:`tee`.
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.. _itertools-example:
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Examples
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--------
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The following examples show common uses for each tool and demonstrate ways they
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can be combined. ::
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>>> amounts = [120.15, 764.05, 823.14]
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>>> for checknum, amount in izip(count(1200), amounts):
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... print('Check %d is for $%.2f' % (checknum, amount))
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...
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Check 1200 is for $120.15
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Check 1201 is for $764.05
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Check 1202 is for $823.14
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>>> import operator
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>>> for cube in imap(operator.pow, range(1,5), repeat(3)):
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... print(cube)
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...
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1
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8
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27
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64
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>>> reportlines = ['EuroPython', 'Roster', '', 'alex', '', 'laura',
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... '', 'martin', '', 'walter', '', 'mark']
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>>> for name in islice(reportlines, 3, None, 2):
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... print(name.title())
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...
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Alex
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Laura
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Martin
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Walter
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Mark
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# Show a dictionary sorted and grouped by value
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>>> from operator import itemgetter
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>>> d = dict(a=1, b=2, c=1, d=2, e=1, f=2, g=3)
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>>> di = sorted(d.items(), key=itemgetter(1))
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>>> for k, g in groupby(di, key=itemgetter(1)):
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... print(k, map(itemgetter(0), g))
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...
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1 ['a', 'c', 'e']
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2 ['b', 'd', 'f']
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3 ['g']
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# Find runs of consecutive numbers using groupby. The key to the solution
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# is differencing with a range so that consecutive numbers all appear in
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# same group.
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>>> data = [ 1, 4,5,6, 10, 15,16,17,18, 22, 25,26,27,28]
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>>> for k, g in groupby(enumerate(data), lambda t:t[0]-t[1]):
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... print(map(operator.itemgetter(1), g))
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...
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[1]
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[4, 5, 6]
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[10]
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[15, 16, 17, 18]
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[22]
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[25, 26, 27, 28]
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.. _itertools-recipes:
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Recipes
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-------
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This section shows recipes for creating an extended toolset using the existing
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itertools as building blocks.
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The extended tools offer the same high performance as the underlying toolset.
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The superior memory performance is kept by processing elements one at a time
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rather than bringing the whole iterable into memory all at once. Code volume is
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kept small by linking the tools together in a functional style which helps
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eliminate temporary variables. High speed is retained by preferring
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"vectorized" building blocks over the use of for-loops and :term:`generator`\s
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which incur interpreter overhead. ::
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def take(n, seq):
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return list(islice(seq, n))
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def enumerate(iterable):
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return izip(count(), iterable)
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def tabulate(function):
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"Return function(0), function(1), ..."
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return imap(function, count())
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def nth(iterable, n):
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"Returns the nth item or raise StopIteration"
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return islice(iterable, n, None).next()
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def all(seq, pred=None):
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"Returns True if pred(x) is true for every element in the iterable"
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for elem in ifilterfalse(pred, seq):
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return False
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return True
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def any(seq, pred=None):
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"Returns True if pred(x) is true for at least one element in the iterable"
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for elem in ifilter(pred, seq):
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return True
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return False
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def no(seq, pred=None):
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"Returns True if pred(x) is false for every element in the iterable"
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for elem in ifilter(pred, seq):
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return False
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return True
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def quantify(seq, pred=None):
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"Count how many times the predicate is true in the sequence"
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return sum(imap(pred, seq))
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def padnone(seq):
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"""Returns the sequence elements and then returns None indefinitely.
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Useful for emulating the behavior of the built-in map() function.
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"""
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return chain(seq, repeat(None))
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def ncycles(seq, n):
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"Returns the sequence elements n times"
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return chain(*repeat(seq, n))
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def dotproduct(vec1, vec2):
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return sum(imap(operator.mul, vec1, vec2))
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def flatten(listOfLists):
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return list(chain(*listOfLists))
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def repeatfunc(func, times=None, *args):
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"""Repeat calls to func with specified arguments.
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Example: repeatfunc(random.random)
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"""
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if times is None:
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return starmap(func, repeat(args))
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else:
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return starmap(func, repeat(args, times))
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def pairwise(iterable):
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"s -> (s0,s1), (s1,s2), (s2, s3), ..."
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a, b = tee(iterable)
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next(b, None)
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return izip(a, b)
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def grouper(n, iterable, padvalue=None):
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"grouper(3, 'abcdefg', 'x') --> ('a','b','c'), ('d','e','f'), ('g','x','x')"
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return izip(*[chain(iterable, repeat(padvalue, n-1))]*n)
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def roundrobin(*iterables):
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"roundrobin('abc', 'd', 'ef') --> 'a', 'd', 'e', 'b', 'f', 'c'"
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# Recipe contributed by George Sakkis
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pending = len(iterables)
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nexts = cycle(iter(it).next for it in iterables)
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while pending:
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try:
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for next in nexts:
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yield next()
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except StopIteration:
|
|
pending -= 1
|
|
nexts = cycle(islice(nexts, pending))
|
|
|