cpython/Doc/lib/libitertools.tex

\section{\module{itertools} ---
         Functions creating iterators for efficient looping}

\declaremodule{standard}{itertools}
\modulesynopsis{Functions creating iterators for efficient looping.}
\moduleauthor{Raymond Hettinger}{python@rcn.com}
\sectionauthor{Raymond Hettinger}{python@rcn.com}
\versionadded{2.3}


This module implements a number of iterator building blocks inspired
by constructs from the Haskell and SML programming languages.  Each
has been recast in a form suitable for Python.

The module standardizes a core set of fast, memory efficient tools
that are useful by themselves or in combination.  Standardization helps
avoid the readability and reliability problems which arise when many
different individuals create their own slightly varying implementations,
each with their own quirks and naming conventions.

The tools are designed to combine readily with each another.  This makes
it easy to construct more specialized tools succinctly and efficiently
in pure Python.

For instance, SML provides a tabulation tool: \code{tabulate(\var{f})}
which produces a sequence \code{f(0), f(1), ...}.  This toolbox
provides \function{imap()} and \function{count()} which can be combined
to form \code{imap(\var{f}, count())} and produce an equivalent result.

Whether cast in pure python form or C code, tools that use iterators
are more memory efficient (and faster) than their list based counterparts.
Adopting the principles of just-in-time manufacturing, they create
data when and where needed instead of consuming memory with the
computer equivalent of ``inventory''.

Some tools were omitted from the module because they offered no
advantage over their pure python counterparts or because their behavior
was too surprising.

For instance, SML provides a tool:  \code{cycle(\var{seq})} which
loops over the sequence elements and then starts again when the
sequence is exhausted.  The surprising behavior is the need for
significant auxiliary storage (which is unusual for an iterator).
If needed, the tool is readily constructible using pure Python.

Other tools are being considered for inclusion in future versions of the
module.  For instance, the function
\function{chain(\var{it0}, \var{it1}, ...})} would return elements from
the first iterator until it was exhausted and then move on to each
successive iterator.  The module author welcomes suggestions for other
basic building blocks.

\begin{seealso}
  \seetext{The Standard ML Basis Library,
           \citetitle[http://www.standardml.org/Basis/]
           {The Standard ML Basis Library}.}

  \seetext{Haskell, A Purely Functional Language,
           \citetitle[http://www.haskell.org/definition/]
           {Definition of Haskell and the Standard Libraries}.}
\end{seealso}


\subsection{Itertool functions \label{itertools-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.

\begin{funcdesc}{count}{\optional{n}}
  Make an iterator that returns consecutive integers starting with \var{n}.
  Does not currently support python long integers.  Often used as an
  argument to \function{imap()} to generate consecutive data points.
  Also, used in \function{izip()} to add sequence numbers.  Equivalent to:

  \begin{verbatim}
     def count(n=0):
         cnt = n
         while True:
             yield cnt
             cnt += 1
  \end{verbatim}

  Note, \function{count()} does not check for overflow and will return
  negative numbers after exceeding \code{sys.maxint}.  This behavior
  may change in the future.
\end{funcdesc}

\begin{funcdesc}{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 \emph{any} output until the predicate
  is true, so it may have a lengthy start-up time.  Equivalent to:

  \begin{verbatim}
     def dropwhile(predicate, iterable):
         iterable = iter(iterable)
         while True:
             x = iterable.next()
             if predicate(x): continue # drop when predicate is true
             yield x
             break
         while True:
             yield iterable.next()
  \end{verbatim}
\end{funcdesc}

\begin{funcdesc}{ifilter}{predicate, iterable}
  Make an iterator that filters elements from iterable returning only
  those for which the predicate is \code{True}.
  If \var{predicate} is \code{None}, return the items that are true.
  Equivalent to:

  \begin{verbatim}
     def ifilter(predicate, iterable):
         if predicate is None:
             def predicate(x):
                 return x
         for x in iterable:
             if predicate(x):
                 yield x
  \end{verbatim}
\end{funcdesc}

\begin{funcdesc}{ifilterfalse}{predicate, iterable}
  Make an iterator that filters elements from iterable returning only
  those for which the predicate is \code{False}.
  If \var{predicate} is \code{None}, return the items that are false.
  Equivalent to:

  \begin{verbatim}
     def ifilterfalse(predicate, iterable):
         if predicate is None:
             def predicate(x):
                 return x
         for x in iterable:
             if not predicate(x):
                 yield x
  \end{verbatim}
\end{funcdesc}

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

  \begin{verbatim}
     def imap(function, *iterables):
         iterables = map(iter, iterables)
         while True:
             args = [i.next() for i in iterables]
             if function is None:
                 yield tuple(args)
             else:
                 yield function(*args)
  \end{verbatim}
\end{funcdesc}

\begin{funcdesc}{islice}{iterable, \optional{start,} stop \optional{, step}}
  Make an iterator that returns selected elements from the iterable.
  If \var{start} is non-zero, then elements from the iterable are skipped
  until start is reached.  Afterward, elements are returned consecutively
  unless \var{step} is set higher than one which results in items being
  skipped.  If \var{stop} is specified, then iteration stops at the
  specified element position; otherwise, it continues indefinitely or
  until the iterable is exhausted.  Unlike regular slicing,
  \function{islice()} does not support negative values for \var{start},
  \var{stop}, or \var{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:

  \begin{verbatim}
     def islice(iterable, *args):
         s = slice(*args)
         next = s.start or 0
         stop = s.stop
         step = s.step or 1
         for cnt, element in enumerate(iterable):
             if cnt < next:
                 continue
             if cnt >= stop:
                 break
             yield element
             next += step
  \end{verbatim}
\end{funcdesc}

\begin{funcdesc}{izip}{*iterables}
  Make an iterator that aggregates elements from each of the iterables.
  Like \function{zip()} except that it returns an iterator instead of
  a list.  Used for lock-step iteration over several iterables at a
  time.  Equivalent to:

  \begin{verbatim}
     def izip(*iterables):
         iterables = map(iter, iterables)
         while True:
             result = [i.next() for i in iterables]
             yield tuple(result)
  \end{verbatim}
\end{funcdesc}

\begin{funcdesc}{repeat}{obj}
  Make an iterator that returns \var{obj} over and over again.
  Used as argument to \function{imap()} for invariant parameters
  to the called function.  Also used with function{izip()} to create
  an invariant part of a tuple record.  Equivalent to:

  \begin{verbatim}
     def repeat(x):
         while True:
             yield x
  \end{verbatim}
\end{funcdesc}

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

  \begin{verbatim}
     def starmap(function, iterable):
         iterable = iter(iterable)
         while True:
             yield function(*iterable.next())
  \end{verbatim}
\end{funcdesc}

\begin{funcdesc}{takewhile}{predicate, iterable}
  Make an iterator that returns elements from the iterable as long as
  the predicate is true.  Equivalent to:

  \begin{verbatim}
     def takewhile(predicate, iterable):
         iterable = iter(iterable)
         while True:
             x = iterable.next()
             if predicate(x):
                 yield x
             else:
                 break
  \end{verbatim}
\end{funcdesc}

\begin{funcdesc}{times}{n, \optional{object}}
  Make an iterator that returns \var{object} \var{n} times.
  \var{object} defaults to \code{None}.  Used for looping a specific
  number of times without creating a number object on each pass.
  Equivalent to:

  \begin{verbatim}
     def times(n, object=None):
         if n<0 : raise ValueError
         for i in xrange(n):
             yield object
  \end{verbatim}
\end{funcdesc}


\subsection{Examples \label{itertools-example}}

The following examples show common uses for each tool and
demonstrate ways they can be combined.

\begin{verbatim}
>>> for i in times(3):
...     print "Hello"
...
Hello
Hello
Hello

>>> amounts = [120.15, 764.05, 823.14]
>>> for checknum, amount in izip(count(1200), amounts):
...     print 'Check %d is for $%.2f' % (checknum, amount)
...
Check 1200 is for $120.15
Check 1201 is for $764.05
Check 1202 is for $823.14

>>> import operator
>>> for cube in imap(operator.pow, xrange(1,4), repeat(3)):
...    print cube
...
1
8
27

>>> reportlines = ['EuroPython', 'Roster', '', 'alex', '', 'laura',
                  '', 'martin', '', 'walter', '', 'samuele']
>>> for name in islice(reportlines, 3, len(reportlines), 2):
...    print name.title()
...
Alex
Laura
Martin
Walter
Samuele

\end{verbatim}

This section has further examples of how itertools can be combined.
Note that \function{enumerate()} and \method{iteritems()} already
have highly efficient implementations in Python.  They are only
included here to illustrate how higher level tools can be created
from building blocks.

\begin{verbatim}
>>> def enumerate(iterable):
...     return izip(count(), iterable)

>>> def tabulate(function):
...     "Return function(0), function(1), ..."
...     return imap(function, count())

>>> def iteritems(mapping):
...     return izip(mapping.iterkeys(), mapping.itervalues())

>>> def nth(iterable, n):
...     "Returns the nth item"
...     return list(islice(iterable, n, n+1))

>>> def all(pred, seq):
...     "Returns True if pred(x) is True for every element in the iterable"
...     return not nth(ifilterfalse(pred, seq), 0)

>>> def some(pred, seq):
...     "Returns True if pred(x) is True at least one element in the iterable"
...     return bool(nth(ifilter(pred, seq), 0))

>>> def no(pred, seq):
...     "Returns True if pred(x) is False for every element in the iterable"
...     return not nth(ifilter(pred, seq), 0)

\end{verbatim}