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<H1><A NAME="SECTION0071600000000000000000">
5.16 <tt class="module">itertools</tt> --
         Functions creating iterators for efficient looping</A>
</H1>

<P>
<A NAME="module-itertools"></A>

<span class="versionnote">New in version 2.3.</span>

<P>
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.

<P>
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.

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

<P>
For instance, SML provides a tabulation tool: <code>tabulate(f)</code>
which produces a sequence <code>f(0), f(1), ...</code>.  This toolbox
provides <tt class="function">imap()</tt> and <tt class="function">count()</tt> which can be combined
to form <code>imap(f, count())</code> and produce an equivalent result.

<P>
Likewise, the functional tools are designed to work well with the
high-speed functions provided by the <tt class="module"><a href="module-operator.html">operator</a></tt> module.

<P>
The module author welcomes suggestions for other basic building blocks
to be added to future versions of the module.

<P>
Whether cast in pure python form or compiled 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''.

<P>
The performance advantage of iterators becomes more acute as the number
of elements increases - at some point, lists grow large enough to
severely impact memory cache performance and start running slowly.

<P>
<div class="seealso">
  <p class="heading">See Also:</p>

  <div class="seetext"><p>The Standard ML Basis Library,
           <em class="citetitle"><a
 href="http://www.standardml.org/Basis/"
 title="The Standard ML Basis Library"
 >The Standard ML Basis Library</a></em>.</p></div>

<P>
<div class="seetext"><p>Haskell, A Purely Functional Language,
           <em class="citetitle"><a
 href="http://www.haskell.org/definition/"
 title="Definition of Haskell and the Standard Libraries"
 >Definition of Haskell and the Standard Libraries</a></em>.</p></div>
</div>

<P>

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	54
	55	<H1><A NAME="SECTION0071600000000000000000">
	56	5.16 <tt class="module">itertools</tt> --
	57	Functions creating iterators for efficient looping</A>
	58	</H1>
	59
	60	<P>
	61	<A NAME="module-itertools"></A>
	62
	63	<span class="versionnote">New in version 2.3.</span>
	64
65	<P>
66	This module implements a number of iterator building blocks inspired
67	by constructs from the Haskell and SML programming languages. Each
68	has been recast in a form suitable for Python.
69
70	<P>
71	The module standardizes a core set of fast, memory efficient tools
72	that are useful by themselves or in combination. Standardization helps
73	avoid the readability and reliability problems which arise when many
74	different individuals create their own slightly varying implementations,
75	each with their own quirks and naming conventions.
76
77	<P>
78	The tools are designed to combine readily with one another. This makes
79	it easy to construct more specialized tools succinctly and efficiently
80	in pure Python.
81
82	<P>
83	For instance, SML provides a tabulation tool: <code>tabulate(f)</code>
84	which produces a sequence <code>f(0), f(1), ...</code>. This toolbox
85	provides <tt class="function">imap()</tt> and <tt class="function">count()</tt> which can be combined
86	to form <code>imap(f, count())</code> and produce an equivalent result.
87
88	<P>
89	Likewise, the functional tools are designed to work well with the
90	high-speed functions provided by the <tt class="module"><a href="module-operator.html">operator</a></tt> module.
91
92	<P>
93	The module author welcomes suggestions for other basic building blocks
94	to be added to future versions of the module.
95
96	<P>
97	Whether cast in pure python form or compiled code, tools that use iterators
98	are more memory efficient (and faster) than their list based counterparts.
99	Adopting the principles of just-in-time manufacturing, they create
100	data when and where needed instead of consuming memory with the
101	computer equivalent of ``inventory''.
102
103	<P>
104	The performance advantage of iterators becomes more acute as the number
105	of elements increases - at some point, lists grow large enough to
106	severely impact memory cache performance and start running slowly.
107
108	<P>
109	<div class="seealso">
110	<p class="heading">See Also:</p>
111
112	<div class="seetext"><p>The Standard ML Basis Library,
113	<em class="citetitle"><a
114	href="http://www.standardml.org/Basis/"
115	title="The Standard ML Basis Library"
116	>The Standard ML Basis Library</a></em>.</p></div>
117
118	<P>
119	<div class="seetext"><p>Haskell, A Purely Functional Language,
120	<em class="citetitle"><a
121	href="http://www.haskell.org/definition/"
122	title="Definition of Haskell and the Standard Libraries"
123	>Definition of Haskell and the Standard Libraries</a></em>.</p></div>
124	</div>
125
126	<P>
127
128	<p><br /></p><hr class='online-navigation' />
129	<div class='online-navigation'>
130	<!--Table of Child-Links-->
131	<A NAME="CHILD_LINKS"><STRONG>Subsections</STRONG></a>
132
133	<UL CLASS="ChildLinks">
134	<LI><A href="itertools-functions.html">5.16.1 Itertool functions</a>
135	<LI><A href="itertools-example.html">5.16.2 Examples</a>
136	<LI><A href="itertools-recipes.html">5.16.3 Recipes</a>
137	</ul>
138	<!--End of Table of Child-Links-->
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