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Added "See also" reference to xdrlib.
Markup nits.
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\section{\module{array} ---
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Efficient arrays of uniformly typed numeric values.}
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\declaremodule{builtin}{array}
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Efficient arrays of numeric values}
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\declaremodule{builtin}{array}
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\modulesynopsis{Efficient arrays of uniformly typed numeric values.}
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\index{arrays}
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This module defines a new object type which can efficiently represent
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an array of basic values: characters, integers, floating point
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numbers. Arrays are sequence types and behave very much like lists,
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except that the type of objects stored in them is constrained. The
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type is specified at object creation time by using a \dfn{type code},
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which is a single character. The following type codes are defined:
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numbers. Arrays\index{arrays} are sequence types and behave very much
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like lists, except that the type of objects stored in them is
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constrained. The type is specified at object creation time by using a
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\dfn{type code}, which is a single character. The following type
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codes are defined:
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\begin{tableiii}{c|l|c}{code}{Type code}{C Type}{Minimum size in bytes}
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\lineiii{'c'}{character}{1}
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\end{tableiii}
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The actual representation of values is determined by the machine
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architecture (strictly speaking, by the \C{} implementation). The actual
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architecture (strictly speaking, by the C implementation). The actual
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size can be accessed through the \member{itemsize} attribute. The values
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stored for \code{'L'} and \code{'I'} items will be represented as
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Python long integers when retrieved, because Python's plain integer
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type cannot represent the full range of \C{}'s unsigned (long) integers.
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type cannot represent the full range of C's unsigned (long) integers.
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The module defines the following function and type object:
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\begin{seealso}
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\seemodule{struct}{Packing and unpacking of heterogeneous binary data.}
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\seemodule{struct}{packing and unpacking of heterogeneous binary data}
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\seemodule{xdrlib{{packing and unpacking of XDR data}
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\end{seealso}
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