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Lots of markup corrections. Some are minor, but others are not: the
contents of an \item should not be enclosed in a group!
This commit is contained in:
Fred Drake
parent
cc2e48dd79
commit
d4a1419f53
@ -6,84 +6,97 @@
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\begin{description}
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\index{...}
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\item[...]{The typical Python prompt of the interactive shell when entering
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code for an indented code block.}
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\item[\code{.\code{.}.}]
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The typical Python prompt of the interactive shell when entering code
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for an indented code block.
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\index{>>>}
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\item[>>>]{The typical Python prompt of the interactive shell. Often seen
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for code examples that can be tried right away in the interpreter.}
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\item[\code{>\code{>}>}]
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The typical Python prompt of the interactive shell. Often seen for
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code examples that can be tried right away in the interpreter.
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\index{__slots__}
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\item[__slots__]{A declaration inside a \emph{new-style class} that saves
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memory by pre-declaring space for instance attributes and eliminating
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instance dictionaries. Though popular, the technique is somewhat tricky to
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get right and is best reserved for rare cases where there are large numbers
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of instances in a memory critical application.}
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\item[__slots__]
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A declaration inside a \emph{new-style class} that saves memory by
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pre-declaring space for instance attributes and eliminating instance
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dictionaries. Though popular, the technique is somewhat tricky to get
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right and is best reserved for rare cases where there are large
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numbers of instances in a memory critical application.
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\index{BDFL}
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\item[BDFL]{Benevolent Dictator For Life, a.k.a. \ulink{Guido van
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Rossum}{http://www.python.org/~guido/}, Python's creator.}
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\item[BDFL]
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Benevolent Dictator For Life, a.k.a. \ulink{Guido van
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Rossum}{http://www.python.org/~guido/}, Python's creator.
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\index{byte code}
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\item[byte code]{The internal representation of a Python program in the
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interpreter. The byte code is also cached in the \code{.pyc} and
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{}\code{.pyo} files so that executing the same file is faster the second
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time (compilation from source to byte code can be saved). This
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"intermediate language" is said to run on a "virtual machine" that calls the
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subroutines corresponding to each bytecode.}
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\item[byte code]
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The internal representation of a Python program in the interpreter.
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The byte code is also cached in the \code{.pyc} and \code{.pyo}
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files so that executing the same file is faster the second time
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(compilation from source to byte code can be saved). This
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\emph{intermediate language} is said to run on a \emph{virtual
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machine} that calls the subroutines corresponding to each bytecode.
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\index{classic class}
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\item[classic class]{Any class which does not inherit from \class{object}.
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See new-style class.}
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\item[classic class]
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Any class which does not inherit from \class{object}. See
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\emph{new-style class}.
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\index{coercion}
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\item[coercion]{Converting data from one type to another. For example,
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{}\code{int(3.15)} coerces the floating point number to the integer, \code{3}.
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Most mathematical operations have rules for coercing their arguments to a common
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type. For instance, adding \code{3 + 4.5}, causes the integer \code{3} to be
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coerced to be a float \code{3.0} before adding to \code{4.5} resulting in the
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float \code{7.5}.}
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\item[coercion]
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Converting data from one type to another. For example,
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{}\code{int(3.15)} coerces the floating point number to the integer,
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{}\code{3}. Most mathematical operations have rules for coercing
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their arguments to a common type. For instance, adding \code{3 +
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4.5}, causes the integer \code{3} to be coerced to be a float
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{}\code{3.0} before adding to \code{4.5} resulting in the float
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{}\code{7.5}.
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\index{descriptor}
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\item[descriptor]{Any object that defines the methods \method{__get__()},
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\method{__set__()}, or \method{__delete__()}. When a class attribute is a
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descriptor, its special binding behavior is triggered upon attribute lookup.
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Normally, writing {}\var{a.b} looks up the object \var{b} in the class
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dictionary for \var{a}, but if \var{b} is a descriptor, the defined method
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gets called. Understanding descriptors is a key to a deep understanding of
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Python because they are the basis for many features including functions,
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methods, properties, class methods, static methods, and reference to super
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classes.}
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\item[descriptor]
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Any \emph{new-style} object that defines the methods
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{}\method{__get__()}, \method{__set__()}, or \method{__delete__()}.
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When a class attribute is a descriptor, its special binding behavior
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is triggered upon attribute lookup. Normally, writing \var{a.b} looks
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up the object \var{b} in the class dictionary for \var{a}, but if
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{}\var{b} is a descriptor, the defined method gets called.
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Understanding descriptors is a key to a deep understanding of Python
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because they are the basis for many features including functions,
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methods, properties, class methods, static methods, and reference to
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super classes.
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\index{dictionary}
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\item[dictionary]{An associative array, where arbitrary keys are mapped to
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values. The use of \class{dict} much resembles that for \class{list}, but
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the keys can be any object with a \function{__hash__} function, not just
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integers starting from zero. Called a hash in Perl.}
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\item[dictionary]
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An associative array, where arbitrary keys are mapped to values. The
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use of \class{dict} much resembles that for \class{list}, but the keys
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can be any object with a \method{__hash__()} function, not just
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integers starting from zero. Called a hash in Perl.
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\index{EAFP}
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\item[EAFP]{Easier to ask for forgiveness than permission. This common
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Python coding style assumes the existence of valid keys or attributes and
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catches exceptions if the assumption proves false. This clean and fast
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style is characterized by the presence of many \keyword{try} and
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{}\keyword{except} statements. The technique contrasts with the \emph{LBYL}
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style that is common in many other languages such as C.}
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\item[EAFP]
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Easier to ask for forgiveness than permission. This common Python
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coding style assumes the existence of valid keys or attributes and
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catches exceptions if the assumption proves false. This clean and
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fast style is characterized by the presence of many \keyword{try} and
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{}\keyword{except} statements. The technique contrasts with the
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{}\emph{LBYL} style that is common in many other languages such as C.
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\index{__future__}
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\item[__future__]{A pseudo module which programmers can use to enable
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new language features which are not compatible with the current interpreter.
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For example, the expression \code{11 / 4} currently evaluates to \code{2}.
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If the module in which it is executed had enabled emph{true division} by
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executing:}
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\item[__future__]
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A pseudo module which programmers can use to enable new language
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features which are not compatible with the current interpreter. For
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example, the expression \code{11 / 4} currently evaluates to \code{2}.
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If the module in which it is executed had enabled emph{true division}
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by executing:
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\begin{verbatim}
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from __future__ import division
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\end{verbatim}
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the expression \code{11 / 4} would evaluate to \code{2.75}. By actually
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importing the \module{__future__} module and evaluating its variables, you
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can see when a new feature was first added to the language and when it will
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become the default:
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the expression \code{11 / 4} would evaluate to \code{2.75}. By
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actually importing the \refmodule[future]{__future__} module and
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evaluating its variables, you can see when a new feature was first
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added to the language and when it will become the default:
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\begin{verbatim}
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>>> import __future__
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@ -92,177 +105,205 @@ _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)
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\end{verbatim}
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\index{generator}
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\item[generator]{A function that returns an iterator. It looks like a
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normal function except that the \keyword{yield} keyword is used instead of
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\item[generator]
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A function that returns an iterator. It looks like a normal function
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except that the \keyword{yield} keyword is used instead of
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{}\keyword{return}. Generator functions often contain one or more
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{}\keyword{for} or \keyword{while} loops that \keyword{yield} elements back to
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the caller. The function execution is stopped at the \keyword{yield} keyword
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(returning the result) and is resumed there when the next element is
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requested by calling the \function{next()} method of the returned iterator.}
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{}\keyword{for} or \keyword{while} loops that \keyword{yield} elements
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back to the caller. The function execution is stopped at the
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{}\keyword{yield} keyword (returning the result) and is resumed there
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when the next element is requested by calling the \method{next()}
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method of the returned iterator.
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\index{GIL}
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\item[GIL]{See \emph{global interpreter lock}.}
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\item[GIL]
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See \emph{global interpreter lock}.
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\index{global interpreter lock}
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\item[global interpreter lock]{the lock used by Python threads to assure
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that only one thread can be run at a time. This simplifies Python by
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assuring that no two processes can access the same memory at the same time.
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Locking the entire interpreter makes it easier for the interpreter to be
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\item[global interpreter lock]
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The lock used by Python threads to assure that only one thread can be
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run at a time. This simplifies Python by assuring that no two
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processes can access the same memory at the same time. Locking the
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entire interpreter makes it easier for the interpreter to be
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multi-threaded, at the expense of some parallelism on multi-processor
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machines. Efforts have been made in the past to create a "free-threaded"
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interpreter (one which locks shared data at a much finer granularity), but
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performance suffered in the common single-processor case.}
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machines. Efforts have been made in the past to create a
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"free-threaded" interpreter (one which locks shared data at a much
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finer granularity), but performance suffered in the common
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single-processor case.
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\index{IDLE}
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\item[IDLE]{an Integrated Development Environment for Python. IDLE is a
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\item[IDLE]
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An Integrated Development Environment for Python. IDLE is a
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basic editor and interpreter environment that ships with the standard
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distribution of Python. Good for beginners, it also serves as clear
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example code for those wanting to implement a moderately
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sophisticated, multi-platform GUI application.}
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sophisticated, multi-platform GUI application.
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\index{immutable}
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\item[immutable]{A object with fixed value. Immutable objects are numbers,
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strings or tuples (and more). Such an object cannot be altered. A new object
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\item[immutable]
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A object with fixed value. Immutable objects are numbers, strings or
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tuples (and more). Such an object cannot be altered. A new object
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has to be created if a different value has to be stored. They play an
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important role in places where a constant hash value is needed. For example
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as a key in a dictionary.}
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important role in places where a constant hash value is needed. For
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example as a key in a dictionary.
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\index{integer division}
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\item[integer division]{Mathematical division discarding any remainder. For
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example, the expression \code{11 / 4} currently evaluates to \code{2} in
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contrast to the \code{2.75} returned by float division. Also called
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\emph{floor division}. When dividing two integers the outcome will always be
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another integer (having the floor function applied to it). However, if one
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of the operands is another numeric type (such as a \class{float}), the result
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will be coerced (see \emph{coercion}) to a common type. For example, a integer
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divided by a float will result in a float value, possibly with a decimal
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fraction. Integer division can be forced by using the \code{//} operator
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instead of the \code{/} operator.
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See also, \emph{__future__}.}
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\item[integer division]
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Mathematical division discarding any remainder. For example, the
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expression \code{11 / 4} currently evaluates to \code{2} in contrast
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to the \code{2.75} returned by float division. Also called
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{}\emph{floor division}. When dividing two integers the outcome will
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always be another integer (having the floor function applied to it).
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However, if one of the operands is another numeric type (such as a
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{}\class{float}), the result will be coerced (see \emph{coercion}) to
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a common type. For example, a integer divided by a float will result
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in a float value, possibly with a decimal fraction. Integer division
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can be forced by using the \code{//} operator instead of the \code{/}
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operator. See also \emph{__future__}.
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\index{interactive}
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\item[interactive]{Python has an interactive interpreter which means that
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you can try out things and directly see its result. Just launch
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{}\code{python} with no arguments (possibly by selecting it from your
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computer's main menu). It is a very powerful way to test out new ideas or
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inspect modules and packages (remember \code{help(x)}).}
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\item[interactive]
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Python has an interactive interpreter which means that you can try out
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things and directly see its result. Just launch \code{python} with no
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arguments (possibly by selecting it from your computer's main menu).
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It is a very powerful way to test out new ideas or inspect modules and
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packages (remember \code{help(x)}).
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\index{interpreted}
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\item[interpreted]{Python is an interpreted language, opposed to a compiled
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one. This means that the source files can be run right away without first
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making an executable which is then run. Interpreted languages typically have
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\item[interpreted]
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Python is an interpreted language, opposed to a compiled one. This
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means that the source files can be run right away without first making
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an executable which is then run. Interpreted languages typically have
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a shorter development/debug cycle than compiled ones. See also
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{}\emph{interactive}.}
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{}\emph{interactive}.
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\index{iterable}
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\item[iterable]{A container object capable of returning its members one at a
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time. Examples of iterables include all sequence types (such as\class{list},
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\item[iterable]
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A container object capable of returning its members one at a time.
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Examples of iterables include all sequence types (such as\class{list},
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{}\class{str}, and \class{tuple}) and some non-sequence types like
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{}\class{dict} and \class{file} and objects of any classes you define with
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an \method{__iter__} or \function{__getitem__} method. Iterables can be
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used in a \keyword{for} loop and in many other places where a sequence is
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needed (\function{zip}, \function{map}, ...). When an iterable object is
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passed as an argument to the builtin function \function{iter()}, it returns
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an iterator for the object. This iterator is good for one pass over the set
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of values. When using iterables, it is usually not necessary to call
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{}\function{iter()} or deal with iterator objects yourself - the \code{for}
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statement does that automatically for you, creating a temporary unnamed
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variable to hold the iterator for the duration of the loop. See also
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\emph{iterator}, \emph{sequence} and \emph{generator}.}
|
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{}\class{dict} and \class{file} and objects of any classes you define
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with an \method{__iter__()} or \method{__getitem__()} method. Iterables
|
||||
can be used in a \keyword{for} loop and in many other places where a
|
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sequence is needed (\function{zip()}, \function{map()}, ...). When an
|
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iterable object is passed as an argument to the builtin function
|
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{}\function{iter()}, it returns an iterator for the object. This
|
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iterator is good for one pass over the set of values. When using
|
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iterables, it is usually not necessary to call \function{iter()} or
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deal with iterator objects yourself---the \code{for} statement does
|
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that automatically for you, creating a temporary unnamed variable to
|
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hold the iterator for the duration of the loop. See also
|
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{}\emph{iterator}, \emph{sequence}, and \emph{generator}.
|
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|
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\index{iterator}
|
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\item[iterator]{An object representing a stream of data. Repeated calls to
|
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the iterator's \function{next()} method return successive items in the
|
||||
\item[iterator]
|
||||
An object representing a stream of data. Repeated calls to the
|
||||
iterator's \method{next()} method return successive items in the
|
||||
stream. When no more data is available a \exception{StopIteration}
|
||||
exception is raised instead. At this point, the iterator object is exhausted
|
||||
and any further calls to its \function{next()} method just raise
|
||||
{}\exception{StopIteration} again. Iterators are required to have an
|
||||
{}\function{__iter__()} method that returns the iterator object itself so
|
||||
every iterator is also iterable and may be used in most places where other
|
||||
iterables are accepted. One notable exception is code that attempts
|
||||
multiple iteration passes. A container object (such as a \class{list})
|
||||
produces a fresh new iterator each time you pass it to the \function{iter()}
|
||||
function or use it in a \function{for} loop. Attempting this with an iterator
|
||||
will just return the same exhausted iterator object from the second iteration
|
||||
pass, making it appear like an empty container.}
|
||||
exception is raised instead. At this point, the iterator object is
|
||||
exhausted and any further calls to its \method{next()} method just
|
||||
raise \exception{StopIteration} again. Iterators are required to have
|
||||
an \method{__iter__()} method that returns the iterator object
|
||||
itself so every iterator is also iterable and may be used in most
|
||||
places where other iterables are accepted. One notable exception is
|
||||
code that attempts multiple iteration passes. A container object
|
||||
(such as a \class{list}) produces a fresh new iterator each time you
|
||||
pass it to the \function{iter()} function or use it in a
|
||||
{}\keyword{for} loop. Attempting this with an iterator will just
|
||||
return the same exhausted iterator object from the second iteration
|
||||
pass, making it appear like an empty container.
|
||||
|
||||
\index{list comprehension}
|
||||
\item[list comprehension]{A compact way to process all or a subset of elements
|
||||
in a sequence and return a list with the results. \code{result = ["0x\%02x"
|
||||
\item[list comprehension]
|
||||
A compact way to process all or a subset of elements in a sequence and
|
||||
return a list with the results. \code{result = ["0x\%02x"
|
||||
\% x for x in range(256) if x \% 2 == 0]} generates a list of strings
|
||||
containing hex numbers (0x..) that are even and in the range from 0 to 255.
|
||||
The \keyword{if} clause is optional. If omitted, all elements in
|
||||
{}\code{range(256)} are processed in that case.}
|
||||
{}\code{range(256)} are processed in that case.
|
||||
|
||||
\index{mapping}
|
||||
\item[mapping]{A container object (such as \class{dict}) that supports
|
||||
arbitrary key lookups using the special method \function{__getitem__()}.}
|
||||
\item[mapping]
|
||||
A container object (such as \class{dict}) that supports arbitrary key
|
||||
lookups using the special method \method{__getitem__()}.
|
||||
|
||||
\index{metaclass}
|
||||
\item[metaclass]{The class of a class. Class definitions create a class
|
||||
name, a class dictionary, and a list of base classes. The metaclass is
|
||||
responsible for taking those three arguments and creating the class. Most
|
||||
object oriented programming languages provide a default implementation.
|
||||
What makes Python special is that it is possible to create custom
|
||||
metaclasses. Most users never need this tool, but when the need arises,
|
||||
metaclasses can provide powerful, elegant solutions. They have been used
|
||||
for logging attribute access, adding thread-safety, tracking object
|
||||
creation, implementing singletons, and many other tasks.}
|
||||
\item[metaclass]
|
||||
The class of a class. Class definitions create a class name, a class
|
||||
dictionary, and a list of base classes. The metaclass is responsible
|
||||
for taking those three arguments and creating the class. Most object
|
||||
oriented programming languages provide a default implementation. What
|
||||
makes Python special is that it is possible to create custom
|
||||
metaclasses. Most users never need this tool, but when the need
|
||||
arises, metaclasses can provide powerful, elegant solutions. They
|
||||
have been used for logging attribute access, adding thread-safety,
|
||||
tracking object creation, implementing singletons, and many other
|
||||
tasks.
|
||||
|
||||
\index{LBYL}
|
||||
\item[LBYL]{Look before you leap. This coding style explicitly tests for
|
||||
pre-conditions before making calls or lookups. This style contrasts with
|
||||
the \emph{EAFP} approach and is characterized the presence of many
|
||||
{}\keyword{if} statements.}
|
||||
\item[LBYL]
|
||||
Look before you leap. This coding style explicitly tests for
|
||||
pre-conditions before making calls or lookups. This style contrasts
|
||||
with the \emph{EAFP} approach and is characterized the presence of
|
||||
many \keyword{if} statements.
|
||||
|
||||
\index{mutable}
|
||||
\item[mutable]{Mutable objects can change their value but keep their
|
||||
\function{id()}. See also immutable.}
|
||||
\item[mutable]
|
||||
Mutable objects can change their value but keep their \function{id()}.
|
||||
See also \emph{immutable}.
|
||||
|
||||
\index{namespace}
|
||||
\item[namespace]{The place where a variable is stored. Namespaces are
|
||||
implemented as dictionary. There is the local, global and builtins
|
||||
namespace and the nested namespaces in objects (in methods). Namespaces
|
||||
support modularity by preventing naming conflicts. For instance, the
|
||||
functions \function{__builtins__.open()} and \function{os.open()} are
|
||||
distinguished by their namespaces. Namespaces also aid readability and
|
||||
maintainability by making it clear which modules implement a function. For
|
||||
instance, writing \function{random.seed()} or \function{itertools.izip()}
|
||||
makes it clear that those functions are implemented by the \module{random}
|
||||
and \module{itertools} modules respectively.}
|
||||
\item[namespace]
|
||||
The place where a variable is stored. Namespaces are implemented as
|
||||
dictionary. There is the local, global and builtins namespace and the
|
||||
nested namespaces in objects (in methods). Namespaces support
|
||||
modularity by preventing naming conflicts. For instance, the
|
||||
functions \function{__builtin__.open()} and \function{os.open()} are
|
||||
distinguished by their namespaces. Namespaces also aid readability
|
||||
and maintainability by making it clear which modules implement a
|
||||
function. For instance, writing \function{random.seed()} or
|
||||
{}\function{itertools.izip()} makes it clear that those functions are
|
||||
implemented by the \refmodule{random} and \refmodule{itertools}
|
||||
modules respectively.
|
||||
|
||||
\index{nested scope}
|
||||
\item[nested scope]{The ability to refer to a variable in an enclosing
|
||||
definition. For instance, a function defined inside another function can
|
||||
refer to variables in the outer function. Note that nested scopes work only
|
||||
\item[nested scope]
|
||||
The ability to refer to a variable in an enclosing definition. For
|
||||
instance, a function defined inside another function can refer to
|
||||
variables in the outer function. Note that nested scopes work only
|
||||
for reference and not for assignment which will always write to the
|
||||
innermost scope. In contrast, local variables both read and write in the
|
||||
innermost scope. Likewise, global variables read and write to the global
|
||||
namespace.}
|
||||
innermost scope. In contrast, local variables both read and write in
|
||||
the innermost scope. Likewise, global variables read and write to the
|
||||
global namespace.
|
||||
|
||||
\index{new-style class}
|
||||
\item[new-style class]{Any class that inherits from \class{object}. This
|
||||
includes all built-in types like \class{list} and \class{dict}. Only
|
||||
new-style classes can use Python's newer, versatile features like
|
||||
{}\var{__slots__}, descriptors, properties, \var{__getattribute__}, class
|
||||
methods, and static methods.}
|
||||
\item[new-style class]
|
||||
Any class that inherits from \class{object}. This includes all
|
||||
built-in types like \class{list} and \class{dict}. Only new-style
|
||||
classes can use Python's newer, versatile features like
|
||||
{}\var{__slots__}, descriptors, properties,
|
||||
\method{__getattribute__()}, class methods, and static methods.
|
||||
|
||||
\index{Python3000}
|
||||
\item[Python3000]{A mythical python release, allowed not to be backward
|
||||
compatible, with telepathic interface.}
|
||||
\item[Python3000]
|
||||
A mythical python release, allowed not to be backward compatible, with
|
||||
telepathic interface.
|
||||
|
||||
\index{sequence}
|
||||
\item[sequence]{An \emph{iterable} which supports efficient element access using
|
||||
integer indices via the \function{__getitem__} and \function{__len()__}
|
||||
special methods. Some builtin sequence types are \class{list}, \class{str},
|
||||
{}\class{tuple}, and \class{unicode}. Note that \class{dict} also supports
|
||||
{}\function{__getitem__} and \function{__len__}, but is considered a mapping
|
||||
rather than a sequence because the lookups use arbitrary \emph{immutable} keys
|
||||
rather than integers.}
|
||||
\item[sequence]
|
||||
An \emph{iterable} which supports efficient element access using
|
||||
integer indices via the \method{__getitem__()} and
|
||||
{}\method{__len__()} special methods. Some built-in sequence types
|
||||
are \class{list}, \class{str}, \class{tuple}, and \class{unicode}.
|
||||
Note that \class{dict} also supports \method{__getitem__()} and
|
||||
{}\method{__len__()}, but is considered a mapping rather than a
|
||||
sequence because the lookups use arbitrary \emph{immutable} keys
|
||||
rather than integers.
|
||||
|
||||
\index{Zen of Python}
|
||||
\item[Zen of Python]{listing of Python design principles and philosophies
|
||||
that are helpful in understanding and using the language. The listing can
|
||||
be found by typing \code{import this} at the interactive prompt.}
|
||||
\item[Zen of Python]
|
||||
Listing of Python design principles and philosophies that are helpful
|
||||
in understanding and using the language. The listing can be found by
|
||||
typing \code{import this} at the interactive prompt.
|
||||
|
||||
\end{description}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user