[Python-checkins]
python/dist/src/Doc/whatsnew whatsnew24.tex, 1.29, 1.30
akuchling at users.sourceforge.net
akuchling at users.sourceforge.net
Mon Feb 9 08:23:38 EST 2004
Update of /cvsroot/python/python/dist/src/Doc/whatsnew
In directory sc8-pr-cvs1.sourceforge.net:/tmp/cvs-serv32441
Modified Files:
whatsnew24.tex
Log Message:
Minor edits
Index: whatsnew24.tex
===================================================================
RCS file: /cvsroot/python/python/dist/src/Doc/whatsnew/whatsnew24.tex,v
retrieving revision 1.29
retrieving revision 1.30
diff -C2 -d -r1.29 -r1.30
*** whatsnew24.tex 29 Jan 2004 06:37:48 -0000 1.29
--- whatsnew24.tex 9 Feb 2004 13:23:34 -0000 1.30
***************
*** 323,330 ****
\end{itemize}
! \item There is a new \module{collections} module which currently offers
! just one new datatype, \class{deque}, which offers high-performance,
! thread-safe, memory friendly appends and pops on either side of the
! deque resulting in efficient stacks and queues:
\begin{verbatim}
--- 323,331 ----
\end{itemize}
! \item There is a new \module{collections} module for
! various specialized collection datatypes.
! Currently it contains just one type, \class{deque},
! a double-ended queue that supports efficiently adding and removing
! elements from either end.
\begin{verbatim}
***************
*** 345,354 ****
\end{verbatim}
! Several modules now take advantage of \class{collections.deque()} for
improved performance: \module{Queue}, \module{mutex}, \module{shlex}
\module{threading}, and \module{pydoc}.
\item The \module{heapq} module has been converted to C. The resulting
! ten-fold improvement in speed makes the module suitable for handling
high volumes of data.
--- 346,355 ----
\end{verbatim}
! Several modules now take advantage of \class{collections.deque} for
improved performance: \module{Queue}, \module{mutex}, \module{shlex}
\module{threading}, and \module{pydoc}.
\item The \module{heapq} module has been converted to C. The resulting
! tenfold improvement in speed makes the module suitable for handling
high volumes of data.
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