lib libcollections.tex, 1.7, 1.8 (original) (raw)

rhettinger at users.sourceforge.net rhettinger at users.sourceforge.net
Sat May 8 21:15:03 EDT 2004

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Update of /cvsroot/python/python/dist/src/Doc/lib In directory sc8-pr-cvs1.sourceforge.net:/tmp/cvs-serv24983/Doc/lib

Modified Files: libcollections.tex Log Message: Add more examples.

Index: libcollections.tex

RCS file: /cvsroot/python/python/dist/src/Doc/lib/libcollections.tex,v retrieving revision 1.7 retrieving revision 1.8 diff -C2 -d -r1.7 -r1.8 *** libcollections.tex 30 Apr 2004 22:52:50 -0000 1.7 --- libcollections.tex 9 May 2004 01:15:00 -0000 1.8

*** 131,134 **** --- 131,163 ---- \end{verbatim}

\subsection{Recipes \label{deque-recipes}}
This section shows various approaches to working with deques.
The \method{rotate()} method provides a way to implement \class{deque}
slicing and deletion:
\begin{verbatim}
def delete_nth(d, n):
```
"del d[n]"
```
```
d.rotate(-n)
```
```
d.popleft()
```
```
d.rotate(n)
```
d = deque('abcdef')
delete_nth(d, 2) # remove the entry at d[2]
d
deque(['a', 'b', 'd', 'e', 'f'])
\end{verbatim}
For slicing, the idea is the same. Use \method{rotate()} to bring a target
element to the left side of the deque. Remove old entries with
\method{popleft()}, add new entries with \method{extend()}, and then
reverse the rotation.
With minor variations on that approach, it is easy to implement Forth style
stack manipulations such as \code{dup}, \code{drop}, \code{swap}, \code{over},
\code{pick}, \code{rot}, and \code{roll}.

A roundrobin task server can be built from a \class{deque} using

*** 148,152 ****

for value in roundrobin('abc', 'd', 'efgh'): ! print value

a --- 177,181 ----

for value in roundrobin('abc', 'd', 'efgh'): ! ... print value

*** 160,161 **** --- 189,212 ----

\end{verbatim}

Multi-pass data reduction algorithms can be succinctly expressed and
efficiently coded by extracting elements using multiple calls to
\method{popleft()}, applying the reduction function, and using
\method{append()} for adding the result back to the queue.
For example, building a balanced binary tree of nested lists entails
reducing two adjacent nodes into one by grouping them in a list:
\begin{verbatim}
def maketree(iterable):
```
d = deque(iterable)
```
```
while len(d) > 1:
```
```
    pair = [d.popleft(), d.popleft()]
```
```
    d.append(pair)
```
```
return list(d)
```
print maketree('abcdefgh')
[[[['a', 'b'], ['c', 'd']], [['e', 'f'], ['g', 'h']]]]
\end{verbatim}

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