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Sync-up examples with Py2.7 updates.
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@ -316,6 +316,28 @@ Example:
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This section shows various approaches to working with deques.
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Bounded length deques provide functionality similar to the ``tail`` filter
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in Unix::
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def tail(filename, n=10):
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'Return the last n lines of a file'
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return deque(open(filename), n)
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Another approach to using deques is to maintain a sequence of recently
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added elements by appending to the right and popping to the left::
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def moving_average(iterable, n=3):
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# moving_average([40, 30, 50, 46, 39, 44]) --> 40.0 42.0 45.0 43.0
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# http://en.wikipedia.org/wiki/Moving_average
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it = iter(iterable)
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d = deque(itertools.islice(it, n-1))
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d.appendleft(0)
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s = sum(d)
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for elem in it:
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s += elem - d.popleft()
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d.append(elem)
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yield s / float(n)
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The :meth:`rotate` method provides a way to implement :class:`deque` slicing and
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deletion. For example, a pure python implementation of ``del d[n]`` relies on
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the :meth:`rotate` method to position elements to be popped::
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@ -333,31 +355,6 @@ With minor variations on that approach, it is easy to implement Forth style
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stack manipulations such as ``dup``, ``drop``, ``swap``, ``over``, ``pick``,
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``rot``, and ``roll``.
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Multi-pass data reduction algorithms can be succinctly expressed and efficiently
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coded by extracting elements with multiple calls to :meth:`popleft`, applying
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a reduction function, and calling :meth:`append` to add the result back to the
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deque.
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For example, building a balanced binary tree of nested lists entails reducing
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two adjacent nodes into one by grouping them in a list:
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>>> def maketree(iterable):
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... d = deque(iterable)
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... while len(d) > 1:
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... pair = [d.popleft(), d.popleft()]
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... d.append(pair)
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... return list(d)
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...
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>>> print maketree('abcdefgh')
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[[[['a', 'b'], ['c', 'd']], [['e', 'f'], ['g', 'h']]]]
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Bounded length deques provide functionality similar to the ``tail`` filter
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in Unix::
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def tail(filename, n=10):
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'Return the last n lines of a file'
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return deque(open(filename), n)
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.. _defaultdict-objects:
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:class:`defaultdict` objects
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