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An Introduction to Python
by Guido van Rossum and Fred L. Drake, Jr.
Paperback (6"x9"), 124 pages
ISBN 0954161769
RRP £12.95 ($19.95)

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11.7 Tools for Working with Lists

Many data structure needs can be met with the built-in list type. However, sometimes there is a need for alternative implementations with different performance trade-offs.

The ‘array’ module provides an array() object that is like a list that stores only homogenous data and stores it more compactly. The following example shows an array of numbers stored as two byte unsigned binary numbers (typecode "H") rather than the usual 16 bytes per entry for regular lists of python int objects:

    >>> from array import array
    >>> a = array('H', [4000, 10, 700, 22222])
    >>> sum(a)
    26932
    >>> a[1:3]
    array('H', [10, 700])

The ‘collections’ module provides a deque() object that is like a list with faster appends and pops from the left side but slower lookups in the middle. These objects are well suited for implementing queues and breadth first tree searches:

    >>> from collections import deque
    >>> d = deque(["task1", "task2", "task3"])
    >>> d.append("task4")
    >>> print "Handling", d.popleft()
    Handling task1

The code for a breadth first search looks like this:

    unsearched = deque([starting_node])
    def breadth_first_search(unsearched):
        node = unsearched.popleft()
        for m in gen_moves(node):
            if is_goal(m):
                return m
            unsearched.append(m)

In addition to alternative list implementations, the library also offers other tools such as the ‘bisect’ module with functions for manipulating sorted lists:

    >>> import bisect
    >>> scores = [(100, 'perl'), (200, 'tcl'), (400, 'lua'),
                  (500, 'python')]
    >>> bisect.insort(scores, (300, 'ruby'))
    >>> scores
    [(100, 'perl'), (200, 'tcl'), (300, 'ruby'),
     (400, 'lua'), (500, 'python')]

The ‘heapq’ module provides functions for implementing heaps based on regular lists. The lowest valued entry is always kept at position zero. This is useful for applications which repeatedly access the smallest element but do not want to run a full list sort:

    >>> from heapq import heapify, heappop, heappush
    >>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
    >>> heapify(data) # rearrange the list into heap order
    >>> heappush(data, -5)         # add a new entry
    >>> [heappop(data) for i in range(3)]  # fetch the three
                                           # smallest entries
    [-5, 0, 1]
ISBN 0954161769An Introduction to PythonSee the print edition