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1007
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1008
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Python does automatic memory management (reference counting for most objects and :term:`garbage collection` to eliminate cycles). The memory is freed shortly after the last reference to it has been eliminated.
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1009
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This approach works fine for most applications but occasionally there is a need to track objects only as long as they are being used by something else. Unfortunately, just tracking them creates a reference that makes them permanent. The :mod:`weakref` module provides tools for tracking objects without creating a reference. When the object is no longer needed, it is automatically removed from a weakref table and a callback is triggered for weakref objects. Typical applications include caching objects that are expensive to create::
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1010
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>>> import weakref, gc >>> class A: ... def __init__(self, value): ... self.value = value ... def __repr__(self): ... return str(self.value) ... >>> a = A(10) # create a reference >>> d = weakref.WeakValueDictionary() >>> d['primary'] = a # does not create a reference >>> d['primary'] # fetch the object if it is still alive 10 >>> del a # remove the one reference >>> gc.collect() # run garbage collection right away 0 >>> d['primary'] # entry was automatically removed Traceback (most recent call last): File "<stdin>", line 1, in <module> d['primary'] # entry was automatically removed File "C:/python31/lib/weakref.py", line 46, in __getitem__ o = self.data[key]() KeyError: 'primary'
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1011
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1012
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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.
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1013
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The :mod:`array` module provides an :class:`array()` object that is like a list that stores only homogeneous 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::
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1014
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>>> from array import array >>> a = array('H', [4000, 10, 700, 22222]) >>> sum(a) 26932 >>> a[1:3] array('H', [10, 700])
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1015
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The :mod:`collections` module provides a :class:`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::
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1016
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>>> from collections import deque >>> d = deque(["task1", "task2", "task3"]) >>> d.append("task4") >>> print("Handling", d.popleft()) Handling task1 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)
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