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a = arrayarray("i", [1,2,3,4,5]) for i, x in enumerate(a): a[i] = 2*x
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bisect
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For large arrays, this in-place modification runs about 15 percent faster than the code that creates a new array with a generator expression
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The arrays created by this module are not suitable for numeric work such as matrix or vector math For example, the addition operator doesn t add the corresponding elements of the arrays; instead, it appends one array to the otherTo create storage and calculation efficient arrays, use the numpy extension available at http://numpysourceforgenet/ Note that the numpy API is completely different The += operator can be used to append the contents of another arrayThe *= operator can be used to repeat an array
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See Also:
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struct (p 290)
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The bisect module provides support for keeping lists in sorted order It uses a bisection algorithm to do most of its work
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bisect(list, item [, low [, high]])
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Returns the index of the insertion point for item to be placed in list in order to maintain list in sorted order low and high are indices specifying a subset of the list to examine If items is already in the list, the insertion point will always be to the right of existing entries in the list
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Returns the index of the insertion point for item to be placed in list in order to maintain list in sorted order low and high are indices specifying a subset of the list to examine If items is already in the list, the insertion point will always be to the left of existing entries in the list
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bisect_right(list, item [, low [, high]])
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insort(list, item [, low [, high]])
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15 Data Structures, Algorithms, and Code Simplification
collections
The collections module contains high-performance implementations of a few useful container types, abstract base classes for various kinds of containers, and a utility function for creating name-tuple objects Each is described in the sections that follow
deque and defaultdict
Two new containers are defined in the collections module: deque and defaultdict
deque([iterable [, maxlen]])
Type representing a double-ended queue (deque, pronounced deck ) object iterable is an iterable object used to populate the deque A deque allows items to be inserted or removed from either end of the queueThe implementation has been optimized so that the performance of these operations is approximately the same as (O(1))This is slightly different from a list where operations at the front of the list may require shifting of all the elements that follow If the optional maxlen argument is supplied, the resulting deque object becomes a circular buffer of that sizeThat is, if new items are added, but there is no more space, items are deleted from the opposite end to make room An instance, d, of deque has the following methods:
dappend(x)
Adds x to the right side of d
dappendleft(x)
Adds x to the left side of d
dclear()
Removes all items from d
dextend(iterable)
Extends d by adding all the items in iterable on the right
dextendleft(iterable)
Extends d by adding all the items in iterable on the left Due to the sequence of left appends that occur, items in iterable will appear in reverse order in d
dpop()
Returns and removes an item from the right side of d Raises IndexError if d is empty
dpopleft()
Returns and removes an item from the left side of d Raises IndexError if d is empty
dremove(item)
Removes the first occurrence of item Raises ValueError if no match is found
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collections
drotate(n)
Rotates all the items n steps to the right If n is negative, items are rotated to the left Deques are often overlooked by many Python programmers However, this type offers many advantages First, the implementation is highly efficient even to a level of using internal data structures that provide good processor cache behavior Appending items at the end is only slightly slower than the built-in list type, whereas inserting items at the front is significantly faster Operations that add new items to a deque are also thread-safe, making this type appropriate for implementing queues deques can also be serialized using the pickle module
defaultdict([default_factory], )
A type that is exactly the same as a dictionary except for the handling of missing keys When a lookup occurs on a key that does not yet exist, the function supplied as default_factory is called to provide a default value which is then saved as the value of the associated keyThe remaining arguments to defaultdict are exactly the same as the built-in dict() function An instance d of defaultdictionary has the same operations as a built-in dictionaryThe attribute ddefault_factory contains the function passed as the first argument and can be modified as necessary A defaultdict object is useful if you are trying to use a dictionary as a container for tracking data For example, suppose you wanted to keep track of the position of each word in a string s Here is how you could use a defaultdict to do this easily:
>>> from collections import defaultdict >>> s = "yeah but no but yeah but no but yeah" >>> words = ssplit() >>> wordlocations = defaultdict(list) >>> for n, w in enumerate(words): wordlocations[w]append(n) >>> wordlocations defaultdict(<type 'list'>, {'yeah': [0, 4, 8], 'but': [1, 3, 5, 7], 'no': [2, 6]}) >>>
In this example, the lookup wordlocations[w] will fail the first time a word is encountered However, instead of raising a KeyError, the function list supplied as default_factory is called to create a new value Built-in dictionaries have a method setdefault() that can be used to achieve a similar result, but it often makes code confusing to read and run slower For example, the statement that appends a new item shown previously could be replaced by wordlocationssetdefault(w,[])append(n)This is not nearly as clear and in a simple timing test, it runs nearly twice as slow as using a defaultdict object