{ "info": { "author": "Stutzbach Enterprises, LLC", "author_email": "daniel@stutzbachenterprises.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Programming Language :: C", "Programming Language :: Python :: 2.5", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.1", "Programming Language :: Python :: 3.2" ], "description": "blist: a list-like type with better performance\n===============================================\n\nThe ``blist`` is a drop-in replacement for the Python list that provides\nbetter performance when modifying large lists. The blist package also\nprovides ``sortedlist``, ``sortedset``, ``weaksortedlist``,\n``weaksortedset``, ``sorteddict``, and ``btuple`` types.\n\nFull documentation is at the link below:\n\nhttp://stutzbachenterprises.com/blist-doc/\n\nPython's built-in list is a dynamically-sized array; to insert or\nremove an item from the beginning or middle of the list, it has to\nmove most of the list in memory, i.e., O(n) operations. The blist\nuses a flexible, hybrid array/tree structure and only needs to move a\nsmall portion of items in memory, specifically using O(log n)\noperations.\n\nFor small lists, the blist and the built-in list have virtually\nidentical performance.\n\nTo use the blist, you simply change code like this:\n\n>>> items = [5, 6, 2]\n>>> more_items = function_that_returns_a_list()\n\nto:\n\n>>> from blist import blist\n>>> items = blist([5, 6, 2])\n>>> more_items = blist(function_that_returns_a_list())\n\nHere are some of the use cases where the blist asymptotically\noutperforms the built-in list:\n\n========================================== ================ =========\nUse Case blist list\n========================================== ================ =========\nInsertion into or removal from a list O(log n) O(n)\nTaking slices of lists O(log n) O(n)\nMaking shallow copies of lists O(1) O(n)\nChanging slices of lists O(log n + log k) O(n+k)\nMultiplying a list to make a sparse list O(log k) O(kn)\nMaintain a sorted lists with bisect.insort O(log**2 n) O(n)\n========================================== ================ =========\n\nSo you can see the performance of the blist in more detail, several\nperformance graphs available at the following link:\nhttp://stutzbachenterprises.com/blist/\n\nExample usage:\n\n>>> from blist import *\n>>> x = blist([0]) # x is a blist with one element\n>>> x *= 2**29 # x is a blist with > 500 million elements\n>>> x.append(5) # append to x\n>>> y = x[4:-234234] # Take a 500 million element slice from x\n>>> del x[3:1024] # Delete a few thousand elements from x\n\nOther data structures\n---------------------\n\nThe blist package provides other data structures based on the blist:\n\n- sortedlist\n- sortedset\n- weaksortedlist\n- weaksortedset\n- sorteddict\n- btuple\n\nThese additional data structures are only available in Python 2.6 or\nhigher, as they make use of Abstract Base Classes.\n\nThe sortedlist is a list that's always sorted. It's iterable and\nindexable like a Python list, but to modify a sortedlist the same\nmethods you would use on a Python set (add, discard, or remove).\n\n>>> from blist import sortedlist\n>>> my_list = sortedlist([3,7,2,1])\n>>> my_list\nsortedlist([1, 2, 3, 7])\n>>> my_list.add(5)\n>>> my_list[3]\n5\n>>>\n\nThe sortedlist constructor takes an optional \"key\" argument, which may\nbe used to change the sort order just like the sorted() function.\n\n>>> from blist import sortedlist\n>>> my_list = sortedlist([3,7,2,1], key=lambda i: -i)\nsortedlist([7, 3, 2, 1]\n>>>\n\nThe sortedset is a set that's always sorted. It's iterable and\nindexable like a Python list, but modified like a set. Essentially,\nit's just like a sortedlist except that duplicates are ignored.\n\n>>> from blist import sortedset\n>>> my_set = sortedset([3,7,2,2])\nsortedset([2, 3, 7]\n>>>\n\nThe weaksortedlist and weaksortedset are weakref variations of the\nsortedlist and sortedset.\n\nThe sorteddict works just like a regular dict, except the keys are\nalways sorted. The sorteddict should not be confused with Python\n2.7's OrderedDict type, which remembers the insertion order of the\nkeys.\n\n>>> from blist import sorteddict\n>>> my_dict = sorteddict({1: 5, 6: 8, -5: 9})\n>>> my_dict.keys()\n[-5, 1, 6]\n>>>\n\nThe btuple is a drop-in replacement for the built-in tuple. Compared\nto the built-in tuple, the btuple offers the following advantages:\n\n- Constructing a btuple from a blist takes O(1) time.\n- Taking a slice of a btuple takes O(n) time, where n is the size of\n the original tuple. The size of the slice does not matter.\n\n>>> from blist import blist, btuple\n>>> x = blist([0]) # x is a blist with one element\n>>> x *= 2**29 # x is a blist with > 500 million elements\n>>> y = btuple(x) # y is a btuple with > 500 million elements\n\nInstallation instructions\n-------------------------\n\nPython 2.5 or higher is required. If building from the source\ndistribution, the Python header files are also required. In either\ncase, just run:\n\n python setup.py install\n\nIf you're running Linux and see a bunch of compilation errors from\nGCC, you probably do not have the Python header files installed.\nThey're usually located in a package called something like\n\"python2.6-dev\".\n\nThe blist package will be installed in the 'site-packages' directory of\nyour Python installation. (Unless directed elsewhere; see the\n\"Installing Python Modules\" section of the Python manuals for details\non customizing installation locations, etc.).\n\nIf you downloaded the source distribution and wish to run the\nassociated test suite, you can also run:\n\n python setup.py test\n\nwhich will verify the correct installation and functioning of the\npackage. The tests require Python 2.6 or higher.\n\nFeedback\n--------\n\nWe're eager to hear about your experiences with the blist. You can\nemail me at daniel@stutzbachenterprises.com. Alternately, bug reports\nand feature requests may be reported on our bug tracker at:\nhttp://github.com/DanielStutzbach/blist/issues\n\nHow we test\n-----------\n\nIn addition to the tests include in the source distribution, we\nperform the following to add extra rigor to our testing process:\n\n 1. We use a \"fuzzer\": a program that randomly generates list\n operations, performs them using both the blist and the built-in\n list, and compares the results.\n\n 2. We use a modified Python interpreter where we have replaced the\n array-based built-in list with the blist. Then, we run all of\n the regular Python unit tests.", "description_content_type": null, "docs_url": null, "download_url": "UNKNOWN", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "http://stutzbachenterprises.com/blist/", "keywords": "blist list b+tree btree fast copy-on-write sparse array sortedlist sorted sortedset weak weaksortedlist weaksortedset sorteddict btuple", "license": "BSD", "maintainer": null, "maintainer_email": null, "name": "blist", "package_url": "https://pypi.org/project/blist/", "platform": "UNKNOWN", "project_url": "https://pypi.org/project/blist/", "project_urls": { "Download": "UNKNOWN", "Homepage": "http://stutzbachenterprises.com/blist/" }, "release_url": "https://pypi.org/project/blist/1.3.6/", "requires_dist": null, "requires_python": null, "summary": "a list-like type with better asymptotic performance and similar performance on small lists", "version": "1.3.6" }, "last_serial": 1028508, "releases": { "0.9.10": [ { 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