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        "description": "===================\nPython lazy-streams\n===================\n\nInspired by Java 8's streams, this Python module provides a fluent syntax for\nmanipulating and querying Python lists.  It's called lazy-streams because\nit *lazy* evaluates the requests to increase performance and decrease resource\nrequirements. Because of the lazy evaluation, lazy-streams can work on really\nlarge data sets with relatively small delays.\n\nHere's a quick example:\n\n::\n\n    >>> from lazy_streams import stream\n    >>> s = stream(range(5000000)) \\\n    ...    .filter(lambda x: str(x)[0] == '3') \\\n    ...    .map(lambda x: -x) \\\n    ...    .map(lambda x: \"Item: %s\" % x) \\\n    ...    .take(75) \\\n    ...    .reverse() \n    >>> print s.first_or_else()\n    Item: -363\n    >>> print s.take(5).to_list()\n    ['Item: -363', 'Item: -362', 'Item: -361', 'Item: -360', 'Item: -359']\n    >>> print s.take(3).to_string()\n    Item: -363, Item: -362, Item: -361\n\n\nLazy-streams is intended to be a small (single-file), light-weight, simple\nimplementation that relies on a mimimal set of dependencies.\n\nAs you can see from the above example, you can stack multiple manipulations\non top of each other.  The module will optimize the execution to only perform\nthe operations on the elements of the list that are involved in the eventual\noutput.\n\nAlso, the original list will remain unchanged as the output of each operation\nsimply returns a delta from the original value.\n\nDocumentation and more examples are available\n`docs <https://github.com/brettschneider/python_lazy_streams/blob/master/README.md>`_.\n\n",
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