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    "info": {
        "author": "Adamos Kyriakou",
        "author_email": "somada141@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5"
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        "description": "========\nvivodict\n========\n\n\n.. image:: https://img.shields.io/pypi/v/vivodict.svg\n        :target: https://pypi.python.org/pypi/vivodict\n\n.. image:: https://img.shields.io/travis/somada141/vivodict.svg\n        :target: https://travis-ci.org/somada141/vivodict\n\n.. image:: https://readthedocs.org/projects/vivodict/badge/?version=latest\n        :target: https://vivodict.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n\nThis package provides a simple implementation of an `auto-vivified\n<https://en.wikipedia.org/wiki/Autovivification>`_ Python ``dict``, i.e., a\ndictionary where accessing a missing key doesn't raise the standard ``KeyError``\nexception but instead implicitly creates and returns an empty auto-vivified\n``dict`` under that key.\n\n\nFeatures\n--------\n\n* Auto-vivified ``VivoDict`` class derived from the standard Python ``dict``\n  class (no third-party dependencies).\n* Auto-vivification of arbitrarily-nested ``dict`` objects.\n* Convenience methods for ``flatten``, ``replace``, and ``apply`` operations.\n* Free software: MIT license\n* Documentation: https://vivodict.readthedocs.io.\n\nMotivation\n----------\n\nMy primary motivation for developing this package is because it contained a\npiece of code I kept copy-pasting like a bloody caveman between projects.\n\nMy typical use-cases for this code include:\n\n* Wrap the decoded JSON ``dict`` from crummy APIs without a schema that just\n  decide to drop keys for which the values are ``null`` resulting in code with\n  nested ``if \"key\" in result:``. This allowed me to either retrieve the value\n  if it was there or at least arriving at an empty ``dict`` which evaluates to\n  ``False`` when mapping their half-formed data to my own data-structures.\n* Create arbitrarily-nested dictionaries of code stats that I can keep organized\n  as I like while using in the code and then quickly ``flatten`` to a Graphite\n  compatible format prior to posting them to ... well Graphite.\n\nBasic Usage\n-----------\n\nThis would be the typical Python ``dict`` behaviour when accessing a missing\nkey::\n\n    >>> d = {\"a\": 1, \"b\": 2}\n    >>> d[\"a\"]\n    1\n    >>> d[\"missing\"]\n    ---------------------------------------------------------------------------\n    KeyError                                  Traceback (most recent call last)\n    <ipython-input-3-d4f58b57b715> in <module>()\n    ----> 1 d[\"missing\"]\n\n    KeyError: 'missing'\n\nWhile if we were using a ``VivoDict``, then upon accessing a missing key we\nwould be provided with an implicitly created empty ``VivoDict`` as such::\n\n    >>> from vivodict import VivoDict\n    >>> d = VivoDict.vivify({\"a\": 1, \"b\": 2})\n    >>> d[\"a\"]\n    1\n    >>> d[\"missing\"]\n    {}\n\n.. note::\n\n    Note that instantiation above is not performed simply by passing an existing\n    ``dict`` to ``VivoDict`` but instead through the ``vivify`` class method\n    which can recursively convert any arbitrarily-nested ``dict`` to a\n    ``VivoDict``.\n\nNow, while the above doesn't seem to offer anything a simple ``try-except`` or\na ``if \"key\" not in d`` wouldn't offer, the ``VivoDict`` becomes useful when\ndealing with arbitrarily nested dictionaries where there may be several levels\nof missing keys. For example::\n\n    >>> from vivodict import VivoDict\n    >>> d = VivoDict({\"a\": 1, \"b\": {\"c\": 2}, \"d\": {\"e\": {\"f\": 3}}})\n    >>> d[\"a\"]\n    1\n    >>> d[\"b\"][\"c\"]\n    2\n    >>> d[\"d\"][\"e\"][\"f\"]\n    3\n    >>> d[\"i\"][\"am\"][\"missing\"][\"eh\"] = 4\n    >>> d\n    {'a': 1,\n    'b': {'c': 2},\n    'd': {'e': {'f': 3}},\n    'i': {'am': {'missing': {'eh': 4}}}}\n\nSo, as can be seen, having auto-vivification allows one to nest keys and values\nto whatever degree.\n\n.. warning::\n\n    The primary caveat of the above functionality is that ``VivoDict`` are very\n    forgiving when it comes to typos which can leads to weird errors. A\n    mistyped key will simply create a new ``VivoDict`` and will allow you to go\n    down some rabbithole of erroneously typed keys your linter won't get you out\n    of.\n\nConvenience Functions\n---------------------\n\nIn addition to the above, a few basic convenience methods have been built into\nthe ``VivoDict`` class, mostly cause they make my life easier and lazier.\n\n\nflatten\n^^^^^^^\n\nAs I mentioned prior one of my typical use-cases for ``vivodict`` is using it\nto store nested metrics which I then post to Graphite via simple HTTP requests.\n\nGraphite, however, bases its structure on ``.`` delimited names where anything\npreceding a ``.`` is considered to be a metric folder with the last token being\nthe metric itself.\n\nThus, I needed a quick way to flatten a nested ``dict`` into a Graphite\ncompatible version.\n\nThe ``flatten`` method does exactly that::\n\n    >>> d = VivoDict.vivify({\"a\": 1, \"b\": {\"c\": 2}, \"d\": {\"e\": {\"f\": 3}}})\n    >>> d.flatten()\n    {'a': 1, 'b.c': 2, 'd.e.f': 3}\n\nreplace\n^^^^^^^\n\nFollowing the same premise as with ``flatten`` I needed to quickly 'reset' my\nmetrics back to 0 between posting cycles.\n\nHence, ``replace`` will replace all 'leaf' node values in what is essentially a\ntree with a given ``value``::\n\n    >>> d = VivoDict.vivify({\"a\": 1, \"b\": {\"c\": 2}, \"d\": {\"e\": {\"f\": 3}}})\n    >>> d.replace(replace_with=0)\n    >>> d\n    {'a': 0, 'b': {'c': 0}, 'd': {'e': {'f': 0}}}\n\n.. warning::\n\n    As you may have noticed from the above snippet, the ``replace`` method\n    performs an **in-place** replacement instead of returning a copy of the\n    original ``VivoDict`` with replaced values.\n\nShould you need to maintain an original copy I'd suggest you use the ``copy``\npackage and its ``deepcopy`` function (cause Python passes by reference) as\nsuch::\n\n    >>> import copy\n    >>> original = VivoDict.vivify({\"a\": 1, \"b\": {\"c\": 2}, \"d\": {\"e\": {\"f\": 3}}})\n    >>> modified = copy.deepcopy(original)\n    >>> modified.replace(replace_with=0)\n    >>> original\n    {'a': 1, 'b': {'c': 2}, 'd': {'e': {'f': 3}}}\n    >>> modified\n    {'a': 0, 'b': {'c': 0}, 'd': {'e': {'f': 0}}}\n\n\napply\n^^^^^\n\nLastly, I often had to modify all values through a given function, typically\ndivide them by a number of observation for average metrics which can be easily\ndone through the ``apply`` method which can take any ``callable`` as an\nargument and replace the original value with its return-value::\n\n    >>> d = VivoDict.vivify({\"a\": 1, \"b\": {\"c\": 2}, \"d\": {\"e\": {\"f\": 3}}})\n    >>> def double(value):\n    >>>     return value * 2\n    >>> d.apply(double)\n    >>> d\n    {'a': 2, 'b': {'c': 4}, 'd': {'e': {'f': 6}}}\n    >>> d.apply(lambda value: value / 2)\n    {'a': 1, 'b': {'c': 2}, 'd': {'e': {'f': 3}}}\n\n.. warning::\n\n    Much like ``replace``, the ``apply`` method replaces values **in-place**.\n\n\n=======\nHistory\n=======\n\n0.3.1 (2017-07-23)\n------------------\n\n* README.rst: Fixed minor formatting typo.\n\n0.3.0 (2017-07-23)\n------------------\n\n* Cleanup the docos and removed a bunch of the unnecessary stuff.\n\n0.2.0 (2017-07-23)\n------------------\n\n* Added more unit-tests and improved docstrings.\n\n0.1.1 (2017-07-23)\n------------------\n\n* Fixed issues with the Python dependencies.\n\n0.1.0 (2017-07-23)\n------------------\n\n* First release on PyPI.\n\n\n",
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