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    "info": {
        "author": "Guillaume Paulet",
        "author_email": "guillaume.paulet@giome.fr",
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            "Intended Audience :: Developers",
            "License :: Public Domain",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": ".. image:: https://raw.githubusercontent.com/ducdetronquito/scalpl/master/assets/scalpl.png\n    :target: https://github.com/ducdetronquito/scalpl\n\nScalpl\n======\n\n.. image:: https://img.shields.io/badge/license-public%20domain-ff69b4.svg\n    :target: https://github.com/ducdetronquito/scalpl#license\n\n.. image:: https://img.shields.io/badge/coverage-100%25-green.svg\n    :target: #\n\n.. image:: https://img.shields.io/badge/pypi-v0.3.0-blue.svg\n    :target: https://pypi.python.org/pypi/scalpl/\n\n.. image:: https://travis-ci.org/ducdetronquito/scalpl.svg?branch=master\n     :target: https://travis-ci.org/ducdetronquito/scalpl\n\n\nOutline\n~~~~~~~\n\n1. `Overview <https://github.com/ducdetronquito/scalpl#overview>`_\n2. `Benefits <https://github.com/ducdetronquito/scalpl#benefits>`_\n3. `Installation <https://github.com/ducdetronquito/scalpl#installation>`_\n4. `Usage <https://github.com/ducdetronquito/scalpl#usage>`_\n5. `Benchmark <https://github.com/ducdetronquito/scalpl#benchmark>`_\n6. `Frequently Asked Questions <https://github.com/ducdetronquito/scalpl#frequently-asked-questions>`_\n7. `How to Contribute <https://github.com/ducdetronquito/scalpl#how-to-contribute>`_\n8. `License <https://github.com/ducdetronquito/scalpl#license>`_\n\n\nOverview\n~~~~~~~~\n\n\n**Scalpl** provides a **lightweight wrapper** that helps you to operate\non **nested dictionaries** seamlessly **through the built-in** ``dict``\n**API**, by using dot-separated string keys.\n\nIt's not a drop-in replacement for your dictionnaries, just syntactic\nsugar to avoid ``this['annoying']['kind']['of']['things']`` and\n``prefer['a.different.approach']``.\n\nNo conversion cost, a thin computation overhead: that's **Scalpl** in a\nnutshell.\n\n\nBenefits\n~~~~~~~~\n\nThere are a lot of good libraries to operate on nested dictionaries,\nsuch as `Addict <https://github.com/mewwts/addict>`_ or \n`Box <https://github.com/cdgriffith/Box>`_ , but if you give **Scalpl**\na try, you will find it:\n\n* \ud83d\ude80 Powerful as the standard dict API\n* \u26a1 Lightweight\n* \ud83d\udc4c Well tested\n\n\nInstallation\n~~~~~~~~~~~~\n\n**Scalpl** is a Python3 library that you can install via ``pip``\n\n.. code:: sh\n\n    pip3 install scalpl\n\n\nUsage\n~~~~~\n\n**Scalpl** provides a simple class named **Cut** that wraps around your dictionary\nand handles operations on nested ``dict`` and that can cut accross ``list`` item.\n\nThis wrapper strictly follows the standard ``dict``\n`API <https://docs.python.org/3/library/stdtypes.html#dict>`_, which\nmeans you can operate seamlessly on ``dict``,\n``collections.defaultdict`` or ``collections.OrderedDict`` by using their methods\nwith dot-separated keys.\n \nLet's see what it looks like with an example ! \ud83d\udc47\n\n.. code:: python\n\n    from scalpl import Cut\n\n    data = {\n        'pokemons': [\n            {\n                'name': 'Bulbasaur',\n                'type': ['Grass', 'Poison'],\n                'category': 'Seed',\n                'ability': 'Overgrow'\n            },\n            {   \n                'name': 'Charmander',\n                'type': 'Fire',\n                'category': 'Lizard',\n                'ability': 'Blaze',\n            },\n            {\n                'name': 'Squirtle',\n                'type': 'Water',\n                'category': 'Tiny Turtle',\n                'ability': 'Torrent',\n            }\n        ],\n        'trainers': [\n            {\n                'name': 'Ash',\n                'hometown': 'Pallet Town'\n            }\n        ]\n    }\n    # Just wrap your data, and you're ready to go deeper !\n    proxy = Cut(data)\n\nYou can use the built-in ``dict`` API to access its values.\n\n.. code:: python\n\n    proxy['pokemons[0].name']\n    # 'Bulbasaur'\n    proxy.get('pokemons[1].sex', 'Unknown')\n    # 'Unknown'\n    'trainers[0].hometown' in proxy\n    # True\n\nBy default, **Scalpl** uses dot as a key separator, but you are free to\nuse a different character that better suits your needs.\n\n.. code:: python\n\n    # You just have to provide one when you wrap your data.\n    proxy = Cut(data, sep='->')\n    # Yarrr!\n    proxy['pokemons[0]->name']\n\nYou can also easily create or update any key/value pair.\n\n.. code:: python\n\n    proxy['pokemons[1].weaknesses'] = ['Ground', 'Rock', 'Water']\n    proxy['pokemons[1].weaknesses']\n    # ['Ground', 'Rock', 'Water']\n    proxy.update({\n        'trainers[0].region': 'Kanto',\n    })\n\n\nFollowing its purpose in the standard API, the *setdefault* method allows\nyou to create any missing dictionary when you try to access a nested key.\n\n.. code:: python\n\n    proxy.setdefault('pokemons[2].moves.Scratch.power', 40)\n    # 40\n\n\nAnd it is still possible to iterate over your data.\n\n.. code:: python\n\n    proxy.items()\n    # [('pokemons', [...]), ('trainers', [...])]\n    proxy.keys()\n    # ['pokemons', 'trainers']\n    proxy.values()\n    # [[...], [...]]\n\nBy the way, if you have to operate on a list of dictionaries, the\n``Cut.all`` method is what you are looking for.\n\n.. code:: python\n\n    # Let's teach these pokemons some sick moves !\n    for pokemon in proxy.all('pokemons'):\n        pokemon.setdefault('moves.Scratch.power', 40)\n\nAlso, you can remove a specific or an arbitrary key/value pair.\n\n.. code:: python\n\n    proxy.pop('pokemons[0].category')\n    # 'Seed'\n    proxy.popitem()\n    # ('trainers', [...])\n    del proxy['pokemons[1].type']\n\nBecause **Scalpl** is only a wrapper around your data, it means you can\nget it back at will without any conversion cost. If you use an external\nAPI that operates on dictionary, it will just work.\n\n.. code:: python\n\n    import json\n    json.dumps(proxy.data)\n    # \"{'pokemons': [...]}\"\n\nFinally, you can retrieve a shallow copy of the inner dictionary or\nremove all keys.\n\n.. code:: python\n\n    shallow_copy = proxy.copy()\n\n    proxy.clear()\n\n\nBenchmark\n~~~~~~~~~\n\nThis humble benchmark is an attempt to give you an overview of the performance\nof `Scalpl <https://github.com/ducdetronquito/scalpl>`_ compared to `Addict <https://github.com/mewwts/addict>`_,\n`Box <https://github.com/cdgriffith/Box>`_ and the built-in ``dict``.\n\nIt will summarize the *number of operations per second* that each library is \nable to perform on a portion of the JSON dump of the `Python subreddit main page <https://www.reddit.com/r/Python.json>`_.\n\nYou can run this benchmark on your machine with the following command:\n\n    python3 ./benchmarks/performance_comparison.py\n\nHere are the results obtained on an Intel Core i5-7500U CPU (2.50GHz) with **Python 3.6.4**.\n\n\n**Addict** 2.2.1::\n\n    instanciate:-------- 271,132  ops per second.\n    get:---------------- 276,090  ops per second.\n    get through list:--- 293,773  ops per second.\n    set:---------------- 300,324  ops per second.\n    set through list:--- 282,149  ops per second.\n\n\n**Box** 3.4.2::\n\n    instanciate:--------- 4,093,439  ops per second.\n    get:-----------------   957,069  ops per second.\n    get through list:----   164,013  ops per second.\n    set:-----------------   900,466  ops per second.\n    set through list:----   165,522  ops per second.\n\n\n**Scalpl** latest::\n\n    instanciate:-------- 183,879,865  ops per second.\n    get:----------------  14,941,355  ops per second.\n    get through list:---  14,175,349  ops per second.\n    set:----------------  11,320,968  ops per second.\n    set through list:---  11,956,001  ops per second.\n\n\n**dict**::\n\n    instanciate:---------  37,816,714  ops per second.\n    get:-----------------  84,317,032  ops per second.\n    get through list:----  62,480,474  ops per second.\n    set:----------------- 146,484,375  ops per second.\n    set through list :--- 122,473,974  ops per second.\n\n\nAs a conclusion and despite being an order of magniture slower than the built-in\n``dict``, **Scalpl** is faster than Box and Addict by an order of magnitude for any operations.\nBesides, the gap increase in favor of **Scalpl** when wrapping large dictionaries.\n\nKeeping in mind that this benchmark may vary depending on your use-case, it is very unlikely that\n**Scalpl** will become a bottleneck of your application.\n\n\nFrequently Asked Questions:\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n* **What if my keys contain dots ?**\n    If your keys contain a lot of dots, you should use an other\n    key separator when wrapping your data::\n\n        proxy = Cut(data, sep='->')\n        proxy['computer->network->127.0.0.1']\n\n    Otherwise, split your key in two part::\n\n        proxy = Cut(data)\n        proxy['computer.network']['127.0.0.1']\n\n* **What if my keys contain spaces ?**::\n    \n    proxy = Cut(data)\n    proxy['it works perfectly'] = 'fine'\n\n\nHow to Contribute\n~~~~~~~~~~~~~~~~~\n\nContributions are welcomed and anyone can feel free to submit a patch, report a bug or ask for a feature. Please open an issue first in order to encourage and keep tracks of potential discussions \u270d\ufe0f\n\n\nLicense\n~~~~~~~\n\n**Scalpl** is released into the **Public Domain**. \ud83c\udf89\n\nPs: If we meet some day, and you think this small stuff worths it, you\ncan give me a beer, a coffee or a high-five in return: I would be really\nhappy to share a moment with you ! \ud83c\udf7b",
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