Metadata-Version: 1.2
Name: scalpl
Version: 0.4.2
Summary: A lightweight wrapper to operate on nested dictionaries seamlessly.
Home-page: https://github.com/ducdetronquito/scalpl
Author: Guillaume Paulet
Author-email: guillaume.paulet@giome.fr
License: Public Domain
Download-URL: https://github.com/ducdetronquito/scalpl/archive/0.4.2.tar.gz
Description: .. image:: https://raw.githubusercontent.com/ducdetronquito/scalpl/master/assets/scalpl.png
            :target: https://github.com/ducdetronquito/scalpl
        
        Scalpl
        ======
        
        .. image:: https://img.shields.io/badge/license-public%20domain-ff69b4.svg
            :target: https://github.com/ducdetronquito/scalpl#license
        
        .. image:: https://img.shields.io/badge/coverage-100%25-green.svg
            :target: #
        
        .. image:: https://img.shields.io/badge/pypi-v0.4.2-blue.svg
            :target: https://pypi.python.org/pypi/scalpl/
        
        .. image:: https://travis-ci.org/ducdetronquito/scalpl.svg?branch=master
             :target: https://travis-ci.org/ducdetronquito/scalpl
        
        
        Outline
        ~~~~~~~
        
        1. `Overview <https://github.com/ducdetronquito/scalpl#overview>`_
        2. `Benefits <https://github.com/ducdetronquito/scalpl#benefits>`_
        3. `Installation <https://github.com/ducdetronquito/scalpl#installation>`_
        4. `Usage <https://github.com/ducdetronquito/scalpl#usage>`_
        5. `Benchmark <https://github.com/ducdetronquito/scalpl#benchmark>`_
        6. `Frequently Asked Questions <https://github.com/ducdetronquito/scalpl#frequently-asked-questions>`_
        7. `How to Contribute <https://github.com/ducdetronquito/scalpl#how-to-contribute>`_
        8. `License <https://github.com/ducdetronquito/scalpl#license>`_
        
        
        Overview
        ~~~~~~~~
        
        
        **Scalpl** provides a **lightweight wrapper** that helps you to operate
        on **nested dictionaries** seamlessly **through the built-in** ``dict``
        **API**, by using dot-separated string keys.
        
        It's not a drop-in replacement for your dictionnaries, just syntactic
        sugar to avoid ``this['annoying']['kind']['of']['things']`` and
        ``prefer['a.different.approach']``.
        
        No conversion cost, a thin computation overhead: that's **Scalpl** in a
        nutshell.
        
        
        Benefits
        ~~~~~~~~
        
        There are a lot of good libraries to operate on nested dictionaries,
        such as `Addict <https://github.com/mewwts/addict>`_ or 
        `Box <https://github.com/cdgriffith/Box>`_ , but if you give **Scalpl**
        a try, you will find it:
        
        * 🚀 Powerful as the standard dict API
        * ⚡ Lightweight
        * 👌 Well tested
        
        
        Installation
        ~~~~~~~~~~~~
        
        **Scalpl** is a Python3 library that you can install via ``pip``
        
        .. code:: sh
        
            pip3 install scalpl
        
        
        Usage
        ~~~~~
        
        **Scalpl** provides a simple class named **Cut** that wraps around your dictionary
        and handles operations on nested ``dict`` and that can cut accross ``list`` item.
        
        This wrapper strictly follows the standard ``dict``
        `API <https://docs.python.org/3/library/stdtypes.html#dict>`_, which
        means you can operate seamlessly on ``dict``,
        ``collections.defaultdict`` or ``collections.OrderedDict`` by using their methods
        with dot-separated keys.
         
        Let's see what it looks like with an example ! 👇
        
        .. code:: python
        
            from scalpl import Cut
        
            data = {
                'pokemon': [
                    {
                        'name': 'Bulbasaur',
                        'type': ['Grass', 'Poison'],
                        'category': 'Seed',
                        'ability': 'Overgrow'
                    },
                    {   
                        'name': 'Charmander',
                        'type': 'Fire',
                        'category': 'Lizard',
                        'ability': 'Blaze',
                    },
                    {
                        'name': 'Squirtle',
                        'type': 'Water',
                        'category': 'Tiny Turtle',
                        'ability': 'Torrent',
                    }
                ],
                'trainers': [
                    {
                        'name': 'Ash',
                        'hometown': 'Pallet Town'
                    }
                ]
            }
            # Just wrap your data, and you're ready to go deeper !
            proxy = Cut(data)
        
        You can use the built-in ``dict`` API to access its values.
        
        .. code:: python
        
            proxy['pokemon[0].name']
            # 'Bulbasaur'
            proxy.get('pokemon[1].sex', 'Unknown')
            # 'Unknown'
            'trainers[0].hometown' in proxy
            # True
        
        By default, **Scalpl** uses dot as a key separator, but you are free to
        use a different character that better suits your needs.
        
        .. code:: python
        
            # You just have to provide one when you wrap your data.
            proxy = Cut(data, sep='->')
            # Yarrr!
            proxy['pokemon[0]->name']
        
        You can also easily create or update any key/value pair.
        
        .. code:: python
        
            proxy['pokemon[1].weaknesses'] = ['Ground', 'Rock', 'Water']
            proxy['pokemon[1].weaknesses']
            # ['Ground', 'Rock', 'Water']
            proxy.update({
                'trainers[0].region': 'Kanto',
            })
        
        
        Following its purpose in the standard API, the *setdefault* method allows
        you to create any missing dictionary when you try to access a nested key.
        
        .. code:: python
        
            proxy.setdefault('pokemon[2].moves.Scratch.power', 40)
            # 40
        
        
        And it is still possible to iterate over your data.
        
        .. code:: python
        
            proxy.items()
            # [('pokemon', [...]), ('trainers', [...])]
            proxy.keys()
            # ['pokemon', 'trainers']
            proxy.values()
            # [[...], [...]]
        
        By the way, if you have to operate on a list of dictionaries, the
        ``Cut.all`` method is what you are looking for.
        
        .. code:: python
        
            # Let's teach these pokemon some sick moves !
            for pokemon in proxy.all('pokemon'):
                pokemon.setdefault('moves.Scratch.power', 40)
        
        Also, you can remove a specific or an arbitrary key/value pair.
        
        .. code:: python
        
            proxy.pop('pokemon[0].category')
            # 'Seed'
            proxy.popitem()
            # ('trainers', [...])
            del proxy['pokemon[1].type']
        
        Because **Scalpl** is only a wrapper around your data, it means you can
        get it back at will without any conversion cost. If you use an external
        API that operates on dictionary, it will just work.
        
        .. code:: python
        
            import json
            json.dumps(proxy.data)
            # "{'pokemon': [...]}"
        
        Finally, you can retrieve a shallow copy of the inner dictionary or
        remove all keys.
        
        .. code:: python
        
            shallow_copy = proxy.copy()
        
            proxy.clear()
        
        
        Benchmark
        ~~~~~~~~~
        
        This humble benchmark is an attempt to give you an overview of the performance
        of `Scalpl <https://github.com/ducdetronquito/scalpl>`_ compared to `Addict <https://github.com/mewwts/addict>`_,
        `Box <https://github.com/cdgriffith/Box>`_ and the built-in ``dict``.
        
        It will summarize the *number of operations per second* that each library is 
        able to perform on a portion of the JSON dump of the `Python subreddit main page <https://www.reddit.com/r/Python.json>`_.
        
        You can run this benchmark on your machine with the following command:
        
            python3 ./benchmarks/performance_comparison.py
        
        Here are the results obtained on an Intel Core i5-7500U CPU (2.50GHz) with **Python 3.6.4**.
        
        
        **Addict** 2.2.1::
        
            instantiate:-------- 271,132  ops per second.
            get:---------------- 276,090  ops per second.
            get through list:--- 293,773  ops per second.
            set:---------------- 300,324  ops per second.
            set through list:--- 282,149  ops per second.
        
        
        **Box** 3.4.2::
        
            instantiate:--------- 4,093,439  ops per second.
            get:-----------------   957,069  ops per second.
            get through list:----   164,013  ops per second.
            set:-----------------   900,466  ops per second.
            set through list:----   165,522  ops per second.
        
        
        **Scalpl** latest::
        
            instantiate:-------- 183,879,865  ops per second.
            get:----------------  14,941,355  ops per second.
            get through list:---  14,175,349  ops per second.
            set:----------------  11,320,968  ops per second.
            set through list:---  11,956,001  ops per second.
        
        
        **dict**::
        
            instantiate:---------  37,816,714  ops per second.
            get:-----------------  84,317,032  ops per second.
            get through list:----  62,480,474  ops per second.
            set:----------------- 146,484,375  ops per second.
            set through list :--- 122,473,974  ops per second.
        
        
        As a conclusion and despite being an order of magniture slower than the built-in
        ``dict``, **Scalpl** is faster than Box and Addict by an order of magnitude for any operations.
        Besides, the gap increase in favor of **Scalpl** when wrapping large dictionaries.
        
        Keeping in mind that this benchmark may vary depending on your use-case, it is very unlikely that
        **Scalpl** will become a bottleneck of your application.
        
        
        Frequently Asked Questions:
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        * **What if my keys contain dots ?**
            If your keys contain a lot of dots, you should use an other
            key separator when wrapping your data::
        
                proxy = Cut(data, sep='->')
                proxy['computer->network->127.0.0.1']
        
            Otherwise, split your key in two part::
        
                proxy = Cut(data)
                proxy['computer.network']['127.0.0.1']
        
        * **What if my keys contain spaces ?**::
            
            proxy = Cut(data)
            proxy['it works perfectly'] = 'fine'
        
        
        How to Contribute
        ~~~~~~~~~~~~~~~~~
        
        Contributions 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 ✍️
        
        
        License
        ~~~~~~~
        
        **Scalpl** is released into the **Public Domain**. 🎉
        
        Ps: If we meet some day, and you think this small stuff worths it, you
        can give me a beer, a coffee or a high-five in return: I would be really
        happy to share a moment with you ! 🍻
        
Keywords: dict,nested,proxy,traversable,dictionary,box,addict,munch,scalpl,scalpel,wrapper
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: Public Domain
Classifier: Operating System :: OS Independent
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
