Metadata-Version: 1.1
Name: items
Version: 0.5.5
Summary: Attribute accessible dicts and collections thereof
Home-page: https://bitbucket.org/jeunice/items
Author: Jonathan Eunice
Author-email: jonathan.eunice@gmail.com
License: Apache License 2.0
Description: 
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            :alt: PyPI Package latest release
            :target: https://pypi.python.org/pypi/items
        
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            :alt: Supported versions
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            :alt: Supported implementations
            :target: https://pypi.python.org/pypi/items
        
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            :alt: Wheel packaging support
            :target: https://pypi.python.org/pypi/items
        
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            :alt: Test line coverage
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        Attribute-accessible dictonaries are the most convenient way to access
        dictionaries and other mappings in many algorithms. ``item.name`` is more
        readable and concise than ``item['name']``. Having attribute access often is
        the difference between being able to easily de-reference a component of
        ``item`` directly and deciding to store that attribute in a completely
        separate variable for clarity (``item_name = item['name']``).
        
        In traversing data structures from XML, JSON, and other typically-nested data
        sources, concise direct access can clean up code considerably.
        
        Items
        -----
        
        ``items`` therefore provides ``Item``, a convenient attribute-accessible ``dict`` subclass,
        plus helper functions to make working with ``Item`` s.
        
        ``itemize``, for example, helps iterate of a list of dictionaries, as often found
        in JSON processing: Each record is handed back as an ``Item`` rather than a Python
        ``dict``.
        
        A typical progression would be from:
        
        .. code-block:: python
        
            for item in data:
                item_name = item['name']
                # ...
                print(item_name)
        
        to
        
        .. code-block:: python
        
            from items import itemize
        
            for item in itemize(data):
                # ...
                print(item.name)
        
        To process a list wholesale:
        
        .. code-block:: python
        
            from items import itemize_all
        
            itemize_all(data)
        
        ``Item`` objects are subclasses of ``collections.OrderedDict``, so that keys
        are ordered the same as when yoor program first encountered them. The
        performance or ordered mappings is minimal in most development contexts,
        especially in exploratory and data-cleanup tasks. Whatever overhead there is is
        more than made up for by the programming and debugging clarity of not having
        keys occur in random locations.
        
        ``Item`` s are also permissive, in a way that ``dict`` and its variants often
        are not: If you access ``item.arbitary_attribute`` where the attribute does not
        exist, you do not raise a ``KeyError`` as you might expect from normal Python
        dictionaries. Instead you get back ``Empty``, a designated, false-y value
        similar to, but distinct from, ``None``. This is convenient for processing data
        which is not irregular and not uniformly filled-in, because you do not need the
        constant "gaurd conditions"--either ``if`` statements or ``try``/``except
        KeyError`` blocks--to protect against cases where this data value or that is
        missing. Using ``Empty`` instead of ``None`` preserves your ability to use
        ``None`` in cases where it's semanticailly important. For example, in parsing
        JSON, ``None`` is returned from JSON's ``null`` value.
        
        ``Empty`` objects are infinitely dereferenceable. No matter how many levels of
        indirection, they always just hand back themselves--the same gentle "nothing
        here, but no exceptions raised" behavior. You can also iterate over an
        ``Empty``--it will simply iterate zero times. This neatly avoids the common
        ``TypeError: 'NoneType' object is not iterable`` error messages in instances
        where a value can be a list--or ``None`` if the list is not present.
        
        .. code-block:: python
        
            e = Empty
            assert e[1].method().there[33][0].no.attributes[99].here is Empty
            for x in Empty:
                print('hey!')     # never prints, because no such iterations occur
        
        For more on the background of ``Empty``, see the `nulltype <https://pypi.org/project/nulltype/>`_
        module. A typical use would be:
        
        .. code-block:: python
        
            for item in itemize(data):
                if item.name:
                    process(item)
        
        Items that lack names are simply not processed.
        
        The more nested, complex, and irregular your data structures, the
        more valueable this becomes.
        
        Serialization and Deserialization
        =================================
        
        Be careful importing data from files. Popular Python modules for reading JSON,
        YAML, and other formats do not believe mappings are ordered. Historically and
        officially, they're not, no matter how ordered they look, no matter that other
        languages such as JavaScript take a different approach, and no matter how many
        Stack Overflow questions demonstrate that ordered import is stronly and broadly
        desired. Therefore stock input/output modules can cause dislocation as data is
        parsed. Take steps to return ordered mappings from them.
        
        .. code-block:: python
        
            # YAML module that will load into OrderedDict instances, which can then
            # be easily converted to Item instances; based on default PyYAML
            import oyaml as yaml
            data = itemize_all(yaml.load(rawyaml))
        
            # modified call to json.load or json.loads to preserve order by instantiating
            # Item instances rather than dict
            import json
            data = json.loads(rawjson, object_pairs_hook=Item)
        
        Cycles
        ======
        
        Not currently organized for handling cyclic data structures. Those do not
        appear in processing JSON, XML, and other common data formats, but still might
        be a nice future extension.
        
        Installation
        ============
        
        To install or upgrade to the latest version::
        
            pip install -U items
        
        Sometimes Python installations have different names for ``pip`` (e.g. ``pip``,
        ``pip2``, and ``pip3``), and on systems with multiple versions of Python, which
        ``pip`` goes with which Python interpreter can become confusing. In those
        cases, try running ``pip`` as a module of the Python version you want to
        install under. This can reduce conflects and confusion::
        
            python3.6 -m pip install -U items
        
        (On Unix, Linux, and macOS you may need to prefix these with ``sudo`` to authorize
        installation. In environments without super-user privileges, you may want to
        use ``pip``'s ``--user`` option, to install only for a single user, rather
        than system-wide.)
        
        Testing
        =======
        
        If you wish to run the module tests locally, you'll need to install
        ``pytest`` and ``tox``.  For full testing, you will also need ``pytest-cov``
        and ``coverage``. Then run one of these commands::
        
            tox                # normal run - speed optimized
            tox -e py27        # run for a specific version only (e.g. py27, py34)
            tox -c toxcov.ini  # run full coverage tests
Keywords: attributes attrs
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Software Development :: Libraries :: Python Modules
