Metadata-Version: 1.1
Name: items
Version: 0.6.1
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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        Attributes are the most straightforward and convenient to access composite data
        in many situations. ``item.name`` is neater, more readable, and more concise
        than the indexing style ``item['name']`` typical of dictionaries. 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 sequence wholesale, returning a ``list``:
        
        .. code-block:: python
        
            from items import itemize_all
        
            itemize_all(data)
        
        For cases in which your initial iteration is over tuples rather than
        dictionaries, you can still use ``itemize`` by providing the field names
        that should be assigned. E.g.:
        
        .. code-block:: python
        
            parser = ...
            for item in itemize(parser, fields='prefix token value'):
                if item.prefix is None and item.token == 'start_array':
                    ...
        
        Here each result returned by ``parser`` (typically a Python generator)
        is converted from a tuple (or list) into an ``Item``.  Now you have several values
        conveniently packaged in a name-accessible way without having to create
        a separate ``namedtuple`` type beforehand, and without any need for
        tuple positional indexing.
        
        You can even do this for a scalar sequence:
        
            for item in itemize('aeiou', fields='vowel'):
                item.value = 20 if item.vowel == 'e' else 15
        
        Beyond graceful handling of single-valued sequences, this example demonstrates
        the mutability of each ``Item``. ``namedtuples`` are grand as return types,
        but they cannot be easily extended or annotated by subsequent processing...and
        subsequent extension is a hyper-common requirement for many algorithms.
        
        Diving Deeper
        -------------
        
        ``Item`` objects are subclasses of ``collections.OrderedDict``, so that keys
        are ordered the same as when your program first encountered them. The
        performance overhead of 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.arbitrary_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 irregular or not uniformly filled-in, because you do
        not need the constant "guard 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 semantically 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, 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
        
            from items import Empty
        
            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 valuable 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 strongly 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 conflicts 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 py37        # run for a specific version only
            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
