items 0.5.1

Attribute accessible dicts and collections thereof

| |travisci| |version| |versions| |impls| |wheel| |coverage|

.. |travisci| image:: https://travis-ci.org/jonathaneunice/items.svg?branch=master
:alt: Travis CI build status
:target: https://travis-ci.org/jonathaneunice/items

.. |version| image:: http://img.shields.io/pypi/v/items.svg?style=flat
:alt: PyPI Package latest release
:target: https://pypi.python.org/pypi/items

.. |versions| image:: https://img.shields.io/pypi/pyversions/items.svg
:alt: Supported versions
:target: https://pypi.python.org/pypi/items

.. |impls| image:: https://img.shields.io/pypi/implementation/items.svg
:alt: Supported implementations
:target: https://pypi.python.org/pypi/items

.. |wheel| image:: https://img.shields.io/pypi/wheel/items.svg
:alt: Wheel packaging support
:target: https://pypi.python.org/pypi/items

.. |coverage| image:: https://img.shields.io/badge/test_coverage-100%25-6600CC.svg
:alt: Test line coverage
:target: https://pypi.python.org/pypi/items

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.

.. code-block:: python

e = Empty
assert e[1].method().there[33][0].no.attributes[99].here is Empty

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:
# if there is a name attribute, it's processed here
# if not, no problem; processing just continues here

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
items = itemize_all(yaml.load(data))

# modified call to json.load or json.loads to preserve order by instantiating
# Item instances rather than dict
items = json.loads(data, object_pairs_hook=Item.from_tuples)

Recursion
=========

Not currently organized for handling recursive data structures. THose do not
appear in processing JSON, XML, and other common data formats, but still might
be a nice future extension.

Alternate Construction
======================

The constructor for ``Item`` is intended to take an existing
mapping, or a set of keywords. Unlike most ``dict`` subtypes,
it will not directly convert a primitive list of two-element
tuples (a historical analog of dictionaries in Python, from
before the advent of ``OrderedDict``) directly. If you need to
convert such a typle list into an ``Item``, use the
class method ``Item.from_tuples(...)`.

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