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Description:

items 0.6.5

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.
itemize, for example, helps iterate through a sequence 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:
for item in data:
item_name = item['name']
# ...
print(item_name)
to
from items import itemize

for item in itemize(data):
# ...
print(item.name)
To process a sequence wholesale, returning a list:
from items import itemize_all

itemize_all(data)
If you’re iterating over a sequence of tuples (or lists) rather than
dictionaries, you can still use itemize by providing the field names
you wnat assigned.
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 for this result type, 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…a
common requirement for many algorithms.

Diving Deeper
Item subclasses collections.OrderedDict, so 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 seemingly randomized order.
Item s are also permissive, in a way that dict and its variants usually
are not: If you access item.some_attribute where the attribute does
not exist, you do not raise a KeyError, unlike typical
Python dictionaries. Instead you get 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”–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.
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
module. A typical use would be:
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. Popular Python modules for reading JSON, YAML, and
other formats do not believe mappings are (or should be) 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 input and output 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.
# 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 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

Notes

Does not work on Python 2. Should work on Python 3 prior to 3.6, but not guaranteed.
Testing more difficult given different dictionary ordering rules prior to 3.6.
Since items intended as forward-looking module, not currently worth energy to retrofit
to archaic dialects.