Attribute accessible dicts and collections thereof
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 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)
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.
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.method().there.no.attributes.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
# 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)
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.
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.
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
- 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.
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