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Superclass for null types parallel to, but different from, None

Project description

Helps define ‘null’ types different from, but parallel to, None.

None is a great sentinel value and a classic implementation of the null object pattern. But there are times that you need more than one to represent different aspects of emptiness. “Nothing there” is logically different from “undefined,” “prohibited,” “end of data” and other kinds of null.

Its core function is representing emptiness and falsity in a way that doesn’t overload None (or False, 0, {}, [] or any of the other “there’s nothing here!”)

This helps create designated identifiers with specific meanings such as Passthrough, Prohibited, and Undefined.

Usage

from nulltype import NullType

Empty = NullType('Empty')

# following just to show it's working
assert bool(Empty) == False
assert len(Empty) == 0
assert list(Empty) == []
assert Empty.some_attribute is Empty
assert Empty[22] is Nothing
assert Empty("hey", 12) is Empty

That created a custom NullType. You can create as many of them as you like. For your convenience, two default values, Null and Nothing, are exported. That way, if you don’t really want to create your own, you can import a pre-constituted null value, such as:

from nulltype import Nothing

Dereferencing

Alternate null types can be particularly useful when parsing data or traversing data structures which might or might not be present. This is common in dealing with the data returned by REST APIs, for instance.

As one example, the documentation for Google’s Gmail API suggests the following code:

threads = gmail_service.users().threads().list(userId='me').execute()
if threads['threads']:
    for thread in threads['threads']:
        print 'Thread ID: %s' % (thread['id'])

But there is a lot going on there to avoid a problematic dereference. If instead you have a Nothing null type defined, the code is shorter (and avoids an extra, very transient variable):

results = gmail_service.users().threads().list(userId='me').execute()
for thread in results.get('threads', Nothing):
    print 'Thread ID: %s' % (thread['id'])

This model of “go ahead, get it if you can” works well for chains of access as well:

results.get("payload", Nothing).get("headers", Nothing)

Will return the correct object if it’s there, but Nothing otherwise. And if you then try to test it (e.g. with if or a logical expression) or iterate over it (e.g. with for), it will act as though it’s an empty list.

Nothing isn’t nothing. It’s something that will simplify your code.

Uniqueness

NullType instances are meant to be singletons, with just one per program. They almost all, though technically multiple NullType instances are reasonable, making it more of a multiton pattern.

The uniqueness of each singleton is currently not enforced, making it a usage convention rather than strict law. With even minimal care, this is a problem roughly 0% of the time.

Notes

Recent Changes

  • Version 2.0 starts major upgrade from just Boolean operations being nulled to essentially all sorts of accesses and updates being nulled. It defines two default NullType instances, Null and Nothing. The ability to have anonymous (unnamed) nulls has been removed as superfluous.
  • Automated multi-version testing is managed with pytest and tox. Now successfully packaged for, and tested against, Python 2.6, 2.7, 3.2, 3.3, and 3.4. Also tested in PyPy 2.4.0 (based on 2.7.8).

Installation

pip install -U nulltype

To easy_install under a specific Python version (3.3 in this example):

python3.3 -m easy_install nulltype

(You may need to prefix these with “sudo ” to authorize installation.)

Project details


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