Javascript style dot notation for dictionary keys unpythonic?

As far as I know, Python classes use dictionaries to store their attributes anyway (that’s hidden from the programmer), so it looks to me that what you’ve done there is effectively emulate a Python class… using a python class.

I like dot notation a lot better than dictionary fields personally. The reason being that it makes autocompletion work a lot better.

With regards to the DictObj, would the following work for you? A blank class will allow you to arbitrarily add to or replace stuff in a container object.

class Container(object):
    pass

>>> myContainer = Container()
>>> myContainer.spam = "in a can"
>>> myContainer.eggs = "in a shell"

If you want to not throw an AttributeError when there is no attribute, what do you think about the following? Personally, I’d prefer to use a dict for clarity, or to use a try/except clause.

class QuietContainer(object):
    def __getattr__(self, attribute):
        try:
            return object.__getattr__(self,attribute)
        except AttributeError:
            return None

>>> cont = QuietContainer()
>>> print cont.me
None

Right?

It’s not bad if it serves your purpose. “Practicality beats purity”.

I saw such approach elserwhere (eg. in Paver), so this can be considered common need (or desire).

Your DictObj example is actually quite common. Object-style dot-notation access can be a win if you are dealing with ‘things that resemble objects’, ie. they have fixed property names containing only characters valid in Python identifiers. Stuff like database rows or form submissions can be usefully stored in this kind of object, making code a little more readable without the excess of [‘item access’].

The implementation is a bit limited – you don’t get the nice constructor syntax of dict, len(), comparisons, ‘in’, iteration or nice reprs. You can of course implement those things yourself, but in the new-style-classes world you can get them for free by simply subclassing dict:

class AttrDict(dict):
    __getattr__ = dict.__getitem__
    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

To get the default-to-None behaviour, simply subclass Python 2.5’s collections.defaultdict class instead of dict.

The one major disadvantage of using something like your DictObj is you either have to limit allowable keys or you can’t have methods on your DictObj such as .keys(), .values(), .items(), etc.

It’s not “wrong” to do this, and it can be nicer if your dictionaries have a strong possibility of turning into objects at some point, but be wary of the reasons for having bracket access in the first place:

1. Dot access can’t use keywords as keys.
2. Dot access has to use Python-identifier-valid characters in the keys.
3. Dictionaries can hold any hashable element — not just strings.

Also keep in mind you can always make your objects access like dictionaries if you decide to switch to objects later on.

For a case like this I would default to the “readability counts” mantra: presumably other Python programmers will be reading your code and they probably won’t be expecting dictionary/object hybrids everywhere. If it’s a good design decision for a particular situation, use it, but I wouldn’t use it without necessity to do so.

Because you ask for undesirable side-effects:

A disadvantage is that in visual editors like eclipse+pyDev, you will see many undefined variable errors on lines using the dot notation. Pydef will not be able to find such runtime “object” definitions. Whereas in the case of a normal dictionary, it knows that you are just getting a dictionary entry.

You would need to 1) ignore those errors and live with red crosses; 2) suppress those warnings on a line by line basis using #@UndefinedVariable or 3) disable undefined variable error entirely, causing you to miss real undefined variable definitions.

There’s a symmetry between this and this answer:

class dotdict(dict):
    __getattr__= dict.__getitem__
    __setattr__= dict.__setitem__
    __delattr__= dict.__delitem__

The same interface, just implemented the other way round…

class container(object):
    __getitem__ = object.__getattribute__
    __setitem__ = object.__setattr__
    __delitem__ = object.__delattr__

Don’t overlook Bunch.

It is a child of dictionary and can import YAML or JSON, or convert any existing dictionary to a Bunch and vice-versa. Once “bunchify”‘d, a dictionary gains dot notations without losing any other dictionary methods.

If you’re looking for an alternative that handles nested dicts:

Recursively transform a dict to instances of the desired class

import json
from collections import namedtuple


class DictTransformer():
    @classmethod
    def constantize(self, d):
        return self.transform(d, klass=namedtuple, klassname='namedtuple')

    @classmethod
    def transform(self, d, klass, klassname):
        return self._from_json(self._to_json(d), klass=klass, klassname=klassname)

    @classmethod
    def _to_json(self, d, access_method='__dict__'):
        return json.dumps(d, default=lambda o: getattr(o, access_method, str(o)))

    @classmethod
    def _from_json(self, jsonstr, klass, klassname):
        return json.loads(jsonstr, object_hook=lambda d: klass(klassname, d.keys())(*d.values()))

Ex:

constants = {
  'A': {
    'B': {
      'C': 'D'
    }
  }
}
CONSTANTS = DictTransformer.transform(d, klass=namedtuple, klassname='namedtuple')
CONSTANTS.A.B.C == 'D'

Pros:

• handles nested dicts
• can potentially generate other classes
• namedtuples provide immutability for constants

Cons:

• may not respond to .keys and .values if those are not provided on your klass (though you can sometimes mimic with ._fields and list(A.B.C))

Thoughts?

h/t to @hlzr for the original class idea