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Tests Coverage Status PyPI version Anaconda-Server Badge

addict is a Python module that gives you dictionaries whose values are both gettable and settable using attributes, in addition to standard item-syntax.

This means that you don't have to write dictionaries like this anymore:

body = {
    'query': {
        'filtered': {
            'query': {
                'match': {'description': 'addictive'}
            'filter': {
                'term': {'created_by': 'Mats'}

Instead, you can simply write the following three lines:

body = Dict()
body.query.filtered.query.match.description = 'addictive'
body.query.filtered.filter.term.created_by = 'Mats'


You can install via pip

pip install addict

or through conda

conda install addict -c conda-forge

Addict runs on Python 2 and Python 3, and every build is tested towards 2.7, 3.6 and 3.7.


addict inherits from dict, but is more flexible in terms of accessing and setting its values. Working with dictionaries are now a joy! Setting the items of a nested Dict is a dream:

>>> from addict import Dict
>>> mapping = Dict()
>>> mapping.a.b.c.d.e = 2
>>> mapping
{'a': {'b': {'c': {'d': {'e': 2}}}}}

If the Dict is instantiated with any iterable values, it will iterate through and clone these values, and turn dicts into Dicts. Hence, the following works

>>> mapping = {'a': [{'b': 3}, {'b': 3}]}
>>> dictionary = Dict(mapping)
>>> dictionary.a[0].b

but mapping['a'] is no longer the same reference as dictionary['a'].

>>> mapping['a'] is dictionary['a']

This behavior is limited to the constructor, and not when items are set using attribute or item syntax, references are untouched:

>>> a = Dict()
>>> b = [1, 2, 3]
>>> a.b = b
>>> a.b is b

Stuff to keep in mind

Remember that ints are not valid attribute names, so keys of the dict that are not strings must be set/get with the get-/setitem syntax

>>> addicted = Dict()
>>> addicted.a.b.c.d.e = 2
>>> addicted[2] = [1, 2, 3]
{2: [1, 2, 3], 'a': {'b': {'c': {'d': {'e': 2}}}}}

However feel free to mix the two syntaxes:

>>> addicted.a.b['c'].d.e

Attributes like keys, items etc.

addict will not let you override attributes that are native to dict, so the following will not work

>>> mapping = Dict()
>>> mapping.keys = 2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "addict/", line 53, in __setattr__
    raise AttributeError("'Dict' object attribute '%s' is read-only" % name)
AttributeError: 'Dict' object attribute 'keys' is read-only

However, the following is fine

>>> a = Dict()
>>> a['keys'] = 2
>>> a
{'keys': 2}
>>> a['keys']

just like a regular dict. There are no restrictions (other than what a regular dict imposes) regarding what keys you can use.

Default values

For keys that are not in the dictionary, addict behaves like defaultdict(Dict), so missing keys return an empty Dict rather than raising KeyError. If this behaviour is not desired, it can be overridden using

>>> class DictNoDefault(Dict):
>>>     def __missing__(self, key):
>>>         raise KeyError(key)

but beware that you will then lose the shorthand assignment functionality (addicted.a.b.c.d.e = 2).

Recursive Fallback to dict

If you don't feel safe shipping your addict around to other modules, use the to_dict()-method, which returns a regular dict clone of the addict dictionary.

>>> regular_dict = my_addict.to_dict()
>>> regular_dict.a = 2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'dict' object has no attribute 'a'

This is perfect for when you wish to create a nested Dict in a few lines, and then ship it on to a different module.

body = Dict()
body.query.filtered.query.match.description = 'addictive'
body.query.filtered.filter.term.created_by = 'Mats'


Dict's ability to easily access and modify deeply-nested attributes makes it ideal for counting. This offers a distinct advantage over collections.Counter, as it will easily allow for counting by multiple levels.

Consider this data:

data = [
    {'born': 1980, 'gender': 'M', 'eyes': 'green'},
    {'born': 1980, 'gender': 'F', 'eyes': 'green'},
    {'born': 1980, 'gender': 'M', 'eyes': 'blue'},
    {'born': 1980, 'gender': 'M', 'eyes': 'green'},
    {'born': 1980, 'gender': 'M', 'eyes': 'green'},
    {'born': 1980, 'gender': 'F', 'eyes': 'blue'},
    {'born': 1981, 'gender': 'M', 'eyes': 'blue'},
    {'born': 1981, 'gender': 'F', 'eyes': 'green'},
    {'born': 1981, 'gender': 'M', 'eyes': 'blue'},
    {'born': 1981, 'gender': 'F', 'eyes': 'blue'},
    {'born': 1981, 'gender': 'M', 'eyes': 'green'},
    {'born': 1981, 'gender': 'F', 'eyes': 'blue'}

If you want to count how many people were born in born of gender gender with eyes eyes, you can easily calculate this information:

counter = Dict()

for row in data:
    born = row['born']
    gender = row['gender']
    eyes = row['eyes']

    counter[born][gender][eyes] += 1

{1980: {'M': {'blue': 1, 'green': 3}, 'F': {'blue': 1, 'green': 1}}, 1981: {'M': {'blue': 2, 'green': 1}, 'F': {'blue': 2, 'green': 1}}}


addicts update functionality is altered for convenience from a normal dict. Where updating nested item using a dict would overwrite it:

>>> d = {'a': {'b': 3}}
>>> d.update({'a': {'c': 4}})
>>> print(d)
{'a': {'c': 4}}

addict will recurse and actually update the nested Dict.

>>> D = Dict({'a': {'b': 3}})
>>> D.update({'a': {'c': 4}})
>>> print(D)
{'a': {'b': 3, 'c': 4}}

When is this especially useful?

This module rose from the entirely tiresome creation of Elasticsearch queries in Python. Whenever you find yourself writing out dicts over multiple lines, just remember that you don't have to. Use addict instead.


As it is a dict, it will serialize into JSON perfectly, and with the to_dict()-method you can feel safe shipping your addict anywhere.

Testing, Development and CI

Issues and Pull Requests are more than welcome. Feel free to open an issue to spark a discussion around a feature or a bug, or simply reply to the existing ones. As for Pull Requests, keeping in touch with the surrounding code style will be appreciated, and as such, writing tests are crucial. Pull requests and commits will be automatically run against TravisCI and coveralls.

The unit tests are implemented in the file and use the unittest python framework. Running the tests is rather simple:

python -m unittest -v test_addict

# - or -


@spiritsack - "Mother of God, this changes everything."

@some guy on Hacker News - "...the purpose itself is grossly unpythonic"