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 instanciated with any iterable values, it will iterate through and clone these values, and turn dict
s into Dict
s.
Hence, the following works
>>> mapping = {'a': [{'b': 3}, {'b': 3}]}
>>> dictionary = Dict(mapping)
>>> dictionary.a[0].b
3
but mapping['a']
is no longer the same reference as dictionary['a']
.
>>> mapping['a'] is dictionary['a']
False
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
True
Remember that int
s 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
2
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/addict.py", 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']
2
just like a regular dict
. There are no restrictions (other than what a regular dict imposes) regarding what keys you can use.
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'
third_party_module.search(query=body.to_dict())
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
print(counter)
{1980: {'M': {'blue': 1, 'green': 3}, 'F': {'blue': 1, 'green': 1}}, 1981: {'M': {'blue': 2, 'green': 1}, 'F': {'blue': 2, 'green': 1}}}
addict
s 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}}
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.
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 test_addict.py
file and use the unittest python framework. Running the tests is rather simple:
python -m unittest -v test_addict
# - or -
python test_addict.py
@spiritsack - "Mother of God, this changes everything."
@some guy on Hacker News - "...the purpose itself is grossly unpythonic"