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python flatten dict with leaving the same key name after flatten

I have the following dict:
{
"a": "b",
"c": {'d':'e', 'g':'f'}
}
and I want to flatten the dict, but in this way:
{
"a": "b",
'd':'e',
'g':'f'
}
You can assume there is no duplicate key.
I read about flatten_dict library but it looks there is no support in this.
How can I do this?
Thank you!

For one level of nesting, a nice loop does the trick:
result = {}
for key, value in my_dict.items():
if isinstance(value, dict):
result.update(value) # add subdict directly into the dict
else:
result[key] = value # non-subdict elements are just copied
If you have more nesting, the if/else should be executed recursively:
def flatten(my_dict):
result = {}
for key, value in my_dict.items():
if isinstance(value, dict):
result.update(flatten(value))
else:
result[key] = value
return result

You can use a modified version of this answer:
from collections.abc import MutableMapping
def flatten(d):
items = []
for k, v in d.items():
if isinstance(v, MutableMapping):
items.extend(flatten(v).items())
else:
items.append((k, v))
return dict(items)
>>> x = {
... "a": "b",
... "c": {'d': 'e', 'g': 'f'}
... }
>>> flatten(x)
{'a': 'b', 'd': 'e', 'g': 'f'}
This will work for all forms of any dictionary

replace all empty string values in a dictionary with null in python

how to replace all empty string values in a dictionary like bellow with null.
values can be dictionary, too.
test_dict = {
'hi': {'h': '', 'i': 'i'},
'bye': {},
'sth': ''
}
my answer:
def empty_str_value_to_none(parent_dict, key=None):
if key is not None:
value = parent_dict[key]
else:
value = parent_dict
if type(value) == dict:
for sub_key in value:
empty_str_value_to_none(value, sub_key)
elif value == "":
parent_dict[key] = None
empty_str_value_to_none(test_dict)
print(test_dict)

The following recursive function will do:
def to_null(obj):
if obj != "":
if isinstance(obj, dict):
return {k: to_null(v) for k, v in obj.items()}
return obj
# return None # happens implicitly
test_dict = to_null(test_dict)
# {'hi': {'h': None, 'i': 'i'}, 'bye': {}, 'sth': None}

how to transfer this flat dict to a nested dict?

There is a flat dict like this:
{"a.b": "foo", "a.c": "bar", "d.e.f":"baz"}
And how to use Python to transfer the dict to this:
{
"a":
{
"b": "foo",
"c": "bar"
},
"d":
{
"e":
{
"f": "baz"
}
}
}

You can split the your keys on . into the last value and everything before with:
*parents, key = k.split('.')
You can dig down into a nested dictionary (creating them if necessary) with something like:
end = reduce(lambda x, y: x.setdefault(y, {}) , parents, new_d)
here end will be the dictionary at the end of the chain. So at that point you can just assign the value. Something like:
from functools import reduce
d = {"a.b": "foo", "a.c": "bar", "d.e.f":"baz", "d.e.g":"M", 'l':"single_key_test" }
new_d = {}
for k in d:
*parents, key = k.split('.')
end = reduce(lambda x, y: x.setdefault(y, {}) , parents, new_d)
end[key] = d[k]
print(new_d)
result:
{'a': {'b': 'foo', 'c': 'bar'}, 'd': {'e': {'f': 'baz', 'g': 'M'}}, 'l': 'single_key_test'}

You would first have to split your keys by '.' to get the paths.
Then you can create a function that creates a nested dictionary from one path:
def make_nested_dict(iterable, final):
"""Make nested dict from iterable"""
if iterable:
head, *tail = iterable
return {head: make_nested_dict(tail, final)}
else:
return final
Which works as follows:
d = {"a.b": "foo", "a.c": "bar", "d.e.f":"baz"}
for key in d:
paths = key.split('.')
nested_path = make_nested_dict(paths, d[key])
print(nested_path)
And gives the following paths:
{'a': {'b': 'foo'}}
{'a': {'c': 'bar'}}
{'d': {'e': {'f': 'baz'}}}
Then you can create a function that merges two nested dictionaries together recursively:
def merge_nested_dicts(d1, d2):
"""Merge two nested dictionaries together"""
for key in d2:
if key in d1:
if isinstance(d2[key], dict) and isinstance(d1[key], dict):
merge_nested_dicts(d1[key], d2[key])
else:
d1[key] = d2[key]
return d1
Which you can merge by updating a resultant nested dict:
nested_dict = {}
for key in d:
paths = key.split('.')
nested_path = make_nested_dict(paths, d[key])
nested_dict = merge_nested_dicts(nested_dict, nested_path)
print(nested_dict)
And now this gives the following:
{'a': {'b': 'foo', 'c': 'bar'}, 'd': {'e': {'f': 'baz'}}}
Full code with some comments:
def make_nested_dict(iterable, final):
"""Make nested dictionary path with a final attribute"""
# If iterable, keep recursing
if iterable:
# Unpack key and rest of dict
head, *tail = iterable
# Return new dictionary, recursing on tail value
return {head: make_nested_dict(tail, final)}
# Otherwise assign final attribute
else:
return final
def merge_nested_dicts(d1, d2):
"""Merge two nested dictionaries together"""
for key in d2:
# If we have matching keys
if key in d1:
# Only merge if both values are dictionaries
if isinstance(d2[key], dict) and isinstance(d1[key], dict):
merge_nested_dicts(d1[key], d2[key])
# Otherwise assign normally
else:
d1[key] = d2[key]
return d1
if __name__ == "__main__":
d = {"a.b": "foo", "a.c": "bar", "d.e.f":"baz", "d.e.g":"M", 'l':"single_key_test" }
nested_dict = {}
for key in d:
# Create path
paths = key.split('.')
# Create nested path
nested_path = make_nested_dict(paths, d[key])
# Update result dict by merging with new dict
nested_dict = merge_nested_dicts(nested_dict, nested_path)
print(nested_dict)

Handle self-references when flattening dictionary

Given some arbitrary dictionary
mydict = {
'first': {
'second': {
'third': {
'fourth': 'the end'
}
}
}
}
I've written a small routine to flatten it in the process of writing an answer to another question.
def recursive_flatten(mydict):
d = {}
for k, v in mydict.items():
if isinstance(v, dict):
for k2, v2 in recursive_flatten(v).items():
d[k + '.' + k2] = v2
else:
d[k] = v
return d
It works, giving me what I want:
new_dict = recursive_flatten(mydict)
print(new_dict)
{'first.second.third.fourth': 'the end'}
And should work for just about any arbitrarily structured dictionary. Unfortunately, it does not:
mydict['new_key'] = mydict
Now recursive_flatten(mydict) will run until I run out of stack space. I'm trying to figure out how to gracefully handle self-references (basically, ignore or remove them). To complicate matters, self-references may occur for any sub-dictionary... not just the top level. How would I handle self-references elegantly? I can think of a mutable default argument, but there should be a better way... right?
Pointers appreciated, thanks for reading. I welcome any other suggestions/improvements to recursive_flatten if you have them.

One way you can do it using set and id. Note this solution also uses generators which means we can start using our flattened dict before the entire result is computed
def recursive_flatten (mydict):
def loop (seen, path, value):
# if we've seen this value, skip it
if id(value) in seen:
return
# if we haven't seen this value, now we have
else:
seen.add(id(value))
# if this value is a dict...
if isinstance (value, dict):
for (k, v) in value.items ():
yield from loop(seen, path + [k], v)
# base case
else:
yield (".".join(path), value)
# init the loop
yield from loop (set(), [], mydict)
Program demo
mydict = {
'first': {
'second': {
'third': {
'fourth': 'the end'
}
}
}
}
for (k,v) in recursive_flatten (mydict):
print (k, v)
# first.second.third.fourth the end
mydict['new_key'] = mydict
for (k,v) in recursive_flatten (mydict):
print (k, v)
# first.second.third.fourth the end
We can make a slight modification if you would like to see output for self-referential values
# if we've seen this value, skip it
if (id(value) in seen):
# this is the new line
yield (".".join(path), "*self-reference* %d" % id(value))
return
Now the output of the program will be
first.second.third.fourth the end
first.second.third.fourth the end
new_key *self-reference* 139700111853032

I'm not sure what your definition of "graceful" is, but this can be done with some bookkeeping of what has been seen before in a set of object ids:
class RecursiveFlatten:
def __init__(self):
self.seen = set()
def __call__(self, mydict):
self.seen.add(id(mydict))
d = {}
for k, v in mydict.items():
if isinstance(v, dict):
if id(v) not in self.seen:
self.seen.add(id(v))
for k2, v2 in self(v).items():
d[k + '.' + k2] = v2
else:
d[k] = v
return d
def recursive_flatten(mydict):
return RecursiveFlatten()(mydict)
Testing it out gives me what I expect
mydict = {
'first': {
'second': {
'third': {
'fourth': 'the end'
}
},
'second2': {
'third2': 'the end2'
}
}
}
mydict['first']['second']['new_key'] = mydict
mydict['new_key'] = mydict
print(recursive_flatten(mydict))
Out:
{'first.second2.third2': 'the end2', 'first.second.third.fourth': 'the end'}

How to convert a nested Python dict to object?

I'm searching for an elegant way to get data using attribute access on a dict with some nested dicts and lists (i.e. javascript-style object syntax).
For example:
>>> d = {'a': 1, 'b': {'c': 2}, 'd': ["hi", {'foo': "bar"}]}
Should be accessible in this way:
>>> x = dict2obj(d)
>>> x.a
1
>>> x.b.c
2
>>> x.d[1].foo
bar
I think, this is not possible without recursion, but what would be a nice way to get an object style for dicts?

Update: In Python 2.6 and onwards, consider whether the namedtuple data structure suits your needs:
>>> from collections import namedtuple
>>> MyStruct = namedtuple('MyStruct', 'a b d')
>>> s = MyStruct(a=1, b={'c': 2}, d=['hi'])
>>> s
MyStruct(a=1, b={'c': 2}, d=['hi'])
>>> s.a
1
>>> s.b
{'c': 2}
>>> s.c
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'MyStruct' object has no attribute 'c'
>>> s.d
['hi']
The alternative (original answer contents) is:
class Struct:
def __init__(self, **entries):
self.__dict__.update(entries)
Then, you can use:
>>> args = {'a': 1, 'b': 2}
>>> s = Struct(**args)
>>> s
<__main__.Struct instance at 0x01D6A738>
>>> s.a
1
>>> s.b
2

Surprisingly no one has mentioned Bunch. This library is exclusively meant to provide attribute style access to dict objects and does exactly what the OP wants. A demonstration:
>>> from bunch import bunchify
>>> d = {'a': 1, 'b': {'c': 2}, 'd': ["hi", {'foo': "bar"}]}
>>> x = bunchify(d)
>>> x.a
1
>>> x.b.c
2
>>> x.d[1].foo
'bar'
A Python 3 library is available at https://github.com/Infinidat/munch - Credit goes to codyzu
>>> from munch import DefaultMunch
>>> d = {'a': 1, 'b': {'c': 2}, 'd': ["hi", {'foo': "bar"}]}
>>> obj = DefaultMunch.fromDict(d)
>>> obj.b.c
2
>>> obj.a
1
>>> obj.d[1].foo
'bar'

class obj(object):
def __init__(self, d):
for k, v in d.items():
if isinstance(k, (list, tuple)):
setattr(self, k, [obj(x) if isinstance(x, dict) else x for x in v])
else:
setattr(self, k, obj(v) if isinstance(v, dict) else v)
>>> d = {'a': 1, 'b': {'c': 2}, 'd': ["hi", {'foo': "bar"}]}
>>> x = obj(d)
>>> x.b.c
2
>>> x.d[1].foo
'bar'

x = type('new_dict', (object,), d)
then add recursion to this and you're done.
edit this is how I'd implement it:
>>> d
{'a': 1, 'b': {'c': 2}, 'd': ['hi', {'foo': 'bar'}]}
>>> def obj_dic(d):
top = type('new', (object,), d)
seqs = tuple, list, set, frozenset
for i, j in d.items():
if isinstance(j, dict):
setattr(top, i, obj_dic(j))
elif isinstance(j, seqs):
setattr(top, i,
type(j)(obj_dic(sj) if isinstance(sj, dict) else sj for sj in j))
else:
setattr(top, i, j)
return top
>>> x = obj_dic(d)
>>> x.a
1
>>> x.b.c
2
>>> x.d[1].foo
'bar'

# Applies to Python-3 Standard Library
class Struct(object):
def __init__(self, data):
for name, value in data.items():
setattr(self, name, self._wrap(value))
def _wrap(self, value):
if isinstance(value, (tuple, list, set, frozenset)):
return type(value)([self._wrap(v) for v in value])
else:
return Struct(value) if isinstance(value, dict) else value
# Applies to Python-2 Standard Library
class Struct(object):
def __init__(self, data):
for name, value in data.iteritems():
setattr(self, name, self._wrap(value))
def _wrap(self, value):
if isinstance(value, (tuple, list, set, frozenset)):
return type(value)([self._wrap(v) for v in value])
else:
return Struct(value) if isinstance(value, dict) else value
Can be used with any sequence/dict/value structure of any depth.

There's a
collection helper called namedtuple, that can do this for you:
from collections import namedtuple
d_named = namedtuple('Struct', d.keys())(*d.values())
In [7]: d_named
Out[7]: Struct(a=1, b={'c': 2}, d=['hi', {'foo': 'bar'}])
In [8]: d_named.a
Out[8]: 1

If your dict is coming from json.loads(), you can turn it into an object instead (rather than a dict) in one line:
import json
from collections import namedtuple
json.loads(data, object_hook=lambda d: namedtuple('X', d.keys())(*d.values()))
See also How to convert JSON data into a Python object.

You can leverage the json module of the standard library with a custom object hook:
import json
class obj(object):
def __init__(self, dict_):
self.__dict__.update(dict_)
def dict2obj(d):
return json.loads(json.dumps(d), object_hook=obj)
Example usage:
>>> d = {'a': 1, 'b': {'c': 2}, 'd': ['hi', {'foo': 'bar'}]}
>>> o = dict2obj(d)
>>> o.a
1
>>> o.b.c
2
>>> o.d[0]
u'hi'
>>> o.d[1].foo
u'bar'
And it is not strictly read-only as it is with namedtuple, i.e. you can change values – not structure:
>>> o.b.c = 3
>>> o.b.c
3

Taking what I feel are the best aspects of the previous examples, here's what I came up with:
class Struct:
"""The recursive class for building and representing objects with."""
def __init__(self, obj):
for k, v in obj.items():
if isinstance(v, dict):
setattr(self, k, Struct(v))
else:
setattr(self, k, v)
def __getitem__(self, val):
return self.__dict__[val]
def __repr__(self):
return '{%s}' % str(', '.join('%s : %s' % (k, repr(v)) for (k, v) in self.__dict__.items()))

I ended up trying BOTH the AttrDict and the Bunch libraries and found them to be way too slow for my uses. After a friend and I looked into it, we found that the main method for writing these libraries results in the library aggressively recursing through a nested object and making copies of the dictionary object throughout. With this in mind, we made two key changes. 1) We made attributes lazy-loaded 2) instead of creating copies of a dictionary object, we create copies of a light-weight proxy object. This is the final implementation. The performance increase of using this code is incredible. When using AttrDict or Bunch, these two libraries alone consumed 1/2 and 1/3 respectively of my request time(what!?). This code reduced that time to almost nothing(somewhere in the range of 0.5ms). This of course depends on your needs, but if you are using this functionality quite a bit in your code, definitely go with something simple like this.
class DictProxy(object):
def __init__(self, obj):
self.obj = obj
def __getitem__(self, key):
return wrap(self.obj[key])
def __getattr__(self, key):
try:
return wrap(getattr(self.obj, key))
except AttributeError:
try:
return self[key]
except KeyError:
raise AttributeError(key)
# you probably also want to proxy important list properties along like
# items(), iteritems() and __len__
class ListProxy(object):
def __init__(self, obj):
self.obj = obj
def __getitem__(self, key):
return wrap(self.obj[key])
# you probably also want to proxy important list properties along like
# __iter__ and __len__
def wrap(value):
if isinstance(value, dict):
return DictProxy(value)
if isinstance(value, (tuple, list)):
return ListProxy(value)
return value
See the original implementation here by https://stackoverflow.com/users/704327/michael-merickel.
The other thing to note, is that this implementation is pretty simple and doesn't implement all of the methods you might need. You'll need to write those as required on the DictProxy or ListProxy objects.

If you want to access dict keys as an object (or as a dict for difficult keys), do it recursively, and also be able to update the original dict, you could do:
class Dictate(object):
"""Object view of a dict, updating the passed in dict when values are set
or deleted. "Dictate" the contents of a dict...: """
def __init__(self, d):
# since __setattr__ is overridden, self.__dict = d doesn't work
object.__setattr__(self, '_Dictate__dict', d)
# Dictionary-like access / updates
def __getitem__(self, name):
value = self.__dict[name]
if isinstance(value, dict): # recursively view sub-dicts as objects
value = Dictate(value)
return value
def __setitem__(self, name, value):
self.__dict[name] = value
def __delitem__(self, name):
del self.__dict[name]
# Object-like access / updates
def __getattr__(self, name):
return self[name]
def __setattr__(self, name, value):
self[name] = value
def __delattr__(self, name):
del self[name]
def __repr__(self):
return "%s(%r)" % (type(self).__name__, self.__dict)
def __str__(self):
return str(self.__dict)
Example usage:
d = {'a': 'b', 1: 2}
dd = Dictate(d)
assert dd.a == 'b' # Access like an object
assert dd[1] == 2 # Access like a dict
# Updates affect d
dd.c = 'd'
assert d['c'] == 'd'
del dd.a
del dd[1]
# Inner dicts are mapped
dd.e = {}
dd.e.f = 'g'
assert dd['e'].f == 'g'
assert d == {'c': 'd', 'e': {'f': 'g'}}

>>> def dict2obj(d):
if isinstance(d, list):
d = [dict2obj(x) for x in d]
if not isinstance(d, dict):
return d
class C(object):
pass
o = C()
for k in d:
o.__dict__[k] = dict2obj(d[k])
return o
>>> d = {'a': 1, 'b': {'c': 2}, 'd': ["hi", {'foo': "bar"}]}
>>> x = dict2obj(d)
>>> x.a
1
>>> x.b.c
2
>>> x.d[1].foo
'bar'

In 2021, use pydantic BaseModel - will convert nested dicts and nested json objects to python objects and vice versa:
https://pydantic-docs.helpmanual.io/usage/models/
>>> class Foo(BaseModel):
... count: int
... size: float = None
...
>>>
>>> class Bar(BaseModel):
... apple = 'x'
... banana = 'y'
...
>>>
>>> class Spam(BaseModel):
... foo: Foo
... bars: List[Bar]
...
>>>
>>> m = Spam(foo={'count': 4}, bars=[{'apple': 'x1'}, {'apple': 'x2'}])
Object to dict
>>> print(m.dict())
{'foo': {'count': 4, 'size': None}, 'bars': [{'apple': 'x1', 'banana': 'y'}, {'apple': 'x2', 'banana': 'y'}]}
Object to JSON
>>> print(m.json())
{"foo": {"count": 4, "size": null}, "bars": [{"apple": "x1", "banana": "y"}, {"apple": "x2", "banana": "y"}]}
Dict to object
>>> spam = Spam.parse_obj({'foo': {'count': 4, 'size': None}, 'bars': [{'apple': 'x1', 'banana': 'y'}, {'apple': 'x2', 'banana': 'y2'}]})
>>> spam
Spam(foo=Foo(count=4, size=None), bars=[Bar(apple='x1', banana='y'), Bar(apple='x2', banana='y2')])
JSON to object
>>> spam = Spam.parse_raw('{"foo": {"count": 4, "size": null}, "bars": [{"apple": "x1", "banana": "y"}, {"apple": "x2", "banana": "y"}]}')
>>> spam
Spam(foo=Foo(count=4, size=None), bars=[Bar(apple='x1', banana='y'), Bar(apple='x2', banana='y')])

x.__dict__.update(d) should do fine.

Typically you want to mirror dict hierarchy into your object but not list or tuples which are typically at lowest level. So this is how I did this:
class defDictToObject(object):
def __init__(self, myDict):
for key, value in myDict.items():
if type(value) == dict:
setattr(self, key, defDictToObject(value))
else:
setattr(self, key, value)
So we do:
myDict = { 'a': 1,
'b': {
'b1': {'x': 1,
'y': 2} },
'c': ['hi', 'bar']
}
and get:
x.b.b1.x 1
x.c ['hi', 'bar']

This should get your started:
class dict2obj(object):
def __init__(self, d):
self.__dict__['d'] = d
def __getattr__(self, key):
value = self.__dict__['d'][key]
if type(value) == type({}):
return dict2obj(value)
return value
d = {'a': 1, 'b': {'c': 2}, 'd': ["hi", {'foo': "bar"}]}
x = dict2obj(d)
print x.a
print x.b.c
print x.d[1].foo
It doesn't work for lists, yet. You'll have to wrap the lists in a UserList and overload __getitem__ to wrap dicts.

I know there's already a lot of answers here already and I'm late to the party but this method will recursively and 'in place' convert a dictionary to an object-like structure... Works in 3.x.x
def dictToObject(d):
for k,v in d.items():
if isinstance(v, dict):
d[k] = dictToObject(v)
return namedtuple('object', d.keys())(*d.values())
# Dictionary created from JSON file
d = {
'primaryKey': 'id',
'metadata':
{
'rows': 0,
'lastID': 0
},
'columns':
{
'col2': {
'dataType': 'string',
'name': 'addressLine1'
},
'col1': {
'datatype': 'string',
'name': 'postcode'
},
'col3': {
'dataType': 'string',
'name': 'addressLine2'
},
'col0': {
'datatype': 'integer',
'name': 'id'
},
'col4': {
'dataType': 'string',
'name': 'contactNumber'
}
},
'secondaryKeys': {}
}
d1 = dictToObject(d)
d1.columns.col1 # == object(datatype='string', name='postcode')
d1.metadata.rows # == 0

from mock import Mock
d = {'a': 1, 'b': {'c': 2}, 'd': ["hi", {'foo': "bar"}]}
my_data = Mock(**d)
# We got
# my_data.a == 1

This also works well too
class DObj(object):
pass
dobj = Dobj()
dobj.__dict__ = {'a': 'aaa', 'b': 'bbb'}
print dobj.a
>>> aaa
print dobj.b
>>> bbb

The simplest way would be using collections.namedtuple.
I find the following 4-liner the most beautiful, which supports nested dictionaries:
def dict_to_namedtuple(typename, data):
return namedtuple(typename, data.keys())(
*(dict_to_namedtuple(typename + '_' + k, v) if isinstance(v, dict) else v for k, v in data.items())
)
The output will look good as well:
>>> nt = dict_to_namedtuple('config', {
... 'path': '/app',
... 'debug': {'level': 'error', 'stream': 'stdout'}
... })
>>> print(nt)
config(path='/app', debug=config_debug(level='error', stream='stdout'))
>>> print(nt.debug.level)
'error'

Let me explain a solution I almost used some time ago. But first, the reason I did not is illustrated by the fact that the following code:
d = {'from': 1}
x = dict2obj(d)
print x.from
gives this error:
File "test.py", line 20
print x.from == 1
^
SyntaxError: invalid syntax
Because "from" is a Python keyword there are certain dictionary keys you cannot allow.
Now my solution allows access to the dictionary items by using their names directly. But it also allows you to use "dictionary semantics". Here is the code with example usage:
class dict2obj(dict):
def __init__(self, dict_):
super(dict2obj, self).__init__(dict_)
for key in self:
item = self[key]
if isinstance(item, list):
for idx, it in enumerate(item):
if isinstance(it, dict):
item[idx] = dict2obj(it)
elif isinstance(item, dict):
self[key] = dict2obj(item)
def __getattr__(self, key):
return self[key]
d = {'a': 1, 'b': {'c': 2}, 'd': ["hi", {'foo': "bar"}]}
x = dict2obj(d)
assert x.a == x['a'] == 1
assert x.b.c == x['b']['c'] == 2
assert x.d[1].foo == x['d'][1]['foo'] == "bar"

Old Q&A, but I get something more to talk. Seems no one talk about recursive dict. This is my code:
#!/usr/bin/env python
class Object( dict ):
def __init__( self, data = None ):
super( Object, self ).__init__()
if data:
self.__update( data, {} )
def __update( self, data, did ):
dataid = id(data)
did[ dataid ] = self
for k in data:
dkid = id(data[k])
if did.has_key(dkid):
self[k] = did[dkid]
elif isinstance( data[k], Object ):
self[k] = data[k]
elif isinstance( data[k], dict ):
obj = Object()
obj.__update( data[k], did )
self[k] = obj
obj = None
else:
self[k] = data[k]
def __getattr__( self, key ):
return self.get( key, None )
def __setattr__( self, key, value ):
if isinstance(value,dict):
self[key] = Object( value )
else:
self[key] = value
def update( self, *args ):
for obj in args:
for k in obj:
if isinstance(obj[k],dict):
self[k] = Object( obj[k] )
else:
self[k] = obj[k]
return self
def merge( self, *args ):
for obj in args:
for k in obj:
if self.has_key(k):
if isinstance(self[k],list) and isinstance(obj[k],list):
self[k] += obj[k]
elif isinstance(self[k],list):
self[k].append( obj[k] )
elif isinstance(obj[k],list):
self[k] = [self[k]] + obj[k]
elif isinstance(self[k],Object) and isinstance(obj[k],Object):
self[k].merge( obj[k] )
elif isinstance(self[k],Object) and isinstance(obj[k],dict):
self[k].merge( obj[k] )
else:
self[k] = [ self[k], obj[k] ]
else:
if isinstance(obj[k],dict):
self[k] = Object( obj[k] )
else:
self[k] = obj[k]
return self
def test01():
class UObject( Object ):
pass
obj = Object({1:2})
d = {}
d.update({
"a": 1,
"b": {
"c": 2,
"d": [ 3, 4, 5 ],
"e": [ [6,7], (8,9) ],
"self": d,
},
1: 10,
"1": 11,
"obj": obj,
})
x = UObject(d)
assert x.a == x["a"] == 1
assert x.b.c == x["b"]["c"] == 2
assert x.b.d[0] == 3
assert x.b.d[1] == 4
assert x.b.e[0][0] == 6
assert x.b.e[1][0] == 8
assert x[1] == 10
assert x["1"] == 11
assert x[1] != x["1"]
assert id(x) == id(x.b.self.b.self) == id(x.b.self)
assert x.b.self.a == x.b.self.b.self.a == 1
x.x = 12
assert x.x == x["x"] == 12
x.y = {"a":13,"b":[14,15]}
assert x.y.a == 13
assert x.y.b[0] == 14
def test02():
x = Object({
"a": {
"b": 1,
"c": [ 2, 3 ]
},
1: 6,
2: [ 8, 9 ],
3: 11,
})
y = Object({
"a": {
"b": 4,
"c": [ 5 ]
},
1: 7,
2: 10,
3: [ 12 , 13 ],
})
z = {
3: 14,
2: 15,
"a": {
"b": 16,
"c": 17,
}
}
x.merge( y, z )
assert 2 in x.a.c
assert 3 in x.a.c
assert 5 in x.a.c
assert 1 in x.a.b
assert 4 in x.a.b
assert 8 in x[2]
assert 9 in x[2]
assert 10 in x[2]
assert 11 in x[3]
assert 12 in x[3]
assert 13 in x[3]
assert 14 in x[3]
assert 15 in x[2]
assert 16 in x.a.b
assert 17 in x.a.c
if __name__ == '__main__':
test01()
test02()

Wanted to upload my version of this little paradigm.
class Struct(dict):
def __init__(self,data):
for key, value in data.items():
if isinstance(value, dict):
setattr(self, key, Struct(value))
else:
setattr(self, key, type(value).__init__(value))
dict.__init__(self,data)
It preserves the attributes for the type that's imported into the class. My only concern would be overwriting methods from within the dictionary your parsing. But otherwise seems solid!

Here is another way to implement SilentGhost's original suggestion:
def dict2obj(d):
if isinstance(d, dict):
n = {}
for item in d:
if isinstance(d[item], dict):
n[item] = dict2obj(d[item])
elif isinstance(d[item], (list, tuple)):
n[item] = [dict2obj(elem) for elem in d[item]]
else:
n[item] = d[item]
return type('obj_from_dict', (object,), n)
else:
return d

I stumbled upon the case I needed to recursively convert a list of dicts to list of objects, so based on Roberto's snippet here what did the work for me:
def dict2obj(d):
if isinstance(d, dict):
n = {}
for item in d:
if isinstance(d[item], dict):
n[item] = dict2obj(d[item])
elif isinstance(d[item], (list, tuple)):
n[item] = [dict2obj(elem) for elem in d[item]]
else:
n[item] = d[item]
return type('obj_from_dict', (object,), n)
elif isinstance(d, (list, tuple,)):
l = []
for item in d:
l.append(dict2obj(item))
return l
else:
return d
Note that any tuple will be converted to its list equivalent, for obvious reasons.
Hope this helps someone as much as all your answers did for me, guys.

What about just assigning your dict to the __dict__ of an empty object?
class Object:
"""If your dict is "flat", this is a simple way to create an object from a dict
>>> obj = Object()
>>> obj.__dict__ = d
>>> d.a
1
"""
pass
Of course this fails on your nested dict example unless you walk the dict recursively:
# For a nested dict, you need to recursively update __dict__
def dict2obj(d):
"""Convert a dict to an object
>>> d = {'a': 1, 'b': {'c': 2}, 'd': ["hi", {'foo': "bar"}]}
>>> obj = dict2obj(d)
>>> obj.b.c
2
>>> obj.d
["hi", {'foo': "bar"}]
"""
try:
d = dict(d)
except (TypeError, ValueError):
return d
obj = Object()
for k, v in d.iteritems():
obj.__dict__[k] = dict2obj(v)
return obj
And your example list element was probably meant to be a Mapping, a list of (key, value) pairs like this:
>>> d = {'a': 1, 'b': {'c': 2}, 'd': [("hi", {'foo': "bar"})]}
>>> obj = dict2obj(d)
>>> obj.d.hi.foo
"bar"

Here's another implementation:
class DictObj(object):
def __init__(self, d):
self.__dict__ = d
def dict_to_obj(d):
if isinstance(d, (list, tuple)): return map(dict_to_obj, d)
elif not isinstance(d, dict): return d
return DictObj(dict((k, dict_to_obj(v)) for (k,v) in d.iteritems()))
[Edit] Missed bit about also handling dicts within lists, not just other dicts. Added fix.

class Struct(dict):
def __getattr__(self, name):
try:
return self[name]
except KeyError:
raise AttributeError(name)
def __setattr__(self, name, value):
self[name] = value
def copy(self):
return Struct(dict.copy(self))
Usage:
points = Struct(x=1, y=2)
# Changing
points['x'] = 2
points.y = 1
# Accessing
points['x'], points.x, points.get('x') # 2 2 2
points['y'], points.y, points.get('y') # 1 1 1
# Accessing inexistent keys/attrs
points['z'] # KeyError: z
points.z # AttributeError: z
# Copying
points_copy = points.copy()
points.x = 2
points_copy.x # 1

How about this:
from functools import partial
d2o=partial(type, "d2o", ())
This can then be used like this:
>>> o=d2o({"a" : 5, "b" : 3})
>>> print o.a
5
>>> print o.b
3

I think a dict consists of number, string and dict is enough most time.
So I ignore the situation that tuples, lists and other types not appearing in the final dimension of a dict.
Considering inheritance, combined with recursion, it solves the print problem conveniently and also provides two ways to query a data,one way to edit a data.
See the example below, a dict that describes some information about students:
group=["class1","class2","class3","class4",]
rank=["rank1","rank2","rank3","rank4","rank5",]
data=["name","sex","height","weight","score"]
#build a dict based on the lists above
student_dic=dict([(g,dict([(r,dict([(d,'') for d in data])) for r in rank ]))for g in group])
#this is the solution
class dic2class(dict):
def __init__(self, dic):
for key,val in dic.items():
self.__dict__[key]=self[key]=dic2class(val) if isinstance(val,dict) else val
student_class=dic2class(student_dic)
#one way to edit:
student_class.class1.rank1['sex']='male'
student_class.class1.rank1['name']='Nan Xiang'
#two ways to query:
print student_class.class1.rank1
print student_class.class1['rank1']
print '-'*50
for rank in student_class.class1:
print getattr(student_class.class1,rank)
Results:
{'score': '', 'sex': 'male', 'name': 'Nan Xiang', 'weight': '', 'height': ''}
{'score': '', 'sex': 'male', 'name': 'Nan Xiang', 'weight': '', 'height': ''}
--------------------------------------------------
{'score': '', 'sex': '', 'name': '', 'weight': '', 'height': ''}
{'score': '', 'sex': '', 'name': '', 'weight': '', 'height': ''}
{'score': '', 'sex': 'male', 'name': 'Nan Xiang', 'weight': '', 'height': ''}
{'score': '', 'sex': '', 'name': '', 'weight': '', 'height': ''}
{'score': '', 'sex': '', 'name': '', 'weight': '', 'height': ''}