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Python JSON - finding elements on a dynamic path

I have to update a nested JSON object.
If I knew the specifics of which items were to be updated I could do :
json_object['basket']['items']['apple'] = 'new value'
However, my list of elements to target is dynamic.
> basket.items.apple = 'green'
> name = 'my shopping'
> basket.cost = '15.43'
I could do this by looping through elements.
Find 'basket' > then find 'items > then find 'apple' > set value
Find 'name' > set value
However, was hoping that there was a way to just reference directly/dynamicaly.
i.e. from a string 'basket.cost', build the expression :
json_object['basket']['cost']
P.s. it has to cope with lists of dictionaries too !
Any guidance appreciated :)

Once you have the string "basket.cost", you can split it on "." and it's pretty easy to drill down into json_object['basket']['cost'] using a loop. Functionally, there is no difference between doing this and doing it "directly": you are still getting the 'basket' key first, and then getting the 'cost' key from the value of json_object['basket'].
def get_element(d, path):
# This function can take the string "basket.cost", or the list ["basket", "cost"]
if isinstance(path, str):
path = path.split(".")
for p in path:
d = d[p]
return d
def set_element(d, path, value):
path = path.split(".")
dict_to_set = get_element(d, path[:-1])
key_to_set = path[-1]
dict_to_set[key_to_set] = value
set_element(json_object, "basket.items.apple", 100)
Now, this assumes all elements of your path already exist, so let's say you create a dictionary that looks like so:
json_object = {"basket": {"items": dict()}}
set_element(json_object, "basket.items.apple", 100)
set_element(json_object, "basket.cost", 10)
print(json_object)
# Output: {'basket': {'items': {'apple': 100}, 'cost': 10}}
print(get_element(json_object, "basket.cost"))
# Output: 10
If you try to access an element that doesn't already exist, you get a KeyError:
get_element(json_object, "basket.date")
# KeyError: 'date'
This also happens if you try to set a value in an element that doesn't exist:
set_element(json_object, "basket.date.day", 1)
# KeyError: 'date'
If we want to allow your function to create the dictionaries when they don't exist, we can modify the get_element function to account for this situation and add the key:
def get_element(d, path, create_missing=False):
# This function can take the string "basket.cost", or an iterable containing the elements "basket" and "cost"
if isinstance(path, str):
path = path.split(".")
for p in path:
if create_missing and p not in d:
d[p] = dict()
d = d[p]
return d
def set_element(d, path, value, create_missing=True):
path = path.split(".")
dict_to_set = get_element(d, path[:-1], create_missing)
key_to_set = path[-1]
dict_to_set[key_to_set] = value
set_element(json_object, "basket.date.day", 1)
print(json_object)
# Output: {'basket': {'items': {'apple': 100}, 'cost': 10, 'date': {'day': 1}}}

If using third party package is an option, you can try python-box. It comes with lots of options and utilities to load from json, yaml files. The implementation is optimized for speed using Cython.
from box import Box
test_data = {
"basket": {
"products": [
{"name": "apple", "colour": "green"}
],
}
}
a = Box(test_data)
a.basket.cost = 12.3
a.basket.products[0].colour = "pink"
a.basket.products.append({"name": "pineapple", "taste": "sweet"})
print(a.basket.products[1].taste)

You can get exactly what you want by overloading some python magic methods: __getattr__ and __setattr__. I'll show an example of the API to wet the appetite and then the full code:
test_data = {'basket': {'items': [{'name': 'apple', 'colour': 'green'},
{'name': 'pineapple', 'taste': 'sweet',},
],
'cost': 12.3,
},
'name': 'Other'}
o = wrap(test_data) # This wraps with the correct class, depending if it is a dict or a list
print(o.name) # Prints 'Other'
print(o.basket.items) # Prints the list of items
print(o.basket.cost) # Prints 12.3
o.basket.cost = 10.0 # Changes the cost
assert o.basket.cost == 10.0
assert len(o) == 2
assert len(o.basket.items) == 2
o.basket.items.append({'name': 'orange'})
o.basket.items[2].colour = 'yellow' # It works with lists!
assert o.basket.items[2].name == 'orange'
assert o.basket.items[2].colour == 'yellow'
# You can get a part of it and it holds a reference to the original
b = o.basket
b.type = 'groceries'
assert o.basket.type == 'groceries'
# It is also possible to create a separate wrapped part and then join:
employees = wrap({})
employees.Clara.id = 101
employees.Clara.age = 23
employees.Lucia.id = 102
employees.Lucia.age = 29
o.employees = employees
The implementation is based on special wrapper classes, one for dicts, another for lists. They all inherit from a base class. Note that the need to use super().__setattr__ instead of simply self._data is because we will override the __getattr__ and __setattr__ methods to look for the data inside _data. Of course it gives an infinite loop when you try to define _data.
from collections.abc import Mapping, Sequence, MutableSequence
class BaseWrapper:
__slots__ = ('_data')
def __init__(self, data):
super().__setattr__('_data', data)
def __repr__(self):
return f'{self.__class__.__name__}({repr(self._data)})'
The wrapper for dictionaries is the most interesting: it uses __getattr__ to look for a key in the wrapped dictionary. This allows for a very natural API: if o is a wrapped dictionary, o.entry will give the same result as o['entry']. Most of the code should be self-explanatory, there are only two tricks: the first is that __getattr__ checks if the output is a dict or list and wraps it. This allows for chaining of calls like o.basket.cost. The downside is that a new wrapper is created every call. The second trick is when setting an attribute: it checks if what is being set is a wrapped instance and un-wraps it. Thus, wrapped dictionaries can be combined and the underlying dictionary is always "clean".
class MappingWrapper(BaseWrapper):
"""Wraps a dictionary and provides the keys of the dictionary as class members.
Create new keys when they do not exist."""
def __getattr__(self, name):
# Note: these two lines allow automatic creation of attributes, e.g. in an object 'obj'
# that doesn't have an attribute 'car', the following is possible:
# >> o.car.colour = 'blue'
# And all the missing levels will be automatically created
if name not in self._data and not name.startswith('_'):
self._data[name] = {}
return wrap(self._data[name])
def __setattr__(self, name, value):
self._data[name] = unwrap(value)
# Implements standard dictionary access
def __getitem__(self, name):
return wrap(self._data[name])
def __setitem__(self, name, value):
self._data[name] = unwrap(value)
def __delitem__(self, name):
del self._data[name]
def __len__(self):
return len(self._data)
The list wrapper is simpler, no need to mess around with attribute access. The only special care we have to take is to wrap and unwrap the list elements when one is requested/set. Note that, just like with the dictionary wrapper, the same wrap and unwrap functions are used (in __getitem__/__setitem__/insert).
class ListWrapper(BaseWrapper, MutableSequence):
"""Wraps a list. Essentially, provides wrapping of elements of the list."""
def __getitem__(self, idx):
return wrap(self._data[idx])
def __setitem__(self, idx, value):
self._data[idx] = unwrap(value)
def __delitem__(self, idx):
del self._data[idx]
def __len__(self):
return len(self._data)
def insert(self, index, obj):
self._data.insert(index, unwrap(obj))
Finally, the definition of wrap, which just selects the correct wrapper based on the type of the input, and unwrap, which extracts the raw data:
def wrap(obj):
if isinstance(obj, dict):
return MappingWrapper(obj)
if isinstance(obj, list):
return ListWrapper(obj)
return obj
def unwrap(obj):
if isinstance(obj, BaseWrapper):
return obj._data
return obj
The full code can be found in this gist.
An important caveat: to keep the implementation simple, wrapper objects are created at every access. Thus using this method inside large loops may cause performance issues (per my measurements, this method of access is between 12 to 30 times slower).

I'm going to assume that you already know how to handle the value errors that will probably come up with this nested collection accessing, so I won't focus on it in my approach.
I would split this in two parts:
Traversing a nested collection according to a list of keys for each level
Getting a list of keys out of a string
The first one is quite trivial, where as you said simply looping through the keys and getting to the end of those gives you access to the collection element in question. A simple implementation of that could look something like this:
def get_nested(collection, key):
for part in key:
collection = collection[part]
return collection
def set_nested(collection, key, value):
for part in key[:-1]:
collection = collection[part]
collection[key[-1]] = value
Here the key is expected to be some iterable of keys, such as a tuple or list.
Of course that means there is an expectation that your string representing a path along the collection is already parsed. We can get to that next.
This step would also be very trivial, since one could simply expression.split(".") it. However, since you also want to be able to index nested lists along with dicts, it get's a little more complicated.
There is a tradeoff to be made here. One could simply say: "Any time that one of the items in expression.split(".") can be parsed to an int, we will do just that, and assume that it was ment as an index in a list", however naturally that isn't necessarily the case. There is nothing preventing you from using a number in string form as a key in a dict. However if you think this is never going to be the case for you, perhaps the you can just call it like this:
set_nested(
collection,
(int(part) if part.isdigit() else part for part in expression.split(".")),
"target value",
)
(or of course wrap it in another function like this).
However if the consideration of using digit keys in dicts is important for you, there is another solution:
Whenever traversing the nested collection downward, we check if the collection we are currently looking at is a list. Only if it is a list, do we actually try to parse the path part as an int.
This would be the respective set_nested and get_nested functions for that:
def get_nested(collection, key: str):
for part in key.split("."):
if type(collection) == list:
part = int(part)
collection = collection[part]
return collection
def set_nested(collection, key: str, val):
key = key.split(".")
for i, part in enumerate(key):
if type(collection) == list:
part = int(part)
if i == len(key) - 1:
collection[part] = val
else:
collection = collection[part]
I believe that's the simplest solution to your problem, though of course it's important to keep in mind:
There is no error handling in this code, and indexing on dynamic paths is a topic where you are bound to run into errors. Depending on where and how you want to handle those it's going to be easy or very tedious.
There is no checking of setting values in dicts that don't exist yet, or for expanding arrays to a specific size, but since you didn't mention those that as a requirement I'm presuming it's not an issue. It might be for others reading this.

This is tricky and I would discourage it unless necessary as it is an easy thing to design and implmenet badly.
First: it's easy to split on path separator and follow the object tree to the desired key.
But after a while questions will start to appear. E.g.: what separator to split on?
A slash? It can appear in the JSON dictionary key... A dot? Same.
We'll need to either restrict legal / handled paths or implement some kind of escaping mechanism.
How do you handle empty strings?
Another goal: handle lists... Ok. So how do we interpret a path a.0? Is it ['a'][0] or ['a']['0'] ?
It seem that we'll have to complicate the language or drop the requirement.
So, in general -- I'd avoid it. Ultimately here's a quick implementation which
desing choices may or may not satisfy you:
there's basic backslash escaping of path separator
empty string is allowed as a key
lists are not handled due to ambiguity
def deep_set(root: dict, path: str, value):
segments = [*iter_segments(path, '.')]
for k in segments[:-1]:
root = root[k]
root[segments[-1]] = value
def iter_segments(path: str, separator: str = '.'):
segment = ''
path_iter = iter(path)
while True:
c = next(path_iter, '')
if c in ('.', ''):
yield segment
segment = ''
if c == '':
break
continue
elif '\\' == c:
c = next(path_iter, '')
segment += c

Construct a data structure with special requirements in python

Requirements:
There is a variable, for example, related_to_dict = 10
Construct a key value pair data, for example, special_dict = {0 :
ref_related_to_dict}
When the variable of related_to_dict changed, the value of
special_dict[0] also changed to the value of related_to_dict
accordingly.
When the value_of special_dict[0], e.g. ref_related_to_dict changed, the
value of related_to_dict also changed to the value of
special_dict[0] accordingly.
Is there a way to achieve this task?

You need to wrap the value in some sort of container.
class Ref:
def __init__(self, v):
self.val = v
And then:
related_to_dict = Ref(10)
special_dict = {0: related_to_dict}
Then it works as desired:
related_to_dict.val = 40
print(special_dict[0].val) # 40

How to get a random value in a very large python dictionary

Given a python dict with multiple million entries, what is the most efficient way to get and remove a random (k,v) pair from it?
The dict is constantly growing and the random remove function is called very often.
The most cited solution for python2 random_key = random.choice(the_dict.keys()) is way too slow as a list of all the keys is created first. With lots of elements in the dict, this solution does not work.
Another proposed solution is the_dict.popitem(), but this does not return a real random object, but depends on the internal ordering of the dict.
The third solution that is also way to slow is an iterator:
it = the_dict.iterkeys()
for i in range (random.randint(0, len(the_dict)-1)):
next(it)
random_key = next(it)
Next to the remove_random(), sometimes a the_dict.pop(x) is required for a specific key. Therefore, a simple list based secondary index does not work.
Can this problem be efficiently be solved with a dict?

A solution is to use a bidirectional map each key to an integer, to allow for randomly selecting a key by using random.randrange(0,N) to select from a range of ints which are bidirectionally mapped to the keys, where N is the number of keys.
Adding a new key simply assigns it the next higher int. Deleting a key reassigns the int for that key to the key that was assigned the previously highest int before deleting the key-value pair. Python code provided for clarity.
Python code:
def create(D): # O(len(D))
# Create the bidirectional maps from the dictionary, D
keys = D.keys()
ints = range(len(keys)
int_to_key = dict(zip(keys, ints))
key_to_int = dict(zip(ints, keys))
return (int_to_key, key_to_int)
def add(D, int_to_key, key_to_int, key, value): # O(1)
# Add key-value pair (no extra work needed for simply changing the value)
new_int = len(D)
D[key] = value
int_to_key[new_int] = key
key_to_int[key] = new_int
def remove(D, int_to_key, key_to_int, key): # O(1)
# Update the bidirectional maps then remove the key-value pair
# Get the two ints and keys.
key_int = key_to_int[key]
swap_int = len(D) - 1 # Should be the highest int
swap_key = int_to_key[swap_int]
# Update the bidirectional maps so that key now has the highest int
key_to_int[key], key_to_int[swap_key] = swap_int, key_int
int_to_key[key_int], int_to_key[swap_int] = swap_key, key
# Remove elements from dictionaries
D.remove(key)
key_to_int.remove(key)
int_to_key.remove(key)
def random_key(D, int_to_key): # O(1)
# Select a random key from the dictionary using the int_to_key map
return int_to_key[random.randrange(0, len(D))]
def remove_random(D, int_to_key, key_to_int): # O(1)
# Randomly remove a key from the dictionary via the bidirectional maps
key = random_key(D, int_to_key)
remove(D, int_to_key, key_to_int, key)
Note: Adding/removing keys from D without using the appropriate above functions will break the bidirectional map. This means it's best to implement this as a class.

No, as you've discovered, this can't be done efficiently with a plain dict. See this issue for some explanations about why implementing random.choice for sets is hard; the same arguments apply to dictionaries.
But it's possible to create a dict-like data structure that does support efficient random selection. Here's a recipe for such an object, based in part on this question and its responses. It's only a starting-point, but it supports most of the existing dict methods, many of which are conveniently filled in by the MutableMapping ABC. Depending on your needs, you may need to flesh it out a bit: for example, to be able to create a RandomChoiceDict directly from a regular dict, or to add a meaningful __repr__, etc.
Essentially, you need to maintain three structures: a list of keys, a list of corresponding values, and a dict that maps keys back to indices (the inverse of the keys list). The basic __getitem__, __setitem__ and __delitem__ operations can be simply implemented in terms of those structures, and if __len__ and __iter__ are specified, the abstract base class takes care of most of the rest.
from collections import MutableMapping
import random
class RandomChoiceDict(MutableMapping):
"""
Dictionary-like object allowing efficient random selection.
"""
def __init__(self):
# Add code to initialize from existing dictionaries.
self._keys = []
self._values = []
self._key_to_index = {}
def __getitem__(self, key):
return self._values[self._key_to_index[key]]
def __setitem__(self, key, value):
try:
index = self._key_to_index[key]
except KeyError:
# Key doesn't exist; add a new one.
index = len(self._keys)
self._key_to_index[key] = index
self._keys.append(key)
self._values.append(value)
else:
# Key already exists; overwrite the value.
self._values[index] = value
def __delitem__(self, key):
index = self._key_to_index.pop(key)
# Remove *last* indexed element, then put
# it back at position 'index' (overwriting the
# one we're actually removing) if necessary.
key, value = self._keys.pop(), self._values.pop()
if index != len(self._key_to_index):
self._keys[index] = key
self._values[index] = value
self._key_to_index[key] = index
def __len__(self):
return len(self._key_to_index)
def __iter__(self):
return iter(self._keys)
def random_key(self):
"""Return a randomly chosen key."""
if not self:
raise KeyError("Empty collection")
index = random.randrange(len(self))
return self._keys[index]
def popitem_random(self):
key = self.random_key()
value = self.pop(key)
return key, value
Example usage:
>>> d = RandomChoiceDict()
>>> for x in range(10**6): # populate with some values
... d[x] = x**2
...
>>> d.popitem_random() # remove and return random item
(132545, 17568177025)
>>> 132545 in d
False
>>> d.popitem_random()
(954424, 910925171776)

Variables as hash values in Python

How can I link a variable to a dictionary value in Python?
Consider the following code:
a_var = 10
a_dict = {'varfield':a_var, 'first':25, 'second':57}
# a_dict['varfield'] == 10 now
a_var = 700
# a_dict['varfield'] == 10 anyway
So is there a way to link the value of a variable to a field in a dictictionary without looking up for that field an updating it's value manually?

You would need to set the value of the dictionary key, to an object that you can change the value of.
For example like this:
class valueContainer(object):
def __init__(self, value):
self.value = value
def __repr__(self):
return self.value.__repr__()
v1 = valueContainer(1)
myDict = {'myvar': v1}
print myDict
#{'myvar': 1}
v1.value = 2
print myDict
#{'myvar': 2}

w = [410]
myDict = {'myvar': w}
print myDict
#{'myvar': [410]}
w[0] = 520
print myDict
#{'myvar': [520]}
That's the version of the code of M4rtini with a list instead of an instance of a class.
He is obliged to modify v1 (in fact its attribute value) with the instruction v1.value = ...,
I am obliged to modify the value in the list with w[0] = ...
The reason to act like this is that what you erroneously called a variable, and that is in fact an identifier, doesn't designates a variable in the sense of a "chunk of memory whose content can change" but references an object to which the identifier is binded, object whose value cannot change because it is an immutable object.
Please read the explanations of the documentation on the data model and the execution model of Python which is quite different from the ones of languages such as Java, PHP, etc.

nesting with namedtuple

I'm having trouble getting my data in the form that I'd like in python.
Basically I have a program that reads in binary data and provides functions for plotting and analysis on said data.
My data has main headings and then subheadings that could be any number of varied datatypes.
I'd like to be able to access my data like for example:
>>> a = myDatafile.readit()
>>> a.elements.hydrogen.distributionfunction
(a big array)
>>> a.elements.hydrogen.mass
1
>>> a.elements.carbon.mass
12
but I don't know the names of the atoms until runtime.
I've tried using namedtuple, for example after I've read in all the atom names:
self.elements = namedtuple('elements',elementlist)
Where elementlist is a list of strings for example ('hydrogen','carbon'). But the problem is I can't nest these using for example:
for i in range(0,self.nelements):
self.elements[i] = namedtuple('details',['ux','uy','uz','mass','distributionfunction'])
and then be able to access the values through for example
self.elements.electron.distributionfunction.
Maybe I'm doing this completely wrong. I'm fairly inexperienced with python. I know this would be easy to do if I wasn't bothered about naming the variables dynamically.
I hope I've made myself clear with what I'm trying to achieve!

Without knowing your data, we can only give a generic solution.
Considering the first two lines contains the headings and Sub-Heading reading it somehow you determined the hierarchy. All you have to do is to create an hierarchical dictionary.
For example, extending your example
data.elements.hydrogen.distributionfunction
data.elements.nitrogen.xyzfunction
data.elements.nitrogen.distributionfunction
data.compound.water.distributionfunction
data.compound.hcl.xyzfunction
So we have to create a dictionary as such
{'data':{'elements':{'hydrogen':{'distributionfunction':<something>}
'nitrogen':{'xyzfunction':<something>,
'distributionfunction':<something>}
}
compound:{'water':{'distributionfunction':<something>}
'hcl':{'xyzfunction':<something>}
}
}
}
how you will populate the dictionary depends on the data which is difficult to say now.
But the keys to the dictionary you should populate from the headers, and somehow you have to map the data to the respective value in the empty slot's of the dictionary.
Once the map is populated, you can access it as
yourDict['data']['compound']['hcl']['xyzfunction']

If your element name are dynamic and obtained from the data at runtime, you can assign them to a dict and access like this
elements['hydrogen'].mass
but if you want dotted notation you can create attributes at run time e.g.
from collections import namedtuple
class Elements(object):
def add_element(self, elementname, element):
setattr(self, elementname, element)
Element = namedtuple('Element', ['ux','uy','uz','mass','distributionfunction'])
elements = Elements()
for data in [('hydrogen',1,1,1,1,1), ('helium',2,2,2,2,2), ('carbon',3,3,3,3,3)]:
elementname = data[0]
element = Element._make(data[1:])
elements.add_element(elementname, element)
print elements.hydrogen.mass
print elements.carbon.distributionfunction
Here I am assuming the data you have, but with data in any other format you can do similar tricks

Here's a method for recursively creating namedtuples from nested data.
from collections import Mapping, namedtuple
def namedtuplify(mapping, name='NT'): # thank you https://gist.github.com/hangtwenty/5960435
""" Convert mappings to namedtuples recursively. """
if isinstance(mapping, Mapping):
for key, value in list(mapping.items()):
mapping[key] = namedtuplify(value)
return namedtuple_wrapper(name, **mapping)
elif isinstance(mapping, list):
return [namedtuplify(item) for item in mapping]
return mapping
def namedtuple_wrapper(name, **kwargs):
wrap = namedtuple(name, kwargs)
return wrap(**kwargs)
stuff = {'data': {'elements': {'hydrogen': {'distributionfunction': 'foo'},
'nitrogen': {'xyzfunction': 'bar',
'distributionfunction': 'baz'}
},
'compound': {'water': {'distributionfunction': 'lorem'},
'hcl': {'xyzfunction': 'ipsum'}}}
}
example = namedtuplify(stuff)
example.data.elements.hydrogen.distributionfunction # 'foo'

I had the same issue with nested json but needed to be able to serialise the output with pickle which doesn't like you creating objects on the fly.
I've taken #bren's answer and enhanced it so that the resulting structure will be serialisable with pickle. You have to save references to each of the structures you create to globals so that pickle can keep tabs on them.
##############################################
class Json2Struct:
'''
Convert mappings to nested namedtuples
Usage:
jStruct = Json2Struct('JS').json2Struct(json)
'''
##############################################
def __init__(self, name):
self.namePrefix = name
self.nameSuffix = 0
def json2Struct(self, jsonObj): # thank you https://gist.github.com/hangtwenty/5960435
"""
Convert mappings to namedtuples recursively.
"""
if isinstance(jsonObj, Mapping):
for key, value in list(jsonObj.items()):
jsonObj[key] = self.json2Struct(value)
return self.namedtuple_wrapper(**jsonObj)
elif isinstance(jsonObj, list):
return [self.json2Struct(item) for item in jsonObj]
return jsonObj
def namedtuple_wrapper(self, **kwargs):
self.nameSuffix += 1
name = self.namePrefix + str(self.nameSuffix)
Jstruct = namedtuple(name, kwargs)
globals()[name] = Jstruct
return Jstruct(**kwargs)
The example below should work as follows and also be serialisable:
stuff = {'data': {'elements': {'hydrogen': {'distributionfunction': 'foo'},
'nitrogen': {'xyzfunction': 'bar',
'distributionfunction': 'baz'}
},
'compound': {'water': {'distributionfunction': 'lorem'},
'hcl': {'xyzfunction': 'ipsum'}}}
}
example = Json2Struct('JS').json2Struct(stuff)
example.data.elements.hydrogen.distributionfunction # 'foo'