I want to implement a function that:
Given a dictionary and an iterable of keys,
deletes the value accessed by iterating over those keys.
Originally I had tried
def delete_dictionary_value(dict, keys):
inner_value = dict
for key in keys:
inner_value = inner_value[key]
del inner_value
return dict
Thinking that since inner_value is assigned to dict by reference, we can mutate dict implcitly by mutating inner_value. However, it seems that assigning inner_value itself creates a new reference (sys.getrefcount(dict[key]) is incremented by assigning inner_value inside the loop) - the result being that the local variable assignment is deled but dict is returned unchanged.
Using inner_value = None has the same effect - presumably because this merely reassigns inner_value.
Other people have posted looking for answers to questions like:
how do I ensure that my dictionary includes no values at the key x - which might be a question about recursion for nested dictionaries, or
how do I iterate over values at a given key (different flavours of this question)
how do I access the value of the key as opposed to the keyed value in a dictionary
This is none of the above - I want to remove a specific key,value pair in a dictionary that may be nested arbitrarily deeply - but I always know the path to the key,value pair I want to delete.
The solution I have hacked together so far is:
def delete_dictionary_value(dict, keys):
base_str = f"del dict"
property_access_str = ''.join([f"['{i}']" for i in keys])
return exec(base_str + property_access_str)
Which doesn't feel right.
This also seems like pretty basic functionality - but I've not found an obvious solution. Most likely I am missing something (most likely something blindingly obvious) - please help me see.
If error checking is not required at all, you just need to iterate to the penultimate key and then delete the value from there:
def del_by_path(d, keys):
for k in keys[:-1]:
d = d[k]
return d.pop(keys[-1])
d = {'a': {'b': {'c': {'d': 'Value'}}}}
del_by_path(d, 'abcd')
# 'Value'
print(d)
# {'a': {'b': {'c': {}}}}
Just for fun, here's a more "functional-style" way to do the same thing:
from functools import reduce
def del_by_path(d, keys):
*init, last = keys
return reduce(dict.get, init, d).pop(last)
Don't use a string-evaluation approach. Try to iteratively move to the last dictionary and delete the key-value pair from it. Here a possibility:
def delete_key(d, value_path):
# move to most internal dictionary
for kp in value_path[:-1]:
if kp in dd and isinstance(d[kp], dict):
d = d[kp]
else:
e_msg = f"Key-value delete-operation failed at key '{kp}'"
raise Exception(e_msg)
# last entry check
lst_kp = value_path[-1]
if lst_kp not in d:
e_msg = f"Key-value delete-operation failed at key '{lst_kp}'"
raise Exception(e_msg)
# delete key-value of most internal dictionary
print(f'Value "{d[lst_kp]}" at position "{value_path}" deleted')
del d[lst_kp]
d = {1: 2, 2:{3: "a"}, 4: {5: 6, 6:{8:9}}}
delete_key(d, [44, 6, 0])
#Value "9" at position "[4, 6, 8]" deleted
#{1: 2, 2: {3: 'a'}, 4: {5: 6, 6: {}}}
I had to remove some fields from a dictionary, the keys for those fields are on a list. So I wrote this function:
def delete_keys_from_dict(dict_del, lst_keys):
"""
Delete the keys present in lst_keys from the dictionary.
Loops recursively over nested dictionaries.
"""
dict_foo = dict_del.copy() #Used as iterator to avoid the 'DictionaryHasChanged' error
for field in dict_foo.keys():
if field in lst_keys:
del dict_del[field]
if type(dict_foo[field]) == dict:
delete_keys_from_dict(dict_del[field], lst_keys)
return dict_del
This code works, but it's not very elegant and I'm sure that there is a better solution.
First of, I think your code is working and not inelegant. There's no immediate reason not to use the code you presented.
There are a few things that could be better though:
Comparing the type
Your code contains the line:
if type(dict_foo[field]) == dict:
That can be definitely improved. Generally (see also PEP8) you should use isinstance instead of comparing types:
if isinstance(dict_foo[field], dict)
However that will also return True if dict_foo[field] is a subclass of dict. If you don't want that, you could also use is instead of ==. That will be marginally (and probably unnoticeable) faster.
If you also want to allow arbitary dict-like objects you could go a step further and test if it's a collections.abc.MutableMapping. That will be True for dict and dict subclasses and for all mutable mappings that explicitly implement that interface without subclassing dict, for example UserDict:
>>> from collections import MutableMapping
>>> # from UserDict import UserDict # Python 2.x
>>> from collections import UserDict # Python 3.x - 3.6
>>> # from collections.abc import MutableMapping # Python 3.7+
>>> isinstance(UserDict(), MutableMapping)
True
>>> isinstance(UserDict(), dict)
False
Inplace modification and return value
Typically functions either modify a data structure inplace or return a new (modified) data structure. Just to mention a few examples: list.append, dict.clear, dict.update all modify the data structure inplace and return None. That makes it easier to keep track what a function does. However that's not a hard rule and there are always valid exceptions from this rule. However personally I think a function like this doesn't need to be an exception and I would simply remove the return dict_del line and let it implicitly return None, but YMMV.
Removing the keys from the dictionary
You copied the dictionary to avoid problems when you remove key-value pairs during the iteration. However, as already mentioned by another answer you could just iterate over the keys that should be removed and try to delete them:
for key in keys_to_remove:
try:
del dict[key]
except KeyError:
pass
That has the additional advantage that you don't need to nest two loops (which could be slower, especially if the number of keys that need to be removed is very long).
If you don't like empty except clauses you can also use: contextlib.suppress (requires Python 3.4+):
from contextlib import suppress
for key in keys_to_remove:
with suppress(KeyError):
del dict[key]
Variable names
There are a few variables I would rename because they are just not descriptive or even misleading:
delete_keys_from_dict should probably mention the subdict-handling, maybe delete_keys_from_dict_recursive.
dict_del sounds like a deleted dict. I tend to prefer names like dictionary or dct because the function name already describes what is done to the dictionary.
lst_keys, same there. I'd probably use just keys there. If you want to be more specific something like keys_sequence would make more sense because it accepts any sequence (you just have to be able to iterate over it multiple times), not just lists.
dict_foo, just no...
field isn't really appropriate either, it's a key.
Putting it all together:
As I said before I personally would modify the dictionary in-place and not return the dictionary again. Because of that I present two solutions, one that modifies it in-place but doesn't return anything and one that creates a new dictionary with the keys removed.
The version that modifies in-place (very much like Ned Batchelders solution):
from collections import MutableMapping
from contextlib import suppress
def delete_keys_from_dict(dictionary, keys):
for key in keys:
with suppress(KeyError):
del dictionary[key]
for value in dictionary.values():
if isinstance(value, MutableMapping):
delete_keys_from_dict(value, keys)
And the solution that returns a new object:
from collections import MutableMapping
def delete_keys_from_dict(dictionary, keys):
keys_set = set(keys) # Just an optimization for the "if key in keys" lookup.
modified_dict = {}
for key, value in dictionary.items():
if key not in keys_set:
if isinstance(value, MutableMapping):
modified_dict[key] = delete_keys_from_dict(value, keys_set)
else:
modified_dict[key] = value # or copy.deepcopy(value) if a copy is desired for non-dicts.
return modified_dict
However it only makes copies of the dictionaries, the other values are not returned as copy, you could easily wrap these in copy.deepcopy (I put a comment in the appropriate place of the code) if you want that.
def delete_keys_from_dict(dict_del, lst_keys):
for k in lst_keys:
try:
del dict_del[k]
except KeyError:
pass
for v in dict_del.values():
if isinstance(v, dict):
delete_keys_from_dict(v, lst_keys)
return dict_del
Since the question requested an elegant way, I'll submit my general-purpose solution to wrangling nested structures. First, install the boltons utility package with pip install boltons, then:
from boltons.iterutils import remap
data = {'one': 'remains', 'this': 'goes', 'of': 'course'}
bad_keys = set(['this', 'is', 'a', 'list', 'of', 'keys'])
drop_keys = lambda path, key, value: key not in bad_keys
clean = remap(data, visit=drop_keys)
print(clean)
# Output:
{'one': 'remains'}
In short, the remap utility is a full-featured, yet succinct approach to handling real-world data structures which are often nested, and can even contain cycles and special containers.
This page has many more examples, including ones working with much larger objects from Github's API.
It's pure-Python, so it works everywhere, and is fully tested in Python 2.7 and 3.3+. Best of all, I wrote it for exactly cases like this, so if you find a case it doesn't handle, you can bug me to fix it right here.
def delete_keys_from_dict(d, to_delete):
if isinstance(to_delete, str):
to_delete = [to_delete]
if isinstance(d, dict):
for single_to_delete in set(to_delete):
if single_to_delete in d:
del d[single_to_delete]
for k, v in d.items():
delete_keys_from_dict(v, to_delete)
elif isinstance(d, list):
for i in d:
delete_keys_from_dict(i, to_delete)
d = {'a': 10, 'b': [{'c': 10, 'd': 10, 'a': 10}, {'a': 10}], 'c': 1 }
delete_keys_from_dict(d, ['a', 'c']) # inplace deletion
print(d)
>>> {'b': [{'d': 10}, {}]}
This solution works for dict and list in a given nested dict. The input to_delete can be a list of str to be deleted or a single str.
Plese note, that if you remove the only key in a dict, you will get an empty dict.
I think the following is more elegant:
def delete_keys_from_dict(dict_del, lst_keys):
if not isinstance(dict_del, dict):
return dict_del
return {
key: value
for key, value in (
(key, delete_keys_from_dict(value, lst_keys))
for key, value in dict_del.items()
)
if key not in lst_keys
}
Example usage:
test_dict_in = {
1: {1: {0: 2, 3: 4}},
0: {2: 3},
2: {5: {0: 4}, 6: {7: 8}},
}
test_dict_out = {
1: {1: {3: 4}},
2: {5: {}, 6: {7: 8}},
}
assert delete_keys_from_dict(test_dict_in, [0]) == test_dict_out
Since you already need to loop through every element in the dict, I'd stick with a single loop and just make sure to use a set for looking up the keys to delete
def delete_keys_from_dict(dict_del, the_keys):
"""
Delete the keys present in the lst_keys from the dictionary.
Loops recursively over nested dictionaries.
"""
# make sure the_keys is a set to get O(1) lookups
if type(the_keys) is not set:
the_keys = set(the_keys)
for k,v in dict_del.items():
if k in the_keys:
del dict_del[k]
if isinstance(v, dict):
delete_keys_from_dict(v, the_keys)
return dict_del
this works with dicts containing Iterables (list, ...) that may contain dict. Python 3. For Python 2 unicode should also be excluded from the iteration. Also there may be some iterables that don't work that I'm not aware of. (i.e. will lead to inifinite recursion)
from collections.abc import Iterable
def deep_omit(d, keys):
if isinstance(d, dict):
for k in keys:
d.pop(k, None)
for v in d.values():
deep_omit(v, keys)
elif isinstance(d, Iterable) and not isinstance(d, str):
for e in d:
deep_omit(e, keys)
return d
Since nobody posted an interactive version that could be useful for someone:
def delete_key_from_dict(adict, key):
stack = [adict]
while stack:
elem = stack.pop()
if isinstance(elem, dict):
if key in elem:
del elem[key]
for k in elem:
stack.append(elem[k])
This version is probably what you would push to production. The recursive version is elegant and easy to write but it scales badly (by default Python uses a maximum recursion depth of 1000).
If you have nested keys as well and based on #John La Rooy's answer here is an elegant solution:
from boltons.iterutils import remap
def sof_solution():
data = {"user": {"name": "test", "pwd": "******"}, "accounts": ["1", "2"]}
sensitive = {"user.pwd", "accounts"}
clean = remap(
data,
visit=lambda path, key, value: drop_keys(path, key, value, sensitive)
)
print(clean)
def drop_keys(path, key, value, sensitive):
if len(path) > 0:
nested_key = f"{'.'.join(path)}.{key}"
return nested_key not in sensitive
return key not in sensitive
sof_solution() # prints {'user': {'name': 'test'}}
Using the awesome code from this post and add a small statement:
def remove_fields(self, d, list_of_keys_to_remove):
if not isinstance(d, (dict, list)):
return d
if isinstance(d, list):
return [v for v in (self.remove_fields(v, list_of_keys_to_remove) for v in d) if v]
return {k: v for k, v in ((k, self.remove_fields(v, list_of_keys_to_remove)) for k, v in d.items()) if k not in list_of_keys_to_remove}
I came here to search for a solution to remove keys from deeply nested Python3 dicts and all solutions seem to be somewhat complex.
Here's a oneliner for removing keys from nested or flat dicts:
nested_dict = {
"foo": {
"bar": {
"foobar": {},
"shmoobar": {}
}
}
}
>>> {'foo': {'bar': {'foobar': {}, 'shmoobar': {}}}}
nested_dict.get("foo", {}).get("bar", {}).pop("shmoobar", None)
>>> {'foo': {'bar': {'foobar': {}}}}
I used .get() to not get KeyError and I also provide empty dict as default value up to the end of the chain. I do pop() for the last element and I provide None as the default there to avoid KeyError.
I have an API that I call that returns a dictionary. Part of that dictionary is itself another dictionary. In that inside dictionary, there are some keys that might not exist, or they might. Those keys could reference another dictionary.
To give an example, say I have the following dictionaries:
dict1 = {'a': {'b': {'c':{'d':3}}}}
dict2 = {'a': {'b': {''f': 2}}}
I would like to write a function that I can pass in the dictionary, and a list of keys that would lead me to the 3 in dict1, and the 2 in dict2. However, it is possible that b and c might not exist in dict1, and b and f might not exist in dict2.
I would like to have a function that I could call like this:
get_value(dict1, ['a', 'b', 'c'])
and that would return a 3, or if the keys are not found, then return a default value of 0.
I know that I can use something like this:
val = dict1.get('a', {}).get('b', {}).get('c', 0)
but that seems to be quite wordy to me.
I can also flatten the dict (see https://stackoverflow.com/a/6043835/1758023), but that can be a bit intensive since my dictionary is actually fairly large, and has about 5 levels of nesting in some keys. And, I only need to get two things from the dict.
Right now I am using the flattenDict function in the SO question, but that seems a bit of overkill for my situation.
You can use a recursive function:
def get_value(mydict, keys):
if not keys:
return mydict
if keys[0] not in mydict:
return 0
return get_value(mydict[keys[0]], keys[1:])
If keys can not only be missing, but be other, non-dict types, you can handle this like so:
def get_value(mydict, keys):
if not keys:
return mydict
key = keys[0]
try:
newdict = mydict[key]
except (TypeError, KeyError):
return 0
return get_value(newdict, keys[1:])
Without recursion, just iterate through the keys and go down one level at a time. Putting that inside a try/except allows you to handle the missing key case. KeyError will be raised when the key is not there, and TypeError will be raised if you hit the "bottom" of the dict too soon and try to apply the [] operator to an int or something.
def get_value(d, ks):
for k in ks:
try:
d = d[k] # descend one level
except (KeyError, TypeError):
return 0 # when any lookup fails, return 0
return d # return the final element
Here is a recursive function that should work for general cases
def recursive_get(d, k):
if len(k) == 0:
return 0
elif len(k) == 1:
return d.get(k[0], 0)
else:
value = d.get(k[0], 0)
if isinstance(value, dict):
return recursive_get(value, k[1:])
else:
return value
It takes arguments of the dict to search, and a list of keys, which it will check one per level
>>> dict1 = {'a': {'b': {'c':{'d':3}}}}
>>> recursive_get(dict1, ['a', 'b', 'c'])
{'d': 3}
>>> dict2 = {'a': {'b': {'f': 2}}}
>>> recursive_get(dict2, ['a', 'b', 'c'])
0
This is a question relative to the solution provided here, it involves the following code as solution:
from collections import MutableMapping
def set_value(d, keys, newkey, newvalue, default_factory=dict):
"""
Equivalent to `reduce(dict.get, keys, d)[newkey] = newvalue`
if all `keys` exists and corresponding values are of correct type
"""
for key in keys:
try:
val = d[key]
except KeyError:
val = d[key] = default_factory()
else:
if not isinstance(val, MutableMapping):
val = d[key] = default_factory()
d = val
d[newkey] = newvalue
I'm hoping someone could provide me some explanation why this code works. I'm confused how the passed in dict 'd' doesn't get constantly overwritten where d = val. How does the dict 'd' keep getting further nested dictionaries without ever indexing to the next node? Sorry, if that doesn't make sense, i don't understand how this works.
Thanks for your help!
d is rebound; the variable is updated to point to val in each loop.
For each key in keys, either the key is found (val = d[key] succeeds) or the default_factory() is used to create a new value for that key.
If the key was found but the value was not a MutableMapping type, the found value is replaced with a new default_factory() result.
Once the new value has been determined for this level, d is told to forget about the old dictionary and pointed to the new instead.
Rebinding does not change the old value. It merely stops referring to that old value.
Let's use a simple example:
>>> d = {'foo': {}}
>>> keys = ['foo']
>>> newkey = 'bar'
>>> newval = 'eggs'
>>> original = d
At the start, original and d are the same object. Think of names here as paper labels, and their values as balloons. The labels are tied with string to the balloons. In the above example, the d and original labels are both tied to the same dictionary balloon.
When we enter the for key in keys loop, the d[key] lookup succeeds and val is tied to the result of d['foo'], an empty dictionary:
>>> key = keys[0]
>>> key
'foo'
>>> val = d[key]
>>> val
{}
This is a regular Python dictionary, and isinstance(val, MutableMapping) is True. Next line rebinds the d label to that dictionary. The string is simply untied from the original dictionary and now attached to the same balloon val is tied to:
>>> d = val
>>> d
{}
>>> original
{'foo': {}}
>>> d is val
True
>>> d is original
False
The original dictionary was not altered by the rebinding!
Having run out of keys (there was only one in keys), the next part then assigns newval to d[newkey]:
>>> d[newkey] = newval
>>> d
{'bar': 'eggs'}
However, d is not the only label attached to this dictionary balloon. Dictionaries themselves contain keys and values, both of which are labels that are tied to balloons too! The original label is still tied to the outer dictionary balloon, and it has a foo key associated value, which was tied to a nested dictionary, and it is this nested dictionary we just changed:
>>> original
{'foo': {'bar': 'eggs'}}
The algorithm merely followed along labels via strings to new dictionaries.
Using more complex key combinations just means more strings are being followed, with perhaps an extra dictionary being pumped up to be tied in.
I think your question boils down to:
Why does d[newkey] = newvalue modify the object, while d = var does not do anything to the object?
It is just the case that in Python, you can modify a mutable object in a function, but you can't change what object the outer name refers to.
I have a nested ordered dictionary i.e. a ordered dictionary, some of whose values are ordered dictionary that contain further ordered dictionaries.
When I use the collections module and use the following call
yOrDict = yaml_ordered_dict.load('ApplyForm.yml')
then I get an ordered dictionary yOrDict from the yml file ApplyForm.yml.
Say that yOrDict has just one key (ApplyForm) and a value which in turn is an ordered dictionary.
Further call gives another ordered dictionary:
yOrDictLevel1 = yOrDict.get('ApplyForm')
say that yOrDictLevel1 has six keys whose values contain ordered dictionaries.
say that at some place one of the ordered dictionary has a value named as Block.
Is there a call/function/method in python by which I can check if Block appears in the top level ordered dictionary - yOrDict ?
i.e. I want to check if the string "Block" is at all there in yOrDict ?
This should do the trick.
def find_value(needle, container):
# Already found the object. Return.
if needle == container:
return True
values = None
if isinstance(container, dict):
values = container.values()
elif hasattr(container, '__iter__'):
values = container.__iter__()
if values is None:
return False
# Check deeper in the container, if needed.
for val in values:
if find_value(needle, val):
return True
# No match found.
return False
Usage:
In [3]: d = { 'test': ['a', 'b', 'c'], 'd1': { 'd2': 'Block', 'a': 'b'} }
In [4]: find_value('Block', d)
Out[4]: True
Edit: testing whether a value contains needle:
def find_value(needle, container):
# Already found the object. Return.
if isinstance(container, basestring) and needle in container:
return True
values = None
if isinstance(container, dict):
values = container.values()
elif hasattr(container, '__iter__'):
values = container.__iter__()
if values is None:
return False
# Check deeper in the container, if needed.
for val in values:
if find_value(needle, val):
return True
# No match found.
return False
if "Block" in yOrDict.get('ApplyForm').values():
print "Found Block value"
It isn't explicitly a method of the higher level meta-dictionary, since it does a get and searches through the values, but its concise and does the trick.
edit response to your comment
if (("Beijing" in val) for key,val in yOrDict.get('ApplyForm')iteritems()):
print "Found Beijing"