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I have a flattened dictionary which I want to make into a nested one, of the form
flat = {'X_a_one': 10,
'X_a_two': 20,
'X_b_one': 10,
'X_b_two': 20,
'Y_a_one': 10,
'Y_a_two': 20,
'Y_b_one': 10,
'Y_b_two': 20}
I want to convert it to the form
nested = {'X': {'a': {'one': 10,
'two': 20},
'b': {'one': 10,
'two': 20}},
'Y': {'a': {'one': 10,
'two': 20},
'b': {'one': 10,
'two': 20}}}
The structure of the flat dictionary is such that there should not be any problems with ambiguities. I want it to work for dictionaries of arbitrary depth, but performance is not really an issue. I've seen lots of methods for flattening a nested dictionary, but basically none for nesting a flattened dictionary. The values stored in the dictionary are either scalars or strings, never iterables.
So far I have got something which can take the input
test_dict = {'X_a_one': '10',
'X_b_one': '10',
'X_c_one': '10'}
to the output
test_out = {'X': {'a_one': '10',
'b_one': '10',
'c_one': '10'}}
using the code
def nest_once(inp_dict):
out = {}
if isinstance(inp_dict, dict):
for key, val in inp_dict.items():
if '_' in key:
head, tail = key.split('_', 1)
if head not in out.keys():
out[head] = {tail: val}
else:
out[head].update({tail: val})
else:
out[key] = val
return out
test_out = nest_once(test_dict)
But I'm having trouble working out how to make this into something which recursively creates all levels of the dictionary.
Any help would be appreciated!
(As for why I want to do this: I have a file whose structure is equivalent to a nested dict, and I want to store this file's contents in the attributes dictionary of a NetCDF file and retrieve it later. However NetCDF only allows you to put flat dictionaries as the attributes, so I want to unflatten the dictionary I previously stored in the NetCDF file.)
Here is my take:
def nest_dict(flat):
result = {}
for k, v in flat.items():
_nest_dict_rec(k, v, result)
return result
def _nest_dict_rec(k, v, out):
k, *rest = k.split('_', 1)
if rest:
_nest_dict_rec(rest[0], v, out.setdefault(k, {}))
else:
out[k] = v
flat = {'X_a_one': 10,
'X_a_two': 20,
'X_b_one': 10,
'X_b_two': 20,
'Y_a_one': 10,
'Y_a_two': 20,
'Y_b_one': 10,
'Y_b_two': 20}
nested = {'X': {'a': {'one': 10,
'two': 20},
'b': {'one': 10,
'two': 20}},
'Y': {'a': {'one': 10,
'two': 20},
'b': {'one': 10,
'two': 20}}}
print(nest_dict(flat) == nested)
# True
output = {}
for k, v in source.items():
# always start at the root.
current = output
# This is the part you're struggling with.
pieces = k.split('_')
# iterate from the beginning until the second to last place
for piece in pieces[:-1]:
if not piece in current:
# if a dict doesn't exist at an index, then create one
current[piece] = {}
# as you walk into the structure, update your current location
current = current[piece]
# The reason you're using the second to last is because the last place
# represents the place you're actually storing the item
current[pieces[-1]] = v
Here's one way using collections.defaultdict, borrowing heavily from this previous answer. There are 3 steps:
Create a nested defaultdict of defaultdict objects.
Iterate items in flat input dictionary.
Build defaultdict result according to the structure derived from splitting keys by _, using getFromDict to iterate the result dictionary.
This is a complete example:
from collections import defaultdict
from functools import reduce
from operator import getitem
def getFromDict(dataDict, mapList):
"""Iterate nested dictionary"""
return reduce(getitem, mapList, dataDict)
# instantiate nested defaultdict of defaultdicts
tree = lambda: defaultdict(tree)
d = tree()
# iterate input dictionary
for k, v in flat.items():
*keys, final_key = k.split('_')
getFromDict(d, keys)[final_key] = v
{'X': {'a': {'one': 10, 'two': 20}, 'b': {'one': 10, 'two': 20}},
'Y': {'a': {'one': 10, 'two': 20}, 'b': {'one': 10, 'two': 20}}}
As a final step, you can convert your defaultdict to a regular dict, though usually this step is not necessary.
def default_to_regular_dict(d):
"""Convert nested defaultdict to regular dict of dicts."""
if isinstance(d, defaultdict):
d = {k: default_to_regular_dict(v) for k, v in d.items()}
return d
# convert back to regular dict
res = default_to_regular_dict(d)
The other answers are cleaner, but since you mentioned recursion we do have other options.
def nest(d):
_ = {}
for k in d:
i = k.find('_')
if i == -1:
_[k] = d[k]
continue
s, t = k[:i], k[i+1:]
if s in _:
_[s][t] = d[k]
else:
_[s] = {t:d[k]}
return {k:(nest(_[k]) if type(_[k])==type(d) else _[k]) for k in _}
You can use itertools.groupby:
import itertools, json
flat = {'Y_a_two': 20, 'Y_a_one': 10, 'X_b_two': 20, 'X_b_one': 10, 'X_a_one': 10, 'X_a_two': 20, 'Y_b_two': 20, 'Y_b_one': 10}
_flat = [[*a.split('_'), b] for a, b in flat.items()]
def create_dict(d):
_d = {a:list(b) for a, b in itertools.groupby(sorted(d, key=lambda x:x[0]), key=lambda x:x[0])}
return {a:create_dict([i[1:] for i in b]) if len(b) > 1 else b[0][-1] for a, b in _d.items()}
print(json.dumps(create_dict(_flat), indent=3))
Output:
{
"Y": {
"b": {
"two": 20,
"one": 10
},
"a": {
"two": 20,
"one": 10
}
},
"X": {
"b": {
"two": 20,
"one": 10
},
"a": {
"two": 20,
"one": 10
}
}
}
Another non-recursive solution with no imports. Splitting the logic between inserting each key-value pair of the flat dict and mapping over key-value pairs of the flat dict.
def insert(dct, lst):
"""
dct: a dict to be modified inplace.
lst: list of elements representing a hierarchy of keys
followed by a value.
dct = {}
lst = [1, 2, 3]
resulting value of dct: {1: {2: 3}}
"""
for x in lst[:-2]:
dct[x] = dct = dct.get(x, dict())
dct.update({lst[-2]: lst[-1]})
def unflat(dct):
# empty dict to store the result
result = dict()
# create an iterator of lists representing hierarchical indices followed by the value
lsts = ([*k.split("_"), v] for k, v in dct.items())
# insert each list into the result
for lst in lsts:
insert(result, lst)
return result
result = unflat(flat)
# {'X': {'a': {'one': 10, 'two': 20}, 'b': {'one': 10, 'two': 20}},
# 'Y': {'a': {'one': 10, 'two': 20}, 'b': {'one': 10, 'two': 20}}}
Here is a reasonably readable recursive result:
def unflatten_dict(a, result = None, sep = '_'):
if result is None:
result = dict()
for k, v in a.items():
k, *rest = k.split(sep, 1)
if rest:
unflatten_dict({rest[0]: v}, result.setdefault(k, {}), sep = sep)
else:
result[k] = v
return result
flat = {'X_a_one': 10,
'X_a_two': 20,
'X_b_one': 10,
'X_b_two': 20,
'Y_a_one': 10,
'Y_a_two': 20,
'Y_b_one': 10,
'Y_b_two': 20}
print(unflatten_dict(flat))
# {'X': {'a': {'one': 10, 'two': 20}, 'b': {'one': 10, 'two': 20}},
# 'Y': {'a': {'one': 10, 'two': 20}, 'b': {'one': 10, 'two': 20}}}
This is based on a couple of the above answers, uses no imports and is only tested in python 3.
Install ndicts
pip install ndicts
Then in your script
from ndicts.ndicts import NestedDict
flat = {'X_a_one': 10,
'X_a_two': 20,
'X_b_one': 10,
'X_b_two': 20,
'Y_a_one': 10,
'Y_a_two': 20,
'Y_b_one': 10,
'Y_b_two': 20}
nd = NestedDict()
for key, value in flat.items():
n_key = tuple(key.split("_"))
nd[n_key] = value
If you need the result as a dictionary:
>>> nd.to_dict()
{'X': {'a': {'one': 10, 'two': 20},
'b': {'one': 10, 'two': 20}},
'Y': {'a': {'one': 10, 'two': 20},
'b': {'one': 10, 'two': 20}}}
If I have a function which has to be executed for the nested dictionaries inside a dictionary. Then how should I execute it ?
For example:
# I have the below dictionary
d = {'a':1, 'b':2, 'c':3, 'd': {'e':4, 'f':5}}
# I wanted a result as below
{'a': 1, 'b': 2, 'c': 3, 'e': 4, 'f': 5}
#I have executed it by
for i, j in d.items():
if type(j) == dict:
for key,value in d[i].items():
d[key] = value
d.pop(i, None)
print d
#output
{'a': 1, 'c': 3, 'b': 2, 'e': 4, 'f': 5}
But what if there are many nested dictionaries? I am kind of confused on this ? Any suggestions?
Thanks in advance.
I would suggest that this be a form of flattening:
def flatten(d):
es = [d.pop(k) for k in sorted(d) if isinstance(d[k], dict)]
# Due to sorted the dictionaries those keys that are lexicographically after
# will overwrite the key-value pairs from those that are before.
# One would expect that `d.update(*es)` would work
# but it doesn't as `update` only takes one argument.
for e in es:
d.update(e)
def flatten_many(d):
while any(isinstance(d[k], dict) for k in d):
flatten(d)
The first function pops every dictionary from d and then updates d with them. The second function applies flatten the first function while there is a value that is a dictionary.
dd={}
def myprint(d):
for k, v in d.iteritems():
if isinstance(v, dict):
myprint(v)
else:
dd.update({k:v})
return dd
d={'a':1, 'b':2, 'c':3, 'd': {'e':4, 'f':5,'g':{'h':6}}}
print(myprint(d))
output-
{'a': 1, 'c': 3, 'b': 2, 'e': 4, 'f': 5, 'h': 6}
dict1 = {'galaxy': 5, 'apple': 6, 'nokia': 5}
Is there is a way to show the keys in a dict with the same values in a dict?
target_value = 5
new_dict = {}
for key, value in dict1:
if value == target_value:
new_dict[key] = value
desired output:
dict1 = {'galaxy':5, 'nokia':5}
If I understand you correctly, you're looking for something like that:
>>> d = {'galaxy': 5, 'apple': 6, 'nokia': 5}
>>> { k:v for k,v in d.items() if v==5 }
{'nokia': 5, 'galaxy': 5}
I have two dictionaries mapping IDs to values. For simplicity, lets say those are the dictionaries:
d_source = {'a': 1, 'b': 2, 'c': 3, '3': 3}
d_target = {'A': 1, 'B': 2, 'C': 3, '1': 1}
As named, the dictionaries are not symmetrical.
I would like to get a dictionary of keys from dictionaries d_source and d_target whose values match. The resulting dictionary would have d_source keys as its own keys, and d_target keys as that keys value (in either a list, tuple or set format).
This would be The expected returned value for the above example should be the following list:
{'a': ('1', 'A'),
'b': ('B',),
'c': ('C',),
'3': ('C',)}
There are two somewhat similar questions, but those solutions can't be easily applied to my question.
Some characteristics of the data:
Source would usually be smaller than target. Having roughly few thousand sources (tops) and a magnitude more targets.
Duplicates in the same dict (both d_source and d_target) are not too likely on values.
matches are expected to be found for (a rough estimate) not more than 50% than d_source items.
All keys are integers.
What is the best (performance wise) solution to this problem?
Modeling data into other datatypes for improved performance is totally ok, even when using third party libraries (i'm thinking numpy)
All answers have O(n^2) efficiency which isn't very good so I thought of answering myself.
I use 2(source_len) + 2(dict_count)(dict_len) memory and I have O(2n) efficiency which is the best you can get here I believe.
Here you go:
from collections import defaultdict
d_source = {'a': 1, 'b': 2, 'c': 3, '3': 3}
d_target = {'A': 1, 'B': 2, 'C': 3, '1': 1}
def merge_dicts(source_dict, *rest):
flipped_rest = defaultdict(list)
for d in rest:
while d:
k, v = d.popitem()
flipped_rest[v].append(k)
return {k: tuple(flipped_rest.get(v, ())) for k, v in source_dict.items()}
new_dict = merge_dicts(d_source, d_target)
By the way, I'm using a tuple in order not to link the resulting lists together.
As you've added specifications for the data, here's a closer matching solution:
d_source = {'a': 1, 'b': 2, 'c': 3, '3': 3}
d_target = {'A': 1, 'B': 2, 'C': 3, '1': 1}
def second_merge_dicts(source_dict, *rest):
"""Optimized for ~50% source match due to if statement addition.
Also uses less memory.
"""
unique_values = set(source_dict.values())
flipped_rest = defaultdict(list)
for d in rest:
while d:
k, v = d.popitem()
if v in unique_values:
flipped_rest[v].append(k)
return {k: tuple(flipped_rest.get(v, ())) for k, v in source_dict.items()}
new_dict = second_merge_dicts(d_source, d_target)
from collections import defaultdict
from pprint import pprint
d_source = {'a': 1, 'b': 2, 'c': 3, '3': 3}
d_target = {'A': 1, 'B': 2, 'C': 3, '1': 1}
d_result = defaultdict(list)
{d_result[a].append(b) for a in d_source for b in d_target if d_source[a] == d_target[b]}
pprint(d_result)
Output:
{'3': ['C'],
'a': ['A', '1'],
'b': ['B'],
'c': ['C']}
Timing results:
from collections import defaultdict
from copy import deepcopy
from random import randint
from timeit import timeit
def Craig_match(source, target):
result = defaultdict(list)
{result[a].append(b) for a in source for b in target if source[a] == target[b]}
return result
def Bharel_match(source_dict, *rest):
flipped_rest = defaultdict(list)
for d in rest:
while d:
k, v = d.popitem()
flipped_rest[v].append(k)
return {k: tuple(flipped_rest.get(v, ())) for k, v in source_dict.items()}
def modified_Bharel_match(source_dict, *rest):
"""Optimized for ~50% source match due to if statement addition.
Also uses less memory.
"""
unique_values = set(source_dict.values())
flipped_rest = defaultdict(list)
for d in rest:
while d:
k, v = d.popitem()
if v in unique_values:
flipped_rest[v].append(k)
return {k: tuple(flipped_rest.get(v, ())) for k, v in source_dict.items()}
# generate source, target such that:
# a) ~10% duplicate values in source and target
# b) 2000 unique source keys, 20000 unique target keys
# c) a little less than 50% matches source value to target value
# d) numeric keys and values
source = {}
for k in range(2000):
source[k] = randint(0, 1800)
target = {}
for k in range(20000):
if k < 1000:
target[k] = randint(0, 2000)
else:
target[k] = randint(2000, 19000)
best_time = {}
approaches = ('Craig', 'Bharel', 'modified_Bharel')
for a in approaches:
best_time[a] = None
for _ in range(3):
for approach in approaches:
test_source = deepcopy(source)
test_target = deepcopy(target)
statement = 'd=' + approach + '_match(test_source,test_target)'
setup = 'from __main__ import test_source, test_target, ' + approach + '_match'
t = timeit(stmt=statement, setup=setup, number=1)
if not best_time[approach] or (t < best_time[approach]):
best_time[approach] = t
for approach in approaches:
print(approach, ':', '%0.5f' % best_time[approach])
Output:
Craig : 7.29259
Bharel : 0.01587
modified_Bharel : 0.00682
Here is another solution. There are a lot of ways to do this
for key1 in d1:
for key2 in d2:
if d1[key1] == d2[key2]:
stuff
Note that you can use any name for key1 and key2.
This maybe "cheating" in some regards, although if you are looking for the matching values of the keys regardless of the case sensitivity then you might be able to do:
import sets
aa = {'a': 1, 'b': 2, 'c':3}
bb = {'A': 1, 'B': 2, 'd': 3}
bbl = {k.lower():v for k,v in bb.items()}
result = {k:k.upper() for k,v in aa.iteritems() & bbl.viewitems()}
print( result )
Output:
{'a': 'A', 'b': 'B'}
The bbl declaration changes the bb keys into lowercase (it could be either aa, or bb).
* I only tested this on my phone, so just throwing this idea out there I suppose... Also, you've changed your question radically since I began composing my answer, so you get what you get.
It is up to you to determine the best solution. Here is a solution:
def dicts_to_tuples(*dicts):
result = {}
for d in dicts:
for k,v in d.items():
result.setdefault(v, []).append(k)
return [tuple(v) for v in result.values() if len(v) > 1]
d1 = {'a': 1, 'b': 2, 'c':3}
d2 = {'A': 1, 'B': 2}
print dicts_to_tuples(d1, d2)
For example I have two dicts:
Dict A: {'a': 1, 'b': 2, 'c': 3}
Dict B: {'b': 3, 'c': 4, 'd': 5}
I need a pythonic way of 'combining' two dicts such that the result is:
{'a': 1, 'b': 5, 'c': 7, 'd': 5}
That is to say: if a key appears in both dicts, add their values, if it appears in only one dict, keep its value.
Use collections.Counter:
>>> from collections import Counter
>>> A = Counter({'a':1, 'b':2, 'c':3})
>>> B = Counter({'b':3, 'c':4, 'd':5})
>>> A + B
Counter({'c': 7, 'b': 5, 'd': 5, 'a': 1})
Counters are basically a subclass of dict, so you can still do everything else with them you'd normally do with that type, such as iterate over their keys and values.
A more generic solution, which works for non-numeric values as well:
a = {'a': 'foo', 'b':'bar', 'c': 'baz'}
b = {'a': 'spam', 'c':'ham', 'x': 'blah'}
r = dict(a.items() + b.items() +
[(k, a[k] + b[k]) for k in set(b) & set(a)])
or even more generic:
def combine_dicts(a, b, op=operator.add):
return dict(a.items() + b.items() +
[(k, op(a[k], b[k])) for k in set(b) & set(a)])
For example:
>>> a = {'a': 2, 'b':3, 'c':4}
>>> b = {'a': 5, 'c':6, 'x':7}
>>> import operator
>>> print combine_dicts(a, b, operator.mul)
{'a': 10, 'x': 7, 'c': 24, 'b': 3}
>>> A = {'a':1, 'b':2, 'c':3}
>>> B = {'b':3, 'c':4, 'd':5}
>>> c = {x: A.get(x, 0) + B.get(x, 0) for x in set(A).union(B)}
>>> print(c)
{'a': 1, 'c': 7, 'b': 5, 'd': 5}
Intro:
There are the (probably) best solutions. But you have to know it and remember it and sometimes you have to hope that your Python version isn't too old or whatever the issue could be.
Then there are the most 'hacky' solutions. They are great and short but sometimes are hard to understand, to read and to remember.
There is, though, an alternative which is to to try to reinvent the wheel.
- Why reinventing the wheel?
- Generally because it's a really good way to learn (and sometimes just because the already-existing tool doesn't do exactly what you would like and/or the way you would like it) and the easiest way if you don't know or don't remember the perfect tool for your problem.
So, I propose to reinvent the wheel of the Counter class from the collections module (partially at least):
class MyDict(dict):
def __add__(self, oth):
r = self.copy()
try:
for key, val in oth.items():
if key in r:
r[key] += val # You can custom it here
else:
r[key] = val
except AttributeError: # In case oth isn't a dict
return NotImplemented # The convention when a case isn't handled
return r
a = MyDict({'a':1, 'b':2, 'c':3})
b = MyDict({'b':3, 'c':4, 'd':5})
print(a+b) # Output {'a':1, 'b': 5, 'c': 7, 'd': 5}
There would probably others way to implement that and there are already tools to do that but it's always nice to visualize how things would basically works.
Definitely summing the Counter()s is the most pythonic way to go in such cases but only if it results in a positive value. Here is an example and as you can see there is no c in result after negating the c's value in B dictionary.
In [1]: from collections import Counter
In [2]: A = Counter({'a':1, 'b':2, 'c':3})
In [3]: B = Counter({'b':3, 'c':-4, 'd':5})
In [4]: A + B
Out[4]: Counter({'d': 5, 'b': 5, 'a': 1})
That's because Counters were primarily designed to work with positive integers to represent running counts (negative count is meaningless). But to help with those use cases,python documents the minimum range and type restrictions as follows:
The Counter class itself is a dictionary
subclass with no restrictions on its keys and values. The values are
intended to be numbers representing counts, but you could store
anything in the value field.
The most_common() method requires only
that the values be orderable.
For in-place operations such as c[key]
+= 1, the value type need only support addition and subtraction. So fractions, floats, and decimals would work and negative values are
supported. The same is also true for update() and subtract() which
allow negative and zero values for both inputs and outputs.
The multiset methods are designed only for use cases with positive values.
The inputs may be negative or zero, but only outputs with positive
values are created. There are no type restrictions, but the value type
needs to support addition, subtraction, and comparison.
The elements() method requires integer counts. It ignores zero and negative counts.
So for getting around that problem after summing your Counter you can use Counter.update in order to get the desire output. It works like dict.update() but adds counts instead of replacing them.
In [24]: A.update(B)
In [25]: A
Out[25]: Counter({'d': 5, 'b': 5, 'a': 1, 'c': -1})
myDict = {}
for k in itertools.chain(A.keys(), B.keys()):
myDict[k] = A.get(k, 0)+B.get(k, 0)
The one with no extra imports!
Their is a pythonic standard called EAFP(Easier to Ask for Forgiveness than Permission). Below code is based on that python standard.
# The A and B dictionaries
A = {'a': 1, 'b': 2, 'c': 3}
B = {'b': 3, 'c': 4, 'd': 5}
# The final dictionary. Will contain the final outputs.
newdict = {}
# Make sure every key of A and B get into the final dictionary 'newdict'.
newdict.update(A)
newdict.update(B)
# Iterate through each key of A.
for i in A.keys():
# If same key exist on B, its values from A and B will add together and
# get included in the final dictionary 'newdict'.
try:
addition = A[i] + B[i]
newdict[i] = addition
# If current key does not exist in dictionary B, it will give a KeyError,
# catch it and continue looping.
except KeyError:
continue
EDIT: thanks to jerzyk for his improvement suggestions.
import itertools
import collections
dictA = {'a':1, 'b':2, 'c':3}
dictB = {'b':3, 'c':4, 'd':5}
new_dict = collections.defaultdict(int)
# use dict.items() instead of dict.iteritems() for Python3
for k, v in itertools.chain(dictA.iteritems(), dictB.iteritems()):
new_dict[k] += v
print dict(new_dict)
# OUTPUT
{'a': 1, 'c': 7, 'b': 5, 'd': 5}
OR
Alternative you can use Counter as #Martijn has mentioned above.
For a more generic and extensible way check mergedict. It uses singledispatch and can merge values based on its types.
Example:
from mergedict import MergeDict
class SumDict(MergeDict):
#MergeDict.dispatch(int)
def merge_int(this, other):
return this + other
d2 = SumDict({'a': 1, 'b': 'one'})
d2.merge({'a':2, 'b': 'two'})
assert d2 == {'a': 3, 'b': 'two'}
From python 3.5: merging and summing
Thanks to #tokeinizer_fsj that told me in a comment that I didn't get completely the meaning of the question (I thought that add meant just adding keys that eventually where different in the two dictinaries and, instead, i meant that the common key values should be summed). So I added that loop before the merging, so that the second dictionary contains the sum of the common keys. The last dictionary will be the one whose values will last in the new dictionary that is the result of the merging of the two, so I thing the problem is solved. The solution is valid from python 3.5 and following versions.
a = {
"a": 1,
"b": 2,
"c": 3
}
b = {
"a": 2,
"b": 3,
"d": 5
}
# Python 3.5
for key in b:
if key in a:
b[key] = b[key] + a[key]
c = {**a, **b}
print(c)
>>> c
{'a': 3, 'b': 5, 'c': 3, 'd': 5}
Reusable code
a = {'a': 1, 'b': 2, 'c': 3}
b = {'b': 3, 'c': 4, 'd': 5}
def mergsum(a, b):
for k in b:
if k in a:
b[k] = b[k] + a[k]
c = {**a, **b}
return c
print(mergsum(a, b))
Additionally, please note a.update( b ) is 2x faster than a + b
from collections import Counter
a = Counter({'menu': 20, 'good': 15, 'happy': 10, 'bar': 5})
b = Counter({'menu': 1, 'good': 1, 'bar': 3})
%timeit a + b;
## 100000 loops, best of 3: 8.62 µs per loop
## The slowest run took 4.04 times longer than the fastest. This could mean that an intermediate result is being cached.
%timeit a.update(b)
## 100000 loops, best of 3: 4.51 µs per loop
One line solution is to use dictionary comprehension.
C = { k: A.get(k,0) + B.get(k,0) for k in list(B.keys()) + list(A.keys()) }
def merge_with(f, xs, ys):
xs = a_copy_of(xs) # dict(xs), maybe generalizable?
for (y, v) in ys.iteritems():
xs[y] = v if y not in xs else f(xs[x], v)
merge_with((lambda x, y: x + y), A, B)
You could easily generalize this:
def merge_dicts(f, *dicts):
result = {}
for d in dicts:
for (k, v) in d.iteritems():
result[k] = v if k not in result else f(result[k], v)
Then it can take any number of dicts.
This is a simple solution for merging two dictionaries where += can be applied to the values, it has to iterate over a dictionary only once
a = {'a':1, 'b':2, 'c':3}
dicts = [{'b':3, 'c':4, 'd':5},
{'c':9, 'a':9, 'd':9}]
def merge_dicts(merged,mergedfrom):
for k,v in mergedfrom.items():
if k in merged:
merged[k] += v
else:
merged[k] = v
return merged
for dct in dicts:
a = merge_dicts(a,dct)
print (a)
#{'c': 16, 'b': 5, 'd': 14, 'a': 10}
Here's yet another option using dictionary comprehensions combined with the behavior of dict():
dict3 = dict(dict1, **{ k: v + dict1.get(k, 0) for k, v in dict2.items() })
# {'a': 4, 'b': 2, 'c': 7, 'g': 1}
From https://docs.python.org/3/library/stdtypes.html#dict:
https://docs.python.org/3/library/stdtypes.html#dict
and also
If keyword arguments are given, the keyword arguments and their values are added to the dictionary created from the positional argument.
The dict comprehension
**{ k: v + dict1.get(v, 0), v in dict2.items() }
handles adding dict1[1] to v. We don't need an explicit if here because the default value for our dict1.get can be set to 0 instead.
This solution is easy to use, it is used as a normal dictionary, but you can use the sum function.
class SumDict(dict):
def __add__(self, y):
return {x: self.get(x, 0) + y.get(x, 0) for x in set(self).union(y)}
A = SumDict({'a': 1, 'c': 2})
B = SumDict({'b': 3, 'c': 4}) # Also works: B = {'b': 3, 'c': 4}
print(A + B) # OUTPUT {'a': 1, 'b': 3, 'c': 6}
The above solutions are great for the scenario where you have a small number of Counters. If you have a big list of them though, something like this is much nicer:
from collections import Counter
A = Counter({'a':1, 'b':2, 'c':3})
B = Counter({'b':3, 'c':4, 'd':5})
C = Counter({'a': 5, 'e':3})
list_of_counts = [A, B, C]
total = sum(list_of_counts, Counter())
print(total)
# Counter({'c': 7, 'a': 6, 'b': 5, 'd': 5, 'e': 3})
The above solution is essentially summing the Counters by:
total = Counter()
for count in list_of_counts:
total += count
print(total)
# Counter({'c': 7, 'a': 6, 'b': 5, 'd': 5, 'e': 3})
This does the same thing but I think it always helps to see what it is effectively doing underneath.
What about:
def dict_merge_and_sum( d1, d2 ):
ret = d1
ret.update({ k:v + d2[k] for k,v in d1.items() if k in d2 })
ret.update({ k:v for k,v in d2.items() if k not in d1 })
return ret
A = {'a': 1, 'b': 2, 'c': 3}
B = {'b': 3, 'c': 4, 'd': 5}
print( dict_merge_and_sum( A, B ) )
Output:
{'d': 5, 'a': 1, 'c': 7, 'b': 5}
More conventional way to combine two dict. Using modules and tools are good but understanding the logic behind it will help in case you don't remember the tools.
Program to combine two dictionary adding values for common keys.
def combine_dict(d1,d2):
for key,value in d1.items():
if key in d2:
d2[key] += value
else:
d2[key] = value
return d2
combine_dict({'a':1, 'b':2, 'c':3},{'b':3, 'c':4, 'd':5})
output == {'b': 5, 'c': 7, 'd': 5, 'a': 1}
Here's a very general solution. You can deal with any number of dict + keys that are only in some dict + easily use any aggregation function you want:
def aggregate_dicts(dicts, operation=sum):
"""Aggregate a sequence of dictionaries using `operation`."""
all_keys = set().union(*[el.keys() for el in dicts])
return {k: operation([dic.get(k, None) for dic in dicts]) for k in all_keys}
example:
dicts_same_keys = [{'x': 0, 'y': 1}, {'x': 1, 'y': 2}, {'x': 2, 'y': 3}]
aggregate_dicts(dicts_same_keys, operation=sum)
#{'x': 3, 'y': 6}
example non-identical keys and generic aggregation:
dicts_diff_keys = [{'x': 0, 'y': 1}, {'x': 1, 'y': 2}, {'x': 2, 'y': 3, 'c': 4}]
def mean_no_none(l):
l_no_none = [el for el in l if el is not None]
return sum(l_no_none) / len(l_no_none)
aggregate_dicts(dicts_diff_keys, operation=mean_no_none)
# {'x': 1.0, 'c': 4.0, 'y': 2.0}
dict1 = {'a':1, 'b':2, 'c':3}
dict2 = {'a':3, 'g':1, 'c':4}
dict3 = {} # will store new values
for x in dict1:
if x in dict2: #sum values with same key
dict3[x] = dict1[x] +dict2[x]
else: #add the values from x to dict1
dict3[x] = dict1[x]
#search for new values not in a
for x in dict2:
if x not in dict1:
dict3[x] = dict2[x]
print(dict3) # {'a': 4, 'b': 2, 'c': 7, 'g': 1}
Merging three dicts a,b,c in a single line without any other modules or libs
If we have the three dicts
a = {"a":9}
b = {"b":7}
c = {'b': 2, 'd': 90}
Merge all with a single line and return a dict object using
c = dict(a.items() + b.items() + c.items())
Returning
{'a': 9, 'b': 2, 'd': 90}