How to Get the Difference Between Two Dictionaries in Python

How to get the difference between two dictionaries in Python?

Try the following snippet, using a dictionary comprehension:

value = { k : second_dict[k] for k in set(second_dict) - set(first_dict) }

In the above code we find the difference of the keys and then rebuild a dict taking the corresponding values.

Comparing two dictionaries and checking how many (key, value) pairs are equal

If you want to know how many values match in both the dictionaries, you should have said that :)

Maybe something like this:

shared_items = {k: x[k] for k in x if k in y and x[k] == y[k]}
print(len(shared_items))

How to find all differences between two dictionaries efficiently in python

You can use dictionary view objects, which act as sets. Subtract sets to get the difference:

missing_in_dict1_but_in_dict2 = dict2.keys() - dict1
missing_in_dict2_but_in_dict1 = dict1.keys() - dict2

For the keys that are the same, use the intersection, with the & operator:

mismatch = {key for key in dict1.keys() & dict2 if dict1[key] != dict2[key]}

If you are still using Python 2, use dict.viewkeys().

Using dictionary views to produce intersections and differences is very efficient, the view objects themselves are very lightweight the algorithms to create the new sets from the set operations can make direct use of the O(1) lookup behaviour of the underlying dictionaries.

Demo:

>>> dict1 = {'foo': 42, 'bar': 81}
>>> dict2 = {'bar': 117, 'spam': 'ham'}
>>> dict2.keys() - dict1
{'spam'}
>>> dict1.keys() - dict2
{'foo'}
>>> [key for key in dict1.keys() & dict2 if dict1[key] != dict2[key]]
{'bar'}

and a performance comparison with creating separate set() objects:

>>> import timeit
>>> import random
>>> def difference_views(d1, d2):
...     missing1 = d2.keys() - d1
...     missing2 = d1.keys() - d2
...     mismatch = {k for k in d1.keys() & d2 if d1[k] != d2[k]}
...     return missing1, missing2, mismatch
...
>>> def difference_sets(d1, d2):
...     missing1 = set(d2) - set(d1)
...     missing2 = set(d1) - set(d2)
...     mismatch = {k for k in set(d1) & set(d2) if d1[k] != d2[k]}
...     return missing1, missing2, mismatch
...
>>> testd1 = {random.randrange(1000000): random.randrange(1000000) for _ in range(10000)}
>>> testd2 = {random.randrange(1000000): random.randrange(1000000) for _ in range(10000)}
>>> timeit.timeit('d(d1, d2)', 'from __main__ import testd1 as d1, testd2 as d2, difference_views as d', number=1000)
1.8643521590274759
>>> timeit.timeit('d(d1, d2)', 'from __main__ import testd1 as d1, testd2 as d2, difference_sets as d', number=1000)
2.811345119960606

Using set() objects is slower, especially when your input dictionaries get larger.

Python: Getting the difference between two dictionaries

You can use a dictionary comprehension for that:

diffdict = {
    key: Dict_1.get(key, 0) - Dict_2.get(key, 0)
    for key in Dict_1.keys() | Dict_2.keys()
}

Here I'm using a default value of zero for the missing keys for both dictionaries, meaning if a value isn't available in Dict_1, but it is in Dict_2, its value will be -Dict_2[key]

The resulting diffdict will look like this:

>>> diffdict
{' Boston ': -4, ' Washington ': -4, ' LA ': -4, ' Chicago ': -1, ' Seattle ': -2}

How to get/print the differences between two dictionaries?

I would suggest making a recursive dictionary comparison function as a generator (thus allowing you to efficiently detect at least one difference using the next function):

def compareDict(before,after):
    for k,old in before.items():
        if k not in after: yield f"{k}: deleted";continue
        new = after[k]
        if type(old) != type(new):
            yield f"{k}: changed from: {old}"
            yield f"{k}: changed   to: {new}"
        elif isinstance(old,dict):
            yield from (f"{k}:{change}" for change in compareDict(old,new))
        elif isinstance(old,(tuple,list)):
            b = {f"[{i}]":v for i,v in enumerate(old)}
            a = {f"[{i}]":v for i,v in enumerate(new)}
            yield from (f"{k}:{change}" for change in compareDict(b,a))
        elif old != new:
            yield f"{k}: changed from: {old}"
            yield f"{k}: changed   to: {new}"
    for k,new in after.items():
        if k not in before: yield f"{k}: added: {new}"

output:

for change in compareDict(attr_prod,attr_test): print(change)

key2:[0]:key3: changed from: n1
key2:[0]:key3: changed   to: different info
key2:[0]:key5:[1]:key8:key10: changed from: g4
key2:[0]:key5:[1]:key8:key10: changed   to: different info
key2:[0]:key5:[2]: added: {'key6': 'this is new', 'key7': 'this is new', 'key8': {'key10': 'new', 'key11': 'new', 'key12': 'new'}}
key20:key22: changed from: g5
key20:key22: changed   to: different info

How to get the symmetric difference of two dictionaries

Here is some code that does timeit speed tests on the various algorithms.

The tests use pairs of dicts of equal sizes. The keys are short random letter strings, with varying proportions of shared keys between the dicts. The dicts are constructed from shuffled lists, so even if they contain lots of shared keys the underlying hash table structure of the two dicts should be rather different.

The exact amount of shared keys is random, the proportion of shared keys is controlled by the shared arg of make_dicts.

The main body of this code will run on Python 2.6+ and Python 3. I have Python 2.6.6 and Python 3.6.0 installed on this machine (which is a single core 32 bit machine with 2GB of RAM running on an old Debian derivative of Linux). Some of the dictionary symmetric difference functions use dictionary comprehensions, which aren't available in Python 2.6, so I couldn't test those functions on Python 2. Also, elmex_dsd_py2 won't run on Python 3, so I've commented it out. I was originally going to post Python 2.6 results too, but I had to reduce the output to fit within the message size limits.

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

''' Dictionary symmetric difference

    Speed tests of various implementations

    See http://stackoverflow.com/q/42650081/4014959

    Speed test code by PM 2Ring 2017.03.08
'''

from __future__ import print_function

from itertools import product
from random import random, seed, shuffle
from string import ascii_letters
from timeit import Timer

seed(163)

# The dict symmetric difference functions ------------------------------

def inbar_dsd_long(a, b):
    # first make sets of the dictionary keys
    keys_in_a = set(a.keys())
    keys_in_b = set(b.keys())
    # get the unique keys
    unique_keys = keys_in_a.symmetric_difference(keys_in_b)
    # start an empty dictionary
    c = {}
    # iterate over the keys
    for key in unique_keys:
        if key in a: 
            # if the key is from a dictionary, take the value from there.
           c[key] = a[key]
        else:
            # the key is in b dictionary, take the value from there.
            c[key] = b[key]
    return c

def pm2r_dsd_py2(a, b):
    return dict((k, a[k] if k in a else b[k]) for k in set(a.keys()) ^ set(b.keys()))

#def elmex_dsd_py2(a, b):
    #symm_diff = set(a) ^ set(b)
    #return dict((k, v) for k, v in a.items() + b.items() if k in symm_diff) 

def raymond_dsd(a, b):
    c = a.copy()
    c.update(b)
    for k in (a.keys() & b.keys()):
        del c[k]
    return c

def inbar_dsd_short(a, b):
    return {k: a[k] if k in a else b[k] for k in  
        set(a.keys()).symmetric_difference(b.keys())}

def pm2r_dsd_py3(a, b):
    return {k: a[k] if k in a else b[k] for k in a.keys() ^ b.keys()}

def evkounis_dsd(a, b):
    res = {k:v for k, v in a.items() if k not in b}
    res.update({k:v for k, v in b.items() if k not in a})
    return res

def elmex_dsd_py3(a, b):
    symm_diff = set(a) ^ set(b)
    return {k: v for k, v in list(a.items()) + list(b.items()) if k in symm_diff} 

funcs = (
    inbar_dsd_long,
    pm2r_dsd_py2,
    #elmex_dsd_py2,
    raymond_dsd,
    inbar_dsd_short,
    pm2r_dsd_py3,
    evkounis_dsd,
    elmex_dsd_py3,
)

# ----------------------------------------------------------------------

# Random key strings
all_keys = [''.join(t) for t in product(ascii_letters, repeat=3)]
shuffle(all_keys)

def make_dicts(size, shared):
    ''' Make a pair of dicts of length `size`, with random key strings.
        `shared` is a real number 0 <= shared <= 1 giving the approximate 
        ratio of shared keys.
    '''
    a, b = [], []
    keys = iter(all_keys)
    shared_count = 0
    for i in range(size):
        ka = next(keys)
        if random() < shared:
            kb = ka
            shared_count += 1
        else:
            kb = next(keys)
        a.append((ka, i))
        b.append((kb, i))
    shuffle(a)
    shuffle(b)
    return dict(a), dict(b), shared_count

def verify(a, b):
    ''' Verify that all functions return the same result '''
    results = [func(a, b) for func in funcs]
    last = results[-1]
    print(all(last == u for u in results[:-1]))

def time_test(loops, reps):
    ''' Print timing stats for all the functions '''
    timings = []
    for func in funcs:
        fname = func.__name__
        setup = 'from __main__ import a, b, ' + fname
        cmd = '{0}(a, b)'.format(fname)
        t = Timer(cmd, setup)
        result = t.repeat(reps, loops)
        result.sort()
        timings.append((result, fname))

    timings.sort()
    for result, fname in timings:
        print('{0:16} {1}'.format(fname, result))

# ----------------------------------------------------------------------

print('Verifying')
size = 1000
a, b, shared_count = make_dicts(size, 0.1)
print('size: {0}, shared count: {1}'.format(size, shared_count))
verify(a, b)

# Timeit tests
reps = 3
fmt = '\nsize: {0}, shared count: {1}, loops: {2}'
for shared in (0.1, 0.25, 0.5, 0.75, 0.9):
    print('\nSHARED: {0:0.2f}'.format(shared))
    #for size in (5, 10, 50, 100, 500, 1000, 5000, 10000, 50000):
    for size in (10, 100, 1000, 10000):
        a, b, shared_count = make_dicts(size, shared)
        loops = 100000 // size
        print(fmt.format(size, shared_count, loops))
        time_test(loops, reps)    

output

Verifying
size: 1000, shared count: 100
True

SHARED: 0.10

size: 10, shared count: 1, loops: 10000
raymond_dsd      [0.13777699099955498, 0.13792390800153953, 0.1381044740010111]
evkounis_dsd     [0.23560065399942687, 0.23752641000100994, 0.2455631840020942]
pm2r_dsd_py3     [0.23770248700020602, 0.23880975800057058, 0.24221741200017277]
inbar_dsd_long   [0.25206301800062647, 0.285963577000075, 0.28780356199786183]
inbar_dsd_short  [0.2636144610005431, 0.2653795980004361, 0.2666834120027488]
elmex_dsd_py3    [0.3290278729982674, 0.33175632400161703, 0.3384615989998565]
pm2r_dsd_py2     [0.3978280019982776, 0.43710133700005827, 0.4523775029992976]

size: 100, shared count: 14, loops: 1000
raymond_dsd      [0.09872918600012781, 0.09888040100122453, 0.10413656799937598]
evkounis_dsd     [0.1804931380029302, 0.1811683220003033, 0.18133216399655794]
pm2r_dsd_py3     [0.20522897000046214, 0.20773609400202986, 0.20979003499815008]
inbar_dsd_short  [0.21217649699974572, 0.21281453499977943, 0.21295483400172088]
inbar_dsd_long   [0.22985933599920827, 0.23097444899758557, 0.24446944000010262]
elmex_dsd_py3    [0.24242248500013375, 0.24477665499944123, 0.24785449900082313]
pm2r_dsd_py2     [0.3103436530000181, 0.31146229099977063, 0.3152951789998042]

size: 1000, shared count: 94, loops: 100
raymond_dsd      [0.10726087399962125, 0.10726979699757067, 0.10853421000138042]
evkounis_dsd     [0.19798667299983208, 0.19957152200004202, 0.20145120699817198]
pm2r_dsd_py3     [0.24767412599976524, 0.25033419099781895, 0.25519442899894784]
inbar_dsd_long   [0.25753367499783053, 0.259813735003263, 0.2615334299989627]
inbar_dsd_short  [0.25835196700063534, 0.2647503340012918, 0.26879757099959534]
elmex_dsd_py3    [0.3065065359987784, 0.3129320820007706, 0.3159641370002646]
pm2r_dsd_py2     [0.32748841799912043, 0.34595297499981825, 0.3797209490003297]

size: 10000, shared count: 987, loops: 10
raymond_dsd      [0.2801321059996553, 0.2831085340003483, 0.28407657299976563]
evkounis_dsd     [0.36119127300116816, 0.36392319399965345, 0.36926983400189783]
pm2r_dsd_py3     [0.5073807749977277, 0.5122791090034298, 0.5579565990010451]
inbar_dsd_short  [0.5086212060014077, 0.5168500030013092, 0.5182715480004845]
inbar_dsd_long   [0.602521363998676, 0.6031914080012939, 0.6047401769974385]
pm2r_dsd_py2     [0.6753699099972437, 0.6772755890015105, 0.6782451350009069]
elmex_dsd_py3    [0.7430517110005894, 0.7464511920006771, 0.7468688779990771]

SHARED: 0.25

size: 10, shared count: 3, loops: 10000
raymond_dsd      [0.1376171269985207, 0.13765478899949812, 0.13801490599871613]
pm2r_dsd_py3     [0.20131645299989032, 0.20166713100115885, 0.20322838700303691]
inbar_dsd_long   [0.20759937799812178, 0.2079929980027373, 0.21979623799779802]
evkounis_dsd     [0.2186124869986088, 0.2202955180000572, 0.223359776999132]
inbar_dsd_short  [0.23444793200178538, 0.23780764999901294, 0.23976211099943612]
elmex_dsd_py3    [0.3178573650002363, 0.3193927319989598, 0.32410190099835745]
pm2r_dsd_py2     [0.3520881920012471, 0.3543025139988458, 0.3581208620016696]

size: 100, shared count: 23, loops: 1000
raymond_dsd      [0.10508492400185787, 0.10563860000183922, 0.10888238600091427]
evkounis_dsd     [0.15686738300064462, 0.15824111300025834, 0.15863642399926903]
pm2r_dsd_py3     [0.1829918709991034, 0.184900373002165, 0.18732255400027498]
inbar_dsd_short  [0.18875792199833086, 0.19031438200181583, 0.19102797700179508]
inbar_dsd_long   [0.21139359699736815, 0.22990316799769062, 0.2418856490003236]
elmex_dsd_py3    [0.22641843899691594, 0.2265430750012456, 0.23143781299950206]
pm2r_dsd_py2     [0.2681290770015039, 0.2703527909980039, 0.27255326500016963]

size: 1000, shared count: 263, loops: 100
raymond_dsd      [0.10895683100170572, 0.11233176399764488, 0.11593639900092967]
evkounis_dsd     [0.17859331599902362, 0.17949835600302322, 0.18466946999978973]
pm2r_dsd_py3     [0.2147589500018512, 0.21515577800164465, 0.21701817199937068]
inbar_dsd_long   [0.21823484400010784, 0.2254721450008219, 0.22556141600216506]
inbar_dsd_short  [0.22114897099891095, 0.22157548800169025, 0.22668778500155895]
pm2r_dsd_py2     [0.2780861230021401, 0.27864550599770155, 0.28336624599978677]
elmex_dsd_py3    [0.28186336900034803, 0.2837228559983487, 0.29606761199829634]

size: 10000, shared count: 2480, loops: 10
raymond_dsd      [0.278912030000356, 0.28916871899855323, 0.2898256120024598]
evkounis_dsd     [0.33290919899809523, 0.3355702890003158, 0.3366183610014559]
pm2r_dsd_py3     [0.4445611189985357, 0.45341551800083835, 0.4544847100005427]
inbar_dsd_short  [0.4466933030016662, 0.4632708070021181, 0.48025122500257567]
inbar_dsd_long   [0.5405201060020772, 0.5567013979998592, 0.5911358039993502]
pm2r_dsd_py2     [0.586115582002094, 0.600204237998696, 0.6029243630000565]
elmex_dsd_py3    [0.7058123890019488, 0.7067292030005774, 0.7115862030004791]

SHARED: 0.50

size: 10, shared count: 6, loops: 10000
raymond_dsd      [0.15135921700129984, 0.1533788429987908, 0.17841531700105406]
pm2r_dsd_py3     [0.15311526600271463, 0.15356177799912984, 0.15895434199774172]
inbar_dsd_long   [0.16137141400031396, 0.1618921000008413, 0.17238240400183713]
inbar_dsd_short  [0.1808154470018053, 0.18266997299724608, 0.1863039679992653]
evkounis_dsd     [0.18221631199776311, 0.18251911100014695, 0.18520446800175705]
pm2r_dsd_py2     [0.2700158850020671, 0.2743520539988822, 0.28932957600045484]
elmex_dsd_py3    [0.28983224500188953, 0.2912340100010624, 0.2933940119983163]

size: 100, shared count: 51, loops: 1000
raymond_dsd      [0.10294843999872683, 0.10327848499946413, 0.10685922099946765]
evkounis_dsd     [0.13586801600104081, 0.13726477299860562, 0.142784658997698]
pm2r_dsd_py3     [0.1435330319982313, 0.14396326799760573, 0.14474550500017358]
inbar_dsd_short  [0.15043617100309348, 0.15080328300246038, 0.1527250040016952]
inbar_dsd_long   [0.1667091649978829, 0.17330403699816088, 0.17601154400108499]
pm2r_dsd_py2     [0.20728979400155367, 0.20776088099955814, 0.2079896369978087]
elmex_dsd_py3    [0.21078268400015077, 0.2123827169998549, 0.21517163300086395]

size: 1000, shared count: 491, loops: 100
raymond_dsd      [0.11212847299975692, 0.11414236799828359, 0.11498476199994911]
evkounis_dsd     [0.14059560900204815, 0.14112727400060976, 0.150327464001748]
pm2r_dsd_py3     [0.14733014900048147, 0.15143406900097034, 0.1542897660001472]
inbar_dsd_short  [0.15075810700000147, 0.151888833999692, 0.15750856500017107]
inbar_dsd_long   [0.16265833400029805, 0.16367860500031384, 0.17333104299905244]
pm2r_dsd_py2     [0.1993612549995305, 0.19947306600079173, 0.20446195700060343]
elmex_dsd_py3    [0.24682135100010782, 0.24862800600021728, 0.25419495800088043]

size: 10000, shared count: 4938, loops: 10
evkounis_dsd     [0.2519790539990936, 0.2573451700009173, 0.2603536310016352]
raymond_dsd      [0.2875208960031159, 0.2887761790007062, 0.30461744100102806]
pm2r_dsd_py3     [0.3364586130010139, 0.342166794998775, 0.3465069459998631]
inbar_dsd_short  [0.3490315640010522, 0.6202766900023562, 0.7155317880024086]
inbar_dsd_long   [0.42809327600116376, 0.4363977649991284, 0.4812496539998392]
pm2r_dsd_py2     [0.46369219400003203, 0.46809901899905526, 0.4706174610000744]
elmex_dsd_py3    [0.6603999830003886, 0.6629649060014344, 0.6652154759976838]

SHARED: 0.75

size: 10, shared count: 7, loops: 10000
pm2r_dsd_py3     [0.14004066000052262, 0.14024711000092793, 0.1411744200013345]
inbar_dsd_long   [0.1457400300023437, 0.1463650259975111, 0.17371471199658117]
raymond_dsd      [0.1495657380000921, 0.15151091000007, 0.1532108950013935]
inbar_dsd_short  [0.16798981899773935, 0.1684792589985591, 0.17371860500134062]
evkounis_dsd     [0.18283682300170767, 0.18351536599948304, 0.18536045300061232]
pm2r_dsd_py2     [0.24651207700298983, 0.24725952299922938, 0.3011513509991346]
elmex_dsd_py3    [0.27965197500088834, 0.2817374969999946, 0.28211258000010275]

size: 100, shared count: 83, loops: 1000
evkounis_dsd     [0.10071835599956103, 0.10109729699979653, 0.1036734150002303]
inbar_dsd_long   [0.10147314599817037, 0.1017698140021821, 0.11575333300061175]
pm2r_dsd_py2     [0.1257392070001515, 0.14690794800117146, 0.2597000979985751]
pm2r_dsd_py3     [0.16547765900031663, 0.17877282599874889, 0.1817621379996126]
elmex_dsd_py3    [0.18176361400037422, 0.18339519599976484, 0.18422297999859438]
inbar_dsd_short  [0.18878075899920077, 0.1932126639985654, 0.201184026998817]
raymond_dsd      [0.23026226100046188, 0.2342098570006783, 0.24134657600006904]

size: 1000, shared count: 751, loops: 100
inbar_dsd_short  [0.0925550639985886, 0.09375216300031752, 0.09518678500171518]
pm2r_dsd_py3     [0.09365715600142721, 0.0952552939997986, 0.0984138530002383]
raymond_dsd      [0.10659463599949959, 0.10675223399812239, 0.1076178000002983]
inbar_dsd_long   [0.10787330499806558, 0.10813268299898482, 0.1191909779990965]
evkounis_dsd     [0.11020168100003502, 0.11101243599841837, 0.11369209199983743]
pm2r_dsd_py2     [0.1283391249999113, 0.12977415000204928, 0.13450328500039177]
elmex_dsd_py3    [0.20605224600149086, 0.20856778099914663, 0.21231961700323154]

size: 10000, shared count: 7525, loops: 10
evkounis_dsd     [0.19238157699874137, 0.19369199399807258, 0.20787687100164476]
pm2r_dsd_py3     [0.237352975000249, 0.2393961540001328, 0.24592895499881706]
inbar_dsd_short  [0.24010049900243757, 0.24383026600116864, 0.246290401002625]
inbar_dsd_long   [0.31666912799846614, 0.3353785740000603, 0.3762496050003392]
raymond_dsd      [0.3268343650015595, 0.3270019219999085, 0.32956799900057376]
pm2r_dsd_py2     [0.3330148269997153, 0.34052117800092674, 0.3426254549995065]
elmex_dsd_py3    [0.6130798710000818, 0.6139247349965444, 0.6146237579996523]

SHARED: 0.90

size: 10, shared count: 10, loops: 10000
pm2r_dsd_py3     [0.09191049900255166, 0.09203974899719469, 0.09560386399971321]
inbar_dsd_long   [0.09304381299807574, 0.09397280899793259, 0.10319281500051147]
inbar_dsd_short  [0.0980829280015314, 0.09835117700276896, 0.0987546550022671]
raymond_dsd      [0.14094099900103174, 0.14119526200011023, 0.14634641500015277]
evkounis_dsd     [0.14480078699853038, 0.1466599049999786, 0.14705315900209825]
pm2r_dsd_py2     [0.16137886599972262, 0.16186897499937913, 0.1626489610025601]
elmex_dsd_py3    [0.24912584599951515, 0.2519607159993029, 0.2550744569998642]

size: 100, shared count: 88, loops: 1000
pm2r_dsd_py3     [0.08017906299937749, 0.08175948099960806, 0.08336899599817116]
inbar_dsd_short  [0.08394136000060826, 0.08467326000027242, 0.08476182100275764]
inbar_dsd_long   [0.09241838099842425, 0.0929719669984479, 0.10157853300188435]
evkounis_dsd     [0.09769711500121048, 0.09770239999852492, 0.10219176600003266]
pm2r_dsd_py2     [0.11295593600152642, 0.11317849099941668, 0.11382339899864746]
raymond_dsd      [0.11950065099881613, 0.11954410699763685, 0.16439275900120265]
elmex_dsd_py3    [0.17893833099878975, 0.18027151500064065, 0.18072834000122384]

size: 1000, shared count: 896, loops: 100
pm2r_dsd_py3     [0.06560493199867778, 0.06627220900190878, 0.06649829500020132]
inbar_dsd_short  [0.067232484001579, 0.06832705600027111, 0.06892605100074434]
inbar_dsd_long   [0.07928322799853049, 0.0793153419981536, 0.0874185499997111]
pm2r_dsd_py2     [0.08986150900091161, 0.09258468600091874, 0.09545781900305883]
evkounis_dsd     [0.09216968399778125, 0.09272978199805948, 0.09716289000061806]
raymond_dsd      [0.11052805100189289, 0.11131704600120429, 0.11136766299750889]
elmex_dsd_py3    [0.18965840600139927, 0.1898866600022302, 0.19107911399987643]

size: 10000, shared count: 9011, loops: 10
evkounis_dsd     [0.1584843410018948, 0.16192917299849796, 0.16836377900108346]
pm2r_dsd_py3     [0.1789340169998468, 0.17990425000243704, 0.1874260629992932]
inbar_dsd_short  [0.18104806900009862, 0.18631987900153035, 0.18891330599944922]
inbar_dsd_long   [0.2561770180000167, 0.2672927259991411, 0.27309057399907033]
pm2r_dsd_py2     [0.26508888299940736, 0.2661178109992761, 0.2812051930013695]
raymond_dsd      [0.3262405569985276, 0.32729987999846344, 0.3313657439975941]
elmex_dsd_py3    [0.5737760600022739, 0.5791283889993792, 0.5847248999998556]

Recursive diff of two dictionaries (keys and values)?

One option would be to convert any lists you run into as dictionaries with the index as a key. For example:

# add this function to the same module
def list_to_dict(l):
    return dict(zip(map(str, range(len(l))), l))

# add this code under the 'if type(d2[k]) == dict' block
                    elif type(d2[k]) == list:
                        dd(list_to_dict(d1[k]), list_to_dict(d2[k]), k)

Here is the output with the sample dictionaries you gave in comments:

>>> d1 = {"name":"Joe", "Pets":[{"name":"spot", "species":"dog"}]}
>>> d2 = {"name":"Joe", "Pets":[{"name":"spot", "species":"cat"}]}
>>> dd(d1, d2, "base")
Changes in base
Changes in Pets
Changes in 0
species changed in d2 to cat
Done with changes in 0
Done with changes in Pets
Done with changes in base

Note that this will compare index by index, so it will need some modification to work well for list items being added or removed.

How to compare two dictionaries using a comprehension?

As you noticed, your syntax is a bit off. I would first assume that where you used in dic2 and in dic1 you meant to use in dic.items() to iterate over key:value pairs. Second, the : can basically only be in the first part of the dict-comp. I guess you meant:

dc3 = {k: v for k,v in dc2.items() if k not in dc1}
dc4 = {**dc1, **dc3}

The creation of dc3 just loops over the key:value pairs in dc2 and only adds them if they are not in dc1.

The creation of dc4 is just a dictionary merge of dc1 and the newly created dc3.

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