I am working on a search program over an inverted index. The index itself is a dictionary whose keys are terms and whose values are themselves dictionaries of short document
In Python, you use the & operator to calculate the intersection of sets, and dictionary keys are set-like objects (in Python 3):
dict_a = {"a": 1, "b": 2}
dict_b = {"a": 2, "c": 3}
intersection = dict_a.keys() & dict_b.keys() # {'a'}
On Python 2 you have to convert the dictionary keys to sets yourself:
keys_a = set(dict_a.keys())
keys_b = set(dict_b.keys())
intersection = keys_a & keys_b
A little known fact is that you don't need to construct set
s to do this:
In Python 2:
In [78]: d1 = {'a': 1, 'b': 2}
In [79]: d2 = {'b': 2, 'c': 3}
In [80]: d1.viewkeys() & d2.viewkeys()
Out[80]: {'b'}
In Python 3 replace viewkeys
with keys
; the same applies to viewvalues
and viewitems
.
From the documentation of viewitems
:
In [113]: d1.viewitems??
Type: builtin_function_or_method
String Form:<built-in method viewitems of dict object at 0x64a61b0>
Docstring: D.viewitems() -> a set-like object providing a view on D's items
For larger dict
s this also slightly faster than constructing set
s and then intersecting them:
In [122]: d1 = {i: rand() for i in range(10000)}
In [123]: d2 = {i: rand() for i in range(10000)}
In [124]: timeit d1.viewkeys() & d2.viewkeys()
1000 loops, best of 3: 714 µs per loop
In [125]: %%timeit
s1 = set(d1)
s2 = set(d2)
res = s1 & s2
1000 loops, best of 3: 805 µs per loop
For smaller `dict`s `set` construction is faster:
In [126]: d1 = {'a': 1, 'b': 2}
In [127]: d2 = {'b': 2, 'c': 3}
In [128]: timeit d1.viewkeys() & d2.viewkeys()
1000000 loops, best of 3: 591 ns per loop
In [129]: %%timeit
s1 = set(d1)
s2 = set(d2)
res = s1 & s2
1000000 loops, best of 3: 477 ns per loop
We're comparing nanoseconds here, which may or may not matter to you. In any case, you get back a set
, so using viewkeys
/keys
eliminates a bit of clutter.
In [1]: d1 = {'a':1, 'b':4, 'f':3}
In [2]: d2 = {'a':1, 'b':4, 'd':2}
In [3]: d = {x:d1[x] for x in d1 if x in d2}
In [4]: d
Out[4]: {'a': 1, 'b': 4}
Just wrap the dictionary instances with a simple class that gets both of the values you want
class DictionaryIntersection(object):
def __init__(self,dictA,dictB):
self.dictA = dictA
self.dictB = dictB
def __getitem__(self,attr):
if attr not in self.dictA or attr not in self.dictB:
raise KeyError('Not in both dictionaries,key: %s' % attr)
return self.dictA[attr],self.dictB[attr]
x = {'foo' : 5, 'bar' :6}
y = {'bar' : 'meow' , 'qux' : 8}
z = DictionaryIntersection(x,y)
print z['bar']
In Python 3, you can use
intersection = dict(dict1.items() & dict2.items())
union = dict(dict1.items() | dict2.items())
difference = dict(dict1.items() ^ dict2.items())
Okay, here is a generalized version of code above in Python3. It is optimized to use comprehensions and set-like dict views which are fast enough.
Function intersects arbitrary many dicts and returns a dict with common keys and a set of common values for each common key:
def dict_intersect(*dicts):
comm_keys = dicts[0].keys()
for d in dicts[1:]:
# intersect keys first
comm_keys &= d.keys()
# then build a result dict with nested comprehension
result = {key:{d[key] for d in dicts} for key in comm_keys}
return result
Usage example:
a = {1: 'ba', 2: 'boon', 3: 'spam', 4:'eggs'}
b = {1: 'ham', 2:'baboon', 3: 'sausages'}
c = {1: 'more eggs', 3: 'cabbage'}
res = dict_intersect(a, b, c)
# Here is res (the order of values may vary) :
# {1: {'ham', 'more eggs', 'ba'}, 3: {'spam', 'sausages', 'cabbage'}}
Here the dict values must be hashable, if they aren't you could simply change set parentheses { } to list [ ]:
result = {key:[d[key] for d in dicts] for key in comm_keys}