Reputation: 97571
If I have these two lists:
la = [1, 2, 3]
lb = [4, 5, 6]
I can iterate over them as follows:
for i in range(min(len(la), len(lb))):
print la[i], lb[i]
Or more pythonically
for a, b in zip(la, lb):
print a, b
What if I have two dictionaries?
da = {'a': 1, 'b': 2, 'c': 3}
db = {'a': 4, 'b': 5, 'c': 6}
Again, I can iterate manually:
for key in set(da.keys()) & set(db.keys()):
print key, da[key], db[key]
Is there some builtin method that allows me to iterate as follows?
for key, value_a, value_b in common_entries(da, db):
print key, value_a, value_b
Upvotes: 52
Views: 65292
Reputation: 55
If it's for iterate them, you can zip like this :
d1 = {"1":1,"2":2}
d2 = {"3":3,"4":4}
for (k1, v1), (k2, v2) in zip(d1.items(), d2.items()):
print(k1,v1,k2,v2)
Output:
1 1 3 3
2 2 4 4
Upvotes: 0
Reputation: 140
I was wondering if taking one of the dict
s as a reference would be efficient, and a quick experiment1 says it depends:
import timeit
dshort = {c: ord(c) for c in "abcdefghijklm"}
dlong = {c: ord(c) for c in "cdefghijklmnopqrstuvwxyz"}
print(timeit.repeat(lambda: {k: (dshort[k], dlong[k]) for k in dshort.keys() & dlong.keys()}))
print(timeit.repeat(lambda: {k: (v1, dlong[k]) for k, v1 in dshort.items() if k in dlong}))
print(timeit.repeat(lambda: {k: (dshort[k], v2) for k, v2 in dlong.items() if k in dshort}))
print(timeit.repeat(lambda: {k: (v1, dlong[k]) for k, v1 in dshort.items() if k in dlong.keys()}))
print(timeit.repeat(lambda: {k: (dshort[k], v2) for k, v2 in dlong.items() if k in dshort.keys()}))
[1.4048387547954917, 1.37691087462008, 1.3789333319291472, 1.3769562682136893, 1.3768599517643452] [1.3084445232525468, 1.2111821668222547, 1.213303911499679, 1.2122336281463504, 1.2120618764311075] [1.544621947221458, 1.5463114874437451, 1.5447452096268535, 1.5450493320822716, 1.5463362047448754] [1.5736573031172156, 1.556909584440291, 1.5570594323799014, 1.5568421771749854, 1.5563717214390635] [2.2037177812308073, 2.204036961309612, 2.2097349502146244, 2.214019880630076, 2.213061122223735]
If you know which dict
is the shortest one, iterate over its items()
and check if its keys are also in d
— However, don't check as in d.keys()
, which worsens performance! I guess this is because time is mostly spent on __getattr__
.
If you are not sure about which is the shortest dict
, just use d1.keys() & d2.keys() & d3.keys() & ...
;)
1 Version: Python 3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:38:13) [GCC 12.3.0] on linux
Upvotes: 0
Reputation: 701
The object returned by dict.keys()
(called a dictionary key view) acts like a set
object, so you can just take the intersection of the keys:
da = {'a': 1, 'b': 2, 'c': 3, 'e': 7}
db = {'a': 4, 'b': 5, 'c': 6, 'd': 9}
common_keys = da.keys() & db.keys()
for k in common_keys:
print(k, da[k], db[k])
On Python 2 you'll need to convert the keys to set
s yourself:
common_keys = set(da) & set(db)
for k in common_keys:
print k, da[k], db[k]
Upvotes: 15
Reputation: 32300
There is no built-in function or method that can do this. However, you could easily define your own.
def common_entries(*dcts):
if not dcts:
return
for i in set(dcts[0]).intersection(*dcts[1:]):
yield (i,) + tuple(d[i] for d in dcts)
This builds on the "manual method" you provide, but, like zip
, can be used for any number of dictionaries.
>>> da = {'a': 1, 'b': 2, 'c': 3}
>>> db = {'a': 4, 'b': 5, 'c': 6}
>>> list(common_entries(da, db))
[('c', 3, 6), ('b', 2, 5), ('a', 1, 4)]
When only one dictionary is provided as an argument, it essentially returns dct.items()
.
>>> list(common_entries(da))
[('c', 3), ('b', 2), ('a', 1)]
With no dictionaries, it returns an empty generator (just like zip()
)
>>> list(common_entries())
[]
Upvotes: 43
Reputation: 956
Python3: How about the following?
da = {'A': 1, 'b': 2, 'c': 3}
db = {'B': 4, 'b': 5, 'c': 6}
for key, (value_a, value_b) in {k:(da[k],db[k]) for k in set(da)&set(db)}.items():
print(key, value_a, value_b)
The above snippet prints values of common keys ('b' and 'c') and discards the keys which don't match ('A' and 'B').
In order to include all keys into the output we could use a slightly modified comprehension: {k:(da.get(k),db.get(k)) for k in set(da)|set(db)}
.
Upvotes: 0
Reputation: 44485
Dictionary key views are already set-like in Python 3. You can remove set()
:
for key in da.keys() & db.keys():
print(key, da[key], db[key])
In Python 2:
for key in da.viewkeys() & db.viewkeys():
print key, da[key], db[key]
Upvotes: 6
Reputation: 10963
In case if someone is looking for generalized solution:
import operator
from functools import reduce
def zip_mappings(*mappings):
keys_sets = map(set, mappings)
common_keys = reduce(set.intersection, keys_sets)
for key in common_keys:
yield (key,) + tuple(map(operator.itemgetter(key), mappings))
or if you like to separate key from values and use syntax like
for key, (values, ...) in zip_mappings(...):
...
we can replace last line with
yield key, tuple(map(operator.itemgetter(key), mappings))
from collections import Counter
counter = Counter('abra')
other_counter = Counter('kadabra')
last_counter = Counter('abbreviation')
for (character,
frequency, other_frequency, last_frequency) in zip_mappings(counter,
other_counter,
last_counter):
print('character "{}" has next frequencies: {}, {}, {}'
.format(character,
frequency,
other_frequency,
last_frequency))
gives us
character "a" has next frequencies: 2, 3, 2
character "r" has next frequencies: 1, 1, 1
character "b" has next frequencies: 1, 1, 2
(tested on Python 2.7.12
& Python 3.5.2
)
Upvotes: 1