我想将两个词典融入一个新的词典。
x = {'a': 1, 'b': 2}
y = {'b': 3, 'c': 4}
z = merge(x, y)
>>> z
{'a': 1, 'b': 3, 'c': 4}
每当两个字典中都有一个关键 k 时,只应保留 y(k) 的值。
我想将两个词典融入一个新的词典。
x = {'a': 1, 'b': 2}
y = {'b': 3, 'c': 4}
z = merge(x, y)
>>> z
{'a': 1, 'b': 3, 'c': 4}
每当两个字典中都有一个关键 k 时,只应保留 y(k) 的值。
当前回答
我认为我的丑陋的单线只需要在这里。
z = next(z.update(y) or z for z in [x.copy()])
# or
z = (lambda z: z.update(y) or z)(x.copy())
单一表达,永远不要敢用它。
我知道Python 3有这个 {**x, **y} 事物,它是正确的事情使用(以及转到Python 3 如果你仍然有Python 2是正确的事情)。
其他回答
深深的定律:
from typing import List, Dict
from copy import deepcopy
def merge_dicts(*from_dicts: List[Dict], no_copy: bool=False) -> Dict :
""" no recursion deep merge of two dicts
By default creates fresh Dict and merges all to it.
no_copy = True, will merge all dicts to a fist one in a list without copy.
Why? Sometime I need to combine one dictionary from "layers".
The "layers" are not in use and dropped immediately after merging.
"""
if no_copy:
xerox = lambda x:x
else:
xerox = deepcopy
result = xerox(from_dicts[0])
for _from in from_dicts[1:]:
merge_queue = [(result, _from)]
for _to, _from in merge_queue:
for k, v in _from.items():
if k in _to and isinstance(_to[k], dict) and isinstance(v, dict):
# key collision add both are dicts.
# add to merging queue
merge_queue.append((_to[k], v))
continue
_to[k] = xerox(v)
return result
使用:
print("=============================")
print("merge all dicts to first one without copy.")
a0 = {"a":{"b":1}}
a1 = {"a":{"c":{"d":4}}}
a2 = {"a":{"c":{"f":5}, "d": 6}}
print(f"a0 id[{id(a0)}] value:{a0}")
print(f"a1 id[{id(a1)}] value:{a1}")
print(f"a2 id[{id(a2)}] value:{a2}")
r = merge_dicts(a0, a1, a2, no_copy=True)
print(f"r id[{id(r)}] value:{r}")
print("=============================")
print("create fresh copy of all")
a0 = {"a":{"b":1}}
a1 = {"a":{"c":{"d":4}}}
a2 = {"a":{"c":{"f":5}, "d": 6}}
print(f"a0 id[{id(a0)}] value:{a0}")
print(f"a1 id[{id(a1)}] value:{a1}")
print(f"a2 id[{id(a2)}] value:{a2}")
r = merge_dicts(a0, a1, a2)
print(f"r id[{id(r)}] value:{r}")
我将所提出的与 perfplot 比较,并发现
x | y # Python 3.9+
是最快的解决方案,与旧的好解决方案
{**x, **y}
和
temp = x.copy()
temp.update(y)
此分類上一篇
重复字符的代码:
from collections import ChainMap
from itertools import chain
import perfplot
def setup(n):
x = dict(zip(range(n), range(n)))
y = dict(zip(range(n, 2 * n), range(n, 2 * n)))
return x, y
def copy_update(x, y):
temp = x.copy()
temp.update(y)
return temp
def add_items(x, y):
return dict(list(x.items()) + list(y.items()))
def curly_star(x, y):
return {**x, **y}
def chain_map(x, y):
return dict(ChainMap({}, y, x))
def itertools_chain(x, y):
return dict(chain(x.items(), y.items()))
def python39_concat(x, y):
return x | y
b = perfplot.bench(
setup=setup,
kernels=[
copy_update,
add_items,
curly_star,
chain_map,
itertools_chain,
python39_concat,
],
labels=[
"copy_update",
"dict(list(x.items()) + list(y.items()))",
"{**x, **y}",
"chain_map",
"itertools.chain",
"x | y",
],
n_range=[2 ** k for k in range(18)],
xlabel="len(x), len(y)",
equality_check=None,
)
b.save("out.png")
b.show()
在这里和其他地方绘制想法,我已经理解了一个功能:
def merge(*dicts, **kv):
return { k:v for d in list(dicts) + [kv] for k,v in d.items() }
使用(在Python 3中测试):
assert (merge({1:11,'a':'aaa'},{1:99, 'b':'bbb'},foo='bar')==\
{1: 99, 'foo': 'bar', 'b': 'bbb', 'a': 'aaa'})
assert (merge(foo='bar')=={'foo': 'bar'})
assert (merge({1:11},{1:99},foo='bar',baz='quux')==\
{1: 99, 'foo': 'bar', 'baz':'quux'})
assert (merge({1:11},{1:99})=={1: 99})
你可以用Lambda。
在Python3中,项目方法不再返回一个列表,而是一个视图,它像一个集一样作用。
dict(x.items() | y.items())
dict(x.viewitems() | y.viewitems())
编辑:
首先,请注意,在 Python 3 中, dic(x、 **y) 技巧不会工作,除非 y 中的键是线条。
此外,Raymond Hettinger的链路图答案是相当优雅的,因为它可以作为论点采取任意数量的论点,但从论点看起来它顺序地通过每个搜索的所有论点的列表:
In [1]: from collections import ChainMap
In [2]: from string import ascii_uppercase as up, ascii_lowercase as lo; x = dict(zip(lo, up)); y = dict(zip(up, lo))
In [3]: chainmap_dict = ChainMap(y, x)
In [4]: union_dict = dict(x.items() | y.items())
In [5]: timeit for k in union_dict: union_dict[k]
100000 loops, best of 3: 2.15 µs per loop
In [6]: timeit for k in chainmap_dict: chainmap_dict[k]
10000 loops, best of 3: 27.1 µs per loop
我是 Chainmap 的粉丝,但看起来不太实用,在那里可能有很多搜索。
def dict_merge(a, b):
c = a.copy()
c.update(b)
return c
new = dict_merge(old, extras)
在如此阴影和可疑的答案中,这个闪发光的例子是在Python中合并独裁的唯一和唯一好的方式,由独裁者为生活Guido van Rossum自己支持! 另一个人提出了一半,但没有把它放在一个功能上。
print dict_merge(
{'color':'red', 'model':'Mini'},
{'model':'Ferrari', 'owner':'Carl'})
给了:
{'color': 'red', 'owner': 'Carl', 'model': 'Ferrari'}