标签归档:dictionary

了解dict.copy()-浅还是深?

问题:了解dict.copy()-浅还是深?

在阅读的文档时dict.copy(),它说它制作了该词典的浅表副本。我关注的书(Beazley的Python参考)也是如此,该书说:

m.copy()方法对映射对象中包含的项目进行浅表复制,并将其放置在新的映射对象中。

考虑一下:

>>> original = dict(a=1, b=2)
>>> new = original.copy()
>>> new.update({'c': 3})
>>> original
{'a': 1, 'b': 2}
>>> new
{'a': 1, 'c': 3, 'b': 2}

因此,我认为这也将更新original(并添加’c’:3)的值,因为我正在执行浅表复制。就像您对列表进行操作一样:

>>> original = [1, 2, 3]
>>> new = original
>>> new.append(4)
>>> new, original
([1, 2, 3, 4], [1, 2, 3, 4])

这按预期工作。

由于两者都是浅表副本,为什么为什么dict.copy()按我的预期无法正常工作?还是我对浅复制和深复制的理解存在缺陷?

While reading up the documentation for dict.copy(), it says that it makes a shallow copy of the dictionary. Same goes for the book I am following (Beazley’s Python Reference), which says:

The m.copy() method makes a shallow copy of the items contained in a mapping object and places them in a new mapping object.

Consider this:

>>> original = dict(a=1, b=2)
>>> new = original.copy()
>>> new.update({'c': 3})
>>> original
{'a': 1, 'b': 2}
>>> new
{'a': 1, 'c': 3, 'b': 2}

So I assumed this would update the value of original (and add ‘c’: 3) also since I was doing a shallow copy. Like if you do it for a list:

>>> original = [1, 2, 3]
>>> new = original
>>> new.append(4)
>>> new, original
([1, 2, 3, 4], [1, 2, 3, 4])

This works as expected.

Since both are shallow copies, why is that the dict.copy() doesn’t work as I expect it to? Or my understanding of shallow vs deep copying is flawed?


回答 0

“浅复制”表示字典的内容不是按值复制,而只是创建一个新引用。

>>> a = {1: [1,2,3]}
>>> b = a.copy()
>>> a, b
({1: [1, 2, 3]}, {1: [1, 2, 3]})
>>> a[1].append(4)
>>> a, b
({1: [1, 2, 3, 4]}, {1: [1, 2, 3, 4]})

相反,深层副本将按值复制所有内容。

>>> import copy
>>> c = copy.deepcopy(a)
>>> a, c
({1: [1, 2, 3, 4]}, {1: [1, 2, 3, 4]})
>>> a[1].append(5)
>>> a, c
({1: [1, 2, 3, 4, 5]}, {1: [1, 2, 3, 4]})

所以:

  1. b = a:参考分配,制造ab指向同一对象。

    'a = b'的图示:'a'和'b'都指向'{1:L}','L'指向'[1、2、3]。

  2. b = a.copy():浅拷贝,a并且b将成为两个独立的对象,但其内容仍共享相同的参考

    'b = a.copy()'的说明:'a'指向'{1:L}','b'指向'{1:M}','L'和'M'都指向'[ 1,2,3]”。

  3. b = copy.deepcopy(a):深度复制,a并且b的结构和内容变得完全孤立。

    'b = copy.deepcopy(a)'的图示:'a'指向'{1:L}','L'指向'[1、2、3]';  'b'指向'{1:M}','M'指向'[1,2,3]'的另一个实例。

By “shallow copying” it means the content of the dictionary is not copied by value, but just creating a new reference.

>>> a = {1: [1,2,3]}
>>> b = a.copy()
>>> a, b
({1: [1, 2, 3]}, {1: [1, 2, 3]})
>>> a[1].append(4)
>>> a, b
({1: [1, 2, 3, 4]}, {1: [1, 2, 3, 4]})

In contrast, a deep copy will copy all contents by value.

>>> import copy
>>> c = copy.deepcopy(a)
>>> a, c
({1: [1, 2, 3, 4]}, {1: [1, 2, 3, 4]})
>>> a[1].append(5)
>>> a, c
({1: [1, 2, 3, 4, 5]}, {1: [1, 2, 3, 4]})

So:

  1. b = a: Reference assignment, Make a and b points to the same object.

    Illustration of 'a = b': 'a' and 'b' both point to '{1: L}', 'L' points to '[1, 2, 3]'.

  2. b = a.copy(): Shallow copying, a and b will become two isolated objects, but their contents still share the same reference

    Illustration of 'b = a.copy()': 'a' points to '{1: L}', 'b' points to '{1: M}', 'L' and 'M' both point to '[1, 2, 3]'.

  3. b = copy.deepcopy(a): Deep copying, a and b‘s structure and content become completely isolated.

    Illustration of 'b = copy.deepcopy(a)': 'a' points to '{1: L}', 'L' points to '[1, 2, 3]'; 'b' points to '{1: M}', 'M' points to a different instance of '[1, 2, 3]'.


回答 1

这不是深拷贝或浅拷贝的问题,您要做的只是深拷贝。

这里:

>>> new = original 

您正在创建对原始引用的列表/字典的新引用。

而在这里:

>>> new = original.copy()
>>> # or
>>> new = list(original) # dict(original)

您正在创建一个新的列表/字典,其中填充了原始容器中包含的对象引用的副本。

It’s not a matter of deep copy or shallow copy, none of what you’re doing is deep copy.

Here:

>>> new = original 

you’re creating a new reference to the the list/dict referenced by original.

while here:

>>> new = original.copy()
>>> # or
>>> new = list(original) # dict(original)

you’re creating a new list/dict which is filled with a copy of the references of objects contained in the original container.


回答 2

举个例子:

original = dict(a=1, b=2, c=dict(d=4, e=5))
new = original.copy()

现在,让我们在“浅”(第一)级别中更改一个值:

new['a'] = 10
# new = {'a': 10, 'b': 2, 'c': {'d': 4, 'e': 5}}
# original = {'a': 1, 'b': 2, 'c': {'d': 4, 'e': 5}}
# no change in original, since ['a'] is an immutable integer

现在让我们将值更深一级地更改:

new['c']['d'] = 40
# new = {'a': 10, 'b': 2, 'c': {'d': 40, 'e': 5}}
# original = {'a': 1, 'b': 2, 'c': {'d': 40, 'e': 5}}
# new['c'] points to the same original['d'] mutable dictionary, so it will be changed

Take this example:

original = dict(a=1, b=2, c=dict(d=4, e=5))
new = original.copy()

Now let’s change a value in the ‘shallow’ (first) level:

new['a'] = 10
# new = {'a': 10, 'b': 2, 'c': {'d': 4, 'e': 5}}
# original = {'a': 1, 'b': 2, 'c': {'d': 4, 'e': 5}}
# no change in original, since ['a'] is an immutable integer

Now let’s change a value one level deeper:

new['c']['d'] = 40
# new = {'a': 10, 'b': 2, 'c': {'d': 40, 'e': 5}}
# original = {'a': 1, 'b': 2, 'c': {'d': 40, 'e': 5}}
# new['c'] points to the same original['d'] mutable dictionary, so it will be changed

回答 3

添加到肯尼的答案。当您进行浅表复制parent.copy()时,会使用相同的键创建一个新字典,但不会复制它们的值。如果将新值添加到parent_copy,则不会影响父对象,因为parent_copy是新字典没有参考。

parent = {1: [1,2,3]}
parent_copy = parent.copy()
parent_reference = parent

print id(parent),id(parent_copy),id(parent_reference)
#140690938288400 140690938290536 140690938288400

print id(parent[1]),id(parent_copy[1]),id(parent_reference[1])
#140690938137128 140690938137128 140690938137128

parent_copy[1].append(4)
parent_copy[2] = ['new']

print parent, parent_copy, parent_reference
#{1: [1, 2, 3, 4]} {1: [1, 2, 3, 4], 2: ['new']} {1: [1, 2, 3, 4]}

parent [1]parent_copy [1]的hash(id)值相同,这意味着存储在id 140690938288400中的parent [1]parent_copy [1]的 [1,2,3] 。

但是parentparent_copy的哈希值不同,这意味着它们是不同的字典,并且parent_copy是一个新字典,其值引用了parent的

Adding to kennytm’s answer. When you do a shallow copy parent.copy() a new dictionary is created with same keys,but the values are not copied they are referenced.If you add a new value to parent_copy it won’t effect parent because parent_copy is a new dictionary not reference.

parent = {1: [1,2,3]}
parent_copy = parent.copy()
parent_reference = parent

print id(parent),id(parent_copy),id(parent_reference)
#140690938288400 140690938290536 140690938288400

print id(parent[1]),id(parent_copy[1]),id(parent_reference[1])
#140690938137128 140690938137128 140690938137128

parent_copy[1].append(4)
parent_copy[2] = ['new']

print parent, parent_copy, parent_reference
#{1: [1, 2, 3, 4]} {1: [1, 2, 3, 4], 2: ['new']} {1: [1, 2, 3, 4]}

The hash(id) value of parent[1], parent_copy[1] are identical which implies [1,2,3] of parent[1] and parent_copy[1] stored at id 140690938288400.

But hash of parent and parent_copy are different which implies They are different dictionaries and parent_copy is a new dictionary having values reference to values of parent


回答 4

“ new”和“ original”是不同的dict,这就是为什么您只能更新其中之一。.这些项目是浅复制的,而不是dict本身。

“new” and “original” are different dicts, that’s why you can update just one of them.. The items are shallow-copied, not the dict itself.


回答 5

内容是浅复制的。

所以,如果原来的dict包含list或另一个dictionary,在原或其浅拷贝修改一个他们将修改他们(listdict)在其他。

Contents are shallow copied.

So if the original dict contains a list or another dictionary, modifying one them in the original or its shallow copy will modify them (the list or the dict) in the other.


回答 6

在第二部分中,您应该使用 new = original.copy()

.copy=是不同的东西。

In your second part, you should use new = original.copy()

.copy and = are different things.


重命名字典键

问题:重命名字典键

有没有一种方法可以重命名字典键,而无需将其值重新分配给新名称并删除旧名称键;而不迭代字典键/值?

对于OrderedDict,在保持键的位置的同时执行相同的操作。

Is there a way to rename a dictionary key, without reassigning its value to a new name and removing the old name key; and without iterating through dict key/value?

In case of OrderedDict, do the same, while keeping that key’s position.


回答 0

对于常规命令,可以使用:

mydict[new_key] = mydict.pop(old_key)

对于OrderedDict,我认为您必须使用一种理解来构建一个全新的。

>>> OrderedDict(zip('123', 'abc'))
OrderedDict([('1', 'a'), ('2', 'b'), ('3', 'c')])
>>> oldkey, newkey = '2', 'potato'
>>> OrderedDict((newkey if k == oldkey else k, v) for k, v in _.viewitems())
OrderedDict([('1', 'a'), ('potato', 'b'), ('3', 'c')])

正如这个问题似乎提出的那样,修改密钥本身是不切实际的,因为dict密钥通常是不可变的对象,例如数字,字符串或元组。您可以尝试在python中实现“重命名”,而不是尝试修改键,而是将值重新分配给新键并删除旧键。

For a regular dict, you can use:

mydict[new_key] = mydict.pop(old_key)

For an OrderedDict, I think you must build an entirely new one using a comprehension.

>>> OrderedDict(zip('123', 'abc'))
OrderedDict([('1', 'a'), ('2', 'b'), ('3', 'c')])
>>> oldkey, newkey = '2', 'potato'
>>> OrderedDict((newkey if k == oldkey else k, v) for k, v in _.viewitems())
OrderedDict([('1', 'a'), ('potato', 'b'), ('3', 'c')])

Modifying the key itself, as this question seems to be asking, is impractical because dict keys are usually immutable objects such as numbers, strings or tuples. Instead of trying to modify the key, reassigning the value to a new key and removing the old key is how you can achieve the “rename” in python.


回答 1

1行中的最佳方法:

>>> d = {'test':[0,1,2]}
>>> d['test2'] = d.pop('test')
>>> d
{'test2': [0, 1, 2]}

best method in 1 line:

>>> d = {'test':[0,1,2]}
>>> d['test2'] = d.pop('test')
>>> d
{'test2': [0, 1, 2]}

回答 2

通过使用check newkey!=oldkey,可以这样:

if newkey!=oldkey:  
    dictionary[newkey] = dictionary[oldkey]
    del dictionary[oldkey]

Using a check for newkey!=oldkey, this way you can do:

if newkey!=oldkey:  
    dictionary[newkey] = dictionary[oldkey]
    del dictionary[oldkey]

回答 3

如果重命名所有字典键:

target_dict = {'k1':'v1', 'k2':'v2', 'k3':'v3'}
new_keys = ['k4','k5','k6']

for key,n_key in zip(target_dict.keys(), new_keys):
    target_dict[n_key] = target_dict.pop(key)

In case of renaming all dictionary keys:

target_dict = {'k1':'v1', 'k2':'v2', 'k3':'v3'}
new_keys = ['k4','k5','k6']

for key,n_key in zip(target_dict.keys(), new_keys):
    target_dict[n_key] = target_dict.pop(key)

回答 4

您可以使用OrderedDict recipeRaymond Hettinger编写的代码并对其进行修改以添加一个rename方法,但这将成为O(N)的复杂性:

def rename(self,key,new_key):
    ind = self._keys.index(key)  #get the index of old key, O(N) operation
    self._keys[ind] = new_key    #replace old key with new key in self._keys
    self[new_key] = self[key]    #add the new key, this is added at the end of self._keys
    self._keys.pop(-1)           #pop the last item in self._keys

例:

dic = OrderedDict((("a",1),("b",2),("c",3)))
print dic
dic.rename("a","foo")
dic.rename("b","bar")
dic["d"] = 5
dic.rename("d","spam")
for k,v in  dic.items():
    print k,v

输出:

OrderedDict({'a': 1, 'b': 2, 'c': 3})
foo 1
bar 2
c 3
spam 5

You can use this OrderedDict recipe written by Raymond Hettinger and modify it to add a rename method, but this is going to be a O(N) in complexity:

def rename(self,key,new_key):
    ind = self._keys.index(key)  #get the index of old key, O(N) operation
    self._keys[ind] = new_key    #replace old key with new key in self._keys
    self[new_key] = self[key]    #add the new key, this is added at the end of self._keys
    self._keys.pop(-1)           #pop the last item in self._keys

Example:

dic = OrderedDict((("a",1),("b",2),("c",3)))
print dic
dic.rename("a","foo")
dic.rename("b","bar")
dic["d"] = 5
dic.rename("d","spam")
for k,v in  dic.items():
    print k,v

output:

OrderedDict({'a': 1, 'b': 2, 'c': 3})
foo 1
bar 2
c 3
spam 5

回答 5

在我之前的一些人提到了.pop一种删除和创建单行密钥的技巧。

我个人认为更明确的实现更具可读性:

d = {'a': 1, 'b': 2}
v = d['b']
del d['b']
d['c'] = v

上面的代码返回 {'a': 1, 'c': 2}

A few people before me mentioned the .pop trick to delete and create a key in a one-liner.

I personally find the more explicit implementation more readable:

d = {'a': 1, 'b': 2}
v = d['b']
del d['b']
d['c'] = v

The code above returns {'a': 1, 'c': 2}


回答 6

其他答案也不错。但是在python3.6中,常规字典也有顺序。因此在正常情况下很难保持钥匙的位置。

def rename(old_dict,old_name,new_name):
    new_dict = {}
    for key,value in zip(old_dict.keys(),old_dict.values()):
        new_key = key if key != old_name else new_name
        new_dict[new_key] = old_dict[key]
    return new_dict

Other answers are pretty good.But in python3.6, regular dict also has order. So it’s hard to keep key’s position in normal case.

def rename(old_dict,old_name,new_name):
    new_dict = {}
    for key,value in zip(old_dict.keys(),old_dict.values()):
        new_key = key if key != old_name else new_name
        new_dict[new_key] = old_dict[key]
    return new_dict

回答 7

在Python 3.6中(继续吗?),我将采用以下一种形式

test = {'a': 1, 'old': 2, 'c': 3}
old_k = 'old'
new_k = 'new'
new_v = 4  # optional

print(dict((new_k, new_v) if k == old_k else (k, v) for k, v in test.items()))

产生

{'a': 1, 'new': 4, 'c': 3}

可能值得注意的是,如果没有print声明,ipython console / jupyter笔记本将按其选择的顺序显示字典。

In Python 3.6 (onwards?) I would go for the following one-liner

test = {'a': 1, 'old': 2, 'c': 3}
old_k = 'old'
new_k = 'new'
new_v = 4  # optional

print(dict((new_k, new_v) if k == old_k else (k, v) for k, v in test.items()))

which produces

{'a': 1, 'new': 4, 'c': 3}

May be worth noting that without the print statement the ipython console/jupyter notebook present the dictionary in an order of their choosing…


回答 8

我想出了这个功能,它不会使原始字典发生变异。该功能也支持字典列表。

import functools
from typing import Union, Dict, List


def rename_dict_keys(
    data: Union[Dict, List[Dict]], old_key: str, new_key: str
):
    """
    This function renames dictionary keys

    :param data:
    :param old_key:
    :param new_key:
    :return: Union[Dict, List[Dict]]
    """
    if isinstance(data, dict):
        res = {k: v for k, v in data.items() if k != old_key}
        try:
            res[new_key] = data[old_key]
        except KeyError:
            raise KeyError(
                "cannot rename key as old key '%s' is not present in data"
                % old_key
            )
        return res
    elif isinstance(data, list):
        return list(
            map(
                functools.partial(
                    rename_dict_keys, old_key=old_key, new_key=new_key
                ),
                data,
            )
        )
    raise ValueError("expected type List[Dict] or Dict got '%s' for data" % type(data))

I came up with this function which does not mutate the original dictionary. This function also supports list of dictionaries too.

import functools
from typing import Union, Dict, List


def rename_dict_keys(
    data: Union[Dict, List[Dict]], old_key: str, new_key: str
):
    """
    This function renames dictionary keys

    :param data:
    :param old_key:
    :param new_key:
    :return: Union[Dict, List[Dict]]
    """
    if isinstance(data, dict):
        res = {k: v for k, v in data.items() if k != old_key}
        try:
            res[new_key] = data[old_key]
        except KeyError:
            raise KeyError(
                "cannot rename key as old key '%s' is not present in data"
                % old_key
            )
        return res
    elif isinstance(data, list):
        return list(
            map(
                functools.partial(
                    rename_dict_keys, old_key=old_key, new_key=new_key
                ),
                data,
            )
        )
    raise ValueError("expected type List[Dict] or Dict got '%s' for data" % type(data))

回答 9

重命名密钥时,我在dict.pop()中使用了@wim的答案,但是我发现了一个问题。在dict上循环以更改键,而又未将旧键列表与dict实例完全分开,导致将新的,已更改的键循环到循环中,并丢失了一些现有键。

首先,我这样做:

for current_key in my_dict:
    new_key = current_key.replace(':','_')
    fixed_metadata[new_key] = fixed_metadata.pop(current_key)

我发现字典以这种方式循环,即使在不应该的时候,字典也一直在寻找键,例如,新的键,即我已更改的键!我需要将实例彼此完全分开,以(a)避免在for循环中找到自己更改的键,以及(b)由于某些原因而在循环中找不到某些键。

我现在正在这样做:

current_keys = list(my_dict.keys())
for current_key in current_keys:
    and so on...

将my_dict.keys()转换为列表对于释放对更改的dict的引用是必要的。仅使用my_dict.keys()就使我与原始实例保持联系,并产生了奇怪的副作用。

I am using @wim ‘s answer above, with dict.pop() when renaming keys, but I found a gotcha. Cycling through the dict to change the keys, without separating the list of old keys completely from the dict instance, resulted in cycling new, changed keys into the loop, and missing some existing keys.

To start with, I did it this way:

for current_key in my_dict:
    new_key = current_key.replace(':','_')
    fixed_metadata[new_key] = fixed_metadata.pop(current_key)

I found that cycling through the dict in this way, the dictionary kept finding keys even when it shouldn’t, i.e., the new keys, the ones I had changed! I needed to separate the instances completely from each other to (a) avoid finding my own changed keys in the for loop, and (b) find some keys that were not being found within the loop for some reason.

I am doing this now:

current_keys = list(my_dict.keys())
for current_key in current_keys:
    and so on...

Converting the my_dict.keys() to a list was necessary to get free of the reference to the changing dict. Just using my_dict.keys() kept me tied to the original instance, with the strange side effects.


回答 10

如果有人想一次重命名所有键,并提供一个包含新名称的列表:

def rename_keys(dict_, new_keys):
    """
     new_keys: type List(), must match length of dict_
    """

    # dict_ = {oldK: value}
    # d1={oldK:newK,} maps old keys to the new ones:  
    d1 = dict( zip( list(dict_.keys()), new_keys) )

          # d1{oldK} == new_key 
    return {d1[oldK]: value for oldK, value in dict_.items()}

In case someone wants to rename all the keys at once providing a list with the new names:

def rename_keys(dict_, new_keys):
    """
     new_keys: type List(), must match length of dict_
    """

    # dict_ = {oldK: value}
    # d1={oldK:newK,} maps old keys to the new ones:  
    d1 = dict( zip( list(dict_.keys()), new_keys) )

          # d1{oldK} == new_key 
    return {d1[oldK]: value for oldK, value in dict_.items()}

回答 11

@ helloswift123我喜欢您的功能。这是在单个调用中重命名多个键的修改:

def rename(d, keymap):
    """
    :param d: old dict
    :type d: dict
    :param keymap: [{:keys from-keys :values to-keys} keymap]
    :returns: new dict
    :rtype: dict
    """
    new_dict = {}
    for key, value in zip(d.keys(), d.values()):
        new_key = keymap.get(key, key)
        new_dict[new_key] = d[key]
    return new_dict

@helloswift123 I like your function. Here is a modification to rename multiple keys in a single call:

def rename(d, keymap):
    """
    :param d: old dict
    :type d: dict
    :param keymap: [{:keys from-keys :values to-keys} keymap]
    :returns: new dict
    :rtype: dict
    """
    new_dict = {}
    for key, value in zip(d.keys(), d.values()):
        new_key = keymap.get(key, key)
        new_dict[new_key] = d[key]
    return new_dict

回答 12

假设您要将键k3重命名为k4:

temp_dict = {'k1':'v1', 'k2':'v2', 'k3':'v3'}
temp_dict['k4']= temp_dict.pop('k3')

Suppose you want to rename key k3 to k4:

temp_dict = {'k1':'v1', 'k2':'v2', 'k3':'v3'}
temp_dict['k4']= temp_dict.pop('k3')

Python:检查“字典”是否为空似乎不起作用

问题:Python:检查“字典”是否为空似乎不起作用

我正在尝试检查字典是否为空,但是行为不正常。它只是跳过它并显示“ 联机”,除了显示消息外没有任何其他内容。有什么主意吗?

 def isEmpty(self, dictionary):
   for element in dictionary:
     if element:
       return True
     return False

 def onMessage(self, socket, message):
  if self.isEmpty(self.users) == False:
     socket.send("Nobody is online, please use REGISTER command" \
                 " in order to register into the server")
  else:
     socket.send("ONLINE " + ' ' .join(self.users.keys())) 

I am trying to check if a dictionary is empty but it doesn’t behave properly. It just skips it and displays ONLINE without anything except of display the message. Any ideas why ?

 def isEmpty(self, dictionary):
   for element in dictionary:
     if element:
       return True
     return False

 def onMessage(self, socket, message):
  if self.isEmpty(self.users) == False:
     socket.send("Nobody is online, please use REGISTER command" \
                 " in order to register into the server")
  else:
     socket.send("ONLINE " + ' ' .join(self.users.keys())) 

回答 0

空字典在Python中的计算结果为False

>>> dct = {}
>>> bool(dct)
False
>>> not dct
True
>>>

因此,您的isEmpty功能是不必要的。您需要做的只是:

def onMessage(self, socket, message):
    if not self.users:
        socket.send("Nobody is online, please use REGISTER command" \
                    " in order to register into the server")
    else:
        socket.send("ONLINE " + ' ' .join(self.users.keys()))

Empty dictionaries evaluate to False in Python:

>>> dct = {}
>>> bool(dct)
False
>>> not dct
True
>>>

Thus, your isEmpty function is unnecessary. All you need to do is:

def onMessage(self, socket, message):
    if not self.users:
        socket.send("Nobody is online, please use REGISTER command" \
                    " in order to register into the server")
    else:
        socket.send("ONLINE " + ' ' .join(self.users.keys()))

回答 1

您可以通过以下三种方法检查dict是否为空。我更喜欢只使用第一种方法。其他两种方式过于罗y。

test_dict = {}

if not test_dict:
    print "Dict is Empty"


if not bool(test_dict):
    print "Dict is Empty"


if len(test_dict) == 0:
    print "Dict is Empty"

Here are three ways you can check if dict is empty. I prefer using the first way only though. The other two ways are way too wordy.

test_dict = {}

if not test_dict:
    print "Dict is Empty"


if not bool(test_dict):
    print "Dict is Empty"


if len(test_dict) == 0:
    print "Dict is Empty"

回答 2

dict = {}
print(len(dict.keys()))

如果length为零,则表示dict为空

dict = {}
print(len(dict.keys()))

if length is zero means that dict is empty


回答 3

检查空字典的简单方法如下:

        a= {}

    1. if a == {}:
           print ('empty dict')
    2. if not a:
           print ('empty dict')

尽管方法1st在a = None时更为严格,但方法1将提供正确的结果,而方法2将提供不正确的结果。

Simple ways to check an empty dict are below:

        a= {}

    1. if a == {}:
           print ('empty dict')
    2. if not a:
           print ('empty dict')

Although method 1st is more strict as when a = None, method 1 will provide correct result but method 2 will give an incorrect result.


回答 4

字典可以自动转换为布尔值,布尔值为False空字典和True非空字典。

if myDictionary: non_empty_clause()
else: empty_clause()

如果这看起来太惯用了,您还可以测试len(myDictionary)零,或测试set(myDictionary.keys())一个空集,或仅测试的相等性{}

isEmpty函数不仅是不必要的,而且您的实现还存在多个我可以发现表面现象的问题。

  1. return False语句缩进太深了一层。它应该在for循环之外,并且与该for语句处于同一级别。如此一来,您的代码将只处理一个任意选择的密钥(如果存在一个密钥)。如果键不存在,则该函数将返回None,并将其强制转换为布尔False。哎哟! 所有空字典将被归类为假阴性。
  2. 如果字典不为空,则代码将仅处理一个键,并将其值强制转换为布尔值。您甚至不能假设每次调用都对同一个键进行评估。因此会有误报。
  3. 假设您更正了return False语句的缩进并将其移出for循环。然后,您得到的是所有键的布尔值OR,或者False如果字典为空。仍然会有误报和误报。进行更正并针对以下词典进行测试以获取证据。

myDictionary={0:'zero', '':'Empty string', None:'None value', False:'Boolean False value', ():'Empty tuple'}

A dictionary can be automatically cast to boolean which evaluates to False for empty dictionary and True for non-empty dictionary.

if myDictionary: non_empty_clause()
else: empty_clause()

If this looks too idiomatic, you can also test len(myDictionary) for zero, or set(myDictionary.keys()) for an empty set, or simply test for equality with {}.

The isEmpty function is not only unnecessary but also your implementation has multiple issues that I can spot prima-facie.

  1. The return False statement is indented one level too deep. It should be outside the for loop and at the same level as the for statement. As a result, your code will process only one, arbitrarily selected key, if a key exists. If a key does not exist, the function will return None, which will be cast to boolean False. Ouch! All the empty dictionaries will be classified as false-nagatives.
  2. If the dictionary is not empty, then the code will process only one key and return its value cast to boolean. You cannot even assume that the same key is evaluated each time you call it. So there will be false positives.
  3. Let us say you correct the indentation of the return False statement and bring it outside the for loop. Then what you get is the boolean OR of all the keys, or False if the dictionary empty. Still you will have false positives and false negatives. Do the correction and test against the following dictionary for an evidence.

myDictionary={0:'zero', '':'Empty string', None:'None value', False:'Boolean False value', ():'Empty tuple'}


回答 5

您也可以使用get()。最初,我认为它只能检查密钥是否存在。

>>> d = { 'a':1, 'b':2, 'c':{}}
>>> bool(d.get('c'))
False
>>> d['c']['e']=1
>>> bool(d.get('c'))
True

我喜欢get的是它不会触发异常,因此可以轻松遍历大型结构。

You can also use get(). Initially I believed it to only check if key existed.

>>> d = { 'a':1, 'b':2, 'c':{}}
>>> bool(d.get('c'))
False
>>> d['c']['e']=1
>>> bool(d.get('c'))
True

What I like with get is that it does not trigger an exception, so it makes it easy to traverse large structures.


回答 6

为什么不使用平等测试?

def is_empty(my_dict):
    """
    Print true if given dictionary is empty
    """
    if my_dict == {}:
        print("Dict is empty !")

Why not use equality test?

def is_empty(my_dict):
    """
    Print true if given dictionary is empty
    """
    if my_dict == {}:
        print("Dict is empty !")

回答 7

使用“任何”

dict = {}

if any(dict) :

     # true
     # dictionary is not empty 

else :

     # false 
     # dictionary is empty

use ‘any’

dict = {}

if any(dict) :

     # true
     # dictionary is not empty 

else :

     # false 
     # dictionary is empty

如何按键对字典排序?

问题:如何按键对字典排序?

这将是一个很好的方式,从去{2:3, 1:89, 4:5, 3:0}{1:89, 2:3, 3:0, 4:5}
我检查了一些帖子,但它们都使用了返回元组的“排序”运算符。

What would be a nice way to go from {2:3, 1:89, 4:5, 3:0} to {1:89, 2:3, 3:0, 4:5}?
I checked some posts but they all use the “sorted” operator that returns tuples.


回答 0

标准Python字典是无序的。即使对(键,值)对进行了排序,也无法以dict保留顺序的方式存储它们。

最简单的方法是使用OrderedDict,它可以记住元素插入的顺序:

In [1]: import collections

In [2]: d = {2:3, 1:89, 4:5, 3:0}

In [3]: od = collections.OrderedDict(sorted(d.items()))

In [4]: od
Out[4]: OrderedDict([(1, 89), (2, 3), (3, 0), (4, 5)])

没关系od打印出来的方式; 它会按预期工作:

In [11]: od[1]
Out[11]: 89

In [12]: od[3]
Out[12]: 0

In [13]: for k, v in od.iteritems(): print k, v
   ....: 
1 89
2 3
3 0
4 5

Python 3

对于Python 3用户,需要使用.items()而不是.iteritems()

In [13]: for k, v in od.items(): print(k, v)
   ....: 
1 89
2 3
3 0
4 5

Standard Python dictionaries are unordered. Even if you sorted the (key,value) pairs, you wouldn’t be able to store them in a dict in a way that would preserve the ordering.

The easiest way is to use OrderedDict, which remembers the order in which the elements have been inserted:

In [1]: import collections

In [2]: d = {2:3, 1:89, 4:5, 3:0}

In [3]: od = collections.OrderedDict(sorted(d.items()))

In [4]: od
Out[4]: OrderedDict([(1, 89), (2, 3), (3, 0), (4, 5)])

Never mind the way od is printed out; it’ll work as expected:

In [11]: od[1]
Out[11]: 89

In [12]: od[3]
Out[12]: 0

In [13]: for k, v in od.iteritems(): print k, v
   ....: 
1 89
2 3
3 0
4 5

Python 3

For Python 3 users, one needs to use the .items() instead of .iteritems():

In [13]: for k, v in od.items(): print(k, v)
   ....: 
1 89
2 3
3 0
4 5

回答 1

字典本身没有这样的有序项目,如果您想按某种顺序将它们打印等,下面是一些示例:

在Python 2.4及更高版本中:

mydict = {'carl':40,
          'alan':2,
          'bob':1,
          'danny':3}

for key in sorted(mydict):
    print "%s: %s" % (key, mydict[key])

给出:

alan: 2
bob: 1
carl: 40
danny: 3

(低于2.4的Python :)

keylist = mydict.keys()
keylist.sort()
for key in keylist:
    print "%s: %s" % (key, mydict[key])

资料来源:http : //www.saltycrane.com/blog/2007/09/how-to-sort-python-dictionary-by-keys/

Dictionaries themselves do not have ordered items as such, should you want to print them etc to some order, here are some examples:

In Python 2.4 and above:

mydict = {'carl':40,
          'alan':2,
          'bob':1,
          'danny':3}

for key in sorted(mydict):
    print "%s: %s" % (key, mydict[key])

gives:

alan: 2
bob: 1
carl: 40
danny: 3

(Python below 2.4:)

keylist = mydict.keys()
keylist.sort()
for key in keylist:
    print "%s: %s" % (key, mydict[key])

Source: http://www.saltycrane.com/blog/2007/09/how-to-sort-python-dictionary-by-keys/


回答 2

Python的collections库文档中

>>> from collections import OrderedDict

>>> # regular unsorted dictionary
>>> d = {'banana': 3, 'apple':4, 'pear': 1, 'orange': 2}

>>> # dictionary sorted by key -- OrderedDict(sorted(d.items()) also works
>>> OrderedDict(sorted(d.items(), key=lambda t: t[0]))
OrderedDict([('apple', 4), ('banana', 3), ('orange', 2), ('pear', 1)])

>>> # dictionary sorted by value
>>> OrderedDict(sorted(d.items(), key=lambda t: t[1]))
OrderedDict([('pear', 1), ('orange', 2), ('banana', 3), ('apple', 4)])

>>> # dictionary sorted by length of the key string
>>> OrderedDict(sorted(d.items(), key=lambda t: len(t[0])))
OrderedDict([('pear', 1), ('apple', 4), ('orange', 2), ('banana', 3)])

From Python’s collections library documentation:

>>> from collections import OrderedDict

>>> # regular unsorted dictionary
>>> d = {'banana': 3, 'apple':4, 'pear': 1, 'orange': 2}

>>> # dictionary sorted by key -- OrderedDict(sorted(d.items()) also works
>>> OrderedDict(sorted(d.items(), key=lambda t: t[0]))
OrderedDict([('apple', 4), ('banana', 3), ('orange', 2), ('pear', 1)])

>>> # dictionary sorted by value
>>> OrderedDict(sorted(d.items(), key=lambda t: t[1]))
OrderedDict([('pear', 1), ('orange', 2), ('banana', 3), ('apple', 4)])

>>> # dictionary sorted by length of the key string
>>> OrderedDict(sorted(d.items(), key=lambda t: len(t[0])))
OrderedDict([('pear', 1), ('apple', 4), ('orange', 2), ('banana', 3)])

回答 3

对于CPython / PyPy 3.6和任何Python 3.7或更高版本,可以使用以下方法轻松完成此操作:

>>> d = {2:3, 1:89, 4:5, 3:0}
>>> dict(sorted(d.items()))
{1: 89, 2: 3, 3: 0, 4: 5}

For CPython/PyPy 3.6, and any Python 3.7 or higher, this is easily done with:

>>> d = {2:3, 1:89, 4:5, 3:0}
>>> dict(sorted(d.items()))
{1: 89, 2: 3, 3: 0, 4: 5}

回答 4

有许多Python模块提供字典实现,这些实现将按顺序自动维护键。考虑sortedcontainers模块,它是纯Python和快速C实现。还与其他基准测试的流行选项进行性能比较

如果您需要在迭代过程中不断添加和删除键/值对,则使用有序dict是不适当的解决方案。

>>> from sortedcontainers import SortedDict
>>> d = {2:3, 1:89, 4:5, 3:0}
>>> s = SortedDict(d)
>>> s.items()
[(1, 89), (2, 3), (3, 0), (4, 5)]

SortedDict类型还支持索引位置查找和删除,这是内置dict类型无法实现的。

>>> s.iloc[-1]
4
>>> del s.iloc[2]
>>> s.keys()
SortedSet([1, 2, 4])

There are a number of Python modules that provide dictionary implementations which automatically maintain the keys in sorted order. Consider the sortedcontainers module which is pure-Python and fast-as-C implementations. There is also a performance comparison with other popular options benchmarked against one another.

Using an ordered dict is an inadequate solution if you need to constantly add and remove key/value pairs while also iterating.

>>> from sortedcontainers import SortedDict
>>> d = {2:3, 1:89, 4:5, 3:0}
>>> s = SortedDict(d)
>>> s.items()
[(1, 89), (2, 3), (3, 0), (4, 5)]

The SortedDict type also supports indexed location lookups and deletion which isn’t possible with the built-in dict type.

>>> s.iloc[-1]
4
>>> del s.iloc[2]
>>> s.keys()
SortedSet([1, 2, 4])

回答 5

只是:

d = {2:3, 1:89, 4:5, 3:0}
sd = sorted(d.items())

for k,v in sd:
    print k, v

输出:

1 89
2 3
3 0
4 5

Simply:

d = {2:3, 1:89, 4:5, 3:0}
sd = sorted(d.items())

for k,v in sd:
    print k, v

Output:

1 89
2 3
3 0
4 5

回答 6

正如其他人所提到的,字典本质上是无序的。但是,如果问题仅在于按顺序显示字典,则可以__str__在字典子类中重写该方法,并使用此字典类而不是Builtin dict。例如。

class SortedDisplayDict(dict):
   def __str__(self):
       return "{" + ", ".join("%r: %r" % (key, self[key]) for key in sorted(self)) + "}"


>>> d = SortedDisplayDict({2:3, 1:89, 4:5, 3:0})
>>> d
{1: 89, 2: 3, 3: 0, 4: 5}

请注意,这不会改变密钥的存储方式,迭代时它们返回的顺序等,也不会改变它们print在python控制台中的显示方式。

As others have mentioned, dictionaries are inherently unordered. However, if the issue is merely displaying dictionaries in an ordered fashion, you can override the __str__ method in a dictionary subclass, and use this dictionary class rather than the builtin dict. Eg.

class SortedDisplayDict(dict):
   def __str__(self):
       return "{" + ", ".join("%r: %r" % (key, self[key]) for key in sorted(self)) + "}"


>>> d = SortedDisplayDict({2:3, 1:89, 4:5, 3:0})
>>> d
{1: 89, 2: 3, 3: 0, 4: 5}

Note, this changes nothing about how the keys are stored, the order they will come back when you iterate over them etc, just how they’re displayed with print or at the python console.


回答 7

找到了另一种方法:

import json
print json.dumps(d, sort_keys = True)

upd:
1.这也会对嵌套对象进行排序(感谢@DanielF)。
2. python字典是无序的,因此可用于打印或仅分配给str。

Found another way:

import json
print json.dumps(d, sort_keys = True)

upd:
1. this also sorts nested objects (thanks @DanielF).
2. python dictionaries are unordered therefore this is sutable for print or assign to str only.


回答 8

在Python 3中。

>>> D1 = {2:3, 1:89, 4:5, 3:0}
>>> for key in sorted(D1):
    print (key, D1[key])

1 89
2 3
3 0
4 5

In Python 3.

>>> D1 = {2:3, 1:89, 4:5, 3:0}
>>> for key in sorted(D1):
    print (key, D1[key])

gives

1 89
2 3
3 0
4 5

回答 9

Python字典在Python 3.6之前是无序的。在Python 3.6的CPython实现中,字典保留插入顺序。从Python 3.7开始,这将成为一种语言功能。

在Python 3.6的更新日志中(https://docs.python.org/3.6/whatsnew/3.6.html#whatsnew36-compactdict):

此新实现的顺序保留方面被认为是实现细节,因此不应依赖(将来可能会更改,但是希望在更改语言规范之前,先在几个发行版中使用该新dict实现该语言,为所有当前和将来的Python实现强制要求保留顺序的语义;这还有助于保留与仍旧有效的随机迭代顺序的旧版本语言(例如Python 3.5)的向后兼容性。

在Python 3.7的文档中(https://docs.python.org/3.7/tutorial/datastructures.html#dictionaries):

在字典上执行list(d)会以插入顺序返回字典中使用的所有键的列表(如果要对其进行排序,请改用sorted(d))。

因此,与以前的版本不同,您可以在Python 3.6 / 3.7之后对字典进行排序。如果要对嵌套的字典(包括其中的子字典)进行排序,则可以执行以下操作:

test_dict = {'a': 1, 'c': 3, 'b': {'b2': 2, 'b1': 1}}

def dict_reorder(item):
    return {k: sort_dict(v) if isinstance(v, dict) else v for k, v in sorted(item.items())}

reordered_dict = dict_reorder(test_dict)

https://gist.github.com/ligyxy/f60f0374defc383aa098d44cfbd318eb

Python dictionary was unordered before Python 3.6. In CPython implementation of Python 3.6, dictionary keeps the insertion order. From Python 3.7, this will become a language feature.

In changelog of Python 3.6 (https://docs.python.org/3.6/whatsnew/3.6.html#whatsnew36-compactdict):

The order-preserving aspect of this new implementation is considered an implementation detail and should not be relied upon (this may change in the future, but it is desired to have this new dict implementation in the language for a few releases before changing the language spec to mandate order-preserving semantics for all current and future Python implementations; this also helps preserve backwards-compatibility with older versions of the language where random iteration order is still in effect, e.g. Python 3.5).

In document of Python 3.7 (https://docs.python.org/3.7/tutorial/datastructures.html#dictionaries):

Performing list(d) on a dictionary returns a list of all the keys used in the dictionary, in insertion order (if you want it sorted, just use sorted(d) instead).

So unlike previous versions, you can sort a dict after Python 3.6/3.7. If you want to sort a nested dict including the sub-dict inside, you can do:

test_dict = {'a': 1, 'c': 3, 'b': {'b2': 2, 'b1': 1}}

def dict_reorder(item):
    return {k: sort_dict(v) if isinstance(v, dict) else v for k, v in sorted(item.items())}

reordered_dict = dict_reorder(test_dict)

https://gist.github.com/ligyxy/f60f0374defc383aa098d44cfbd318eb


回答 10

在这里,我找到了一些最简单的解决方案,以使用键对python字典进行排序pprint。例如。

>>> x = {'a': 10, 'cd': 20, 'b': 30, 'az': 99} 
>>> print x
{'a': 10, 'b': 30, 'az': 99, 'cd': 20}

但是在使用pprint时,它将返回排序的字典

>>> import pprint 
>>> pprint.pprint(x)
{'a': 10, 'az': 99, 'b': 30, 'cd': 20}

Here I found some simplest solution to sort the python dict by key using pprint. eg.

>>> x = {'a': 10, 'cd': 20, 'b': 30, 'az': 99} 
>>> print x
{'a': 10, 'b': 30, 'az': 99, 'cd': 20}

but while using pprint it will return sorted dict

>>> import pprint 
>>> pprint.pprint(x)
{'a': 10, 'az': 99, 'b': 30, 'cd': 20}

回答 11

有一种简单的方法可以对字典进行排序。

根据您的问题,

解决方案是:

c={2:3, 1:89, 4:5, 3:0}
y=sorted(c.items())
print y

(其中c是您的字典的名称。)

该程序提供以下输出:

[(1, 89), (2, 3), (3, 0), (4, 5)]

就像你想要的。

另一个示例是:

d={"John":36,"Lucy":24,"Albert":32,"Peter":18,"Bill":41}
x=sorted(d.keys())
print x

给出输出:['Albert', 'Bill', 'John', 'Lucy', 'Peter']

y=sorted(d.values())
print y

给出输出:[18, 24, 32, 36, 41]

z=sorted(d.items())
print z

给出输出:

[('Albert', 32), ('Bill', 41), ('John', 36), ('Lucy', 24), ('Peter', 18)]

因此,通过将其更改为键,值和项,您可以按照自己的需要进行打印。希望这会有所帮助!

There is an easy way to sort a dictionary.

According to your question,

The solution is :

c={2:3, 1:89, 4:5, 3:0}
y=sorted(c.items())
print y

(Where c,is the name of your dictionary.)

This program gives the following output:

[(1, 89), (2, 3), (3, 0), (4, 5)]

like u wanted.

Another example is:

d={"John":36,"Lucy":24,"Albert":32,"Peter":18,"Bill":41}
x=sorted(d.keys())
print x

Gives the output:['Albert', 'Bill', 'John', 'Lucy', 'Peter']

y=sorted(d.values())
print y

Gives the output:[18, 24, 32, 36, 41]

z=sorted(d.items())
print z

Gives the output:

[('Albert', 32), ('Bill', 41), ('John', 36), ('Lucy', 24), ('Peter', 18)]

Hence by changing it into keys, values and items , you can print like what u wanted.Hope this helps!


回答 12

将会生成您想要的东西:

 D1 = {2:3, 1:89, 4:5, 3:0}

 sort_dic = {}

 for i in sorted(D1):
     sort_dic.update({i:D1[i]})
 print sort_dic


{1: 89, 2: 3, 3: 0, 4: 5}

但这不是执行此操作的正确方法,因为它可能会显示不同词典的不同行为,这是我最近学到的。因此,Tim在我在这里分享的Query的响应中提出了一种完美的方法。

from collections import OrderedDict
sorted_dict = OrderedDict(sorted(D1.items(), key=lambda t: t[0]))

Will generate exactly what you want:

 D1 = {2:3, 1:89, 4:5, 3:0}

 sort_dic = {}

 for i in sorted(D1):
     sort_dic.update({i:D1[i]})
 print sort_dic


{1: 89, 2: 3, 3: 0, 4: 5}

But this is not the correct way to do this, because, It could show a distinct behavior with different dictionaries, which I have learned recently. Hence perfect way has been suggested by Tim In the response of my Query which I am sharing here.

from collections import OrderedDict
sorted_dict = OrderedDict(sorted(D1.items(), key=lambda t: t[0]))

回答 13

我认为最简单的方法是按键对字典进行排序,然后将排序后的键:值对保存在新字典中。

dict1 = {'renault': 3, 'ford':4, 'volvo': 1, 'toyota': 2} 
dict2 = {}                  # create an empty dict to store the sorted values
for key in sorted(dict1.keys()):
    if not key in dict2:    # Depending on the goal, this line may not be neccessary
        dict2[key] = dict1[key]

为了更清楚一点:

dict1 = {'renault': 3, 'ford':4, 'volvo': 1, 'toyota': 2} 
dict2 = {}                  # create an empty dict to store the sorted     values
for key in sorted(dict1.keys()):
    if not key in dict2:    # Depending on the goal, this line may not be  neccessary
        value = dict1[key]
        dict2[key] = value

I think the easiest thing is to sort the dict by key and save the sorted key:value pair in a new dict.

dict1 = {'renault': 3, 'ford':4, 'volvo': 1, 'toyota': 2} 
dict2 = {}                  # create an empty dict to store the sorted values
for key in sorted(dict1.keys()):
    if not key in dict2:    # Depending on the goal, this line may not be neccessary
        dict2[key] = dict1[key]

To make it clearer:

dict1 = {'renault': 3, 'ford':4, 'volvo': 1, 'toyota': 2} 
dict2 = {}                  # create an empty dict to store the sorted     values
for key in sorted(dict1.keys()):
    if not key in dict2:    # Depending on the goal, this line may not be  neccessary
        value = dict1[key]
        dict2[key] = value

回答 14

您可以根据问题按关键字对当前词典进行排序,从而创建新词典。

这是你的字典

d = {2:3, 1:89, 4:5, 3:0}

通过使用lambda函数对d排序来创建新字典d1

d1 = dict(sorted(d.items(), key = lambda x:x[0]))

d1应该为{1:89,2:3,3:0,4:5},根据d中的键排序。

You can create a new dictionary by sorting the current dictionary by key as per your question.

This is your dictionary

d = {2:3, 1:89, 4:5, 3:0}

Create a new dictionary d1 by sorting this d using lambda function

d1 = dict(sorted(d.items(), key = lambda x:x[0]))

d1 should be {1: 89, 2: 3, 3: 0, 4: 5}, sorted based on keys in d.


回答 15

Python字典是无序的。通常,这不是问题,因为最常见的用例是进行查找。

执行所需操作的最简单方法是创建collections.OrderedDict按排序顺序插入元素。

ordered_dict = collections.OrderedDict([(k, d[k]) for k in sorted(d.keys())])

如上面其他建议那样,如果需要迭代,则最简单的方法是迭代已排序的键。例子-

打印按键排序的值:

# create the dict
d = {k1:v1, k2:v2,...}
# iterate by keys in sorted order
for k in sorted(d.keys()):
    value = d[k]
    # do something with k, value like print
    print k, value

获取按键排序的值列表:

values = [d[k] for k in sorted(d.keys())]

Python dicts are un-ordered. Usually, this is not a problem since the most common use case is to do a lookup.

The simplest way to do what you want would be to create a collections.OrderedDict inserting the elements in sorted order.

ordered_dict = collections.OrderedDict([(k, d[k]) for k in sorted(d.keys())])

If you need to iterated, as others above have suggested, the simplest way would be to iterate over sorted keys. Examples-

Print values sorted by keys:

# create the dict
d = {k1:v1, k2:v2,...}
# iterate by keys in sorted order
for k in sorted(d.keys()):
    value = d[k]
    # do something with k, value like print
    print k, value

Get list of values sorted by keys:

values = [d[k] for k in sorted(d.keys())]

回答 16

我提出单行字典排序。

>> a = {2:3, 1:89, 4:5, 3:0}
>> c = {i:a[i] for i in sorted(a.keys())}
>> print(c)
{1: 89, 2: 3, 3: 0, 4: 5}
[Finished in 0.4s]

希望这会有所帮助。

I come up with single line dict sorting.

>> a = {2:3, 1:89, 4:5, 3:0}
>> c = {i:a[i] for i in sorted(a.keys())}
>> print(c)
{1: 89, 2: 3, 3: 0, 4: 5}
[Finished in 0.4s]

Hope this will be helpful.


回答 17

此函数将按其键对任何字典进行递归排序。也就是说,如果字典中的任何值也是字典,则也将通过其键对它进行排序。如果您在CPython 3.6或更高版本上运行,则可以简单地更改为使用a dict而不是an OrderedDict

from collections import OrderedDict

def sort_dict(d):
    items = [[k, v] for k, v in sorted(d.items(), key=lambda x: x[0])]
    for item in items:
        if isinstance(item[1], dict):
            item[1] = sort_dict(item[1])
    return OrderedDict(items)
    #return dict(items)

This function will sort any dictionary recursively by its key. That is, if any value in the dictionary is also a dictionary, it too will be sorted by its key. If you are running on CPython 3.6 or greater, than a simple change to use a dict rather than an OrderedDict can be made.

from collections import OrderedDict

def sort_dict(d):
    items = [[k, v] for k, v in sorted(d.items(), key=lambda x: x[0])]
    for item in items:
        if isinstance(item[1], dict):
            item[1] = sort_dict(item[1])
    return OrderedDict(items)
    #return dict(items)

回答 18

伙计们,你让事情变得复杂了……这很简单

from pprint import pprint
Dict={'B':1,'A':2,'C':3}
pprint(Dict)

输出为:

{'A':2,'B':1,'C':3}

Guys you are making things complicated … it’s really simple

from pprint import pprint
Dict={'B':1,'A':2,'C':3}
pprint(Dict)

The output is:

{'A':2,'B':1,'C':3}

回答 19

最简单的解决方案是,您应该获得一个dict键的列表,该键是排序顺序,然后遍历dict。例如

a1 = {'a':1, 'b':13, 'd':4, 'c':2, 'e':30}
a1_sorted_keys = sorted(a1, key=a1.get, reverse=True)
for r in a1_sorted_keys:
    print r, a1[r]

以下是输出(降序)

e 30
b 13
d 4
c 2
a 1

Simplest solution is that you should get a list of dict key is sorted order and then iterate over dict. For example

a1 = {'a':1, 'b':13, 'd':4, 'c':2, 'e':30}
a1_sorted_keys = sorted(a1, key=a1.get, reverse=True)
for r in a1_sorted_keys:
    print r, a1[r]

Following will be the output (desending order)

e 30
b 13
d 4
c 2
a 1

回答 20

一种简单的方法:

d = {2:3, 1:89, 4:5, 3:0}

s = {k : d[k] for k in sorted(d)}

s
Out[1]: {1: 89, 2: 3, 3: 0, 4: 5} 

An easy way to do this:

d = {2:3, 1:89, 4:5, 3:0}

s = {k : d[k] for k in sorted(d)}

s
Out[1]: {1: 89, 2: 3, 3: 0, 4: 5} 

回答 21

2.7中这两种方法的时序比较表明它们实际上是相同的:

>>> setup_string = "a = sorted(dict({2:3, 1:89, 4:5, 3:0}).items())"
>>> timeit.timeit(stmt="[(k, val) for k, val in a]", setup=setup_string, number=10000)
0.003599141953657181

>>> setup_string = "from collections import OrderedDict\n"
>>> setup_string += "a = OrderedDict({1:89, 2:3, 3:0, 4:5})\n"
>>> setup_string += "b = a.items()"
>>> timeit.timeit(stmt="[(k, val) for k, val in b]", setup=setup_string, number=10000)
0.003581275490432745 

A timing comparison of the two methods in 2.7 shows them to be virtually identical:

>>> setup_string = "a = sorted(dict({2:3, 1:89, 4:5, 3:0}).items())"
>>> timeit.timeit(stmt="[(k, val) for k, val in a]", setup=setup_string, number=10000)
0.003599141953657181

>>> setup_string = "from collections import OrderedDict\n"
>>> setup_string += "a = OrderedDict({1:89, 2:3, 3:0, 4:5})\n"
>>> setup_string += "b = a.items()"
>>> timeit.timeit(stmt="[(k, val) for k, val in b]", setup=setup_string, number=10000)
0.003581275490432745 

回答 22

from operator import itemgetter
# if you would like to play with multiple dictionaries then here you go:
# Three dictionaries that are composed of first name and last name.
user = [
    {'fname': 'Mo', 'lname': 'Mahjoub'},
    {'fname': 'Abdo', 'lname': 'Al-hebashi'},
    {'fname': 'Ali', 'lname': 'Muhammad'}
]
#  This loop will sort by the first and the last names.
# notice that in a dictionary order doesn't matter. So it could put the first name first or the last name first. 
for k in sorted (user, key=itemgetter ('fname', 'lname')):
    print (k)

# This one will sort by the first name only.
for x in sorted (user, key=itemgetter ('fname')):
    print (x)
from operator import itemgetter
# if you would like to play with multiple dictionaries then here you go:
# Three dictionaries that are composed of first name and last name.
user = [
    {'fname': 'Mo', 'lname': 'Mahjoub'},
    {'fname': 'Abdo', 'lname': 'Al-hebashi'},
    {'fname': 'Ali', 'lname': 'Muhammad'}
]
#  This loop will sort by the first and the last names.
# notice that in a dictionary order doesn't matter. So it could put the first name first or the last name first. 
for k in sorted (user, key=itemgetter ('fname', 'lname')):
    print (k)

# This one will sort by the first name only.
for x in sorted (user, key=itemgetter ('fname')):
    print (x)

回答 23

dictionary = {1:[2],2:[],5:[4,5],4:[5],3:[1]}

temp=sorted(dictionary)
sorted_dict = dict([(k,dictionary[k]) for i,k in enumerate(temp)])

sorted_dict:
         {1: [2], 2: [], 3: [1], 4: [5], 5: [4, 5]}
dictionary = {1:[2],2:[],5:[4,5],4:[5],3:[1]}

temp=sorted(dictionary)
sorted_dict = dict([(k,dictionary[k]) for i,k in enumerate(temp)])

sorted_dict:
         {1: [2], 2: [], 3: [1], 4: [5], 5: [4, 5]}

回答 24

或使用pandas

演示:

>>> d={'B':1,'A':2,'C':3}
>>> df=pd.DataFrame(d,index=[0]).sort_index(axis=1)
   A  B  C
0  2  1  3
>>> df.to_dict('int')[0]
{'A': 2, 'B': 1, 'C': 3}
>>> 

看到:

这个文档

大熊猫的文献资料

Or use pandas,

Demo:

>>> d={'B':1,'A':2,'C':3}
>>> df=pd.DataFrame(d,index=[0]).sort_index(axis=1)
   A  B  C
0  2  1  3
>>> df.to_dict('int')[0]
{'A': 2, 'B': 1, 'C': 3}
>>> 

See:

Docs of this

Documentation of whole pandas


回答 25

我的建议是这样,因为它允许您在添加项目时对字典进行排序或使字典保持排序,并且将来可能需要添加项目:

dict从头开始构建。有第二个数据结构,一个列表,以及您的键列表。bisect软件包具有insort函数,该函数允许插入排序列表中,或者在完全填充字典后对列表进行排序。现在,当您遍历字典时,您将遍历列表以按顺序访问每个键,而不必担心dict结构的表示(不是为排序而设计的)。

My suggestion is this as it allows you to sort a dict or keep a dict sorted as you are adding items and might need to add items in the future:

Build a dict from scratch as you go along. Have a second data structure, a list, with your list of keys. The bisect package has an insort function which allows inserting into a sorted list, or sort your list after completely populating your dict. Now, when you iterate over your dict, you instead iterate over the list to access each key in an in-order fashion without worrying about the representation of the dict structure (which was not made for sorting).


回答 26

l = dict.keys()
l2 = l
l2.append(0)
l3 = []
for repeater in range(0, len(l)):
    smallnum = float("inf")
    for listitem in l2:
        if listitem < smallnum:
            smallnum = listitem
    l2.remove(smallnum)
    l3.append(smallnum)
l3.remove(0)
l = l3

for listitem in l:
    print(listitem)
l = dict.keys()
l2 = l
l2.append(0)
l3 = []
for repeater in range(0, len(l)):
    smallnum = float("inf")
    for listitem in l2:
        if listitem < smallnum:
            smallnum = listitem
    l2.remove(smallnum)
    l3.append(smallnum)
l3.remove(0)
l = l3

for listitem in l:
    print(listitem)

我应该在Python字典上使用’has_key()’或’in’吗?

问题:我应该在Python字典上使用’has_key()’或’in’吗?

我不知道该怎么办:

d = {'a': 1, 'b': 2}
'a' in d
True

要么:

d = {'a': 1, 'b': 2}
d.has_key('a')
True

I wonder what is better to do:

d = {'a': 1, 'b': 2}
'a' in d
True

or:

d = {'a': 1, 'b': 2}
d.has_key('a')
True

回答 0

in 绝对更pythonic。

实际上has_key()已在Python 3.x中删除

in is definitely more pythonic.

In fact has_key() was removed in Python 3.x.


回答 1

in 不仅在优雅方面(而且不被弃用;-),而且在性能方面,都赢得了放手,例如:

$ python -mtimeit -s'd=dict.fromkeys(range(99))' '12 in d'
10000000 loops, best of 3: 0.0983 usec per loop
$ python -mtimeit -s'd=dict.fromkeys(range(99))' 'd.has_key(12)'
1000000 loops, best of 3: 0.21 usec per loop

尽管以下观察并非总是正确的,但您会注意到,通常在Python中,更快的解决方案更加优雅和Pythonic。这就是为什么如此-mtimeit有用的原因- 不仅仅是在这里和那里节省一百纳秒!-)

in wins hands-down, not just in elegance (and not being deprecated;-) but also in performance, e.g.:

$ python -mtimeit -s'd=dict.fromkeys(range(99))' '12 in d'
10000000 loops, best of 3: 0.0983 usec per loop
$ python -mtimeit -s'd=dict.fromkeys(range(99))' 'd.has_key(12)'
1000000 loops, best of 3: 0.21 usec per loop

While the following observation is not always true, you’ll notice that usually, in Python, the faster solution is more elegant and Pythonic; that’s why -mtimeit is SO helpful — it’s not just about saving a hundred nanoseconds here and there!-)


回答 2

根据python docs

has_key()不推荐使用 key in d

According to python docs:

has_key() is deprecated in favor of key in d.


回答 3

使用dict.has_key()如果(且仅当)你的代码是要求Python版本早于2.3(当为可运key in dict介绍)。

Use dict.has_key() if (and only if) your code is required to be runnable by Python versions earlier than 2.3 (when key in dict was introduced).


回答 4

有一个例子in实际上会削弱您的表现。

如果你使用in一个O(1)集装箱只实现__getitem__has_key()而不是__contains__你会变成一个O(1)搜索到O(N),搜索(如in回落到通过线性搜索__getitem__)。

修复显然是微不足道的:

def __contains__(self, x):
    return self.has_key(x)

There is one example where in actually kills your performance.

If you use in on a O(1) container that only implements __getitem__ and has_key() but not __contains__ you will turn an O(1) search into an O(N) search (as in falls back to a linear search via __getitem__).

Fix is obviously trivial:

def __contains__(self, x):
    return self.has_key(x)

回答 5

has_key是一个字典方法,但是in可以在任何集合上使用,即使__contains__丢失,in也可以使用任何其他方法来迭代该集合以找出答案。

has_key is a dictionary method, but in will work on any collection, and even when __contains__ is missing, in will use any other method to iterate the collection to find out.


回答 6

dict.has_key()的解决方案已弃用,请使用“ in”-sublime文本编辑器3

在这里,我举了一个名为“ age”的字典的例子-

ages = {}

# Add a couple of names to the dictionary
ages['Sue'] = 23

ages['Peter'] = 19

ages['Andrew'] = 78

ages['Karren'] = 45

# use of 'in' in if condition instead of function_name.has_key(key-name).
if 'Sue' in ages:

    print "Sue is in the dictionary. She is", ages['Sue'], "years old"

else:

    print "Sue is not in the dictionary"

Solution to dict.has_key() is deprecated, use ‘in’ — sublime text editor 3

Here I have taken an example of dictionary named ‘ages’ –

ages = {}

# Add a couple of names to the dictionary
ages['Sue'] = 23

ages['Peter'] = 19

ages['Andrew'] = 78

ages['Karren'] = 45

# use of 'in' in if condition instead of function_name.has_key(key-name).
if 'Sue' in ages:

    print "Sue is in the dictionary. She is", ages['Sue'], "years old"

else:

    print "Sue is not in the dictionary"

回答 7

亚当·帕金(Adam Parkin)的评论扩展了Alex Martelli的性能测试…

$ python3.5 -mtimeit -s'd=dict.fromkeys(range( 99))' 'd.has_key(12)'
Traceback (most recent call last):
  File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/timeit.py", line 301, in main
    x = t.timeit(number)
  File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/timeit.py", line 178, in timeit
    timing = self.inner(it, self.timer)
  File "<timeit-src>", line 6, in inner
    d.has_key(12)
AttributeError: 'dict' object has no attribute 'has_key'

$ python2.7 -mtimeit -s'd=dict.fromkeys(range(  99))' 'd.has_key(12)'
10000000 loops, best of 3: 0.0872 usec per loop

$ python2.7 -mtimeit -s'd=dict.fromkeys(range(1999))' 'd.has_key(12)'
10000000 loops, best of 3: 0.0858 usec per loop

$ python3.5 -mtimeit -s'd=dict.fromkeys(range(  99))' '12 in d'
10000000 loops, best of 3: 0.031 usec per loop

$ python3.5 -mtimeit -s'd=dict.fromkeys(range(1999))' '12 in d'
10000000 loops, best of 3: 0.033 usec per loop

$ python3.5 -mtimeit -s'd=dict.fromkeys(range(  99))' '12 in d.keys()'
10000000 loops, best of 3: 0.115 usec per loop

$ python3.5 -mtimeit -s'd=dict.fromkeys(range(1999))' '12 in d.keys()'
10000000 loops, best of 3: 0.117 usec per loop

Expanding on Alex Martelli’s performance tests with Adam Parkin’s comments…

$ python3.5 -mtimeit -s'd=dict.fromkeys(range( 99))' 'd.has_key(12)'
Traceback (most recent call last):
  File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/timeit.py", line 301, in main
    x = t.timeit(number)
  File "/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/lib/python3.5/timeit.py", line 178, in timeit
    timing = self.inner(it, self.timer)
  File "<timeit-src>", line 6, in inner
    d.has_key(12)
AttributeError: 'dict' object has no attribute 'has_key'

$ python2.7 -mtimeit -s'd=dict.fromkeys(range(  99))' 'd.has_key(12)'
10000000 loops, best of 3: 0.0872 usec per loop

$ python2.7 -mtimeit -s'd=dict.fromkeys(range(1999))' 'd.has_key(12)'
10000000 loops, best of 3: 0.0858 usec per loop

$ python3.5 -mtimeit -s'd=dict.fromkeys(range(  99))' '12 in d'
10000000 loops, best of 3: 0.031 usec per loop

$ python3.5 -mtimeit -s'd=dict.fromkeys(range(1999))' '12 in d'
10000000 loops, best of 3: 0.033 usec per loop

$ python3.5 -mtimeit -s'd=dict.fromkeys(range(  99))' '12 in d.keys()'
10000000 loops, best of 3: 0.115 usec per loop

$ python3.5 -mtimeit -s'd=dict.fromkeys(range(1999))' '12 in d.keys()'
10000000 loops, best of 3: 0.117 usec per loop

回答 8

如果您有这样的事情:

t.has_key(ew)

将其更改为以下版本以在Python 3.X及更高版本上运行:

key = ew
if key not in t

If you have something like this:

t.has_key(ew)

change it to below for running on Python 3.X and above:

key = ew
if key not in t

获取字典中具有最大值的键?

问题:获取字典中具有最大值的键?

我有一个dictionary:键是字符串,值是整数。

例:

stats = {'a':1000, 'b':3000, 'c': 100}

我想得到'b'一个答案,因为它是具有更高价值的关键。

我使用带有反向键值元组的中间列表进行了以下操作:

inverse = [(value, key) for key, value in stats.items()]
print max(inverse)[1]

那是更好(或更优雅)的方法吗?

I have a dictionary: keys are strings, values are integers.

Example:

stats = {'a':1000, 'b':3000, 'c': 100}

I’d like to get 'b' as an answer, since it’s the key with a higher value.

I did the following, using an intermediate list with reversed key-value tuples:

inverse = [(value, key) for key, value in stats.items()]
print max(inverse)[1]

Is that one the better (or even more elegant) approach?


回答 0

您可以使用operator.itemgetter

import operator
stats = {'a':1000, 'b':3000, 'c': 100}
max(stats.iteritems(), key=operator.itemgetter(1))[0]

而不是在内存使用中构建新列表stats.iteritems()。该函数的key参数max()是一个函数,该函数计算用于确定如何对项目进行排名的键。

请注意,如果要使用另一个键值对“ d”:3000,则此方法将仅返回两个值中的一个,即使它们都具有最大值。

>>> import operator
>>> stats = {'a':1000, 'b':3000, 'c': 100, 'd':3000}
>>> max(stats.iteritems(), key=operator.itemgetter(1))[0]
'b' 

如果使用Python3:

>>> max(stats.items(), key=operator.itemgetter(1))[0]
'b'

You can use operator.itemgetter for that:

import operator
stats = {'a':1000, 'b':3000, 'c': 100}
max(stats.iteritems(), key=operator.itemgetter(1))[0]

And instead of building a new list in memory use stats.iteritems(). The key parameter to the max() function is a function that computes a key that is used to determine how to rank items.

Please note that if you were to have another key-value pair ‘d’: 3000 that this method will only return one of the two even though they both have the maximum value.

>>> import operator
>>> stats = {'a':1000, 'b':3000, 'c': 100, 'd':3000}
>>> max(stats.iteritems(), key=operator.itemgetter(1))[0]
'b' 

If using Python3:

>>> max(stats.items(), key=operator.itemgetter(1))[0]
'b'

回答 1

max(stats, key=stats.get)
max(stats, key=stats.get)

回答 2

我已经测试了许多变体,这是用最大值返回字典键的最快方法:

def keywithmaxval(d):
     """ a) create a list of the dict's keys and values; 
         b) return the key with the max value"""  
     v=list(d.values())
     k=list(d.keys())
     return k[v.index(max(v))]

为了给您一个想法,以下是一些候选方法:

def f1():  
     v=list(d1.values())
     k=list(d1.keys())
     return k[v.index(max(v))]

def f2():
    d3={v:k for k,v in d1.items()}
    return d3[max(d3)]

def f3():
    return list(filter(lambda t: t[1]==max(d1.values()), d1.items()))[0][0]    

def f3b():
    # same as f3 but remove the call to max from the lambda
    m=max(d1.values())
    return list(filter(lambda t: t[1]==m, d1.items()))[0][0]        

def f4():
    return [k for k,v in d1.items() if v==max(d1.values())][0]    

def f4b():
    # same as f4 but remove the max from the comprehension
    m=max(d1.values())
    return [k for k,v in d1.items() if v==m][0]        

def f5():
    return max(d1.items(), key=operator.itemgetter(1))[0]    

def f6():
    return max(d1,key=d1.get)     

def f7():
     """ a) create a list of the dict's keys and values; 
         b) return the key with the max value"""    
     v=list(d1.values())
     return list(d1.keys())[v.index(max(v))]    

def f8():
     return max(d1, key=lambda k: d1[k])     

tl=[f1,f2, f3b, f4b, f5, f6, f7, f8, f4,f3]     
cmpthese.cmpthese(tl,c=100) 

测试字典:

d1={1: 1, 2: 2, 3: 8, 4: 3, 5: 6, 6: 9, 7: 17, 8: 4, 9: 20, 10: 7, 11: 15, 
    12: 10, 13: 10, 14: 18, 15: 18, 16: 5, 17: 13, 18: 21, 19: 21, 20: 8, 
    21: 8, 22: 16, 23: 16, 24: 11, 25: 24, 26: 11, 27: 112, 28: 19, 29: 19, 
    30: 19, 3077: 36, 32: 6, 33: 27, 34: 14, 35: 14, 36: 22, 4102: 39, 38: 22, 
    39: 35, 40: 9, 41: 110, 42: 9, 43: 30, 44: 17, 45: 17, 46: 17, 47: 105, 48: 12, 
    49: 25, 50: 25, 51: 25, 52: 12, 53: 12, 54: 113, 1079: 50, 56: 20, 57: 33, 
    58: 20, 59: 33, 60: 20, 61: 20, 62: 108, 63: 108, 64: 7, 65: 28, 66: 28, 67: 28, 
    68: 15, 69: 15, 70: 15, 71: 103, 72: 23, 73: 116, 74: 23, 75: 15, 76: 23, 77: 23, 
    78: 36, 79: 36, 80: 10, 81: 23, 82: 111, 83: 111, 84: 10, 85: 10, 86: 31, 87: 31, 
    88: 18, 89: 31, 90: 18, 91: 93, 92: 18, 93: 18, 94: 106, 95: 106, 96: 13, 9232: 35, 
    98: 26, 99: 26, 100: 26, 101: 26, 103: 88, 104: 13, 106: 13, 107: 101, 1132: 63, 
    2158: 51, 112: 21, 113: 13, 116: 21, 118: 34, 119: 34, 7288: 45, 121: 96, 122: 21, 
    124: 109, 125: 109, 128: 8, 1154: 32, 131: 29, 134: 29, 136: 16, 137: 91, 140: 16, 
    142: 104, 143: 104, 146: 117, 148: 24, 149: 24, 152: 24, 154: 24, 155: 86, 160: 11, 
    161: 99, 1186: 76, 3238: 49, 167: 68, 170: 11, 172: 32, 175: 81, 178: 32, 179: 32, 
    182: 94, 184: 19, 31: 107, 188: 107, 190: 107, 196: 27, 197: 27, 202: 27, 206: 89, 
    208: 14, 214: 102, 215: 102, 220: 115, 37: 22, 224: 22, 226: 14, 232: 22, 233: 84, 
    238: 35, 242: 97, 244: 22, 250: 110, 251: 66, 1276: 58, 256: 9, 2308: 33, 262: 30, 
    263: 79, 268: 30, 269: 30, 274: 92, 1300: 27, 280: 17, 283: 61, 286: 105, 292: 118, 
    296: 25, 298: 25, 304: 25, 310: 87, 1336: 71, 319: 56, 322: 100, 323: 100, 325: 25, 
    55: 113, 334: 69, 340: 12, 1367: 40, 350: 82, 358: 33, 364: 95, 376: 108, 
    377: 64, 2429: 46, 394: 28, 395: 77, 404: 28, 412: 90, 1438: 53, 425: 59, 430: 103, 
    1456: 97, 433: 28, 445: 72, 448: 23, 466: 85, 479: 54, 484: 98, 485: 98, 488: 23, 
    6154: 37, 502: 67, 4616: 34, 526: 80, 538: 31, 566: 62, 3644: 44, 577: 31, 97: 119, 
    592: 26, 593: 75, 1619: 48, 638: 57, 646: 101, 650: 26, 110: 114, 668: 70, 2734: 41, 
    700: 83, 1732: 30, 719: 52, 728: 96, 754: 65, 1780: 74, 4858: 47, 130: 29, 790: 78, 
    1822: 43, 2051: 38, 808: 29, 850: 60, 866: 29, 890: 73, 911: 42, 958: 55, 970: 99, 
    976: 24, 166: 112}

以及在Python 3.2下的测试结果:

    rate/sec       f4      f3    f3b     f8     f5     f2    f4b     f6     f7     f1
f4       454       --   -2.5% -96.9% -97.5% -98.6% -98.6% -98.7% -98.7% -98.9% -99.0%
f3       466     2.6%      -- -96.8% -97.4% -98.6% -98.6% -98.6% -98.7% -98.9% -99.0%
f3b   14,715  3138.9% 3057.4%     -- -18.6% -55.5% -56.0% -56.4% -58.3% -63.8% -68.4%
f8    18,070  3877.3% 3777.3%  22.8%     -- -45.4% -45.9% -46.5% -48.8% -55.5% -61.2%
f5    33,091  7183.7% 7000.5% 124.9%  83.1%     --  -1.0%  -2.0%  -6.3% -18.6% -29.0%
f2    33,423  7256.8% 7071.8% 127.1%  85.0%   1.0%     --  -1.0%  -5.3% -17.7% -28.3%
f4b   33,762  7331.4% 7144.6% 129.4%  86.8%   2.0%   1.0%     --  -4.4% -16.9% -27.5%
f6    35,300  7669.8% 7474.4% 139.9%  95.4%   6.7%   5.6%   4.6%     -- -13.1% -24.2%
f7    40,631  8843.2% 8618.3% 176.1% 124.9%  22.8%  21.6%  20.3%  15.1%     -- -12.8%
f1    46,598 10156.7% 9898.8% 216.7% 157.9%  40.8%  39.4%  38.0%  32.0%  14.7%     --

在Python 2.7下:

    rate/sec       f3       f4     f8    f3b     f6     f5     f2    f4b     f7     f1
f3       384       --    -2.6% -97.1% -97.2% -97.9% -97.9% -98.0% -98.2% -98.5% -99.2%
f4       394     2.6%       -- -97.0% -97.2% -97.8% -97.9% -98.0% -98.1% -98.5% -99.1%
f8    13,079  3303.3%  3216.1%     --  -5.6% -28.6% -29.9% -32.8% -38.3% -49.7% -71.2%
f3b   13,852  3504.5%  3412.1%   5.9%     -- -24.4% -25.8% -28.9% -34.6% -46.7% -69.5%
f6    18,325  4668.4%  4546.2%  40.1%  32.3%     --  -1.8%  -5.9% -13.5% -29.5% -59.6%
f5    18,664  4756.5%  4632.0%  42.7%  34.7%   1.8%     --  -4.1% -11.9% -28.2% -58.8%
f2    19,470  4966.4%  4836.5%  48.9%  40.6%   6.2%   4.3%     --  -8.1% -25.1% -57.1%
f4b   21,187  5413.0%  5271.7%  62.0%  52.9%  15.6%  13.5%   8.8%     -- -18.5% -53.3%
f7    26,002  6665.8%  6492.4%  98.8%  87.7%  41.9%  39.3%  33.5%  22.7%     -- -42.7%
f1    45,354 11701.5% 11399.0% 246.8% 227.4% 147.5% 143.0% 132.9% 114.1%  74.4%     -- 

您可以看到f1在Python 3.2和2.7下这是最快的(或者更完整地说,keywithmaxval在本文的顶部)

I have tested MANY variants, and this is the fastest way to return the key of dict with the max value:

def keywithmaxval(d):
     """ a) create a list of the dict's keys and values; 
         b) return the key with the max value"""  
     v=list(d.values())
     k=list(d.keys())
     return k[v.index(max(v))]

To give you an idea, here are some candidate methods:

def f1():  
     v=list(d1.values())
     k=list(d1.keys())
     return k[v.index(max(v))]

def f2():
    d3={v:k for k,v in d1.items()}
    return d3[max(d3)]

def f3():
    return list(filter(lambda t: t[1]==max(d1.values()), d1.items()))[0][0]    

def f3b():
    # same as f3 but remove the call to max from the lambda
    m=max(d1.values())
    return list(filter(lambda t: t[1]==m, d1.items()))[0][0]        

def f4():
    return [k for k,v in d1.items() if v==max(d1.values())][0]    

def f4b():
    # same as f4 but remove the max from the comprehension
    m=max(d1.values())
    return [k for k,v in d1.items() if v==m][0]        

def f5():
    return max(d1.items(), key=operator.itemgetter(1))[0]    

def f6():
    return max(d1,key=d1.get)     

def f7():
     """ a) create a list of the dict's keys and values; 
         b) return the key with the max value"""    
     v=list(d1.values())
     return list(d1.keys())[v.index(max(v))]    

def f8():
     return max(d1, key=lambda k: d1[k])     

tl=[f1,f2, f3b, f4b, f5, f6, f7, f8, f4,f3]     
cmpthese.cmpthese(tl,c=100) 

The test dictionary:

d1={1: 1, 2: 2, 3: 8, 4: 3, 5: 6, 6: 9, 7: 17, 8: 4, 9: 20, 10: 7, 11: 15, 
    12: 10, 13: 10, 14: 18, 15: 18, 16: 5, 17: 13, 18: 21, 19: 21, 20: 8, 
    21: 8, 22: 16, 23: 16, 24: 11, 25: 24, 26: 11, 27: 112, 28: 19, 29: 19, 
    30: 19, 3077: 36, 32: 6, 33: 27, 34: 14, 35: 14, 36: 22, 4102: 39, 38: 22, 
    39: 35, 40: 9, 41: 110, 42: 9, 43: 30, 44: 17, 45: 17, 46: 17, 47: 105, 48: 12, 
    49: 25, 50: 25, 51: 25, 52: 12, 53: 12, 54: 113, 1079: 50, 56: 20, 57: 33, 
    58: 20, 59: 33, 60: 20, 61: 20, 62: 108, 63: 108, 64: 7, 65: 28, 66: 28, 67: 28, 
    68: 15, 69: 15, 70: 15, 71: 103, 72: 23, 73: 116, 74: 23, 75: 15, 76: 23, 77: 23, 
    78: 36, 79: 36, 80: 10, 81: 23, 82: 111, 83: 111, 84: 10, 85: 10, 86: 31, 87: 31, 
    88: 18, 89: 31, 90: 18, 91: 93, 92: 18, 93: 18, 94: 106, 95: 106, 96: 13, 9232: 35, 
    98: 26, 99: 26, 100: 26, 101: 26, 103: 88, 104: 13, 106: 13, 107: 101, 1132: 63, 
    2158: 51, 112: 21, 113: 13, 116: 21, 118: 34, 119: 34, 7288: 45, 121: 96, 122: 21, 
    124: 109, 125: 109, 128: 8, 1154: 32, 131: 29, 134: 29, 136: 16, 137: 91, 140: 16, 
    142: 104, 143: 104, 146: 117, 148: 24, 149: 24, 152: 24, 154: 24, 155: 86, 160: 11, 
    161: 99, 1186: 76, 3238: 49, 167: 68, 170: 11, 172: 32, 175: 81, 178: 32, 179: 32, 
    182: 94, 184: 19, 31: 107, 188: 107, 190: 107, 196: 27, 197: 27, 202: 27, 206: 89, 
    208: 14, 214: 102, 215: 102, 220: 115, 37: 22, 224: 22, 226: 14, 232: 22, 233: 84, 
    238: 35, 242: 97, 244: 22, 250: 110, 251: 66, 1276: 58, 256: 9, 2308: 33, 262: 30, 
    263: 79, 268: 30, 269: 30, 274: 92, 1300: 27, 280: 17, 283: 61, 286: 105, 292: 118, 
    296: 25, 298: 25, 304: 25, 310: 87, 1336: 71, 319: 56, 322: 100, 323: 100, 325: 25, 
    55: 113, 334: 69, 340: 12, 1367: 40, 350: 82, 358: 33, 364: 95, 376: 108, 
    377: 64, 2429: 46, 394: 28, 395: 77, 404: 28, 412: 90, 1438: 53, 425: 59, 430: 103, 
    1456: 97, 433: 28, 445: 72, 448: 23, 466: 85, 479: 54, 484: 98, 485: 98, 488: 23, 
    6154: 37, 502: 67, 4616: 34, 526: 80, 538: 31, 566: 62, 3644: 44, 577: 31, 97: 119, 
    592: 26, 593: 75, 1619: 48, 638: 57, 646: 101, 650: 26, 110: 114, 668: 70, 2734: 41, 
    700: 83, 1732: 30, 719: 52, 728: 96, 754: 65, 1780: 74, 4858: 47, 130: 29, 790: 78, 
    1822: 43, 2051: 38, 808: 29, 850: 60, 866: 29, 890: 73, 911: 42, 958: 55, 970: 99, 
    976: 24, 166: 112}

And the test results under Python 3.2:

    rate/sec       f4      f3    f3b     f8     f5     f2    f4b     f6     f7     f1
f4       454       --   -2.5% -96.9% -97.5% -98.6% -98.6% -98.7% -98.7% -98.9% -99.0%
f3       466     2.6%      -- -96.8% -97.4% -98.6% -98.6% -98.6% -98.7% -98.9% -99.0%
f3b   14,715  3138.9% 3057.4%     -- -18.6% -55.5% -56.0% -56.4% -58.3% -63.8% -68.4%
f8    18,070  3877.3% 3777.3%  22.8%     -- -45.4% -45.9% -46.5% -48.8% -55.5% -61.2%
f5    33,091  7183.7% 7000.5% 124.9%  83.1%     --  -1.0%  -2.0%  -6.3% -18.6% -29.0%
f2    33,423  7256.8% 7071.8% 127.1%  85.0%   1.0%     --  -1.0%  -5.3% -17.7% -28.3%
f4b   33,762  7331.4% 7144.6% 129.4%  86.8%   2.0%   1.0%     --  -4.4% -16.9% -27.5%
f6    35,300  7669.8% 7474.4% 139.9%  95.4%   6.7%   5.6%   4.6%     -- -13.1% -24.2%
f7    40,631  8843.2% 8618.3% 176.1% 124.9%  22.8%  21.6%  20.3%  15.1%     -- -12.8%
f1    46,598 10156.7% 9898.8% 216.7% 157.9%  40.8%  39.4%  38.0%  32.0%  14.7%     --

And under Python 2.7:

    rate/sec       f3       f4     f8    f3b     f6     f5     f2    f4b     f7     f1
f3       384       --    -2.6% -97.1% -97.2% -97.9% -97.9% -98.0% -98.2% -98.5% -99.2%
f4       394     2.6%       -- -97.0% -97.2% -97.8% -97.9% -98.0% -98.1% -98.5% -99.1%
f8    13,079  3303.3%  3216.1%     --  -5.6% -28.6% -29.9% -32.8% -38.3% -49.7% -71.2%
f3b   13,852  3504.5%  3412.1%   5.9%     -- -24.4% -25.8% -28.9% -34.6% -46.7% -69.5%
f6    18,325  4668.4%  4546.2%  40.1%  32.3%     --  -1.8%  -5.9% -13.5% -29.5% -59.6%
f5    18,664  4756.5%  4632.0%  42.7%  34.7%   1.8%     --  -4.1% -11.9% -28.2% -58.8%
f2    19,470  4966.4%  4836.5%  48.9%  40.6%   6.2%   4.3%     --  -8.1% -25.1% -57.1%
f4b   21,187  5413.0%  5271.7%  62.0%  52.9%  15.6%  13.5%   8.8%     -- -18.5% -53.3%
f7    26,002  6665.8%  6492.4%  98.8%  87.7%  41.9%  39.3%  33.5%  22.7%     -- -42.7%
f1    45,354 11701.5% 11399.0% 246.8% 227.4% 147.5% 143.0% 132.9% 114.1%  74.4%     -- 

You can see that f1 is the fastest under Python 3.2 and 2.7 (or, more completely, keywithmaxval at the top of this post)


回答 3

如果你只需要知道与最高值的键,你可以不用iterkeys或者iteritems因为迭代通过Python字典是迭代通过它的键。

max_key = max(stats, key=lambda k: stats[k])

编辑:

从评论@ user1274878:

我是python的新手。您能分步解释您的答案吗?

是的

最高

max(iterable [,key])

max(arg1,arg2,* args [,key])

返回可迭代的最大项或两个或多个参数中的最大项。

可选key参数描述如何比较元素以获得最大的元素:

lambda <item>: return <a result of operation with item> 

返回的值将被比较。

辞典

Python dict是一个哈希表。dict的键是声明为键的对象的哈希。由于性能原因,尽管通过字典的键实现了迭代,但仍执行迭代。

因此,我们可以使用它来摆脱获取密钥列表的操作。

关闭

在另一个函数内部定义的函数称为嵌套函数。嵌套函数可以访问封闭范围的变量。

stats通过函数的__closure__属性可用的变量,lambda作为指向父范围中定义的变量值的指针。

If you need to know only a key with the max value you can do it without iterkeys or iteritems because iteration through dictionary in Python is iteration through it’s keys.

max_key = max(stats, key=lambda k: stats[k])

EDIT:

From comments, @user1274878 :

I am new to python. Can you please explain your answer in steps?

Yep…

max

max(iterable[, key])

max(arg1, arg2, *args[, key])

Return the largest item in an iterable or the largest of two or more arguments.

The optional key argument describes how to compare elements to get maximum among them:

lambda <item>: return <a result of operation with item> 

Returned values will be compared.

Dict

Python dict is a hash table. A key of dict is a hash of an object declared as a key. Due to performance reasons iteration though a dict implemented as iteration through it’s keys.

Therefore we can use it to rid operation of obtaining a keys list.

Closure

A function defined inside another function is called a nested function. Nested functions can access variables of the enclosing scope.

The stats variable available through __closure__ attribute of the lambda function as a pointer to the value of the variable defined in the parent scope.


回答 4

例:

stats = {'a':1000, 'b':3000, 'c': 100}

如果您想通过键找到最大值,则跟随可能很简单,而无需任何相关功能。

max(stats, key=stats.get)

输出是具有最大值的键。

Example:

stats = {'a':1000, 'b':3000, 'c': 100}

if you wanna find the max value with its key, maybe follwing could be simple, without any relevant functions.

max(stats, key=stats.get)

the output is the key which has the max value.


回答 5

这是另一个:

stats = {'a':1000, 'b':3000, 'c': 100}
max(stats.iterkeys(), key=lambda k: stats[k])

该函数key仅返回应用于排序的值,并立即max()返回所需的元素。

Here is another one:

stats = {'a':1000, 'b':3000, 'c': 100}
max(stats.iterkeys(), key=lambda k: stats[k])

The function key simply returns the value that should be used for ranking and max() returns the demanded element right away.


回答 6

key, value = max(stats.iteritems(), key=lambda x:x[1])

如果您不在乎价值(我会很惊讶,但是),您可以这样做:

key, _ = max(stats.iteritems(), key=lambda x:x[1])

与表达式末尾的[0]下标相比,我更喜欢将元组拆包。我从不非常喜欢lambda表达式的可读性,但是发现它比operator.itemgetter(1)更好。

key, value = max(stats.iteritems(), key=lambda x:x[1])

If you don’t care about value (I’d be surprised, but) you can do:

key, _ = max(stats.iteritems(), key=lambda x:x[1])

I like the tuple unpacking better than a [0] subscript at the end of the expression. I never like the readability of lambda expressions very much, but find this one better than the operator.itemgetter(1) IMHO.


回答 7

鉴于有多个条目,我拥有最大值。我将列出具有最大值的键。

>>> stats = {'a':1000, 'b':3000, 'c': 100, 'd':3000}
>>> [key for m in [max(stats.values())] for key,val in stats.iteritems() if val == m]
['b', 'd']

这也将为您提供“ b”和任何其他最大键。

注意:对于python 3,请使用stats.items()代替stats.iteritems()

Given that more than one entry my have the max value. I would make a list of the keys that have the max value as their value.

>>> stats = {'a':1000, 'b':3000, 'c': 100, 'd':3000}
>>> [key for m in [max(stats.values())] for key,val in stats.iteritems() if val == m]
['b', 'd']

This will give you ‘b’ and any other max key as well.

Note: For python 3 use stats.items() instead of stats.iteritems()


回答 8

您可以使用:

max(d, key = d.get) 
# which is equivalent to 
max(d, key = lambda k : d.get(k))

要返回键,值对使用:

max(d.items(), key = lambda k : k[1])

You can use:

max(d, key = d.get) 
# which is equivalent to 
max(d, key = lambda k : d.get(k))

To return the key, value pair use:

max(d.items(), key = lambda k : k[1])

回答 9

要获得字典的最大键/值stats

stats = {'a':1000, 'b':3000, 'c': 100}
  • 基于

>>> max(stats.items(), key = lambda x: x[0]) ('c', 100)

  • 基于价值

>>> max(stats.items(), key = lambda x: x[1]) ('b', 3000)

当然,如果只想从结果中获取键或值,则可以使用元组索引。例如,要获取对应于最大值的密钥:

>>> max(stats.items(), key = lambda x: x[1])[0] 'b'

说明

items()Python 3中的dictionary方法返回字典的view对象。通过该max函数迭代该视图对象时,它会将字典项生成为form的元组(key, value)

>>> list(stats.items()) [('c', 100), ('b', 3000), ('a', 1000)]

使用lambda表达式时lambda x: x[1],在每次迭代中,x 都是这些元组之一(key, value)。因此,通过选择正确的索引,您可以选择是按键还是按值进行比较。

Python 2

对于Python 2.2+版本,相同的代码将起作用。但是,最好使用iteritems()字典方法而不是items()性能。

笔记

To get the maximum key/value of the dictionary stats:

stats = {'a':1000, 'b':3000, 'c': 100}
  • Based on keys

>>> max(stats.items(), key = lambda x: x[0]) ('c', 100)

  • Based on values

>>> max(stats.items(), key = lambda x: x[1]) ('b', 3000)

Of course, if you want to get only the key or value from the result, you can use tuple indexing. For Example, to get the key corresponding to the maximum value:

>>> max(stats.items(), key = lambda x: x[1])[0] 'b'

Explanation

The dictionary method items() in Python 3 returns a view object of the dictionary. When this view object is iterated over, by the max function, it yields the dictionary items as tuples of the form (key, value).

>>> list(stats.items()) [('c', 100), ('b', 3000), ('a', 1000)]

When you use the lambda expression lambda x: x[1], in each iteration, x is one of these tuples (key, value). So, by choosing the right index, you select whether you want to compare by keys or by values.

Python 2

For Python 2.2+ releases, the same code will work. However, it is better to use iteritems() dictionary method instead of items() for performance.

Notes


回答 10

d = {'A': 4,'B':10}

min_v = min(zip(d.values(), d.keys()))
# min_v is (4,'A')

max_v = max(zip(d.values(), d.keys()))
# max_v is (10,'B')
d = {'A': 4,'B':10}

min_v = min(zip(d.values(), d.keys()))
# min_v is (4,'A')

max_v = max(zip(d.values(), d.keys()))
# max_v is (10,'B')

回答 11

通过在选定答案中的注释进行迭代的解决方案…

在Python 3中:

max(stats.keys(), key=(lambda k: stats[k]))

在Python 2中:

max(stats.iterkeys(), key=(lambda k: stats[k]))

Per the iterated solutions via comments in the selected answer…

In Python 3:

max(stats.keys(), key=(lambda k: stats[k]))

In Python 2:

max(stats.iterkeys(), key=(lambda k: stats[k]))

回答 12

我到达这里是mydict.keys()根据的值寻找如何返回mydict.values()。我不只是返回一个键,而是希望返回前x个值。

该解决方案比使用该max()函数更简单,并且您可以轻松更改返回的值数量:

stats = {'a':1000, 'b':3000, 'c': 100}

x = sorted(stats, key=(lambda key:stats[key]), reverse=True)
['b', 'a', 'c']

如果要使用单个最高排名的键,只需使用索引:

x[0]
['b']

如果要使用排名最高的前两个键,请使用列表切片:

x[:2]
['b', 'a']

I got here looking for how to return mydict.keys() based on the value of mydict.values(). Instead of just the one key returned, I was looking to return the top x number of values.

This solution is simpler than using the max() function and you can easily change the number of values returned:

stats = {'a':1000, 'b':3000, 'c': 100}

x = sorted(stats, key=(lambda key:stats[key]), reverse=True)
['b', 'a', 'c']

If you want the single highest ranking key, just use the index:

x[0]
['b']

If you want the top two highest ranking keys, just use list slicing:

x[:2]
['b', 'a']

回答 13

我对这些答案都不满意。max总是选择具有最大值的第一个键。字典可以有多个具有该值的键。

def keys_with_top_values(my_dict):
    return [key  for (key, value) in my_dict.items() if value == max(my_dict.values())]

发布此答案,以防有人帮忙。见下面的SO帖子

如果是平局,Python会选择哪个最大值?

I was not satisfied with any of these answers. max always picks the first key with the max value. The dictionary could have multiple keys with that value.

def keys_with_top_values(my_dict):
    return [key  for (key, value) in my_dict.items() if value == max(my_dict.values())]

Posting this answer in case it helps someone out. See the below SO post

Which maximum does Python pick in the case of a tie?


回答 14

collections.Counter你可以做

>>> import collections
>>> stats = {'a':1000, 'b':3000, 'c': 100}
>>> stats = collections.Counter(stats)
>>> stats.most_common(1)
[('b', 3000)]

如果合适,您可以简单地以空开头collections.Counter并添加到其中

>>> stats = collections.Counter()
>>> stats['a'] += 1
:
etc. 

With collections.Counter you could do

>>> import collections
>>> stats = {'a':1000, 'b':3000, 'c': 100}
>>> stats = collections.Counter(stats)
>>> stats.most_common(1)
[('b', 3000)]

If appropriate, you could simply start with an empty collections.Counter and add to it

>>> stats = collections.Counter()
>>> stats['a'] += 1
:
etc. 

回答 15

堆队列是一种通用解决方案,它允许您提取按值排序的前n个键:

from heapq import nlargest

stats = {'a':1000, 'b':3000, 'c': 100}

res1 = nlargest(1, stats, key=stats.__getitem__)  # ['b']
res2 = nlargest(2, stats, key=stats.__getitem__)  # ['b', 'a']

res1_val = next(iter(res1))                       # 'b'

注意dict.__getitem__是语法糖调用的方法dict[]。与相对dict.getKeyError如果未找到密钥,它将返回,在此不会发生。

A heap queue is a generalised solution which allows you to extract the top n keys ordered by value:

from heapq import nlargest

stats = {'a':1000, 'b':3000, 'c': 100}

res1 = nlargest(1, stats, key=stats.__getitem__)  # ['b']
res2 = nlargest(2, stats, key=stats.__getitem__)  # ['b', 'a']

res1_val = next(iter(res1))                       # 'b'

Note dict.__getitem__ is the method called by the syntactic sugar dict[]. As opposed to dict.get, it will return KeyError if a key is not found, which here cannot occur.


回答 16

max((value, key) for key, value in stats.items())[1]

max((value, key) for key, value in stats.items())[1]


回答 17

+1 @Aric Coady的最简单的解决方案。
还有一种在字典中随机选择具有最大值的键之一的方法:

stats = {'a':1000, 'b':3000, 'c': 100, 'd':3000}

import random
maxV = max(stats.values())
# Choice is one of the keys with max value
choice = random.choice([key for key, value in stats.items() if value == maxV])

+1 to @Aric Coady‘s simplest solution.
And also one way to random select one of keys with max value in the dictionary:

stats = {'a':1000, 'b':3000, 'c': 100, 'd':3000}

import random
maxV = max(stats.values())
# Choice is one of the keys with max value
choice = random.choice([key for key, value in stats.items() if value == maxV])

回答 18

Counter = 0
for word in stats.keys():
    if stats[word]> counter:
        Counter = stats [word]
print Counter
Counter = 0
for word in stats.keys():
    if stats[word]> counter:
        Counter = stats [word]
print Counter

回答 19

怎么样:

 max(zip(stats.keys(), stats.values()), key=lambda t : t[1])[0]

How about:

 max(zip(stats.keys(), stats.values()), key=lambda t : t[1])[0]

回答 20

我在一个非常基本的循环中测试了可接受的答案和@thewolf最快的解决方案,该循环比两者都快:

import time
import operator


d = {"a"+str(i): i for i in range(1000000)}

def t1(dct):
    mx = float("-inf")
    key = None
    for k,v in dct.items():
        if v > mx:
            mx = v
            key = k
    return key

def t2(dct):
    v=list(dct.values())
    k=list(dct.keys())
    return k[v.index(max(v))]

def t3(dct):
    return max(dct.items(),key=operator.itemgetter(1))[0]

start = time.time()
for i in range(25):
    m = t1(d)
end = time.time()
print ("Iterating: "+str(end-start))

start = time.time()
for i in range(25):
    m = t2(d)
end = time.time()
print ("List creating: "+str(end-start))

start = time.time()
for i in range(25):
    m = t3(d)
end = time.time()
print ("Accepted answer: "+str(end-start))

结果:

Iterating: 3.8201940059661865
List creating: 6.928712844848633
Accepted answer: 5.464320182800293

I tested the accepted answer AND @thewolf’s fastest solution against a very basic loop and the loop was faster than both:

import time
import operator


d = {"a"+str(i): i for i in range(1000000)}

def t1(dct):
    mx = float("-inf")
    key = None
    for k,v in dct.items():
        if v > mx:
            mx = v
            key = k
    return key

def t2(dct):
    v=list(dct.values())
    k=list(dct.keys())
    return k[v.index(max(v))]

def t3(dct):
    return max(dct.items(),key=operator.itemgetter(1))[0]

start = time.time()
for i in range(25):
    m = t1(d)
end = time.time()
print ("Iterating: "+str(end-start))

start = time.time()
for i in range(25):
    m = t2(d)
end = time.time()
print ("List creating: "+str(end-start))

start = time.time()
for i in range(25):
    m = t3(d)
end = time.time()
print ("Accepted answer: "+str(end-start))

results:

Iterating: 3.8201940059661865
List creating: 6.928712844848633
Accepted answer: 5.464320182800293

回答 21

对于科学的python用户,以下是使用Pandas的简单解决方案:

import pandas as pd
stats = {'a': 1000, 'b': 3000, 'c': 100}
series = pd.Series(stats)
series.idxmax()

>>> b

For scientific python users, here is a simple solution using Pandas:

import pandas as pd
stats = {'a': 1000, 'b': 3000, 'c': 100}
series = pd.Series(stats)
series.idxmax()

>>> b

回答 22

如果您有多个具有相同值的键,例如:

stats = {'a':1000, 'b':3000, 'c': 100, 'd':3000, 'e':3000}

您可以获得具有最大值的所有键的集合,如下所示:

from collections import defaultdict
from collections import OrderedDict

groupedByValue = defaultdict(list)
for key, value in sorted(stats.items()):
    groupedByValue[value].append(key)

# {1000: ['a'], 3000: ['b', 'd', 'e'], 100: ['c']}

groupedByValue[max(groupedByValue)]
# ['b', 'd', 'e']

In the case you have more than one key with the same value, for example:

stats = {'a':1000, 'b':3000, 'c': 100, 'd':3000, 'e':3000}

You could get a collection with all the keys with max value as follow:

from collections import defaultdict
from collections import OrderedDict

groupedByValue = defaultdict(list)
for key, value in sorted(stats.items()):
    groupedByValue[value].append(key)

# {1000: ['a'], 3000: ['b', 'd', 'e'], 100: ['c']}

groupedByValue[max(groupedByValue)]
# ['b', 'd', 'e']

回答 23

更简单易懂的方法:

dict = { 'a':302, 'e':53, 'g':302, 'h':100 }
max_value_keys = [key for key in dict.keys() if dict[key] == max(dict.values())]
print(max_value_keys) # prints a list of keys with max value

输出: [‘a’,’g’]

现在您只能选择一个键:

maximum = dict[max_value_keys[0]]

Much simpler to understand approach:

dict = { 'a':302, 'e':53, 'g':302, 'h':100 }
max_value_keys = [key for key in dict.keys() if dict[key] == max(dict.values())]
print(max_value_keys) # prints a list of keys with max value

Output: [‘a’, ‘g’]

Now you can choose only one key:

maximum = dict[max_value_keys[0]]

如何复制字典并仅编辑副本

问题:如何复制字典并仅编辑副本

有人可以向我解释一下吗?这对我来说毫无意义。

我将字典复制到另一个字典中,然后编辑第二个字典,并且两者都已更改。为什么会这样呢?

>>> dict1 = {"key1": "value1", "key2": "value2"}
>>> dict2 = dict1
>>> dict2
{'key2': 'value2', 'key1': 'value1'}
>>> dict2["key2"] = "WHY?!"
>>> dict1
{'key2': 'WHY?!', 'key1': 'value1'}

Can someone please explain this to me? This doesn’t make any sense to me.

I copy a dictionary into another and edit the second and both are changed. Why is this happening?

>>> dict1 = {"key1": "value1", "key2": "value2"}
>>> dict2 = dict1
>>> dict2
{'key2': 'value2', 'key1': 'value1'}
>>> dict2["key2"] = "WHY?!"
>>> dict1
{'key2': 'WHY?!', 'key1': 'value1'}

回答 0

Python 绝不会隐式复制对象。设置时dict2 = dict1,将使它们引用同一精确的dict对象,因此,在对它进行突变时,对其的所有引用都将始终引用该对象的当前状态。

如果要复制字典(这种情况很少见),则必须使用

dict2 = dict(dict1)

要么

dict2 = dict1.copy()

Python never implicitly copies objects. When you set dict2 = dict1, you are making them refer to the same exact dict object, so when you mutate it, all references to it keep referring to the object in its current state.

If you want to copy the dict (which is rare), you have to do so explicitly with

dict2 = dict(dict1)

or

dict2 = dict1.copy()

回答 1

分配时dict2 = dict1,您并没有复制该文件的副本dict1,导致dict2它只是它的另一个名称dict1

要复制字典等可变类型,请使用copy/ deepcopycopy模块。

import copy

dict2 = copy.deepcopy(dict1)

When you assign dict2 = dict1, you are not making a copy of dict1, it results in dict2 being just another name for dict1.

To copy the mutable types like dictionaries, use copy / deepcopy of the copy module.

import copy

dict2 = copy.deepcopy(dict1)

回答 2

虽然dict.copy()dict(dict1)生成副本,但它们只是浅表副本。如果要拷贝,copy.deepcopy(dict1)则是必需的。一个例子:

>>> source = {'a': 1, 'b': {'m': 4, 'n': 5, 'o': 6}, 'c': 3}
>>> copy1 = x.copy()
>>> copy2 = dict(x)
>>> import copy
>>> copy3 = copy.deepcopy(x)
>>> source['a'] = 10  # a change to first-level properties won't affect copies
>>> source
{'a': 10, 'c': 3, 'b': {'m': 4, 'o': 6, 'n': 5}}
>>> copy1
{'a': 1, 'c': 3, 'b': {'m': 4, 'o': 6, 'n': 5}}
>>> copy2
{'a': 1, 'c': 3, 'b': {'m': 4, 'o': 6, 'n': 5}}
>>> copy3
{'a': 1, 'c': 3, 'b': {'m': 4, 'o': 6, 'n': 5}}
>>> source['b']['m'] = 40  # a change to deep properties WILL affect shallow copies 'b.m' property
>>> source
{'a': 10, 'c': 3, 'b': {'m': 40, 'o': 6, 'n': 5}}
>>> copy1
{'a': 1, 'c': 3, 'b': {'m': 40, 'o': 6, 'n': 5}}
>>> copy2
{'a': 1, 'c': 3, 'b': {'m': 40, 'o': 6, 'n': 5}}
>>> copy3  # Deep copy's 'b.m' property is unaffected
{'a': 1, 'c': 3, 'b': {'m': 4, 'o': 6, 'n': 5}}

关于浅层副本与深层副本,来自Python copy模块docs

浅表复制和深度复制之间的区别仅与复合对象(包含其他对象的对象,如列表或类实例)有关:

  • 浅表副本构造一个新的复合对象,然后(在可能的范围内)将对原始对象中找到的对象的引用插入其中。
  • 深层副本将构造一个新的复合对象,然后递归地将原始对象中发现的对象的副本插入其中。

While dict.copy() and dict(dict1) generates a copy, they are only shallow copies. If you want a deep copy, copy.deepcopy(dict1) is required. An example:

>>> source = {'a': 1, 'b': {'m': 4, 'n': 5, 'o': 6}, 'c': 3}
>>> copy1 = x.copy()
>>> copy2 = dict(x)
>>> import copy
>>> copy3 = copy.deepcopy(x)
>>> source['a'] = 10  # a change to first-level properties won't affect copies
>>> source
{'a': 10, 'c': 3, 'b': {'m': 4, 'o': 6, 'n': 5}}
>>> copy1
{'a': 1, 'c': 3, 'b': {'m': 4, 'o': 6, 'n': 5}}
>>> copy2
{'a': 1, 'c': 3, 'b': {'m': 4, 'o': 6, 'n': 5}}
>>> copy3
{'a': 1, 'c': 3, 'b': {'m': 4, 'o': 6, 'n': 5}}
>>> source['b']['m'] = 40  # a change to deep properties WILL affect shallow copies 'b.m' property
>>> source
{'a': 10, 'c': 3, 'b': {'m': 40, 'o': 6, 'n': 5}}
>>> copy1
{'a': 1, 'c': 3, 'b': {'m': 40, 'o': 6, 'n': 5}}
>>> copy2
{'a': 1, 'c': 3, 'b': {'m': 40, 'o': 6, 'n': 5}}
>>> copy3  # Deep copy's 'b.m' property is unaffected
{'a': 1, 'c': 3, 'b': {'m': 4, 'o': 6, 'n': 5}}

Regarding shallow vs deep copies, from the Python copy module docs:

The difference between shallow and deep copying is only relevant for compound objects (objects that contain other objects, like lists or class instances):

  • A shallow copy constructs a new compound object and then (to the extent possible) inserts references into it to the objects found in the original.
  • A deep copy constructs a new compound object and then, recursively, inserts copies into it of the objects found in the original.

回答 3

在python 3.5+上,有一种更简单的方法可以通过使用**解包运算符来实现浅表副本。由Pep 448定义。

>>>dict1 = {"key1": "value1", "key2": "value2"}
>>>dict2 = {**dict1}
>>>print(dict2)
{'key1': 'value1', 'key2': 'value2'}
>>>dict2["key2"] = "WHY?!"
>>>print(dict1)
{'key1': 'value1', 'key2': 'value2'}
>>>print(dict2)
{'key1': 'value1', 'key2': 'WHY?!'}

**将字典解包为新字典,然后将其分配给dict2。

我们还可以确认每个词典都有不同的ID。

>>>id(dict1)
 178192816

>>>id(dict2)
 178192600

如果需要深层副本,那么仍然可以使用copy.deepcopy()

On python 3.5+ there is an easier way to achieve a shallow copy by using the ** unpackaging operator. Defined by Pep 448.

>>>dict1 = {"key1": "value1", "key2": "value2"}
>>>dict2 = {**dict1}
>>>print(dict2)
{'key1': 'value1', 'key2': 'value2'}
>>>dict2["key2"] = "WHY?!"
>>>print(dict1)
{'key1': 'value1', 'key2': 'value2'}
>>>print(dict2)
{'key1': 'value1', 'key2': 'WHY?!'}

** unpackages the dictionary into a new dictionary that is then assigned to dict2.

We can also confirm that each dictionary has a distinct id.

>>>id(dict1)
 178192816

>>>id(dict2)
 178192600

If a deep copy is needed then copy.deepcopy() is still the way to go.


回答 4

最好的和最简单的方法创建一个副本一个的字典中都Python的2.7和3是…

要创建简单(单级)字典的副本:

1.使用dict()方法,而不是生成指向现有dict的引用。

my_dict1 = dict()
my_dict1["message"] = "Hello Python"
print(my_dict1)  # {'message':'Hello Python'}

my_dict2 = dict(my_dict1)
print(my_dict2)  # {'message':'Hello Python'}

# Made changes in my_dict1 
my_dict1["name"] = "Emrit"
print(my_dict1)  # {'message':'Hello Python', 'name' : 'Emrit'}
print(my_dict2)  # {'message':'Hello Python'}

2.使用python字典的内置update()方法。

my_dict2 = dict()
my_dict2.update(my_dict1)
print(my_dict2)  # {'message':'Hello Python'}

# Made changes in my_dict1 
my_dict1["name"] = "Emrit"
print(my_dict1)  # {'message':'Hello Python', 'name' : 'Emrit'}
print(my_dict2)  # {'message':'Hello Python'}

要创建嵌套或复杂字典的副本:

使用内置的复制模块,该模块提供通用的浅层和深层复制操作。Python 2.7和3中都提供了此模块。*

import copy

my_dict2 = copy.deepcopy(my_dict1)

The best and the easiest ways to create a copy of a dict in both Python 2.7 and 3 are…

To create a copy of simple(single-level) dictionary:

1. Using dict() method, instead of generating a reference that points to the existing dict.

my_dict1 = dict()
my_dict1["message"] = "Hello Python"
print(my_dict1)  # {'message':'Hello Python'}

my_dict2 = dict(my_dict1)
print(my_dict2)  # {'message':'Hello Python'}

# Made changes in my_dict1 
my_dict1["name"] = "Emrit"
print(my_dict1)  # {'message':'Hello Python', 'name' : 'Emrit'}
print(my_dict2)  # {'message':'Hello Python'}

2. Using the built-in update() method of python dictionary.

my_dict2 = dict()
my_dict2.update(my_dict1)
print(my_dict2)  # {'message':'Hello Python'}

# Made changes in my_dict1 
my_dict1["name"] = "Emrit"
print(my_dict1)  # {'message':'Hello Python', 'name' : 'Emrit'}
print(my_dict2)  # {'message':'Hello Python'}

To create a copy of nested or complex dictionary:

Use the built-in copy module, which provides a generic shallow and deep copy operations. This module is present in both Python 2.7 and 3.*

import copy

my_dict2 = copy.deepcopy(my_dict1)

回答 5

您也可以使用字典理解功能来制作新字典。这样可以避免导入副本。

dout = dict((k,v) for k,v in mydict.items())

当然,在python> = 2.7中,您可以执行以下操作:

dout = {k:v for k,v in mydict.items()}

但是对于向后兼容,顶级方法更好。

You can also just make a new dictionary with a dictionary comprehension. This avoids importing copy.

dout = dict((k,v) for k,v in mydict.items())

Of course in python >= 2.7 you can do:

dout = {k:v for k,v in mydict.items()}

But for backwards compat., the top method is better.


回答 6

除了提供的其他解决方案外,您还可以**将字典集成到一个空字典中,例如,

shallow_copy_of_other_dict = {**other_dict}

现在,您将拥有的“浅”副本other_dict

应用于您的示例:

>>> dict1 = {"key1": "value1", "key2": "value2"}
>>> dict2 = {**dict1}
>>> dict2
{'key1': 'value1', 'key2': 'value2'}
>>> dict2["key2"] = "WHY?!"
>>> dict1
{'key1': 'value1', 'key2': 'value2'}
>>>

指针:浅拷贝和深拷贝之间的区别

In addition to the other provided solutions, you can use ** to integrate the dictionary into an empty dictionary, e.g.,

shallow_copy_of_other_dict = {**other_dict}.

Now you will have a “shallow” copy of other_dict.

Applied to your example:

>>> dict1 = {"key1": "value1", "key2": "value2"}
>>> dict2 = {**dict1}
>>> dict2
{'key1': 'value1', 'key2': 'value2'}
>>> dict2["key2"] = "WHY?!"
>>> dict1
{'key1': 'value1', 'key2': 'value2'}
>>>

Pointer: Difference between shallow and deep copys


回答 7

Python中的赋值语句不复制对象,它们在目标和对象之间创建绑定。

因此,dict2 = dict1它会在dict2dict1引用的对象之间产生另一个绑定。

如果要复制字典,可以使用copy module。复制模块有两个接口:

copy.copy(x)
Return a shallow copy of x.

copy.deepcopy(x)
Return a deep copy of x.

浅表复制和深度复制之间的区别仅与复合对象(包含其他对象的对象,如列表或类实例)有关:

浅拷贝构造新化合物对象,然后(在可能的范围)插入到它的对象引用原始发现。

深层副本构造新化合物的对象,然后,递归地,插入拷贝到它的目的在原始发现。

例如,在python 2.7.9中:

>>> import copy
>>> a = [1,2,3,4,['a', 'b']]
>>> b = a
>>> c = copy.copy(a)
>>> d = copy.deepcopy(a)
>>> a.append(5)
>>> a[4].append('c')

结果是:

>>> a
[1, 2, 3, 4, ['a', 'b', 'c'], 5]
>>> b
[1, 2, 3, 4, ['a', 'b', 'c'], 5]
>>> c
[1, 2, 3, 4, ['a', 'b', 'c']]
>>> d
[1, 2, 3, 4, ['a', 'b']]

Assignment statements in Python do not copy objects, they create bindings between a target and an object.

so, dict2 = dict1, it results another binding between dict2and the object that dict1 refer to.

if you want to copy a dict, you can use the copy module. The copy module has two interface:

copy.copy(x)
Return a shallow copy of x.

copy.deepcopy(x)
Return a deep copy of x.

The difference between shallow and deep copying is only relevant for compound objects (objects that contain other objects, like lists or class instances):

A shallow copy constructs a new compound object and then (to the extent possible) inserts references into it to the objects found in the original.

A deep copy constructs a new compound object and then, recursively, inserts copies into it of the objects found in the original.

For example, in python 2.7.9:

>>> import copy
>>> a = [1,2,3,4,['a', 'b']]
>>> b = a
>>> c = copy.copy(a)
>>> d = copy.deepcopy(a)
>>> a.append(5)
>>> a[4].append('c')

and the result is:

>>> a
[1, 2, 3, 4, ['a', 'b', 'c'], 5]
>>> b
[1, 2, 3, 4, ['a', 'b', 'c'], 5]
>>> c
[1, 2, 3, 4, ['a', 'b', 'c']]
>>> d
[1, 2, 3, 4, ['a', 'b']]

回答 8

您可以通过dict使用其他关键字参数调用构造函数来一次性复制和编辑新构造的副本:

>>> dict1 = {"key1": "value1", "key2": "value2"}
>>> dict2 = dict(dict1, key2="WHY?!")
>>> dict1
{'key2': 'value2', 'key1': 'value1'}
>>> dict2
{'key2': 'WHY?!', 'key1': 'value1'}

You can copy and edit the newly constructed copy in one go by calling the dict constructor with additional keyword arguments:

>>> dict1 = {"key1": "value1", "key2": "value2"}
>>> dict2 = dict(dict1, key2="WHY?!")
>>> dict1
{'key2': 'value2', 'key1': 'value1'}
>>> dict2
{'key2': 'WHY?!', 'key1': 'value1'}

回答 9

最初,这也使我感到困惑,因为我来自C语言。

在C语言中,变量是内存中定义类型的位置。分配给变量会将数据复制到变量的存储位置。

但是在Python中,变量的作用更像是指向对象的指针。因此,将一个变量分配给另一个变量不会产生副本,只会使该变量名称指向同一对象。

This confused me too, initially, because I was coming from a C background.

In C, a variable is a location in memory with a defined type. Assigning to a variable copies the data into the variable’s memory location.

But in Python, variables act more like pointers to objects. So assigning one variable to another doesn’t make a copy, it just makes that variable name point to the same object.


回答 10

python中的每个变量(类似于dict1str或的东西__builtins__都是指向机器内部某些隐藏的柏拉图“对象”的指针。

如果设置dict1 = dict2,则只需指向dict1与相同的对象(或内存位置,或类似的对象)dict2。现在,所引用的对象与所引用的对象dict1相同dict2

您可以检查:dict1 is dict2应该是True。另外,id(dict1)应与相同id(dict2)

您想要dict1 = copy(dict2)dict1 = deepcopy(dict2)

copy和之间的区别deepcopydeepcopy将确保dict2(您是否将其指向列表?)的元素也是副本。

我用的不是deepcopy很多-在我看来,编写需要它的代码通常是不明智的做法。

Every variable in python (stuff like dict1 or str or __builtins__ is a pointer to some hidden platonic “object” inside the machine.

If you set dict1 = dict2,you just point dict1 to the same object (or memory location, or whatever analogy you like) as dict2. Now, the object referenced by dict1 is the same object referenced by dict2.

You can check: dict1 is dict2 should be True. Also, id(dict1) should be the same as id(dict2).

You want dict1 = copy(dict2), or dict1 = deepcopy(dict2).

The difference between copy and deepcopy? deepcopy will make sure that the elements of dict2 (did you point it at a list?) are also copies.

I don’t use deepcopy much – it’s usually poor practice to write code that needs it (in my opinion).


回答 11

dict1是引用基础字典对象的符号。分配dict1dict2仅分配相同的参考。通过dict2符号更改键的值会更改基础对象,这也会影响dict1。这很混乱。

关于不可变值的推理要比引用容易得多,因此请尽可能制作副本:

person = {'name': 'Mary', 'age': 25}
one_year_later = {**person, 'age': 26}  # does not mutate person dict

在语法上与以下内容相同:

one_year_later = dict(person, age=26)

dict1 is a symbol that references an underlying dictionary object. Assigning dict1 to dict2 merely assigns the same reference. Changing a key’s value via the dict2 symbol changes the underlying object, which also affects dict1. This is confusing.

It is far easier to reason about immutable values than references, so make copies whenever possible:

person = {'name': 'Mary', 'age': 25}
one_year_later = {**person, 'age': 26}  # does not mutate person dict

This is syntactically the same as:

one_year_later = dict(person, age=26)

回答 12

dict2 = dict1不复制字典。它只是为程序员提供了第二种方法(dict2)来引用同一词典。

dict2 = dict1 does not copy the dictionary. It simply gives you the programmer a second way (dict2) to refer to the same dictionary.


回答 13

>>> dict2 = dict1
# dict2 is bind to the same Dict object which binds to dict1, so if you modify dict2, you will modify the dict1

复制Dict对象的方法很多,我只是简单地使用

dict_1 = {
           'a':1,
           'b':2
         }
dict_2 = {}
dict_2.update(dict_1)
>>> dict2 = dict1
# dict2 is bind to the same Dict object which binds to dict1, so if you modify dict2, you will modify the dict1

There are many ways to copy Dict object, I simply use

dict_1 = {
           'a':1,
           'b':2
         }
dict_2 = {}
dict_2.update(dict_1)

回答 14

正如其他人所解释的,内置dict功能无法满足您的需求。但是在Python2中(可能还有3个),您可以轻松地创建一个ValueDict用于复制的类,=因此可以确保原始版本不会更改。

class ValueDict(dict):

    def __ilshift__(self, args):
        result = ValueDict(self)
        if isinstance(args, dict):
            dict.update(result, args)
        else:
            dict.__setitem__(result, *args)
        return result # Pythonic LVALUE modification

    def __irshift__(self, args):
        result = ValueDict(self)
        dict.__delitem__(result, args)
        return result # Pythonic LVALUE modification

    def __setitem__(self, k, v):
        raise AttributeError, \
            "Use \"value_dict<<='%s', ...\" instead of \"d[%s] = ...\"" % (k,k)

    def __delitem__(self, k):
        raise AttributeError, \
            "Use \"value_dict>>='%s'\" instead of \"del d[%s]" % (k,k)

    def update(self, d2):
        raise AttributeError, \
            "Use \"value_dict<<=dict2\" instead of \"value_dict.update(dict2)\""


# test
d = ValueDict()

d <<='apples', 5
d <<='pears', 8
print "d =", d

e = d
e <<='bananas', 1
print "e =", e
print "d =", d

d >>='pears'
print "d =", d
d <<={'blueberries': 2, 'watermelons': 315}
print "d =", d
print "e =", e
print "e['bananas'] =", e['bananas']


# result
d = {'apples': 5, 'pears': 8}
e = {'apples': 5, 'pears': 8, 'bananas': 1}
d = {'apples': 5, 'pears': 8}
d = {'apples': 5}
d = {'watermelons': 315, 'blueberries': 2, 'apples': 5}
e = {'apples': 5, 'pears': 8, 'bananas': 1}
e['bananas'] = 1

# e[0]=3
# would give:
# AttributeError: Use "value_dict<<='0', ..." instead of "d[0] = ..."

请参考此处讨论的左值修改模式:Python 2.7-左值修改的纯语法。关键的观察是,strint表现为在Python值(即使它们实际上是引擎盖下的不可变对象)。当您观察到这一点时,也请注意,关于str或,没有什么神奇的特别之处intdict可以以几乎相同的方式使用,我可以想到很多ValueDict有意义的情况。

As others have explained, the built-in dict does not do what you want. But in Python2 (and probably 3 too) you can easily create a ValueDict class that copies with = so you can be sure that the original will not change.

class ValueDict(dict):

    def __ilshift__(self, args):
        result = ValueDict(self)
        if isinstance(args, dict):
            dict.update(result, args)
        else:
            dict.__setitem__(result, *args)
        return result # Pythonic LVALUE modification

    def __irshift__(self, args):
        result = ValueDict(self)
        dict.__delitem__(result, args)
        return result # Pythonic LVALUE modification

    def __setitem__(self, k, v):
        raise AttributeError, \
            "Use \"value_dict<<='%s', ...\" instead of \"d[%s] = ...\"" % (k,k)

    def __delitem__(self, k):
        raise AttributeError, \
            "Use \"value_dict>>='%s'\" instead of \"del d[%s]" % (k,k)

    def update(self, d2):
        raise AttributeError, \
            "Use \"value_dict<<=dict2\" instead of \"value_dict.update(dict2)\""


# test
d = ValueDict()

d <<='apples', 5
d <<='pears', 8
print "d =", d

e = d
e <<='bananas', 1
print "e =", e
print "d =", d

d >>='pears'
print "d =", d
d <<={'blueberries': 2, 'watermelons': 315}
print "d =", d
print "e =", e
print "e['bananas'] =", e['bananas']


# result
d = {'apples': 5, 'pears': 8}
e = {'apples': 5, 'pears': 8, 'bananas': 1}
d = {'apples': 5, 'pears': 8}
d = {'apples': 5}
d = {'watermelons': 315, 'blueberries': 2, 'apples': 5}
e = {'apples': 5, 'pears': 8, 'bananas': 1}
e['bananas'] = 1

# e[0]=3
# would give:
# AttributeError: Use "value_dict<<='0', ..." instead of "d[0] = ..."

Please refer to the lvalue modification pattern discussed here: Python 2.7 – clean syntax for lvalue modification. The key observation is that str and int behave as values in Python (even though they’re actually immutable objects under the hood). While you’re observing that, please also observe that nothing is magically special about str or int. dict can be used in much the same ways, and I can think of many cases where ValueDict makes sense.


回答 15

以下代码,是遵循json语法的字典的,比deepcopy快3倍以上

def CopyDict(dSrc):
    try:
        return json.loads(json.dumps(dSrc))
    except Exception as e:
        Logger.warning("Can't copy dict the preferred way:"+str(dSrc))
        return deepcopy(dSrc)

the following code, which is on dicts which follows json syntax more than 3 times faster than deepcopy

def CopyDict(dSrc):
    try:
        return json.loads(json.dumps(dSrc))
    except Exception as e:
        Logger.warning("Can't copy dict the preferred way:"+str(dSrc))
        return deepcopy(dSrc)

回答 16

尝试深复制类w / o将其分配给变量的字典属性时,遇到了一种特殊的行为

new = copy.deepcopy(my_class.a)不起作用,即修改new修改my_class.a

但是,如果您这样做old = my_class.a,那么new = copy.deepcopy(old)它会完美运行,即修改new不会影响my_class.a

我不确定为什么会发生这种情况,但希望它可以节省一些时间!:)

i ran into a peculiar behavior when trying to deep copy dictionary property of class w/o assigning it to variable

new = copy.deepcopy(my_class.a) doesn’t work i.e. modifying new modifies my_class.a

but if you do old = my_class.a and then new = copy.deepcopy(old) it works perfectly i.e. modifying new does not affect my_class.a

I am not sure why this happens, but hope it helps save some hours! :)


回答 17

因为dict2 = dict1,dict2保存了对dict1的引用。dict1和dict2都指向内存中的同一位置。这是在python中使用可变对象时的正常情况。使用python中的可变对象时,必须小心,因为它很难调试。如下面的例子。

 my_users = {
        'ids':[1,2],
        'blocked_ids':[5,6,7]
 }
 ids = my_users.get('ids')
 ids.extend(my_users.get('blocked_ids')) #all_ids
 print ids#output:[1, 2, 5, 6, 7]
 print my_users #output:{'blocked_ids': [5, 6, 7], 'ids': [1, 2, 5, 6, 7]}

此示例意图是获取所有用户ID,包括被阻止的ID。我们是从ids变量获得的,但是我们也无意间更新了my_users的值。当你扩展的IDSblocked_ids my_users得到了更新,因为IDS参考my_users

because, dict2 = dict1, dict2 holds the reference to dict1. Both dict1 and dict2 points to the same location in the memory. This is just a normal case while working with mutable objects in python. When you are working with mutable objects in python you must be careful as it is hard to debug. Such as the following example.

 my_users = {
        'ids':[1,2],
        'blocked_ids':[5,6,7]
 }
 ids = my_users.get('ids')
 ids.extend(my_users.get('blocked_ids')) #all_ids
 print ids#output:[1, 2, 5, 6, 7]
 print my_users #output:{'blocked_ids': [5, 6, 7], 'ids': [1, 2, 5, 6, 7]}

This example intention is to get all the user ids including blocked ids. That we got from ids variable but we also updated the value of my_users unintentionally. when you extended the ids with blocked_ids my_users got updated because ids refer to my_users.


回答 18

使用for循环进行复制:

orig = {"X2": 674.5, "X3": 245.0}

copy = {}
for key in orig:
    copy[key] = orig[key]

print(orig) # {'X2': 674.5, 'X3': 245.0}
print(copy) # {'X2': 674.5, 'X3': 245.0}
copy["X2"] = 808
print(orig) # {'X2': 674.5, 'X3': 245.0}
print(copy) # {'X2': 808, 'X3': 245.0}

Copying by using a for loop:

orig = {"X2": 674.5, "X3": 245.0}

copy = {}
for key in orig:
    copy[key] = orig[key]

print(orig) # {'X2': 674.5, 'X3': 245.0}
print(copy) # {'X2': 674.5, 'X3': 245.0}
copy["X2"] = 808
print(orig) # {'X2': 674.5, 'X3': 245.0}
print(copy) # {'X2': 808, 'X3': 245.0}

回答 19

您可以直接使用:

dict2 = eval(repr(dict1))

其中对象dict2是dict1的独立副本,因此您可以修改dict2而不会影响dict1。

这适用于任何类型的对象。

You can use directly:

dict2 = eval(repr(dict1))

where object dict2 is an independent copy of dict1, so you can modify dict2 without affecting dict1.

This works for any kind of object.


如何在Python中将字典键作为列表返回?

问题:如何在Python中将字典键作为列表返回?

Python 2.7中,我可以将字典作为列表获取:

>>> newdict = {1:0, 2:0, 3:0}
>>> newdict.keys()
[1, 2, 3]

现在,在Python> = 3.3中,我得到如下信息:

>>> newdict.keys()
dict_keys([1, 2, 3])

因此,我必须这样做以获得列表:

newlist = list()
for i in newdict.keys():
    newlist.append(i)

我想知道,是否有更好的方法在Python 3中返回列表?

In Python 2.7, I could get dictionary keys, values, or items as a list:

>>> newdict = {1:0, 2:0, 3:0}
>>> newdict.keys()
[1, 2, 3]

Now, in Python >= 3.3, I get something like this:

>>> newdict.keys()
dict_keys([1, 2, 3])

So, I have to do this to get a list:

newlist = list()
for i in newdict.keys():
    newlist.append(i)

I’m wondering, is there a better way to return a list in Python 3?


回答 0

尝试list(newdict.keys())

这会将dict_keys对象转换为列表。

另一方面,您应该问自己是否重要。Python的编码方式是假设鸭子输入(如果它看起来像鸭子,而像鸭子一样嘎嘎叫,那就是鸭子)。在dict_keys大多数情况下,该对象的作用类似于列表。例如:

for key in newdict.keys():
  print(key)

显然,插入运算符可能不起作用,但是对于字典关键字列表而言,这并没有多大意义。

Try list(newdict.keys()).

This will convert the dict_keys object to a list.

On the other hand, you should ask yourself whether or not it matters. The Pythonic way to code is to assume duck typing (if it looks like a duck and it quacks like a duck, it’s a duck). The dict_keys object will act like a list for most purposes. For instance:

for key in newdict.keys():
  print(key)

Obviously, insertion operators may not work, but that doesn’t make much sense for a list of dictionary keys anyway.


回答 1

Python> = 3.5替代方法:解压缩为列表文字 [*newdict]

Python 3.5引入了新的拆包概括(PEP 448),使您现在可以轻松进行以下操作:

>>> newdict = {1:0, 2:0, 3:0}
>>> [*newdict]
[1, 2, 3]

与解压缩的对象可*任何可迭代的对象一起使用,并且由于字典在迭代过程中会返回其键,因此您可以在列表文字中使用它轻松创建列表。

添加.keys()ie [*newdict.keys()]可能有助于使您的意图更加明确,尽管这将花费您函数查找和调用的费用。(实际上,这不是您真正应该担心的事情)。

*iterable语法类似于做list(iterable)其行为最初记录在呼叫部分 Python的参考手册。对于PEP 448,放宽了对*iterable可能出现的位置的限制,使其也可以放置在列表,集合和元组文字中,“ 表达式”列表上的参考手册也进行了更新以说明这一点。


尽管这等效于list(newdict)它更快(至少对于小型词典而言),因为实际上没有执行任何函数调用:

%timeit [*newdict]
1000000 loops, best of 3: 249 ns per loop

%timeit list(newdict)
1000000 loops, best of 3: 508 ns per loop

%timeit [k for k in newdict]
1000000 loops, best of 3: 574 ns per loop

对于较大的字典,速度几乎是相同的(遍历大量集合的开销胜过了函数调用的小开销)。


您可以用类似的方式创建元组和字典键集:

>>> *newdict,
(1, 2, 3)
>>> {*newdict}
{1, 2, 3}

在元组的情况下要小心尾随逗号!

Python >= 3.5 alternative: unpack into a list literal [*newdict]

New unpacking generalizations (PEP 448) were introduced with Python 3.5 allowing you to now easily do:

>>> newdict = {1:0, 2:0, 3:0}
>>> [*newdict]
[1, 2, 3]

Unpacking with * works with any object that is iterable and, since dictionaries return their keys when iterated through, you can easily create a list by using it within a list literal.

Adding .keys() i.e [*newdict.keys()] might help in making your intent a bit more explicit though it will cost you a function look-up and invocation. (which, in all honesty, isn’t something you should really be worried about).

The *iterable syntax is similar to doing list(iterable) and its behaviour was initially documented in the Calls section of the Python Reference manual. With PEP 448 the restriction on where *iterable could appear was loosened allowing it to also be placed in list, set and tuple literals, the reference manual on Expression lists was also updated to state this.


Though equivalent to list(newdict) with the difference that it’s faster (at least for small dictionaries) because no function call is actually performed:

%timeit [*newdict]
1000000 loops, best of 3: 249 ns per loop

%timeit list(newdict)
1000000 loops, best of 3: 508 ns per loop

%timeit [k for k in newdict]
1000000 loops, best of 3: 574 ns per loop

with larger dictionaries the speed is pretty much the same (the overhead of iterating through a large collection trumps the small cost of a function call).


In a similar fashion, you can create tuples and sets of dictionary keys:

>>> *newdict,
(1, 2, 3)
>>> {*newdict}
{1, 2, 3}

beware of the trailing comma in the tuple case!


回答 2

list(newdict)在Python 2和Python 3中均可使用,在中提供了键的简单列表newdictkeys()没必要 (:

list(newdict) works in both Python 2 and Python 3, providing a simple list of the keys in newdict. keys() isn’t necessary. (:


回答 3

在“鸭子类型”定义上有一点点偏离- dict.keys()返回一个可迭代的对象,而不是类似列表的对象。它可以在任何可迭代的地方都可以使用-列表不能在任何地方使用。列表也是可迭代的,但可迭代的不是列表(或序列…)

在实际的用例中,与字典中的键有关的最常见的事情是遍历它们,因此这很有意义。如果确实需要它们作为清单,则可以调用list()

非常相似zip()-在大多数情况下,它会被迭代-为什么创建一个新的元组列表只是为了对其进行迭代,然后又将其丢弃?

这是python中使用更多迭代器(和生成器),而不是到处都是列表副本的一种大趋势的一部分。

dict.keys() 不过,应该可以理解-仔细检查是否有错别字或其他内容…对我来说效果很好:

>>> d = dict(zip(['Sounder V Depth, F', 'Vessel Latitude, Degrees-Minutes'], [None, None]))
>>> [key.split(", ") for key in d.keys()]
[['Sounder V Depth', 'F'], ['Vessel Latitude', 'Degrees-Minutes']]

A bit off on the “duck typing” definition — dict.keys() returns an iterable object, not a list-like object. It will work anywhere an iterable will work — not any place a list will. a list is also an iterable, but an iterable is NOT a list (or sequence…)

In real use-cases, the most common thing to do with the keys in a dict is to iterate through them, so this makes sense. And if you do need them as a list you can call list().

Very similarly for zip() — in the vast majority of cases, it is iterated through — why create an entire new list of tuples just to iterate through it and then throw it away again?

This is part of a large trend in python to use more iterators (and generators), rather than copies of lists all over the place.

dict.keys() should work with comprehensions, though — check carefully for typos or something… it works fine for me:

>>> d = dict(zip(['Sounder V Depth, F', 'Vessel Latitude, Degrees-Minutes'], [None, None]))
>>> [key.split(", ") for key in d.keys()]
[['Sounder V Depth', 'F'], ['Vessel Latitude', 'Degrees-Minutes']]

回答 4

您还可以使用列表推导

>>> newdict = {1:0, 2:0, 3:0}
>>> [k  for  k in  newdict.keys()]
[1, 2, 3]

或更短一点

>>> [k  for  k in  newdict]
[1, 2, 3]

注意:在3.7版以下的版本中,不能保证订购(订购仍然只是CPython 3.6的实现细节)。

You can also use a list comprehension:

>>> newdict = {1:0, 2:0, 3:0}
>>> [k  for  k in  newdict.keys()]
[1, 2, 3]

Or, shorter,

>>> [k  for  k in  newdict]
[1, 2, 3]

Note: Order is not guaranteed on versions under 3.7 (ordering is still only an implementation detail with CPython 3.6).


回答 5

不使用该keys方法转换为列表使其更具可读性:

list(newdict)

并且,当遍历字典时,不需要keys()

for key in newdict:
    print key

除非您要在循环中进行修改,否则将需要预先创建的键列表:

for key in list(newdict):
    del newdict[key]

在Python 2上,使用会产生少量性能提升keys()

Converting to a list without using the keys method makes it more readable:

list(newdict)

and, when looping through dictionaries, there’s no need for keys():

for key in newdict:
    print key

unless you are modifying it within the loop which would require a list of keys created beforehand:

for key in list(newdict):
    del newdict[key]

On Python 2 there is a marginal performance gain using keys().


回答 6

如果您需要单独存储密钥,那么此解决方案使用扩展的可迭代拆包(python3.x +),与迄今为止提供的所有其他解决方案相比,它的键入次数更少。

newdict = {1: 0, 2: 0, 3: 0}
*k, = newdict

k
# [1, 2, 3]

            ╒═══════════════╤═════════════════════════════════════════╕
             k = list(d)      9 characters (excluding whitespace)   
            ├───────────────┼─────────────────────────────────────────┤
             k = [*d]         6 characters                          
            ├───────────────┼─────────────────────────────────────────┤
             *k, = d          5 characters                          
            ╘═══════════════╧═════════════════════════════════════════╛

If you need to store the keys separately, here’s a solution that requires less typing than every other solution presented thus far, using Extended Iterable Unpacking (python3.x+).

newdict = {1: 0, 2: 0, 3: 0}
*k, = newdict

k
# [1, 2, 3]

            ╒═══════════════╤═════════════════════════════════════════╕
            │ k = list(d)   │   9 characters (excluding whitespace)   │
            ├───────────────┼─────────────────────────────────────────┤
            │ k = [*d]      │   6 characters                          │
            ├───────────────┼─────────────────────────────────────────┤
            │ *k, = d       │   5 characters                          │
            ╘═══════════════╧═════════════════════════════════════════╛

回答 7

我可以想到两种从字典中提取键的方法。

方法1:- 使用.keys()方法获取密钥,然后将其转换为列表。

some_dict = {1: 'one', 2: 'two', 3: 'three'}
list_of_keys = list(some_dict.keys())
print(list_of_keys)
-->[1,2,3]

方法2:- 创建一个空列表,然后通过循环将键附加到列表中。您也可以通过此循环获取值(仅将.keys()用于键,将.items()用于键和值提取)

list_of_keys = []
list_of_values = []
for key,val in some_dict.items():
    list_of_keys.append(key)
    list_of_values.append(val)

print(list_of_keys)
-->[1,2,3]

print(list_of_values)
-->['one','two','three']

I can think of 2 ways in which we can extract the keys from the dictionary.

Method 1: – To get the keys using .keys() method and then convert it to list.

some_dict = {1: 'one', 2: 'two', 3: 'three'}
list_of_keys = list(some_dict.keys())
print(list_of_keys)
-->[1,2,3]

Method 2: – To create an empty list and then append keys to the list via a loop. You can get the values with this loop as well (use .keys() for just keys and .items() for both keys and values extraction)

list_of_keys = []
list_of_values = []
for key,val in some_dict.items():
    list_of_keys.append(key)
    list_of_values.append(val)

print(list_of_keys)
-->[1,2,3]

print(list_of_values)
-->['one','two','three']

将字典的字符串表示形式转换为字典?

问题:将字典的字符串表示形式转换为字典?

如何将a的str表示形式(dict例如以下字符串)转换为a dict

s = "{'muffin' : 'lolz', 'foo' : 'kitty'}"

我宁愿不使用eval。我还能使用什么?

这样做的主要原因是他写的我的同事类之一,将所有输入都转换为字符串。我不打算去修改他的类,以解决这个问题。

How can I convert the str representation of a dict, such as the following string, into a dict?

s = "{'muffin' : 'lolz', 'foo' : 'kitty'}"

I prefer not to use eval. What else can I use?

The main reason for this, is one of my coworkers classes he wrote, converts all input into strings. I’m not in the mood to go and modify his classes, to deal with this issue.


回答 0

从Python 2.6开始,您可以使用内置的ast.literal_eval

>>> import ast
>>> ast.literal_eval("{'muffin' : 'lolz', 'foo' : 'kitty'}")
{'muffin': 'lolz', 'foo': 'kitty'}

这比使用更为安全eval。正如其文档所说:

>>>帮助(ast.literal_eval)
帮助ast模块中的literal_eval函数:

literal_eval(node_or_string)
    安全地评估表达式节点或包含Python的字符串
    表达。提供的字符串或节点只能由以下内容组成
    Python文字结构:字符串,数字,元组,列表,字典,布尔值,
    和没有。

例如:

>>> eval("shutil.rmtree('mongo')")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<string>", line 1, in <module>
  File "/opt/Python-2.6.1/lib/python2.6/shutil.py", line 208, in rmtree
    onerror(os.listdir, path, sys.exc_info())
  File "/opt/Python-2.6.1/lib/python2.6/shutil.py", line 206, in rmtree
    names = os.listdir(path)
OSError: [Errno 2] No such file or directory: 'mongo'
>>> ast.literal_eval("shutil.rmtree('mongo')")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/Python-2.6.1/lib/python2.6/ast.py", line 68, in literal_eval
    return _convert(node_or_string)
  File "/opt/Python-2.6.1/lib/python2.6/ast.py", line 67, in _convert
    raise ValueError('malformed string')
ValueError: malformed string

Starting in Python 2.6 you can use the built-in ast.literal_eval:

>>> import ast
>>> ast.literal_eval("{'muffin' : 'lolz', 'foo' : 'kitty'}")
{'muffin': 'lolz', 'foo': 'kitty'}

This is safer than using eval. As its own docs say:

>>> help(ast.literal_eval)
Help on function literal_eval in module ast:

literal_eval(node_or_string)
    Safely evaluate an expression node or a string containing a Python
    expression.  The string or node provided may only consist of the following
    Python literal structures: strings, numbers, tuples, lists, dicts, booleans,
    and None.

For example:

>>> eval("shutil.rmtree('mongo')")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<string>", line 1, in <module>
  File "/opt/Python-2.6.1/lib/python2.6/shutil.py", line 208, in rmtree
    onerror(os.listdir, path, sys.exc_info())
  File "/opt/Python-2.6.1/lib/python2.6/shutil.py", line 206, in rmtree
    names = os.listdir(path)
OSError: [Errno 2] No such file or directory: 'mongo'
>>> ast.literal_eval("shutil.rmtree('mongo')")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/Python-2.6.1/lib/python2.6/ast.py", line 68, in literal_eval
    return _convert(node_or_string)
  File "/opt/Python-2.6.1/lib/python2.6/ast.py", line 67, in _convert
    raise ValueError('malformed string')
ValueError: malformed string

回答 1

https://docs.python.org/3.8/library/json.html

JSON可以解决此问题,尽管其解码器希望在键和值周围使用双引号。如果您不介意更换骇客…

import json
s = "{'muffin' : 'lolz', 'foo' : 'kitty'}"
json_acceptable_string = s.replace("'", "\"")
d = json.loads(json_acceptable_string)
# d = {u'muffin': u'lolz', u'foo': u'kitty'}

请注意,如果将单引号作为键或值的一部分,则由于字符替换不当而导致此操作失败。仅当您对评估解决方案强烈反对时,才建议使用此解决方案。

有关JSON单引号的更多信息:jQuery.parseJSON由于JSON中的单引号已转义而引发“无效JSON”错误

https://docs.python.org/3.8/library/json.html

JSON can solve this problem though its decoder wants double quotes around keys and values. If you don’t mind a replace hack…

import json
s = "{'muffin' : 'lolz', 'foo' : 'kitty'}"
json_acceptable_string = s.replace("'", "\"")
d = json.loads(json_acceptable_string)
# d = {u'muffin': u'lolz', u'foo': u'kitty'}

NOTE that if you have single quotes as a part of your keys or values this will fail due to improper character replacement. This solution is only recommended if you have a strong aversion to the eval solution.

More about json single quote: jQuery.parseJSON throws “Invalid JSON” error due to escaped single quote in JSON


回答 2

使用json.loads

>>> import json
>>> h = '{"foo":"bar", "foo2":"bar2"}'
>>> d = json.loads(h)
>>> d
{u'foo': u'bar', u'foo2': u'bar2'}
>>> type(d)
<type 'dict'>

using json.loads:

>>> import json
>>> h = '{"foo":"bar", "foo2":"bar2"}'
>>> d = json.loads(h)
>>> d
{u'foo': u'bar', u'foo2': u'bar2'}
>>> type(d)
<type 'dict'>

回答 3

以OP为例:

s = "{'muffin' : 'lolz', 'foo' : 'kitty'}"

我们可以使用Yaml处理字符串中的这种非标准json:

>>> import yaml
>>> s = "{'muffin' : 'lolz', 'foo' : 'kitty'}"
>>> s
"{'muffin' : 'lolz', 'foo' : 'kitty'}"
>>> yaml.load(s)
{'muffin': 'lolz', 'foo': 'kitty'}

To OP’s example:

s = "{'muffin' : 'lolz', 'foo' : 'kitty'}"

We can use Yaml to deal with this kind of non-standard json in string:

>>> import yaml
>>> s = "{'muffin' : 'lolz', 'foo' : 'kitty'}"
>>> s
"{'muffin' : 'lolz', 'foo' : 'kitty'}"
>>> yaml.load(s)
{'muffin': 'lolz', 'foo': 'kitty'}

回答 4

如果始终可以信任该字符串,则可以使用eval(或literal_eval按建议使用;无论该字符串是什么都是安全的。)否则,您需要一个解析器。如果JSON解析器(例如simplejson)仅存储符合JSON方案的内容,则该解析器将起作用。

If the string can always be trusted, you could use eval (or use literal_eval as suggested; it’s safe no matter what the string is.) Otherwise you need a parser. A JSON parser (such as simplejson) would work if he only ever stores content that fits with the JSON scheme.


回答 5

使用json。该ast库消耗大量内存,并且速度较慢。我有一个过程需要读取156Mb的文本文件。Ast转换字典需要5分钟的延迟,json而使用内存减少60%则需要1分钟!

Use json. the ast library consumes a lot of memory and and slower. I have a process that needs to read a text file of 156Mb. Ast with 5 minutes delay for the conversion dictionary json and 1 minutes using 60% less memory!


回答 6

总结一下:

import ast, yaml, json, timeit

descs=['short string','long string']
strings=['{"809001":2,"848545":2,"565828":1}','{"2979":1,"30581":1,"7296":1,"127256":1,"18803":2,"41619":1,"41312":1,"16837":1,"7253":1,"70075":1,"3453":1,"4126":1,"23599":1,"11465":3,"19172":1,"4019":1,"4775":1,"64225":1,"3235":2,"15593":1,"7528":1,"176840":1,"40022":1,"152854":1,"9878":1,"16156":1,"6512":1,"4138":1,"11090":1,"12259":1,"4934":1,"65581":1,"9747":2,"18290":1,"107981":1,"459762":1,"23177":1,"23246":1,"3591":1,"3671":1,"5767":1,"3930":1,"89507":2,"19293":1,"92797":1,"32444":2,"70089":1,"46549":1,"30988":1,"4613":1,"14042":1,"26298":1,"222972":1,"2982":1,"3932":1,"11134":1,"3084":1,"6516":1,"486617":1,"14475":2,"2127":1,"51359":1,"2662":1,"4121":1,"53848":2,"552967":1,"204081":1,"5675":2,"32433":1,"92448":1}']
funcs=[json.loads,eval,ast.literal_eval,yaml.load]

for  desc,string in zip(descs,strings):
    print('***',desc,'***')
    print('')
    for  func in funcs:
        print(func.__module__+' '+func.__name__+':')
        %timeit func(string)        
    print('')

结果:

*** short string ***

json loads:
4.47 µs ± 33.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
builtins eval:
24.1 µs ± 163 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
ast literal_eval:
30.4 µs ± 299 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
yaml load:
504 µs ± 1.29 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

*** long string ***

json loads:
29.6 µs ± 230 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
builtins eval:
219 µs ± 3.92 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
ast literal_eval:
331 µs ± 1.89 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
yaml load:
9.02 ms ± 92.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

结论:更喜欢json.loads

To summarize:

import ast, yaml, json, timeit

descs=['short string','long string']
strings=['{"809001":2,"848545":2,"565828":1}','{"2979":1,"30581":1,"7296":1,"127256":1,"18803":2,"41619":1,"41312":1,"16837":1,"7253":1,"70075":1,"3453":1,"4126":1,"23599":1,"11465":3,"19172":1,"4019":1,"4775":1,"64225":1,"3235":2,"15593":1,"7528":1,"176840":1,"40022":1,"152854":1,"9878":1,"16156":1,"6512":1,"4138":1,"11090":1,"12259":1,"4934":1,"65581":1,"9747":2,"18290":1,"107981":1,"459762":1,"23177":1,"23246":1,"3591":1,"3671":1,"5767":1,"3930":1,"89507":2,"19293":1,"92797":1,"32444":2,"70089":1,"46549":1,"30988":1,"4613":1,"14042":1,"26298":1,"222972":1,"2982":1,"3932":1,"11134":1,"3084":1,"6516":1,"486617":1,"14475":2,"2127":1,"51359":1,"2662":1,"4121":1,"53848":2,"552967":1,"204081":1,"5675":2,"32433":1,"92448":1}']
funcs=[json.loads,eval,ast.literal_eval,yaml.load]

for  desc,string in zip(descs,strings):
    print('***',desc,'***')
    print('')
    for  func in funcs:
        print(func.__module__+' '+func.__name__+':')
        %timeit func(string)        
    print('')

Results:

*** short string ***

json loads:
4.47 µs ± 33.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
builtins eval:
24.1 µs ± 163 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
ast literal_eval:
30.4 µs ± 299 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
yaml load:
504 µs ± 1.29 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

*** long string ***

json loads:
29.6 µs ± 230 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
builtins eval:
219 µs ± 3.92 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
ast literal_eval:
331 µs ± 1.89 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
yaml load:
9.02 ms ± 92.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Conclusion: prefer json.loads


回答 7

string = "{'server1':'value','server2':'value'}"

#Now removing { and }
s = string.replace("{" ,"")
finalstring = s.replace("}" , "")

#Splitting the string based on , we get key value pairs
list = finalstring.split(",")

dictionary ={}
for i in list:
    #Get Key Value pairs separately to store in dictionary
    keyvalue = i.split(":")

    #Replacing the single quotes in the leading.
    m= keyvalue[0].strip('\'')
    m = m.replace("\"", "")
    dictionary[m] = keyvalue[1].strip('"\'')

print dictionary
string = "{'server1':'value','server2':'value'}"

#Now removing { and }
s = string.replace("{" ,"")
finalstring = s.replace("}" , "")

#Splitting the string based on , we get key value pairs
list = finalstring.split(",")

dictionary ={}
for i in list:
    #Get Key Value pairs separately to store in dictionary
    keyvalue = i.split(":")

    #Replacing the single quotes in the leading.
    m= keyvalue[0].strip('\'')
    m = m.replace("\"", "")
    dictionary[m] = keyvalue[1].strip('"\'')

print dictionary

回答 8

没有使用任何库:

dict_format_string = "{'1':'one', '2' : 'two'}"
d = {}
elems  = filter(str.isalnum,dict_format_string.split("'"))
values = elems[1::2]
keys   = elems[0::2]
d.update(zip(keys,values))

注意:由于已进行硬编码,split("'")因此仅适用于“单引号”数据的字符串。

no any libs are used:

dict_format_string = "{'1':'one', '2' : 'two'}"
d = {}
elems  = filter(str.isalnum,dict_format_string.split("'"))
values = elems[1::2]
keys   = elems[0::2]
d.update(zip(keys,values))

NOTE: As it has hardcoded split("'") will work only for strings where data is “single quoted”.


Python2中的dict.items()和dict.iteritems()有什么区别?

问题:Python2中的dict.items()和dict.iteritems()有什么区别?

dict.items()和之间有适用的区别dict.iteritems()吗?

Python文档

dict.items():返回字典的(键,值)对列表的副本

dict.iteritems():在字典的(键,值)对上返回迭代器

如果我运行下面的代码,每个似乎都返回对同一对象的引用。我缺少任何细微的差异吗?

#!/usr/bin/python

d={1:'one',2:'two',3:'three'}
print 'd.items():'
for k,v in d.items():
   if d[k] is v: print '\tthey are the same object' 
   else: print '\tthey are different'

print 'd.iteritems():'   
for k,v in d.iteritems():
   if d[k] is v: print '\tthey are the same object' 
   else: print '\tthey are different'   

输出:

d.items():
    they are the same object
    they are the same object
    they are the same object
d.iteritems():
    they are the same object
    they are the same object
    they are the same object

Are there any applicable differences between dict.items() and dict.iteritems()?

From the Python docs:

dict.items(): Return a copy of the dictionary’s list of (key, value) pairs.

dict.iteritems(): Return an iterator over the dictionary’s (key, value) pairs.

If I run the code below, each seems to return a reference to the same object. Are there any subtle differences that I am missing?

#!/usr/bin/python

d={1:'one',2:'two',3:'three'}
print 'd.items():'
for k,v in d.items():
   if d[k] is v: print '\tthey are the same object' 
   else: print '\tthey are different'

print 'd.iteritems():'   
for k,v in d.iteritems():
   if d[k] is v: print '\tthey are the same object' 
   else: print '\tthey are different'   

Output:

d.items():
    they are the same object
    they are the same object
    they are the same object
d.iteritems():
    they are the same object
    they are the same object
    they are the same object

回答 0

这是演变的一部分。

最初,Python items()构建了一个真正的元组列表,并将其返回。这可能会占用大量额外的内存。

然后,一般将生成器引入该语言,然后将该方法重新实现为名为的迭代器-生成器方法iteritems()。保留原始版本是为了向后兼容。

Python 3的更改之一是 items()现在返回迭代器,并且列表从未完全构建。该iteritems()方法也消失了,因为items()在Python 3中的工作方式与viewitems()在Python 2.7中一样。

It’s part of an evolution.

Originally, Python items() built a real list of tuples and returned that. That could potentially take a lot of extra memory.

Then, generators were introduced to the language in general, and that method was reimplemented as an iterator-generator method named iteritems(). The original remains for backwards compatibility.

One of Python 3’s changes is that items() now return iterators, and a list is never fully built. The iteritems() method is also gone, since items() in Python 3 works like viewitems() in Python 2.7.


回答 1

dict.items()返回2元组([(key, value), (key, value), ...])的列表,而是dict.iteritems()生成2元组的生成器。前者最初占用更多空间和时间,但是访问每个元素的速度很快,而前者最初占用较少的空间和时间,但是在生成每个元素时要花费更多的时间。

dict.items() returns a list of 2-tuples ([(key, value), (key, value), ...]), whereas dict.iteritems() is a generator that yields 2-tuples. The former takes more space and time initially, but accessing each element is fast, whereas the second takes less space and time initially, but a bit more time in generating each element.


回答 2

在Py2.x中

该命令dict.items()dict.keys()dict.values()返回一个副本字典的的列表(k, v)对,键和值。如果复制的列表很大,则可能会占用大量内存。

该命令dict.iteritems()dict.iterkeys()dict.itervalues()返回一个迭代器在字典的(k, v)对,键和值。

该命令dict.viewitems()dict.viewkeys()dict.viewvalues()返回视图对象,它可以体现字典的变化。(即,如果您在字典中del添加了项或(k,v)在字典中添加了对,则视图对象可以同时自动更改。)

$ python2.7

>>> d = {'one':1, 'two':2}
>>> type(d.items())
<type 'list'>
>>> type(d.keys())
<type 'list'>
>>> 
>>> 
>>> type(d.iteritems())
<type 'dictionary-itemiterator'>
>>> type(d.iterkeys())
<type 'dictionary-keyiterator'>
>>> 
>>> 
>>> type(d.viewitems())
<type 'dict_items'>
>>> type(d.viewkeys())
<type 'dict_keys'>

在Py3.x中

在Py3.x,事情比较干净,因为只有dict.items()dict.keys()dict.values()可用,这回该视图对象,就像dict.viewitems()在Py2.x一样。

就像@lvc指出的那样,view对象iterator并不相同,因此,如果要在Py3.x中返回迭代器,可以使用iter(dictview)

$ python3.3

>>> d = {'one':'1', 'two':'2'}
>>> type(d.items())
<class 'dict_items'>
>>>
>>> type(d.keys())
<class 'dict_keys'>
>>>
>>>
>>> ii = iter(d.items())
>>> type(ii)
<class 'dict_itemiterator'>
>>>
>>> ik = iter(d.keys())
>>> type(ik)
<class 'dict_keyiterator'>

In Py2.x

The commands dict.items(), dict.keys() and dict.values() return a copy of the dictionary’s list of (k, v) pair, keys and values. This could take a lot of memory if the copied list is very large.

The commands dict.iteritems(), dict.iterkeys() and dict.itervalues() return an iterator over the dictionary’s (k, v) pair, keys and values.

The commands dict.viewitems(), dict.viewkeys() and dict.viewvalues() return the view objects, which can reflect the dictionary’s changes. (I.e. if you del an item or add a (k,v) pair in the dictionary, the view object can automatically change at the same time.)

$ python2.7

>>> d = {'one':1, 'two':2}
>>> type(d.items())
<type 'list'>
>>> type(d.keys())
<type 'list'>
>>> 
>>> 
>>> type(d.iteritems())
<type 'dictionary-itemiterator'>
>>> type(d.iterkeys())
<type 'dictionary-keyiterator'>
>>> 
>>> 
>>> type(d.viewitems())
<type 'dict_items'>
>>> type(d.viewkeys())
<type 'dict_keys'>

While in Py3.x

In Py3.x, things are more clean, since there are only dict.items(), dict.keys() and dict.values() available, which return the view objects just as dict.viewitems() in Py2.x did.

But

Just as @lvc noted, view object isn’t the same as iterator, so if you want to return an iterator in Py3.x, you could use iter(dictview) :

$ python3.3

>>> d = {'one':'1', 'two':'2'}
>>> type(d.items())
<class 'dict_items'>
>>>
>>> type(d.keys())
<class 'dict_keys'>
>>>
>>>
>>> ii = iter(d.items())
>>> type(ii)
<class 'dict_itemiterator'>
>>>
>>> ik = iter(d.keys())
>>> type(ik)
<class 'dict_keyiterator'>

回答 3

您问:“ dict.items()和dict.iteritems()之间是否有适用的区别”

这可能会有所帮助(对于Python 2.x):

>>> d={1:'one',2:'two',3:'three'}
>>> type(d.items())
<type 'list'>
>>> type(d.iteritems())
<type 'dictionary-itemiterator'>

您将看到d.items()返回键,值对的元组列表,并d.iteritems()返回一个字典迭代器。

清单d.items()是可切片的:

>>> l1=d.items()[0]
>>> l1
(1, 'one')   # an unordered value!

但是没有__iter__方法:

>>> next(d.items())
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: list object is not an iterator

作为迭代器,d.iteritems()不可切片:

>>> i1=d.iteritems()[0]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: 'dictionary-itemiterator' object is not subscriptable

但是确实有__iter__

>>> next(d.iteritems())
(1, 'one')               # an unordered value!

因此,物品本身是相同的-运送物品的容器是不同的。一个是列表,另一个是迭代器(取决于Python版本…)

因此,dict.items()和dict.iteritems()之间的适用差异与列表和迭代器之间的适用差异相同。

You asked: ‘Are there any applicable differences between dict.items() and dict.iteritems()’

This may help (for Python 2.x):

>>> d={1:'one',2:'two',3:'three'}
>>> type(d.items())
<type 'list'>
>>> type(d.iteritems())
<type 'dictionary-itemiterator'>

You can see that d.items() returns a list of tuples of the key, value pairs and d.iteritems() returns a dictionary-itemiterator.

As a list, d.items() is slice-able:

>>> l1=d.items()[0]
>>> l1
(1, 'one')   # an unordered value!

But would not have an __iter__ method:

>>> next(d.items())
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: list object is not an iterator

As an iterator, d.iteritems() is not slice-able:

>>> i1=d.iteritems()[0]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: 'dictionary-itemiterator' object is not subscriptable

But does have __iter__:

>>> next(d.iteritems())
(1, 'one')               # an unordered value!

So the items themselves are same — the container delivering the items are different. One is a list, the other an iterator (depending on the Python version…)

So the applicable differences between dict.items() and dict.iteritems() are the same as the applicable differences between a list and an iterator.


回答 4

dict.items()返回元组列表,并dict.iteritems()在字典中返回元组的迭代器对象为(key,value)。元组相同,但容器不同。

dict.items()基本上将所有字典复制到列表中。尝试使用下面的代码的执行时间比较dict.items()dict.iteritems()。您将看到差异。

import timeit

d = {i:i*2 for i in xrange(10000000)}  
start = timeit.default_timer() #more memory intensive
for key,value in d.items():
    tmp = key + value #do something like print
t1 = timeit.default_timer() - start

start = timeit.default_timer()
for key,value in d.iteritems(): #less memory intensive
    tmp = key + value
t2 = timeit.default_timer() - start

在我的机器上输出:

Time with d.items(): 9.04773592949
Time with d.iteritems(): 2.17707300186

这清楚地表明这dictionary.iteritems()是非常有效的。

dict.items() return list of tuples, and dict.iteritems() return iterator object of tuple in dictionary as (key,value). The tuples are the same, but container is different.

dict.items() basically copies all dictionary into list. Try using following code to compare the execution times of the dict.items() and dict.iteritems(). You will see the difference.

import timeit

d = {i:i*2 for i in xrange(10000000)}  
start = timeit.default_timer() #more memory intensive
for key,value in d.items():
    tmp = key + value #do something like print
t1 = timeit.default_timer() - start

start = timeit.default_timer()
for key,value in d.iteritems(): #less memory intensive
    tmp = key + value
t2 = timeit.default_timer() - start

Output in my machine:

Time with d.items(): 9.04773592949
Time with d.iteritems(): 2.17707300186

This clearly shows that dictionary.iteritems() is much more efficient.


回答 5

如果你有

dict = {key1:value1, key2:value2, key3:value3,...}

Python 2中dict.items()复制每个元组并返回字典中的元组列表,即[(key1,value1), (key2,value2), ...]。这意味着整个字典将被复制到包含元组的新列表中

dict = {i: i * 2 for i in xrange(10000000)}  
# Slow and memory hungry.
for key, value in dict.items():
    print(key,":",value)

dict.iteritems()返回字典项迭代器。返回的项的值也相同,即(key1,value1), (key2,value2), ...,但这不是列表。这只是字典项迭代器对象。这意味着更少的内存使用量(减少了50%)。

  • 列出为可变快照: d.items() -> list(d.items())
  • 迭代器对象: d.iteritems() -> iter(d.items())

元组是相同的。您比较了每个中的元组,因此您得到相同的元组。

dict = {i: i * 2 for i in xrange(10000000)}  
# More memory efficient.
for key, value in dict.iteritems():
    print(key,":",value)

Python 3中dict.items()返回迭代器对象。dict.iteritems()已删除,因此不再有问题。

If you have

dict = {key1:value1, key2:value2, key3:value3,...}

In Python 2, dict.items() copies each tuples and returns the list of tuples in dictionary i.e. [(key1,value1), (key2,value2), ...]. Implications are that the whole dictionary is copied to new list containing tuples

dict = {i: i * 2 for i in xrange(10000000)}  
# Slow and memory hungry.
for key, value in dict.items():
    print(key,":",value)

dict.iteritems() returns the dictionary item iterator. The value of the item returned is also the same i.e. (key1,value1), (key2,value2), ..., but this is not a list. This is only dictionary item iterator object. That means less memory usage (50% less).

  • Lists as mutable snapshots: d.items() -> list(d.items())
  • Iterator objects: d.iteritems() -> iter(d.items())

The tuples are the same. You compared tuples in each so you get same.

dict = {i: i * 2 for i in xrange(10000000)}  
# More memory efficient.
for key, value in dict.iteritems():
    print(key,":",value)

In Python 3, dict.items() returns iterator object. dict.iteritems() is removed so there is no more issue.


回答 6

dict.iteritems在Python3.x中已经不存在了,因此用于iter(dict.items())获得相同的输出和内存分配

dict.iteritems is gone in Python3.x So use iter(dict.items()) to get the same output and memory alocation


回答 7

如果您想要一种方法来迭代同时适用于Python 2和3的字典的项对,请尝试如下操作:

DICT_ITER_ITEMS = (lambda d: d.iteritems()) if hasattr(dict, 'iteritems') else (lambda d: iter(d.items()))

像这样使用它:

for key, value in DICT_ITER_ITEMS(myDict):
    # Do something with 'key' and/or 'value'.

If you want a way to iterate the item pairs of a dictionary that works with both Python 2 and 3, try something like this:

DICT_ITER_ITEMS = (lambda d: d.iteritems()) if hasattr(dict, 'iteritems') else (lambda d: iter(d.items()))

Use it like this:

for key, value in DICT_ITER_ITEMS(myDict):
    # Do something with 'key' and/or 'value'.

回答 8

dict.iteritems():给您一个迭代器。您可以在循环外的其他模式中使用迭代器。

student = {"name": "Daniel", "student_id": 2222}

for key,value in student.items():
    print(key,value)

('student_id', 2222)
('name', 'Daniel')

for key,value in student.iteritems():
    print(key,value)

('student_id', 2222)
('name', 'Daniel')

studentIterator = student.iteritems()

print(studentIterator.next())
('student_id', 2222)

print(studentIterator.next())
('name', 'Daniel')

dict.iteritems(): gives you an iterator. You may use the iterator in other patterns outside of the loop.

student = {"name": "Daniel", "student_id": 2222}

for key,value in student.items():
    print(key,value)

('student_id', 2222)
('name', 'Daniel')

for key,value in student.iteritems():
    print(key,value)

('student_id', 2222)
('name', 'Daniel')

studentIterator = student.iteritems()

print(studentIterator.next())
('student_id', 2222)

print(studentIterator.next())
('name', 'Daniel')

回答 9

python 2中的dict.iteritems()与python 3中的dict.items()等效。

dict.iteritems() in python 2 is equivalent to dict.items() in python 3.