标签归档:sorting

按列对NumPy中的数组排序

问题:按列对NumPy中的数组排序

如何按第n列对NumPy中的数组排序?

例如,

a = array([[9, 2, 3],
           [4, 5, 6],
           [7, 0, 5]])

我想按第二列对行进行排序,以便返回:

array([[7, 0, 5],
       [9, 2, 3],
       [4, 5, 6]])

How can I sort an array in NumPy by the nth column?

For example,

a = array([[9, 2, 3],
           [4, 5, 6],
           [7, 0, 5]])

I’d like to sort rows by the second column, such that I get back:

array([[7, 0, 5],
       [9, 2, 3],
       [4, 5, 6]])

回答 0

@steve答案实际上是最优雅的方法。

对于“正确”的方式,请参见numpy.ndarray.sort的order关键字参数。

但是,您需要将数组视为具有字段的数组(结构化数组)。

如果您最初没有使用字段定义数组,那么“正确”的方法就很难看了。

作为一个简单的示例,对其进行排序并返回副本:

In [1]: import numpy as np

In [2]: a = np.array([[1,2,3],[4,5,6],[0,0,1]])

In [3]: np.sort(a.view('i8,i8,i8'), order=['f1'], axis=0).view(np.int)
Out[3]: 
array([[0, 0, 1],
       [1, 2, 3],
       [4, 5, 6]])

对其进行原位排序:

In [6]: a.view('i8,i8,i8').sort(order=['f1'], axis=0) #<-- returns None

In [7]: a
Out[7]: 
array([[0, 0, 1],
       [1, 2, 3],
       [4, 5, 6]])

据我所知,@ Steve确实是最优雅的方式…

此方法的唯一优点是,“ order”参数是用来对搜索进行排序的字段列表。例如,您可以通过提供order = [‘f1’,’f2’,’f0’]来对第二列,第三列,第一列进行排序。

@steve‘s is actually the most elegant way of doing it.

For the “correct” way see the order keyword argument of numpy.ndarray.sort

However, you’ll need to view your array as an array with fields (a structured array).

The “correct” way is quite ugly if you didn’t initially define your array with fields…

As a quick example, to sort it and return a copy:

In [1]: import numpy as np

In [2]: a = np.array([[1,2,3],[4,5,6],[0,0,1]])

In [3]: np.sort(a.view('i8,i8,i8'), order=['f1'], axis=0).view(np.int)
Out[3]: 
array([[0, 0, 1],
       [1, 2, 3],
       [4, 5, 6]])

To sort it in-place:

In [6]: a.view('i8,i8,i8').sort(order=['f1'], axis=0) #<-- returns None

In [7]: a
Out[7]: 
array([[0, 0, 1],
       [1, 2, 3],
       [4, 5, 6]])

@Steve’s really is the most elegant way to do it, as far as I know…

The only advantage to this method is that the “order” argument is a list of the fields to order the search by. For example, you can sort by the second column, then the third column, then the first column by supplying order=[‘f1′,’f2′,’f0’].


回答 1

我想这可行: a[a[:,1].argsort()]

这表示的第二列,a并据此对其进行排序。

I suppose this works: a[a[:,1].argsort()]

This indicates the second column of a and sort it based on it accordingly.


回答 2

您可以按照Steve Tjoa的方法对多个列进行排序,方法是使用诸如mergesort之类的稳定排序并对索引从最低有效列到最高有效列进行排序:

a = a[a[:,2].argsort()] # First sort doesn't need to be stable.
a = a[a[:,1].argsort(kind='mergesort')]
a = a[a[:,0].argsort(kind='mergesort')]

排序方式为:第0列,然后是1,然后是2。

You can sort on multiple columns as per Steve Tjoa’s method by using a stable sort like mergesort and sorting the indices from the least significant to the most significant columns:

a = a[a[:,2].argsort()] # First sort doesn't need to be stable.
a = a[a[:,1].argsort(kind='mergesort')]
a = a[a[:,0].argsort(kind='mergesort')]

This sorts by column 0, then 1, then 2.


回答 3

我认为您可以从Python文档Wiki中进行以下操作:

a = ([[1, 2, 3], [4, 5, 6], [0, 0, 1]]); 
a = sorted(a, key=lambda a_entry: a_entry[1]) 
print a

输出为:

[[[0, 0, 1], [1, 2, 3], [4, 5, 6]]]

From the Python documentation wiki, I think you can do:

a = ([[1, 2, 3], [4, 5, 6], [0, 0, 1]]); 
a = sorted(a, key=lambda a_entry: a_entry[1]) 
print a

The output is:

[[[0, 0, 1], [1, 2, 3], [4, 5, 6]]]

回答 4

如果有人想在他们程序的关键部分使用排序,下面是对不同提案的性能比较:

import numpy as np
table = np.random.rand(5000, 10)

%timeit table.view('f8,f8,f8,f8,f8,f8,f8,f8,f8,f8').sort(order=['f9'], axis=0)
1000 loops, best of 3: 1.88 ms per loop

%timeit table[table[:,9].argsort()]
10000 loops, best of 3: 180 µs per loop

import pandas as pd
df = pd.DataFrame(table)
%timeit df.sort_values(9, ascending=True)
1000 loops, best of 3: 400 µs per loop

因此,似乎使用argsort进行索引是迄今为止最快的方法…

In case someone wants to make use of sorting at a critical part of their programs here’s a performance comparison for the different proposals:

import numpy as np
table = np.random.rand(5000, 10)

%timeit table.view('f8,f8,f8,f8,f8,f8,f8,f8,f8,f8').sort(order=['f9'], axis=0)
1000 loops, best of 3: 1.88 ms per loop

%timeit table[table[:,9].argsort()]
10000 loops, best of 3: 180 µs per loop

import pandas as pd
df = pd.DataFrame(table)
%timeit df.sort_values(9, ascending=True)
1000 loops, best of 3: 400 µs per loop

So, it looks like indexing with argsort is the quickest method so far…


回答 5

该NumPy的邮件列表,这里是另一种解决方案:

>>> a
array([[1, 2],
       [0, 0],
       [1, 0],
       [0, 2],
       [2, 1],
       [1, 0],
       [1, 0],
       [0, 0],
       [1, 0],
      [2, 2]])
>>> a[np.lexsort(np.fliplr(a).T)]
array([[0, 0],
       [0, 0],
       [0, 2],
       [1, 0],
       [1, 0],
       [1, 0],
       [1, 0],
       [1, 2],
       [2, 1],
       [2, 2]])

From the NumPy mailing list, here’s another solution:

>>> a
array([[1, 2],
       [0, 0],
       [1, 0],
       [0, 2],
       [2, 1],
       [1, 0],
       [1, 0],
       [0, 0],
       [1, 0],
      [2, 2]])
>>> a[np.lexsort(np.fliplr(a).T)]
array([[0, 0],
       [0, 0],
       [0, 2],
       [1, 0],
       [1, 0],
       [1, 0],
       [1, 0],
       [1, 2],
       [2, 1],
       [2, 2]])

回答 6

我有一个类似的问题。

我的问题:

我想计算SVD,需要按降序对我的特征值进行排序。但是我想保留特征值和特征向量之间的映射。我的特征值在第一行中,而对应的特征向量在同一列中。

因此,我想按降序按第一行在列中对二维数组进行排序。

我的解决方案

a = a[::, a[0,].argsort()[::-1]]

那么这是如何工作的呢?

a[0,] 只是我要排序的第一行。

现在,我使用argsort来获取索引的顺序。

我用 [::-1]是因为我需要降序排列。

最后,我使用a[::, ...]正确的顺序查看各列。

I had a similar problem.

My Problem:

I want to calculate an SVD and need to sort my eigenvalues in descending order. But I want to keep the mapping between eigenvalues and eigenvectors. My eigenvalues were in the first row and the corresponding eigenvector below it in the same column.

So I want to sort a two-dimensional array column-wise by the first row in descending order.

My Solution

a = a[::, a[0,].argsort()[::-1]]

So how does this work?

a[0,] is just the first row I want to sort by.

Now I use argsort to get the order of indices.

I use [::-1] because I need descending order.

Lastly I use a[::, ...] to get a view with the columns in the right order.


回答 7

稍微复杂一点的lexsort例子-在第一列下降,在第二列上升。的窍门lexsort是,它对行进行排序(因此.T),并优先考虑最后一行。

In [120]: b=np.array([[1,2,1],[3,1,2],[1,1,3],[2,3,4],[3,2,5],[2,1,6]])
In [121]: b
Out[121]: 
array([[1, 2, 1],
       [3, 1, 2],
       [1, 1, 3],
       [2, 3, 4],
       [3, 2, 5],
       [2, 1, 6]])
In [122]: b[np.lexsort(([1,-1]*b[:,[1,0]]).T)]
Out[122]: 
array([[3, 1, 2],
       [3, 2, 5],
       [2, 1, 6],
       [2, 3, 4],
       [1, 1, 3],
       [1, 2, 1]])

A little more complicated lexsort example – descending on the 1st column, secondarily ascending on the 2nd. The tricks with lexsort are that it sorts on rows (hence the .T), and gives priority to the last.

In [120]: b=np.array([[1,2,1],[3,1,2],[1,1,3],[2,3,4],[3,2,5],[2,1,6]])
In [121]: b
Out[121]: 
array([[1, 2, 1],
       [3, 1, 2],
       [1, 1, 3],
       [2, 3, 4],
       [3, 2, 5],
       [2, 1, 6]])
In [122]: b[np.lexsort(([1,-1]*b[:,[1,0]]).T)]
Out[122]: 
array([[3, 1, 2],
       [3, 2, 5],
       [2, 1, 6],
       [2, 3, 4],
       [1, 1, 3],
       [1, 2, 1]])

回答 8

这是考虑所有列的另一种解决方案(JJ的答案的更紧凑方式);

ar=np.array([[0, 0, 0, 1],
             [1, 0, 1, 0],
             [0, 1, 0, 0],
             [1, 0, 0, 1],
             [0, 0, 1, 0],
             [1, 1, 0, 0]])

用lexsort排序,

ar[np.lexsort(([ar[:, i] for i in range(ar.shape[1]-1, -1, -1)]))]

输出:

array([[0, 0, 0, 1],
       [0, 0, 1, 0],
       [0, 1, 0, 0],
       [1, 0, 0, 1],
       [1, 0, 1, 0],
       [1, 1, 0, 0]])

Here is another solution considering all columns (more compact way of J.J‘s answer);

ar=np.array([[0, 0, 0, 1],
             [1, 0, 1, 0],
             [0, 1, 0, 0],
             [1, 0, 0, 1],
             [0, 0, 1, 0],
             [1, 1, 0, 0]])

Sort with lexsort,

ar[np.lexsort(([ar[:, i] for i in range(ar.shape[1]-1, -1, -1)]))]

Output:

array([[0, 0, 0, 1],
       [0, 0, 1, 0],
       [0, 1, 0, 0],
       [1, 0, 0, 1],
       [1, 0, 1, 0],
       [1, 1, 0, 0]])

回答 9

只需使用排序,即可使用要排序的列号。

a = np.array([1,1], [1,-1], [-1,1], [-1,-1]])
print (a)
a=a.tolist() 
a = np.array(sorted(a, key=lambda a_entry: a_entry[0]))
print (a)

Simply using sort, use coloumn number based on which you want to sort.

a = np.array([1,1], [1,-1], [-1,1], [-1,-1]])
print (a)
a=a.tolist() 
a = np.array(sorted(a, key=lambda a_entry: a_entry[0]))
print (a)

回答 10

这是一个古老的问题,但是如果您需要将其推广到2维以上的数组,则可以采用以下解决方案:

np.einsum('ij->ij', a[a[:,1].argsort(),:])

这对于两个维度来说是一个过大的杀伤力,并且a[a[:,1].argsort()]每个@steve的答案就足够了,但是不能将该答案推广到更高的维度。您可以在此问题中找到3D阵列的示例。

输出:

[[7 0 5]
 [9 2 3]
 [4 5 6]]

It is an old question but if you need to generalize this to a higher than 2 dimension arrays, here is the solution than can be easily generalized:

np.einsum('ij->ij', a[a[:,1].argsort(),:])

This is an overkill for two dimensions and a[a[:,1].argsort()] would be enough per @steve’s answer, however that answer cannot be generalized to higher dimensions. You can find an example of 3D array in this question.

Output:

[[7 0 5]
 [9 2 3]
 [4 5 6]]

Python列表按降序排序

问题:Python列表按降序排序

如何按降序对列表进行排序?

timestamp = [
    "2010-04-20 10:07:30",
    "2010-04-20 10:07:38",
    "2010-04-20 10:07:52",
    "2010-04-20 10:08:22",
    "2010-04-20 10:08:22",
    "2010-04-20 10:09:46",
    "2010-04-20 10:10:37",
    "2010-04-20 10:10:58",
    "2010-04-20 10:11:50",
    "2010-04-20 10:12:13",
    "2010-04-20 10:12:13",
    "2010-04-20 10:25:38"
]

How can I sort this list in descending order?

timestamp = [
    "2010-04-20 10:07:30",
    "2010-04-20 10:07:38",
    "2010-04-20 10:07:52",
    "2010-04-20 10:08:22",
    "2010-04-20 10:08:22",
    "2010-04-20 10:09:46",
    "2010-04-20 10:10:37",
    "2010-04-20 10:10:58",
    "2010-04-20 10:11:50",
    "2010-04-20 10:12:13",
    "2010-04-20 10:12:13",
    "2010-04-20 10:25:38"
]

回答 0

在一行中,使用lambda

timestamp.sort(key=lambda x: time.strptime(x, '%Y-%m-%d %H:%M:%S')[0:6], reverse=True)

将函数传递给list.sort

def foo(x):
    return time.strptime(x, '%Y-%m-%d %H:%M:%S')[0:6]

timestamp.sort(key=foo, reverse=True)

In one line, using a lambda:

timestamp.sort(key=lambda x: time.strptime(x, '%Y-%m-%d %H:%M:%S')[0:6], reverse=True)

Passing a function to list.sort:

def foo(x):
    return time.strptime(x, '%Y-%m-%d %H:%M:%S')[0:6]

timestamp.sort(key=foo, reverse=True)

回答 1

这将为您提供阵列的排序版本。

sorted(timestamp, reverse=True)

如果要就地排序:

timestamp.sort(reverse=True)

This will give you a sorted version of the array.

sorted(timestamp, reverse=True)

If you want to sort in-place:

timestamp.sort(reverse=True)

回答 2

您可以简单地做到这一点:

timestamp.sort(reverse=True)

You can simply do this:

timestamp.sort(reverse=True)

回答 3

由于您的列表已经按升序排列,因此我们可以简单地反转列表。

>>> timestamp.reverse()
>>> timestamp
['2010-04-20 10:25:38', 
'2010-04-20 10:12:13', 
'2010-04-20 10:12:13', 
'2010-04-20 10:11:50', 
'2010-04-20 10:10:58', 
'2010-04-20 10:10:37', 
'2010-04-20 10:09:46', 
'2010-04-20 10:08:22',
'2010-04-20 10:08:22', 
'2010-04-20 10:07:52', 
'2010-04-20 10:07:38', 
'2010-04-20 10:07:30']

Since your list is already in ascending order, we can simply reverse the list.

>>> timestamp.reverse()
>>> timestamp
['2010-04-20 10:25:38', 
'2010-04-20 10:12:13', 
'2010-04-20 10:12:13', 
'2010-04-20 10:11:50', 
'2010-04-20 10:10:58', 
'2010-04-20 10:10:37', 
'2010-04-20 10:09:46', 
'2010-04-20 10:08:22',
'2010-04-20 10:08:22', 
'2010-04-20 10:07:52', 
'2010-04-20 10:07:38', 
'2010-04-20 10:07:30']

回答 4

您简单的输入:

timestamp.sort()
timestamp=timestamp[::-1]

you simple type:

timestamp.sort()
timestamp=timestamp[::-1]

回答 5

这是另一种方式


timestamp.sort()
timestamp.reverse()
print(timestamp)

Here is another way


timestamp.sort()
timestamp.reverse()
print(timestamp)

按多个属性对列表进行排序?

问题:按多个属性对列表进行排序?

我有一个清单清单:

[[12, 'tall', 'blue', 1],
[2, 'short', 'red', 9],
[4, 'tall', 'blue', 13]]

如果我想按一个元素(例如,高/短元素)排序,则可以通过进行s = sorted(s, key = itemgetter(1))

如果我想同时根据高/短和颜色进行排序,我可以进行两次排序,每个元素一次,但是有一种更快的方法吗?

I have a list of lists:

[[12, 'tall', 'blue', 1],
[2, 'short', 'red', 9],
[4, 'tall', 'blue', 13]]

If I wanted to sort by one element, say the tall/short element, I could do it via s = sorted(s, key = itemgetter(1)).

If I wanted to sort by both tall/short and colour, I could do the sort twice, once for each element, but is there a quicker way?


回答 0

键可以是返回元组的函数:

s = sorted(s, key = lambda x: (x[1], x[2]))

或者,您可以使用来达到相同的效果itemgetter(速度更快,并且避免了Python函数调用):

import operator
s = sorted(s, key = operator.itemgetter(1, 2))

并请注意,您可以在此处使用sort而不是使用sorted,然后重新分配:

s.sort(key = operator.itemgetter(1, 2))

A key can be a function that returns a tuple:

s = sorted(s, key = lambda x: (x[1], x[2]))

Or you can achieve the same using itemgetter (which is faster and avoids a Python function call):

import operator
s = sorted(s, key = operator.itemgetter(1, 2))

And notice that here you can use sort instead of using sorted and then reassigning:

s.sort(key = operator.itemgetter(1, 2))

回答 1

我不确定这是否是最pythonic的方法……我有一个元组列表,需要按降序对整数值进行排序,然后按字母顺序对第二个进行排序。这需要反转整数排序,而不是字母排序。这是我的解决方案:(在一次考试中,我什至不知道您可以“嵌套”排序功能)

a = [('Al', 2),('Bill', 1),('Carol', 2), ('Abel', 3), ('Zeke', 2), ('Chris', 1)]  
b = sorted(sorted(a, key = lambda x : x[0]), key = lambda x : x[1], reverse = True)  
print(b)  
[('Abel', 3), ('Al', 2), ('Carol', 2), ('Zeke', 2), ('Bill', 1), ('Chris', 1)]

I’m not sure if this is the most pythonic method … I had a list of tuples that needed sorting 1st by descending integer values and 2nd alphabetically. This required reversing the integer sort but not the alphabetical sort. Here was my solution: (on the fly in an exam btw, I was not even aware you could ‘nest’ sorted functions)

a = [('Al', 2),('Bill', 1),('Carol', 2), ('Abel', 3), ('Zeke', 2), ('Chris', 1)]  
b = sorted(sorted(a, key = lambda x : x[0]), key = lambda x : x[1], reverse = True)  
print(b)  
[('Abel', 3), ('Al', 2), ('Carol', 2), ('Zeke', 2), ('Bill', 1), ('Chris', 1)]

回答 2

看来您可以使用list而不是tuple。我认为,当您获取属性而不是列表/元组的“魔术索引”时,这变得尤为重要。

在我的情况下,我想按类的多个属性进行排序,其中传入的键是字符串。我需要在不同的地方进行不同的排序,并且我希望为与客户进行交互的父类提供一个通用的默认排序。只需在真正需要时覆盖“排序键”,而且还可以将它们存储为类可以共享的列表

所以首先我定义了一个辅助方法

def attr_sort(self, attrs=['someAttributeString']:
  '''helper to sort by the attributes named by strings of attrs in order'''
  return lambda k: [ getattr(k, attr) for attr in attrs ]

然后使用它

# would defined elsewhere but showing here for consiseness
self.SortListA = ['attrA', 'attrB']
self.SortListB = ['attrC', 'attrA']
records = .... #list of my objects to sort
records.sort(key=self.attr_sort(attrs=self.SortListA))
# perhaps later nearby or in another function
more_records = .... #another list
more_records.sort(key=self.attr_sort(attrs=self.SortListB))

这将使用生成的lambda函数对列表进行排序object.attrA,然后object.attrB假定object具有与提供的字符串名称相对应的getter。到object.attrC那时,第二种情况将得到解决object.attrA

这还允许您潜在地暴露向外的排序选择,以供使用者,单元测试共享,或者让他们告诉您他们希望如何对api中的某些操作进行排序,而只需给您一个列表,而不是将它们耦合到您的后端实现。

It appears you could use a list instead of a tuple. This becomes more important I think when you are grabbing attributes instead of ‘magic indexes’ of a list/tuple.

In my case I wanted to sort by multiple attributes of a class, where the incoming keys were strings. I needed different sorting in different places, and I wanted a common default sort for the parent class that clients were interacting with; only having to override the ‘sorting keys’ when I really ‘needed to’, but also in a way that I could store them as lists that the class could share

So first I defined a helper method

def attr_sort(self, attrs=['someAttributeString']:
  '''helper to sort by the attributes named by strings of attrs in order'''
  return lambda k: [ getattr(k, attr) for attr in attrs ]

then to use it

# would defined elsewhere but showing here for consiseness
self.SortListA = ['attrA', 'attrB']
self.SortListB = ['attrC', 'attrA']
records = .... #list of my objects to sort
records.sort(key=self.attr_sort(attrs=self.SortListA))
# perhaps later nearby or in another function
more_records = .... #another list
more_records.sort(key=self.attr_sort(attrs=self.SortListB))

This will use the generated lambda function sort the list by object.attrA and then object.attrB assuming object has a getter corresponding to the string names provided. And the second case would sort by object.attrC then object.attrA.

This also allows you to potentially expose outward sorting choices to be shared alike by a consumer, a unit test, or for them to perhaps tell you how they want sorting done for some operation in your api by only have to give you a list and not coupling them to your back end implementation.


回答 3

几年迟到了,但我想这两个排序2个标准使用reverse=True。如果其他人想知道如何做,则可以将您的条件(函数)括在括号中:

s = sorted(my_list, key=lambda i: ( criteria_1(i), criteria_2(i) ), reverse=True)

Several years late to the party but I want to both sort on 2 criteria and use reverse=True. In case someone else wants to know how, you can wrap your criteria (functions) in parenthesis:

s = sorted(my_list, key=lambda i: ( criteria_1(i), criteria_2(i) ), reverse=True)

回答 4

这是一种方法:您基本上是重写您的排序函数以获取一个排序函数列表,每个排序函数都会比较您要测试的属性,在每次排序测试中,您都会查看并查看cmp函数是否返回非零返回值如果是这样,则中断并发送返回值。您可以通过调用Lambda列表功能的Lambda来调用它。

它的优点是它可以单次通过数据,而不像其他方法那样通过以前的排序。另一件事是,它排序到位,而排序似乎可以复制。

我用它编写了一个等级函数,该函数对每个对象在一个组中并具有得分函数的类列表进行排名,但是您可以添加任何属性列表。请注意类似unlambda的内容,尽管会使用lambda来调用setter。等级部分不适用于列表数组,但排序可以。

#First, here's  a pure list version
my_sortLambdaLst = [lambda x,y:cmp(x[0], y[0]), lambda x,y:cmp(x[1], y[1])]
def multi_attribute_sort(x,y):
    r = 0
    for l in my_sortLambdaLst:
        r = l(x,y)
        if r!=0: return r #keep looping till you see a difference
    return r

Lst = [(4, 2.0), (4, 0.01), (4, 0.9), (4, 0.999),(4, 0.2), (1, 2.0), (1, 0.01), (1, 0.9), (1, 0.999), (1, 0.2) ]
Lst.sort(lambda x,y:multi_attribute_sort(x,y)) #The Lambda of the Lambda
for rec in Lst: print str(rec)

这是一种对对象列表进行排名的方法

class probe:
    def __init__(self, group, score):
        self.group = group
        self.score = score
        self.rank =-1
    def set_rank(self, r):
        self.rank = r
    def __str__(self):
        return '\t'.join([str(self.group), str(self.score), str(self.rank)]) 


def RankLst(inLst, group_lambda= lambda x:x.group, sortLambdaLst = [lambda x,y:cmp(x.group, y.group), lambda x,y:cmp(x.score, y.score)], SetRank_Lambda = lambda x, rank:x.set_rank(rank)):
    #Inner function is the only way (I could think of) to pass the sortLambdaLst into a sort function
    def multi_attribute_sort(x,y):
        r = 0
        for l in sortLambdaLst:
            r = l(x,y)
            if r!=0: return r #keep looping till you see a difference
        return r

    inLst.sort(lambda x,y:multi_attribute_sort(x,y))
    #Now Rank your probes
    rank = 0
    last_group = group_lambda(inLst[0])
    for i in range(len(inLst)):
        rec = inLst[i]
        group = group_lambda(rec)
        if last_group == group: 
            rank+=1
        else:
            rank=1
            last_group = group
        SetRank_Lambda(inLst[i], rank) #This is pure evil!! The lambda purists are gnashing their teeth

Lst = [probe(4, 2.0), probe(4, 0.01), probe(4, 0.9), probe(4, 0.999), probe(4, 0.2), probe(1, 2.0), probe(1, 0.01), probe(1, 0.9), probe(1, 0.999), probe(1, 0.2) ]

RankLst(Lst, group_lambda= lambda x:x.group, sortLambdaLst = [lambda x,y:cmp(x.group, y.group), lambda x,y:cmp(x.score, y.score)], SetRank_Lambda = lambda x, rank:x.set_rank(rank))
print '\t'.join(['group', 'score', 'rank']) 
for r in Lst: print r

Here’s one way: You basically re-write your sort function to take a list of sort functions, each sort function compares the attributes you want to test, on each sort test, you look and see if the cmp function returns a non-zero return if so break and send the return value. You call it by calling a Lambda of a function of a list of Lambdas.

Its advantage is that it does single pass through the data not a sort of a previous sort as other methods do. Another thing is that it sorts in place, whereas sorted seems to make a copy.

I used it to write a rank function, that ranks a list of classes where each object is in a group and has a score function, but you can add any list of attributes. Note the un-lambda-like, though hackish use of a lambda to call a setter. The rank part won’t work for an array of lists, but the sort will.

#First, here's  a pure list version
my_sortLambdaLst = [lambda x,y:cmp(x[0], y[0]), lambda x,y:cmp(x[1], y[1])]
def multi_attribute_sort(x,y):
    r = 0
    for l in my_sortLambdaLst:
        r = l(x,y)
        if r!=0: return r #keep looping till you see a difference
    return r

Lst = [(4, 2.0), (4, 0.01), (4, 0.9), (4, 0.999),(4, 0.2), (1, 2.0), (1, 0.01), (1, 0.9), (1, 0.999), (1, 0.2) ]
Lst.sort(lambda x,y:multi_attribute_sort(x,y)) #The Lambda of the Lambda
for rec in Lst: print str(rec)

Here’s a way to rank a list of objects

class probe:
    def __init__(self, group, score):
        self.group = group
        self.score = score
        self.rank =-1
    def set_rank(self, r):
        self.rank = r
    def __str__(self):
        return '\t'.join([str(self.group), str(self.score), str(self.rank)]) 


def RankLst(inLst, group_lambda= lambda x:x.group, sortLambdaLst = [lambda x,y:cmp(x.group, y.group), lambda x,y:cmp(x.score, y.score)], SetRank_Lambda = lambda x, rank:x.set_rank(rank)):
    #Inner function is the only way (I could think of) to pass the sortLambdaLst into a sort function
    def multi_attribute_sort(x,y):
        r = 0
        for l in sortLambdaLst:
            r = l(x,y)
            if r!=0: return r #keep looping till you see a difference
        return r

    inLst.sort(lambda x,y:multi_attribute_sort(x,y))
    #Now Rank your probes
    rank = 0
    last_group = group_lambda(inLst[0])
    for i in range(len(inLst)):
        rec = inLst[i]
        group = group_lambda(rec)
        if last_group == group: 
            rank+=1
        else:
            rank=1
            last_group = group
        SetRank_Lambda(inLst[i], rank) #This is pure evil!! The lambda purists are gnashing their teeth

Lst = [probe(4, 2.0), probe(4, 0.01), probe(4, 0.9), probe(4, 0.999), probe(4, 0.2), probe(1, 2.0), probe(1, 0.01), probe(1, 0.9), probe(1, 0.999), probe(1, 0.2) ]

RankLst(Lst, group_lambda= lambda x:x.group, sortLambdaLst = [lambda x,y:cmp(x.group, y.group), lambda x,y:cmp(x.score, y.score)], SetRank_Lambda = lambda x, rank:x.set_rank(rank))
print '\t'.join(['group', 'score', 'rank']) 
for r in Lst: print r

如何对字符串列表进行排序?

问题:如何对字符串列表进行排序?

在Python中创建按字母顺序排序的列表的最佳方法是什么?

What is the best way of creating an alphabetically sorted list in Python?


回答 0

基本答案:

mylist = ["b", "C", "A"]
mylist.sort()

这会修改您的原始列表(即就地排序)。要获得列表的排序副本,而无需更改原始副本,请使用以下sorted()函数:

for x in sorted(mylist):
    print x

但是,上面的示例有些天真,因为它们没有考虑区域设置,而是执行区分大小写的排序。您可以利用可选参数key指定自定义排序顺序(使用的替代方法cmp是不推荐使用的解决方案,因为它必须多次评估- key每个元素仅计算一次)。

因此,要根据当前语言环境进行排序,并考虑到特定于语言的规则(这cmp_to_key是functools的帮助函数):

sorted(mylist, key=cmp_to_key(locale.strcoll))

最后,如果需要,您可以指定自定义语言环境进行排序:

import locale
locale.setlocale(locale.LC_ALL, 'en_US.UTF-8') # vary depending on your lang/locale
assert sorted((u'Ab', u'ad', u'aa'),
  key=cmp_to_key(locale.strcoll)) == [u'aa', u'Ab', u'ad']

最后要注意的是:您将看到使用该lower()方法的不区分大小写的排序示例-这些是不正确的,因为它们仅适用于ASCII字符子集。对于任何非英语数据,这两个错误:

# this is incorrect!
mylist.sort(key=lambda x: x.lower())
# alternative notation, a bit faster, but still wrong
mylist.sort(key=str.lower)

Basic answer:

mylist = ["b", "C", "A"]
mylist.sort()

This modifies your original list (i.e. sorts in-place). To get a sorted copy of the list, without changing the original, use the sorted() function:

for x in sorted(mylist):
    print x

However, the examples above are a bit naive, because they don’t take locale into account, and perform a case-sensitive sorting. You can take advantage of the optional parameter key to specify custom sorting order (the alternative, using cmp, is a deprecated solution, as it has to be evaluated multiple times – key is only computed once per element).

So, to sort according to the current locale, taking language-specific rules into account (cmp_to_key is a helper function from functools):

sorted(mylist, key=cmp_to_key(locale.strcoll))

And finally, if you need, you can specify a custom locale for sorting:

import locale
locale.setlocale(locale.LC_ALL, 'en_US.UTF-8') # vary depending on your lang/locale
assert sorted((u'Ab', u'ad', u'aa'),
  key=cmp_to_key(locale.strcoll)) == [u'aa', u'Ab', u'ad']

Last note: you will see examples of case-insensitive sorting which use the lower() method – those are incorrect, because they work only for the ASCII subset of characters. Those two are wrong for any non-English data:

# this is incorrect!
mylist.sort(key=lambda x: x.lower())
# alternative notation, a bit faster, but still wrong
mylist.sort(key=str.lower)

回答 1

还值得注意的sorted()功能:

for x in sorted(list):
    print x

这将返回列表的新排序版本,而不更改原始列表。

It is also worth noting the sorted() function:

for x in sorted(list):
    print x

This returns a new, sorted version of a list without changing the original list.


回答 2

list.sort()

真的就是这么简单:)

list.sort()

It really is that simple :)


回答 3

字符串排序的正确方法是:

import locale
locale.setlocale(locale.LC_ALL, 'en_US.UTF-8') # vary depending on your lang/locale
assert sorted((u'Ab', u'ad', u'aa'), cmp=locale.strcoll) == [u'aa', u'Ab', u'ad']

# Without using locale.strcoll you get:
assert sorted((u'Ab', u'ad', u'aa')) == [u'Ab', u'aa', u'ad']

前面的示例mylist.sort(key=lambda x: x.lower())对于仅ASCII上下文适用。

The proper way to sort strings is:

import locale
locale.setlocale(locale.LC_ALL, 'en_US.UTF-8') # vary depending on your lang/locale
assert sorted((u'Ab', u'ad', u'aa'), cmp=locale.strcoll) == [u'aa', u'Ab', u'ad']

# Without using locale.strcoll you get:
assert sorted((u'Ab', u'ad', u'aa')) == [u'Ab', u'aa', u'ad']

The previous example of mylist.sort(key=lambda x: x.lower()) will work fine for ASCII-only contexts.


回答 4

请在Python3中使用sorted()函数

items = ["love", "like", "play", "cool", "my"]
sorted(items2)

Please use sorted() function in Python3

items = ["love", "like", "play", "cool", "my"]
sorted(items2)

回答 5

但是,这如何处理特定于语言的排序规则?是否考虑到语言环境?

不,list.sort()是通用排序功能。如果要根据Unicode规则进行排序,则必须定义一个自定义的排序键函数。您可以尝试使用pyuca模块,但我不知道它的完整性。

But how does this handle language specific sorting rules? Does it take locale into account?

No, list.sort() is a generic sorting function. If you want to sort according to the Unicode rules, you’ll have to define a custom sort key function. You can try using the pyuca module, but I don’t know how complete it is.


回答 6

这是一个老问题,但是如果您想在不进行设置的情况下进行 locale.LC_ALL感知区域设置的排序,则可以按照此答案的建议使用PyICU库

import icu # PyICU

def sorted_strings(strings, locale=None):
    if locale is None:
       return sorted(strings)
    collator = icu.Collator.createInstance(icu.Locale(locale))
    return sorted(strings, key=collator.getSortKey)

然后用例如:

new_list = sorted_strings(list_of_strings, "de_DE.utf8")

这对我有用,而无需安装任何语言环境或更改其他系统设置。

(这已经在上面的评论中建议,但是我想让它更加突出,因为我一开始就很想念它。)

Old question, but if you want to do locale-aware sorting without setting locale.LC_ALL you can do so by using the PyICU library as suggested by this answer:

import icu # PyICU

def sorted_strings(strings, locale=None):
    if locale is None:
       return sorted(strings)
    collator = icu.Collator.createInstance(icu.Locale(locale))
    return sorted(strings, key=collator.getSortKey)

Then call with e.g.:

new_list = sorted_strings(list_of_strings, "de_DE.utf8")

This worked for me without installing any locales or changing other system settings.

(This was already suggested in a comment above, but I wanted to give it more prominence, because I missed it myself at first.)


回答 7

假设 s = "ZWzaAd"

要在字符串上方排序,简单的解决方案将是在字符串下方。

print ''.join(sorted(s))

Suppose s = "ZWzaAd"

To sort above string the simple solution will be below one.

print ''.join(sorted(s))

回答 8

或许:

names = ['Jasmine', 'Alberto', 'Ross', 'dig-dog']
print ("The solution for this is about this names being sorted:",sorted(names, key=lambda name:name.lower()))

Or maybe:

names = ['Jasmine', 'Alberto', 'Ross', 'dig-dog']
print ("The solution for this is about this names being sorted:",sorted(names, key=lambda name:name.lower()))

回答 9

l =['abc' , 'cd' , 'xy' , 'ba' , 'dc']
l.sort()
print(l1)

结果

[‘abc’,’ba’,’cd’,’dc’,’xy’]

l =['abc' , 'cd' , 'xy' , 'ba' , 'dc']
l.sort()
print(l1)

Result

[‘abc’, ‘ba’, ‘cd’, ‘dc’, ‘xy’]


回答 10

很简单:https : //trinket.io/library/trinkets/5db81676e4

scores = '54 - Alice,35 - Bob,27 - Carol,27 - Chuck,05 - Craig,30 - Dan,27 - Erin,77 - Eve,14 - Fay,20 - Frank,48 - Grace,61 - Heidi,03 - Judy,28 - Mallory,05 - Olivia,44 - Oscar,34 - Peggy,30 - Sybil,82 - Trent,75 - Trudy,92 - Victor,37 - Walter'

得分= scores.split(’,’)for x in sorted(scores):print(x)

It is simple: https://trinket.io/library/trinkets/5db81676e4

scores = '54 - Alice,35 - Bob,27 - Carol,27 - Chuck,05 - Craig,30 - Dan,27 - Erin,77 - Eve,14 - Fay,20 - Frank,48 - Grace,61 - Heidi,03 - Judy,28 - Mallory,05 - Olivia,44 - Oscar,34 - Peggy,30 - Sybil,82 - Trent,75 - Trudy,92 - Victor,37 - Walter'

scores = scores.split(‘,’) for x in sorted(scores): print(x)


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)

如何根据对象的属性对对象列表进行排序?

问题:如何根据对象的属性对对象列表进行排序?

我有一个Python对象列表,我想按对象本身的属性对其进行排序。该列表如下所示:

>>> ut
[<Tag: 128>, <Tag: 2008>, <Tag: <>, <Tag: actionscript>, <Tag: addresses>,
 <Tag: aes>, <Tag: ajax> ...]

每个对象都有一个计数:

>>> ut[1].count
1L

我需要按递减计数对列表进行排序。

我已经看到了几种方法,但是我正在寻找Python的最佳实践。

I’ve got a list of Python objects that I’d like to sort by an attribute of the objects themselves. The list looks like:

>>> ut
[<Tag: 128>, <Tag: 2008>, <Tag: <>, <Tag: actionscript>, <Tag: addresses>,
 <Tag: aes>, <Tag: ajax> ...]

Each object has a count:

>>> ut[1].count
1L

I need to sort the list by number of counts descending.

I’ve seen several methods for this, but I’m looking for best practice in Python.


回答 0

# To sort the list in place...
ut.sort(key=lambda x: x.count, reverse=True)

# To return a new list, use the sorted() built-in function...
newlist = sorted(ut, key=lambda x: x.count, reverse=True)

有关按键排序的更多信息。

# To sort the list in place...
ut.sort(key=lambda x: x.count, reverse=True)

# To return a new list, use the sorted() built-in function...
newlist = sorted(ut, key=lambda x: x.count, reverse=True)

More on sorting by keys.


回答 1

可以使用最快的方法,尤其是在您的列表中有很多记录的情况下operator.attrgetter("count")。但是,它可以在预操作者版本的Python上运行,因此具有后备机制会很好。然后,您可能需要执行以下操作:

try: import operator
except ImportError: keyfun= lambda x: x.count # use a lambda if no operator module
else: keyfun= operator.attrgetter("count") # use operator since it's faster than lambda

ut.sort(key=keyfun, reverse=True) # sort in-place

A way that can be fastest, especially if your list has a lot of records, is to use operator.attrgetter("count"). However, this might run on an pre-operator version of Python, so it would be nice to have a fallback mechanism. You might want to do the following, then:

try: import operator
except ImportError: keyfun= lambda x: x.count # use a lambda if no operator module
else: keyfun= operator.attrgetter("count") # use operator since it's faster than lambda

ut.sort(key=keyfun, reverse=True) # sort in-place

回答 2

读者应注意,key =方法:

ut.sort(key=lambda x: x.count, reverse=True)

比向对象添加丰富的比较运算符快许多倍。我很惊讶地阅读了这篇文章(“ Python in a Nutshell”的第485页)。您可以通过在这个小程序上运行测试来确认这一点:

#!/usr/bin/env python
import random

class C:
    def __init__(self,count):
        self.count = count

    def __cmp__(self,other):
        return cmp(self.count,other.count)

longList = [C(random.random()) for i in xrange(1000000)] #about 6.1 secs
longList2 = longList[:]

longList.sort() #about 52 - 6.1 = 46 secs
longList2.sort(key = lambda c: c.count) #about 9 - 6.1 = 3 secs

我的非常少的测试表明,第一种方法的运行速度要慢10倍以上,但书中说,一般而言,它仅慢5倍左右。他们说的原因是由于python(timsort)中使用了高度优化的排序算法。

仍然,.sort(lambda)比普通的旧.sort()快是很奇怪的。我希望他们能解决这个问题。

Readers should notice that the key= method:

ut.sort(key=lambda x: x.count, reverse=True)

is many times faster than adding rich comparison operators to the objects. I was surprised to read this (page 485 of “Python in a Nutshell”). You can confirm this by running tests on this little program:

#!/usr/bin/env python
import random

class C:
    def __init__(self,count):
        self.count = count

    def __cmp__(self,other):
        return cmp(self.count,other.count)

longList = [C(random.random()) for i in xrange(1000000)] #about 6.1 secs
longList2 = longList[:]

longList.sort() #about 52 - 6.1 = 46 secs
longList2.sort(key = lambda c: c.count) #about 9 - 6.1 = 3 secs

My, very minimal, tests show the first sort is more than 10 times slower, but the book says it is only about 5 times slower in general. The reason they say is due to the highly optimizes sort algorithm used in python (timsort).

Still, its very odd that .sort(lambda) is faster than plain old .sort(). I hope they fix that.


回答 3

面向对象的方法

最好将对象排序逻辑(如果适用)设置为类的属性,而不是在每个实例中都要求进行排序。

这样可以确保一致性,并且不需要样板代码。

至少,您应该指定__eq____lt__操作此功能。然后使用sorted(list_of_objects)

class Card(object):

    def __init__(self, rank, suit):
        self.rank = rank
        self.suit = suit

    def __eq__(self, other):
        return self.rank == other.rank and self.suit == other.suit

    def __lt__(self, other):
        return self.rank < other.rank

hand = [Card(10, 'H'), Card(2, 'h'), Card(12, 'h'), Card(13, 'h'), Card(14, 'h')]
hand_order = [c.rank for c in hand]  # [10, 2, 12, 13, 14]

hand_sorted = sorted(hand)
hand_sorted_order = [c.rank for c in hand_sorted]  # [2, 10, 12, 13, 14]

Object-oriented approach

It’s good practice to make object sorting logic, if applicable, a property of the class rather than incorporated in each instance the ordering is required.

This ensures consistency and removes the need for boilerplate code.

At a minimum, you should specify __eq__ and __lt__ operations for this to work. Then just use sorted(list_of_objects).

class Card(object):

    def __init__(self, rank, suit):
        self.rank = rank
        self.suit = suit

    def __eq__(self, other):
        return self.rank == other.rank and self.suit == other.suit

    def __lt__(self, other):
        return self.rank < other.rank

hand = [Card(10, 'H'), Card(2, 'h'), Card(12, 'h'), Card(13, 'h'), Card(14, 'h')]
hand_order = [c.rank for c in hand]  # [10, 2, 12, 13, 14]

hand_sorted = sorted(hand)
hand_sorted_order = [c.rank for c in hand_sorted]  # [2, 10, 12, 13, 14]

回答 4

from operator import attrgetter
ut.sort(key = attrgetter('count'), reverse = True)
from operator import attrgetter
ut.sort(key = attrgetter('count'), reverse = True)

回答 5

它看起来很像Django ORM模型实例的列表。

为什么不对这样的查询进行排序:

ut = Tag.objects.order_by('-count')

It looks much like a list of Django ORM model instances.

Why not sort them on query like this:

ut = Tag.objects.order_by('-count')

回答 6

将丰富的比较运算符添加到对象类,然后使用列表的sort()方法。
参见python中的丰富比较


更新:尽管此方法可行,但我认为Triptych的解决方案更简单,因此更适合您的情况。

Add rich comparison operators to the object class, then use sort() method of the list.
See rich comparison in python.


Update: Although this method would work, I think solution from Triptych is better suited to your case because way simpler.


回答 7

如果要排序的属性property,则可以避免导入,operator.attrgetter而可以使用属性的fget方法。

例如,对于Circle具有属性的类,radius我们可以circles按如下所示对半径列表进行排序:

result = sorted(circles, key=Circle.radius.fget)

这不是最知名的功能,但通常使我免于导入的麻烦。

If the attribute you want to sort by is a property, then you can avoid importing operator.attrgetter and use the property’s fget method instead.

For example, for a class Circle with a property radius we could sort a list of circles by radii as follows:

result = sorted(circles, key=Circle.radius.fget)

This is not the most well-known feature but often saves me a line with the import.


如何按给定索引处的元素对列表/元组的列表/元组进行排序?

问题:如何按给定索引处的元素对列表/元组的列表/元组进行排序?

我在列表列表或元组列表中都有一些数据,如下所示:

data = [[1,2,3], [4,5,6], [7,8,9]]
data = [(1,2,3), (4,5,6), (7,8,9)]

我想按子集中的第二个元素排序。这意味着,由2,5,8,其中排序2(1,2,3)5是从(4,5,6)。常见的做法是什么?我应该将元组或列表存储在列表中吗?

I have some data either in a list of lists or a list of tuples, like this:

data = [[1,2,3], [4,5,6], [7,8,9]]
data = [(1,2,3), (4,5,6), (7,8,9)]

And I want to sort by the 2nd element in the subset. Meaning, sorting by 2,5,8 where 2 is from (1,2,3), 5 is from (4,5,6). What is the common way to do this? Should I store tuples or lists in my list?


回答 0

sorted_by_second = sorted(data, key=lambda tup: tup[1])

要么:

data.sort(key=lambda tup: tup[1])  # sorts in place
sorted_by_second = sorted(data, key=lambda tup: tup[1])

or:

data.sort(key=lambda tup: tup[1])  # sorts in place

回答 1

from operator import itemgetter
data.sort(key=itemgetter(1))
from operator import itemgetter
data.sort(key=itemgetter(1))

回答 2

如果您想将数组从高到低排序,我只想添加到Stephen的答案中,除了上面的注释中的另一种方法就是将其添加到行中:

reverse = True

结果将如下所示:

data.sort(key=lambda tup: tup[1], reverse=True)

I just want to add to Stephen’s answer if you want to sort the array from high to low, another way other than in the comments above is just to add this to the line:

reverse = True

and the result will be as follows:

data.sort(key=lambda tup: tup[1], reverse=True)

回答 3

为了按照多个条件进行排序,例如按元组中的第二个和第三个元素进行排序,

data = [(1,2,3),(1,2,1),(1,1,4)]

并定义一个lambda来返回描述优先级的元组,例如

sorted(data, key=lambda tup: (tup[1],tup[2]) )
[(1, 1, 4), (1, 2, 1), (1, 2, 3)]

For sorting by multiple criteria, namely for instance by the second and third elements in a tuple, let

data = [(1,2,3),(1,2,1),(1,1,4)]

and so define a lambda that returns a tuple that describes priority, for instance

sorted(data, key=lambda tup: (tup[1],tup[2]) )
[(1, 1, 4), (1, 2, 1), (1, 2, 3)]

回答 4

斯蒂芬的答案就是我会用的答案。为了完整起见,这是带有列表推导的DSU(装饰-排序-取消装饰)模式:

decorated = [(tup[1], tup) for tup in data]
decorated.sort()
undecorated = [tup for second, tup in decorated]

或者,更简洁地说:

[b for a,b in sorted((tup[1], tup) for tup in data)]

Python Sorting HowTo中所述,自Python 2.4启用关键功能以来,就没有必要这样做

Stephen’s answer is the one I’d use. For completeness, here’s the DSU (decorate-sort-undecorate) pattern with list comprehensions:

decorated = [(tup[1], tup) for tup in data]
decorated.sort()
undecorated = [tup for second, tup in decorated]

Or, more tersely:

[b for a,b in sorted((tup[1], tup) for tup in data)]

As noted in the Python Sorting HowTo, this has been unnecessary since Python 2.4, when key functions became available.


回答 5

为了对元组列表进行排序(<word>, <count>),以count降序和word字母顺序:

data = [
('betty', 1),
('bought', 1),
('a', 1),
('bit', 1),
('of', 1),
('butter', 2),
('but', 1),
('the', 1),
('was', 1),
('bitter', 1)]

我使用这种方法:

sorted(data, key=lambda tup:(-tup[1], tup[0]))

它给了我结果:

[('butter', 2),
('a', 1),
('betty', 1),
('bit', 1),
('bitter', 1),
('bought', 1),
('but', 1),
('of', 1),
('the', 1),
('was', 1)]

In order to sort a list of tuples (<word>, <count>), for count in descending order and word in alphabetical order:

data = [
('betty', 1),
('bought', 1),
('a', 1),
('bit', 1),
('of', 1),
('butter', 2),
('but', 1),
('the', 1),
('was', 1),
('bitter', 1)]

I use this method:

sorted(data, key=lambda tup:(-tup[1], tup[0]))

and it gives me the result:

[('butter', 2),
('a', 1),
('betty', 1),
('bit', 1),
('bitter', 1),
('bought', 1),
('but', 1),
('of', 1),
('the', 1),
('was', 1)]

回答 6

没有lambda:

def sec_elem(s):
    return s[1]

sorted(data, key=sec_elem)

Without lambda:

def sec_elem(s):
    return s[1]

sorted(data, key=sec_elem)

回答 7

itemgetter() 比…快一点 lambda tup: tup[1],但增长幅度相对较小(大约10%到25%)。

(IPython会话)

>>> from operator import itemgetter
>>> from numpy.random import randint
>>> values = randint(0, 9, 30000).reshape((10000,3))
>>> tpls = [tuple(values[i,:]) for i in range(len(values))]

>>> tpls[:5]    # display sample from list
[(1, 0, 0), 
 (8, 5, 5), 
 (5, 4, 0), 
 (5, 7, 7), 
 (4, 2, 1)]

>>> sorted(tpls[:5], key=itemgetter(1))    # example sort
[(1, 0, 0), 
 (4, 2, 1), 
 (5, 4, 0), 
 (8, 5, 5), 
 (5, 7, 7)]

>>> %timeit sorted(tpls, key=itemgetter(1))
100 loops, best of 3: 4.89 ms per loop

>>> %timeit sorted(tpls, key=lambda tup: tup[1])
100 loops, best of 3: 6.39 ms per loop

>>> %timeit sorted(tpls, key=(itemgetter(1,0)))
100 loops, best of 3: 16.1 ms per loop

>>> %timeit sorted(tpls, key=lambda tup: (tup[1], tup[0]))
100 loops, best of 3: 17.1 ms per loop

itemgetter() is somewhat faster than lambda tup: tup[1], but the increase is relatively modest (around 10 to 25 percent).

(IPython session)

>>> from operator import itemgetter
>>> from numpy.random import randint
>>> values = randint(0, 9, 30000).reshape((10000,3))
>>> tpls = [tuple(values[i,:]) for i in range(len(values))]

>>> tpls[:5]    # display sample from list
[(1, 0, 0), 
 (8, 5, 5), 
 (5, 4, 0), 
 (5, 7, 7), 
 (4, 2, 1)]

>>> sorted(tpls[:5], key=itemgetter(1))    # example sort
[(1, 0, 0), 
 (4, 2, 1), 
 (5, 4, 0), 
 (8, 5, 5), 
 (5, 7, 7)]

>>> %timeit sorted(tpls, key=itemgetter(1))
100 loops, best of 3: 4.89 ms per loop

>>> %timeit sorted(tpls, key=lambda tup: tup[1])
100 loops, best of 3: 6.39 ms per loop

>>> %timeit sorted(tpls, key=(itemgetter(1,0)))
100 loops, best of 3: 16.1 ms per loop

>>> %timeit sorted(tpls, key=lambda tup: (tup[1], tup[0]))
100 loops, best of 3: 17.1 ms per loop

回答 8

@Stephen的答案很关键!这是一个更好的可视化示例,

为Ready Player One粉丝大喊大叫!=)

>>> gunters = [('2044-04-05', 'parzival'), ('2044-04-07', 'aech'), ('2044-04-06', 'art3mis')]
>>> gunters.sort(key=lambda tup: tup[0])
>>> print gunters
[('2044-04-05', 'parzival'), ('2044-04-06', 'art3mis'), ('2044-04-07', 'aech')]

key是一个函数,将调用该函数来转换集合的项目以进行比较compareTo

传递给key的参数必须是可调用的。在这里,使用lambdacreate创建一个匿名函数(可调用)。
lambda的语法是单词lambda,后跟一个可迭代的名称,然后是单个代码块。

在下面的示例中,我们正在对元组列表进行排序,该元组列表包含某些事件和演员名称的信息记录时间。

我们按照事件发生的时间对该列表进行排序-这是元组的第0个元素。

注意- s.sort([cmp[, key[, reverse]]]) 将s的项目排序到位

@Stephen ‘s answer is to the point! Here is an example for better visualization,

Shout out for the Ready Player One fans! =)

>>> gunters = [('2044-04-05', 'parzival'), ('2044-04-07', 'aech'), ('2044-04-06', 'art3mis')]
>>> gunters.sort(key=lambda tup: tup[0])
>>> print gunters
[('2044-04-05', 'parzival'), ('2044-04-06', 'art3mis'), ('2044-04-07', 'aech')]

key is a function that will be called to transform the collection’s items for comparison.. like compareTo method in Java.

The parameter passed to key must be something that is callable. Here, the use of lambda creates an anonymous function (which is a callable).
The syntax of lambda is the word lambda followed by a iterable name then a single block of code.

Below example, we are sorting a list of tuple that holds the info abt time of certain event and actor name.

We are sorting this list by time of event occurrence – which is the 0th element of a tuple.

Note – s.sort([cmp[, key[, reverse]]]) sorts the items of s in place


回答 9

对元组进行排序非常简单:

tuple(sorted(t))

Sorting a tuple is quite simple:

tuple(sorted(t))

如何按字典值对字典列表进行排序?

问题:如何按字典值对字典列表进行排序?

我有一个字典列表,希望每个项目都按特定的属性值排序。

考虑下面的数组,

[{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}]

当排序name,应该成为

[{'name':'Bart', 'age':10}, {'name':'Homer', 'age':39}]

I have a list of dictionaries and want each item to be sorted by a specific property values.

Take into consideration the array below,

[{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}]

When sorted by name, should become

[{'name':'Bart', 'age':10}, {'name':'Homer', 'age':39}]

回答 0

使用密钥而不是cmp看起来更干净:

newlist = sorted(list_to_be_sorted, key=lambda k: k['name']) 

或如JFSebastian和其他人所建议的,

from operator import itemgetter
newlist = sorted(list_to_be_sorted, key=itemgetter('name')) 

为了完整性(如fitzgeraldsteele的评论中指出的那样),请添加reverse=True降序排列

newlist = sorted(l, key=itemgetter('name'), reverse=True)

It may look cleaner using a key instead a cmp:

newlist = sorted(list_to_be_sorted, key=lambda k: k['name']) 

or as J.F.Sebastian and others suggested,

from operator import itemgetter
newlist = sorted(list_to_be_sorted, key=itemgetter('name')) 

For completeness (as pointed out in comments by fitzgeraldsteele), add reverse=True to sort descending

newlist = sorted(l, key=itemgetter('name'), reverse=True)

回答 1

import operator

通过key =’name’对字典列表进行排序:

list_of_dicts.sort(key=operator.itemgetter('name'))

按照key =’age’对字典列表进行排序:

list_of_dicts.sort(key=operator.itemgetter('age'))
import operator

To sort the list of dictionaries by key=’name’:

list_of_dicts.sort(key=operator.itemgetter('name'))

To sort the list of dictionaries by key=’age’:

list_of_dicts.sort(key=operator.itemgetter('age'))

回答 2

my_list = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}]

my_list.sort(lambda x,y : cmp(x['name'], y['name']))

my_list 现在将成为您想要的。

(3年后)进行编辑以添加:

新的key论点更加有效和整洁。更好的答案现在看起来像:

my_list = sorted(my_list, key=lambda k: k['name'])

…IMO比operator.itemgetterymmv 更容易理解。

my_list = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}]

my_list.sort(lambda x,y : cmp(x['name'], y['name']))

my_list will now be what you want.

(3 years later) Edited to add:

The new key argument is more efficient and neater. A better answer now looks like:

my_list = sorted(my_list, key=lambda k: k['name'])

…the lambda is, IMO, easier to understand than operator.itemgetter, but YMMV.


回答 3

如果要按多个键对列表进行排序,可以执行以下操作:

my_list = [{'name':'Homer', 'age':39}, {'name':'Milhouse', 'age':10}, {'name':'Bart', 'age':10} ]
sortedlist = sorted(my_list , key=lambda elem: "%02d %s" % (elem['age'], elem['name']))

它相当骇人听闻,因为它依赖于将值转换为单个字符串表示形式进行比较,但是它对于包括负数在内的数字也可以正常工作(尽管如果使用数字,则需要使用零填充来适当格式化字符串)

If you want to sort the list by multiple keys you can do the following:

my_list = [{'name':'Homer', 'age':39}, {'name':'Milhouse', 'age':10}, {'name':'Bart', 'age':10} ]
sortedlist = sorted(my_list , key=lambda elem: "%02d %s" % (elem['age'], elem['name']))

It is rather hackish, since it relies on converting the values into a single string representation for comparison, but it works as expected for numbers including negative ones (although you will need to format your string appropriately with zero paddings if you are using numbers)


回答 4

import operator
a_list_of_dicts.sort(key=operator.itemgetter('name'))

‘key’用于按任意值排序,’itemgetter’将该值设置为每个项目的’name’属性。

import operator
a_list_of_dicts.sort(key=operator.itemgetter('name'))

‘key’ is used to sort by an arbitrary value and ‘itemgetter’ sets that value to each item’s ‘name’ attribute.


回答 5

a = [{'name':'Homer', 'age':39}, ...]

# This changes the list a
a.sort(key=lambda k : k['name'])

# This returns a new list (a is not modified)
sorted(a, key=lambda k : k['name']) 
a = [{'name':'Homer', 'age':39}, ...]

# This changes the list a
a.sort(key=lambda k : k['name'])

# This returns a new list (a is not modified)
sorted(a, key=lambda k : k['name']) 

回答 6

我想你的意思是:

[{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}]

排序如下:

sorted(l,cmp=lambda x,y: cmp(x['name'],y['name']))

I guess you’ve meant:

[{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}]

This would be sorted like this:

sorted(l,cmp=lambda x,y: cmp(x['name'],y['name']))

回答 7

您可以使用自定义比较函数,也可以传入一个计算自定义排序键的函数。通常,这样做效率更高,因为每个项只计算一次密钥,而比较函数将被调用多次。

您可以这样进行:

def mykey(adict): return adict['name']
x = [{'name': 'Homer', 'age': 39}, {'name': 'Bart', 'age':10}]
sorted(x, key=mykey)

但是标准库包含用于获取任意对象项的通用例程:itemgetter。因此,请尝试以下操作:

from operator import itemgetter
x = [{'name': 'Homer', 'age': 39}, {'name': 'Bart', 'age':10}]
sorted(x, key=itemgetter('name'))

You could use a custom comparison function, or you could pass in a function that calculates a custom sort key. That’s usually more efficient as the key is only calculated once per item, while the comparison function would be called many more times.

You could do it this way:

def mykey(adict): return adict['name']
x = [{'name': 'Homer', 'age': 39}, {'name': 'Bart', 'age':10}]
sorted(x, key=mykey)

But the standard library contains a generic routine for getting items of arbitrary objects: itemgetter. So try this instead:

from operator import itemgetter
x = [{'name': 'Homer', 'age': 39}, {'name': 'Bart', 'age':10}]
sorted(x, key=itemgetter('name'))

回答 8

使用Perl的Schwartzian变换,

py = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}]

sort_on = "name"
decorated = [(dict_[sort_on], dict_) for dict_ in py]
decorated.sort()
result = [dict_ for (key, dict_) in decorated]

>>> result
[{'age': 10, 'name': 'Bart'}, {'age': 39, 'name': 'Homer'}]

有关Perl Schwartzian变换的更多信息

在计算机科学中,Schwartzian变换是一种Perl编程习惯用法,用于提高对项目列表进行排序的效率。当排序实际上是基于元素的某个属性(键)的排序时,此惯用法适用于基于比较的排序,其中计算该属性是一项应执行最少次数的密集操作。Schwartzian转换的显着之处在于它不使用命名的临时数组。

Using Schwartzian transform from Perl,

py = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}]

do

sort_on = "name"
decorated = [(dict_[sort_on], dict_) for dict_ in py]
decorated.sort()
result = [dict_ for (key, dict_) in decorated]

gives

>>> result
[{'age': 10, 'name': 'Bart'}, {'age': 39, 'name': 'Homer'}]

More on Perl Schwartzian transform

In computer science, the Schwartzian transform is a Perl programming idiom used to improve the efficiency of sorting a list of items. This idiom is appropriate for comparison-based sorting when the ordering is actually based on the ordering of a certain property (the key) of the elements, where computing that property is an intensive operation that should be performed a minimal number of times. The Schwartzian Transform is notable in that it does not use named temporary arrays.


回答 9

您必须实现自己的比较功能,该功能将通过名称键的值比较字典。请参阅从PythonInfo Wiki对Mini-HOW TO进行排序

You have to implement your own comparison function that will compare the dictionaries by values of name keys. See Sorting Mini-HOW TO from PythonInfo Wiki


回答 10

有时我们需要使用lower()例如

lists = [{'name':'Homer', 'age':39},
  {'name':'Bart', 'age':10},
  {'name':'abby', 'age':9}]

lists = sorted(lists, key=lambda k: k['name'])
print(lists)
# [{'name':'Bart', 'age':10}, {'name':'Homer', 'age':39}, {'name':'abby', 'age':9}]

lists = sorted(lists, key=lambda k: k['name'].lower())
print(lists)
# [ {'name':'abby', 'age':9}, {'name':'Bart', 'age':10}, {'name':'Homer', 'age':39}]

sometime we need to use lower() for example

lists = [{'name':'Homer', 'age':39},
  {'name':'Bart', 'age':10},
  {'name':'abby', 'age':9}]

lists = sorted(lists, key=lambda k: k['name'])
print(lists)
# [{'name':'Bart', 'age':10}, {'name':'Homer', 'age':39}, {'name':'abby', 'age':9}]

lists = sorted(lists, key=lambda k: k['name'].lower())
print(lists)
# [ {'name':'abby', 'age':9}, {'name':'Bart', 'age':10}, {'name':'Homer', 'age':39}]

回答 11

这是另一种通用解决方案-它按键和值对dict的元素进行排序。它的优点-无需指定键,并且如果某些词典中缺少某些键,它将仍然有效。

def sort_key_func(item):
    """ helper function used to sort list of dicts

    :param item: dict
    :return: sorted list of tuples (k, v)
    """
    pairs = []
    for k, v in item.items():
        pairs.append((k, v))
    return sorted(pairs)
sorted(A, key=sort_key_func)

Here is the alternative general solution – it sorts elements of dict by keys and values. The advantage of it – no need to specify keys, and it would still work if some keys are missing in some of dictionaries.

def sort_key_func(item):
    """ helper function used to sort list of dicts

    :param item: dict
    :return: sorted list of tuples (k, v)
    """
    pairs = []
    for k, v in item.items():
        pairs.append((k, v))
    return sorted(pairs)
sorted(A, key=sort_key_func)

回答 12

使用pandas包是另一种方法,尽管它的大规模运行比其他人提出的更传统的方法要慢得多:

import pandas as pd

listOfDicts = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}]
df = pd.DataFrame(listOfDicts)
df = df.sort_values('name')
sorted_listOfDicts = df.T.to_dict().values()

以下是一些小型词典和大型(100k +)字典的一些基准值:

setup_large = "listOfDicts = [];\
[listOfDicts.extend(({'name':'Homer', 'age':39}, {'name':'Bart', 'age':10})) for _ in range(50000)];\
from operator import itemgetter;import pandas as pd;\
df = pd.DataFrame(listOfDicts);"

setup_small = "listOfDicts = [];\
listOfDicts.extend(({'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}));\
from operator import itemgetter;import pandas as pd;\
df = pd.DataFrame(listOfDicts);"

method1 = "newlist = sorted(listOfDicts, key=lambda k: k['name'])"
method2 = "newlist = sorted(listOfDicts, key=itemgetter('name')) "
method3 = "df = df.sort_values('name');\
sorted_listOfDicts = df.T.to_dict().values()"

import timeit
t = timeit.Timer(method1, setup_small)
print('Small Method LC: ' + str(t.timeit(100)))
t = timeit.Timer(method2, setup_small)
print('Small Method LC2: ' + str(t.timeit(100)))
t = timeit.Timer(method3, setup_small)
print('Small Method Pandas: ' + str(t.timeit(100)))

t = timeit.Timer(method1, setup_large)
print('Large Method LC: ' + str(t.timeit(100)))
t = timeit.Timer(method2, setup_large)
print('Large Method LC2: ' + str(t.timeit(100)))
t = timeit.Timer(method3, setup_large)
print('Large Method Pandas: ' + str(t.timeit(1)))

#Small Method LC: 0.000163078308105
#Small Method LC2: 0.000134944915771
#Small Method Pandas: 0.0712950229645
#Large Method LC: 0.0321750640869
#Large Method LC2: 0.0206089019775
#Large Method Pandas: 5.81405615807

Using the pandas package is another method, though it’s runtime at large scale is much slower than the more traditional methods proposed by others:

import pandas as pd

listOfDicts = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}]
df = pd.DataFrame(listOfDicts)
df = df.sort_values('name')
sorted_listOfDicts = df.T.to_dict().values()

Here are some benchmark values for a tiny list and a large (100k+) list of dicts:

setup_large = "listOfDicts = [];\
[listOfDicts.extend(({'name':'Homer', 'age':39}, {'name':'Bart', 'age':10})) for _ in range(50000)];\
from operator import itemgetter;import pandas as pd;\
df = pd.DataFrame(listOfDicts);"

setup_small = "listOfDicts = [];\
listOfDicts.extend(({'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}));\
from operator import itemgetter;import pandas as pd;\
df = pd.DataFrame(listOfDicts);"

method1 = "newlist = sorted(listOfDicts, key=lambda k: k['name'])"
method2 = "newlist = sorted(listOfDicts, key=itemgetter('name')) "
method3 = "df = df.sort_values('name');\
sorted_listOfDicts = df.T.to_dict().values()"

import timeit
t = timeit.Timer(method1, setup_small)
print('Small Method LC: ' + str(t.timeit(100)))
t = timeit.Timer(method2, setup_small)
print('Small Method LC2: ' + str(t.timeit(100)))
t = timeit.Timer(method3, setup_small)
print('Small Method Pandas: ' + str(t.timeit(100)))

t = timeit.Timer(method1, setup_large)
print('Large Method LC: ' + str(t.timeit(100)))
t = timeit.Timer(method2, setup_large)
print('Large Method LC2: ' + str(t.timeit(100)))
t = timeit.Timer(method3, setup_large)
print('Large Method Pandas: ' + str(t.timeit(1)))

#Small Method LC: 0.000163078308105
#Small Method LC2: 0.000134944915771
#Small Method Pandas: 0.0712950229645
#Large Method LC: 0.0321750640869
#Large Method LC2: 0.0206089019775
#Large Method Pandas: 5.81405615807

回答 13

如果你不需要原来listdictionaries,你可以用修改就地sort()使用自定义按键功能的方法。

按键功能:

def get_name(d):
    """ Return the value of a key in a dictionary. """

    return d["name"]

list进行排序:

data_one = [{'name': 'Homer', 'age': 39}, {'name': 'Bart', 'age': 10}]

就地排序:

data_one.sort(key=get_name)

如果您需要原始的list,请调用将sorted()函数传递给的函数list和键函数,然后将返回的排序list后的变量分配给新变量:

data_two = [{'name': 'Homer', 'age': 39}, {'name': 'Bart', 'age': 10}]
new_data = sorted(data_two, key=get_name)

印刷data_onenew_data

>>> print(data_one)
[{'name': 'Bart', 'age': 10}, {'name': 'Homer', 'age': 39}]
>>> print(new_data)
[{'name': 'Bart', 'age': 10}, {'name': 'Homer', 'age': 39}]

If you do not need the original list of dictionaries, you could modify it in-place with sort() method using a custom key function.

Key function:

def get_name(d):
    """ Return the value of a key in a dictionary. """

    return d["name"]

The list to be sorted:

data_one = [{'name': 'Homer', 'age': 39}, {'name': 'Bart', 'age': 10}]

Sorting it in-place:

data_one.sort(key=get_name)

If you need the original list, call the sorted() function passing it the list and the key function, then assign the returned sorted list to a new variable:

data_two = [{'name': 'Homer', 'age': 39}, {'name': 'Bart', 'age': 10}]
new_data = sorted(data_two, key=get_name)

Printing data_one and new_data.

>>> print(data_one)
[{'name': 'Bart', 'age': 10}, {'name': 'Homer', 'age': 39}]
>>> print(new_data)
[{'name': 'Bart', 'age': 10}, {'name': 'Homer', 'age': 39}]

回答 14

假设我有一本D包含以下内容的字典。要进行排序,只需使用sort中的key参数来传递自定义函数,如下所示:

D = {'eggs': 3, 'ham': 1, 'spam': 2}
def get_count(tuple):
    return tuple[1]

sorted(D.items(), key = get_count, reverse=True)
# or
sorted(D.items(), key = lambda x: x[1], reverse=True)  # avoiding get_count function call

检查这个出来。

Let’s say I have a dictionary D with elements below. To sort just use key argument in sorted to pass custom function as below :

D = {'eggs': 3, 'ham': 1, 'spam': 2}
def get_count(tuple):
    return tuple[1]

sorted(D.items(), key = get_count, reverse=True)
# or
sorted(D.items(), key = lambda x: x[1], reverse=True)  # avoiding get_count function call

Check this out.


回答 15

我一直是lambda过滤器的忠实拥护者,但是如果您考虑时间复杂性,则不是最佳选择

第一选择

sorted_list = sorted(list_to_sort, key= lambda x: x['name'])
# returns list of values

第二选择

list_to_sort.sort(key=operator.itemgetter('name'))
#edits the list, does not return a new list

快速比较执行时间

# First option
python3.6 -m timeit -s "list_to_sort = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}, {'name':'Faaa', 'age':57}, {'name':'Errr', 'age':20}]" -s "sorted_l=[]" "sorted_l = sorted(list_to_sort, key=lambda e: e['name'])"

1000000次循环,最好为3:每个循环0.736微秒

# Second option 
python3.6 -m timeit -s "list_to_sort = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}, {'name':'Faaa', 'age':57}, {'name':'Errr', 'age':20}]" -s "sorted_l=[]" -s "import operator" "list_to_sort.sort(key=operator.itemgetter('name'))"

1000000次循环,最好为3:每个循环0.438微秒

I have been a big fan of filter w/ lambda however it is not best option if you considering time complexity

First option

sorted_list = sorted(list_to_sort, key= lambda x: x['name'])
# returns list of values

Second option

list_to_sort.sort(key=operator.itemgetter('name'))
#edits the list, does not return a new list

Fast comparison of exec times

# First option
python3.6 -m timeit -s "list_to_sort = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}, {'name':'Faaa', 'age':57}, {'name':'Errr', 'age':20}]" -s "sorted_l=[]" "sorted_l = sorted(list_to_sort, key=lambda e: e['name'])"

1000000 loops, best of 3: 0.736 usec per loop

# Second option 
python3.6 -m timeit -s "list_to_sort = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}, {'name':'Faaa', 'age':57}, {'name':'Errr', 'age':20}]" -s "sorted_l=[]" -s "import operator" "list_to_sort.sort(key=operator.itemgetter('name'))"

1000000 loops, best of 3: 0.438 usec per loop


回答 16

如果需要考虑性能,我会使用内置函数operator.itemgetter来代替lambda手工函数,而使用内置函数来代替。该itemgetter功能似乎比lambda根据我的测试快约20%。

https://wiki.python.org/moin/PythonSpeed

同样,内置函数比手工生成的等效函数运行得更快。例如,map(operator.add,v1,v2)比map(lambda x,y:x + y,v1,v2)快。

这是使用lambdavs 进行排序速度的比较itemgetter

import random
import operator

# create a list of 100 dicts with random 8-letter names and random ages from 0 to 100.
l = [{'name': ''.join(random.choices(string.ascii_lowercase, k=8)), 'age': random.randint(0, 100)} for i in range(100)]

# Test the performance with a lambda function sorting on name
%timeit sorted(l, key=lambda x: x['name'])
13 µs ± 388 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

# Test the performance with itemgetter sorting on name
%timeit sorted(l, key=operator.itemgetter('name'))
10.7 µs ± 38.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

# Check that each technique produces same sort order
sorted(l, key=lambda x: x['name']) == sorted(l, key=operator.itemgetter('name'))
True

两种技术都以相同的顺序对列表进行排序(通过执行代码块中的final语句进行验证),但是一种方法要快一些。

If performance is a concern, I would use operator.itemgetter instead of lambda as built-in functions perform faster than hand-crafted functions. The itemgetter function seems to perform approximately 20% faster than lambda based on my testing.

From https://wiki.python.org/moin/PythonSpeed:

Likewise, the builtin functions run faster than hand-built equivalents. For example, map(operator.add, v1, v2) is faster than map(lambda x,y: x+y, v1, v2).

Here is a comparison of sorting speed using lambda vs itemgetter.

import random
import operator

# create a list of 100 dicts with random 8-letter names and random ages from 0 to 100.
l = [{'name': ''.join(random.choices(string.ascii_lowercase, k=8)), 'age': random.randint(0, 100)} for i in range(100)]

# Test the performance with a lambda function sorting on name
%timeit sorted(l, key=lambda x: x['name'])
13 µs ± 388 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

# Test the performance with itemgetter sorting on name
%timeit sorted(l, key=operator.itemgetter('name'))
10.7 µs ± 38.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

# Check that each technique produces same sort order
sorted(l, key=lambda x: x['name']) == sorted(l, key=operator.itemgetter('name'))
True

Both techniques sort the list in the same order (verified by execution of the final statement in the code block) but one is a little faster.


回答 17

您可以使用以下代码

sorted_dct = sorted(dct_name.items(), key = lambda x : x[1])

You may use the following code

sorted_dct = sorted(dct_name.items(), key = lambda x : x[1])

如何按值对字典排序?

问题:如何按值对字典排序?

我有一个从数据库的两个字段中读取的值的字典:字符串字段和数字字段。字符串字段是唯一的,因此这是字典的键。

我可以对键进行排序,但是如何根据值进行排序?

注意:我在这里阅读了堆栈溢出问题,如何按字典值对字典列表进行排序?可能会更改我的代码以包含字典列表,但是由于我实际上并不需要字典列表,因此我想知道是否存在一种更简单的解决方案来按升序或降序进行排序。

I have a dictionary of values read from two fields in a database: a string field and a numeric field. The string field is unique, so that is the key of the dictionary.

I can sort on the keys, but how can I sort based on the values?

Note: I have read Stack Overflow question here How do I sort a list of dictionaries by a value of the dictionary? and probably could change my code to have a list of dictionaries, but since I do not really need a list of dictionaries I wanted to know if there is a simpler solution to sort either in ascending or descending order.


回答 0

Python 3.6+

x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
{k: v for k, v in sorted(x.items(), key=lambda item: item[1])}
{0: 0, 2: 1, 1: 2, 4: 3, 3: 4}

旧版Python

无法对字典进行排序,只能获得已排序字典的表示形式。字典本质上是无序的,但其他类型(例如列表和元组)不是。因此,您需要一个有序的数据类型来表示排序后的值,该值将是一个列表-可能是一个元组列表。

例如,

import operator
x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
sorted_x = sorted(x.items(), key=operator.itemgetter(1))

sorted_x将是按每个元组中第二个元素排序的元组列表。dict(sorted_x) == x

对于那些希望对键而不是值进行排序的人:

import operator
x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
sorted_x = sorted(x.items(), key=operator.itemgetter(0))

在Python3中,由于不允许拆包[1],我们可以使用

x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
sorted_x = sorted(x.items(), key=lambda kv: kv[1])

如果要将输出作为字典,则可以使用collections.OrderedDict

import collections

sorted_dict = collections.OrderedDict(sorted_x)

Python 3.6+

x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
{k: v for k, v in sorted(x.items(), key=lambda item: item[1])}
{0: 0, 2: 1, 1: 2, 4: 3, 3: 4}

Older Python

It is not possible to sort a dictionary, only to get a representation of a dictionary that is sorted. Dictionaries are inherently orderless, but other types, such as lists and tuples, are not. So you need an ordered data type to represent sorted values, which will be a list—probably a list of tuples.

For instance,

import operator
x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
sorted_x = sorted(x.items(), key=operator.itemgetter(1))

sorted_x will be a list of tuples sorted by the second element in each tuple. dict(sorted_x) == x.

And for those wishing to sort on keys instead of values:

import operator
x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
sorted_x = sorted(x.items(), key=operator.itemgetter(0))

In Python3 since unpacking is not allowed [1] we can use

x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
sorted_x = sorted(x.items(), key=lambda kv: kv[1])

If you want the output as a dict, you can use collections.OrderedDict:

import collections

sorted_dict = collections.OrderedDict(sorted_x)

回答 1

简单如: sorted(dict1, key=dict1.get)

好吧,实际上可以执行“按字典值排序”。最近,我不得不在Code Golf(堆栈溢出问题Code golf:单词频率图表)中进行此操作。简而言之,问题是这样的:给定文本,计算遇到每个单词的频率,并显示按频率递减排序的最重要单词列表。

如果您以单词为键构建字典,每个单词的出现次数为值,则简化为:

from collections import defaultdict
d = defaultdict(int)
for w in text.split():
    d[w] += 1

那么您可以获取单词列表,sorted(d, key=d.get)并按使用频率排序-使用单词出现的次数作为排序键,对字典键进行排序迭代。

for w in sorted(d, key=d.get, reverse=True):
    print(w, d[w])

我正在写这个详细的说明,以说明人们通常所说的“我可以很容易地按键对字典进行排序,但是如何按值进行排序”的意思-我认为原始帖子试图解决这样的问题。解决方案是根据值对键列表进行排序,如上所示。

As simple as: sorted(dict1, key=dict1.get)

Well, it is actually possible to do a “sort by dictionary values”. Recently I had to do that in a Code Golf (Stack Overflow question Code golf: Word frequency chart). Abridged, the problem was of the kind: given a text, count how often each word is encountered and display a list of the top words, sorted by decreasing frequency.

If you construct a dictionary with the words as keys and the number of occurrences of each word as value, simplified here as:

from collections import defaultdict
d = defaultdict(int)
for w in text.split():
    d[w] += 1

then you can get a list of the words, ordered by frequency of use with sorted(d, key=d.get) – the sort iterates over the dictionary keys, using the number of word occurrences as a sort key .

for w in sorted(d, key=d.get, reverse=True):
    print(w, d[w])

I am writing this detailed explanation to illustrate what people often mean by “I can easily sort a dictionary by key, but how do I sort by value” – and I think the original post was trying to address such an issue. And the solution is to do sort of list of the keys, based on the values, as shown above.


回答 2

您可以使用:

sorted(d.items(), key=lambda x: x[1])

这将按照字典中每个条目的值(从最小到最大)对字典进行排序。

要对其进行降序排序,只需添加reverse=True

sorted(d.items(), key=lambda x: x[1], reverse=True)

输入:

d = {'one':1,'three':3,'five':5,'two':2,'four':4}
a = sorted(d.items(), key=lambda x: x[1])    
print(a)

输出:

[('one', 1), ('two', 2), ('three', 3), ('four', 4), ('five', 5)]

You could use:

sorted(d.items(), key=lambda x: x[1])

This will sort the dictionary by the values of each entry within the dictionary from smallest to largest.

To sort it in descending order just add reverse=True:

sorted(d.items(), key=lambda x: x[1], reverse=True)

Input:

d = {'one':1,'three':3,'five':5,'two':2,'four':4}
a = sorted(d.items(), key=lambda x: x[1])    
print(a)

Output:

[('one', 1), ('two', 2), ('three', 3), ('four', 4), ('five', 5)]

回答 3

字典无法排序,但您可以从中建立排序列表。

字典值的排序列表:

sorted(d.values())

(键,值)对的列表,按值排序:

from operator import itemgetter
sorted(d.items(), key=itemgetter(1))

Dicts can’t be sorted, but you can build a sorted list from them.

A sorted list of dict values:

sorted(d.values())

A list of (key, value) pairs, sorted by value:

from operator import itemgetter
sorted(d.items(), key=itemgetter(1))

回答 4

在最近的Python 2.7中,我们有了新的OrderedDict类型,该类型可以记住添加项目的顺序。

>>> d = {"third": 3, "first": 1, "fourth": 4, "second": 2}

>>> for k, v in d.items():
...     print "%s: %s" % (k, v)
...
second: 2
fourth: 4
third: 3
first: 1

>>> d
{'second': 2, 'fourth': 4, 'third': 3, 'first': 1}

要从原始字典中重新排序,请按以下值排序:

>>> from collections import OrderedDict
>>> d_sorted_by_value = OrderedDict(sorted(d.items(), key=lambda x: x[1]))

OrderedDict的行为类似于普通字典:

>>> for k, v in d_sorted_by_value.items():
...     print "%s: %s" % (k, v)
...
first: 1
second: 2
third: 3
fourth: 4

>>> d_sorted_by_value
OrderedDict([('first': 1), ('second': 2), ('third': 3), ('fourth': 4)])

In recent Python 2.7, we have the new OrderedDict type, which remembers the order in which the items were added.

>>> d = {"third": 3, "first": 1, "fourth": 4, "second": 2}

>>> for k, v in d.items():
...     print "%s: %s" % (k, v)
...
second: 2
fourth: 4
third: 3
first: 1

>>> d
{'second': 2, 'fourth': 4, 'third': 3, 'first': 1}

To make a new ordered dictionary from the original, sorting by the values:

>>> from collections import OrderedDict
>>> d_sorted_by_value = OrderedDict(sorted(d.items(), key=lambda x: x[1]))

The OrderedDict behaves like a normal dict:

>>> for k, v in d_sorted_by_value.items():
...     print "%s: %s" % (k, v)
...
first: 1
second: 2
third: 3
fourth: 4

>>> d_sorted_by_value
OrderedDict([('first': 1), ('second': 2), ('third': 3), ('fourth': 4)])

回答 5

更新:2015年12月5日使用Python 3.5

尽管我发现接受的答案很有用,但令我感到惊讶的是,它没有被更新为从标准库集合模块中引用OrderedDict作为可行的现代替代方案,旨在解决这类问题。

from operator import itemgetter
from collections import OrderedDict

x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
sorted_x = OrderedDict(sorted(x.items(), key=itemgetter(1)))
# OrderedDict([(0, 0), (2, 1), (1, 2), (4, 3), (3, 4)])

官方的OrderedDict文档也提供了一个非常相似的示例,但是对排序函数使用了lambda:

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

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

UPDATE: 5 DECEMBER 2015 using Python 3.5

Whilst I found the accepted answer useful, I was also surprised that it hasn’t been updated to reference OrderedDict from the standard library collections module as a viable, modern alternative – designed to solve exactly this type of problem.

from operator import itemgetter
from collections import OrderedDict

x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
sorted_x = OrderedDict(sorted(x.items(), key=itemgetter(1)))
# OrderedDict([(0, 0), (2, 1), (1, 2), (4, 3), (3, 4)])

The official OrderedDict documentation offers a very similar example too, but using a lambda for the sort function:

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

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

回答 6

Hank Gay的答案几乎相同:

sorted([(value,key) for (key,value) in mydict.items()])

或根据John Fouhy的建议进行了稍微优化:

sorted((value,key) for (key,value) in mydict.items())

Pretty much the same as Hank Gay’s answer:

sorted([(value,key) for (key,value) in mydict.items()])

Or optimized slightly as suggested by John Fouhy:

sorted((value,key) for (key,value) in mydict.items())

回答 7

使用namedtuple通常很方便。例如,您有一个“名称”作为键,而“分数”作为值的字典,并且您想对“分数”进行排序:

import collections
Player = collections.namedtuple('Player', 'score name')
d = {'John':5, 'Alex':10, 'Richard': 7}

首先以最低分数排序:

worst = sorted(Player(v,k) for (k,v) in d.items())

首先以最高分排序:

best = sorted([Player(v,k) for (k,v) in d.items()], reverse=True)

现在您可以得到Python的第二好玩家(index = 1)的名称和得分,如下所示:

player = best[1]
player.name
    'Richard'
player.score
    7

It can often be very handy to use namedtuple. For example, you have a dictionary of ‘name’ as keys and ‘score’ as values and you want to sort on ‘score’:

import collections
Player = collections.namedtuple('Player', 'score name')
d = {'John':5, 'Alex':10, 'Richard': 7}

sorting with lowest score first:

worst = sorted(Player(v,k) for (k,v) in d.items())

sorting with highest score first:

best = sorted([Player(v,k) for (k,v) in d.items()], reverse=True)

Now you can get the name and score of, let’s say the second-best player (index=1) very Pythonically like this:

player = best[1]
player.name
    'Richard'
player.score
    7

回答 8

Python 3.6开始,将对内置字典进行排序

好消息,因此OP从数据库中检索到的映射对的原始用例(以唯一的字符串ID作为键,将数值作为值)转换为内置Python v3.6 + dict,现在应该遵守插入顺序。

如果说从数据库查询中得到的两个列表表达式如下:

SELECT a_key, a_value FROM a_table ORDER BY a_value;

将存储在两个Python元组k_seq和v_seq中(按数字索引对齐,并且具有相同的长度),然后:

k_seq = ('foo', 'bar', 'baz')
v_seq = (0, 1, 42)
ordered_map = dict(zip(k_seq, v_seq))

允许以后输出为:

for k, v in ordered_map.items():
    print(k, v)

在这种情况下产生(对于新的Python 3.6+内置字典!):

foo 0
bar 1
baz 42

以v的每个值相同的顺序排列。

当前在我的机器上的Python 3.5安装位置生成:

bar 1
foo 0
baz 42

细节:

正如Raymond Hettinger在2012年提出的(请参见python-dev上的邮件,主题为“更紧凑的字典,迭代速度更快”),现在(2016年),Victor Stinner在给主题为“ Python 3.6 dict的 python-dev的邮件”中宣布紧凑并获得私有版本;由于在Python 3.6中已解决/实现了问题27350 “紧凑且有序的字典”,因此关键字变得有序”,我们现在可以使用内置的字典来维护插入顺序!!

希望这将导致第一步的薄层OrderedDict实现。就像@ JimFasarakis-Hilliard指出的那样,将来还会看到一些OrderedDict类型的用例。我认为整个Python社区都会仔细检查,是否经得起时间的考验以及下一步将是什么。

是时候重新考虑一下我们的编码习惯,不要错过以下稳定排序所带来的可能性:

  • 关键字参数和
  • (中级)字典存储

第一个是因为它在某些情况下简化了函数和方法的实现中的分派。

第二个参数鼓励使用dicts作为处理管道中的中间存储。

Raymond Hettinger 从旧金山Python Meetup Group的演讲2016-DEC-08中提供了解释“ Python 3.6词典背后的技术文档。

也许相当一部分Stack Overflow高修饰度的问答页面会收到此信息的变体,并且许多高质量的答案也需要按版本进行更新。

警告购买者(另请参阅下面的2017年12月15日更新):

正如@ajcr正确指出的那样:“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。” (摘自whatsnew36)并不是很挑剔,引文有点悲观了;-)。它继续显示为“(将来可能会改变,但是希望在更改语言规范以强制所有当前和将来的Python实现保留顺序语义之前,先在几个版本中使用该语言的新dict实现;有助于保持与仍旧有效的随机迭代顺序的旧版语言(例如Python 3.5)的向后兼容性。”

因此,就像在某些人类语言(例如德语)中一样,用法决定了语言的使用方式,现在遗嘱已在whatsnew36中声明。

更新2017-12-15:

发给python-dev列表邮件中,Guido van Rossum声明:

做到这一点。裁定“裁定保留插入顺序”。谢谢!

因此,dict插入顺序的3.6 CPython版本的副作用现在已成为语言规范的一部分(并且不再仅仅是实现细节)。collections.OrderedDict正如雷蒙德·海廷格(Raymond Hettinger)在讨论中所提醒的那样,该邮件线程还浮出了一些与众不同的设计目标。

As of Python 3.6 the built-in dict will be ordered

Good news, so the OP’s original use case of mapping pairs retrieved from a database with unique string ids as keys and numeric values as values into a built-in Python v3.6+ dict, should now respect the insert order.

If say the resulting two column table expressions from a database query like:

SELECT a_key, a_value FROM a_table ORDER BY a_value;

would be stored in two Python tuples, k_seq and v_seq (aligned by numerical index and with the same length of course), then:

k_seq = ('foo', 'bar', 'baz')
v_seq = (0, 1, 42)
ordered_map = dict(zip(k_seq, v_seq))

Allow to output later as:

for k, v in ordered_map.items():
    print(k, v)

yielding in this case (for the new Python 3.6+ built-in dict!):

foo 0
bar 1
baz 42

in the same ordering per value of v.

Where in the Python 3.5 install on my machine it currently yields:

bar 1
foo 0
baz 42

Details:

As proposed in 2012 by Raymond Hettinger (cf. mail on python-dev with subject “More compact dictionaries with faster iteration”) and now (in 2016) announced in a mail by Victor Stinner to python-dev with subject “Python 3.6 dict becomes compact and gets a private version; and keywords become ordered” due to the fix/implementation of issue 27350 “Compact and ordered dict” in Python 3.6 we will now be able, to use a built-in dict to maintain insert order!!

Hopefully this will lead to a thin layer OrderedDict implementation as a first step. As @JimFasarakis-Hilliard indicated, some see use cases for the OrderedDict type also in the future. I think the Python community at large will carefully inspect, if this will stand the test of time, and what the next steps will be.

Time to rethink our coding habits to not miss the possibilities opened by stable ordering of:

  • Keyword arguments and
  • (intermediate) dict storage

The first because it eases dispatch in the implementation of functions and methods in some cases.

The second as it encourages to more easily use dicts as intermediate storage in processing pipelines.

Raymond Hettinger kindly provided documentation explaining “The Tech Behind Python 3.6 Dictionaries” – from his San Francisco Python Meetup Group presentation 2016-DEC-08.

And maybe quite some Stack Overflow high decorated question and answer pages will receive variants of this information and many high quality answers will require a per version update too.

Caveat Emptor (but also see below update 2017-12-15):

As @ajcr rightfully notes: “The order-preserving aspect of this new implementation is considered an implementation detail and should not be relied upon.” (from the whatsnew36) not nit picking, but the citation was cut a bit pessimistic ;-). It continues as ” (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).”

So as in some human languages (e.g. German), usage shapes the language, and the will now has been declared … in whatsnew36.

Update 2017-12-15:

In a mail to the python-dev list, Guido van Rossum declared:

Make it so. “Dict keeps insertion order” is the ruling. Thanks!

So, the version 3.6 CPython side-effect of dict insertion ordering is now becoming part of the language spec (and not anymore only an implementation detail). That mail thread also surfaced some distinguishing design goals for collections.OrderedDict as reminded by Raymond Hettinger during discussion.


回答 9

我有同样的问题,我这样解决了:

WantedOutput = sorted(MyDict, key=lambda x : MyDict[x]) 

(回答“无法对字典进行排序的人没有读过这个问题!实际上,“我可以对键进行排序,但是如何根据值进行排序?”显然意味着他想要一个列表)键根据其值的值排序。)

请注意,顺序定义不正确(具有相同值的键在输出列表中将以任意顺序排列)。

I had the same problem, and I solved it like this:

WantedOutput = sorted(MyDict, key=lambda x : MyDict[x]) 

(People who answer “It is not possible to sort a dict” did not read the question! In fact, “I can sort on the keys, but how can I sort based on the values?” clearly means that he wants a list of the keys sorted according to the value of their values.)

Please notice that the order is not well defined (keys with the same value will be in an arbitrary order in the output list).


回答 10

如果值是数字,则也可以Countercollections中使用。

from collections import Counter

x = {'hello': 1, 'python': 5, 'world': 3}
c = Counter(x)
print(c.most_common())

>> [('python', 5), ('world', 3), ('hello', 1)]    

If values are numeric you may also use Counter from collections.

from collections import Counter

x = {'hello': 1, 'python': 5, 'world': 3}
c = Counter(x)
print(c.most_common())

>> [('python', 5), ('world', 3), ('hello', 1)]    

回答 11

在Python 2.7中,只需执行以下操作:

from collections import OrderedDict
# regular unsorted dictionary
d = {'banana': 3, 'apple':4, 'pear': 1, 'orange': 2}

# dictionary sorted by key
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)])

复制粘贴自:http : //docs.python.org/dev/library/collections.html#ordereddict-examples-and-recipes

请享用 ;-)

In Python 2.7, simply do:

from collections import OrderedDict
# regular unsorted dictionary
d = {'banana': 3, 'apple':4, 'pear': 1, 'orange': 2}

# dictionary sorted by key
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)])

copy-paste from : http://docs.python.org/dev/library/collections.html#ordereddict-examples-and-recipes

Enjoy ;-)


回答 12

这是代码:

import operator
origin_list = [
    {"name": "foo", "rank": 0, "rofl": 20000},
    {"name": "Silly", "rank": 15, "rofl": 1000},
    {"name": "Baa", "rank": 300, "rofl": 20},
    {"name": "Zoo", "rank": 10, "rofl": 200},
    {"name": "Penguin", "rank": -1, "rofl": 10000}
]
print ">> Original >>"
for foo in origin_list:
    print foo

print "\n>> Rofl sort >>"
for foo in sorted(origin_list, key=operator.itemgetter("rofl")):
    print foo

print "\n>> Rank sort >>"
for foo in sorted(origin_list, key=operator.itemgetter("rank")):
    print foo

结果如下:

原版的

{'name': 'foo', 'rank': 0, 'rofl': 20000}
{'name': 'Silly', 'rank': 15, 'rofl': 1000}
{'name': 'Baa', 'rank': 300, 'rofl': 20}
{'name': 'Zoo', 'rank': 10, 'rofl': 200}
{'name': 'Penguin', 'rank': -1, 'rofl': 10000}

罗夫

{'name': 'Baa', 'rank': 300, 'rofl': 20}
{'name': 'Zoo', 'rank': 10, 'rofl': 200}
{'name': 'Silly', 'rank': 15, 'rofl': 1000}
{'name': 'Penguin', 'rank': -1, 'rofl': 10000}
{'name': 'foo', 'rank': 0, 'rofl': 20000}

{'name': 'Penguin', 'rank': -1, 'rofl': 10000}
{'name': 'foo', 'rank': 0, 'rofl': 20000}
{'name': 'Zoo', 'rank': 10, 'rofl': 200}
{'name': 'Silly', 'rank': 15, 'rofl': 1000}
{'name': 'Baa', 'rank': 300, 'rofl': 20}

This is the code:

import operator
origin_list = [
    {"name": "foo", "rank": 0, "rofl": 20000},
    {"name": "Silly", "rank": 15, "rofl": 1000},
    {"name": "Baa", "rank": 300, "rofl": 20},
    {"name": "Zoo", "rank": 10, "rofl": 200},
    {"name": "Penguin", "rank": -1, "rofl": 10000}
]
print ">> Original >>"
for foo in origin_list:
    print foo

print "\n>> Rofl sort >>"
for foo in sorted(origin_list, key=operator.itemgetter("rofl")):
    print foo

print "\n>> Rank sort >>"
for foo in sorted(origin_list, key=operator.itemgetter("rank")):
    print foo

Here are the results:

Original

{'name': 'foo', 'rank': 0, 'rofl': 20000}
{'name': 'Silly', 'rank': 15, 'rofl': 1000}
{'name': 'Baa', 'rank': 300, 'rofl': 20}
{'name': 'Zoo', 'rank': 10, 'rofl': 200}
{'name': 'Penguin', 'rank': -1, 'rofl': 10000}

Rofl

{'name': 'Baa', 'rank': 300, 'rofl': 20}
{'name': 'Zoo', 'rank': 10, 'rofl': 200}
{'name': 'Silly', 'rank': 15, 'rofl': 1000}
{'name': 'Penguin', 'rank': -1, 'rofl': 10000}
{'name': 'foo', 'rank': 0, 'rofl': 20000}

Rank

{'name': 'Penguin', 'rank': -1, 'rofl': 10000}
{'name': 'foo', 'rank': 0, 'rofl': 20000}
{'name': 'Zoo', 'rank': 10, 'rofl': 200}
{'name': 'Silly', 'rank': 15, 'rofl': 1000}
{'name': 'Baa', 'rank': 300, 'rofl': 20}

回答 13

请尝试以下方法。让我们用以下数据定义一个名为mydict的字典:

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

如果要按键对字典排序,可以执行以下操作:

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

这应该返回以下输出:

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

另一方面,如果要按值对字典排序(如问题中所述),则可以执行以下操作:

for key, value in sorted(mydict.iteritems(), key=lambda (k,v): (v,k)):
    print "%s: %s" % (key, value)

该命令的结果(按值对字典进行排序)应返回以下内容:

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

Try the following approach. Let us define a dictionary called mydict with the following data:

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

If one wanted to sort the dictionary by keys, one could do something like:

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

This should return the following output:

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

On the other hand, if one wanted to sort a dictionary by value (as is asked in the question), one could do the following:

for key, value in sorted(mydict.iteritems(), key=lambda (k,v): (v,k)):
    print "%s: %s" % (key, value)

The result of this command (sorting the dictionary by value) should return the following:

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

回答 14

从Python 3.6开始,dict对象现在按插入顺序排序。它正式在Python 3.7的规范中。

>>> words = {"python": 2, "blah": 4, "alice": 3}
>>> dict(sorted(words.items(), key=lambda x: x[1]))
{'python': 2, 'alice': 3, 'blah': 4}

在此之前,您必须使用OrderedDict

Python 3.7文档说:

在版本3.7中更改:保证字典顺序为插入顺序。此行为是3.6版CPython的实现细节。

Starting from Python 3.6, dict objects are now ordered by insertion order. It’s officially in the specs of Python 3.7.

>>> words = {"python": 2, "blah": 4, "alice": 3}
>>> dict(sorted(words.items(), key=lambda x: x[1]))
{'python': 2, 'alice': 3, 'blah': 4}

Before that, you had to use OrderedDict.

Python 3.7 documentation says:

Changed in version 3.7: Dictionary order is guaranteed to be insertion order. This behavior was implementation detail of CPython from 3.6.


回答 15

您可以创建一个“倒排索引”

from collections import defaultdict
inverse= defaultdict( list )
for k, v in originalDict.items():
    inverse[v].append( k )

现在您的逆数具有值;每个值都有一个适用键的列表。

for k in sorted(inverse):
    print k, inverse[k]

You can create an “inverted index”, also

from collections import defaultdict
inverse= defaultdict( list )
for k, v in originalDict.items():
    inverse[v].append( k )

Now your inverse has the values; each value has a list of applicable keys.

for k in sorted(inverse):
    print k, inverse[k]

回答 16

您可以使用collections.Counter。请注意,这对于数字和非数字值均适用。

>>> x = {1: 2, 3: 4, 4:3, 2:1, 0:0}
>>> from collections import Counter
>>> #To sort in reverse order
>>> Counter(x).most_common()
[(3, 4), (4, 3), (1, 2), (2, 1), (0, 0)]
>>> #To sort in ascending order
>>> Counter(x).most_common()[::-1]
[(0, 0), (2, 1), (1, 2), (4, 3), (3, 4)]
>>> #To get a dictionary sorted by values
>>> from collections import OrderedDict
>>> OrderedDict(Counter(x).most_common()[::-1])
OrderedDict([(0, 0), (2, 1), (1, 2), (4, 3), (3, 4)])

You can use the collections.Counter. Note, this will work for both numeric and non-numeric values.

>>> x = {1: 2, 3: 4, 4:3, 2:1, 0:0}
>>> from collections import Counter
>>> #To sort in reverse order
>>> Counter(x).most_common()
[(3, 4), (4, 3), (1, 2), (2, 1), (0, 0)]
>>> #To sort in ascending order
>>> Counter(x).most_common()[::-1]
[(0, 0), (2, 1), (1, 2), (4, 3), (3, 4)]
>>> #To get a dictionary sorted by values
>>> from collections import OrderedDict
>>> OrderedDict(Counter(x).most_common()[::-1])
OrderedDict([(0, 0), (2, 1), (1, 2), (4, 3), (3, 4)])

回答 17

您可以使用skip dict,这是一个按值永久排序的字典。

>>> data = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
>>> SkipDict(data)
{0: 0.0, 2: 1.0, 1: 2.0, 4: 3.0, 3: 4.0}

如果使用keys()values()或者items()那么你会在排序顺序通过值迭代。

它是使用跳过列表数据结构实现的。

You can use a skip dict which is a dictionary that’s permanently sorted by value.

>>> data = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
>>> SkipDict(data)
{0: 0.0, 2: 1.0, 1: 2.0, 4: 3.0, 3: 4.0}

If you use keys(), values() or items() then you’ll iterate in sorted order by value.

It’s implemented using the skip list datastructure.


回答 18

您还可以使用可以传递给键的自定义函数。

def dict_val(x):
    return x[1]
x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
sorted_x = sorted(x.items(), key=dict_val)

You can also use custom function that can be passed to key.

def dict_val(x):
    return x[1]
x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
sorted_x = sorted(x.items(), key=dict_val)

回答 19

from django.utils.datastructures import SortedDict

def sortedDictByKey(self,data):
    """Sorted dictionary order by key"""
    sortedDict = SortedDict()
    if data:
        if isinstance(data, dict):
            sortedKey = sorted(data.keys())
            for k in sortedKey:
                sortedDict[k] = data[k]
    return sortedDict
from django.utils.datastructures import SortedDict

def sortedDictByKey(self,data):
    """Sorted dictionary order by key"""
    sortedDict = SortedDict()
    if data:
        if isinstance(data, dict):
            sortedKey = sorted(data.keys())
            for k in sortedKey:
                sortedDict[k] = data[k]
    return sortedDict

回答 20

正如Dilettant指出的那样,Python 3.6现在将保持顺序!我以为我会分享我编写的一个函数,该函数可以简化可迭代对象(元组,列表,字典)的排序。在后一种情况下,您可以对键或值进行排序,并且可以考虑数字比较。仅适用于> = 3.6!

当您尝试在包含字符串和整数的可迭代对象上使用sorted时,sorted()将失败。当然,您可以使用str()强制进行字符串比较。但是,在某些情况下,您想进行小于的实际数字比较(在字符串比较中不是这种情况)。因此,我提出了以下建议。当您需要显式数值比较时,可以使用该标志,该标志将尝试通过将所有值都转换为浮点数来进行显式数值排序。如果成功,它将进行数字排序,否则将使用字符串比较。1220num_as_num

欢迎提出改进或推送要求的评论。

def sort_iterable(iterable, sort_on=None, reverse=False, num_as_num=False):
    def _sort(i):
      # sort by 0 = keys, 1 values, None for lists and tuples
      try:
        if num_as_num:
          if i is None:
            _sorted = sorted(iterable, key=lambda v: float(v), reverse=reverse)
          else:
            _sorted = dict(sorted(iterable.items(), key=lambda v: float(v[i]), reverse=reverse))
        else:
          raise TypeError
      except (TypeError, ValueError):
        if i is None:
          _sorted = sorted(iterable, key=lambda v: str(v), reverse=reverse)
        else:
          _sorted = dict(sorted(iterable.items(), key=lambda v: str(v[i]), reverse=reverse))

      return _sorted

    if isinstance(iterable, list):
      sorted_list = _sort(None)
      return sorted_list
    elif isinstance(iterable, tuple):
      sorted_list = tuple(_sort(None))
      return sorted_list
    elif isinstance(iterable, dict):
      if sort_on == 'keys':
        sorted_dict = _sort(0)
        return sorted_dict
      elif sort_on == 'values':
        sorted_dict = _sort(1)
        return sorted_dict
      elif sort_on is not None:
        raise ValueError(f"Unexpected value {sort_on} for sort_on. When sorting a dict, use key or values")
    else:
      raise TypeError(f"Unexpected type {type(iterable)} for iterable. Expected a list, tuple, or dict")

As pointed out by Dilettant, Python 3.6 will now keep the order! I thought I’d share a function I wrote that eases the sorting of an iterable (tuple, list, dict). In the latter case, you can sort either on keys or values, and it can take numeric comparison into account. Only for >= 3.6!

When you try using sorted on an iterable that holds e.g. strings as well as ints, sorted() will fail. Of course you can force string comparison with str(). However, in some cases you want to do actual numeric comparison where 12 is smaller than 20 (which is not the case in string comparison). So I came up with the following. When you want explicit numeric comparison you can use the flag num_as_num which will try to do explicit numeric sorting by trying to convert all values to floats. If that succeeds, it will do numeric sorting, otherwise it’ll resort to string comparison.

Comments for improvement or push requests welcome.

def sort_iterable(iterable, sort_on=None, reverse=False, num_as_num=False):
    def _sort(i):
      # sort by 0 = keys, 1 values, None for lists and tuples
      try:
        if num_as_num:
          if i is None:
            _sorted = sorted(iterable, key=lambda v: float(v), reverse=reverse)
          else:
            _sorted = dict(sorted(iterable.items(), key=lambda v: float(v[i]), reverse=reverse))
        else:
          raise TypeError
      except (TypeError, ValueError):
        if i is None:
          _sorted = sorted(iterable, key=lambda v: str(v), reverse=reverse)
        else:
          _sorted = dict(sorted(iterable.items(), key=lambda v: str(v[i]), reverse=reverse))

      return _sorted

    if isinstance(iterable, list):
      sorted_list = _sort(None)
      return sorted_list
    elif isinstance(iterable, tuple):
      sorted_list = tuple(_sort(None))
      return sorted_list
    elif isinstance(iterable, dict):
      if sort_on == 'keys':
        sorted_dict = _sort(0)
        return sorted_dict
      elif sort_on == 'values':
        sorted_dict = _sort(1)
        return sorted_dict
      elif sort_on is not None:
        raise ValueError(f"Unexpected value {sort_on} for sort_on. When sorting a dict, use key or values")
    else:
      raise TypeError(f"Unexpected type {type(iterable)} for iterable. Expected a list, tuple, or dict")

回答 21

这是在d.values()d.keys()上使用zip的解决方案。该链接(在Dictionary视图对象上)下面的几行是:

这允许使用zip()创建(值,键)对:pair = zip(d.values(),d.keys())。

因此,我们可以执行以下操作:

d = {'key1': 874.7, 'key2': 5, 'key3': 8.1}

d_sorted = sorted(zip(d.values(), d.keys()))

print d_sorted 
# prints: [(5, 'key2'), (8.1, 'key3'), (874.7, 'key1')]

Here is a solution using zip on d.values() and d.keys(). A few lines down this link (on Dictionary view objects) is:

This allows the creation of (value, key) pairs using zip(): pairs = zip(d.values(), d.keys()).

So we can do the following:

d = {'key1': 874.7, 'key2': 5, 'key3': 8.1}

d_sorted = sorted(zip(d.values(), d.keys()))

print d_sorted 
# prints: [(5, 'key2'), (8.1, 'key3'), (874.7, 'key1')]

回答 22

当然,请记住,您需要使用它,OrderedDict因为常规Python字典不会保留原始顺序。

from collections import OrderedDict
a = OrderedDict(sorted(originalDict.items(), key=lambda x: x[1]))

如果您没有Python 2.7或更高版本,则最好的办法是迭代生成器函数中的值。(有一个OrderedDict2.4和2.6 在这里,但

a)我不知道它的效果如何

b)当然,您必须下载并安装它。如果您没有管理权限,那么恐怕该选项不可用了。)


def gen(originalDict):
    for x, y in sorted(zip(originalDict.keys(), originalDict.values()), key=lambda z: z[1]):
        yield (x, y)
    #Yields as a tuple with (key, value). You can iterate with conditional clauses to get what you want. 

for bleh, meh in gen(myDict):
    if bleh == "foo":
        print(myDict[bleh])

您还可以打印出每个值

for bleh, meh in gen(myDict):
    print(bleh, meh)

如果未使用Python 3.0或更高版本,请记住在打印后删除括号

Of course, remember, you need to use OrderedDict because regular Python dictionaries don’t keep the original order.

from collections import OrderedDict
a = OrderedDict(sorted(originalDict.items(), key=lambda x: x[1]))

If you do not have Python 2.7 or higher, the best you can do is iterate over the values in a generator function. (There is an OrderedDict for 2.4 and 2.6 here, but

a) I don’t know about how well it works

and

b) You have to download and install it of course. If you do not have administrative access, then I’m afraid the option’s out.)


def gen(originalDict):
    for x, y in sorted(zip(originalDict.keys(), originalDict.values()), key=lambda z: z[1]):
        yield (x, y)
    #Yields as a tuple with (key, value). You can iterate with conditional clauses to get what you want. 

for bleh, meh in gen(myDict):
    if bleh == "foo":
        print(myDict[bleh])

You can also print out every value

for bleh, meh in gen(myDict):
    print(bleh, meh)

Please remember to remove the parentheses after print if not using Python 3.0 or above


回答 23

使用ValueSortedDicthttp://stardict.sourceforge.net/Dictionaries.php下载

from dicts.sorteddict import ValueSortedDict
d = {1: 2, 3: 4, 4:3, 2:1, 0:0}
sorted_dict = ValueSortedDict(d)
print sorted_dict.items() 

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

Use ValueSortedDict from dicts:

from dicts.sorteddict import ValueSortedDict
d = {1: 2, 3: 4, 4:3, 2:1, 0:0}
sorted_dict = ValueSortedDict(d)
print sorted_dict.items() 

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

回答 24

这适用于3.1.x:

import operator
slovar_sorted=sorted(slovar.items(), key=operator.itemgetter(1), reverse=True)
print(slovar_sorted)

This works in 3.1.x:

import operator
slovar_sorted=sorted(slovar.items(), key=operator.itemgetter(1), reverse=True)
print(slovar_sorted)

回答 25

刚刚从Python for Everyone中学习了相关技能。

您可以使用一个临时列表来帮助您对字典进行排序:

#Assume dictionary to be:
d = {'apple': 500.1, 'banana': 1500.2, 'orange': 1.0, 'pineapple': 789.0}

# create a temporary list
tmp = []

# iterate through the dictionary and append each tuple into the temporary list 
for key, value in d.items():
    tmptuple = (value, key)
    tmp.append(tmptuple)

# sort the list in ascending order
tmp = sorted(tmp)

print (tmp)

如果要按降序对列表进行排序,只需将原始排序行更改为:

tmp = sorted(tmp, reverse=True)

使用列表推导,一个衬里将是:

#Assuming the dictionary looks like
d = {'apple': 500.1, 'banana': 1500.2, 'orange': 1.0, 'pineapple': 789.0}
#One liner for sorting in ascending order
print (sorted([(v, k) for k, v in d.items()]))
#One liner for sorting in descending order
print (sorted([(v, k) for k, v in d.items()], reverse=True))

样本输出:

#Asending order
[(1.0, 'orange'), (500.1, 'apple'), (789.0, 'pineapple'), (1500.2, 'banana')]
#Descending order
[(1500.2, 'banana'), (789.0, 'pineapple'), (500.1, 'apple'), (1.0, 'orange')]

Just learned relevant skill from Python for Everybody.

You may use a temporary list to help you to sort the dictionary:

#Assume dictionary to be:
d = {'apple': 500.1, 'banana': 1500.2, 'orange': 1.0, 'pineapple': 789.0}

# create a temporary list
tmp = []

# iterate through the dictionary and append each tuple into the temporary list 
for key, value in d.items():
    tmptuple = (value, key)
    tmp.append(tmptuple)

# sort the list in ascending order
tmp = sorted(tmp)

print (tmp)

If you want to sort the list in descending order, simply change the original sorting line to:

tmp = sorted(tmp, reverse=True)

Using list comprehension, the one liner would be:

#Assuming the dictionary looks like
d = {'apple': 500.1, 'banana': 1500.2, 'orange': 1.0, 'pineapple': 789.0}
#One liner for sorting in ascending order
print (sorted([(v, k) for k, v in d.items()]))
#One liner for sorting in descending order
print (sorted([(v, k) for k, v in d.items()], reverse=True))

Sample Output:

#Asending order
[(1.0, 'orange'), (500.1, 'apple'), (789.0, 'pineapple'), (1500.2, 'banana')]
#Descending order
[(1500.2, 'banana'), (789.0, 'pineapple'), (500.1, 'apple'), (1.0, 'orange')]

回答 26

遍历字典并按其值降序对其进行排序:

$ python --version
Python 3.2.2

$ cat sort_dict_by_val_desc.py 
dictionary = dict(siis = 1, sana = 2, joka = 3, tuli = 4, aina = 5)
for word in sorted(dictionary, key=dictionary.get, reverse=True):
  print(word, dictionary[word])

$ python sort_dict_by_val_desc.py 
aina 5
tuli 4
joka 3
sana 2
siis 1

Iterate through a dict and sort it by its values in descending order:

$ python --version
Python 3.2.2

$ cat sort_dict_by_val_desc.py 
dictionary = dict(siis = 1, sana = 2, joka = 3, tuli = 4, aina = 5)
for word in sorted(dictionary, key=dictionary.get, reverse=True):
  print(word, dictionary[word])

$ python sort_dict_by_val_desc.py 
aina 5
tuli 4
joka 3
sana 2
siis 1

回答 27

如果您的值是整数,并且使用的是Python 2.7或更高版本,则可以使用collections.Counter代替dict。该most_common方法将为您提供所有项目,并按值排序。

If your values are integers, and you use Python 2.7 or newer, you can use collections.Counter instead of dict. The most_common method will give you all items, sorted by the value.


回答 28

为了完整起见,我发布了一个使用heapq的解决方案。请注意,此方法适用于数值和非数值

>>> x = {1: 2, 3: 4, 4:3, 2:1, 0:0}
>>> x_items = x.items()
>>> heapq.heapify(x_items)
>>> #To sort in reverse order
>>> heapq.nlargest(len(x_items),x_items, operator.itemgetter(1))
[(3, 4), (4, 3), (1, 2), (2, 1), (0, 0)]
>>> #To sort in ascending order
>>> heapq.nsmallest(len(x_items),x_items, operator.itemgetter(1))
[(0, 0), (2, 1), (1, 2), (4, 3), (3, 4)]

For the sake of completeness, I am posting a solution using heapq. Note, this method will work for both numeric and non-numeric values

>>> x = {1: 2, 3: 4, 4:3, 2:1, 0:0}
>>> x_items = x.items()
>>> heapq.heapify(x_items)
>>> #To sort in reverse order
>>> heapq.nlargest(len(x_items),x_items, operator.itemgetter(1))
[(3, 4), (4, 3), (1, 2), (2, 1), (0, 0)]
>>> #To sort in ascending order
>>> heapq.nsmallest(len(x_items),x_items, operator.itemgetter(1))
[(0, 0), (2, 1), (1, 2), (4, 3), (3, 4)]

回答 29

由于需要保持与旧版本Python的向后兼容性,我认为OrderedDict解决方案非常不明智。您需要适用于Python 2.7和更早版本的工具。

但是,另一个答案中提到的集合解决方案绝对是极好的,因为您可以重新训练键和值之间的联系,这对于字典而言极为重要。

我不同意另一个答案中提出的第一选择,因为它会丢掉钥匙。

我使用了上面提到的解决方案(如下所示的代码),并保留了对键和值的访问,在我的情况下,排序是在值上进行的,但重要的是在对值进行排序之后对键进行排序。

from collections import Counter

x = {'hello':1, 'python':5, 'world':3}
c=Counter(x)
print c.most_common()


>> [('python', 5), ('world', 3), ('hello', 1)]

Because of requirements to retain backward compatability with older versions of Python I think the OrderedDict solution is very unwise. You want something that works with Python 2.7 and older versions.

But the collections solution mentioned in another answer is absolutely superb, because you retrain a connection between the key and value which in the case of dictionaries is extremely important.

I don’t agree with the number one choice presented in another answer, because it throws away the keys.

I used the solution mentioned above (code shown below) and retained access to both keys and values and in my case the ordering was on the values, but the importance was the ordering of the keys after ordering the values.

from collections import Counter

x = {'hello':1, 'python':5, 'world':3}
c=Counter(x)
print c.most_common()


>> [('python', 5), ('world', 3), ('hello', 1)]