标签归档:combinations

统计信息:Python中的组合

问题:统计信息:Python中的组合

我需要计算在Python combinatorials(NCR),但无法找到的功能做在mathnumpystat 图书馆。类似于函数的类型:

comb = calculate_combinations(n, r)

我需要可能的组合数量,而不是实际组合,因此itertools.combinations我对此并不感兴趣。

最后,我要避免使用阶乘,因为我将要计算其组合的数字可能会太大,并且阶乘将变得非常可怕。

这似乎是一个非常容易回答的问题,但是我被有关生成所有实际组合的问题淹没了,这不是我想要的。

I need to compute combinatorials (nCr) in Python but cannot find the function to do that in math, numpy or stat libraries. Something like a function of the type:

comb = calculate_combinations(n, r)

I need the number of possible combinations, not the actual combinations, so itertools.combinations does not interest me.

Finally, I want to avoid using factorials, as the numbers I’ll be calculating the combinations for can get too big and the factorials are going to be monstrous.

This seems like a REALLY easy to answer question, however I am being drowned in questions about generating all the actual combinations, which is not what I want.


回答 0

请参阅scipy.special.comb(旧版本的scipy中的scipy.misc.comb)。当exact为False时,它使用伽马函数来获得良好的精度而无需花费很多时间。在确切的情况下,它返回一个任意精度的整数,这可能需要很长时间才能计算出来。

See scipy.special.comb (scipy.misc.comb in older versions of scipy). When exact is False, it uses the gammaln function to obtain good precision without taking much time. In the exact case it returns an arbitrary-precision integer, which might take a long time to compute.


回答 1

为什么不自己写呢?这是一线之类的:

from operator import mul    # or mul=lambda x,y:x*y
from fractions import Fraction

def nCk(n,k): 
  return int( reduce(mul, (Fraction(n-i, i+1) for i in range(k)), 1) )

测试-打印Pascal的三角形:

>>> for n in range(17):
...     print ' '.join('%5d'%nCk(n,k) for k in range(n+1)).center(100)
...     
                                                   1                                                
                                                1     1                                             
                                             1     2     1                                          
                                          1     3     3     1                                       
                                       1     4     6     4     1                                    
                                    1     5    10    10     5     1                                 
                                 1     6    15    20    15     6     1                              
                              1     7    21    35    35    21     7     1                           
                           1     8    28    56    70    56    28     8     1                        
                        1     9    36    84   126   126    84    36     9     1                     
                     1    10    45   120   210   252   210   120    45    10     1                  
                  1    11    55   165   330   462   462   330   165    55    11     1               
               1    12    66   220   495   792   924   792   495   220    66    12     1            
            1    13    78   286   715  1287  1716  1716  1287   715   286    78    13     1         
         1    14    91   364  1001  2002  3003  3432  3003  2002  1001   364    91    14     1      
      1    15   105   455  1365  3003  5005  6435  6435  5005  3003  1365   455   105    15     1   
    1    16   120   560  1820  4368  8008 11440 12870 11440  8008  4368  1820   560   120    16     1
>>> 

PS。编辑以替换int(round(reduce(mul, (float(n-i)/(i+1) for i in range(k)), 1)))int(reduce(mul, (Fraction(n-i, i+1) for i in range(k)), 1))因此对于大N / K不会出错

Why not write it yourself? It’s a one-liner or such:

from operator import mul    # or mul=lambda x,y:x*y
from fractions import Fraction

def nCk(n,k): 
  return int( reduce(mul, (Fraction(n-i, i+1) for i in range(k)), 1) )

Test – printing Pascal’s triangle:

>>> for n in range(17):
...     print ' '.join('%5d'%nCk(n,k) for k in range(n+1)).center(100)
...     
                                                   1                                                
                                                1     1                                             
                                             1     2     1                                          
                                          1     3     3     1                                       
                                       1     4     6     4     1                                    
                                    1     5    10    10     5     1                                 
                                 1     6    15    20    15     6     1                              
                              1     7    21    35    35    21     7     1                           
                           1     8    28    56    70    56    28     8     1                        
                        1     9    36    84   126   126    84    36     9     1                     
                     1    10    45   120   210   252   210   120    45    10     1                  
                  1    11    55   165   330   462   462   330   165    55    11     1               
               1    12    66   220   495   792   924   792   495   220    66    12     1            
            1    13    78   286   715  1287  1716  1716  1287   715   286    78    13     1         
         1    14    91   364  1001  2002  3003  3432  3003  2002  1001   364    91    14     1      
      1    15   105   455  1365  3003  5005  6435  6435  5005  3003  1365   455   105    15     1   
    1    16   120   560  1820  4368  8008 11440 12870 11440  8008  4368  1820   560   120    16     1
>>> 

PS. edited to replace int(round(reduce(mul, (float(n-i)/(i+1) for i in range(k)), 1))) with int(reduce(mul, (Fraction(n-i, i+1) for i in range(k)), 1)) so it won’t err for big N/K


回答 2

在Google代码上快速搜索给出了(它使用了@Mark Byers的答案中的公式):

def choose(n, k):
    """
    A fast way to calculate binomial coefficients by Andrew Dalke (contrib).
    """
    if 0 <= k <= n:
        ntok = 1
        ktok = 1
        for t in xrange(1, min(k, n - k) + 1):
            ntok *= n
            ktok *= t
            n -= 1
        return ntok // ktok
    else:
        return 0

choose()scipy.misc.comb()您需要确切答案快10倍(在所有0 <=(n,k)<1e3对上测试)。

def comb(N,k): # from scipy.comb(), but MODIFIED!
    if (k > N) or (N < 0) or (k < 0):
        return 0L
    N,k = map(long,(N,k))
    top = N
    val = 1L
    while (top > (N-k)):
        val *= top
        top -= 1
    n = 1L
    while (n < k+1L):
        val /= n
        n += 1
    return val

A quick search on google code gives (it uses formula from @Mark Byers’s answer):

def choose(n, k):
    """
    A fast way to calculate binomial coefficients by Andrew Dalke (contrib).
    """
    if 0 <= k <= n:
        ntok = 1
        ktok = 1
        for t in xrange(1, min(k, n - k) + 1):
            ntok *= n
            ktok *= t
            n -= 1
        return ntok // ktok
    else:
        return 0

choose() is 10 times faster (tested on all 0 <= (n,k) < 1e3 pairs) than scipy.misc.comb() if you need an exact answer.

def comb(N,k): # from scipy.comb(), but MODIFIED!
    if (k > N) or (N < 0) or (k < 0):
        return 0L
    N,k = map(long,(N,k))
    top = N
    val = 1L
    while (top > (N-k)):
        val *= top
        top -= 1
    n = 1L
    while (n < k+1L):
        val /= n
        n += 1
    return val

回答 3

如果您想要确切的结果速度,请尝试gmpygmpy.comb应该完全按照您的要求进行操作,而且速度非常快(当然,作为gmpy的原始作者,我偏见;-)。

If you want exact results and speed, try gmpygmpy.comb should do exactly what you ask for, and it’s pretty fast (of course, as gmpy‘s original author, I am biased;-).


回答 4

如果您想要精确的结果,请使用sympy.binomial。看来这是最快的方法。

x = 1000000
y = 234050

%timeit scipy.misc.comb(x, y, exact=True)
1 loops, best of 3: 1min 27s per loop

%timeit gmpy.comb(x, y)
1 loops, best of 3: 1.97 s per loop

%timeit int(sympy.binomial(x, y))
100000 loops, best of 3: 5.06 µs per loop

If you want an exact result, use sympy.binomial. It seems to be the fastest method, hands down.

x = 1000000
y = 234050

%timeit scipy.misc.comb(x, y, exact=True)
1 loops, best of 3: 1min 27s per loop

%timeit gmpy.comb(x, y)
1 loops, best of 3: 1.97 s per loop

%timeit int(sympy.binomial(x, y))
100000 loops, best of 3: 5.06 µs per loop

回答 5

在许多情况下,数学定义的字面翻译是足够的(记住Python将自动使用大数算法):

from math import factorial

def calculate_combinations(n, r):
    return factorial(n) // factorial(r) // factorial(n-r)

对于我测试的某些输入(例如n = 1000 r = 500),这比reduce另一种(目前投票率最高)答案中建议的一种衬板的速度快10倍以上。另一方面,@ JF Sebastian提供的代码片段的性能优于。

A literal translation of the mathematical definition is quite adequate in a lot of cases (remembering that Python will automatically use big number arithmetic):

from math import factorial

def calculate_combinations(n, r):
    return factorial(n) // factorial(r) // factorial(n-r)

For some inputs I tested (e.g. n=1000 r=500) this was more than 10 times faster than the one liner reduce suggested in another (currently highest voted) answer. On the other hand, it is out-performed by the snippit provided by @J.F. Sebastian.


回答 6

从开始Python 3.8,标准库现在包括math.comb用于计算二项式系数的函数:

math.comb(n,k)

这是从n个项中不重复选择k个项的方法的数量
n! / (k! (n - k)!)

import math
math.comb(10, 5) # 252

Starting Python 3.8, the standard library now includes the math.comb function to compute the binomial coefficient:

math.comb(n, k)

which is the number of ways to choose k items from n items without repetition
n! / (k! (n - k)!):

import math
math.comb(10, 5) # 252

回答 7

这是另一种选择。该代码最初是用C ++编写的,因此可以将其反向移植到C ++以获取有限精度的整数(例如__int64)。优点是(1)它仅涉及整数运算,(2)通过执行连续的乘法和除法对,避免了膨胀整数值。我已经用Nas Banov的Pascal三角形测试了结果,它得到了正确的答案:

def choose(n,r):
  """Computes n! / (r! (n-r)!) exactly. Returns a python long int."""
  assert n >= 0
  assert 0 <= r <= n

  c = 1L
  denom = 1
  for (num,denom) in zip(xrange(n,n-r,-1), xrange(1,r+1,1)):
    c = (c * num) // denom
  return c

基本原理:为了最小化乘法和除法的数量,我们将表达式重写为

    n!      n(n-1)...(n-r+1)
--------- = ----------------
 r!(n-r)!          r!

为了尽可能避免乘法溢出,我们将按照以下STRICT顺序从左到右进行评估:

n / 1 * (n-1) / 2 * (n-2) / 3 * ... * (n-r+1) / r

我们可以证明按此顺序运算的整数算术是精确的(即无舍入误差)。

Here’s another alternative. This one was originally written in C++, so it can be backported to C++ for a finite-precision integer (e.g. __int64). The advantage is (1) it involves only integer operations, and (2) it avoids bloating the integer value by doing successive pairs of multiplication and division. I’ve tested the result with Nas Banov’s Pascal triangle, it gets the correct answer:

def choose(n,r):
  """Computes n! / (r! (n-r)!) exactly. Returns a python long int."""
  assert n >= 0
  assert 0 <= r <= n

  c = 1L
  denom = 1
  for (num,denom) in zip(xrange(n,n-r,-1), xrange(1,r+1,1)):
    c = (c * num) // denom
  return c

Rationale: To minimize the # of multiplications and divisions, we rewrite the expression as

    n!      n(n-1)...(n-r+1)
--------- = ----------------
 r!(n-r)!          r!

To avoid multiplication overflow as much as possible, we will evaluate in the following STRICT order, from left to right:

n / 1 * (n-1) / 2 * (n-2) / 3 * ... * (n-r+1) / r

We can show that integer arithmatic operated in this order is exact (i.e. no roundoff error).


回答 8

使用动态编程,时间复杂度为Θ(n * m),空间复杂度为Θ(m):

def binomial(n, k):
""" (int, int) -> int

         | c(n-1, k-1) + c(n-1, k), if 0 < k < n
c(n,k) = | 1                      , if n = k
         | 1                      , if k = 0

Precondition: n > k

>>> binomial(9, 2)
36
"""

c = [0] * (n + 1)
c[0] = 1
for i in range(1, n + 1):
    c[i] = 1
    j = i - 1
    while j > 0:
        c[j] += c[j - 1]
        j -= 1

return c[k]

Using dynamic programming, the time complexity is Θ(n*m) and space complexity Θ(m):

def binomial(n, k):
""" (int, int) -> int

         | c(n-1, k-1) + c(n-1, k), if 0 < k < n
c(n,k) = | 1                      , if n = k
         | 1                      , if k = 0

Precondition: n > k

>>> binomial(9, 2)
36
"""

c = [0] * (n + 1)
c[0] = 1
for i in range(1, n + 1):
    c[i] = 1
    j = i - 1
    while j > 0:
        c[j] += c[j - 1]
        j -= 1

return c[k]

回答 9

如果您的程序有上限n(例如n <= N),并且需要重复计算nCr(最好是>> N次),则使用lru_cache可以极大地提高性能:

from functools import lru_cache

@lru_cache(maxsize=None)
def nCr(n, r):
    return 1 if r == 0 or r == n else nCr(n - 1, r - 1) + nCr(n - 1, r)

构造缓存(隐式完成)需要花费O(N^2)时间。随后的所有对的调用都nCr将返回O(1)

If your program has an upper bound to n (say n <= N) and needs to repeatedly compute nCr (preferably for >>N times), using lru_cache can give you a huge performance boost:

from functools import lru_cache

@lru_cache(maxsize=None)
def nCr(n, r):
    return 1 if r == 0 or r == n else nCr(n - 1, r - 1) + nCr(n - 1, r)

Constructing the cache (which is done implicitly) takes up to O(N^2) time. Any subsequent calls to nCr will return in O(1).


回答 10

您可以编写2个简单的函数,实际上比使用scipy.special.comb快5到8倍。实际上,您不需要导入任何额外的程序包,并且该函数非常易于阅读。诀窍是使用备忘录存储先前计算的值,并使用nCr的定义

# create a memoization dictionary
memo = {}
def factorial(n):
    """
    Calculate the factorial of an input using memoization
    :param n: int
    :rtype value: int
    """
    if n in [1,0]:
        return 1
    if n in memo:
        return memo[n]
    value = n*factorial(n-1)
    memo[n] = value
    return value

def ncr(n, k):
    """
    Choose k elements from a set of n elements - n must be larger than or equal to k
    :param n: int
    :param k: int
    :rtype: int
    """
    return factorial(n)/(factorial(k)*factorial(n-k))

如果我们比较时间

from scipy.special import comb
%timeit comb(100,48)
>>> 100000 loops, best of 3: 6.78 µs per loop

%timeit ncr(100,48)
>>> 1000000 loops, best of 3: 1.39 µs per loop

You can write 2 simple functions that actually turns out to be about 5-8 times faster than using scipy.special.comb. In fact, you don’t need to import any extra packages, and the function is quite easily readable. The trick is to use memoization to store previously computed values, and using the definition of nCr

# create a memoization dictionary
memo = {}
def factorial(n):
    """
    Calculate the factorial of an input using memoization
    :param n: int
    :rtype value: int
    """
    if n in [1,0]:
        return 1
    if n in memo:
        return memo[n]
    value = n*factorial(n-1)
    memo[n] = value
    return value

def ncr(n, k):
    """
    Choose k elements from a set of n elements - n must be larger than or equal to k
    :param n: int
    :param k: int
    :rtype: int
    """
    return factorial(n)/(factorial(k)*factorial(n-k))

If we compare times

from scipy.special import comb
%timeit comb(100,48)
>>> 100000 loops, best of 3: 6.78 µs per loop

%timeit ncr(100,48)
>>> 1000000 loops, best of 3: 1.39 µs per loop

回答 11

使用sympy很容易。

import sympy

comb = sympy.binomial(n, r)

It’s pretty easy with sympy.

import sympy

comb = sympy.binomial(n, r)

回答 12

仅使用随Python分发的标准库

import itertools

def nCk(n, k):
    return len(list(itertools.combinations(range(n), k)))

Using only standard library distributed with Python:

import itertools

def nCk(n, k):
    return len(list(itertools.combinations(range(n), k)))

回答 13

当n大于20时,直接公式会产生大整数。

因此,另一个回应是:

from math import factorial

reduce(long.__mul__, range(n-r+1, n+1), 1L) // factorial(r)

简短,准确和高效,因为它通过坚持使用long避免了python大整数。

与scipy.special.comb相比,它更准确,更快捷:

 >>> from scipy.special import comb
 >>> nCr = lambda n,r: reduce(long.__mul__, range(n-r+1, n+1), 1L) // factorial(r)
 >>> comb(128,20)
 1.1965669823265365e+23
 >>> nCr(128,20)
 119656698232656998274400L  # accurate, no loss
 >>> from timeit import timeit
 >>> timeit(lambda: comb(n,r))
 8.231969118118286
 >>> timeit(lambda: nCr(128, 20))
 3.885951042175293

The direct formula produces big integers when n is bigger than 20.

So, yet another response:

from math import factorial

reduce(long.__mul__, range(n-r+1, n+1), 1L) // factorial(r)

short, accurate and efficient because this avoids python big integers by sticking with longs.

It is more accurate and faster when comparing to scipy.special.comb:

 >>> from scipy.special import comb
 >>> nCr = lambda n,r: reduce(long.__mul__, range(n-r+1, n+1), 1L) // factorial(r)
 >>> comb(128,20)
 1.1965669823265365e+23
 >>> nCr(128,20)
 119656698232656998274400L  # accurate, no loss
 >>> from timeit import timeit
 >>> timeit(lambda: comb(n,r))
 8.231969118118286
 >>> timeit(lambda: nCr(128, 20))
 3.885951042175293

回答 14

这是使用内置备忘录修饰器的@ killerT2333代码。

from functools import lru_cache

@lru_cache()
def factorial(n):
    """
    Calculate the factorial of an input using memoization
    :param n: int
    :rtype value: int
    """
    return 1 if n in (1, 0) else n * factorial(n-1)

@lru_cache()
def ncr(n, k):
    """
    Choose k elements from a set of n elements,
    n must be greater than or equal to k.
    :param n: int
    :param k: int
    :rtype: int
    """
    return factorial(n) / (factorial(k) * factorial(n - k))

print(ncr(6, 3))

This is @killerT2333 code using the builtin memoization decorator.

from functools import lru_cache

@lru_cache()
def factorial(n):
    """
    Calculate the factorial of an input using memoization
    :param n: int
    :rtype value: int
    """
    return 1 if n in (1, 0) else n * factorial(n-1)

@lru_cache()
def ncr(n, k):
    """
    Choose k elements from a set of n elements,
    n must be greater than or equal to k.
    :param n: int
    :param k: int
    :rtype: int
    """
    return factorial(n) / (factorial(k) * factorial(n - k))

print(ncr(6, 3))

回答 15

这是为您提供的高效算法

for i = 1.....r

   p = p * ( n - i ) / i

print(p)

例如nCr(30,7)= fact(30)/(fact(7)* fact(23))=(30 * 29 * 28 * 27 * 26 * 25 * 24)/(1 * 2 * 3 * 4 * 5 * 6 * 7)

因此,只需从1到r运行循环即可获得结果。

Here is an efficient algorithm for you

for i = 1.....r

   p = p * ( n - i ) / i

print(p)

For example nCr(30,7) = fact(30) / ( fact(7) * fact(23)) = ( 30 * 29 * 28 * 27 * 26 * 25 * 24 ) / (1 * 2 * 3 * 4 * 5 * 6 * 7)

So just run the loop from 1 to r can get the result.


回答 16

对于相当大的输入,这可能与在纯python中完成的速度一样快:

def choose(n, k):
    if k == n: return 1
    if k > n: return 0
    d, q = max(k, n-k), min(k, n-k)
    num =  1
    for n in xrange(d+1, n+1): num *= n
    denom = 1
    for d in xrange(1, q+1): denom *= d
    return num / denom

That’s probably as fast as you can do it in pure python for reasonably large inputs:

def choose(n, k):
    if k == n: return 1
    if k > n: return 0
    d, q = max(k, n-k), min(k, n-k)
    num =  1
    for n in xrange(d+1, n+1): num *= n
    denom = 1
    for d in xrange(1, q+1): denom *= d
    return num / denom

回答 17

此功能非常优化。

def nCk(n,k):
    m=0
    if k==0:
        m=1
    if k==1:
        m=n
    if k>=2:
        num,dem,op1,op2=1,1,k,n
        while(op1>=1):
            num*=op2
            dem*=op1
            op1-=1
            op2-=1
        m=num//dem
    return m

This function is very optimazed.

def nCk(n,k):
    m=0
    if k==0:
        m=1
    if k==1:
        m=n
    if k>=2:
        num,dem,op1,op2=1,1,k,n
        while(op1>=1):
            num*=op2
            dem*=op1
            op1-=1
            op2-=1
        m=num//dem
    return m

两个列表之间的组合?

问题:两个列表之间的组合?

已经有一段时间了,我无法将自己的头围绕着我尝试制定的算法。基本上,我有两个列表,并且想要获得两个列表的所有组合。

我可能没有解释正确,所以这里有个例子。

name = 'a', 'b'
number = 1, 2

在这种情况下的输出将是:

1.  A1 B2
2.  B1 A2

棘手的部分是,“名称”变量中的项目可能比“数字”变量中的项目更多(数字将始终等于或小于名称变量)。

我很困惑如何进行所有组合(是否嵌套到循环?),甚至在名称中的项目比数字列表中的项目多的情况下,对于将名称变量中的项目进行移位的逻辑更加困惑。

我不是最好的程序员,但是如果有人可以帮助我阐明实现这一目标的逻辑/算法,我想可以试一试。所以我只是停留在嵌套的循环上。

更新:

这是带有3个变量和2个数字的输出:

name = 'a', 'b', 'c'
number = 1, 2

输出:

1.  A1 B2
2.  B1 A2
3.  A1 C2
4.  C1 A2
5.  B1 C2
6.  C1 B2

I’m having trouble wrapping my head around a algorithm I’m try to implement. I have two lists and want to take particular combinations from the two lists.

Here’s an example.

names = 'a', 'b'
numbers = 1, 2

the output in this case would be:

[('a', 1), ('b', 2)]
[('b', 1), ('a', 2)]

I might have more names than numbers, i.e. len(names) >= len(numbers). Here’s an example with 3 names and 2 numbers:

names = 'a', 'b', 'c'
numbers = 1, 2

output:

[('a', 1), ('b', 2)]
[('b', 1), ('a', 2)]
[('a', 1), ('c', 2)]
[('c', 1), ('a', 2)]
[('b', 1), ('c', 2)]
[('c', 1), ('b', 2)]

回答 0

注意:此答案是针对上面提出的特定问题的。如果您来自Google,只是在寻找一种使用Python获得笛卡尔积的方法,itertools.product或者您可能正在寻找简单的列表理解方法-请参见其他答案。


假设len(list1) >= len(list2)。然后,你似乎需要的是采取长的所有排列len(list2)list1,并与列表2项匹配。在python中:

import itertools
list1=['a','b','c']
list2=[1,2]

[list(zip(x,list2)) for x in itertools.permutations(list1,len(list2))]

退货

[[('a', 1), ('b', 2)], [('a', 1), ('c', 2)], [('b', 1), ('a', 2)], [('b', 1), ('c', 2)], [('c', 1), ('a', 2)], [('c', 1), ('b', 2)]]

Note: This answer is for the specific question asked above. If you are here from Google and just looking for a way to get a Cartesian product in Python, itertools.product or a simple list comprehension may be what you are looking for – see the other answers.


Suppose len(list1) >= len(list2). Then what you appear to want is to take all permutations of length len(list2) from list1 and match them with items from list2. In python:

import itertools
list1=['a','b','c']
list2=[1,2]

[list(zip(x,list2)) for x in itertools.permutations(list1,len(list2))]

Returns

[[('a', 1), ('b', 2)], [('a', 1), ('c', 2)], [('b', 1), ('a', 2)], [('b', 1), ('c', 2)], [('c', 1), ('a', 2)], [('c', 1), ('b', 2)]]

回答 1

最简单的方法是使用itertools.product

a = ["foo", "melon"]
b = [True, False]
c = list(itertools.product(a, b))
>> [("foo", True), ("foo", False), ("melon", True), ("melon", False)]

The simplest way is to use itertools.product:

a = ["foo", "melon"]
b = [True, False]
c = list(itertools.product(a, b))
>> [("foo", True), ("foo", False), ("melon", True), ("melon", False)]

回答 2

可能比上面最简单的方法更简单:

>>> a = ["foo", "bar"]
>>> b = [1, 2, 3]
>>> [(x,y) for x in a for y in b]  # for a list
[('foo', 1), ('foo', 2), ('foo', 3), ('bar', 1), ('bar', 2), ('bar', 3)]
>>> ((x,y) for x in a for y in b)  # for a generator if you worry about memory or time complexity.
<generator object <genexpr> at 0x1048de850>

没有任何进口

May be simpler than the simplest one above:

>>> a = ["foo", "bar"]
>>> b = [1, 2, 3]
>>> [(x,y) for x in a for y in b]  # for a list
[('foo', 1), ('foo', 2), ('foo', 3), ('bar', 1), ('bar', 2), ('bar', 3)]
>>> ((x,y) for x in a for y in b)  # for a generator if you worry about memory or time complexity.
<generator object <genexpr> at 0x1048de850>

without any import


回答 3

我一直在寻找一个仅由唯一组合乘以自身的列表,该组合是作为此功能提供的。

import itertools
itertools.combinations(list, n_times)

这里作为Python文档 itertools的摘录,可能会帮助您找到所需的内容。

Combinatoric generators:

Iterator                                 | Results
-----------------------------------------+----------------------------------------
product(p, q, ... [repeat=1])            | cartesian product, equivalent to a 
                                         |   nested for-loop
-----------------------------------------+----------------------------------------
permutations(p[, r])                     | r-length tuples, all possible 
                                         |   orderings, no repeated elements
-----------------------------------------+----------------------------------------
combinations(p, r)                       | r-length tuples, in sorted order, no 
                                         |   repeated elements
-----------------------------------------+----------------------------------------
combinations_with_replacement(p, r)      | r-length tuples, in sorted order, 
                                         | with repeated elements
-----------------------------------------+----------------------------------------
product('ABCD', repeat=2)                | AA AB AC AD BA BB BC BD CA CB CC CD DA DB DC DD
permutations('ABCD', 2)                  | AB AC AD BA BC BD CA CB CD DA DB DC
combinations('ABCD', 2)                  | AB AC AD BC BD CD
combinations_with_replacement('ABCD', 2) | AA AB AC AD BB BC BD CC CD DD

I was looking for a list multiplied by itself with only unique combinations, which is provided as this function.

import itertools
itertools.combinations(list, n_times)

Here as an excerpt from the Python docs on itertools That might help you find what your looking for.

Combinatoric generators:

Iterator                                 | Results
-----------------------------------------+----------------------------------------
product(p, q, ... [repeat=1])            | cartesian product, equivalent to a 
                                         |   nested for-loop
-----------------------------------------+----------------------------------------
permutations(p[, r])                     | r-length tuples, all possible 
                                         |   orderings, no repeated elements
-----------------------------------------+----------------------------------------
combinations(p, r)                       | r-length tuples, in sorted order, no 
                                         |   repeated elements
-----------------------------------------+----------------------------------------
combinations_with_replacement(p, r)      | r-length tuples, in sorted order, 
                                         | with repeated elements
-----------------------------------------+----------------------------------------
product('ABCD', repeat=2)                | AA AB AC AD BA BB BC BD CA CB CC CD DA DB DC DD
permutations('ABCD', 2)                  | AB AC AD BA BC BD CA CB CD DA DB DC
combinations('ABCD', 2)                  | AB AC AD BC BD CD
combinations_with_replacement('ABCD', 2) | AA AB AC AD BB BC BD CC CD DD

回答 4

您可能想尝试一个单行列表理解:

>>> [name+number for name in 'ab' for number in '12']
['a1', 'a2', 'b1', 'b2']
>>> [name+number for name in 'abc' for number in '12']
['a1', 'a2', 'b1', 'b2', 'c1', 'c2']

You might want to try a one line list comprehension:

>>> [name+number for name in 'ab' for number in '12']
['a1', 'a2', 'b1', 'b2']
>>> [name+number for name in 'abc' for number in '12']
['a1', 'a2', 'b1', 'b2', 'c1', 'c2']

回答 5

找出大量列表的所有组合的最佳方法是:

import itertools
from pprint import pprint

inputdata = [
    ['a', 'b', 'c'],
    ['d'],
    ['e', 'f'],
]
result = list(itertools.product(*inputdata))
pprint(result)

结果将是:

[('a', 'd', 'e'),
 ('a', 'd', 'f'),
 ('b', 'd', 'e'),
 ('b', 'd', 'f'),
 ('c', 'd', 'e'),
 ('c', 'd', 'f')]

the best way to find out all the combinations for large number of lists is:

import itertools
from pprint import pprint

inputdata = [
    ['a', 'b', 'c'],
    ['d'],
    ['e', 'f'],
]
result = list(itertools.product(*inputdata))
pprint(result)

the result will be:

[('a', 'd', 'e'),
 ('a', 'd', 'f'),
 ('b', 'd', 'e'),
 ('b', 'd', 'f'),
 ('c', 'd', 'e'),
 ('c', 'd', 'f')]

回答 6

或KISS回答的简短清单:

[(i, j) for i in list1 for j in list2]

性能不如itertools,但您使用的是python,因此性能已经不是您的头等大事…

我也喜欢所有其他答案!

Or the KISS answer for short lists:

[(i, j) for i in list1 for j in list2]

Not as performant as itertools but you’re using python so performance is already not your top concern…

I like all the other answers too!


回答 7

对interjay答案的微小改进,使结果成为一个扁平的列表。

>>> list3 = [zip(x,list2) for x in itertools.permutations(list1,len(list2))]
>>> import itertools
>>> chain = itertools.chain(*list3)
>>> list4 = list(chain)
[('a', 1), ('b', 2), ('a', 1), ('c', 2), ('b', 1), ('a', 2), ('b', 1), ('c', 2), ('c', 1), ('a', 2), ('c', 1), ('b', 2)]

来自此链接的参考

a tiny improvement for the answer from interjay, to make the result as a flatten list.

>>> list3 = [zip(x,list2) for x in itertools.permutations(list1,len(list2))]
>>> import itertools
>>> chain = itertools.chain(*list3)
>>> list4 = list(chain)
[('a', 1), ('b', 2), ('a', 1), ('c', 2), ('b', 1), ('a', 2), ('b', 1), ('c', 2), ('c', 1), ('a', 2), ('c', 1), ('b', 2)]

reference from this link


回答 8

没有itertools

[(list1[i], list2[j]) for i in xrange(len(list1)) for j in xrange(len(list2))]

Without itertools

[(list1[i], list2[j]) for i in xrange(len(list1)) for j in xrange(len(list2))]

回答 9

回答问题“给定两个列表,从每个列表中找到一个项目对的所有可能的排列”,并使用基本的Python功能(即,没有itertools),因此可以轻松地复制其他编程语言:

def rec(a, b, ll, size):
    ret = []
    for i,e in enumerate(a):
        for j,f in enumerate(b):
            l = [e+f]
            new_l = rec(a[i+1:], b[:j]+b[j+1:], ll, size)
            if not new_l:
                ret.append(l)
            for k in new_l:
                l_k = l + k
                ret.append(l_k)
                if len(l_k) == size:
                    ll.append(l_k)
    return ret

a = ['a','b','c']
b = ['1','2']
ll = []
rec(a,b,ll, min(len(a),len(b)))
print(ll)

退货

[['a1', 'b2'], ['a1', 'c2'], ['a2', 'b1'], ['a2', 'c1'], ['b1', 'c2'], ['b2', 'c1']]

Answering the question “given two lists, find all possible permutations of pairs of one item from each list” and using basic Python functionality (i.e., without itertools) and, hence, making it easy to replicate for other programming languages:

def rec(a, b, ll, size):
    ret = []
    for i,e in enumerate(a):
        for j,f in enumerate(b):
            l = [e+f]
            new_l = rec(a[i+1:], b[:j]+b[j+1:], ll, size)
            if not new_l:
                ret.append(l)
            for k in new_l:
                l_k = l + k
                ret.append(l_k)
                if len(l_k) == size:
                    ll.append(l_k)
    return ret

a = ['a','b','c']
b = ['1','2']
ll = []
rec(a,b,ll, min(len(a),len(b)))
print(ll)

Returns

[['a1', 'b2'], ['a1', 'c2'], ['a2', 'b1'], ['a2', 'c1'], ['b1', 'c2'], ['b2', 'c1']]

回答 10

更好的答案仅适用于所提供列表的特定长度。

这是一个适用于任何长度输入的版本。就组合和置换的数学概念而言,这也使算法更加清晰。

from itertools import combinations, permutations
list1 = ['1', '2']
list2 = ['A', 'B', 'C']

num_elements = min(len(list1), len(list2))
list1_combs = list(combinations(list1, num_elements))
list2_perms = list(permutations(list2, num_elements))
result = [
  tuple(zip(perm, comb))
  for comb in list1_combs
  for perm in list2_perms
]

for idx, ((l11, l12), (l21, l22)) in enumerate(result):
  print(f'{idx}: {l11}{l12} {l21}{l22}')

输出:

0: A1 B2
1: A1 C2
2: B1 A2
3: B1 C2
4: C1 A2
5: C1 B2

The better answers to this only work for specific lengths of lists that are provided.

Here’s a version that works for any lengths of input. It also makes the algorithm clear in terms of the mathematical concepts of combination and permutation.

from itertools import combinations, permutations
list1 = ['1', '2']
list2 = ['A', 'B', 'C']

num_elements = min(len(list1), len(list2))
list1_combs = list(combinations(list1, num_elements))
list2_perms = list(permutations(list2, num_elements))
result = [
  tuple(zip(perm, comb))
  for comb in list1_combs
  for perm in list2_perms
]

for idx, ((l11, l12), (l21, l22)) in enumerate(result):
  print(f'{idx}: {l11}{l12} {l21}{l22}')

This outputs:

0: A1 B2
1: A1 C2
2: B1 A2
3: B1 C2
4: C1 A2
5: C1 B2

清单清单的所有组合

问题:清单清单的所有组合

我基本上是在寻找组合的 python版本List<List<int>>

给定一个列表列表,我需要一个新列表,该列表给出列表之间所有可能的项目组合。

[[1,2,3],[4,5,6],[7,8,9,10]] -> [[1,4,7],[1,4,8],...,[3,6,10]]

列表的数量是未知的,因此我需要适用于所有情况的东西。优雅奖励积分!

I’m basically looking for a python version of Combination of List<List<int>>

Given a list of lists, I need a new list that gives all the possible combinations of items between the lists.

[[1,2,3],[4,5,6],[7,8,9,10]] -> [[1,4,7],[1,4,8],...,[3,6,10]]

The number of lists is unknown, so I need something that works for all cases. Bonus points for elegance!


回答 0

您需要itertools.product

>>> import itertools
>>> a = [[1,2,3],[4,5,6],[7,8,9,10]]
>>> list(itertools.product(*a))
[(1, 4, 7), (1, 4, 8), (1, 4, 9), (1, 4, 10), (1, 5, 7), (1, 5, 8), (1, 5, 9), (1, 5, 10), (1, 6, 7), (1, 6, 8), (1, 6, 9), (1, 6, 10), (2, 4, 7), (2, 4, 8), (2, 4, 9), (2, 4, 10), (2, 5, 7), (2, 5, 8), (2, 5, 9), (2, 5, 10), (2, 6, 7), (2, 6, 8), (2, 6, 9), (2, 6, 10), (3, 4, 7), (3, 4, 8), (3, 4, 9), (3, 4, 10), (3, 5, 7), (3, 5, 8), (3, 5, 9), (3, 5, 10), (3, 6, 7), (3, 6, 8), (3, 6, 9), (3, 6, 10)]

you need itertools.product:

>>> import itertools
>>> a = [[1,2,3],[4,5,6],[7,8,9,10]]
>>> list(itertools.product(*a))
[(1, 4, 7), (1, 4, 8), (1, 4, 9), (1, 4, 10), (1, 5, 7), (1, 5, 8), (1, 5, 9), (1, 5, 10), (1, 6, 7), (1, 6, 8), (1, 6, 9), (1, 6, 10), (2, 4, 7), (2, 4, 8), (2, 4, 9), (2, 4, 10), (2, 5, 7), (2, 5, 8), (2, 5, 9), (2, 5, 10), (2, 6, 7), (2, 6, 8), (2, 6, 9), (2, 6, 10), (3, 4, 7), (3, 4, 8), (3, 4, 9), (3, 4, 10), (3, 5, 7), (3, 5, 8), (3, 5, 9), (3, 5, 10), (3, 6, 7), (3, 6, 8), (3, 6, 9), (3, 6, 10)]

回答 1

最优雅的解决方案是在python 2.6中使用itertools.product

如果您不使用Python 2.6,itertools.product的文档实际上会显示等效的功能,以“手动”方式完成产品:

def product(*args, **kwds):
    # product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy
    # product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111
    pools = map(tuple, args) * kwds.get('repeat', 1)
    result = [[]]
    for pool in pools:
        result = [x+[y] for x in result for y in pool]
    for prod in result:
        yield tuple(prod)

The most elegant solution is to use itertools.product in python 2.6.

If you aren’t using Python 2.6, the docs for itertools.product actually show an equivalent function to do the product the “manual” way:

def product(*args, **kwds):
    # product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy
    # product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111
    pools = map(tuple, args) * kwds.get('repeat', 1)
    result = [[]]
    for pool in pools:
        result = [x+[y] for x in result for y in pool]
    for prod in result:
        yield tuple(prod)

回答 2

listOLists = [[1,2,3],[4,5,6],[7,8,9,10]]
for list in itertools.product(*listOLists):
  print list;

希望您能像我初次接触时一样优雅。

listOLists = [[1,2,3],[4,5,6],[7,8,9,10]]
for list in itertools.product(*listOLists):
  print list;

I hope you find that as elegant as I did when I first encountered it.


回答 3

Numpy可以做到:

 >>> import numpy
 >>> a = [[1,2,3],[4,5,6],[7,8,9,10]]
 >>> [list(x) for x in numpy.array(numpy.meshgrid(*a)).T.reshape(-1,len(a))]
[[ 1, 4, 7], [1, 5, 7], [1, 6, 7], ....]

Numpy can do it:

 >>> import numpy
 >>> a = [[1,2,3],[4,5,6],[7,8,9,10]]
 >>> [list(x) for x in numpy.array(numpy.meshgrid(*a)).T.reshape(-1,len(a))]
[[ 1, 4, 7], [1, 5, 7], [1, 6, 7], ....]

回答 4

直接执行此任务的递归没有什么问题,如果您需要使用字符串的版本,则可以满足您的需求:

combinations = []

def combine(terms, accum):
    last = (len(terms) == 1)
    n = len(terms[0])
    for i in range(n):
        item = accum + terms[0][i]
        if last:
            combinations.append(item)
        else:
            combine(terms[1:], item)


>>> a = [['ab','cd','ef'],['12','34','56']]
>>> combine(a, '')
>>> print(combinations)
['ab12', 'ab34', 'ab56', 'cd12', 'cd34', 'cd56', 'ef12', 'ef34', 'ef56']

Nothing wrong with straight up recursion for this task, and if you need a version that works with strings, this might fit your needs:

combinations = []

def combine(terms, accum):
    last = (len(terms) == 1)
    n = len(terms[0])
    for i in range(n):
        item = accum + terms[0][i]
        if last:
            combinations.append(item)
        else:
            combine(terms[1:], item)


>>> a = [['ab','cd','ef'],['12','34','56']]
>>> combine(a, '')
>>> print(combinations)
['ab12', 'ab34', 'ab56', 'cd12', 'cd34', 'cd56', 'ef12', 'ef34', 'ef56']

回答 5

一个人可以为此使用基本的python。该代码需要一个函数来拉平列表的列表:

def flatten(B):    # function needed for code below;
    A = []
    for i in B:
        if type(i) == list: A.extend(i)
        else: A.append(i)
    return A

然后可以运行:

L = [[1,2,3],[4,5,6],[7,8,9,10]]

outlist =[]; templist =[[]]
for sublist in L:
    outlist = templist; templist = [[]]
    for sitem in sublist:
        for oitem in outlist:
            newitem = [oitem]
            if newitem == [[]]: newitem = [sitem]
            else: newitem = [newitem[0], sitem]
            templist.append(flatten(newitem))

outlist = list(filter(lambda x: len(x)==len(L), templist))  # remove some partial lists that also creep in;
print(outlist)

输出:

[[1, 4, 7], [2, 4, 7], [3, 4, 7], 
[1, 5, 7], [2, 5, 7], [3, 5, 7], 
[1, 6, 7], [2, 6, 7], [3, 6, 7], 
[1, 4, 8], [2, 4, 8], [3, 4, 8], 
[1, 5, 8], [2, 5, 8], [3, 5, 8], 
[1, 6, 8], [2, 6, 8], [3, 6, 8], 
[1, 4, 9], [2, 4, 9], [3, 4, 9], 
[1, 5, 9], [2, 5, 9], [3, 5, 9], 
[1, 6, 9], [2, 6, 9], [3, 6, 9], 
[1, 4, 10], [2, 4, 10], [3, 4, 10], 
[1, 5, 10], [2, 5, 10], [3, 5, 10], 
[1, 6, 10], [2, 6, 10], [3, 6, 10]]

One can use base python for this. The code needs a function to flatten lists of lists:

def flatten(B):    # function needed for code below;
    A = []
    for i in B:
        if type(i) == list: A.extend(i)
        else: A.append(i)
    return A

Then one can run:

L = [[1,2,3],[4,5,6],[7,8,9,10]]

outlist =[]; templist =[[]]
for sublist in L:
    outlist = templist; templist = [[]]
    for sitem in sublist:
        for oitem in outlist:
            newitem = [oitem]
            if newitem == [[]]: newitem = [sitem]
            else: newitem = [newitem[0], sitem]
            templist.append(flatten(newitem))

outlist = list(filter(lambda x: len(x)==len(L), templist))  # remove some partial lists that also creep in;
print(outlist)

Output:

[[1, 4, 7], [2, 4, 7], [3, 4, 7], 
[1, 5, 7], [2, 5, 7], [3, 5, 7], 
[1, 6, 7], [2, 6, 7], [3, 6, 7], 
[1, 4, 8], [2, 4, 8], [3, 4, 8], 
[1, 5, 8], [2, 5, 8], [3, 5, 8], 
[1, 6, 8], [2, 6, 8], [3, 6, 8], 
[1, 4, 9], [2, 4, 9], [3, 4, 9], 
[1, 5, 9], [2, 5, 9], [3, 5, 9], 
[1, 6, 9], [2, 6, 9], [3, 6, 9], 
[1, 4, 10], [2, 4, 10], [3, 4, 10], 
[1, 5, 10], [2, 5, 10], [3, 5, 10], 
[1, 6, 10], [2, 6, 10], [3, 6, 10]]

回答 6

from itertools import product 
list_vals = [['Brand Acronym:CBIQ', 'Brand Acronym :KMEFIC'],['Brand Country:DXB','Brand Country:BH']]
list(product(*list_vals))

输出:

[(”品牌缩写:CBIQ’,’品牌国家:DXB’),
(’品牌缩写:CBIQ’,’品牌国家:BH’),
(’品牌缩写:KMEFIC’,’品牌国家:DXB’),
( ‘品牌缩写:KMEFIC’,’品牌国家:BH’)]

from itertools import product 
list_vals = [['Brand Acronym:CBIQ', 'Brand Acronym :KMEFIC'],['Brand Country:DXB','Brand Country:BH']]
list(product(*list_vals))

Output:

[(‘Brand Acronym:CBIQ’, ‘Brand Country :DXB’),
(‘Brand Acronym:CBIQ’, ‘Brand Country:BH’),
(‘Brand Acronym :KMEFIC’, ‘Brand Country :DXB’),
(‘Brand Acronym :KMEFIC’, ‘Brand Country:BH’)]


如何获得列表元素的所有可能组合?

问题:如何获得列表元素的所有可能组合?

我有一个包含15个数字的列表,我需要编写一些代码来生成这些数字的所有32,768个组合。

我已经找到了一些代码(通过Googling),这些代码显然可以满足我的需求,但是我发现代码相当不透明并且对使用它很谨慎。另外,我觉得必须有一个更优雅的解决方案。

对我而言,唯一发生的就是循环遍历十进制整数1–32768,并将其转换为二进制,然后使用二进制表示形式作为筛选器来选择适当的数字。

有谁知道更好的方法吗?使用map(),也许?

I have a list with 15 numbers in, and I need to write some code that produces all 32,768 combinations of those numbers.

I’ve found some code (by Googling) that apparently does what I’m looking for, but I found the code fairly opaque and am wary of using it. Plus I have a feeling there must be a more elegant solution.

The only thing that occurs to me would be to just loop through the decimal integers 1–32768 and convert those to binary, and use the binary representation as a filter to pick out the appropriate numbers.

Does anyone know of a better way? Using map(), maybe?


回答 0

看看itertools.combinations

itertools.combinations(iterable, r)

从可迭代的输入中返回元素的r长度子序列。

组合按字典顺序排序。因此,如果将输入的iterable排序,则将按排序顺序生成组合元组。

从2.6开始,包括电池!

Have a look at itertools.combinations:

itertools.combinations(iterable, r)

Return r length subsequences of elements from the input iterable.

Combinations are emitted in lexicographic sort order. So, if the input iterable is sorted, the combination tuples will be produced in sorted order.

Since 2.6, batteries are included!


回答 1

这个答案错过了一个方面:OP要求所有组合……而不仅仅是长度“ r”的组合。

因此,您要么必须遍历所有长度“ L”:

import itertools

stuff = [1, 2, 3]
for L in range(0, len(stuff)+1):
    for subset in itertools.combinations(stuff, L):
        print(subset)

或者-如果您想变得眼花or乱(或者想让以后阅读代码的人都屈服)-您可以生成“ combinations()”生成器链,然后进行迭代:

from itertools import chain, combinations
def all_subsets(ss):
    return chain(*map(lambda x: combinations(ss, x), range(0, len(ss)+1)))

for subset in all_subsets(stuff):
    print(subset)

This answer missed one aspect: the OP asked for ALL combinations… not just combinations of length “r”.

So you’d either have to loop through all lengths “L”:

import itertools

stuff = [1, 2, 3]
for L in range(0, len(stuff)+1):
    for subset in itertools.combinations(stuff, L):
        print(subset)

Or — if you want to get snazzy (or bend the brain of whoever reads your code after you) — you can generate the chain of “combinations()” generators, and iterate through that:

from itertools import chain, combinations
def all_subsets(ss):
    return chain(*map(lambda x: combinations(ss, x), range(0, len(ss)+1)))

for subset in all_subsets(stuff):
    print(subset)

回答 2

这是一个懒惰的单行代码,也使用itertools:

from itertools import compress, product

def combinations(items):
    return ( set(compress(items,mask)) for mask in product(*[[0,1]]*len(items)) )
    # alternative:                      ...in product([0,1], repeat=len(items)) )

答案背后的主要思想是:有2 ^ N个组合-与长度为N的二进制字符串的数目相同。对于每个二进制字符串,请选择与“ 1”相对应的所有元素。

items=abc * mask=###
 |
 V
000 -> 
001 ->   c
010 ->  b
011 ->  bc
100 -> a
101 -> a c
110 -> ab
111 -> abc

注意事项:

  • 这就需要你可以调用len(...)items(解决方法:如果items是这样的迭代就像一台生成器,与第一把它变成一个列表items=list(_itemsArg)
  • 这要求迭代的顺序items不是随机的(解决方法:不要疯了)
  • 这就要求项目是独一无二的,要不然{2,2,1}{2,1,1}都将崩溃{2,1}(解决方法:使用collections.Counter作为一个下拉更换set;它基本上是一个多集…尽管你可能需要以后使用tuple(sorted(Counter(...).elements())),如果你需要它是可哈希)

演示版

>>> list(combinations(range(4)))
[set(), {3}, {2}, {2, 3}, {1}, {1, 3}, {1, 2}, {1, 2, 3}, {0}, {0, 3}, {0, 2}, {0, 2, 3}, {0, 1}, {0, 1, 3}, {0, 1, 2}, {0, 1, 2, 3}]

>>> list(combinations('abcd'))
[set(), {'d'}, {'c'}, {'c', 'd'}, {'b'}, {'b', 'd'}, {'c', 'b'}, {'c', 'b', 'd'}, {'a'}, {'a', 'd'}, {'a', 'c'}, {'a', 'c', 'd'}, {'a', 'b'}, {'a', 'b', 'd'}, {'a', 'c', 'b'}, {'a', 'c', 'b', 'd'}]

Here’s a lazy one-liner, also using itertools:

from itertools import compress, product

def combinations(items):
    return ( set(compress(items,mask)) for mask in product(*[[0,1]]*len(items)) )
    # alternative:                      ...in product([0,1], repeat=len(items)) )

Main idea behind this answer: there are 2^N combinations — same as the number of binary strings of length N. For each binary string, you pick all elements corresponding to a “1”.

items=abc * mask=###
 |
 V
000 -> 
001 ->   c
010 ->  b
011 ->  bc
100 -> a
101 -> a c
110 -> ab
111 -> abc

Things to consider:

  • This requires that you can call len(...) on items (workaround: if items is something like an iterable like a generator, turn it into a list first with items=list(_itemsArg))
  • This requires that the order of iteration on items is not random (workaround: don’t be insane)
  • This requires that the items are unique, or else {2,2,1} and {2,1,1} will both collapse to {2,1} (workaround: use collections.Counter as a drop-in replacement for set; it’s basically a multiset… though you may need to later use tuple(sorted(Counter(...).elements())) if you need it to be hashable)

Demo

>>> list(combinations(range(4)))
[set(), {3}, {2}, {2, 3}, {1}, {1, 3}, {1, 2}, {1, 2, 3}, {0}, {0, 3}, {0, 2}, {0, 2, 3}, {0, 1}, {0, 1, 3}, {0, 1, 2}, {0, 1, 2, 3}]

>>> list(combinations('abcd'))
[set(), {'d'}, {'c'}, {'c', 'd'}, {'b'}, {'b', 'd'}, {'c', 'b'}, {'c', 'b', 'd'}, {'a'}, {'a', 'd'}, {'a', 'c'}, {'a', 'c', 'd'}, {'a', 'b'}, {'a', 'b', 'd'}, {'a', 'c', 'b'}, {'a', 'c', 'b', 'd'}]

回答 3

在@Dan H 极力支持的答案的评论powerset()中,itertools文档中提到了该配方—包括Dan本人的配方。但是,到目前为止,还没有人将其发布为答案。由于它可能是解决问题的最佳方法,即使不是最好的方法之一,并且在另一位评论者的鼓励下,它显示如下。该函数产生的所有列表中的元件的独特组合长度可能的(包括那些含有零和所有的元素)。

注意:如果,微妙的不同,目标是获得唯一的独特元素的组合,改线s = list(iterable),以s = list(set(iterable))消除任何重复的元素。无论如何,iterable最终会变成的事实list意味着它将与生成器一起使用(与其他几个答案不同)。

from itertools import chain, combinations

def powerset(iterable):
    "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
    s = list(iterable)  # allows duplicate elements
    return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))

stuff = [1, 2, 3]
for i, combo in enumerate(powerset(stuff), 1):
    print('combo #{}: {}'.format(i, combo))

输出:

combo #1: ()
combo #2: (1,)
combo #3: (2,)
combo #4: (3,)
combo #5: (1, 2)
combo #6: (1, 3)
combo #7: (2, 3)
combo #8: (1, 2, 3)

In comments under the highly upvoted answer by @Dan H, mention is made of the powerset() recipe in the itertools documentation—including one by Dan himself. However, so far no one has posted it as an answer. Since it’s probably one of the better if not the best approach to the problem—and given a little encouragement from another commenter, it’s shown below. The function produces all unique combinations of the list elements of every length possible (including those containing zero and all the elements).

Note: If the, subtly different, goal is to obtain only combinations of unique elements, change the line s = list(iterable) to s = list(set(iterable)) to eliminate any duplicate elements. Regardless, the fact that the iterable is ultimately turned into a list means it will work with generators (unlike several of the other answers).

from itertools import chain, combinations

def powerset(iterable):
    "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
    s = list(iterable)  # allows duplicate elements
    return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))

stuff = [1, 2, 3]
for i, combo in enumerate(powerset(stuff), 1):
    print('combo #{}: {}'.format(i, combo))

Output:

combo #1: ()
combo #2: (1,)
combo #3: (2,)
combo #4: (3,)
combo #5: (1, 2)
combo #6: (1, 3)
combo #7: (2, 3)
combo #8: (1, 2, 3)

回答 4

这是使用递归的一种:

>>> import copy
>>> def combinations(target,data):
...     for i in range(len(data)):
...         new_target = copy.copy(target)
...         new_data = copy.copy(data)
...         new_target.append(data[i])
...         new_data = data[i+1:]
...         print new_target
...         combinations(new_target,
...                      new_data)
...                      
... 
>>> target = []
>>> data = ['a','b','c','d']
>>> 
>>> combinations(target,data)
['a']
['a', 'b']
['a', 'b', 'c']
['a', 'b', 'c', 'd']
['a', 'b', 'd']
['a', 'c']
['a', 'c', 'd']
['a', 'd']
['b']
['b', 'c']
['b', 'c', 'd']
['b', 'd']
['c']
['c', 'd']
['d']

Here is one using recursion:

>>> import copy
>>> def combinations(target,data):
...     for i in range(len(data)):
...         new_target = copy.copy(target)
...         new_data = copy.copy(data)
...         new_target.append(data[i])
...         new_data = data[i+1:]
...         print new_target
...         combinations(new_target,
...                      new_data)
...                      
... 
>>> target = []
>>> data = ['a','b','c','d']
>>> 
>>> combinations(target,data)
['a']
['a', 'b']
['a', 'b', 'c']
['a', 'b', 'c', 'd']
['a', 'b', 'd']
['a', 'c']
['a', 'c', 'd']
['a', 'd']
['b']
['b', 'c']
['b', 'c', 'd']
['b', 'd']
['c']
['c', 'd']
['d']

回答 5

这种单行代码为您提供了所有组合(如果原始列表/集合包含不同的元素,则在0n项之间n),并使用本机方法itertools.combinations

Python 2

from itertools import combinations

input = ['a', 'b', 'c', 'd']

output = sum([map(list, combinations(input, i)) for i in range(len(input) + 1)], [])

Python 3

from itertools import combinations

input = ['a', 'b', 'c', 'd']

output = sum([list(map(list, combinations(input, i))) for i in range(len(input) + 1)], [])

输出将是:

[[],
 ['a'],
 ['b'],
 ['c'],
 ['d'],
 ['a', 'b'],
 ['a', 'c'],
 ['a', 'd'],
 ['b', 'c'],
 ['b', 'd'],
 ['c', 'd'],
 ['a', 'b', 'c'],
 ['a', 'b', 'd'],
 ['a', 'c', 'd'],
 ['b', 'c', 'd'],
 ['a', 'b', 'c', 'd']]

在线尝试:

http://ideone.com/COghfX

This one-liner gives you all the combinations (between 0 and n items if the original list/set contains n distinct elements) and uses the native method itertools.combinations:

Python 2

from itertools import combinations

input = ['a', 'b', 'c', 'd']

output = sum([map(list, combinations(input, i)) for i in range(len(input) + 1)], [])

Python 3

from itertools import combinations

input = ['a', 'b', 'c', 'd']

output = sum([list(map(list, combinations(input, i))) for i in range(len(input) + 1)], [])

The output will be:

[[],
 ['a'],
 ['b'],
 ['c'],
 ['d'],
 ['a', 'b'],
 ['a', 'c'],
 ['a', 'd'],
 ['b', 'c'],
 ['b', 'd'],
 ['c', 'd'],
 ['a', 'b', 'c'],
 ['a', 'b', 'd'],
 ['a', 'c', 'd'],
 ['b', 'c', 'd'],
 ['a', 'b', 'c', 'd']]

Try it online:

http://ideone.com/COghfX


回答 6

我同意Dan H的观点,Ben确实要求所有组合。itertools.combinations()没有给出所有组合。

另一个问题是,如果可迭代的输入很大,则最好返回一个生成器而不是列表中的所有内容:

iterable = range(10)
for s in xrange(len(iterable)+1):
  for comb in itertools.combinations(iterable, s):
    yield comb

I agree with Dan H that Ben indeed asked for all combinations. itertools.combinations() does not give all combinations.

Another issue is, if the input iterable is big, it is perhaps better to return a generator instead of everything in a list:

iterable = range(10)
for s in xrange(len(iterable)+1):
  for comb in itertools.combinations(iterable, s):
    yield comb

回答 7

这是一种可以轻松转移到支持递归的所有编程语言的方法(没有itertools,没有yield,没有列表理解)

def combs(a):
    if len(a) == 0:
        return [[]]
    cs = []
    for c in combs(a[1:]):
        cs += [c, c+[a[0]]]
    return cs

>>> combs([1,2,3,4,5])
[[], [1], [2], [2, 1], [3], [3, 1], [3, 2], ..., [5, 4, 3, 2, 1]]

This is an approach that can be easily transfered to all programming languages supporting recursion (no itertools, no yield, no list comprehension):

def combs(a):
    if len(a) == 0:
        return [[]]
    cs = []
    for c in combs(a[1:]):
        cs += [c, c+[a[0]]]
    return cs

>>> combs([1,2,3,4,5])
[[], [1], [2], [2, 1], [3], [3, 1], [3, 2], ..., [5, 4, 3, 2, 1]]

回答 8

您可以使用以下简单代码在python中生成列表的所有组合

import itertools

a = [1,2,3,4]
for i in xrange(0,len(a)+1):
   print list(itertools.combinations(a,i))

结果将是:

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

You can generating all combinations of a list in python using this simple code

import itertools

a = [1,2,3,4]
for i in xrange(0,len(a)+1):
   print list(itertools.combinations(a,i))

Result would be :

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

回答 9

我认为我可以为那些寻求答案的人添加此功能,而无需导入itertools或任何其他额外的库。

def powerSet(items):
    """
    Power set generator: get all possible combinations of a list’s elements

    Input:
        items is a list
    Output:
        returns 2**n combination lists one at a time using a generator 

    Reference: edx.org 6.00.2x Lecture 2 - Decision Trees and dynamic programming
    """

    N = len(items)
    # enumerate the 2**N possible combinations
    for i in range(2**N):
        combo = []
        for j in range(N):
            # test bit jth of integer i
            if (i >> j) % 2 == 1:
                combo.append(items[j])
        yield combo

简单良率生成器用法:

for i in powerSet([1,2,3,4]):
    print (i, ", ",  end="")

上面用法示例的输出:

[],[1],[2],[1,2],[3],[1,3],[2,3],[1,2,3],[4],[1,4] ,[2、4],[1、2、4],[3、4],[1、3、4],[2、3、4],[1、2、3、4],

I thought I would add this function for those seeking an answer without importing itertools or any other extra libraries.

def powerSet(items):
    """
    Power set generator: get all possible combinations of a list’s elements

    Input:
        items is a list
    Output:
        returns 2**n combination lists one at a time using a generator 

    Reference: edx.org 6.00.2x Lecture 2 - Decision Trees and dynamic programming
    """

    N = len(items)
    # enumerate the 2**N possible combinations
    for i in range(2**N):
        combo = []
        for j in range(N):
            # test bit jth of integer i
            if (i >> j) % 2 == 1:
                combo.append(items[j])
        yield combo

Simple Yield Generator Usage:

for i in powerSet([1,2,3,4]):
    print (i, ", ",  end="")

Output from Usage example above:

[] , [1] , [2] , [1, 2] , [3] , [1, 3] , [2, 3] , [1, 2, 3] , [4] , [1, 4] , [2, 4] , [1, 2, 4] , [3, 4] , [1, 3, 4] , [2, 3, 4] , [1, 2, 3, 4] ,


回答 10

这是涉及使用itertools.combinations函数的另一种解决方案(单行),但是这里我们使用了双重列表理解(与for循环或求和相对):

def combs(x):
    return [c for i in range(len(x)+1) for c in combinations(x,i)]

演示:

>>> combs([1,2,3,4])
[(), 
 (1,), (2,), (3,), (4,), 
 (1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4), 
 (1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4), 
 (1, 2, 3, 4)]

Here is yet another solution (one-liner), involving using the itertools.combinations function, but here we use a double list comprehension (as opposed to a for loop or sum):

def combs(x):
    return [c for i in range(len(x)+1) for c in combinations(x,i)]

Demo:

>>> combs([1,2,3,4])
[(), 
 (1,), (2,), (3,), (4,), 
 (1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4), 
 (1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4), 
 (1, 2, 3, 4)]

回答 11

from itertools import permutations, combinations


features = ['A', 'B', 'C']
tmp = []
for i in range(len(features)):
    oc = combinations(features, i + 1)
    for c in oc:
        tmp.append(list(c))

输出

[
 ['A'],
 ['B'],
 ['C'],
 ['A', 'B'],
 ['A', 'C'],
 ['B', 'C'],
 ['A', 'B', 'C']
]
from itertools import permutations, combinations


features = ['A', 'B', 'C']
tmp = []
for i in range(len(features)):
    oc = combinations(features, i + 1)
    for c in oc:
        tmp.append(list(c))

output

[
 ['A'],
 ['B'],
 ['C'],
 ['A', 'B'],
 ['A', 'C'],
 ['B', 'C'],
 ['A', 'B', 'C']
]

回答 12

下面是一个“标准递归答案”,类似于其他类似的答案https://stackoverflow.com/a/23743696/711085。(我们实际上不必担心会耗尽堆栈空间,因为我们不可能处理所有N!个置换。)

它依次访问每个元素,然后接受或离开它(我们可以从该算法直接看到2 ^ N基数)。

def combs(xs, i=0):
    if i==len(xs):
        yield ()
        return
    for c in combs(xs,i+1):
        yield c
        yield c+(xs[i],)

演示:

>>> list( combs(range(5)) )
[(), (0,), (1,), (1, 0), (2,), (2, 0), (2, 1), (2, 1, 0), (3,), (3, 0), (3, 1), (3, 1, 0), (3, 2), (3, 2, 0), (3, 2, 1), (3, 2, 1, 0), (4,), (4, 0), (4, 1), (4, 1, 0), (4, 2), (4, 2, 0), (4, 2, 1), (4, 2, 1, 0), (4, 3), (4, 3, 0), (4, 3, 1), (4, 3, 1, 0), (4, 3, 2), (4, 3, 2, 0), (4, 3, 2, 1), (4, 3, 2, 1, 0)]

>>> list(sorted( combs(range(5)), key=len))
[(), 
 (0,), (1,), (2,), (3,), (4,), 
 (1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2), (4, 0), (4, 1), (4, 2), (4, 3), 
 (2, 1, 0), (3, 1, 0), (3, 2, 0), (3, 2, 1), (4, 1, 0), (4, 2, 0), (4, 2, 1), (4, 3, 0), (4, 3, 1), (4, 3, 2), 
 (3, 2, 1, 0), (4, 2, 1, 0), (4, 3, 1, 0), (4, 3, 2, 0), (4, 3, 2, 1), 
 (4, 3, 2, 1, 0)]

>>> len(set(combs(range(5))))
32

Below is a “standard recursive answer”, similar to the other similar answer https://stackoverflow.com/a/23743696/711085 . (We don’t realistically have to worry about running out of stack space since there’s no way we could process all N! permutations.)

It visits every element in turn, and either takes it or leaves it (we can directly see the 2^N cardinality from this algorithm).

def combs(xs, i=0):
    if i==len(xs):
        yield ()
        return
    for c in combs(xs,i+1):
        yield c
        yield c+(xs[i],)

Demo:

>>> list( combs(range(5)) )
[(), (0,), (1,), (1, 0), (2,), (2, 0), (2, 1), (2, 1, 0), (3,), (3, 0), (3, 1), (3, 1, 0), (3, 2), (3, 2, 0), (3, 2, 1), (3, 2, 1, 0), (4,), (4, 0), (4, 1), (4, 1, 0), (4, 2), (4, 2, 0), (4, 2, 1), (4, 2, 1, 0), (4, 3), (4, 3, 0), (4, 3, 1), (4, 3, 1, 0), (4, 3, 2), (4, 3, 2, 0), (4, 3, 2, 1), (4, 3, 2, 1, 0)]

>>> list(sorted( combs(range(5)), key=len))
[(), 
 (0,), (1,), (2,), (3,), (4,), 
 (1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2), (4, 0), (4, 1), (4, 2), (4, 3), 
 (2, 1, 0), (3, 1, 0), (3, 2, 0), (3, 2, 1), (4, 1, 0), (4, 2, 0), (4, 2, 1), (4, 3, 0), (4, 3, 1), (4, 3, 2), 
 (3, 2, 1, 0), (4, 2, 1, 0), (4, 3, 1, 0), (4, 3, 2, 0), (4, 3, 2, 1), 
 (4, 3, 2, 1, 0)]

>>> len(set(combs(range(5))))
32

回答 13

使用列表理解:

def selfCombine( list2Combine, length ):
    listCombined = str( ['list2Combine[i' + str( i ) + ']' for i in range( length )] ).replace( "'", '' ) \
                     + 'for i0 in range(len( list2Combine ) )'
    if length > 1:
        listCombined += str( [' for i' + str( i ) + ' in range( i' + str( i - 1 ) + ', len( list2Combine ) )' for i in range( 1, length )] )\
            .replace( "', '", ' ' )\
            .replace( "['", '' )\
            .replace( "']", '' )

    listCombined = '[' + listCombined + ']'
    listCombined = eval( listCombined )

    return listCombined

list2Combine = ['A', 'B', 'C']
listCombined = selfCombine( list2Combine, 2 )

输出为:

['A', 'A']
['A', 'B']
['A', 'C']
['B', 'B']
['B', 'C']
['C', 'C']

Using list comprehension:

def selfCombine( list2Combine, length ):
    listCombined = str( ['list2Combine[i' + str( i ) + ']' for i in range( length )] ).replace( "'", '' ) \
                     + 'for i0 in range(len( list2Combine ) )'
    if length > 1:
        listCombined += str( [' for i' + str( i ) + ' in range( i' + str( i - 1 ) + ', len( list2Combine ) )' for i in range( 1, length )] )\
            .replace( "', '", ' ' )\
            .replace( "['", '' )\
            .replace( "']", '' )

    listCombined = '[' + listCombined + ']'
    listCombined = eval( listCombined )

    return listCombined

list2Combine = ['A', 'B', 'C']
listCombined = selfCombine( list2Combine, 2 )

Output would be:

['A', 'A']
['A', 'B']
['A', 'C']
['B', 'B']
['B', 'C']
['C', 'C']

回答 14

该代码采用带有嵌套列表的简单算法…

# FUNCTION getCombos: To generate all combos of an input list, consider the following sets of nested lists...
#
#           [ [ [] ] ]
#           [ [ [] ], [ [A] ] ]
#           [ [ [] ], [ [A],[B] ],         [ [A,B] ] ]
#           [ [ [] ], [ [A],[B],[C] ],     [ [A,B],[A,C],[B,C] ],                   [ [A,B,C] ] ]
#           [ [ [] ], [ [A],[B],[C],[D] ], [ [A,B],[A,C],[B,C],[A,D],[B,D],[C,D] ], [ [A,B,C],[A,B,D],[A,C,D],[B,C,D] ], [ [A,B,C,D] ] ]
#
#  There is a set of lists for each number of items that will occur in a combo (including an empty set).
#  For each additional item, begin at the back of the list by adding an empty list, then taking the set of
#  lists in the previous column (e.g., in the last list, for sets of 3 items you take the existing set of
#  3-item lists and append to it additional lists created by appending the item (4) to the lists in the
#  next smallest item count set. In this case, for the three sets of 2-items in the previous list. Repeat
#  for each set of lists back to the initial list containing just the empty list.
#

def getCombos(listIn = ['A','B','C','D','E','F'] ):
    listCombos = [ [ [] ] ]     # list of lists of combos, seeded with a list containing only the empty list
    listSimple = []             # list to contain the final returned list of items (e.g., characters)

    for item in listIn:
        listCombos.append([])   # append an emtpy list to the end for each new item added
        for index in xrange(len(listCombos)-1, 0, -1):  # set the index range to work through the list
            for listPrev in listCombos[index-1]:        # retrieve the lists from the previous column
                listCur = listPrev[:]                   # create a new temporary list object to update
                listCur.append(item)                    # add the item to the previous list to make it current
                listCombos[index].append(listCur)       # list length and append it to the current list

                itemCombo = ''                          # Create a str to concatenate list items into a str
                for item in listCur:                    # concatenate the members of the lists to create
                    itemCombo += item                   # create a string of items
                listSimple.append(itemCombo)            # add to the final output list

    return [listSimple, listCombos]
# END getCombos()

This code employs a simple algorithm with nested lists…

# FUNCTION getCombos: To generate all combos of an input list, consider the following sets of nested lists...
#
#           [ [ [] ] ]
#           [ [ [] ], [ [A] ] ]
#           [ [ [] ], [ [A],[B] ],         [ [A,B] ] ]
#           [ [ [] ], [ [A],[B],[C] ],     [ [A,B],[A,C],[B,C] ],                   [ [A,B,C] ] ]
#           [ [ [] ], [ [A],[B],[C],[D] ], [ [A,B],[A,C],[B,C],[A,D],[B,D],[C,D] ], [ [A,B,C],[A,B,D],[A,C,D],[B,C,D] ], [ [A,B,C,D] ] ]
#
#  There is a set of lists for each number of items that will occur in a combo (including an empty set).
#  For each additional item, begin at the back of the list by adding an empty list, then taking the set of
#  lists in the previous column (e.g., in the last list, for sets of 3 items you take the existing set of
#  3-item lists and append to it additional lists created by appending the item (4) to the lists in the
#  next smallest item count set. In this case, for the three sets of 2-items in the previous list. Repeat
#  for each set of lists back to the initial list containing just the empty list.
#

def getCombos(listIn = ['A','B','C','D','E','F'] ):
    listCombos = [ [ [] ] ]     # list of lists of combos, seeded with a list containing only the empty list
    listSimple = []             # list to contain the final returned list of items (e.g., characters)

    for item in listIn:
        listCombos.append([])   # append an emtpy list to the end for each new item added
        for index in xrange(len(listCombos)-1, 0, -1):  # set the index range to work through the list
            for listPrev in listCombos[index-1]:        # retrieve the lists from the previous column
                listCur = listPrev[:]                   # create a new temporary list object to update
                listCur.append(item)                    # add the item to the previous list to make it current
                listCombos[index].append(listCur)       # list length and append it to the current list

                itemCombo = ''                          # Create a str to concatenate list items into a str
                for item in listCur:                    # concatenate the members of the lists to create
                    itemCombo += item                   # create a string of items
                listSimple.append(itemCombo)            # add to the final output list

    return [listSimple, listCombos]
# END getCombos()

回答 15

我知道这是更为实际使用itertools得到所有的组合,但你可以想代码实现这个部分,只有列表中理解,如果你这么碰巧的愿望,给予了很多

对于两对组合:

    lambda l: [(a, b) for i, a in enumerate(l) for b in l[i+1:]]


而且,对于三对组合,就这么简单:

    lambda l: [(a, b, c) for i, a in enumerate(l) for ii, b in enumerate(l[i+1:]) for c in l[i+ii+2:]]


结果与使用itertools.combinations相同:

import itertools
combs_3 = lambda l: [
    (a, b, c) for i, a in enumerate(l) 
    for ii, b in enumerate(l[i+1:]) 
    for c in l[i+ii+2:]
]
data = ((1, 2), 5, "a", None)
print("A:", list(itertools.combinations(data, 3)))
print("B:", combs_3(data))
# A: [((1, 2), 5, 'a'), ((1, 2), 5, None), ((1, 2), 'a', None), (5, 'a', None)]
# B: [((1, 2), 5, 'a'), ((1, 2), 5, None), ((1, 2), 'a', None), (5, 'a', None)]

I know it’s far more practical to use itertools to get the all the combinations, but you can achieve this partly with only list comprehension if you so happen to desire, granted you want to code a lot

For combinations of two pairs:

    lambda l: [(a, b) for i, a in enumerate(l) for b in l[i+1:]]


And, for combinations of three pairs, it’s as easy as this:

    lambda l: [(a, b, c) for i, a in enumerate(l) for ii, b in enumerate(l[i+1:]) for c in l[i+ii+2:]]


The result is identical to using itertools.combinations:
import itertools
combs_3 = lambda l: [
    (a, b, c) for i, a in enumerate(l) 
    for ii, b in enumerate(l[i+1:]) 
    for c in l[i+ii+2:]
]
data = ((1, 2), 5, "a", None)
print("A:", list(itertools.combinations(data, 3)))
print("B:", combs_3(data))
# A: [((1, 2), 5, 'a'), ((1, 2), 5, None), ((1, 2), 'a', None), (5, 'a', None)]
# B: [((1, 2), 5, 'a'), ((1, 2), 5, None), ((1, 2), 'a', None), (5, 'a', None)]

回答 16

不使用itertools:

def combine(inp):
    return combine_helper(inp, [], [])


def combine_helper(inp, temp, ans):
    for i in range(len(inp)):
        current = inp[i]
        remaining = inp[i + 1:]
        temp.append(current)
        ans.append(tuple(temp))
        combine_helper(remaining, temp, ans)
        temp.pop()
    return ans


print(combine(['a', 'b', 'c', 'd']))

Without using itertools:

def combine(inp):
    return combine_helper(inp, [], [])


def combine_helper(inp, temp, ans):
    for i in range(len(inp)):
        current = inp[i]
        remaining = inp[i + 1:]
        temp.append(current)
        ans.append(tuple(temp))
        combine_helper(remaining, temp, ans)
        temp.pop()
    return ans


print(combine(['a', 'b', 'c', 'd']))

回答 17

这是两个实现 itertools.combinations

返回列表的一个

def combinations(lst, depth, start=0, items=[]):
    if depth <= 0:
        return [items]
    out = []
    for i in range(start, len(lst)):
        out += combinations(lst, depth - 1, i + 1, items + [lst[i]])
    return out

一个还生成器

def combinations(lst, depth, start=0, prepend=[]):
    if depth <= 0:
        yield prepend
    else:
        for i in range(start, len(lst)):
            for c in combinations(lst, depth - 1, i + 1, prepend + [lst[i]]):
                yield c

请注意,建议为这些函数提供帮助函数,因为prepend参数是静态的,并且不会在每次调用时更改

print([c for c in combinations([1, 2, 3, 4], 3)])
# [[1, 2, 3], [1, 2, 4], [1, 3, 4], [2, 3, 4]]

# get a hold of prepend
prepend = [c for c in combinations([], -1)][0]
prepend.append(None)

print([c for c in combinations([1, 2, 3, 4], 3)])
# [[None, 1, 2, 3], [None, 1, 2, 4], [None, 1, 3, 4], [None, 2, 3, 4]]

这是一个非常肤浅的案例,但总比后悔要安全

Here are two implementations of itertools.combinations

One that returns a list

def combinations(lst, depth, start=0, items=[]):
    if depth <= 0:
        return [items]
    out = []
    for i in range(start, len(lst)):
        out += combinations(lst, depth - 1, i + 1, items + [lst[i]])
    return out

One returns a generator

def combinations(lst, depth, start=0, prepend=[]):
    if depth <= 0:
        yield prepend
    else:
        for i in range(start, len(lst)):
            for c in combinations(lst, depth - 1, i + 1, prepend + [lst[i]]):
                yield c

Please note that providing a helper function to those is advised because the prepend argument is static and is not changing with every call

print([c for c in combinations([1, 2, 3, 4], 3)])
# [[1, 2, 3], [1, 2, 4], [1, 3, 4], [2, 3, 4]]

# get a hold of prepend
prepend = [c for c in combinations([], -1)][0]
prepend.append(None)

print([c for c in combinations([1, 2, 3, 4], 3)])
# [[None, 1, 2, 3], [None, 1, 2, 4], [None, 1, 3, 4], [None, 2, 3, 4]]

This is a very superficial case but better be safe than sorry


回答 18

怎么样..使用字符串而不是列表,但是同样的事情..字符串可以像Python中的列表一样对待:

def comb(s, res):
    if not s: return
    res.add(s)
    for i in range(0, len(s)):
        t = s[0:i] + s[i + 1:]
        comb(t, res)

res = set()
comb('game', res) 

print(res)

How about this.. used a string instead of list, but same thing.. string can be treated like a list in Python:

def comb(s, res):
    if not s: return
    res.add(s)
    for i in range(0, len(s)):
        t = s[0:i] + s[i + 1:]
        comb(t, res)

res = set()
comb('game', res) 

print(res)

回答 19

来自itertools的组合

import itertools
col_names = ["aa","bb", "cc", "dd"]
all_combinations = itertools.chain(*[itertools.combinations(col_names,i+1) for i,_ in enumerate(col_names)])
print(list(all_combinations))

谢谢

Combination from itertools

import itertools
col_names = ["aa","bb", "cc", "dd"]
all_combinations = itertools.chain(*[itertools.combinations(col_names,i+1) for i,_ in enumerate(col_names)])
print(list(all_combinations))

Thanks


回答 20

没有 itertools Python 3,您可以执行以下操作:

def combinations(arr, carry):
    for i in range(len(arr)):
        yield carry + arr[i]
        yield from combinations(arr[i + 1:], carry + arr[i])

最初在哪里 carry = "".

Without itertools in Python 3 you could do something like this:

def combinations(arr, carry):
    for i in range(len(arr)):
        yield carry + arr[i]
        yield from combinations(arr[i + 1:], carry + arr[i])

where initially carry = "".


回答 21

3个功能:

  1. n个元素的所有组合列表
  2. n个元素的所有组合列出了顺序不同的地方
  3. 所有排列
import sys

def permutations(a):
    return combinations(a, len(a))

def combinations(a, n):
    if n == 1:
        for x in a:
            yield [x]
    else:
        for i in range(len(a)):
            for x in combinations(a[:i] + a[i+1:], n-1):
                yield [a[i]] + x

def combinationsNoOrder(a, n):
    if n == 1:
        for x in a:
            yield [x]
    else:
        for i in range(len(a)):
            for x in combinationsNoOrder(a[:i], n-1):
                yield [a[i]] + x

if __name__ == "__main__":
    for s in combinations(list(map(int, sys.argv[2:])), int(sys.argv[1])):
        print(s)

3 functions:

  1. all combinations of n elements list
  2. all combinations of n elements list where order is not distinct
  3. all permutations
import sys

def permutations(a):
    return combinations(a, len(a))

def combinations(a, n):
    if n == 1:
        for x in a:
            yield [x]
    else:
        for i in range(len(a)):
            for x in combinations(a[:i] + a[i+1:], n-1):
                yield [a[i]] + x

def combinationsNoOrder(a, n):
    if n == 1:
        for x in a:
            yield [x]
    else:
        for i in range(len(a)):
            for x in combinationsNoOrder(a[:i], n-1):
                yield [a[i]] + x

if __name__ == "__main__":
    for s in combinations(list(map(int, sys.argv[2:])), int(sys.argv[1])):
        print(s)

回答 22

这是我的实现

    def get_combinations(list_of_things):
    """gets every combination of things in a list returned as a list of lists

    Should be read : add all combinations of a certain size to the end of a list for every possible size in the
    the list_of_things.

    """
    list_of_combinations = [list(combinations_of_a_certain_size)
                            for possible_size_of_combinations in range(1,  len(list_of_things))
                            for combinations_of_a_certain_size in itertools.combinations(list_of_things,
                                                                                         possible_size_of_combinations)]
    return list_of_combinations

This is my implementation

    def get_combinations(list_of_things):
    """gets every combination of things in a list returned as a list of lists

    Should be read : add all combinations of a certain size to the end of a list for every possible size in the
    the list_of_things.

    """
    list_of_combinations = [list(combinations_of_a_certain_size)
                            for possible_size_of_combinations in range(1,  len(list_of_things))
                            for combinations_of_a_certain_size in itertools.combinations(list_of_things,
                                                                                         possible_size_of_combinations)]
    return list_of_combinations

回答 23

您也可以使用优质包装中的Powerset功能more_itertools

from more_itertools import powerset

l = [1,2,3]
list(powerset(l))

# [(), (1,), (2,), (3,), (1, 2), (1, 3), (2, 3), (1, 2, 3)]

我们还可以验证它是否符合OP的要求

from more_itertools import ilen

assert ilen(powerset(range(15))) == 32_768

You can also use the powerset function from the excellent more_itertools package.

from more_itertools import powerset

l = [1,2,3]
list(powerset(l))

# [(), (1,), (2,), (3,), (1, 2), (1, 3), (2, 3), (1, 2, 3)]

We can also verify, that it meets OP’s requirement

from more_itertools import ilen

assert ilen(powerset(range(15))) == 32_768

回答 24

def combinations(iterable, r):
# combinations('ABCD', 2) --> AB AC AD BC BD CD
# combinations(range(4), 3) --> 012 013 023 123
pool = tuple(iterable)
n = len(pool)
if r > n:
    return
indices = range(r)
yield tuple(pool[i] for i in indices)
while True:
    for i in reversed(range(r)):
        if indices[i] != i + n - r:
            break
    else:
        return
    indices[i] += 1
    for j in range(i+1, r):
        indices[j] = indices[j-1] + 1
    yield tuple(pool[i] for i in indices)


x = [2, 3, 4, 5, 1, 6, 4, 7, 8, 3, 9]
for i in combinations(x, 2):
    print i
def combinations(iterable, r):
# combinations('ABCD', 2) --> AB AC AD BC BD CD
# combinations(range(4), 3) --> 012 013 023 123
pool = tuple(iterable)
n = len(pool)
if r > n:
    return
indices = range(r)
yield tuple(pool[i] for i in indices)
while True:
    for i in reversed(range(r)):
        if indices[i] != i + n - r:
            break
    else:
        return
    indices[i] += 1
    for j in range(i+1, r):
        indices[j] = indices[j-1] + 1
    yield tuple(pool[i] for i in indices)


x = [2, 3, 4, 5, 1, 6, 4, 7, 8, 3, 9]
for i in combinations(x, 2):
    print i

回答 25

如果有人正在寻找反向列表,就像我曾经那样:

stuff = [1, 2, 3, 4]

def reverse(bla, y):
    for subset in itertools.combinations(bla, len(bla)-y):
        print list(subset)
    if y != len(bla):
        y += 1
        reverse(bla, y)

reverse(stuff, 1)

If someone is looking for a reversed list, like I was:

stuff = [1, 2, 3, 4]

def reverse(bla, y):
    for subset in itertools.combinations(bla, len(bla)-y):
        print list(subset)
    if y != len(bla):
        y += 1
        reverse(bla, y)

reverse(stuff, 1)

回答 26

flag = 0
requiredCals =12
from itertools import chain, combinations

def powerset(iterable):
    s = list(iterable)  # allows duplicate elements
    return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))

stuff = [2,9,5,1,6]
for i, combo in enumerate(powerset(stuff), 1):
    if(len(combo)>0):
        #print(combo , sum(combo))
        if(sum(combo)== requiredCals):
            flag = 1
            break
if(flag==1):
    print('True')
else:
    print('else')
flag = 0
requiredCals =12
from itertools import chain, combinations

def powerset(iterable):
    s = list(iterable)  # allows duplicate elements
    return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))

stuff = [2,9,5,1,6]
for i, combo in enumerate(powerset(stuff), 1):
    if(len(combo)>0):
        #print(combo , sum(combo))
        if(sum(combo)== requiredCals):
            flag = 1
            break
if(flag==1):
    print('True')
else:
    print('else')