下面是两种简单的方法从给定的字典中提取键的最大值

import time
stats = {
   "a" : 1000,
   "b" : 3000,
   "c" : 90,
   "d" : 74,
   "e" : 72,
 }

start_time = time.time_ns()
max_key = max(stats, key = stats.get)
print("Max Key [", max_key, "]Time taken (ns)", time.time_ns() - start_time)

start_time = time.time_ns()
max_key = max(stats, key=lambda key: stats[key])
print("Max Key with Lambda[", max_key, "]Time taken (ns)", time.time_ns() - start_time)

输出

Max Key [ b ] Time taken (ns) 3100
Max Key with Lambda [ b ] Time taken (ns) 1782

使用Lambda表达式的解决方案似乎对较小的输入执行得更好。

我测试了接受的答案和@thewolf的最快的解决方案，对一个非常基本的循环，循环比两者都快:

import time
import operator


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

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

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

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

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

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

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

结果:

Iterating: 3.8201940059661865
List creating: 6.928712844848633
Accepted answer: 5.464320182800293

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

如果你不关心价值(我会很惊讶，但是)，你可以做:

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

我更喜欢元组解包，而不是表达式末尾的[0]下标。 我从来都不太喜欢lambda表达式的可读性，但我发现这个比operator.itemgetter(1)更好。

这是另一个:

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

函数键只是返回应该用于排序的值，max()立即返回所需的元素。

如果你只需要知道一个带有最大值的键，你可以不使用iterkeys或iteritems，因为在Python中迭代字典就是迭代它的键。

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

编辑:

来自评论，@user1274878:

我不熟悉python。你能分步骤解释一下你的答案吗?

是的……

max

马克斯(iterable(关键)) Max (arg1, arg2， *args[， key]) 返回可迭代对象中最大的项或两个或多个参数中最大的项。

可选参数key描述了如何比较元素以获得它们之间的最大值:

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

返回值将进行比较。

Dict

Python dict是一个哈希表。dict的键是声明为键的对象的哈希值。由于性能原因，迭代通过字典实现迭代通过它的键。

因此，我们可以用它来消除获取键列表的操作。

关闭

定义在另一个函数内部的函数称为嵌套函数。嵌套函数可以访问封闭范围的变量。

通过lambda函数的__closure__属性可用的stats变量，作为指向父作用域中定义的变量值的指针。

我已经测试了许多变体，这是返回最大值的dict键的最快方法:

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

为了给你一个概念，这里有一些候选方法:

def f1():  
     v = list(d1.values())
     k = list(d1.keys())
     return k[v.index(max(v))]
    
def f2():
    d3 = {v: k for k,v in d1.items()}
    return d3[max(d3)]
    
def f3():
    return list(filter(lambda t: t[1] == max(d1.values()), d1.items()))[0][0]    
    
def f3b():
    # same as f3 but remove the call to max from the lambda
    m = max(d1.values())
    return list(filter(lambda t: t[1] == m, d1.items()))[0][0]        
    
def f4():
    return [k for k, v in d1.items() if v == max(d1.values())][0]    
    
def f4b():
    # same as f4 but remove the max from the comprehension
    m = max(d1.values())
    return [k for k,v in d1.items() if v == m][0]        
    
def f5():
    return max(d1.items(), key=operator.itemgetter(1))[0]    
    
def f6():
    return max(d1, key=d1.get)     
    
def f7():
     """ a) create a list of the dict's keys and values; 
         b) return the key with the max value"""    
     v = list(d1.values())
     return list(d1.keys())[v.index(max(v))]    
     
def f8():
     return max(d1, key=lambda k: d1[k])     
     
tl = [f1, f2, f3b, f4b, f5, f6, f7, f8, f4, f3]     
cmpthese.cmpthese(tl, c=100) 

测试词典:

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

以及Python 3.2下的测试结果:

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

在Python 2.7中:

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

你可以看到f1在Python 3.2和2.7下是最快的(或者，更完整地说，在本文顶部的keywithmaxval)