python性能调优---优化字典查找-优快云博客

本文链接：https://blog.youkuaiyun.com/yufeng0528/article/details/83977075

本文通过四个阶段逐步优化了Soundex算法的实现方式，并对比了不同优化方案的性能表现。从最初的逐字符处理到利用Python内置函数提升效率，最终采用字符串映射表实现了最佳性能。

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代码：

charToSoundex = {"A": "9",
"B": "1",
"C": "2",
"D": "3",
"E": "9",
"F": "1",
"G": "2",
"H": "9",
"I": "9",
"J": "2",
"K": "2",
"L": "4",
"M": "5",
"N": "5",
"O": "9",
"P": "1",
"Q": "2",
"R": "6",
"S": "2",
"T": "3",
"U": "9",
"V": "1",
"W": "9",
"X": "2",
"Y": "9",
"Z": "2"}
def soundex(source):
# ... input check omitted for brevity ...
    source = source[0].upper() + source[1:]
    digits = source[0]
    for s in source[1:]:
        s = s.upper()
        digits += charToSoundex[s]

测试性能

C:\samples\soundex\stage1>python soundex1c.py

Woo W000 14.5341678901
Pilgrim P426 19.2650071448
Flingjingwaller F452 30.1003563302

优化代码2

def soundex(source):
# ...
source = source.upper()
digits = source[0] + "".join(map(lambda c: charToSoundex[c], source[1:]))

测试性能

C:\samples\soundex\stage2>python soundex2a.py
Woo W000 15.0097526362
Pilgrim P426 19.254806407
Flingjingwaller F452 29.3790847719

匿名函数的开销抵消了用过把string作为一组字符进行出来带来的性能优化

优化代码3

source = source.upper()
digits = source[0] + "".join([charToSoundex[c] for c in source[1:]])

测试性能

C:\samples\soundex\stage2>python soundex2b.py
Woo W000 13.4221324219
Pilgrim P426 16.4901234654
Flingjingwaller F452 25.8186157738

可以字符串处理最好用list

优化代码4

allChar = string.uppercase + string.lowercase
charToSoundex = string.maketrans(allChar, "91239129922455912623919292" * 2)
def soundex(source):
    # ...
    digits = source[0].upper() + source[1:].translate(charToSoundex)

测试性能

C:\samples\soundex\stage2>python soundex2c.py
Woo W000 11.437645008
Pilgrim P426 13.2825062962
Flingjingwaller F452 18.5570110168

string.maketrans创建了字符和数字之间转换的联系，这种数据结构比map要快很多