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100 lines
1.5 KiB
Markdown
100 lines
1.5 KiB
Markdown
# 列表与元组的速度比较
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IPython 中用 `magic` 命令 `%timeit` 来计时。
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## 比较生成速度
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In [1]:
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```py
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%timeit [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25]
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```
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```py
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1000000 loops, best of 3: 456 ns per loop
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```
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In [2]:
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```py
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%timeit (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25)
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```
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```py
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10000000 loops, best of 3: 23 ns per loop
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```
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可以看到，元组的生成速度要比列表的生成速度快得多，相差大概一个数量级。
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## 比较遍历速度
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产生内容相同的随机列表和元组：
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In [3]:
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```py
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from numpy.random import rand
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values = rand(10000,4)
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lst = [list(row) for row in values]
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tup = tuple(tuple(row) for row in values)
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```
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In [4]:
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```py
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%timeit for row in lst: list(row)
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```
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```py
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100 loops, best of 3: 4.12 ms per loop
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```
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In [5]:
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```py
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%timeit for row in tup: tuple(row)
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```
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```py
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100 loops, best of 3: 2.07 ms per loop
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```
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在遍历上，元组和列表的速度表现差不多。
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## 比较遍历和索引速度：
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In [6]:
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```py
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%timeit for row in lst: a = row[0] + 1
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```
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```py
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The slowest run took 12.20 times longer than the fastest. This could mean that an intermediate result is being cached
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100 loops, best of 3: 3.73 ms per loop
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```
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In [7]:
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```py
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%timeit for row in tup: a = row[0] + 1
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```
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```py
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100 loops, best of 3: 3.82 ms per loop
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```
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元组的生成速度会比列表快很多，迭代速度快一点，索引速度差不多。 |