所以自从python 3之后,
map()
是迭代器,您需要记住您需要什么:迭代器
list
对象。
已经是@alexmartelli了
mentioned
,
MAP()
如果你不使用
lambda
功能。
我会给你一些时间比较。
python 3.5.2和cpython
我已经用过
Jupiter notebook
尤其是
%timeit
内置魔术指令
测量
:s==1000ms==1000*1000s=1000*1000*1000ns
设置:
x_list = [(i, i+1, i+2, i*2, i-9) for i in range(1000)]
i_list = list(range(1000))
内置功能:
%timeit map(sum, x_list) # creating iterator object
# Output: The slowest run took 9.91 times longer than the fastest.
# This could mean that an intermediate result is being cached.
# 1000000 loops, best of 3: 277 ns per loop
%timeit list(map(sum, x_list)) # creating list with map
# Output: 1000 loops, best of 3: 214 µs per loop
%timeit [sum(x) for x in x_list] # creating list with list comprehension
# Output: 1000 loops, best of 3: 290 µs per loop
兰姆达
功能:
%timeit map(lambda i: i+1, i_list)
# Output: The slowest run took 8.64 times longer than the fastest.
# This could mean that an intermediate result is being cached.
# 1000000 loops, best of 3: 325 ns per loop
%timeit list(map(lambda i: i+1, i_list))
# Output: 1000 loops, best of 3: 183 µs per loop
%timeit [i+1 for i in i_list]
# Output: 10000 loops, best of 3: 84.2 µs per loop
还有生成器表达式,请参见
PEP-0289
. 所以我觉得把它加到比较中是有用的
%timeit (sum(i) for i in x_list)
# Output: The slowest run took 6.66 times longer than the fastest.
# This could mean that an intermediate result is being cached.
# 1000000 loops, best of 3: 495 ns per loop
%timeit list((sum(x) for x in x_list))
# Output: 1000 loops, best of 3: 319 µs per loop
%timeit (i+1 for i in i_list)
# Output: The slowest run took 6.83 times longer than the fastest.
# This could mean that an intermediate result is being cached.
# 1000000 loops, best of 3: 506 ns per loop
%timeit list((i+1 for i in i_list))
# Output: 10000 loops, best of 3: 125 µs per loop
你需要
列表
对象:
使用列表理解如果是自定义函数,则使用
list(map())
如果有内置函数
你不需要
列表
对象,你只需要一个可接受的:
总是使用
MAP()
!
