list型別資料執行效率測試

測試**如下

from timeit import

timer
deffun1():
a =for i in range(10000):
li =[i]
a = a +li
deffun2():
a = [i for i in range(10000)]
deffun3():
a =for i in range(10000):
deffun4():
a = list(range(10000))
deffun5():
a =for i in range(10000):
a.extend([i])
#fun1()
#fun2()
#fun3()
#fun4()
#fun5()
time1 = timer("
fun1()
", "
from __main__ import fun1")
print("
time1:
", time1.timeit(500))
time2 = timer("
fun2()
", "
from __main__ import fun2")
print("
time2:
", time2.timeit(500))
time3 = timer("
fun3()
", "
from __main__ import fun3")
print("
time3:
", time3.timeit(500))
time4 = timer("
fun4()
", "
from __main__ import fun4")
print("
time4:
", time4.timeit(500))
time5 = timer("
fun5()
", "
from __main__ import fun5")
print("
time5:
", time5.timeit(500))

#插入到頭部

a.insert(0, i)

deffun8():

a =for i in range(10000):

#插入到尾部

a.insert(-1, i)

time6 = timer("

fun6()

", "

from __main__ import fun6")

print("

time6:

", time6.timeit(500))

time7 = timer("

fun7()

", "

from __main__ import fun7")

print("

time7:

", time7.timeit(500))

time8 = timer("

fun8()

", "

from __main__ import fun8")

print("

time8:

", time8.timeit(500))

執行結果，插入到頭部需要更新列表中後面所有資料的位置，所以最耗時：

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