python:用列表值遍历一个字典 [英] python: iterating through a dictionary with list values
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问题描述
给出列表的字典,例如
d = {'1':[11,12],'2 ':[21,21]}
哪些是更多的pythonic或其他更好:
$在d中,$ b#无论k,x
或
在d.iteritems()中的k,dk:
在dk中的x:
#无论与k,x
还是有其他要考虑的东西?
编辑,如果列表可能有用(例如,标准版不保存订单),这可能是适当的,虽然要慢一些。
d2 = d.items()
for k在d2中:
for d2 [1]:
#无论与k,x
解决方案
这是一个速度测试,为什么不:
import random
numEntries = 1000000
d = dict(zip(range(numEntries)),[ra ndom.sample(range(0,100),2)for x in range(numEntries)]))
def m1(d):
for d in:
对于d [k]中的x:
pass
def m2(d):
for k,dk in d.iteritems():
for x in dk :
pass
import cProfile
cProfile.run('m1(d)')
打印
cProfile.run('m2(d)')
#冉3试验:
#m1:0.205,0.194,0.193:平均0.197 s
#m2:0.176 ,0.166,0.173:平均0.172 s
#方法1比方法2多15%的时间
cProfile示例输出:
0.194秒内的3个函数调用
按:标准名称
ncalls tottime percall cumtime percall文件名:lineno(功能)
1 0.000 0.000 0.194 0.194< string>:1(< module>)
1 0.194 0.194 0.194 0.194 stackoverflow.py:7(m1)
1 0.000 0.000 0.000 0.000 {方法'禁用''_lsprof.Profiler'对象}
4个函数调用0.179秒
订购者:标准名称
ncalls tottime percall cumtime percall文件名:lineno(功能)
1 0.000 0.000 0.179 0.179< string>:1(< module>)
1 0.179 0.179 0.179 0.179 stackoverflow .py:12(m2)
1 0.000 0.000 0.000 0.000 {method'disable'of'_lsprof.Profiler'objects}
1 0.000 0.000 0.000 0.000 {method'iteritems'of'dict'objects}
Given a dictionary of lists, such as
d = {'1':[11,12], '2':[21,21]}
Which is more pythonic or otherwise preferable:
for k in d:
for x in d[k]:
# whatever with k, x
or
for k, dk in d.iteritems():
for x in dk:
# whatever with k, x
or is there something else to consider?
EDIT, in case a list might be useful (e.g., standard dicts don't preserve order), this might be appropriate, although it's much slower.
d2 = d.items()
for k in d2:
for x in d2[1]:
# whatever with k, x
解决方案
Here's a speed test, why not:
import random
numEntries = 1000000
d = dict(zip(range(numEntries), [random.sample(range(0, 100), 2) for x in range(numEntries)]))
def m1(d):
for k in d:
for x in d[k]:
pass
def m2(d):
for k, dk in d.iteritems():
for x in dk:
pass
import cProfile
cProfile.run('m1(d)')
print
cProfile.run('m2(d)')
# Ran 3 trials:
# m1: 0.205, 0.194, 0.193: average 0.197 s
# m2: 0.176, 0.166, 0.173: average 0.172 s
# Method 1 takes 15% more time than method 2
cProfile example output:
3 function calls in 0.194 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.194 0.194 <string>:1(<module>)
1 0.194 0.194 0.194 0.194 stackoverflow.py:7(m1)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
4 function calls in 0.179 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.179 0.179 <string>:1(<module>)
1 0.179 0.179 0.179 0.179 stackoverflow.py:12(m2)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
1 0.000 0.000 0.000 0.000 {method 'iteritems' of 'dict' objects}
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