python：遍历具有列表值的字典
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python: iterating through a dictionary with list values
提问by foosion
Given a dictionary of lists, such as
给定一个列表字典，例如
d = {'1':[11,12], '2':[21,21]}
Which is more pythonic or otherwise preferable:
哪个更pythonic或更可取：
for k in d:
for x in d[k]:
# whatever with k, x
or
或者
for k, dk in d.iteritems():
for x in dk:
# whatever with k, x
or is there something else to consider?
或者还有什么需要考虑的吗？
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.
编辑，如果列表可能有用（例如，标准字典不保留顺序），这可能是合适的，尽管它要慢得多。
d2 = d.items()
for k in d2:
for x in d2[1]:
# whatever with k, x
回答by Brionius
Here's a speed test, why not:
这是一个速度测试，为什么不：
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:
cProfile 示例输出：
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}
回答by kelorek
Here's the list comprehension approach. Nested...
这是列表理解方法。嵌套...
r = [[i for i in d[x]] for x in d.keys()]
print r
[[11, 12], [21, 21]]
回答by foosion
My results from Brionius code:
我从 Brionius 代码得到的结果：
3 function calls in 0.173 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.173 0.173 <string>:1(<module>)
1 0.173 0.173 0.173 0.173 speed.py:5(m1)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Prof
iler' objects}
4 function calls in 0.185 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.185 0.185 <string>:1(<module>)
1 0.185 0.185 0.185 0.185 speed.py:10(m2)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Prof
iler' objects}
1 0.000 0.000 0.000 0.000 {method 'iteritems' of 'dict' obje
cts}
回答by Ryne Everett
I considered a couple methods:
我考虑了几种方法：
import itertools
COLORED_THINGS = {'blue': ['sky', 'jeans', 'powerline insert mode'],
'yellow': ['sun', 'banana', 'phone book/monitor stand'],
'red': ['blood', 'tomato', 'test failure']}
def forloops():
""" Nested for loops. """
for color, things in COLORED_THINGS.items():
for thing in things:
pass
def iterator():
""" Use itertools and list comprehension to construct iterator. """
for color, thing in (
itertools.chain.from_iterable(
[itertools.product((k,), v) for k, v in COLORED_THINGS.items()])):
pass
def iterator_gen():
""" Use itertools and generator to construct iterator. """
for color, thing in (
itertools.chain.from_iterable(
(itertools.product((k,), v) for k, v in COLORED_THINGS.items()))):
pass
I used ipython and memory_profilerto test performance:
我使用 ipython 和memory_profiler来测试性能：
>>> %timeit forloops()
1000000 loops, best of 3: 1.31 μs per loop
>>> %timeit iterator()
100000 loops, best of 3: 3.58 μs per loop
>>> %timeit iterator_gen()
100000 loops, best of 3: 3.91 μs per loop
>>> %memit -r 1000 forloops()
peak memory: 35.79 MiB, increment: 0.02 MiB
>>> %memit -r 1000 iterator()
peak memory: 35.79 MiB, increment: 0.00 MiB
>>> %memit -r 1000 iterator_gen()
peak memory: 35.79 MiB, increment: 0.00 MiB
As you can see, the method had no observable impact on peak memory usage, but nested forloops were unbeatable for speed (not to mention readability).
如您所见，该方法对峰值内存使用没有明显影响，但嵌套for循环在速度方面是无与伦比的（更不用说可读性了）。
