थोड़ा गति तुलना:
Python 2.7.11 |Anaconda 2.4.1 (64-bit)| (default, Dec 7 2015, 14:10:42) [MSC v.1500 64 bit (AMD64)] on win32
In[1]: l = [0,1,2,3,2,3,1,2,0]
In[2]: m = {0:10, 1:11, 2:12, 3:13}
In[3]: %timeit [m[_] for _ in l] # list comprehension
1000000 loops, best of 3: 762 ns per loop
In[4]: %timeit map(lambda _: m[_], l) # using 'map'
1000000 loops, best of 3: 1.66 µs per loop
In[5]: %timeit list(m[_] for _ in l) # a generator expression passed to a list constructor.
1000000 loops, best of 3: 1.65 µs per loop
In[6]: %timeit map(m.__getitem__, l)
The slowest run took 4.01 times longer than the fastest. This could mean that an intermediate result is being cached
1000000 loops, best of 3: 853 ns per loop
In[7]: %timeit map(m.get, l)
1000000 loops, best of 3: 908 ns per loop
In[33]: from operator import itemgetter
In[34]: %timeit list(itemgetter(*l)(m))
The slowest run took 9.26 times longer than the fastest. This could mean that an intermediate result is being cached
1000000 loops, best of 3: 739 ns per loop
इसलिए लिस्ट कॉम्प्रिहेंशन और आइटमगेटर ऐसा करने के सबसे तेज़ तरीके हैं।
अद्यतन: बड़ी यादृच्छिक सूचियों और मानचित्रों के लिए मेरे पास कुछ अलग परिणाम थे:
Python 2.7.11 |Anaconda 2.4.1 (64-bit)| (default, Dec 7 2015, 14:10:42) [MSC v.1500 64 bit (AMD64)] on win32
In[2]: import numpy.random as nprnd
l = nprnd.randint(1000, size=10000)
m = dict([(_, nprnd.rand()) for _ in range(1000)])
from operator import itemgetter
import operator
f = operator.itemgetter(*l)
%timeit f(m)
%timeit list(itemgetter(*l)(m))
%timeit [m[_] for _ in l] # list comprehension
%timeit map(m.__getitem__, l)
%timeit list(m[_] for _ in l) # a generator expression passed to a list constructor.
%timeit map(m.get, l)
%timeit map(lambda _: m[_], l)
1000 loops, best of 3: 1.14 ms per loop
1000 loops, best of 3: 1.68 ms per loop
100 loops, best of 3: 2 ms per loop
100 loops, best of 3: 2.05 ms per loop
100 loops, best of 3: 2.19 ms per loop
100 loops, best of 3: 2.53 ms per loop
100 loops, best of 3: 2.9 ms per loop
तो इस मामले में स्पष्ट विजेता है f = operator.itemgetter(*l); f(m)
, और स्पष्ट बाहरी व्यक्ति map(lambda _: m[_], l)
:।
अजगर के लिए अद्यतन 3.6.4:
import numpy.random as nprnd
l = nprnd.randint(1000, size=10000)
m = dict([(_, nprnd.rand()) for _ in range(1000)])
from operator import itemgetter
import operator
f = operator.itemgetter(*l)
%timeit f(m)
%timeit list(itemgetter(*l)(m))
%timeit [m[_] for _ in l] # list comprehension
%timeit list(map(m.__getitem__, l))
%timeit list(m[_] for _ in l) # a generator expression passed to a list constructor.
%timeit list(map(m.get, l))
%timeit list(map(lambda _: m[_], l)
1.66 ms ± 74.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
2.1 ms ± 93.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
2.58 ms ± 88.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
2.36 ms ± 60.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
2.98 ms ± 142 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
2.7 ms ± 284 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
3.14 ms ± 62.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
तो, पायथन 3.6.4 के परिणाम लगभग समान हैं।
mydict
कोई फ़ंक्शन कॉल है (जो एक तानाशाह को लौटाता है) तो यह फ़ंक्शन को कई बार कॉल करता है, है ना?