built-in range or numpy.arange: which is more efficient?
Solution 1
For large arrays, a vectorised numpy operation is the fastest. If you must loop, prefer xrange
/range
and avoid using np.arange
.
In numpy you should use combinations of vectorized calculations, ufuncs and indexing to solve your problems as it runs at C
speed.
Looping over numpy arrays is inefficient compared to this.
(Something like the worst thing you could do would be to iterate over the array with an index created with range
or np.arange
as the first sentence in your question suggests, but I'm not sure if you really mean that.)
import numpy as np
import sys
sys.version
# out: '2.7.3rc2 (default, Mar 22 2012, 04:35:15) \n[GCC 4.6.3]'
np.version.version
# out: '1.6.2'
size = int(1E6)
%timeit for x in range(size): x ** 2
# out: 10 loops, best of 3: 136 ms per loop
%timeit for x in xrange(size): x ** 2
# out: 10 loops, best of 3: 88.9 ms per loop
# avoid this
%timeit for x in np.arange(size): x ** 2
#out: 1 loops, best of 3: 1.16 s per loop
# use this
%timeit np.arange(size) ** 2
#out: 100 loops, best of 3: 19.5 ms per loop
So for this case numpy is 4 times faster than using xrange
if you do it right. Depending on your problem numpy can be much faster than a 4 or 5 times speed up.
The answers to this question explain some more advantages of using numpy arrays instead of python lists for large data sets.
Solution 2
First of all, as written by @bmu, you should use combinations of vectorized calculations, ufuncs and indexing. There are indeed some cases where explicit looping is required, but those are really rare.
If explicit loop is needed, with python 2.6 and 2.7, you should use xrange (see below). From what you say, in Python 3, range is the same as xrange (returns a generator). So maybe range is as good for you.
Now, you should try it yourself (using timeit: - here the ipython "magic function"):
%timeit for i in range(1000000): pass
[out] 10 loops, best of 3: 63.6 ms per loop
%timeit for i in np.arange(1000000): pass
[out] 10 loops, best of 3: 158 ms per loop
%timeit for i in xrange(1000000): pass
[out] 10 loops, best of 3: 23.4 ms per loop
Again, as mentioned above, most of the time it is possible to use numpy vector/array formula (or ufunc etc...) which run a c speed: much faster. This is what we could call "vector programming". It makes program easier to implement than C (and more readable) but almost as fast in the end.
clstaudt
Updated on July 24, 2021Comments
-
clstaudt almost 3 years
When iterating over a large array with a range expression, should I use Python's built-in
range
function, or numpy'sarange
to get the best performance?My reasoning so far:
range
probably resorts to a native implementation and might be faster therefore. On the other hand,arange
returns a full array, which occupies memory, so there might be an overhead. Python 3's range expression is a generator, which does not hold all the values in memory. -
Juh_ almost 12 yearsThere is a standard python 'timeit' module which allows to do the same without IPython. But it is just much more easy to use this magic function.
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Juh_ almost 12 yearsI fully agree, but the question was about "iterating over a large array". In some situation, it is not possible to use the numpy vectorized, ufunc or indexing. On example I encountered is to compute the eigenvectors of a list of matrices (>= 3x3)
-
Guimoute over 5 yearsI don't know if
np.arange
has been made more efficient since 2012, but it's main interest compared to the built-inrange
is that you can use float number as start:stop:step. -
Maulik Madhavi over 3 yearsnp.arange is useful when we have non integer step size
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Benyamin Jafari - aGn over 2 yearsIt's worth mentioning that, there is no
xrange
in Python3.x, andrange
is the same withxrange
on it. -
Benyamin Jafari - aGn over 2 yearsAlso, I tested
for x in np.arange(size): x ** 2
which was faster thanfor x in range(size): x ** 2
innumpy==1.16.4
andpython 3.6