Python的总和与NumPy的numpy.sum [英] Python's sum vs. NumPy's numpy.sum

问题描述

使用Python的本机sum函数和NumPy的numpy.sum在性能和行为上有什么区别? sum适用于NumPy的数组，而numpy.sum适用于Python列表，它们都返回相同的有效结果(未经测试的边缘情况，如溢出)，但类型不同.

What are the differences in performance and behavior between using Python's native sum function and NumPy's numpy.sum? sum works on NumPy's arrays and numpy.sum works on Python lists and they both return the same effective result (haven't tested edge cases such as overflow) but different types.

>>> import numpy as np
>>> np_a = np.array(range(5))
>>> np_a
array([0, 1, 2, 3, 4])
>>> type(np_a)
<class 'numpy.ndarray')

>>> py_a = list(range(5))
>>> py_a
[0, 1, 2, 3, 4]
>>> type(py_a)
<class 'list'>

# The numerical answer (10) is the same for the following sums:
>>> type(np.sum(np_a))
<class 'numpy.int32'>
>>> type(sum(np_a))
<class 'numpy.int32'>
>>> type(np.sum(py_a))
<class 'numpy.int32'>
>>> type(sum(py_a))
<class 'int'>

我认为我的实际问题是，在Python整数列表上使用numpy.sum会比使用Python自己的sum快吗?

I think my practical question here is would using numpy.sum on a list of Python integers be any faster than using Python's own sum?

此外，使用Python整数与标量numpy.int32有什么关系(包括性能)?例如，对于a += 1，如果a的类型是Python整数或numpy.int32，是否在行为或性能上有所不同?我很好奇，如果将NumPy标量数据类型(例如numpy.int32)用于在Python代码中增加或减少很多的值，是否更快.

Additionally, what are the implications (including performance) of using a Python integer versus a scalar numpy.int32? For example, for a += 1, is there a behavior or performance difference if the type of a is a Python integer or a numpy.int32? I am curious if it is faster to use a NumPy scalar datatype such as numpy.int32 for a value that is added or subtracted a lot in Python code.

为澄清起见，我正在进行生物信息学模拟，其中一部分包括将多维numpy.ndarray分解为单个标量和，然后对其进行额外处理.我正在使用Python 3.2和NumPy 1.6.

For clarification, I am working on a bioinformatics simulation which partly consists of collapsing multidimensional numpy.ndarrays into single scalar sums which are then additionally processed. I am using Python 3.2 and NumPy 1.6.

提前谢谢！

推荐答案

我很好奇并定时了. numpy.sum对于numpy数组似乎快得多，但在列表上要慢得多.

I got curious and timed it. numpy.sum seems much faster for numpy arrays, but much slower on lists.

import numpy as np
import timeit

x = range(1000)
# or 
#x = np.random.standard_normal(1000)

def pure_sum():
    return sum(x)

def numpy_sum():
    return np.sum(x)

n = 10000

t1 = timeit.timeit(pure_sum, number = n)
print 'Pure Python Sum:', t1
t2 = timeit.timeit(numpy_sum, number = n)
print 'Numpy Sum:', t2

x = range(1000)时的结果:

Pure Python Sum: 0.445913167735
Numpy Sum: 8.54926219673

x = np.random.standard_normal(1000)时的结果:

Pure Python Sum: 12.1442425643
Numpy Sum: 0.303303771848

我正在使用Python 2.7.2和Numpy 1.6.1

I am using Python 2.7.2 and Numpy 1.6.1

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