Python：math.exp 和 numpy.exp 有什么区别，为什么 numpy 创造者选择再次引入 exp
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Python: What is the difference between math.exp and numpy.exp and why do numpy creators choose to introduce exp again
提问by Ka-Wa Yip
expmeans exponential function
exp表示指数函数
expin math module: https://docs.python.org/2/library/math.html
exp在math module：https: //docs.python.org/2/library/math.html
expin numpy module: http://docs.scipy.org/doc/numpy/reference/generated/numpy.exp.html
exp在numpy module：http: //docs.scipy.org/doc/numpy/reference/generated/numpy.exp.html
Why do numpycreators introduce this function again?
为什么numpy创作者又要引入这个功能？
采纳答案by ThePredator
The math.expworks only for scalars as EdChummentions. Whereas numpy.expwill work for arrays.
在math.exp作为标量只能EdChum提到。而numpy.exp将适用于数组。
Example:
例子：
>>> import math
>>> import numpy as np
>>> x = [1.,2.,3.,4.,5.]
>>> math.exp(x)
Traceback (most recent call last):
File "<pyshell#10>", line 1, in <module>
math.exp(x)
TypeError: a float is required
>>> np.exp(x)
array([ 2.71828183, 7.3890561 , 20.08553692, 54.59815003,
148.4131591 ])
>>>
It is the same case for other mathfunctions.
其他math函数的情况也是如此。
>>> math.sin(x)
Traceback (most recent call last):
File "<pyshell#12>", line 1, in <module>
math.sin(x)
TypeError: a float is required
>>> np.sin(x)
array([ 0.84147098, 0.90929743, 0.14112001, -0.7568025 , -0.95892427])
>>>
Also refer to THIS ANSWERto check out how numpyis faster than math.
另请参阅此答案以查看numpy比math.
回答by Chinmay Kanchi
math.expworks on a single number, the numpy version works on numpy arrays and is tremendously faster due to the benefits of vectorization. The expfunction isn't alone in this - several mathfunctions have numpy counterparts, such as sin, pow, etc.
math.exp适用于单个数字，numpy 版本适用于 numpy 数组，并且由于矢量化的好处而速度更快。该exp功能不单单是这一点-几个math函数有numpy的同行，如sin，pow等。
Consider the following:
考虑以下：
In [10]: import math
In [11]: import numpy
In [13]: arr = numpy.random.random_integers(0, 500, 100000)
In [14]: %timeit numpy.exp(arr)
100 loops, best of 3: 1.89 ms per loop
In [15]: %timeit [math.exp(i) for i in arr]
100 loops, best of 3: 17.9 ms per loop
The numpy version is ~9x faster (and probably can be made faster still by a careful choice of optimized math libraries)
numpy 版本的速度提高了大约 9 倍（通过仔细选择优化的数学库，可能还可以使速度更快）
As @camz states below - the mathversion will be faster when working on single values (in a quick test, ~7.5x faster).
正如@camz 在下面指出的那样 -math处理单个值时版本会更快（在快速测试中，大约快 7.5 倍）。
回答by Z Che
If you manually vectorize math.exp using map, it is faster than numpy. As far as I tested..
如果您使用 map 手动矢量化 math.exp，它比 numpy 更快。据我测试..
%timeit np.exp(arr)
%timeit np.exp(arr)
500 μs ± 3.37 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
每个循环 500 μs ± 3.37 μs（平均值 ± 标准偏差，7 次运行，每次 1000 次循环）
%timeit map(math.exp, arr)
%timeit map(math.exp, arr)
148 ns ± 4 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
每个循环 148 ns ± 4 ns（7 次运行的平均值 ± 标准偏差，每次 10000000 次循环）
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