What is the difference between math.exp and numpy.exp and why do numpy creators choose to introduce exp again?
Question:
Answers:
The math.exp works only for scalars, whereas numpy.exp will work for arrays.
Example:
>>> 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 math functions.
>>> 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 answer to check out how numpy is faster than math.
math.exp works on a single number, the numpy version works on numpy arrays and is tremendously faster due to the benefits of vectorization. The exp function isn’t alone in this – several math functions have numpy counterparts, such as sin, pow, etc.
Consider the following:
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)
As @camz states below – the math version will be faster when working on single values (in a quick test, ~7.5x faster).
If you manually vectorize math.exp using map, it is faster than numpy. As far as I tested..
%timeit np.exp(arr)
500 µs ± 3.37 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit map(math.exp, arr)
148 ns ± 4 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
The math.exp works only for scalars, whereas numpy.exp will work for arrays.
Example:
>>> 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 math functions.
>>> 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 answer to check out how numpy is faster than math.
math.exp works on a single number, the numpy version works on numpy arrays and is tremendously faster due to the benefits of vectorization. The exp function isn’t alone in this – several math functions have numpy counterparts, such as sin, pow, etc.
Consider the following:
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)
As @camz states below – the math version will be faster when working on single values (in a quick test, ~7.5x faster).
If you manually vectorize math.exp using map, it is faster than numpy. As far as I tested..
%timeit np.exp(arr)
500 µs ± 3.37 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit map(math.exp, arr)
148 ns ± 4 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)