问题：如何执行两个列表的按元素相乘？
我想执行元素明智的乘法，将两个列表按值在Python中相乘，就像我们在Matlab中可以做到的那样。
这就是我在Matlab中要做的。
a = [1,2,3,4]
b = [2,3,4,5]
a .* b = [2, 6, 12, 20]
对于from 和from的每个组合x * y，列表理解将给出16个列表条目。不确定如何映射。xayb
如果有人对此感兴趣，我有一个数据集，并想乘以Numpy.linspace(1.0, 0.5, num=len(dataset)) =)。
I want to perform an element wise multiplication, to multiply two lists together by value in Python, like we can do it in Matlab.
This is how I would do it in Matlab.
a = [1,2,3,4]
b = [2,3,4,5]
a .* b = [2, 6, 12, 20]
A list comprehension would give 16 list entries, for every combination x * y of x from a and y from b. Unsure of how to map this.
If anyone is interested why, I have a dataset, and want to multiply it by Numpy.linspace(1.0, 0.5, num=len(dataset)) =).
回答 0
使用列表理解与zip()：混合。
[a*b for a,b in zip(lista,listb)]
Use a list comprehension mixed with zip():.
[a*b for a,b in zip(lista,listb)]
回答 1
由于您已经在使用numpy，因此将数据存储在numpy数组而不是列表中很有意义。完成此操作后，您将免费获得类似智能元素的产品：
In [1]: import numpy as np
In [2]: a = np.array([1,2,3,4])
In [3]: b = np.array([2,3,4,5])
In [4]: a * b
Out[4]: array([ 2, 6, 12, 20])
Since you’re already using numpy, it makes sense to store your data in a numpy array rather than a list. Once you do this, you get things like element-wise products for free:
In [1]: import numpy as np
In [2]: a = np.array([1,2,3,4])
In [3]: b = np.array([2,3,4,5])
In [4]: a * b
Out[4]: array([ 2, 6, 12, 20])
回答 2
使用np.multiply（a，b）：
import numpy as np
a = [1,2,3,4]
b = [2,3,4,5]
np.multiply(a,b)
Use np.multiply(a,b):
import numpy as np
a = [1,2,3,4]
b = [2,3,4,5]
np.multiply(a,b)
回答 3
您可以尝试将每个元素乘以一个循环。这样做的捷径是
ab = [a[i]*b[i] for i in range(len(a))]
You can try multiplying each element in a loop. The short hand for doing that is
ab = [a[i]*b[i] for i in range(len(a))]
回答 4
还有一个答案：
-1…需要导入
+1…非常易读
import operator
a = [1,2,3,4]
b = [10,11,12,13]
list(map(operator.mul, a, b))
输出[10、22、36、52]
Yet another answer:
-1 … requires import
+1 … is very readable
import operator
a = [1,2,3,4]
b = [10,11,12,13]
list(map(operator.mul, a, b))
outputs [10, 22, 36, 52]
回答 5
相当直观的方法：
a = [1,2,3,4]
b = [2,3,4,5]
ab = [] #Create empty list
for i in range(0, len(a)):
ab.append(a[i]*b[i]) #Adds each element to the list
Fairly intuitive way of doing this:
a = [1,2,3,4]
b = [2,3,4,5]
ab = [] #Create empty list
for i in range(0, len(a)):
ab.append(a[i]*b[i]) #Adds each element to the list
回答 6
您可以使用 lambda
foo=[1,2,3,4]
bar=[1,2,5,55]
l=map(lambda x,y:x*y,foo,bar)
you can multiplication using lambda
foo=[1,2,3,4]
bar=[1,2,5,55]
l=map(lambda x,y:x*y,foo,bar)
回答 7
对于大型列表，我们可以反复进行：
product_iter_object = itertools.imap(operator.mul, [1,2,3,4], [2,3,4,5])
product_iter_object.next() 给出输出列表中的每个元素。
输出将是两个输入列表中较短者的长度。
For large lists, we can do it the iter-way:
product_iter_object = itertools.imap(operator.mul, [1,2,3,4], [2,3,4,5])
product_iter_object.next() gives each of the element in the output list.
The output would be the length of the shorter of the two input lists.
回答 8
创建一个数组；将每个列表乘以数组；将数组转换为列表
import numpy as np
a = [1,2,3,4]
b = [2,3,4,5]
c = (np.ones(len(a))*a*b).tolist()
[2.0, 6.0, 12.0, 20.0]
create an array of ones;
multiply each list times the array;
convert array to a list
import numpy as np
a = [1,2,3,4]
b = [2,3,4,5]
c = (np.ones(len(a))*a*b).tolist()
[2.0, 6.0, 12.0, 20.0]
回答 9
gahooa的答案对于标题中所述的问题是正确的，但是如果列表已经是numpy格式或大于十，它将更快（3个数量级）并且可读性更高，如NPE。我得到这些时间：
0.0049ms -> N = 4, a = [i for i in range(N)], c = [a*b for a,b in zip(a, b)]
0.0075ms -> N = 4, a = [i for i in range(N)], c = a * b
0.0167ms -> N = 4, a = np.arange(N), c = [a*b for a,b in zip(a, b)]
0.0013ms -> N = 4, a = np.arange(N), c = a * b
0.0171ms -> N = 40, a = [i for i in range(N)], c = [a*b for a,b in zip(a, b)]
0.0095ms -> N = 40, a = [i for i in range(N)], c = a * b
0.1077ms -> N = 40, a = np.arange(N), c = [a*b for a,b in zip(a, b)]
0.0013ms -> N = 40, a = np.arange(N), c = a * b
0.1485ms -> N = 400, a = [i for i in range(N)], c = [a*b for a,b in zip(a, b)]
0.0397ms -> N = 400, a = [i for i in range(N)], c = a * b
1.0348ms -> N = 400, a = np.arange(N), c = [a*b for a,b in zip(a, b)]
0.0020ms -> N = 400, a = np.arange(N), c = a * b
即从以下测试程序。
import timeit
init = ['''
import numpy as np
N = {}
a = {}
b = np.linspace(0.0, 0.5, len(a))
'''.format(i, j) for i in [4, 40, 400]
for j in ['[i for i in range(N)]', 'np.arange(N)']]
func = ['''c = [a*b for a,b in zip(a, b)]''',
'''c = a * b''']
for i in init:
for f in func:
lines = i.split('\n')
print('{:6.4f}ms -> {}, {}, {}'.format(
timeit.timeit(f, setup=i, number=1000), lines[2], lines[3], f))
gahooa’s answer is correct for the question as phrased in the heading, but if the lists are already numpy format or larger than ten it will be MUCH faster (3 orders of magnitude) as well as more readable, to do simple numpy multiplication as suggested by NPE. I get these timings:
0.0049ms -> N = 4, a = [i for i in range(N)], c = [a*b for a,b in zip(a, b)]
0.0075ms -> N = 4, a = [i for i in range(N)], c = a * b
0.0167ms -> N = 4, a = np.arange(N), c = [a*b for a,b in zip(a, b)]
0.0013ms -> N = 4, a = np.arange(N), c = a * b
0.0171ms -> N = 40, a = [i for i in range(N)], c = [a*b for a,b in zip(a, b)]
0.0095ms -> N = 40, a = [i for i in range(N)], c = a * b
0.1077ms -> N = 40, a = np.arange(N), c = [a*b for a,b in zip(a, b)]
0.0013ms -> N = 40, a = np.arange(N), c = a * b
0.1485ms -> N = 400, a = [i for i in range(N)], c = [a*b for a,b in zip(a, b)]
0.0397ms -> N = 400, a = [i for i in range(N)], c = a * b
1.0348ms -> N = 400, a = np.arange(N), c = [a*b for a,b in zip(a, b)]
0.0020ms -> N = 400, a = np.arange(N), c = a * b
i.e. from the following test program.
import timeit
init = ['''
import numpy as np
N = {}
a = {}
b = np.linspace(0.0, 0.5, len(a))
'''.format(i, j) for i in [4, 40, 400]
for j in ['[i for i in range(N)]', 'np.arange(N)']]
func = ['''c = [a*b for a,b in zip(a, b)]''',
'''c = a * b''']
for i in init:
for f in func:
lines = i.split('\n')
print('{:6.4f}ms -> {}, {}, {}'.format(
timeit.timeit(f, setup=i, number=1000), lines[2], lines[3], f))
回答 10
可以使用枚举。
a = [1, 2, 3, 4]
b = [2, 3, 4, 5]
ab = [val * b[i] for i, val in enumerate(a)]
Can use enumerate.
a = [1, 2, 3, 4]
b = [2, 3, 4, 5]
ab = [val * b[i] for i, val in enumerate(a)]
回答 11
该map功能在这里可能非常有用。使用map我们可以将任何函数应用于可迭代对象的每个元素。
Python 3.x
>>> def my_mul(x,y):
... return x*y
...
>>> a = [1,2,3,4]
>>> b = [2,3,4,5]
>>>
>>> list(map(my_mul,a,b))
[2, 6, 12, 20]
>>>
当然：
map(f, iterable)
相当于
[f(x) for x in iterable]
因此，我们可以通过以下方式获得解决方案：
>>> [my_mul(x,y) for x, y in zip(a,b)]
[2, 6, 12, 20]
>>>
在Python 2.x中map()意味着：将函数应用于可迭代的每个元素并构造一个新列表。在Python 3.x中，map构造迭代器而不是列表。
代替my_mul我们可以使用 mul运算符
Python 2.7
>>>from operator import mul # import mul operator
>>>a = [1,2,3,4]
>>>b = [2,3,4,5]
>>>map(mul,a,b)
[2, 6, 12, 20]
>>>
Python 3.5+
>>> from operator import mul
>>> a = [1,2,3,4]
>>> b = [2,3,4,5]
>>> [*map(mul,a,b)]
[2, 6, 12, 20]
>>>
请注意，由于map()构造了迭代器，因此我们使用*可迭代的拆包运算符来获取列表。解压缩方法比list构造函数要快一些：
>>> list(map(mul,a,b))
[2, 6, 12, 20]
>>>
The map function can be very useful here.
Using map we can apply any function to each element of an iterable.
Python 3.x
>>> def my_mul(x,y):
... return x*y
...
>>> a = [1,2,3,4]
>>> b = [2,3,4,5]
>>>
>>> list(map(my_mul,a,b))
[2, 6, 12, 20]
>>>
Of course:
map(f, iterable)
is equivalent to
[f(x) for x in iterable]
So we can get our solution via:
>>> [my_mul(x,y) for x, y in zip(a,b)]
[2, 6, 12, 20]
>>>
In Python 2.x map() means: apply a function to each element of an iterable and construct a new list.
In Python 3.x, map construct iterators instead of lists.
Instead of my_mul we could use mul operator
Python 2.7
>>>from operator import mul # import mul operator
>>>a = [1,2,3,4]
>>>b = [2,3,4,5]
>>>map(mul,a,b)
[2, 6, 12, 20]
>>>
Python 3.5+
>>> from operator import mul
>>> a = [1,2,3,4]
>>> b = [2,3,4,5]
>>> [*map(mul,a,b)]
[2, 6, 12, 20]
>>>
Please note that since map() constructs an iterator we use * iterable unpacking operator to get a list.
The unpacking approach is a bit faster then the list constructor:
>>> list(map(mul,a,b))
[2, 6, 12, 20]
>>>
回答 12
要维护列表类型，并在一行中完成（当然，在将numpy导入为np之后）：
list(np.array([1,2,3,4]) * np.array([2,3,4,5]))
要么
list(np.array(a) * np.array(b))
To maintain the list type, and do it in one line (after importing numpy as np, of course):
list(np.array([1,2,3,4]) * np.array([2,3,4,5]))
or
list(np.array(a) * np.array(b))
回答 13
您可以将其用于相同长度的列表
def lstsum(a, b):
c=0
pos = 0
for element in a:
c+= element*b[pos]
pos+=1
return c
you can use this for lists of the same length
def lstsum(a, b):
c=0
pos = 0
for element in a:
c+= element*b[pos]
pos+=1
return c
