python原生list数组与numpy的array
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python原生list数组与numpy的array
2021年12月22日
在python中存储集合数据可以选择多种原生数据类型,包括list,array,tuple,dictionary四种类型.其中list可变性强,可存储任意内容并且可变,应用范围广泛.而在进行科学运算,存储纯数字时,numpy被广泛应用,可以说基本完全替代了list.那么它们之间有何不同,差距到底有多大,实际过程中应该如何应用呢?
当然,使用实际案例最能说明问题.
运算速度比较
简单的加减乘除,以10000内的数字做个比较.
首先是求和
mylist = []
for i in range(1,10001):
mylist.append(i)
# list
from time import time
start = time()
total=sum(mylist)
print(total)
end = time()
print(f"total:{end-start}s")
## 50005000
## total:0.0003197193145751953s
# numpy np.sum
import numpy as np
myarray = np.array(mylist)
start = time()
total = np.sum(myarray)
print(total)
end = time()
print(f"total:{end-start}s")
## 50005000
## total:0.00041031837463378906s
# numpy sum
start = time()
total = sum(myarray)
print(total)
end = time()
print(f"total:{end-start}s")
## 50005000
## total:0.0012726783752441406s
可以看到,在使用求和时原生的数组求和时间为0.0003,而使用numpy的np.sum却需要0.0004,采用内置的sum求numpy中array的和时耗时最久,为0.001,差不多两倍的时间了.而这还不包括将list转换为array的时间,可见在求和上内置的list明显占据上风.而很多其他文章比较时采用循环的方式当然会慢,但是不符合真实速度.
其次是求积
同样采用mylist数据作为基础,再次比较两者速度
# list
from time import time
start = time()
total = 1
for i in total:
total *= i
end = time()
print(f"total:{end-start}s")
## 50005000
## total:0.0003197193145751953s
# numpy np.sum
import numpy as np
myarray = np.array(mylist)
start = time()
total = np.prod(myarray)
end = time()
print(f"total:{end-start}s")
## total:0.01838994026184082s
## total:0.000213623046875s
在进行连乘时,由于没有内置的乘法,只能采用循环的方式进行,不可避免的造成速度的降低,而numpy由于有prod函数,极大的提升了连乘的计算速度.
本文从实际运算的角度比较了python内置的list与numpy的array的计算速度,发现在计算加和是numpy并不占优势,而且类型转换上会多消耗时间,而在计算连乘时numpy速度提升非常大,因此在计算连乘时numpy下频率高. 总的来说,list应用范围广,求和速度快.而在科学运算,机器学习等领域则使用numpy.因numpy的array在计算连乘等方面速度极快,并且由于pandas的dataframe,series等广泛应用在科学计算上占据绝对优势,也使得其依赖numpy在科学运算中占据绝对优势.
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