NumPy–算术运算
原文:https://www.geeksforgeeks.org/numpy-arithmetic-operations/
NumPy 是一个执行数组计算(矩阵运算)的开源 Python 库。它是用 C 语言实现的库的包装器，用于执行几个三角、代数和统计运算。NumPy 对象可以很容易地转换成其他类型的对象，如熊猫数据框和张量流张量。Python 列表可以用于数组计算，但是比 NumPy 慢很多。NumPy 使用矢量化实现了快速实现。NumPy 数组的一个重要特性是，开发人员可以用一个命令对每个元素执行相同的数学运算。
让我们理解使用 NumPy 的算术运算。
添加
Python 3
import numpy as np
# Defining both the matrices
a = np.array([5, 72, 13, 100])
b = np.array([2, 5, 10, 30])
# Performing addition using arithmetic operator
add_ans = a+b
print(add_ans)
# Performing addition using numpy function
add_ans = np.add(a, b)
print(add_ans)
# The same functions and operations can be used for multiple matrices
c = np.array([1, 2, 3, 4])
add_ans = a+b+c
print(add_ans)
add_ans = np.add(a, b, c)
print(add_ans)
输出
[ 7 77 23 130]
[ 7 77 23 130]
[ 8 79 26 134]
[ 8 79 26 134]
我们可以看到，矩阵的形状是一样的，如果不同，如果可能的话，Numpy 会尝试广播。读者可以看到，使用算术运算(+)和 numpy 函数(np.add)可以完成相同的运算(加法)。
减法
Python 3
import numpy as np
# Defining both the matrices
a = np.array([5, 72, 13, 100])
b = np.array([2, 5, 10, 30])
# Performing subtraction using arithmetic operator
sub_ans = a-b
print(sub_ans)
# Performing subtraction using numpy function
sub_ans = np.subtract(a, b)
print(sub_ans)
输出
[ 3 67 3 70]
[ 3 67 3 70]
用户也可以用矩阵和常数进行广播
Python 3
import numpy as np
# Defining both the matrices
a = np.array([5, 72, 13, 100])
b = np.array([2, 5, 10, 30])
# Performing subtraction using arithmetic operator
sub_ans = a-b-1
print(sub_ans)
# Performing subtraction using numpy function
sub_ans = np.subtract(a, b, 1)
print(sub_ans)
输出
[ 2 66 2 69]
[ 2 66 2 69]
乘法器
Python 3
import numpy as np
# Defining both the matrices
a = np.array([5, 72, 13, 100])
b = np.array([2, 5, 10, 30])
# Performing multiplication using arithmetic operator
mul_ans = a*b
print(mul_ans)
# Performing multiplication using numpy function
mul_ans = np.multiply(a, b)
print(mul_ans)
输出
[ 10 360 130 3000]
[ 10 360 130 3000]
分部
Python 3
import numpy as np
# Defining both the matrices
a = np.array([5, 72, 13, 100])
b = np.array([2, 5, 10, 30])
# Performing division using arithmetic operators
div_ans = a/b
print(div_ans)
# Performing division using numpy functions
div_ans = np.divide(a, b)
print(div_ans)
输出
[ 2.5 14.4 1.3 3.33333333]
[ 2.5 14.4 1.3 3.33333333]
在 NumPy 中有无数的其他函数，让我们一个接一个地看到其中的一些。
mod()和 power()函数
例
Python 3
# Performing mod on two matrices
mod_ans = np.mod(a, b)
print(mod_ans)
#Performing remainder on two matrices
rem_ans=np.remainder(a,b)
print(rem_ans)
# Performing power of two matrices
pow_ans = np.power(a, b)
print(pow_ans)
输出
[ 1 2 3 10]
[ 1 2 3 10]
[ 25 1934917632 137858491849
1152921504606846976]
一些聚合和统计函数
例
Python 3
# Getting mean of all numbers in 'a'
mean_a = np.mean(a)
print(mean_a)
# Getting average of all numbers in 'b'
mean_b = np.average(b)
print(mean_b)
# Getting sum of all numbers in 'a'
sum_a = np.sum(a)
print(sum_a)
# Getting variance of all number in 'b'
var_b = np.var(b)
print(var_b)
输出
47.5
11.75
190
119.1875
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