在 NumPy 中将 ufunc outer() 函数应用于所有二维数组对

将 **ufunc outer()** 函数应用于所有 2D 数组对。**numpy.ufunc** 包含逐元素操作整个数组的函数。ufunc是用C语言编写的（为了速度），并通过NumPy的ufunc工具链接到Python。

泛型函数（简称ufunc）是在逐元素基础上操作ndarray的函数，支持数组广播、类型转换以及其他一些标准特性。也就是说，ufunc是针对需要固定数量的特定输入并产生固定数量的特定输出的函数的“矢量化”包装器。

步骤

首先，导入所需的库 &minusl;

import numpy as np

创建两个二维数组 -

arr1 = np.array([[5, 10, 15, 20], [25, 30, 35, 40]])
arr2 = np.array([[7, 14, 21, 28, 35]])

显示数组 -

print("Array 1...
", arr1)
print("
Array 2...
", arr2)

获取数组的类型 -

print("
Our Array 1 type...
", arr1.dtype)
print("
Our Array 2 type...
", arr2.dtype)

获取数组的维度 -

print("
Our Array 1 Dimensions...
",arr1.ndim)
print("
Our Array 2 Dimensions...
",arr2.ndim)

获取数组的形状 -

print("
Our Array 1 Shape...
",arr1.shape)
print("
Our Array 2 Shape...
",arr2.shape)

将 ufunc outer() 函数应用于所有数组对 -

res = np.multiply.outer(arr1, arr2)
print("
Result...
",res)
print("
Shape...
",res.shape)

示例

import numpy as np

# The numpy.ufunc has functions that operate element by element on whole arrays.
# ufuncs are written in C (for speed) and linked into Python with NumPy's ufunc facility

# Create two 2D arrays
arr1 = np.array([[5, 10, 15, 20], [25, 30, 35, 40]])
arr2 = np.array([[7, 14, 21, 28, 35]])

# Display the arrays
print("Array 1...
", arr1)
print("
Array 2...
", arr2)

# Get the type of the arrays
print("
Our Array 1 type...
", arr1.dtype)
print("
Our Array 2 type...
", arr2.dtype)

# Get the dimensions of the Arrays
print("
Our Array 1 Dimensions...
",arr1.ndim)
print("
Our Array 2 Dimensions...
",arr2.ndim)

# Get the shape of the Arrays
print("
Our Array 1 Shape...
",arr1.shape)
print("
Our Array 2 Shape...
",arr2.shape)

# Apply the ufunc outer() function to all pairs
res = np.multiply.outer(arr1, arr2)
print("
Result...
",res)
print("
Shape...
",res.shape)

输出

Array 1...
[[ 5 10 15 20]
[25 30 35 40]]

Array 2...
[[ 7 14 21 28 35]]

Our Array 1 type...
int64

Our Array 2 type...
int64

Our Array 1 Dimensions...
2

Our Array 2 Dimensions...
2

Our Array 1 Shape...
(2, 4)

Our Array 2 Shape...
(1, 5)

Result...
[[[[ 35 70 105 140 175]]

[[ 70 140 210 280 350]]

[[ 105 210 315 420 525]]

[[ 140 280 420 560 700]]]


[[[ 175 350 525 700 875]]

[[ 210 420 630 840 1050]]

[[ 245 490 735 980 1225]]

[[ 280 560 840 1120 1400]]]]

Shape...
(2, 4, 1, 5)

更新于：2022年2月7日

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