12-numpy笔记-莫烦基本操作2-优快云博客

本文详细介绍了使用NumPy进行数组创建、索引、迭代、连接、分割等操作的方法。通过实例演示了如何进行数组的重塑、切片、迭代、合并及拆分，以及数组元素的复制与深拷贝，为读者提供了全面的NumPy数组操作指南。

代码

import numpy as np
 
A = np.arange(3,15)
print('-1-')
print(A)
print('-2-')
print(A[3])
 
A = np.arange(3,15).reshape((3,4))
print('-3-')
print(A[1])
 
print('-4-')
print(A[2][1])
 
# 第一行和第二行
print('-5-')
print(A[1:3])

print('-6-')
for row in A:
    print (row)
 
print('-7-')
for column in A.T:
    print (column)
 
print('-8-')
for item in A.flat:
    print (item)
 
# 迭代器
print('-9-')
print(A.flat)
 
# 展成一行
print('-10-')
print(A.flatten())
 
A = np.array([1,1,1])
B = np.array([2,2,2])
 
# 上下合并
print('-11-')
print(np.vstack((A,B)))
 
C = np.vstack((A,B))
print('-12-')
print(A.shape, C.shape)
 
# 左右合并
D = np.hstack((A,B))
print('-13-')
print(D)
print('-14-')
print(A.shape, D.shape)
 
#横向的数列转化到列，行向加维度
print('-15-')
print(A[np.newaxis,:])
 
#纵向的数列转化到行，纵向的添加维度
print('-16-')
print(A[:,np.newaxis])
 
A = np.array([1,1,1])[:,np.newaxis]
B = np.array([2,2,2])[:,np.newaxis]
 
# 三个向量的横向合并
print('-17-')
print(np.hstack((A,A,B)))
 
# 三个向量的竖向合并
C = np.concatenate((A,B,B,A))
print('-18-')
print(C)
 
# 三个向量的竖向合并
C = np.concatenate((A,B,B,A), axis=0)
print('-19-')
print(C)
 
# 三个向量的横向合并
C = np.concatenate((A,B,B,A), axis=1)
print('-20-')
print(C)
 
A = np.arange(12).reshape((3,4))
print('-21-')
print(A)
 
#分成两块，按列划分，只能进行相等的划分
print('-22-')
print(np.split(A,2,axis = 1))
 
print('-23-')
print(np.split(A,3,axis = 0))
 
#分成两块，按列划分，进行不相等的划分
print('-24-')
print(np.array_split(A,3,axis = 1))
 
# 垂直划分
print('-25-')
print(np.vsplit(A,3))
# 竖直划分
print('-26-')
print(np.hsplit(A,2))
 
a=np.arange(4)
b = a # 引用复制
c = a # abcd都是一样
d = a
 
a[0] = 11
 
print('-27-')
print(b,c,d) # 都是11
 
d is a
 
e = a.copy() # deep copy

输出

-1-
[ 3  4  5  6  7  8  9 10 11 12 13 14]
-2-
6
-3-
[ 7  8  9 10]
-4-
12
-5-
[[ 7  8  9 10]
 [11 12 13 14]]
-6-
[3 4 5 6]
[ 7  8  9 10]
[11 12 13 14]
-7-
[ 3  7 11]
[ 4  8 12]
[ 5  9 13]
[ 6 10 14]
-8-
3
4
5
6
7
8
9
10
11
12
13
14
-9-
<numpy.flatiter object at 0x000002A6F47AB7B0>
-10-
[ 3  4  5  6  7  8  9 10 11 12 13 14]
-11-
[[1 1 1]
 [2 2 2]]
-12-
(3,) (2, 3)
-13-
[1 1 1 2 2 2]
-14-
(3,) (6,)
-15-
[[1 1 1]]
-16-
[[1]
 [1]
 [1]]
-17-
[[1 1 2]
 [1 1 2]
 [1 1 2]]
-18-
[[1]
 [1]
 [1]
 [2]
 [2]
 [2]
 [2]
 [2]
 [2]
 [1]
 [1]
 [1]]
-19-
[[1]
 [1]
 [1]
 [2]
 [2]
 [2]
 [2]
 [2]
 [2]
 [1]
 [1]
 [1]]
-20-
[[1 2 2 1]
 [1 2 2 1]
 [1 2 2 1]]
-21-
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
-22-
[array([[0, 1],
       [4, 5],
       [8, 9]]), array([[ 2,  3],
       [ 6,  7],
       [10, 11]])]
-23-
[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,
9, 10, 11]])]
-24-
[array([[0, 1],
       [4, 5],
       [8, 9]]), array([[ 2],
       [ 6],
       [10]]), array([[ 3],
       [ 7],
       [11]])]
-25-
[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,
9, 10, 11]])]
-26-
[array([[0, 1],
       [4, 5],
       [8, 9]]), array([[ 2,  3],
       [ 6,  7],
       [10, 11]])]
-27-
[11  1  2  3] [11  1  2  3] [11  1  2  3]

转载于:https://www.cnblogs.com/alexYuin/p/9599746.html