Numpy学习（2）

Numpy基本知识

1、基本运算

import numpy as np
# 仿照列表排序
A = np.arange(14,2,-1).reshape((3,4)) # -1表示反向递减一个步长
print(A)
'''
[[14 13 12 11]
 [10  9  8  7]
 [ 6  5  4  3]]
'''
print(np.sort(A))
'''
[[11 12 13 14]
 [ 7  8  9 10]
 [ 3  4  5  6]]
'''

# 矩阵转置
print(np.transpose(A))\
'''
[[14 10  6]
 [13  9  5]
 [12  8  4]
 [11  7  3]]
'''

clip(Array,Array_min,Array_max)

将Array_min<X<Array_max X表示矩阵A中的数，如果满足上述关系，则原数不变。

否则，如果X<Array_min，则将矩阵中X变为Array_min;

如果X>Array_max，则将矩阵中X变为Array_max.

print(np.clip(A,5,9))
'''
[[9 9 9 9]
 [9 9 8 7]
 [6 5 5 5]]
'''

累差运算

B = np.array([[3,5,9],
              [4,8,10]])
print(np.diff(B))
'''
[[2 4]
 [4 2]]
'''

多维转一维

A = np.arange(3,15).reshape((3,4))
# print(A)
print(A.flatten())
# flat是一个迭代器，本身是一个object属性
'''
[ 3  4  5  6  7  8  9 10 11 12 13 14]
'''

for item in A.flat:
    print(item)
'''
3
4
5
6
7
8
9
10
11
12
13
14
'''

2、数组合并

import numpy as np
A = np.array([1,1,1])
B = np.array([2,2,2])
print(np.vstack((A,B)))
# vertical stack 上下合并,对括号的两个整体操作。
'''
[[1 1 1]
 [2 2 2]]
'''

C = np.vstack((A,B))
print(C)
'''
[[1 1 1]
 [2 2 2]]
'''
print(A.shape,B.shape,C.shape)# 从shape中看出A,B均为拥有3项的数组(数列)
'''
(3,) (3,) (2, 3)
'''

# horizontal stack左右合并
D = np.hstack((A,B))
print(D)
'''
[1 1 1 2 2 2]
'''
print(A.shape,B.shape,D.shape)
# (3,) (3,) (6,)
# 对于A,B这种，为数组或数列，无法进行转置，需要借助其他函数进行转置
'''
(3,) (3,) (6,)
'''

3、数组转置为矩阵

print(A[np.newaxis,:]) # [1 1 1]变为[[1 1 1]]
'''
[[1 1 1]]
'''
print(A[np.newaxis,:].shape) # (3,)变为(1, 3)
'''
(1, 3)
'''
print(A[:,np.newaxis])
'''
[[1]
 [1]
 [1]]
'''

4、多个矩阵合并

A = A[:,np.newaxis] # 数组转为矩阵
B = B[:,np.newaxis] # 数组转为矩阵

print(A)
'''
[[1]
 [1]
 [1]]
'''

print(B)
'''
[[2]
 [2]
 [2]]
'''

# axis=0纵向合并
C = np.concatenate((A,B,B,A),axis=0)
print(C)
'''
[[1]
 [1]
 [1]
 [2]
 [2]
 [2]
 [2]
 [2]
 [2]
 [1]
 [1]
 [1]]
'''

# axis=1横向合并
C = np.concatenate((A,B),axis=1)
print(C)
'''
[[1 2]
 [1 2]
 [1 2]]
'''

5、array分割

import numpy as np
A = np.arange(12).reshape((3,4))
print(A)
'''
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
'''

等量分割

# 等量分割
# 纵向分割同横向合并的axis
print(np.split(A, 2, axis=1))
'''
[array([[0, 1],
       [4, 5],
       [8, 9]]), array([[ 2,  3],
       [ 6,  7],
       [10, 11]])]
'''

# 横向分割同纵向合并的axis
print(np.split(A,3,axis=0))
'''
[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,  9, 10, 11]])]
'''

不等量分割

print(np.array_split(A,3,axis=1))
'''
[array([[0, 1],
       [4, 5],
       [8, 9]]), array([[ 2],
       [ 6],
       [10]]), array([[ 3],
       [ 7],
       [11]])]
'''

其他的分割方式

# 横向分割
print(np.vsplit(A,3)) # 等价于print(np.split(A,3,axis=0))
'''
[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,  9, 10, 11]])]
'''

# 纵向分割
print(np.hsplit(A,2)) # 等价于print(np.split(A,2,axis=1))
'''
[array([[0, 1],
       [4, 5],
       [8, 9]]), array([[ 2,  3],
       [ 6,  7],
       [10, 11]])]
'''

6、Numpy copy与 =

=赋值方式会带有关联性

import numpy as np
# `=`赋值方式会带有关联性
a = np.arange(4)
print(a) # [0 1 2 3]

b = a
c = a
d = b
a[0] = 11
print(a) # [11  1  2  3]

print(b) # [11  1  2  3]

print(c) # [11  1  2  3]

print(d) # [11  1  2  3]

print(b is a) # True

print(c is a) # True

print(d is a) # True

d[1:3] = [22,33]
print(a) # [11 22 33  3]

print(b) # [11 22 33  3]

print(c) # [11 22 33  3]

copy()赋值方式没有关联性

a = np.arange(4)
print(a) # [0 1 2 3]

b =a.copy() # deep copy
print(b) # [0 1 2 3]

a[3] = 44
print(a) # [ 0  1  2 44]
print(b) # [0 1 2 3]

# 此时a与b已经没有关联