python--numpy下_a.reshape(1,a.size)-优快云博客

本文详细介绍了使用Python的NumPy库进行数组操作的方法，包括数组的合并、分割以及赋值等核心操作。通过实例展示了如何利用NumPy进行高效的数据处理。

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import numpy as np

A = np.array([1,1,1])
B = np.array([2,2,2])

#数组上下合并
C = np.vstack((A,B))
print(C)
"""
[[1 1 1]
 [2 2 2]]
"""

#左右合并
D = np.hstack((A,B))
print(D)
"""
[1 1 1 2 2 2]
"""

print(A.shape,C.shape,D.shape)
"""
(3,) (2, 3) (6,)
"""

#将数组转化为纵向
print(A[:,np.newaxis])
"""
[[1]
 [1]
 [1]]
"""
print(A.reshape(A.size,1))
"""
[[1]
 [1]
 [1]]
"""
print(A.reshape(-1,1))
"""
[[1]
 [1]
 [1]]
"""

#纵向组合
D = np.hstack((A[:,np.newaxis],B[:,np.newaxis]))
print(D)
"""
[[1 2]
 [1 2]
 [1 2]]
"""

E = np.c_[A,B]
print(E)
"""
[[1 2]
 [1 2]
 [1 2]]
"""

D = np.concatenate((A[:,np.newaxis],B[:,np.newaxis],B[:,np.newaxis]),axis=1)
print(D)
"""
[[1 2 2]
 [1 2 2]
 [1 2 2]]
"""

D = np.hstack((A[:,np.newaxis],B[:,np.newaxis],B[:,np.newaxis]))
print(D)
"""
[[1 2 2]
 [1 2 2]
 [1 2 2]]
"""




#数组分割
a = np.arange(12).reshape((3,4))
print(a)
"""
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
"""

#横向分割,列分割(均等)
c,d = np.split(a,2,axis=1)
print(c,d)
"""
[[0 1]
 [4 5]
 [8 9]] [[ 2  3]
 [ 6  7]
 [10 11]]
"""

print(np.hsplit(a,2))
"""
[array([[0, 1],
       [4, 5],
       [8, 9]]), array([[ 2,  3],
       [ 6,  7],
       [10, 11]])]
"""


#纵向分割,行分割(均等)
print(np.split(a,1,axis=0))
"""
[array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])]
"""
print(np.split(a,3,axis=0))
"""
[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,  9, 10, 11]])]
"""
#print(np.split(a,6,axis=0))
"""
ValueError: array split does not result in an equal division
"""

print(np.vsplit(a,3))
"""
[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,  9, 10, 11]])]
"""



#纵向分割,行分割(不均等)
print(np.array_split(a,3,axis=1))
"""
[array([[0, 1],
       [4, 5],
       [8, 9]]), array([[ 2],
       [ 6],
       [10]]), array([[ 3],
       [ 7],
       [11]])]
"""



#赋值篇
a = np.arange(4)
b=a
d=b
print(a)
"""
[0 1 2 3]
"""
a[0]=5
print(a)
"""
[5 1 2 3]
"""
#判断b是否完全是a(地址和值)
print(b is a)
"""
True
"""
print(b)
"""
[5 1 2 3]
"""

print(d is a)
"""
True
"""
d[1:3] = [22,33]
print(a)
"""
[ 5 22 33  3]
"""

#copy拥有一样的值，但不关联
b = a.copy()
print(b is a)
"""
False
"""
print(b)
""""
[ 5 22 33  3]
"""
a[1] = 222
print(b)
"""
[ 5 22 33  3]
"""

b = a[:]
print(b is a)
"""
False
"""

B站：https://www.bilibili.com/video/av16378934/?p=1