一、列表切片（一维数组）
1.1、切片原理
列表切片是从原始列表中提取列表的一部分的过程。在列表切片中，我们将根据所需内容（如，从何处开始，结束以及增量进行切片）剪切列表。Python中符合序列的有序序列都支持切片（slice），例如列表，字符串，元组。
规则：
存储对象[start : end : step]
start : 起始索引，从0开始，-1表示结束
end：结束索引,不包含
step：步长；步长为正时，从左向右取值。步长为负时，反向取值
1.2、切片使用
1.2.1、获取列表中的元素
>>> l1 = [3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1
[3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> midd_num=int(len(l1)/2)
>>> midd_num
5
>>> l1[midd_num:] # 获取下标 5 及其之后的数据
[15, 17, 20, 23, 25]
>>> l1[:midd_num] # 获取下标 5 之前的数据
[3, 5, 7, 10, 13]
>>> l1[-1] # 获取列表最后一个数据
25
>>> l1[-2] # 获取列表逆序第二个数据
23
>>> l1[-2:] # 获取列表逆序后两个数据
[23, 25]
>>> l1[2:8] # 获取列表3-8d的数据
[7, 10, 13, 15, 17, 20]
>>> l1[::2] # 获取整个列表且步长为2
[3, 7, 13, 17, 23]
>>> l1[1::2] # 从第二个开始获取整个列表且步长为2
[5, 10, 15, 20, 25]
>>> l1[0:90:2] # ！！！不存在越界问题，体现了健壮性
[3, 7, 13, 17, 23]
1.2.2、列表逆序
通过设置步长为 -1实现，如下：
>>> l1[::-1]
[25, 23, 20, 17, 15, 13, 10, 7, 5, 3]
1.2.3、修改列表元素
切片赋值的办法实现，如下：
>>> l1
[3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1[0:1]
[3]
>>> l1[0:1]=["hello"]
>>> l1
['hello', 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1[1:2]
[5]
>>> l1[1:2]="world" # 注意赋值类型需要为列表
>>> l1
['hello', 'w', 'o', 'r', 'l', 'd', 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1[0:2]
[3, 5]
>>> l1[0:2]=["hello", "world"] # 同时修改多个数据
>>> l1
['hello', 'world', 7, 10, 13, 15, 17, 20, 23, 25]
1.2.4、插入新元素
在空白处插入，如下：
>>> l1=[3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1[:0]=["nihao"]
>>> l1
['nihao', 3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1=[3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1[:1]=["nihao","shijie"] # 会替换掉 '3'
>>> l1
['nihao', 'shijie', 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1=[3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1[:1]
[3]
>>> l1[:0]=["nihao","shijie"] # 插入多个
>>> l1
['nihao', 'shijie', 3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1=[3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1[5]
15
>>> l1[5:5]=["nihao", "shijie"]
>>> l1
[3, 5, 7, 10, 13, 'nihao', 'shijie', 15, 17, 20, 23, 25]
1.2.5、删除元素
给列表某个值赋空值，如下：
>>> l1=[3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1[:3]
[3, 5, 7]
>>> l1[:3]=[]
>>> l1
[10, 13, 15, 17, 20, 23, 25]
>>> l1=[3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l1[:3]
[3, 5, 7]
>>> del(l1[:3]) # 同样可以实现上面结果
>>> l1
[10, 13, 15, 17, 20, 23, 25]
1.2.6、复制元素（浅拷贝）
>>> l1=[3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l2=l1[:]
>>> l2
[3, 5, 7, 10, 13, 15, 17, 20, 23, 25]
>>> l2 is l1
False
>>> l2=l1
>>> l2 is l1
True
>>> import copy
>>> l2=copy.copy(l1) # 浅拷贝
>>> l2 is l1
False
>>> l2=copy.deepcopy(l1) # 深拷贝
>>> l2 is l1
False
二、多维数组切片
多为数组的切片操作与一维数组一致，不同维度上的操作使用’,'隔开就好
2.1、认识数组的维度
arr.ndim
>>> ar1=np.arange(12).reshape((4, 3))
>>> ar1
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
>>> ar1.ndim
2
>>> ar1=np.arange(27).reshape((3,3,3))
>>> ar1
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]],
[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]]])
>>> ar1.ndim
3
>>> ar1[:] # 0 维取全部
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]],
[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]]])
>>> ar1[:,0] # 获取每维数组的第一行
array([[ 0, 1, 2],
[ 9, 10, 11],
[18, 19, 20]])
>>> ar1[:,0,0] # 获取每维数组的第一行的第一个元素
array([ 0, 9, 18])
# 认识数组的维度可以查看ar1.ndim ,也可以查看数组的'['层数
2.2、多维数组切片使用
2.2.1、获取列表中的元素
>>> ar1=np.arange(27).reshape((3,3,3))
>>> ar1
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]],
[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]]])
>>> ar1[0] # 获取数组的0维
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> ar1[1] # 获取数组的1维
array([[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]])
>>> ar1[2] # 获取数组的2维
array([[18, 19, 20],
[21, 22, 23],
[24, 25, 26]])
>>> ar1[0,0]
array([0, 1, 2])
>>> ar1[0,0,1]
1
>>> ar1[1,2,1]
16
>>> ar1[1,0,0:2]
array([ 9, 10])
>>> ar1[1,0,-2]
10
>>> ar1[1,0,-2:]
array([10, 11])
2.2.2、数组逆序
>>> ar1=np.arange(27).reshape((3,3,3))
>>> ar1
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]],
[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]]])
>>> ar1[1,0,::-1] # 第2维逆序
array([11, 10, 9])
>>> ar1[1,::-1] # 第1 维逆序
array([[15, 16, 17],
[12, 13, 14],
[ 9, 10, 11]])
>>> ar1[::-1] # 整个数组逆序
array([[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]],
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]],
[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]]])
>>> ar1[::-1,::-1] # 第0、1维逆序
array([[[24, 25, 26],
[21, 22, 23],
[18, 19, 20]],
[[15, 16, 17],
[12, 13, 14],
[ 9, 10, 11]],
[[ 6, 7, 8],
[ 3, 4, 5],
[ 0, 1, 2]]])
>>> ar1[::-1,::-1,::-1] # 第0、1和2维逆序
array([[[26, 25, 24],
[23, 22, 21],
[20, 19, 18]],
[[17, 16, 15],
[14, 13, 12],
[11, 10, 9]],
[[ 8, 7, 6],
[ 5, 4, 3],
[ 2, 1, 0]]])
>>> ar1[1,:,:]
array([[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]])
>>> ar1[1,...] # 对于大于等于三维的数组，可以使用...来简化操作
array([[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]])
2.2.3、修改列表元素
>>> ar1[0,0,1]=999
>>> ar1
array([[[ 0, 999, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
[[ 9, 10, 11],
[ 12, 13, 14],
[ 15, 16, 17]],
[[ 18, 19, 20],
[ 21, 22, 23],
[ 24, 25, 26]]])
>>> ar1[0,1]
array([3, 4, 5])
>>> ar1[0,1]=[123, 456, 789]
>>> ar1
array([[[ 0, 999, 2],
[123, 456, 789],
[ 6, 7, 8]],
[[ 9, 10, 11],
[ 12, 13, 14],
[ 15, 16, 17]],
[[ 18, 19, 20],
[ 21, 22, 23],
[ 24, 25, 26]]])
2.2.4、插入新元素
多维数组空白处插入数据不生效
>>> ar1[0,0,:0]=[58]
>>> ar1[0,0]
array([0, 1, 2])
>>> ar1
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]],
[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]]])
2.2.5、删除元素
多维数组无法直接删除，报错如下：
>>> ar1[0,1,2]=[]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: setting an array element with a sequence.
2.2.6、复制元素（浅拷贝）
>>> ar1
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]],
[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]]])
>>> ar3=ar1[:]
>>> ar3
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]],
[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]]])
>>> ar3 is ar1
False
>>> ar3=ar1
>>> ar3 is ar1
True
>>> import copy
>>> ar3=copy.copy(ar1)
>>> ar3
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]],
[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]]])
>>> ar3 is ar1
False
>>> ar3=copy.deepcopy(ar1)
>>> ar3
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]],
[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]]])
>>> ar3 is ar1
False
三、参考文档
1、https://blog.csdn.net/hlx20080808/article/details/127610664
2、http://coolpython.net/data_analysis/numpy/numpy-del-item.html
3、https://www.bbsmax.com/A/gAJGw4g1JZ/文章来源：https://www.toymoban.com/news/detail-418057.html
4、https://blog.csdn.net/weixin_36670529/article/details/111307004文章来源地址https://www.toymoban.com/news/detail-418057.html
到了这里，关于Numpy 数组切片的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！
