NumPy User Guide, Release 1.11.0
>>> b[ : ,1] # equivalent to the previous example
array([ 1, 11, 21, 31, 41])
>>> b[1:3, : ] # each column in the second and third row of b
array([[10, 11, 12, 13],
[20, 21, 22, 23]])
When fewer indices are provided than the number of axes, the missing indices are considered complete slices:
>>> b[-1] # the last row. Equivalent to b[-1,:]
array([40, 41, 42, 43])
The expression within brackets in b[i] is treated as an i followed by as many instances of : as needed to represent
the remaining axes. NumPy also allows you to write this using dots as b[i,...].
The dots (...) represent as many colons as needed to produce a complete indexing tuple. For example, if x is a rank
5 array (i.e., it has 5 axes), then
• x[1,2,...] is equivalent to x[1,2,:,:,:],
• x[...,3] to x[:,:,:,:,3] and
• x[4,...,5,:] to x[4,:,:,5,:].
>>> c = np.array( [[[ 0, 1, 2], # a 3D array (two stacked 2D arrays)
... [ 10, 12, 13]],
... [[100,101,102],
... [110,112,113]]])
>>> c.shape
(2, 2, 3)
>>> c[1,...] # same as c[1,:,:] or c[1]
array([[100, 101, 102],
[110, 112, 113]])
>>> c[...,2] # same as c[:,:,2]
array([[ 2, 13],
[102, 113]])
Iterating over multidimensional arrays is done with respect to the first axis:
>>> for row in b:
... print(row)
...
[0 1 2 3]
[10 11 12 13]
[20 21 22 23]
[30 31 32 33]
[40 41 42 43]
However, if one wants to perform an operation on each element in the array, one can use the flat attribute which is
an iterator over all the elements of the array:
>>> for element in b.flat:
... print(element)
...
0
1
2
3
10
11
12
13
12 Chapter 2. Quickstart tutorial
剩余134页未读，继续阅读
k_shmily
- 粉丝: 79
- 资源: 102
我的内容管理
展开
最新资源
- 多模态联合稀疏表示在视频目标跟踪中的应用
- Kubernetes资源管控与Gardener开源软件实践解析
- MPI集群监控与负载平衡策略
- 自动化PHP安全漏洞检测：静态代码分析与数据流方法
- 青苔数据CEO程永：技术生态与阿里云开放创新
- 制造业转型： HyperX引领企业上云策略
- 赵维五分享：航空工业电子采购上云实战与运维策略
- 单片机控制的LED点阵显示屏设计及其实现
- 驻云科技李俊涛：AI驱动的云上服务新趋势与挑战
- 6LoWPAN物联网边界路由器：设计与实现
- 猩便利工程师仲小玉：Terraform云资源管理最佳实践与团队协作
- 类差分度改进的互信息特征选择提升文本分类性能
- VERITAS与阿里云合作的混合云转型与数据保护方案
- 云制造中的生产线仿真模型设计与虚拟化研究
- 汪洋在PostgresChina2018分享：高可用 PostgreSQL 工具与架构设计
- 2018 PostgresChina大会：阿里云时空引擎Ganos在PostgreSQL中的创新应用与多模型存储
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
