November 2023
Intermediate to advanced
512 pages
11h 53m
Chinese
Content preview from 利用 Python 进行数据分析：原书第 3 版
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448
|
附录
A
表
A-1
中列出了所有关于拼接和拆分的函数，其中一些仅作为通用 concatenate 的便捷
方法。
表 A-1：数组拼接和拆分函数
函数 说明
concatenate
最通用的函数
，
沿特定轴拼接数组集合
vstack、 row_stack
按行堆叠数组
（
沿着轴 0
）
hstack
按列堆叠数组
（
沿着轴 1
）
column_stack
类似于 hstack
，
但是会先将一维数组转换为二维列向量
dstack
按
“
深度
”
堆叠数组
（
沿着轴 2
）
split
沿指定轴
，
在传入的位置拆分数组
hsplit/vsplit
分别沿轴 0
、
轴 1 进行拆分的便捷函数
堆叠辅助方法：
r_
和
c_
NumPy
命名空间中有两个特殊的对象 r_ 和 c_，它们可以使数组的堆叠操作更简洁：
它们还可以将切片转换成数组：
高阶
NumPy
|
449
r_ 和 c_ 的具体功能请参考其文档字符串。
A.2.4
元素的重复操作：
tile
和
repeat
对数组进行重复操作或复制数组以生成更大数组的工具，主要是 repeat 和 tile 这两个
函数。repeat 会将数组中的各个元素重复一定次数，从而产生一个更大的数组：
与其他流行的数组编程语言（如
MATLAB
）不同，
NumPy
中很少需要对数
组进行复制或重复。这主要是因为广播机制能更好地满足该需求 ...
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