如何在python中将向量附加到矩阵
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How to append a vector to a matrix in python
提问by Hadi
I want to append a vector to a matrix in python. I tried appendor concatenatemethods but I didn't get the answer. I was previously working with Matlab and there I used this:
我想在 python 中将一个向量附加到一个矩阵。我试过append或concatenate方法，但我没有得到答案。我以前使用过 Matlab，在那里我使用了这个：
m = zeros(10, 4) % define my matrix, 10x4
v = ones(10, 1) % my vecto, 10x1
c = [m,v] % so simple! the result is: 10x5 (the vector added as the last column)
How can I do that in python using numpy?
我如何使用 numpy 在 python 中做到这一点？
采纳答案by Joe Kington
You're looking for np.r_and np.c_. (Think "column stack" and "row stack" (which are also functions) but with matlab-style range generations.)
您正在寻找np.r_和np.c_。（想想“列堆栈”和“行堆栈”（它们也是函数），但使用 matlab 样式的范围生成。）
Also see np.concatenate, np.vstack, np.hstack, np.dstack, np.row_stack, np.column_stacketc.
另见np.concatenate，np.vstack，np.hstack，np.dstack，np.row_stack，np.column_stack等。
For example:
例如：
import numpy as np
m = np.zeros((10, 4))
v = np.ones((10, 1))
c = np.c_[m, v]
Yields:
产量：
array([[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.]])
This is also equivalent to np.hstack([m, v])or np.column_stack([m, v])
这也相当于np.hstack([m, v])或np.column_stack([m, v])
If you're not coming from matlab, hstackand column_stackprobably seem much more readable and descriptive. (And they're arguably better in this case for that reason.)
如果您不是来自 matlab，hstack并且column_stack可能看起来更具可读性和描述性。（出于这个原因，他们在这种情况下可以说更好。）
However, np.c_and np.r_have additional functionality that folks coming from matlab tend to expect. For example:
但是，np.c_并np.r_具有来自 matlab 的人们倾向于期望的附加功能。例如：
In [7]: np.r_[1:5, 2]
Out[7]: array([1, 2, 3, 4, 2])
Or:
或者：
In [8]: np.c_[m, 0:10]
Out[8]:
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 2.],
[ 0., 0., 0., 0., 3.],
[ 0., 0., 0., 0., 4.],
[ 0., 0., 0., 0., 5.],
[ 0., 0., 0., 0., 6.],
[ 0., 0., 0., 0., 7.],
[ 0., 0., 0., 0., 8.],
[ 0., 0., 0., 0., 9.]])
At any rate, for matlab folks, it's handy to know about np.r_and np.c_in addition to vstack, hstack, etc.
无论如何，对于MATLAB人，这是很方便的了解np.r_和np.c_除vstack，hstack等等。
回答by dawg
In numpy it is similar:
在 numpy 中它是类似的：
>>> m=np.zeros((10,4))
>>> m
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]])
>>> v=np.ones((10,1))
>>> v
array([[ 1.],
[ 1.],
[ 1.],
[ 1.],
[ 1.],
[ 1.],
[ 1.],
[ 1.],
[ 1.],
[ 1.]])
>>> np.c_[m,v]
array([[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.],
[ 0., 0., 0., 0., 1.]])
