在python中向矩阵添加列

Question

在python中有什么方法可以将列添加到矩阵中吗？我想在python中的mxn矩阵的开头添加一个列。例如，我有1000x100矩阵，我想将它变成1000x101矩阵。我想在开头插入一个新的列，其中包含所有的ones，也就是说，它将是我的新的第一个列。在python中有可能吗？

这是我的密码- vector1是一个列表，cnt是1000

data=np.array(vector1)  
shape = ( cnt, 100 )
data=data.reshape(shape)

现在，我想在所有列的开头添加一个新列

Answer 1

我发现numpy.c_函数在向矩阵添加列时非常方便。 下面的代码将向矩阵中添加一个全为零的列。

import numpy as np

np.c_[np.ones((100,1)),X]

这里，X是原始矩阵。

Answer 2

在numpy.hstack和numpy.ones中查找的函数：

例如

import numpy as np

X = np.random.uniform(size=(10,3))
n,m = X.shape # for generality
X0 = np.ones((n,1))
Xnew = np.hstack((X,X0))

print(X)
[[ 0.78614426  0.24150772  0.94330932]
 [ 0.60088812  0.20427371  0.19453546]
 [ 0.31853252  0.31669057  0.82782995]
 [ 0.71749368  0.54609844  0.74924888]
 [ 0.86883981  0.54634575  0.83232409]
 [ 0.89313181  0.8006561   0.05072146]
 [ 0.79492088  0.07750024  0.45762175]
 [ 0.92350837  0.20587178  0.76987197]
 [ 0.0092076   0.0044617   0.04673518]
 [ 0.69569363  0.3315923   0.15093861]]

print(X0)
[[ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]]

print(Xnew)
[[ 0.78614426  0.24150772  0.94330932  1.        ]
 [ 0.60088812  0.20427371  0.19453546  1.        ]
 [ 0.31853252  0.31669057  0.82782995  1.        ]
 [ 0.71749368  0.54609844  0.74924888  1.        ]
 [ 0.86883981  0.54634575  0.83232409  1.        ]
 [ 0.89313181  0.8006561   0.05072146  1.        ]
 [ 0.79492088  0.07750024  0.45762175  1.        ]
 [ 0.92350837  0.20587178  0.76987197  1.        ]
 [ 0.0092076   0.0044617   0.04673518  1.        ]
 [ 0.69569363  0.3315923   0.15093861  1.        ]]

Answer 3

data=np.loadtxt('ex1data1.txt',delimiter=',')
X=data[:,0]
Y=data[:,1]
X=(np.column_stack((np.ones(np.size(X)),X)))

打印x给出

[[ 1.      6.1101]
 [ 1.      5.5277]
 [ 1.      8.5186]
 [ 1.      7.0032]
 [ 1.      5.8598]
 [ 1.      8.3829]
 [ 1.      7.4764]
 [ 1.      8.5781]
 [ 1.      6.4862]
 [ 1.      5.0546]
 [ 1.      5.7107]
 [ 1.     14.164 ]
 [ 1.      5.734 ]
 [ 1.      8.4084]
 [ 1.      5.6407]
 [ 1.      5.3794]
 [ 1.      6.3654]
 [ 1.      5.1301]
 [ 1.      6.4296]
 [ 1.      7.0708]
 [ 1.      6.1891]
 [ 1.     20.27  ]
 [ 1.      5.4901]
 [ 1.      6.3261]
 [ 1.      5.5649]
 [ 1.     18.945 ]
 [ 1.     12.828 ]
 [ 1.     10.957 ]
 [ 1.     13.176 ]
 [ 1.     22.203 ]
 [ 1.      5.2524]
 [ 1.      6.5894]
 [ 1.      9.2482]
 [ 1.      5.8918]
 [ 1.      8.2111]
 [ 1.      7.9334]
 [ 1.      8.0959]
 [ 1.      5.6063]
 [ 1.     12.836 ]
 [ 1.      6.3534]
 [ 1.      5.4069]
 [ 1.      6.8825]
 [ 1.     11.708 ]
 [ 1.      5.7737]
 [ 1.      7.8247]
 [ 1.      7.0931]
 [ 1.      5.0702]
 [ 1.      5.8014]
 [ 1.     11.7   ]
 [ 1.      5.5416]
 [ 1.      7.5402]
 [ 1.      5.3077]
 [ 1.      7.4239]
 [ 1.      7.6031]
 [ 1.      6.3328]
 [ 1.      6.3589]
 [ 1.      6.2742]
 [ 1.      5.6397]
 [ 1.      9.3102]
 [ 1.      9.4536]
 [ 1.      8.8254]
 [ 1.      5.1793]
 [ 1.     21.279 ]
 [ 1.     14.908 ]
 [ 1.     18.959 ]
 [ 1.      7.2182]
 [ 1.      8.2951]
 [ 1.     10.236 ]
 [ 1.      5.4994]
 [ 1.     20.341 ]
 [ 1.     10.136 ]
 [ 1.      7.3345]
 [ 1.      6.0062]
 [ 1.      7.2259]
 [ 1.      5.0269]
 [ 1.      6.5479]
 [ 1.      7.5386]
 [ 1.      5.0365]
 [ 1.     10.274 ]
 [ 1.      5.1077]
 [ 1.      5.7292]
 [ 1.      5.1884]
 [ 1.      6.3557]
 [ 1.      9.7687]
 [ 1.      6.5159]
 [ 1.      8.5172]
 [ 1.      9.1802]
 [ 1.      6.002 ]
 [ 1.      5.5204]
 [ 1.      5.0594]
 [ 1.      5.7077]
 [ 1.      7.6366]
 [ 1.      5.8707]
 [ 1.      5.3054]
 [ 1.      8.2934]
 [ 1.     13.394 ]
 [ 1.      5.4369]]

我发现column_stack工作得很完美…希望它能帮上忙