Add a Vector to All Rows of a Matrix

add a vector to all rows of a matrix

We can use col to replicate the 'a' elements

b + a[col(b)]
#     [,1] [,2] [,3] [,4] [,5]
#[1,]    2    3    1    1    1
#[2,]    2    3    1    1    1
#[3,]    2    3    1    1    1
#[4,]    2    3    1    1    1
#[5,]    2    3    1    1    1
#[6,]    2    3    1    1    1

Or a faster option would be to use rep

b + rep(a, each = nrow(b))

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Or use sweep

sweep(b, 2, a, "+")

Benchmarks

set.seed(24)
b <- matrix(sample(0:9, 8000*5000, replace=TRUE), ncol=5000)
a <- sample(0:3, 5000, replace=TRUE)
system.time(b + a[col(b)])
#  user  system elapsed 
#  1.08    0.06    1.14 
system.time(b + rep(a, each = nrow(b)))
#   user  system elapsed 
#   0.83    0.03    0.86 

system.time(t(a+t(b)))
#   user  system elapsed 
#   1.14    0.03    1.17 

system.time(sweep(b, 2, a, "+"))
#  user  system elapsed 
#  0.62    0.06    0.69 

Adding a vector to matrix rows in numpy

For adding a 1d array to every row, broadcasting already takes care of things for you:

mat += vec

However more generally you can use np.newaxis to coerce the array into a broadcastable form. For example:

mat + np.ones(3)[np.newaxis,:]

While not necessary for adding the array to every row, this is necessary to do the same for column-wise addition:

mat + np.ones(5)[:,np.newaxis]

EDIT: as Sebastian mentions, for row addition, mat + vec already handles the broadcasting correctly. It is also faster than using np.newaxis. I've edited my original answer to make this clear.

How to assign the same vector in every row of a matrix

You could use replicate to copy directly, without needing to start with an empty matrix

vec <- 1:5

t(replicate(10, vec))
#>       [,1] [,2] [,3] [,4] [,5]
#>  [1,]    1    2    3    4    5
#>  [2,]    1    2    3    4    5
#>  [3,]    1    2    3    4    5
#>  [4,]    1    2    3    4    5
#>  [5,]    1    2    3    4    5
#>  [6,]    1    2    3    4    5
#>  [7,]    1    2    3    4    5
#>  [8,]    1    2    3    4    5
#>  [9,]    1    2    3    4    5
#> [10,]    1    2    3    4    5

How do I (efficiently) add a vector to every row of a matrix in Julia?

Simply prepend . to the + which will allow per element addition:

matr .+ centre

How do I add a vector from a matrix into an empty matrix using for loop in python?

for ii in d:
    yf.append(a[ii])

or

for row in a: 
     yf.append(row)

will clean up the list append

or

yf = [ii for ii in a]

Working example:

In [65]: a = np.linspace(1,30,30).reshape(3,10)
    ...: yf = []
    ...: d = [0,1,2]
    ...: for ii in d:
    ...:   yf.append(a[ii])
    ...: 
In [66]: yf
Out[66]: 
[array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]),
 array([11., 12., 13., 14., 15., 16., 17., 18., 19., 20.]),
 array([21., 22., 23., 24., 25., 26., 27., 28., 29., 30.])]

Turn the list of arrays into an array with:

In [67]: np.array(yf)
Out[67]: 
array([[ 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., 27., 28., 29., 30.]])

or with a function in the concatenate/stack family:

In [69]: np.vstack(yf)
Out[69]: 
array([[ 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., 27., 28., 29., 30.]])

We often get SO questions where people try to use np.append iteratively to do this, but this is harder to get right, and is slower. Repeated list append followed by one array construction at the end is better.

Python Numpy: Adding vector to existing matrix row

In [74]: A = np.arange(1,10).reshape(3,3); v = np.arange(1,4)                                                        
In [75]: A                                                                                                           
Out[75]: 
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])
In [77]: v                                                                                                           
Out[77]: array([1, 2, 3])

Expand A to a (3,3,3):

In [78]: A[None,:,:].repeat(3,0)                                                                                     
Out[78]: 
array([[[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]],

       [[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]],

       [[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]]])

Do the same with v:

In [79]: np.eye(3)                                                                                                   
Out[79]: 
array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])
In [80]: np.eye(3)[:,:,None]*v                                                                                       
Out[80]: 
array([[[1., 2., 3.],
        [0., 0., 0.],
        [0., 0., 0.]],

       [[0., 0., 0.],
        [1., 2., 3.],
        [0., 0., 0.]],

       [[0., 0., 0.],
        [0., 0., 0.],
        [1., 2., 3.]]])

add the two:

In [81]: _78+_80                                                                                                     
Out[81]: 
array([[[ 2.,  4.,  6.],
        [ 4.,  5.,  6.],
        [ 7.,  8.,  9.]],

       [[ 1.,  2.,  3.],
        [ 5.,  7.,  9.],
        [ 7.,  8.,  9.]],

       [[ 1.,  2.,  3.],
        [ 4.,  5.,  6.],
        [ 8., 10., 12.]]])

or in one step:

A+np.eye(3)[:,:,None]*v  

Adding a vector to a specific row of a matrix with numpy

Lets assume you have 2 matrices:

import numpy as np

m1 = np.matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9,]])
m2 = np.empty([8, 8])
m2.fill(0)

And a list of positions is defined:

li = [1, 3, 6]

The list defines to replace values of the matrix m2, by the rows of the matrix m1, at the positions [0][0:2], [2][2:4] and [5][5:7].

Values of numpy arrays can be replaced by numpy.put().

Calculate the indices of the values to be replaced and replace the values:

ri = [(v-1) * m2.shape[1] + v - 1 + j for v in li for j in range(m1.shape[1])]
np.put(m2, ri, m1)

Output:

print(m1)
print(li)
print(m2)

[[1 2 3]
 [4 5 6]
 [7 8 9]]
[1, 3, 6]
[[1. 2. 3. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 4. 5. 6. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 7. 8. 9.]
 [0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0.]]

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If you don't want to replace the indices, but you wat to add the values to the current matrix, then you have to sum the values in a loop, instead of replacing by np.put:

for i in range(len(ri)):
    m2.flat[ri[i]] += m1.flat[i] 

How to append a vector as a column in R matrix?

use cbind

cbind(c(1,2), matrix(1:6, nrow=2))

So in case you work with bigger data, imagine your matrix is saved as m and you have a vector my_vector you want to add as a column in front of this matrix, the command would be

new_m <- cbind(my_vector, m)

Make sure the dimension of your vector fit the number of rows in your matrix.

In case you want to add rows instead of columns, the command is called rbind and is used in exactly the same way.

How to add matrix and vector column-wise?

Make the vector a column:

matrix + vector[:, None]

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