Numpy array (list of lists), add values at the end

Question

I have two array's:

In [32]: a                                                                      
Out[32]: 
array([[1, 2, 3],
       [2, 3, 4]])

In [33]: b                                                                      
Out[33]: 
array([[ 8,  9],
       [ 9, 10]])

I would like to get the following:

In [35]: c                                                                      
Out[35]: 
array([[ 1,  2,  3,  8,  9],
       [ 2,  3,  4,  9, 10]])

i.e. apped the first and second value of b[0] = array([8, 9]) as the last two values of a[0] and append the first and second value of b[1] = array([9,10]) as the last two values of a[1].

The second answer in this link: How to add multiple extra columns to a NumPy array does not work and I do not understand the accepted answer.

MrNobody33 · Accepted Answer · 2020-06-26 01:38:02Z

You could try with np.hstack:

a=np.array([[1, 2, 3],
           [2, 3, 4]])
    

b=np.array([[ 8,  9],
       [ 9, 10]])
print(np.hstack((a,b)))

output:

[[ 1  2  3  8  9]
 [ 2  3  4  9 10]]

Or since the first answer of link you attached is faster than concatenate, and as you can see G.Anderson's timings, the fastest was concatenate, here is an explanation, so you can use that first answer:

#So you create an array of the same shape that the expected concatenate output:
res = np.zeros((2,5),int)
res
[[0 0 0 0 0]
 [0 0 0 0 0]]

#Then you assign res[:,:3] to fisrt array, where res[:,:3] that is the first 3 elements of each row
res[:,:3]
[[0 0 0]
 [0 0 0]]
res[:,:3]=a   #assign
res[:,:3]
[[1, 2, 3]
 [2, 3, 4]]


#Then you assign res[:,3:] to fisrt array, where res[:,3:] that is the last two elements of eah row
res[:,3:]
[[0 0]
 [0 0]]
res[:,3:]=b  #assign
res[:,3:]
[[ 8,  9]
 [ 9, 10]]

#And finally:
res
[[ 1  2  3  8  9]
 [ 2  3  4  9 10]]

Quang Hoang · Accepted Answer · 2020-06-24 15:44:01Z

1

You can do concatenate:

np.concatenate([a,b], axis=1)

Output:

array([[ 1,  2,  3,  8,  9],
       [ 2,  3,  4,  9, 10]])

answered Jun 24, 2020 at 15:44

Quang Hoang

151k11 gold badges64 silver badges86 bronze badges

Comments

G. Anderson · Accepted Answer · 2020-06-24 15:44:05Z

1

You can use np.append with the axis parameter for joining two arrays on a given axis

np.append(a,b, axis=1)

array([[ 1,  2,  3,  8,  9],
       [ 2,  3,  4,  9, 10]])

Adding timings for the top three answers, for completeness sake. Note that these timings will vary based on the machine running the code, and may scale at different rates for different sizes of array

%timeit np.append(a,b, axis=1)
2.81 µs ± 438 ns per loop

%timeit np.concatenate([a,b], axis=1)
2.32 µs ± 375 ns per loop

%timeit np.hstack((a,b))
4.41 µs ± 489 ns per loop

answered Jun 24, 2020 at 15:44

G. Anderson

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1 Comment

hpaulj Over a year ago

I discourage the use of np.append. It's too tempting to treat it like a list append clone, which it is not.

Thulfiqar · Accepted Answer · 2020-06-24 16:09:34Z

1

from numpy documentation about numpy.concatenate

Join a sequence of arrays along an existing axis.

and from the question, I understood is that what you want

import numpy as np

a = np.array([[1, 2, 3],
       [2, 3, 4]])

b = np.array([[ 8,  9],
       [ 9, 10]])

c = np.concatenate((a, b), axis=1)

print ("a: ", a)
print ("b: ", b)
print ("c: ", c)

output:

a:  [[1 2 3]
 [2 3 4]]

b:  [[ 8  9]
 [ 9 10]]

c:  [[ 1  2  3  8  9]
 [ 2  3  4  9 10]]

answered Jun 24, 2020 at 16:09

Thulfiqar

3927 silver badges16 bronze badges

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