Appending to 2 dimensional array in python

Question

I am reading Data from CSV file which comes similar to the below matrix/array

b = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]

I would like to change the index of every element greater than 1 to a new row in the arraylist

this will make the above array as below

b = [[1,2],[5,6],[9,10],[3,4],[7,8][11,12]]

what i have done in python (but couldn't get the answer)

b = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
c = b
rows = len(b)
columns = len(b[0])
c[4].append(1)
count = 3
for i in  range(rows):
 for j in  range(columns):
     if i > 1:
         for k in columns
             list1 = 
             c.insert(count,list1)
             count = count + 1

Austin · Accepted Answer · 2018-05-01 13:36:18Z

2

You can use numpy. Perform indexing and concatenate at the end:

import numpy as np
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print(np.concatenate((b[:,:2], b[:,2:])))

# [[ 1  2]
#  [ 5  6]                                                    
#  [ 9 10]                                                  
#  [ 3  4]                 
#  [ 7  8]                                                     
#  [11 12]]

edited May 1, 2018 at 13:36

answered May 1, 2018 at 12:41

Austin

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Dariswan Janweri P. · Accepted Answer · 2019-11-29 19:59:33Z

1

data =[]
data.append(['a',1])
data.append(['b',2])
data.append(['c',3])
data.append(['d',4])
print(data)

output

[['a', 1], ['b', 2], ['c', 3], ['d', 4]]

answered Nov 29, 2019 at 19:59

Dariswan Janweri P.

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seralouk · Accepted Answer · 2019-11-29 20:03:37Z

1

One line solution `np.array(b).reshape(-1,2)`:

import numpy as np
b = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]

np.array(b).reshape(-1,2)

array([[ 1,  2],
       [ 3,  4],
       [ 5,  6],
       [ 7,  8],
       [ 9, 10],
       [11, 12]])

answered Nov 29, 2019 at 20:03

seralouk

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Probie · Accepted Answer · 2018-05-01 12:36:05Z

0

Why not just split it into two lists, and then recombine them?

new_elements = []
for i in range(len(b)):
    if len(b[i]) > 2:
        new_elements.append(b[i][2:])
    b[i] = b[i][:2]
b.extend(new_elements)

answered May 1, 2018 at 12:36

Probie

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snooze_bear · Accepted Answer · 2018-05-01 12:47:54Z

0

You might want to use numpy arrays and the concatenate function.

import numpy as np
b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) # or b = np.array(b)
c = np.concatenate((b[:, :2], b[:, 2:]),0)

If you prefer working with python arrays, you can use list interpretation:

c = [row[:2] for row in b]
c.extend([row[2:] for row in b])

which returns

[[1, 2], [5, 6], [9, 10], [3, 4], [7, 8], [11, 12]]

answered May 1, 2018 at 12:47

snooze_bear

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Comments

Rakesh · Accepted Answer · 2018-05-01 12:48:58Z

0

Using a list comprehension.

Ex:

b = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
n = 2
b = [j[i:i+n] for j in b for i in range(0, len(j), n)]
b = b[0::2] + b[1::2]
print(b)

Output:

[[1, 2], [5, 6], [9, 10], [3, 4], [7, 8], [11, 12]]

edited May 1, 2018 at 12:48

answered May 1, 2018 at 12:31

Rakesh

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4 Comments

Androidy Over a year ago

Rakesh, thanks for the quick response but the answer should be [[1,2],[5,6],[9,10],[3,4],[7,8][11,12]]

Androidy Over a year ago

the data actually represent the below

Androidy Over a year ago

studentID StudentName MathGrade ScienceGrade studentID StudentName MathGrade ScienceGrade studentID StudentName MathGrade ScienceGrade (all students data comes in rows which i try to get in column)

Rakesh Over a year ago

Updated snippet.

Vasilis G. · Accepted Answer · 2018-05-01 18:59:10Z

0

Another approach would be this:

b = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]

step = 2
length = len(b[0])
b = [elem[i:i+step] for i in range(0,length,step) for elem in b]
print(b)

Output:

[[1, 2], [5, 6], [9, 10], [3, 4], [7, 8], [11, 12]]

answered May 1, 2018 at 18:59

Vasilis G.

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Mou · Accepted Answer · 2024-10-21 05:29:46Z

0

Use numpy.expand_dims() to have one more axis and append your 2D arrays along that axis.

a1=np.expand_dims(np.array(np.arange(4).reshape(2,-1))+0,0)
a2=np.expand_dims(np.array(np.arange(4).reshape(2,-1))+10,0)
a1_a2=np.concatenate([a1,a2],axis=0)

Outcome shown as print(a1_a2):

array([[[ 0,  1],
        [ 2,  3]],
       [[10, 11],
        [12, 13]]])

answered Oct 21, 2024 at 5:29

Mou

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One line solution `np.array(b).reshape(-1,2)`: