Transform a column into specific rows using numpy

Question

I am trying to extract a column and arrange into multiple rows. My Input: data

-2.74889,1.585,223.60

-2.74889,1.553,228.60

-2.74889,1.423,246.00

-2.74889,1.236,249.10

-2.74889,0.928,243.80

-2.74889,0.710,242.20

-2.74889,0.558,243.50
...
...
...

k = np.reshape(data[:,2], (2,10))

Output:

[[ 223.6   228.6   246.    249.1   243.8   242.2   243.5   244.    244.8
   245.2 ]

 [ 224.6   230.    250.7   249.3   244.4   242.1   242.8   243.8   244.7
   245.1 ]]

My question is how to add square brackets for each number(for example 223.6) and remain them in 1 row?

Thanks, Prasad.

Paul Panzer · Accepted Answer · 2018-07-02 10:35:27Z

1

It's not entirely clear what you mean, but perhaps it's something like this?

>>> import numpy as np
>>> data = np.arange(30).reshape(10,3)
>>> data
array([[ 0,  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]])
>>> data[:, 2, None]
array([[ 2],
       [ 5],
       [ 8],
       [11],
       [14],
       [17],
       [20],
       [23],
       [26],
       [29]])

answered Jul 2, 2018 at 10:35

Paul Panzer

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user3483203 · Accepted Answer · 2018-07-02 10:38:58Z

0

You need to expand the dimensions of the array when you reshape.

Setup

x = np.arange(60).reshape(20, 3)

reshape with an additional dimension

x[:, 2].reshape((-1, 10, 1))

expand_dims with axis=2

np.expand_dims(x[:, 2].reshape(-1, 10), axis=2)

atleast_3d

np.atleast_3d(x[:, 2].reshape(-1, 10))

All three produce:

array([[[ 2],
        [ 5],
        [ 8],
        [11],
        [14],
        [17],
        [20],
        [23],
        [26],
        [29]],

       [[32],
        [35],
        [38],
        [41],
        [44],
        [47],
        [50],
        [53],
        [56],
        [59]]])

edited Jul 2, 2018 at 10:38

answered Jul 2, 2018 at 10:31

user3483203

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