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I have a 2d array of numbers coming from a csv

this is just a example of the data shape

[[ 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]
 [30 31 32 33 34 35 36 37 38 39]
 [40 41 42 43 44 45 46 47 48 49]
 [50 51 52 53 54 55 56 57 58 59]
 [60 61 62 63 64 65 66 67 68 69]
 [70 71 72 73 74 75 76 77 78 79]
 [80 81 82 83 84 85 86 87 88 89]
 [90 91 92 93 94 95 96 97 98 99]]

Im learning to use numpy, my goal is to convert that 2d array into a 3d array of shape (10,2,4) for example

index 0

[[ 0  1  2  3  4  5  6  7  8  9]
 [10 11 12 13 14 15 16 17 18 19]]

index 1

 [[20 21 22 23 24 25 26 27 28 29]
 [30 31 32 33 34 35 36 37 38 39]]

index 2

 [[40 41 42 43 44 45 46 47 48 49]
 [50 51 52 53 54 55 56 57 58 59]]

index 3

 [[60 61 62 63 64 65 66 67 68 69]
 [70 71 72 73 74 75 76 77 78 79]]

index 4

 [[80 81 82 83 84 85 86 87 88 89]
 [90 91 92 93 94 95 96 97 98 99]]

I can do this by using a loop, but i wonder if there is a better way

also concatenating rows in one column would also work

my goal is to fit a keras model where a single sample is composed of multiple rows of a dataframe

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1 Answer 1

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You can use slicing and list comprehension

>>> src = [[ 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],
 [30, 31, 32, 33 ,34 ,35 ,36 ,37 ,38 ,39],
 [40, 41, 42, 43 ,44 ,45 ,46 ,47 ,48 ,49],
 [50, 51, 52, 53 ,54 ,55 ,56 ,57 ,58 ,59],
 [60, 61, 62, 63 ,64 ,65 ,66 ,67 ,68 ,69],
 [70, 71, 72, 73 ,74 ,75 ,76 ,77 ,78 ,79],
 [80, 81, 82, 83 ,84 ,85 ,86 ,87 ,88 ,89],
 [90, 91, 92, 93 ,94 ,95 ,96 ,97 ,98 ,99]]

>>> [src[i:i+2] for i in range(0,len(src),2)]
[[[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],
  [30, 31, 32, 33, 34, 35, 36, 37, 38, 39]],
 [[40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
  [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]],
 [[60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
  [70, 71, 72, 73, 74, 75, 76, 77, 78, 79]],
 [[80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
  [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]]]

Hope this answer your question

Edit : I re-read your question and look for the comment that @Anshumaan-mishra mention. You can also use numpy reshape.

>>> import numpy as np
>>> src = np.reshape(src,(5, 2, 10))
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],
        [30, 31, 32, 33, 34, 35, 36, 37, 38, 39]],

       [[40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
        [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]],

       [[60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
        [70, 71, 72, 73, 74, 75, 76, 77, 78, 79]],

       [[80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
        [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]]])

I answer with my code above because I'm focusing on the data structure that you want

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