Let's say I have data structured in a 2D array like this:
[[1, 3, 4, 6],
[1, 4, 8, 2],
[1, 3, 2, 9],
[2, 2, 4, 8],
[2, 4, 9, 1],
[2, 2, 9, 3]]
The first column denotes a third dimension, so I want to convert this to the following 3D array:
[[[3, 4, 6],
[4, 8, 2],
[3, 2, 9]],
[[2, 4, 8],
[4, 9, 1],
[2, 9, 3]]]
Is there a built-in numpy function to do this?
You can try code below:
import numpy as np
array = np.array([[1, 3, 4, 6],
[1, 4, 8, 2],
[1, 3, 2, 9],
[2, 2, 4, 8],
[2, 4, 9, 1],
[2, 2, 9, 3]])
array = np.delete(array, 0, 1)
array.reshape(2,3,-1)
array([[[3, 4, 6],
[4, 8, 2],
[3, 2, 9]],
[[2, 4, 8],
[4, 9, 1],
[2, 9, 3]]])
However, this code can be used when you are aware of the array's shape. But if you are sure that the number of columns in the array is a multiple of 3, you can simply use code below to show the array in the desired format.
array.reshape(array.shape[0]//3,3,-3)
Use numpy array slicing with reshape function.
import numpy as np
arr = [[1, 3, 4, 6],
[1, 4, 8, 2],
[1, 3, 2, 9],
[2, 2, 4, 8],
[2, 4, 9, 1],
[2, 2, 9, 3]]
# convert the list to numpy array
arr = np.array(arr)
# remove first column from numpy array
arr = arr[:,1:]
# reshape the remaining array to desired shape
arr = arr.reshape(len(arr)//3,3,-1)
print(arr)
Output:
[[[3 4 6]
[4 8 2]
[3 2 9]]
[[2 4 8]
[4 9 1]
[2 9 3]]]
You list a non numpy array. I am unsure if you are just suggesting numpy as a means to get a non numpy result, or you are actually looking for a numpy array as result. If you don't actually need numpy, you could do something like this:
arr = [[1, 3, 4, 6],
[1, 4, 8, 2],
[1, 3, 2, 9],
[2, 2, 4, 8],
[2, 4, 9, 1],
[2, 2, 9, 3]]
# Length of the 3rd and 2nd dimension.
nz = arr[-1][0] + (arr[0][0]==0)
ny = int(len(arr)/nz)
res = [[arr[ny*z_idx+y_idx][1:] for y_idx in range(ny)] for z_idx in range(nz)]
OUTPUT:
[[[3, 4, 6], [4, 8, 2], [3, 2, 9]], [[2, 4, 8], [4, 9, 1], [2, 9, 3]]]
Note that the calculation of nz takes into account that the 3rd dimension index in your array is either 0-based (as python is per default) or 1-based (as you show in your example).
