I have an array:
arr = [
['00', '01', '02'],
['10', '11', '12'],
]
I want to reshape this array considering its indices:
reshaped = [
[0, 0, '00'],
[0, 1, '01'],
[0, 2, '02'],
[1, 0, '10'],
[1, 1, '11'],
[1, 2, '12'],
]
Is there a numpy or pandas way to do that? Or do I have to do the good old for?
for x, arr_x in enumerate(arr):
for y, val in enumerate(arr_x):
print(x, y, val)
You can use np.indices to get the indices and then stitch everything together...
arr = np.array(arr)
i, j = np.indices(arr.shape)
np.concatenate([i.reshape(-1, 1), j.reshape(-1, 1), arr.reshape(-1, 1)], axis=1)
np.column_stack([*map(np.ravel, [*np.indices(np.shape(arr)), arr])])I would use numpy.ndenumerate for that purpose, following way:
import numpy as np
arr = np.array([['00', '01', '02'],['10', '11', '12']])
output = [[*inx,x] for inx,x in np.ndenumerate(arr)]
print(*output,sep='\n') # print sublists in separate lines to enhance readibility
Output:
[0, 0, '00']
[0, 1, '01']
[0, 2, '02']
[1, 0, '10']
[1, 1, '11']
[1, 2, '12']
As side note: this action is not reshaping, as reshaping mean movement of elements, as output contain more cells it is impossible to do it with just reshaping.
