Python reshape list to ndim array

You can think of reshaping that the new shape is filled row by row (last dimension varies fastest) from the flattened original list/array.

An easy solution is to shape the list into a (100, 28) array and then transpose it:

x = np.reshape(list_data, (100, 28)).T

Update regarding the updated example:

np.reshape([0, 0, 1, 1, 2, 2, 3, 3], (4, 2)).T# array([[0, 1, 2, 3],#        [0, 1, 2, 3]])np.reshape([0, 0, 1, 1, 2, 2, 3, 3], (2, 4))# array([[0, 0, 1, 1],#        [2, 2, 3, 3]])

Step by step:

# import numpy libraryimport numpy as np# create listmy_list = [0,0,1,1,2,2,3,3]# convert list to numpy arraynp_array=np.asarray(my_list)# reshape array into 4 rows x 2 columns, and transpose the resultreshaped_array = np_array.reshape(4, 2).T #check the resultreshaped_arrayarray([[0, 1, 2, 3],       [0, 1, 2, 3]])

The answers above are good. Adding a case that I used. Just if you don't want to use numpy and keep it as list without changing the contents.

You can run a small loop and change the dimension from 1xN to Nx1.

    tmp=[]    for b in bus:        tmp.append([b])    bus=tmp

It is maybe not efficient while in case of very large numbers. But it works for a small set of numbers.Thanks