Python Reshape List To Ndim Array

Hi I have a list flat which is length 2800, it contains 100 results for each of 28 variables: Below is an example of 4 results for 2 variables [0, 0, 1, 1, 2, 2, 3, 3] I wo

Solution 1:

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]])

Solution 2:

Step by step:

# import numpy library
import numpy as np
# create list
my_list = [0,0,1,1,2,2,3,3]
# convert list to numpy array
np_array=np.asarray(my_list)
# reshape array into 4 rows x 2 columns, and transpose the result
reshaped_array = np_array.reshape(4, 2).T 

#check the result
reshaped_array
array([[0, 1, 2, 3],
       [0, 1, 2, 3]])

Solution 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

Solution 4:

You can specify the interpretation order of the axes using the order parameter:

np.reshape(arr, (2, -1), order='F')