How to loop through a nested numpy array to obtain column-wise values

Question

I have an input numpy array as follows:

import numpy as np

my_array = [
        np.array([[[1,  10]],
                 [[2,  11]]], dtype=np.int32),
        np.array([[[3, 12]],
                  [[4, 13]],
                  [[5, 14]]], dtype=np.int32),
        np.array([[[6,  15]],
                  [[7,  16]],
                  [[8,  17]]], dtype=np.int32)
]

I want to get two arrays (1 for each column) so that:

array1 = [1, 2, 3, 4, 5, 6, 7 ,8]
array2 = [10, 11, 12, 13, 14, 15, 16, 17]

I tried with a list comprehension but it didn't work:

[col[:] for col in my_array]

Alexander Rossa · Accepted Answer · 2019-11-26 12:06:46Z

3

You can loop through the arrays and append to the new ones:

array1 = []
array2 = []

for array in my_array:
  for nested_array in array:
    # nested_array is of form [[ 1 10]] here, you need to index it
    # first with [0] then with the element you want to access [0] or [1]
    array1.append(nested_array[0][0])
    array2.append(nested_array[0][1])

You just have to think about the structure of the input data and how to get the values you need in the order that you need.

The output:

>>> array1
[1, 2, 3, 4, 5, 6, 7, 8]
>>> array2
[10, 11, 12, 13, 14, 15, 16, 17]

answered Nov 26, 2019 at 12:06

Alexander Rossa

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Comments

Sayandip Dutta · Accepted Answer · 2019-11-26 12:06:38Z

1

You can try this:

>>> from numpy import array
>>> import numpy as np
>>> my_array = [array([[[1,  10]],
        [[2,  11]]], dtype='int32'), array([[[3, 12]],

        [[4, 13]],
        [[5, 14]]], dtype='int32'), array([[[6,  15]],

        [[7,  16]],
        [[8,  17]]], dtype='int32')]
# One way
>>> np.concatenate(my_array,axis=0)[...,0]    # [...,1] would give the other one
array([[1],
       [2],
       [3],
       [4],
       [5],
       [6],
       [7],
       [8]], dtype=int32)
# Other way:
>>> np.concatenate(my_array,axis=0)[...,0].reshape(-1,) # [...,1].reshape(-1,0) would be the other one
array([1, 2, 3, 4, 5, 6, 7, 8], dtype=int32)

answered Nov 26, 2019 at 12:06

Sayandip Dutta

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