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Program to access different columns of a multidimensional Numpy array

Prerequisite: Numpy module

The following article discusses how we can access different columns of multidimensional Numpy array. Here, we are using Slicing method to obtain the required functionality.

Example 1: (Accessing the First and Last column of Numpy array)

Python3

 `# Importing Numpy module``import` `numpy as np`` ` `# Creating a 3x3 Numpy array``arr ``=` `np.array([[``11``, ``20``, ``3``], ``                ``[``89``, ``5``, ``66``], ``                ``[``71``, ``88``, ``39``]])`` ` `print``(``"Given Array :"``)``print``(arr)`` ` `# Access the First and Last column of array``res_arr ``=` `arr[:,[``0``,``2``]]``print``(``"\nAccessed Columns :"``)``print``(res_arr)`

Output:

Given Array :

[[11 20  3]

[89  5 66]

[71 88 39]]

Accessed Columns :

[[11  3]

[89 66]

[71 39]]

Example 2: (Accessing the Middle and Last column of Numpy array)

Python3

 `# Importing Numpy module``import` `numpy as np`` ` `# Creating a 4x4 Numpy array``arr ``=` `np.array([[``1``, ``20``, ``3``, ``1``], ``                ``[``40``, ``5``, ``66``, ``7``], ``                ``[``70``, ``88``, ``9``, ``11``],``               ``[``80``, ``100``, ``50``, ``77``]])`` ` `print``(``"Given Array :"``)``print``(arr)`` ` `# Access the Middle and Last column of array``res_arr ``=` `arr[:,[``1``,``3``]]``print``(``"\nAccessed Columns :"``)``print``(res_arr)`

Output:

Given Array :

[[  1  20   3   1]

[ 40   5  66   7]

[ 70  88   9  11]

[ 80 100  50  77]]

Accessed Columns :

[[ 20   1]

[  5   7]

[ 88  11]

[100  77]]

Example 3: (Accessing the Last two columns of Numpy array)

Python3

 `# Importing Numpy module``import` `numpy as np`` ` `# Creating a 3d (3X4X4) Numpy array``arr ``=` `np.array([[[``21``, ``20``, ``3``, ``1``], ``                ``[``40``, ``5``, ``66``, ``7``], ``                ``[``70``, ``88``, ``9``, ``11``],``               ``[``80``, ``100``, ``50``, ``77``]],`` ` `               ``[[``65``, ``120``, ``53``, ``73``], ``                ``[``49``, ``50``, ``56``, ``11``], ``                ``[``81``, ``88``, ``34``, ``22``],``               ``[``564``,``56``, ``76``, ``99``]],``                ` `               ``[[``45``, ``85``, ``38``, ``455``], ``                ``[``40``, ``53``, ``69``, ``6``], ``                ``[``50``, ``528``, ``654``, ``11``],``               ``[``54``, ``87``, ``78``, ``77``]]])`` ` `print``(``"Given Array :"``)``print``(arr)`` ` `# Access the Last two columns of array``res_arr ``=` `arr[``2``,:,[``2``,``3``]]``print``(``"\nAccessed Columns :"``)``print``(res_arr)`

Output:

Given Array :

[[[ 21  20   3   1]

[ 40   5  66   7]

[ 70  88   9  11]

[ 80 100  50  77]]

[[ 65 120  53  73]

[ 49  50  56  11]

[ 81  88  34  22]

[564  56  76  99]]

[[ 45  85  38 455]

[ 40  53  69   6]

[ 50 528 654  11]

[ 54  87  78  77]]]

Accessed Columns :

[[ 38  69 654  78]

[455   6  11  77]]

Example 4: (Accessing the First column of a 4D Numpy array)

Python3

 `# Importing Numpy module``import` `numpy as np`` ` `# Creating a 4D Numpy array``arr ``=` `np.array([``  ``[``    ``[``      ``[``1``,``2``],``      ``[``3``,``4``]``    ``],``    ``[``      ``[``5``,``6``],``      ``[``7``,``8``]``    ``]``  ``],``   ``[``    ``[``      ``[``9``,``10``],``      ``[``11``,``12``]``    ``],``    ``[``      ``[``13``,``14``],``      ``[``15``,``16``]``    ``]``  ``]`` ` `])`` ` `print``(``"Given Array :"``)``print``(arr)`` ` `# Access the First three columns of array``res_arr ``=` `arr[``0``,``0``,:,[``0``]]``print``(``"\nAccessed Columns :"``)``print``(res_arr)`

Output:

Given Array :

[[[[ 1  2]

[ 3  4]]

[[ 5  6]

[ 7  8]]]

[[[ 9 10]

[11 12]]

[[13 14]

[15 16]]]]

Accessed Columns :

[[1 3]]

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