# Program to access different columns of a multidimensional Numpy array

Last Updated : 01 Nov, 2020

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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