Indexing Multi-dimensional arrays in Python using NumPy

In this article, we will cover the Indexing of Multi-dimensional arrays in Python using NumPy.

NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python. It contains various features.
Note: For more information, refer to Python Numpy

Example 1: 

Creating the single-dimensional array.

# numpy library imported
import numpy as np

# creating single-dimensional array
arr_s = np.arange(5)
print(arr_s)

Output: 

[0 1 2 3 4]

Example 2:

The arrange() method in NumPy creates a single-dimension array of length 5. A single parameter inside the arrange() method acts as the end element for the range. arrange() also takes start and end arguments with steps. 

import numpy as np

# here inside arrange method we
# provide start, end, step as
# arguments.
arr_b = np.arange(20, 30, 2)

# step argument helps in printing 
# every said step and skipping the 
# rest.
print(arr_b)

Output: 

[20 22 24 26 28]

Example 3:

Indexing these arrays is simple. Every array element has a particular index associated with them. Indexing starts at 0 and goes on till the length of array-1. In the previous example, arr_b has 5 elements within itself. Accessing these elements can be done with:  array_name[index_number]

import numpy as np

# provide start, end, step as
# arguments.
arr_b = np.arange(20, 30, 2)

# step argument helps in printing 
# every said step and skipping the 
# rest.
print(arr_b)


print(arr_b[2])

# Slicing operation from index 
# 1 to 3 
print(arr_b[1:4])

Output:

[20 22 24 26 28]
24
[22 24 26]

Example 4:

For Multidimensional array, you can use reshape() method along with arrange() 

import numpy as np

arr_m = np.arange(12).reshape(6, 2)
print(arr_m)

Output: 

[[ 0  1]
 [ 2  3]
 [ 4  5]
 [ 6  7]
 [ 8  9]
 [10 11]]

Example 5:

Inside reshape() the parameters should be the multiple of the arrange() parameter. In our previous example, we had 6 rows and 2 columns. You can specify another parameter whereby you define the dimension of the array. By default, it is a 2d array. 

import numpy as np

arr_m = np.arange(12).reshape(2, 2, 3)
print(arr_m)

Output:

[[[ 0  1  2]
  [ 3  4  5]]

 [[ 6  7  8]
  [ 9 10 11]]]

Example 6: 

To index a multi-dimensional array you can index with a slicing operation similar to a single dimension array.

import numpy as np

arr_m = np.arange(12).reshape(2, 2, 3)

# Indexing
print(arr_m[0:3])
print()
print(arr_m[1:5:2,::3])

Output:

[[[ 0  1  2]
  [ 3  4  5]]

 [[ 6  7  8]
  [ 9 10 11]]]

[[[6 7 8]]]