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Indexing Multi-dimensional arrays in Python using NumPy

  • Difficulty Level : Hard
  • Last Updated : 05 Aug, 2021

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: 
 

Python3




# numpy library imported
import numpy as np
 
# creating single-dimensional array
arr_s = np.arrange(5)
print(arr_s)

Output: 
 

[0 1 2 3 4]

arrange() method in numpy creates single dimension array of length 5. Single parameter inside the arrange() method acts as the end element for the range. arrange() also takes start and end arguments with steps. 
Example:
 

Python3




import numpy as np
 
# here inside arrange method we
# provide start, end, step as
# arguments.
arr_b = np.arrange(20, 30, 2)
 
# step argument helps in printing
# every said step and skipping the
# rest.
print(arr_b)

Output: 

[20 22 24 26 28]

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]

Example: 
 

Python3




import numpy as np
 
# here inside arrange method we
# provide start, end, step as
# arguments.
arr_b = np.arrange(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]

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

Python3




import numpy as np
 
arr_m = np.arrange(12).reshape(6, 2)
print(arr_m)

Output: 
 

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

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 an 2d array. 
Example:
 

Python3




import numpy as np
 
arr_m = np.arrange(12).reshape(2, 2, 3)
print(arr_m)

Output 
 

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

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

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

Python3




import numpy as np
 
arr_m = np.arrange(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]]]

 


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