Skip to content
Related Articles

Related Articles

Indexing Multi-dimensional arrays in Python using NumPy
  • Difficulty Level : Hard
  • Last Updated : 21 Apr, 2020

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:




# 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]

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



Example:




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]

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:




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)
  
  
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 arange()




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]]

Inside reshape() the parameters should be the multiple of the arange() 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:




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]]]

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

Example:




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]]]

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course

My Personal Notes arrow_drop_up
Recommended Articles
Page :