Skip to content
Related Articles

Related Articles

Improve Article
Save Article
Like Article

numpy.indices() function – Python

  • Last Updated : 11 Jun, 2020

numpy.indices() function return an array representing the indices of a grid. Compute an array where the subarrays contain index values 0, 1, … varying only along the corresponding axis.

Syntax : numpy.indices(dimensions, dtype, sparse = False)

 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

Parameters :
dimensions : [sequence of ints] The shape of the grid.
dtype: [dtype, optional] Data type of the result.
sparse: [boolean, optional] Return a sparse representation of the grid instead of a dense representation. Default is False.



Return : [ndarray or tuple of ndarrays]
If sparse is False:
Returns one array of grid indices, grid.shape = (len(dimensions), ) + tuple(dimensions).

If sparse is True:
Returns a tuple of arrays, with grid[i].shape = (1, …, 1, dimensions[i], 1, …, 1) with dimensions[i] in the ith place

Code #1 :




# Python program explaining
# numpy.indices() function
       
# importing numpy as geek 
import numpy as geek 
   
gfg = geek.indices((2, 3))
  
print (gfg)

Output :

[[[0 0 0]
  [1 1 1]]

 [[0 1 2]
  [0 1 2]]]

 
Code #2 :




# Python program explaining
# numpy.indices() function
       
# importing numpy as geek 
import numpy as geek 
   
grid = geek.indices((2, 3))
gfg = grid[1]
  
print (gfg)

Output :

[[0 1 2]
 [0 1 2]]



My Personal Notes arrow_drop_up
Recommended Articles
Page :

Start Your Coding Journey Now!