NumPy indices() Method | Create Array of Indices

The indices() method returns an array representing the indices of a grid.

It computes an array where the subarrays contain index values 0, 1, … varying only along the corresponding axis.

Example

Python3

import numpy as np

gfg = np.indices((2, 3))

print (gfg)

Output :

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

Syntax

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

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

How to Generate a Grid of Indices for a Given Shape in NumPy

To generate a grid of indices for a given shape we use numpy.indices() method of the NumPy library in Python.

Let us understand it better with an example:

Python3

import numpy as np

grid = np.indices((2, 3))
gfg = grid[1]

print (gfg)

Output :

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