# Return a 2-D array with ones on the diagonal and zeros elsewhere in Numpy

NumpyServer Side ProgrammingProgramming

The numpy.eye() returns a 2-D array with 1’s as the diagonal and 0’s elsewhere. Here, the 1st parameter means the "Number of rows in the output" i.e. 4 means 4x4 array. The 2nd parameter is the number of columns in the output.

The function eye() returns an array where all elements are equal to zero, except for the k-th diagonal, whose values are equal to one. The dtype is the data-type of the returned array. The order suggests whether the output should be stored in row-major (C-style) or column-major (Fortran-style) order in memory.

The like parameter is a reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

## Steps

At first, import the required library −

import numpy as np

Create a 2d array. The numpy.eye() returns a 2-D array with 1’s as the diagonal and 0’s elsewhere. Here, the 1st parameter means the "Number of rows in the output" i.e. 4 means 4x4 array. The 2nd parameter is the number of columns in the output. If None, defaults to the 1st parameter i.e. 4x4 here −

arr = np.eye(4)


Display the array −

print("Array...\n", arr)

Get the type of the array −

print("\nArray type...\n", arr.dtype)


Get the shape of the array −

print("\nArray shape...\n", arr.shape)

Get the dimensions of the Array −

print("\nArray Dimensions...\n",arr.ndim)

Get the number of elements of the Array −

print("\nArray (count of elements)...\n",arr.size)

## Example

import numpy as np

# Create a 2d array
# The numpy.eye() returns a 2-D array with 1’s as the diagonal and 0’s elsewhere.
# Here, the 1st parameter means the "Number of rows in the output" i.e. 4 means 4x4 array
# The 2nd parameter is the number of columns in the output. If None, defaults to the 1st parameter i.e. 4x4 here.
arr = np.eye(4)

# Display the array
print("Array...\n", arr)

# Get the type of the array
print("\nArray type...\n", arr.dtype)

# Get the shape of the array
print("\nArray shape...\n", arr.shape)

# Get the dimensions of the Array
print("\nArray Dimensions...\n",arr.ndim)

# Get the number of elements of the Array
print("\nArray (count of elements)...\n",arr.size)

## Output

Array...
[[1. 0. 0. 0.]
[0. 1. 0. 0.]
[0. 0. 1. 0.]
[0. 0. 0. 1.]]

Array type...
float64

Array shape...
(4, 4)

Array Dimensions...
2

Array (count of elements)...
16