Return an array of ones with the same shape and type as a given array in Numpy

To return an array of ones with the same shape and type as a given array, use the numpy.ones_like() method in Python Numpy. The 1st parameter here is the shape and data-type of array-like that define these same attributes of the returned array.

The dtypes overrides the data type of the result. The order overrides the memory layout of the result. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible.

The subok parameter, if True, then the newly created array will use the sub-class type of a, otherwise it will be a base-class array. Defaults to True.

Steps

At first, import the required library −

import numpy as np

Create a new array using the numpy.array() method in Python Numpy −

arr = np.array([[35, 56, 66], [88, 73, 98]])

Display the array −

print("Array...
",arr)

Get the type of the array −

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

Get the dimensions of the Array −

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

To return an array of ones with the same shape and type as a given array, use the numpy.ones_like() method in Python Numpy. The 1st parameter here is the shape and data-type of array-like that define these same attributes of the returned array −

newArr = np.ones_like(arr)
print("\nNew Array..
", newArr)

Get the type of the new array −

print("\nNew Array type...
", newArr.dtype)

Get the dimensions of the new array −

print("\nNew Array Dimensions...
", newArr.ndim)

Example

import numpy as np

# Create a new array using the numpy.array() method in Python Numpy
arr = np.array([[35, 56, 66], [88, 73, 98]])

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

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

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

# To return an array of ones with the same shape and type as a given array, use the numpy.ones_like() method in Python Numpy
# The 1st parameter here is the shape and data-type of array-like that define these same attributes of the returned array.
newArr = np.ones_like(arr)

print("\nNew Array..
", newArr)

# Get the type of the new array
print("\nNew Array type...
", newArr.dtype)

# Get the dimensions of the new array
print("\nNew Array Dimensions...
", newArr.ndim)

Output

Array...
[[35 56 66]
[88 73 98]]

Array type...
int64

Array Dimensions...
2

New Array..
[[1 1 1]
[1 1 1]]

New Array type...
int64

New Array Dimensions...
2