# Return a full array with the same shape and type as a given array in Numpy

To return a full array with the same shape and type as a given array, use the numpy.full_like() method in Python Numpy. The 1st parameter here is the shape and data-type, define these same attributes of the returned array. The 2nd parameter is the fill value.

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. The shape parameter overrides the shape of the result. If order=’K’ and the number of dimensions is unchanged, will try to keep order, otherwise, order=’C’ is implied.

## 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([[91, 34, 89], [29, 87, 55]])


Display the array −

print("Array...",arr)

Get the type of the array −

print("Array type...", arr.dtype)


Get the dimensions of the Array −

print("Array Dimensions...", arr.ndim)

To return a full array with the same shape and type as a given array, use the numpy.full_like() method in Python Numpy −

newArr = np.full_like(arr, 999)
print("New Array..", newArr)

Get the type of the new array −

print("New Array type...", newArr.dtype)


Get the dimensions of the new array −

print("New Array Dimensions...", newArr.ndim)

## Example

import numpy as np

# Create a new array using the numpy.array() method in Python Numpy
arr = np.array([[91, 34, 89], [29, 87, 55]])

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

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

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

# To return a full array with the same shape and type as a given array, use the numpy.full_like() method in Python Numpy
# The 1st parameter here is the shape and data-type, define these same attributes of the returned array.
# The 2nd parameter is the fill value
newArr = np.full_like(arr, 999)
print("New Array..", newArr)

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

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

## Output

Array...
[[91 34 89]
[29 87 55]]

Array type...
int64

Array Dimensions...
2

New Array..
[[999 999 999]
[999 999 999]]

New Array type...
int64

New Array Dimensions...
2

Updated on: 10-Feb-2022

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