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# Return a new array with the same shape and type as a given array and change the order to C style in Numpy

To return a new array with the same shape and type as a given array, use the **numpy.empty_like()** method in Python Numpy. It returns the array of uninitialized (arbitrary) data with the same shape and type as prototype. The 1st parameter here is the shape and data-type of prototype(array-like) that define these same attributes of the returned array. We have set the order to 'C' style using the "**order**" parameter.

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

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

## Steps

At first, import the required library −

import numpy as np

Creating a numpy Three-Dimensional array using the array() method. We have added elements of int type −

arr = np.array([[[5,10],[15,20]],[[25,30],[35,40]],[[50,60],[70,80]]])

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 new array with the same shape and type as a given array, use the numpy.empty_like() method in Python Numpy. We have set the order to 'C' style using the "order" parameter −

newArr = np.empty_like(arr, order = 'C') 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 # Creating a numpy Three-Dimensional array using the array() method # We have added elements of int type arr = np.array([[[5,10],[15,20]],[[25,30],[35,40]],[[50,60],[70,80]]]) # 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 new array with the same shape and type as a given array, use the numpy.empty_like() method in Python Numpy # It returns the array of uninitialized (arbitrary) data with the same shape and type as prototype. # The 1st parameter here is the shape and data-type of prototype(array-like) that define these same attributes of the returned array. # We have set the order to 'C' style using the "order" parameter. newArr = np.empty_like(arr, order = 'C') 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... [[[ 5 10] [15 20]] [[25 30] [35 40]] [[50 60] [70 80]]] Array type... int64 Array Dimensions... 3 New Array.. [[[ 0 0] [ 0 0]] [[140006423023024 140006423022960] [140006423023088 140006422893488]] [[140006422893680 140006422893744] [140006422893808 140006422894384]]] New Array type... int64 New Array Dimensions... 3

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