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Return a new array with the same shape and type as a given array and change the order 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 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 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 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]] [[140556668570992 140556671038576] [140556668571056 140556668441520]] [[140556668441712 140556668441776] [140556668441840 140556668442416]]] New Array type... int64 New Array Dimensions... 3
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