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Python | Numpy ndarray.__array__()

  • Last Updated : 29 Mar, 2019

With the help of ndarray.__array__() method, we can create a new array as we want by giving a parameter as dtype and we can get a copy of an array that doesn’t change the data element of original array if we change any element in the new one.

Syntax : ndarray.__array__()

Return :

  • Returns either a new reference to self if dtype is not given
  • New array of provided data type if dtype is different from the current dtype of the array.

Example #1 :

In this example we can see that we change the dtype of a new array by just using ndarray.__array__() method.




# import the important module in python
import numpy as np
        
# make an array with numpy
gfg = np.array([1, 2, 3, 4, 5])
        
# applying ndarray.__array__() method
geeks = gfg.__array__(float)
  
print(geeks)
Output:
[ 1.  2.  3.  4.  5.]

 
Example #2 :




# import the important module in python
import numpy as np
        
# make an array with numpy
gfg = np.array([[1.1, 2, 3.3, 4, 5],
                [6, 5.2, 4, 3, 2.2]])
        
# applying ndarray.__array__() method
geeks = gfg.__array__(int)
  
print(geeks)
Output:
[[1 2 3 4 5]
 [6 5 4 3 2]]


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