numpy.delete() in Python

About :
numpy.delete(array, object, axis = None) : returns a new array with the deletion of sub-arrays along with the mentioned axis.
Parameters : 

array   : [array_like]Input array. 
object  : [int, array of ints]Sub-array to delete
axis    : Axis along which we want to delete sub-arrays. By default, it object is applied to
              applied to flattened array

Return : 

An array with sub-array being deleted as per the mentioned object along a given axis.

Code 1 : Deletion from 1D array



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# Python Program illustrating
# numpy.delete()
  
import numpy as geek
  
#Working on 1D
arr = geek.arange(5)
print("arr : \n", arr)
print("Shape : ", arr.shape)
  
# deletion from 1D array 
  
object = 2
a = geek.delete(arr, object)
print("\ndeleteing arr 2 times : \n", a)
print("Shape : ", a.shape)
  
object = [1, 2]
b = geek.delete(arr, object)
print("\ndeleteing arr 3 times : \n", b)
print("Shape : ", a.shape)

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Output : 


arr : 
 [0 1 2 3 4]

Repeating arr 2 times : 
 [0 0 1 1 2 2 3 3 4 4]
Shape :  (10,)

Repeating arr 3 times : 
 [0 0 0 ..., 4 4 4]
Shape :  (15,)



Code 2 : 











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# Python Program illustrating 


# numpy.delete() 


  


import numpy as geek 


  


#Working on 1D 


arr = geek.arange(12).reshape(3, 4) 


print("arr : \n", arr) 


print("Shape : ", arr.shape) 


  


# deletion from 2D array  


a = geek.delete(arr, 1, 0) 


''' 


        [[ 0  1  2  3] 


         [ 4  5  6  7] -> deleted 


         [ 8  9 10 11]] 


'''


print("\ndeleteing arr 2 times : \n", a) 


print("Shape : ", a.shape) 


  


# deletion from 2D array  


a = geek.delete(arr, 1, 1) 


''' 


        [[ 0  1*  2  3] 


         [ 4  5*  6  7]  


         [ 8  9* 10 11]] 


              ^ 


              Deletion 


'''


print("\ndeleteing arr 2 times : \n", a) 


print("Shape : ", a.shape) 



















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Output : 


arr : 
 [[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
Shape :  (3, 4)

deleteing arr 2 times : 
 [[ 0  1  2  3]
 [ 8  9 10 11]]
Shape :  (2, 4)

deleteing arr 2 times : 
 [[ 0  2  3]
 [ 4  6  7]
 [ 8 10 11]]
Shape :  (3, 3)

deleteing arr 3 times : 
 [ 0  3  4  5  6  7  8  9 10 11]
Shape :  (3, 3)



Code 3 : Deletion performed using Boolean Mask











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# Python Program illustrating 


# numpy.delete() 


  


import numpy as geek 


  


arr = geek.arange(5) 


print("Original array : ", arr) 


mask = geek.ones(len(arr), dtype=bool) 


  


# Equivalent to np.delete(arr, [0,2,4], axis=0) 


mask[[0,2]] = False


print("\nMask set as : ", mask) 


result = arr[mask,...] 


print("\nDeletion Using a Boolean Mask : ", result) 



















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Output : 


Original array :  [0 1 2 3 4]

Mask set as :  [False  True False  True  True]

Deletion Using a Boolean Mask :  [1 3 4]


References : 

https://docs.scipy.org/doc/numpy/reference/generated/numpy.delete.html


Note : 

These codes won’t run on online-ID. Please run them on your systems to explore the working

.

This article is contributed by Mohit Gupta_OMG 😀. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.


Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.



          
          
          
          

            

    

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