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.


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