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numpy.delete() in Python

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  • Difficulty Level : Expert
  • Last Updated : 09 Aug, 2022
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The numpy.delete() function returns a new array with the deletion of sub-arrays along with the mentioned axis. 
 

Syntax:

numpy.delete(array, object, axis = None)

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  
                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 
 

Python




# 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 {} from array : \n {}".format(object,a))
print("Shape : ", a.shape)
 
object = [1, 2]
b = geek.delete(arr, object)
print("\ndeleteing {} from array : \n {}".format(object,a))
print("Shape : ", a.shape)

Output : 
 

arr : 
 [0 1 2 3 4]
Shape :  (5,)

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

deleting arr 3 times : 
 [0 3 4]
Shape :  (4,)

Code 2 : 
 

Python




# 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)

Output : 
 

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

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

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

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

Code 3: Deletion performed using Boolean Mask 
 

Python




# 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)

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 IDE’s. Please run them on your systems to explore the working 

This article is contributed by Mohit Gupta_OMG πŸ˜€. If you like GeeksforGeeks and would like to contribute, you can also write an article using write.geeksforgeeks.org or mail your article to review-team@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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