numpy.delete() in Python - GeeksforGeeks

28 Mar, 2022
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 1Darr = geek.arange(5)print("arr : \n", arr)print("Shape : ", arr.shape) # deletion from 1D array object = 2a = 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,)

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

deleteing arr 3 times : 
 [0 3 4]
Shape :  (4,)
Code 2 :  
Python
# Python Program illustrating# numpy.delete() import numpy as geek #Working on 1Darr = geek.arange(12).reshape(3, 4)print("arr : \n", arr)print("Shape : ", arr.shape) # deletion from 2D arraya = 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 arraya = 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)

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  
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]] = Falseprint("\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.htmlNote : 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 . 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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Python numpy-arrayManipulation
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