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numpy.delete() in Python

  • Difficulty Level : Expert
  • Last Updated : 23 Oct, 2020

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,)

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 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)

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]] = 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-ID. 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 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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