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 arrayobject = 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,) 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 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) 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 geekarr = 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.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 😀. If you like Lazyroar 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 Lazyroar 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.
