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
import numpy as geek
arr = geek.arange( 5 )
print ( "arr : \n" , arr)
print ( "Shape : " , arr.shape)
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)
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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
import numpy as geek
arr = geek.arange( 12 ).reshape( 3 , 4 )
print ( "arr : \n" , arr)
print ( "Shape : " , arr.shape)
a = geek.delete(arr, 1 , 0 )
print ( "\ndeleteing arr 2 times : \n" , a)
print ( "Shape : " , a.shape)
a = geek.delete(arr, 1 , 1 )
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)
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
import numpy as geek
arr = geek.arange( 5 )
print ( "Original array : " , arr)
mask = geek.ones( len (arr), dtype = bool )
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 IDE’s. Please run them on your systems to explore the working
.
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