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 wonβt run on online-ID. Please run them on your systems to explore the working
.
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