 # numpy.delete () in Python

numpy.delete (array, object, axis = None): returns a new array with subarrays deleted along with the mentioned axis.
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 applied to flattened array `

Return:

` An array with sub-array being deleted as per the mentioned object along a given axis. `

Code 1: removing from 1D array

 ` # Python program illustrating ` ` # numpy.delete () `   ` import ` ` numpy as geek `   ` # Working on 1D ` ` arr ` ` = ` ` geek.arange (` ` 5 ` `) ` ` print ` ` (` ` "arr:" ` `, arr) ` ` print ` ` (` ` "Shape:" ` `, arr.shape) ` ` `  ` # removing from 1D array ` ` `  ` object ` ` = ` ` 2 ` ` a ` ` = ` ` geek.delete (arr, ` ` object ` `) ` ` print ` ` ( ` ` "deleteing arr 2 times:" ` `, a) ` ` print ` ` (` `" Shape: "` `, a.shape) `   ` object ` ` = ` ` [` ` 1 ` `, ` ` 2 ` `] ` ` b = geek.delete (arr, object ) ```` print ( "deleteing arr 3 times:" , b) print ( "Shape:" , a.shape) ```

Output:

``` arr: [0 1 2 3 4 ] Repeating arr 2 times: [0 0 1 1 2 2 3 3 4 4] Shape: (10,) Repeating arr 3 times: [0 0 0 ..., 4 4 4] Shape: (15,)    Code 2:            ` # Python program illustrating `  ` # numpy.delete () `   ` `   ` import ` ` numpy as geek `  ` `  ` # Working on 1D `   ` arr ` ` = ` ` geek.arange (` ` 12 ` `). reshape (` ` 3 ` `, ` ` 4 ` `) `  ` print ` ` (` ` "arr:" ` ` , arr) `  ` print ` ` (` ` "Shape : "` `, arr.shape) `  ` `  ` # remove from 2D array `   ` a ` ` = ` ` geek.delete (arr, ` ` 1 ` `, ` ` 0 ` `) `  ` "" "`    ` [[0 1 2 3] `    ` [4 5 6 7] - & gt; removed `    ` [8 9 10 11]] `   ` "" "`   ` print ` ` (` ` "deleteing arr 2 times:" ` `, a) `  ` print ` ` (` ` "Shape:" ` `, a.shape) `     ` # remove from 2D array `   ` a ` ` = ` ` geek.delete (arr, ` ` 1 ` `, ` ` 1 ` `) `  ` "" "`   ` ` ` [[0 1 * 2 3 ] `    ` [4 5 * 6 7] `     ` [8 9 * 10 11]] `    ` ^ `    ` deletion `   ` "" "`   ` print ` ` (` ` "deleteing arr 2 times:" ` `, 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 is performed using boolean m asks

` # 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 ` ` (` `" Mask set as: "` `, mask) `
` result ` ` = ` ` arr [mask, ...] `
` print ` ` (` ` "Deletion 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]
Links:    https://docs.scipy.org/ doc / numpy / reference / generated / numpy.delete.html
Notes:   These codes will not work for online IDs. Please run them on your systems to see how they work