# numpy.delete() in Python

numpy.delete(array, object, axis = None) : returns a new array with the deletion of sub-arrays 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 : Deletion from 1D array

 `# 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 arr 2 times : \n"``, a) ` `print``(``"Shape : "``, a.shape) ` ` `  `object` `=` `[``1``, ``2``] ` `b ``=` `geek.delete(arr, ``object``) ` `print``(``"\ndeleteing arr 3 times : \n"``, 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 : \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 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]```

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 😀. 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.