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# numpy.delete() in Python

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,)

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 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)

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 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 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 GeeksforGeeks 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 GeeksforGeeks main page and help other Geeks.