## Introduction to NumPy delete

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 a flattened array

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

## Code Examples of NumPy delete

**Example 01:** Deletion from 1D array

```
# welcome to softhunt.net
# Python Program illustrating
# numpy.delete()
import numpy as np
#Working on 1D
arr = np.arange(5)
print("arr : \n", arr)
print("Shape : ", arr.shape)
# deletion from 1D array
object = 2
a = np.delete(arr, object)
print("\ndeleteing {} from array : \n {}".format(object,a))
print("Shape : ", a.shape)
object = [1, 2]
b = np.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 2 from array : [0 1 3 4] Shape : (4,) deleteing [1, 2] from array : [0 1 3 4] Shape : (4,)

**Example 02:**

```
# welcome to softhunt.net
# Python Program illustrating
# numpy.delete()
import numpy as np
#Working on 1D
arr = np.arange(12).reshape(3, 4)
print("arr : \n", arr)
print("Shape : ", arr.shape)
# deletion from 2D array
a = np.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 = np.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)

**Example 03:** Deletion performed using Boolean Mask

```
# welcome to softhunt.net
# Python Program illustrating
# numpy.delete()
import numpy as np
arr = np.arange(5)
print("Original array : ", arr)
mask = np.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 programs will not run in online IDEs. Please test them on your systems to see how they operate.

## FAQs

### What does NumPy delete do?

Return a new array with sub-arrays along an axis deleted. For a one-dimensional array, this returns those entries not returned by arr[obj].

### Why is NumPy faster than for loop?

Because of the following reasons, NumPy Arrays are quicker than Python Lists: A collection of homogenous data types stored in contiguous memory spaces is referred to as an array. A list, on the other hand, is a collection of disparate data types store in non-contiguous memory locations in Python.

### Is Julia faster than NumPy?

For small arrays (up to 1000 elements) Julia is actually faster than Python/NumPy. For intermediate size arrays (100,000 elements), Julia is nearly 2.5 times slower (and in fact, without the sum, Julia is up to 4 times slower). Finally, at the largest array sizes, Julia catches up again. (It is unclear to me why; it seems like the Python/NumPy performance should scale linearly above n=100,000, but it does not.)

## Conclusion

That’s all for this article, if you have any confusion contact us through our website or email us at *[email protected]* or by using LinkedIn. And make sure you check out our NumPy tutorials.

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