# Remove Elements From Array in Numpy

In this article, we will learn about two ways to remove elements from a NumPy array.

## Remove Elements Using `numpy.delete()`

Function

Refer to the following code.

```
import numpy as np
myArray = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
indexes = [3, 5, 7]
modifiedArray = np.delete(myArray, indexes)
print(modifiedArray)
```

Output:

```
[ 1 2 3 5 7 9 10]
```

In the above code, we use the `delete()`

function of the `numpy`

library. The `delete()`

function accepts three parameters, namely, `arr`

, `obj`

, and `axis`

and outputs a NumPy array. The `arr`

is the NumPy array we wish to delete elements from. `obj`

is a list of integer numbers. These numbers represent the indexes of the elements that should be deleted from the array. Lastly, the `axis`

is an optional argument. `axis`

refers to the axis along which the elements targetted by the `obj`

should be deleted. If a `None`

value is assigned to this parameter, `arr`

is flattened, and deletion is carried out on this flattened array.

As usual, if an index that lies outside the range of `arr`

is provided to this method, it throws an `IndexError`

exception.

```
import numpy as np
myArray = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
indexes = [3, 5, 7, 34]
modifiedArray = np.delete(myArray, indexes)
print(modifiedArray)
```

Output:

```
Traceback (most recent call last):
File "<string>", line 5, in <module>
File "<__array_function__ internals>", line 5, in delete
File "/path/to/library/numpy/lib/function_base.py", line 4480, in delete
keep[obj,] = False
IndexError: index 34 is out of bounds for axis 0 with size 10
```

To learn more about this function, refer to the official documentation of this function here

Here are two more examples for deletion in a multidimensional NumPy array.

```
import numpy as np
myArray = np.array([[1, 2, 3, 4, 5], [11, 12, 13, 14, 15], [21, 22, 23, 24, 25]])
modifiedArray = np.delete(myArray, [1, 2], 1)
print(modifiedArray)
```

Output:

```
[[ 1 4 5]
[11 14 15]
[21 24 25]]
```

Use `None`

as the value for parameter `axis`

.

```
import numpy as np
myArray = np.array([[1, 2, 3, 4, 5], [11, 12, 13, 14, 15], [21, 22, 23, 24, 25]])
modifiedArray = np.delete(myArray, [1, 2], None)
print(modifiedArray)
```

Output:

```
[ 1 4 5 11 12 13 14 15 21 22 23 24 25]
```

## Remove Elements Using `numpy.setdiff1d()`

Function

This time we will use the `setdiff1d()`

function from `numpy`

. This function accepts three parameters, `ar1`

, `ar2`

, and `assume_unique`

. `ar1`

and `ar2`

are two NumPy arrays. And `assume_unique`

is an optional boolean argument. Its default value is `False`

. When it’s `True`

, then the two input arrays are assumed to be unique, and this assumption can speed up the calculation time.

`setdiff1d()`

return the unique values in `ar1`

that are not in `ar2`

.

Refer to the following code.

```
import numpy as np
myArray = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
indexes = [3, 5, 7]
modifiedArray = np.setdiff1d(myArray, indexes)
print(modifiedArray)
```

Output:

```
[ 1 2 4 6 8 9 10]
```

Unlike `numpy.delete()`

, both the arrays are NumPy arrays with actual elements in them but not indexes.

To learn more about this function, refer to the official documentation of this function here.