I'm trying

`numpy.newaxis`

and the result gives me a 2-d plot frame with x-axis from 0 to 1.

However, when I try using numpy.newaxis to slice a vector,

```vector[0:4,]
[ 0.04965172  0.04979645  0.04994022  0.05008303]
vector[:, np.newaxis][0:4,]
[[ 0.04965172]
[ 0.04979645]
[ 0.04994022]
[ 0.05008303]]```

Is it the same thing except that it changes a row vector to a column vector?

Generally, what is the use of numpy.ne,waxis and in which circumstances should we use it?

Apr 14, 2020 in Python 15,558 views

## 1 answer to this question.

Hello,

The np.newaxis is generally used with slicing. It indicates that you want to add an additional dimension to the array.

The position of the np.newaxis represents where I want to add dimensions.

```>>> import numpy as np
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> a.shape
(10,)```

In the first example I use all elements from the first dimension and add a second dimension:

```>>> a[:, np.newaxis]
array([[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
[9]])
>>> a[:, np.newaxis].shape
(10, 1)```

The second example adds a dimension as first dimension and then uses all elements from the first dimension of the original array as elements in the second dimension of the result array:

```>>> a[np.newaxis, :]  # The output has 2 [] pairs!
array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])
>>> a[np.newaxis, :].shape
(1, 10)```

Similarly you can use multiple np.newaxis to add multiple dimensions:

```>>> a[np.newaxis, :, np.newaxis]  # note the 3 [] pairs in the output
array([[[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
[9]]])
>>> a[np.newaxis, :, np.newaxis].shape
(1, 10, 1)```
• 82,880 points

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