Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How does numpy.newaxis work and when to use it** **in Python**. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

## How does numpy.newaxis work and when to use it?

**How does numpy.newaxis work and when to use it?**You started with a one-dimensional list of numbers. Once you used

`numpy.newaxis`

, you turned it into a two-dimensional matrix, consisting of four rows of one column each.**numpy.newaxis work and when to use it**You started with a one-dimensional list of numbers. Once you used

`numpy.newaxis`

, you turned it into a two-dimensional matrix, consisting of four rows of one column each.

## Method 1

You started with a one-dimensional list of numbers. Once you used `numpy.newaxis`

, you turned it into a two-dimensional matrix, consisting of four rows of one column each.

You could then use that matrix for matrix multiplication, or involve it in the construction of a larger 4 x n matrix.

## Method 2

`newaxis`

object in the selection tuple serves to **expand the dimensions** of the resulting selection by **one unit-length** dimension.

It is not just conversion of row matrix to column matrix.

Consider the example below:

In [1]:x1 = np.arange(1,10).reshape(3,3) print(x1) Out[1]: array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

Now lets add new dimension to our data,

In [2]:x1_new = x1[:,np.newaxis] print(x1_new) Out[2]:array([[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]]])

You can see that `newaxis`

added the extra dimension here, x1 had dimension (3,3) and X1_new has dimension (3,1,3).

How our new dimension enables us to different operations:

In [3]:x2 = np.arange(11,20).reshape(3,3) print(x2) Out[3]:array([[11, 12, 13], [14, 15, 16], [17, 18, 19]])

Adding x1_new and x2, we get:

In [4]:x1_new+x2 Out[4]:array([[[12, 14, 16], [15, 17, 19], [18, 20, 22]], [[15, 17, 19], [18, 20, 22], [21, 23, 25]], [[18, 20, 22], [21, 23, 25], [24, 26, 28]]])

Thus, `newaxis`

is not just conversion of row to column matrix. It increases the dimension of matrix, thus enabling us to do more operations on it.

**Conclusion**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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