# np newaxis with code examples

NumPy is a popular Python library that is widely used for working with arrays and performing mathematical operations on them. A key feature of NumPy is its ability to create and manipulate arrays with ease and efficiency. One of the essential functions in NumPy is `np.newaxis`, which provides a straightforward way to reshape arrays. In this article, we'll explore how `np.newaxis` works and provide some code examples to help you understand its usage.

Understanding `np.newaxis`

Before we dive into the code examples, it's essential to understand what `np.newaxis` does. It is primarily used to insert a new axis into an existing array. In simpler terms, it allows us to change the dimensionality of an array. It is a useful tool when working with arrays of different shapes and sizes to align them in a way that they can be mathematically operated on.

The `np.newaxis` object can be used in two different ways:

1. Explicitly add a new axis to an array.

2. Implicitly add a new axis to an array by using it as an index.

Let's start with explicit addition. Let's say we have a one-dimensional NumPy array, and we want to convert it into a two-dimensional array by adding an axis. Here's how we can do it with `np.newaxis`:

```import numpy as np

# create a one-dimensional array
x = np.array([1, 2, 3])

# add a new axis to make it a two-dimensional array
y = x[:, np.newaxis]

print(y.shape)
# Output: (3, 1)
```

In the above example, we're using `np.newaxis` to add an extra axis to the one-dimensional array `x`. When we use `x[:, np.newaxis]`, we're telling NumPy to add a new axis at the second position. This results in the creation of a two-dimensional array `y` of shape `(3, 1)`.

We could also add the new axis at different positions depending on the shape we want to achieve. Here's an example:

```import numpy as np

# create a one-dimensional array
x = np.array([1, 2, 3])

# add a new axis at the beginning to make it a two-dimensional array
y = x[np.newaxis, :]

print(y.shape)
# Output: (1, 3)
```

In the above example, we're adding a new axis at the first position by using `x[np.newaxis, :]`. This results in the creation of a two-dimensional array `y` of shape `(1, 3)`.

Another way to use `np.newaxis` is to implicitly add a new axis while indexing an array. Here's an example:

```import numpy as np

# create a two-dimensional array
x = np.array([[1, 2, 3], [4, 5, 6]])

# index using np.newaxis to add a new axis
y = x[:, :, np.newaxis]

print(y.shape)
# Output: (2, 3, 1)
```

In the above example, we're using `np.newaxis` to add a new axis at the last position while indexing the two-dimensional array `x`. This results in the creation of a three-dimensional array `y` of shape `(2, 3, 1)`.

`np.newaxis` can also be used to add a new axis at the beginning and the middle positions. Here's an example:

```import numpy as np

# create a two-dimensional array
x = np.array([[1, 2, 3], [4, 5, 6]])

# add a new axis at the beginning and the middle position
y = x[:, np.newaxis, :, np.newaxis]

print(y.shape)
# Output: (2, 1, 3, 1)
```

In the above example, we're using `np.newaxis` twice to add a new axis at the beginning and the middle positions while indexing the two-dimensional array `x`. This results in a four-dimensional array `y` of shape `(2, 1, 3, 1)`.

Uses of `np.newaxis`

Now that we have seen how to use `np.newaxis`, let's look at why it can be useful. The primary use of `np.newaxis` is to convert one-dimensional arrays to two-dimensional arrays, making them easier to work with. There are other advantages of using `np.newaxis`, which we'll explore below.

Broadcasting is a crucial feature of NumPy arrays. When performing operations between two arrays of different shapes, NumPy will often adjust the shapes automatically to make the operation possible. The way broadcasting works in NumPy is by adding new axes to the smaller array so that its shape matches the larger array. Here's an example:

```import numpy as np

# create two arrays of different shapes
a = np.array([1, 2, 3])
b = np.array([, ])

# add new axis to array a
c = a[:, np.newaxis]

d = c + b

print(d)
# Output: array([[5, 6, 7],
#                [6, 7, 8]])
```

In the above example, we're using `np.newaxis` to add a new axis to the one-dimensional array `a`, making it a two-dimensional array. This makes it possible to add it to the two-dimensional array `b`. The result is another two-dimensional array `d`, which has the shape `(2, 3)`.

Matrix multiplication

Another use case of `np.newaxis` is in matrix multiplication. Matrix multiplication can only be performed between arrays that have compatible shapes. By using `np.newaxis`, we can change the shape of an array to make it compatible with another array for multiplication. Here's an example:

```import numpy as np

# create two arrays of different shapes
a = np.array([1, 2, 3])
b = np.array([, , ])

# add new axis to array a
c = a[:, np.newaxis]

# perform matrix multiplication
d = np.dot(c, b)

print(d)
# Output: array([])
```

In the above example, we're using `np.newaxis` to add a new axis to the one-dimensional array `a`, making it a two-dimensional array. We're then performing matrix multiplication between the two-dimensional arrays `c` and `b`. The result is a one-dimensional array `d` of shape `(1,)`.

Conclusion

In conclusion, `np.newaxis` is a powerful tool for reshaping arrays in NumPy. By adding a new axis to an array, we can change its dimensionality and align it with other arrays of different shapes. One of the primary uses of `np.newaxis` is to convert one-dimensional arrays to two-dimensional arrays, which makes them easier to work with. Other use cases of `np.newaxis` include broadcasting and matrix multiplication. With an understanding of `np.newaxis`, you'll be able to achieve more with NumPy arrays and perform more complex operations with ease.

let's dive a little deeper into some of the topics covered earlier.

Adding a new axis to an array with `np.newaxis`

As we saw earlier, `np.newaxis` is used to add a new axis to an array. This can be very useful when working with arrays of different sizes and shapes. Let's take a closer look at how it works.

When an array is created, it has a certain number of dimensions (also called the array's rank). For example, a one-dimensional array (also called a vector) has one dimension, a two-dimensional array (also called a matrix) has two dimensions, and so on. However, sometimes we need to reshape an array to make it easier to work with.

This is where `np.newaxis` comes in. By adding a new axis to an array, we can effectively reshape the array to have an additional dimension. For example, consider the following code:

```import numpy as np

# create a one-dimensional array
x = np.array([1, 2, 3])

# add a new axis to make it a row vector
y = x[np.newaxis, :]

print(y)
# Output: array([[1, 2, 3]])
```

In this code, we use `np.newaxis` to add a new axis at the first position of the array `x`. This effectively gives us a new row dimension, and `x` becomes a row vector. The resulting array `y` has shape `(1, 3)`, which means it's a one-row, three-column matrix.

Implicitly adding a new axis with `np.newaxis`

Another way to use `np.newaxis` is to implicitly add an axis while indexing an array. This can be useful when we want to slice an array in a particular way and reshape it at the same time. Here's an example:

```import numpy as np

# create a two-dimensional array
x = np.array([[1, 2, 3], [4, 5, 6]])

# add a new axis at the second position while slicing
y = x[:, np.newaxis, :]

print(y.shape)
# Output: (2, 1, 3)
```

In this code, we use `np.newaxis` to add a new axis at the second position while slicing the array `x`. This gives us a new depth dimension, and `x` becomes a three-dimensional array. The resulting array `y` has shape `(2, 1, 3)`, which means it's a two-row, one-column, three-depth array.

Broadcasting arrays with `np.newaxis`

Another common use of `np.newaxis` is in broadcasting arrays of different shapes. Broadcasting is a feature of NumPy that allows us to perform operations between arrays of incompatible shapes. For example, we can add a scalar value to a one-dimensional array and NumPy will automatically broadcast the scalar to every element in the array.

Similarly, we can use `np.newaxis` to add new dimensions to an array and broadcast it against another array. Here's an example:

```import numpy as np

# create two arrays of different shapes
x = np.array([[1, 2], [3, 4]])
y = np.array([10, 20])

# add a new axis to y and broadcast it against x
z = x + y[:, np.newaxis]

print(z)
# Output: array([[11, 12],
#                 [23, 24]])
```

In this code, we use `np.newaxis` to add a new axis to the one-dimensional array `y`. This gives us a new row dimension, and `y` becomes a two-row, one-column matrix. We then add `y` to `x` using broadcasting, and the result is an array `z` of shape `(2, 2)`.

Matrix multiplication with `np.newaxis`

Finally, `np.newaxis` can be used to perform matrix multiplication between arrays of different shapes. Matrix multiplication requires that the number of columns in the left-hand matrix matches the number of rows in the right-hand matrix. If the matrices don't have matching dimensions, we can use `np.newaxis` to reshape the arrays and perform matrix multiplication anyway. Here's an example:

```import numpy as np

# create two arrays of different shapes
x = np.array([1, 2, 3])
y = np.array([, , ])

# add new axes to x and y and compute their dot product
z = np.dot(x[:, np.newaxis], y)

print(z)
# Output: array([[ 4,  5,  6],
#                 [ 8, 10, 12],
#                 [12, 15, 18]])
```

In this code, we use `np.newaxis` to add a new axis to `x`. This gives us a new row dimension, and `x` becomes a one-row, three-column matrix. We then use `np.dot()` to compute the dot product of `x` and `y`. The resulting array `z` has shape `(3, 3)`, which means it's a three-row, three-column matrix.

Conclusion

In summary, `np.newaxis` is a useful tool for working with arrays in NumPy. We can use it to add new dimensions to an array, reshape an array, broadcast arrays, and perform matrix multiplication. By using `np.newaxis` effectively, we can create more complex and flexible programs with NumPy.

## Popular questions

1. What is `np.newaxis` used for?

`np.newaxis` is used to add a new axis to an array, which effectively reshapes the array to have an additional dimension.

2. How do you add a new axis to a one-dimensional array using `np.newaxis`?

To add a new axis to a one-dimensional array, you can use the following syntax:

```x = np.array([1, 2, 3])
y = x[:, np.newaxis]
```

This adds a new column dimension to the array `x` and creates a new two-dimensional array `y`.

3. How can `np.newaxis` be used for broadcasting?

`np.newaxis` can be used to add new dimensions to an array and broadcast it against another array. Here's an example:

```import numpy as np

x = np.array([[1, 2], [3, 4]])
y = np.array([10, 20])

z = x + y[:, np.newaxis]

print(z)  # Output: array([[11, 12], [23, 24]])
```

In this example, `y[:, np.newaxis]` is used to add a new column dimension to `y`, which enables NumPy to broadcast `y` to every element in `x`.

4. How can `np.newaxis` be used for matrix multiplication?

We can use `np.newaxis` to perform matrix multiplication between arrays of different shapes. Here's an example:

```import numpy as np

x = np.array([1, 2, 3])
y = np.array([, , ])

z = np.dot(x[:, np.newaxis], y)

print(z)  # Output: array([[ 4,  5,  6], [ 8, 10, 12], [12, 15, 18]])
```

In this example, `x[:, np.newaxis]` is used to add a new row dimension to `x`, which enables NumPy to perform matrix multiplication with `y`.

5. In what situations might you use `np.newaxis`?

`np.newaxis` can be useful in situations where you need to reshape an array or add new dimensions to an array to align it with other arrays of different sizes and shapes. It can be used for broadcasting, matrix multiplication, and any other situation where you need to change the dimensions of an array.

### Tag

Axis. ##### Ajay Kemparaj
Throughout my career, I have held positions ranging from Associate Software Engineer to Principal Engineer and have excelled in high-pressure environments. My passion and enthusiasm for my work drive me to get things done efficiently and effectively. I have a balanced mindset towards software development and testing, with a focus on design and underlying technologies. My experience in software development spans all aspects, including requirements gathering, design, coding, testing, and infrastructure. I specialize in developing distributed systems, web services, high-volume web applications, and ensuring scalability and availability using Amazon Web Services (EC2, ELBs, autoscaling, SimpleDB, SNS, SQS). Currently, I am focused on honing my skills in algorithms, data structures, and fast prototyping to develop and implement proof of concepts. Additionally, I possess good knowledge of analytics and have experience in implementing SiteCatalyst. As an open-source contributor, I am dedicated to contributing to the community and staying up-to-date with the latest technologies and industry trends.
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