## Reshaping

Sometimes situations arise when we need to somehow **change** our array, for example, change the **size** of the array or go from an array of one **dimension** to an array of another dimension, but with the same data that was originally used. But it is not always convenient to recreate the array from scratch, so some **functions** modify the array as we need it.

Let's have a look at some of them:

`np.reshape()`

- this function changes the shape of an N-dimensional array while maintaining the same total number of elements;`np.transpose()`

- this function transposes the array, essentially swapping its axes;`np.concatenate()`

- this function creates a new array by appending arrays one after another along the specified axis;`np.resize()`

- this function is used to resize an array, creating a copy of the original array with the specified size.

Reshape one-dimensional array into a two-dimensional array:

Reshape one-dimensional into a three-dimensional array:

# Task

Consider the following array: `[11, 56, 78, 45, 1, 5]`

.
You should obtain the following array:
`[[11, 56], [78, 45], [1, 5]]`

.

Please use the `.reshape()`

method.

Everything was clear?

Course Content

# NumPy in a Nutshell

1. Getting Started with NumPy

4. Important Functions

NumPy in a Nutshell

## Reshaping

Sometimes situations arise when we need to somehow **change** our array, for example, change the **size** of the array or go from an array of one **dimension** to an array of another dimension, but with the same data that was originally used. But it is not always convenient to recreate the array from scratch, so some **functions** modify the array as we need it.

Let's have a look at some of them:

`np.reshape()`

- this function changes the shape of an N-dimensional array while maintaining the same total number of elements;`np.transpose()`

- this function transposes the array, essentially swapping its axes;`np.concatenate()`

- this function creates a new array by appending arrays one after another along the specified axis;`np.resize()`

- this function is used to resize an array, creating a copy of the original array with the specified size.

Reshape one-dimensional array into a two-dimensional array:

Reshape one-dimensional into a three-dimensional array:

# Task

Consider the following array: `[11, 56, 78, 45, 1, 5]`

.
You should obtain the following array:
`[[11, 56], [78, 45], [1, 5]]`

.

Please use the `.reshape()`

method.

Everything was clear?