Course Content

NumPy in a Nutshell

## 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:

`import numpy as np array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) new_array = array.reshape(4, 3) print(new_array)`

Reshape one-dimensional into a three-dimensional array:

`import numpy as np array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) new_array = array.reshape(2, 3, 2) print(new_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.

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?

# 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:

`import numpy as np array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) new_array = array.reshape(4, 3) print(new_array)`

Reshape one-dimensional into a three-dimensional array:

`import numpy as np array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) new_array = array.reshape(2, 3, 2) print(new_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.

Task

`[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?

# 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:

`import numpy as np array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) new_array = array.reshape(4, 3) print(new_array)`

Reshape one-dimensional into a three-dimensional array:

`import numpy as np array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) new_array = array.reshape(2, 3, 2) print(new_array)`

Task

`[11, 56, 78, 45, 1, 5]`

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

.

Please use the `.reshape()`

method.

Task

`[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?

**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

`[11, 56, 78, 45, 1, 5]`

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

.

Please use the `.reshape()`

method.