Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Reshaping | Important Functions
NumPy in a Nutshell
course content

Course Content

NumPy in a Nutshell

NumPy in a Nutshell

1. Getting Started with NumPy
What is NumPy?Function array()Basic Array Creation Quiz
2. Dimensions in Arrays
1-D ArraysMathematical Operations with 1-D Arrays2-D Arrays3-D ArraysMultidimensional Arrays Quiz
3. Indexing and Slicing
Access Array ElementsMatch Math Operations Access 2-D and 3-D ArraysMatch the Slicing Negative IndexingMatch the IndicesGet One Dimensional Array Using Slice and Only Positive Indexes Get One Dimensional Array Using Only Negative Indexes
4. Important Functions
ReshapingFlatteningJoining Sorting ArraysCopying Arrays

bookReshaping

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:


              
123456

            
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)
copy

Reshape one-dimensional into a three-dimensional array:


              
123456

            
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)
copy
Task
test

Swipe to show code editor

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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 4. Chapter 1
toggle bottom row

bookReshaping

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:


              
123456

            
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)
copy

Reshape one-dimensional into a three-dimensional array:


              
123456

            
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)
copy
Task
test

Swipe to show code editor

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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 4. Chapter 1
toggle bottom row

bookReshaping

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:


              
123456

            
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)
copy

Reshape one-dimensional into a three-dimensional array:


              
123456

            
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)
copy
Task
test

Swipe to show code editor

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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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:


              
123456

            
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)
copy

Reshape one-dimensional into a three-dimensional array:


              
123456

            
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)
copy
Task
test

Swipe to show code editor

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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. Chapter 1
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
We're sorry to hear that something went wrong. What happened?
some-alt