Skip to content
pythoneo

Pythoneo

Online How to Python stuff

How to reshape array in Numpy?

Posted on March 18, 2021August 10, 2023 By Pythoneo

Let’s see how to use numpy reshape method to reshape an array in Numpy Python module.

reshape array numpy

Reshaping an array

Suppose you would like to reshape a one-dimensional array into a four-dimensional one.

import numpy as np

my_array = np.arange(24)

reshaped_array = my_array.reshape(4, 6)
print("my array")
print(my_array)
print("reshaped array")
print(reshaped_array)

I used the np.reshape function. The first element is the new number of dimensions.

In other words, the first argument will be the number of rows and the second one will be the number of columns.

The `reshape` method was able to reshape the one-dimensional array into a four-dimensional array with 4 rows and 6 columns.

Other parameters of Numpy reshape method

The `reshape` method in Numpy has several other parameters that can be used to customize the output.

  • `order`: The order in which the elements of the array are reshaped. The default value is `C`.
    • `C`: The elements are reshaped in column-major order. This is the default order for Numpy arrays.
    • `F`: The elements are reshaped in row-major order. This is the order that is used by most other programming languages.
  • `axes`: A list of integers that specifies the new dimensions of the array. The length of the list must be equal to the number of dimensions in the output array.
  • `copy`: If set to True, the output array will be a copy of the input array. The default value is False.

When to use Numpy reshape method?

The `reshape` method in Numpy can be used in a variety of situations, but it is most commonly used when you need to change the shape of an array. For example, you might use the `reshape` method to:

* Change the number of dimensions of an array.
* Change the size of each dimension of an array.
* Align the array with the shape of another array.
* Prepare an array for plotting or saving to a file.

Key Takeaways

  • The `reshape` method in Numpy can be used to change the shape of an array.
  • The `reshape` method takes two parameters: the new number of dimensions and the new shape of the array.
  • The `reshape` method can be used to change the number of dimensions of an array, the size of each dimension of an array, or to align the array with the shape of another array.

FAQ

  • **What is the difference between `reshape` and `resize`?**
    • reshape method changes the shape of an array without changing the data in the array. resize method changes the shape of an array and may also change the data in the array.
  • **What is the order of the elements in a reshaped array?**
    • The order of the elements in a reshaped array is determined by the `order` parameter of the `reshape` method. The default value of `order` is `C`, which means that the elements are ordered in column-major order.
  • **What happens if I try to reshape an array to a shape that is not compatible with the data in the array?**
    • If you try to reshape an array to a shape that is not compatible with the data in the array, the `reshape` method will raise an error.
See also  How to empty an array in Numpy?
numpy Tags:reshape

Post navigation

Previous Post: How to calculate square root in Numpy?
Next Post: How to create empty array in Numpy?

Categories

  • bokeh (1)
  • Django (5)
  • matplotlib (11)
  • numpy (99)
  • OpenCV (4)
  • Pandas (3)
  • paramiko (12)
  • Pillow (3)
  • Plotly (3)
  • Python (30)
  • Scipy (4)
  • Seaborn (7)
  • statistics (7)
  • Tkinter (8)
  • turtle (2)

RSS RSS

  • OpenCV FindContours: Detecting and Analyzing Objects in Images
  • How to create a simple animation in Tkinter
  • Adaptive Thresholding with OpenCV
  • Hot to use the grid geometry manager in Tkinter
  • How to install and use paramiko for SSH connections in Python
  • How to automate file transfers with paramiko and SFTP
  • How to Execute Remote Commands with Paramiko and SSHClient
  • Handling Paramiko Errors and Timeouts
  • How to use paramiko with multiprocessing and threading
  • How to use matplotlib cmap?

Tags

arithmetic mean array axis button calculations chart column conversion count data type dictionary dimension draw error files fill float generate grid GUI image index integer list matrix max mean min mode multiply normal distribution plot random reshape rotate round rows size string sum test text time type zero

Copyright © 2023 Pythoneo.

Powered by PressBook WordPress theme

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept”, you consent to the use of ALL the cookies.
Cookie settingsACCEPT
Manage consent

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Non-necessary
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
SAVE & ACCEPT
Go to mobile version