Tutorial on Using Jupyter (IPython) Notebook

When it comes to tasks related to data processing, numerical simulation, statistical modelling, and machine learning, Jupyter is the application of choice. Developed and maintained by Organisation Jupyter, a non-profit project that was originally a part of the IPython Project in 2014, Jupyter is an open-source software which can be freely downloaded and used by anyone without any costs associated. Its name is derived from the Julia, Python, and R programming languages that it supports.

In this article, we will demonstrate the steps required to set up a local version of the Jupyter (IPython) Notebook and execute it without connecting to a server. We will also provide guidelines for constructing a notebook, populating it with data, exporting it to another platform, extending it, and more.

To what do you refer when you say “Jupyter (IPython) Notebook”?

Previously referred to as IPython Notebook, Jupyter Notebook is an easy-to-use web application that provides a platform for creating and sharing documents containing live code, equations, visualisations, and narrative text. It enables users to develop and disseminate documents that combine the computational power of scripting languages with the narrative strength of formatted text, equations, and visualisations. Jupyter Notebook can be used for a variety of tasks, including data cleaning and transformation, numerical simulation, statistical modelling, machine learning, and much more.

  • Active ciphers
  • Equations
  • Visualisations
  • An account or story.

The Jupyter Notebook can be an effective tool when employed correctly. It can be used for a range of activities, such as data storage and transfer, as well as the learning and teaching of programming languages like Python.

Part 1: The Setup

In order to use the Jupyter Notebook with Python, it is necessary to separately download Python and manually install the Jupyter Notebook. To successfully complete this process, please take the following steps:
Check out https://jupyter.org/, the project’s official home page.

If you want to proceed, you have two choices: Install the Notebook and give it a try in your browser.

Put it to the test in your browser: If you would prefer to not have the program installed on your device, you may choose to opt for the hosted version of the Jupyter Notebook. This option allows you to access the program without having to install it onto your hardware.
Setup of the Notebook Computer: If you click this, you’ll be redirected to another website that provides comprehensive instructions for setting up your device.

Next, choose one of the two methods for putting the program on your computer, as described further below.

  • Using Python’s package manager, pip, to set up the Jupyter Notebook (CPython)
  • The Anaconda distribution installation process is used to set up the program.

Take the second route if you’re just getting started with Python.

If you would like to download Anaconda, you can find the appropriate installers for Windows, macOS, and Linux on this page. The links provided are tailored to optimise the installation process for each of the operating systems.

By installing Anaconda on your computer, you gain access to a plethora of scientific libraries, such as the Jupyter Notebook, without requiring any additional programs. This makes Anaconda a convenient, all-in-one solution for your data science needs.

The second stage entails kicking off the server for the Jupyter Notebook.

Let’s get down to business now that you’ve got the Anaconda distribution set up.

  • Launch the terminal and go to the desired directory.
  • In the terminal, go to the desired place (the folder, or subfolder if one has been created), and then execute the following command:
    To the tune of jupyter notebook $

It should now launch Jupyter.

Third, make the notepad.

Now that you know how to launch the Notebook server, you may investigate making a working Jupyter Notebook.

  • Navigate to the “New” submenu by clicking the corresponding button.
  • Check the box next to “Python 3” to use this version.
  • A fresh Python notebook will be generated.

Fourth, give the notebook a title.

  • To rename the file, select the word “untitled” in the document’s header and click it.
  • Once you’ve decided on a name for the file, save it with the.ipynb extension.

The file is now open, as shown by the Running status at the top. If you want to turn off the same, you may do so by clicking the Shutdown button.

Phase 5: Cellular Operations

  • Cells form the notepad. Once a new notebook is created, the first blank page is immediately accessible for use.
  • You may just enter the Python code in text form.
  • Clicking the run cell button or hitting Shift + Return will cause the code in this cell to be executed.
  • The results are shown in a cell directly under the input.
  • The system mechanically generates the subsequent blank cell. There’s room for extra code in that cell if you need it.

Stage 6: Including Explanatory Text

  • Adding explanatory text to your notebook requires switching the cell type from Code to Markdown.
  • After switching the cell’s type, typing in the markdown code and clicking the Shift + Return keys will compile the cell.
  • The output will now appear in the cell formerly occupied by the markdown editor.

Seventh, switch between editing and command modes

If a cell is currently active, it will have one of two states: command mode or edit mode.

  • When you double-click a cell, it switches to Command mode. The left margin’s blue tint indicates this.
  • You may enter edit mode by clicking the cell’s code area. A green border on the left side of the page indicates this.
  • The ESC key will put you back into the Command menu.
  • To get a list of all the features that can be accessed by the keyboard in either mode, go to Help > Keyboard shortcuts.

The Eighth Stage: Inspecting Progress

The Jupyter Notebook provides a useful feature for users in the form of checkpointing. This allows users to save the state of their notebook so that any modifications made after the checkpoint can be reversed, allowing the notebook to be restored to its prior condition.

  • You may save your progress and set a new checkpoint by selecting File Save and Checkpoint from the main menu.
  • By doing so, a checkpoint is made in the notebook and the file is saved.
  • Later, if you decide you want to revert to the checkpoint, you may do so by selecting File > Revert to Checkpoint.

The Notebook Will Be Exported (Step 9)

  • To save the notebook to your computer, go to File, then Download as.
  • Downloadable file types vary from.ipynb to.pdf; choose one that best suits your needs.

You should now be able to create your own Jupyter Notebook and use it for your future Python projects.

If you are looking to share your notes with others, you may want to consider organising them into a slideshow or posting them to GitHub. This allows anyone to access and comment on your notebook, without the need for installation. Another option is to use a binder, which also allows users to access your notebook without the need to install it.

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