Getting started with Jupyter Notebooks

This post is for those who want to get started with Jupyter Notebooks. You don’t need to know anything beyond Python to start using Jupyter notebook. In this post I will be covering below topics.

  • What is a Jupyter Notebook and app?
  • How to install and use Jupyter Notebook
  • Jupyter notebook user interface
  • Sharing Jupyter Notebooks via GitHub Gist and nbviewer
  • How to use a shared Jupyter Notebook
  • Built-in magic commands in Jupyter Notebook

  • What is a Jupyter Notebook?

    Jupyter Notebook is a web application where you can create and share documents that contain source code, equations, visualizations as well as text. It is an ideal tool to help you to gain the data science skills you need. “Jupyter” is a loose acronym meaning Julia, Python and R. These programming languages were the first target languages of Jupyter application.

    Jupyter Notebook App

    It is a server-client application, which allows you to edit and run notebooks via a web browser. Its two main components are the kernel and a dashboard. A kernel is a program that runs and examines the code. The dashboard of the UI application which shows you the notebook documents that you have created and is used to manage the kernels.

    How to install Jupyter Notebook

    Running Jupyter Notebooks with the Anaconda Python Distribution:- One of the basic requirement for running Jupyter notebook is Python(either Python 3.3 or greater or Python 2.7). The general recommendation is that you use the Anaconda distribution to install both Python and the notebook application. The advantage of Anaconda is that you have access to over 720 packages that can easily be installed with Anaconda’s Conda, a package, dependency, and environment manager. You can download and follow the instructions for the installation of Anaconda here.

    Running Jupyter Notebook using Pip :- If you don’t want to install Anaconda, you just have to make sure that you have the latest version of pip. Once you have pip, you can just run

     # Python2
     pip install jupyter
     # Python 3
     pip3 install jupyter

    Running Jupyter Notebooks in Docker Containers:- Docker is an excellent platform to run software in containers. To run the Jupyter Notebook image in your Docker container, give in the below command in your Docker Quickstart Terminal

    docker run --rm -it -p 8888:8888 -v "$(pwd):/notebooks" jupyter/notebook

    The newest Jupyter HTML Notebook image will be downloaded and it will be started.

    How to use Jupyter Notebook

    To launch a Jupyter Notebook application run the following command in the command prompt from the working directory.

      jupyter notebook

    This will start the Jupyter server and open a new window in your browser (if that’s not the case, go to URL: http://localhost:8888). Below is the screenshot of Jupyter application.

    Jupyter application

    For closing the notebook server, go to the command prompt where you launched the server from, and press Ctrl+C.

    The notebook dashboard consists of several tabs.

  • The Files tab is where all the files are kept
  • Running tab keeps track of all the processes
  • Clusters tab is provided by ipython parallel, which allows you to control many individual engines.
  • A Notebook is a interactive document containing code, text, and other elements. A notebook is saved in a file with the .ipynb extension
  • A Kernel is a process running an interactive session. When using jupyter notebook, this kernel is a python process
  • A notebook is a file and Kernel is a process. The kernel receives snippets of code from the Notebook interface, executes them, and sends the outputs and possible errors back to the Notebook interface.

    Jupyter notebook user interface

    To start a new notebook, Click on the New drop down in the Files tab. You see the options make a regular text file, a folder and a terminal. Lastly, you will see an option to make a Python 3 notebook. This option will depend on the version of python that you have installed. If you want to start on a new notebook, click on the “Python3” option in the ‘Notebook’ category.

    Sample Jupyter User Interface

    You can import libraries that you need, you can add, remove and edit the cells according to your needs. You can also add explanatory text, titles and subtitles to explain the code.

    A notebook may be downloaded in either a .ipynb or .py file from the menu option File | Download as. Choosing the .py option downloads a Python .py script, in which all rich output has been removed and the content of markdown cells have been inserted as comments.

    Share your Jupyter Notebooks

    Sometimes you may want to share your notebook with colleagues or friends. Notebook documents are JSON documents that contain text, source code, rich media output and metadata. Each segment of the document is stored in a cell.

    There are different ways to share notebook document with others:

  • For those who are not using notebooks, you can convert to HTML or PDF using the file menu option. Jupyter gives you an option to download your notebook as an HTML, Markdown or a Python script or a Notebook file.
  • You can use nbconvert command to convert the notebook document file to another static format such as HTML,PDF,LaTex, Markdown,reStructuredText etc., Use command to convert your notebook –
    jupyter nbconvert --to html sample.ipynb
  • You can create, list and load GitHub Gists from the notebook documents. Gists are a way to share your work because you can share single files, parts of files or full applications
  • With JupyterHub, you can manage and proxy multiple instances of the single-user Jupyter notebook server. In other words, its a platform for hosting notebooks on a server with multiple users. Note:- JupyterHub is supported on Linux/Unix based systems. JupyterHub officially does not support Windows
  • You can use nbviewer to render notebooks as static web pages. I will show how to share Jupyter notebook using Gists and nbviewer in the later sections
  • You can create a blog from your notebook with Pelican plugin
  • To turn your notebooks into slideshows, you can turn to nbpresent and RISE
  • You can use jupyter_dashboards if you want to display notebooks as interactive dashboards

  • Sharing notebooks via GitHub Gist and nbviewer

    The below example notebook gender_wise_marks.ipynb has been created with a sample code for matplotlib usage. Below are the steps to share this notebook to Github Gist.

    • Clear all the output from the notebook
    • Save the notebook as .ipynb file
    • Open the .ipynb in notepad++ and copy the JSON content
    • Login into github Gist –
    • Paste the JSON content in the Gist code snippet area
    • Give a filename with extension as .ipynb and save the file
    • Click on Create Public Gist. This will generate a number with Gist URL as shown below
    GithubGist with the .ipynb code
    GithubGist with the .ipynb code
  • Copy that number from the URL(In this case 3df0b9d10b07d2c136b2dc9934f861a1)
  • Go to URL and paste the copied number in the URL|Gist ID field and click Go!
  • You can view the notebook now in nbviewer here

    Jupyter nbviewer page

    How to use a shared Jupyter Notebook

    Loading Notebook Files – You can also load Jupyter Notebooks that other people have created, saved as Jupyter Notebook files (File extension .ipynb). After downloading the Notebook file from Github or Jupyterhub move it into your Jupyter Notebook working directory and then choose File -> Open in Notebook to open it. Now you can make some changes and edit the existing files.

    Import .ipynb files into another notebook – You can import .ipynb into another notebook by using the %run command. You can simply say %run ‘gender_wise_marks.ipynb’ to share code between notebooks(without cutting and pasting them between different notebooks). Jupyter’s %run magic allows you execute python files and ipython scripts in a notebook. You can also import Jupyter notebooks as modules. For more information refer here

    Loading Python Files – You can also load a pre-existing Python file into a Jupyter Notebook cell by simply typing

            %load ""

    This loads up a new cell containing the contents of There is one other useful built-in tool for working with Python files:

            %run ""

    This will run and load the output into a Notebook cell.

    Built-in magic commands in Jupyter Notebook

    Jupyter kernels support a set of predefined ‘magic functions’. The builtin magics are two kinds line-oriented and cell-oriented.

    Line magics are prefixed with the % character and work much like OS command-line calls: they get as an argument the rest of the line, where arguments are passed without parentheses or quotes.
    Cell magics are prefixed with a double %%, and they are functions that get as an argument not only the rest of the line, but also the lines below it in a separate argument.

    The built-in magics include:

  • Functions that work with code: %run, %edit, %save, %macro, %recall, %lsmagic, %pdb etc.
  • Functions which affect the shell: %colors, %xmode, %autoindent, %automagic, etc.
  • Other functions such as %reset, %timeit, %%writefile, %% HTML, %load, or %paste.

  • Hope this post helps you get started with Jupyter Notebooks!

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    1) Boost Your Jupyter Notebook Productivity
    2) Jupyter Notebook Tutorial: The Definitive Guide
    3) IPython Extensions
    4) Jupyter Builtin magics

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