Introduction and installation of Jupyter notebook
This article provides an overview of Jupyter notebook. This page introduces Jupyter notebook and its features.
The Jupyter Notebook is an open source web application that you can use to create and share documents that contain live code, equations, visualizations, and text. Jupyter Notebook is maintained by the people at Project Jupyter.
Jupyter Notebooks are a spin-off project from the IPython project, which used to have an IPython Notebook project itself. The name, Jupyter, comes from the core supported programming languages that it supports: Julia, Python, and R. Jupyter ships with the IPython kernel, which allows you to write your programs in Python, but there are currently over 100 other kernels that you can also use.
Installing Jupyter Notebook
The Jupyter Notebook is not included with Python, so if you want to try it out, you will need to install Jupyter.
There are many distributions of the Python language. This article will focus on just two of them for the purposes of installing Jupyter Notebook. The most popular is CPython, which is the reference version of Python that you can get from their website. It is also assumed that you are using Python 3.
Installation
If so, then you can use a handy tool that comes with Python called pip to install Jupyter Notebook like this:
The next most popular distribution of Python is Anaconda. Anaconda has its own installer tool called conda that you could use for installing a third-party package. However, Anaconda comes with many scientific libraries preinstalled, including the Jupyter Notebook, so you don’t actually need to do anything other than install Anaconda itself.
Starting the Jupyter Notebook Server
Now that you have Jupyter installed, let’s learn how to use it. To get started, all you need to do is open up your terminal application and go to a folder of your choice. I recommend using something like your Documents folder to start out with and create a subfolder there called Notebooks or something else that is easy to remember.
Then just go to that location in your terminal and run the following command:
This will start up Jupyter and your default browser should start (or open a new tab) to the following URL: http://localhost:8888/tree
Your browser should now look something like this:
Note that right now you are not actually running a Notebook, but instead you are just running the Notebook server. Let’s actually create a Notebook now!
Creating a Notebook
Now that you know how to start a Notebook server, you should probably learn how to create an actual Notebook document.
All you need to do is click on the New button (upper right), and it will open up a list of choices. On my machine, I happen to have Python 2 and Python 3 installed, so I can create a Notebook that uses either of these. For simplicity’s sake, let’s choose Python 3.
Your web page should now look like this:
Naming
You will notice that at the top of the page is the word Untitled. This is the title for the page and the name of your Notebook. Since that isn’t a very descriptive name, let’s change it!
Just move your mouse over the word Untitled and click on the text. You should now see an in-browser dialog titled Rename Notebook. Let’s rename this one to Hello Jupyter:
Running Cells
A Notebook’s cell defaults to using code whenever you first create one, and that cell uses the kernel that you chose when you started your Notebook.
In this case, you started yours with Python 3 as your kernel, so that means you can write Python code in your code cells. Since your initial Notebook has only one empty cell in it, the Notebook can’t really do anything.
Thus, to verify that everything is working as it should, you can add some Python code to the cell and try running its contents.
Let’s try adding the following code to that cell:
Running a cell means that you will execute the cell’s contents. To execute a cell, you can just select the cell and click the Run button that is in the row of buttons along the top. It’s towards the middle. If you prefer using your keyboard, you can just press Shift+Enter.
When I ran the code above, the output looked like this:
If you have multiple cells in your Notebook, and you run the cells in order, you can share your variables and imports across cells. This makes it easy to separate out your code into logical chunks without needing to reimport libraries or recreate variables or functions in every cell.
When you run a cell, you will notice that there are some square braces next to the word In to the left of the cell. The square braces will auto fill with a number that indicates the order that you ran the cells. For example, if you open a fresh Notebook and run the first cell at the top of the Notebook, the square braces will fill with the number 1.
Exporting Notebooks
When you are working with Jupyter Notebooks, you will find that you need to share your results with non-technical people. When that happens, you can use the nbconvert tool which comes with Jupyter Notebook to convert or export your Notebook into one of the following formats:
HTML
LaTeX
PDF
RevealJS
Markdown
ReStructured Text
Executable script
The nbconvert tool uses Jinja templates under the covers to convert your Notebook files (.ipynb) into these other formats.
Jinja is a template engine that was made for Python. Also note that nbconvert also depends on Pandoc and TeX to be able to export to all the formats above. If you don’t have one or more of these, some of the export types may not work. For more information, you should check out the documentation.
How to Use nbconvert
nbconvertThe nbconvert command does not take very many parameters, which makes learning how to use it easier. Open up a terminal and navigate to the folder that contains the Notebook you wish to convert. The basic conversion command looks like this:
Example Usage
Let’s pretend that you have a Notebook named py_examples.ipynb and you want to convert it to PDF. Here is the command you would use to do that:
When you run this command, you should see some output that tells you about the conversion process. nbconvert will display warnings and errors if there are any. Assuming everything goes according to plan, you will now have a py_examples.pdf file in your folder.
The conversion process for the other file types is quite similar. You just have to tell nbconvert what type to convert to (PDF, Markdown, HTML, and so on).
Conclusion
The Jupyter Notebook is quite useful not only for learning and teaching a programming language such as Python but also for sharing your data.
You can turn your Notebook into a slideshow or share it online with GitHub. If you want to share a Notebook without requiring your users to install anything, you can use binder for that.
Google and Microsoft both have their own version of the Notebook that you can use to create and share your Notebooks at Google Colaboratory and Microsoft Azure Notebooks respectively. You can browse really interesting Notebooks there as well.
Project Jupyter recently launched their latest product, JupyterLab. JupyterLab incorporates Jupyter Notebook into an Integrated Development type Editor that you run in your browser. You can kind of think of JupyterLab as an advanced version of Jupyter Notebook. JupyterLab allows you to run terminals, text editors and code consoles in your browser in addition to Notebooks.
As always, it is best to try out a new piece of software yourself to see if it suits you and is worth using. I encourage you to give Jupyter Notebook or JupyterLab a spin and see what you think!
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