Hello friend! Welcome to this beginner‘s guide on understanding what Jupyter Notebook is and how you can install the powerful tool on your own machine.
Over the course of this article, I will explain:
- The origins of Jupyter project and Notebooks
- Why Jupyter Notebooks have become so integral for data science workflows
- Components that make up the anatomy of Jupyter Notebooks
- Step-by-step instructions tailored to your operating system to setup Jupyter Notebooks with or without Anaconda distribution
- Real-world applications and examples where Jupyter Notebooks have proven highly valuable
- Additional pointers to customize, extend notebooks functionality further as per your needs
So let‘s get started!
A Quick History of Project Jupyter
Project Jupyter originated in 2014 when scientist Fernando Pérez announced the launch of what was then called the IPython Notebook as an evolution of his earlier IPython project started in 2001.
IPython targeted interactive scientific computing for Python while the Notebook tool specifically focused on:
- Creating documents containing live code, visualizations, text narrations exploring an analysis or problem
- Supporting execution environments in various programming languages apart from Python
Over the next couple of years, languages like Julia and R got notebook kernel supports and people from disciplines like physics, astronomy, biology etc started leveraging IPython notebooks extensively for computational tasks.
The project was eventually renamed to Jupyter to symbolize language agnosticism – with the core Ju coming from Julia and pyt from Python that were the initial supported languages.
Ever since, the Jupyter Notebook has become one of the most widely used data science tools with latest surveys showing close to 70% of data scientists and ML developers leveraging it for their workflows.
The rich ecosystem of Jupyter tools has also grown to include JupyterLab, JupyterHub for multi-user servers and Voilà for creating standalone web applications – all advancing Fernando Pérez‘s vision of interactive and collaborative computing environments suitable for diverse use cases.
So now that you know about Project Jupyter‘s history, let‘s talk about why Jupyter Notebooks themselves have specifically become so indispensable for data science practitioners.
Why Jupyter Notebooks are Loved by Data Scientists
While Jupyter Notebooks support over 100 programming languages like C++, Ruby, Rust etc thanks to the community developed language kernels, they have found maximum adoption among data scientists using Python.
Here are some key reasons for this widespread usage:
✔️ Interactive and Exploratory Analysis ➡️ The notebook structure with interleaved code and markdown cells allows quick iteration of ideas and seeing results in real time.
✔️ Single Environment for Entire Data Pipeline ➡️ There is no need to switch between different tools as you can perform ETL, analysis, visualization and modeling all within Jupyter Notebook using Python libraries like Pandas, Matplotlib etc.
✔️ Improved Collaboration ➡️ Notebooks can be easily shared internally and externally with explanations provided in markdown cells. Changes can also be tracked like scripts under version control.
✔️ Reproducible Analyses and Logging ➡️ Entire end-to-end working contained in notebooks acts like lab notebooks providing clearer record keeping. This reproducibility enables peer reviews.
✔️Simpler Pipeline Maintenance ➡️ Notebooks allow breaking down complex data pipelines into smaller components like functions and classes that can be refined iteratively.
✔️Easier Reporting of Results ➡️ Using nbconvert, notebooks containing key findings can be exported as reports in HTML, PDF formats ready for dashboarding tools.
Let‘s now dive deeper into understanding the core components that make up Jupyter notebooks enabling these useful capabilities before we install Jupyter on our machines.
Anatomy of Jupyter Notebooks
A Jupyter Notebook comprises of a stack of cells that can contain either code or text that get executed or converted by underlying computational engines called kernels.
Let‘s go through the key elements:
1. Notebook Cells
The cells form the heart of a notebook – they allow creating blocks of executable code as well as text with multimedia elements. There are two main cell types in notebooks:
Code Cells
- Used to write code in Python/R/Scala and execute it
- Useful for data exploration and analysis
- Results get displayed immediately below the cell
Markdown Cells
- Used to add headings, text paragraphs, links images etc
- Provide explanation and clarifications for the code cells
- Support formatting options like bullet points, tables etc
Source: Nature Computational Science Journal
Based on whether you specify a cell to be Code type or Markdown type, Jupyter notebook renders it accordingly.
Thisability to intersperse code and descriptive cells is what enables literate programming allowing you to create computational narratives rather than just scripts.
2. Notebooks Kernels
Kernels are essentially the computational engines that execute and process the code written inside notebook cells.
Some examples of popular kernels include:
- Python – For data analysis stack using Numpy, Pandas, Matplotlib etc
- R – For statistical modeling and visualizations through libraries like ggplot2
- Scala – For Spark based distributed big data processing
- Julia – For high performance numerical analysis and ML
Based on the choice of kernel during notebook setup, the corresponding runtime is used to evaluate the code cells. Multiple kernels allow Jupyter users leverage their language of choice.
In data science specifically, the Python kernel along with key libraries like Numpy, Pandas, Scikit-Learn, TensorFlow etc is used extensively.
3. Notebook Documents
Jupyter Notebook files themselves are called notebook documents and use the .ipynb file extension. They are essentially JSON structured text files consisting of:
- Metadata like kernel details
- Cell contents for code and text
- Cell level outputs and states
Notebooks allows users to write and execute code incrementally in small sections rather than entire scripts in one go. This encourages more modular design.
Additionally, notebook files get auto-saved avoiding any loss of data. Coupled with features like version controlling using Git, collaborative editing capabilities Jupyter notebooks provide a full-featured environment for data analysis.
Now that you know the overall architecture and benefits of Jupyter notebooks, let me show you how to install Jupyter notebook on your system.
Installing Jupyter Notebook
While there are a couple ways of installing Jupyter, I highly recommend using the Anaconda distribution as it comes prebundled with Python and 200+ science and data science packages saving tons of manual configuration.
However, I will also show how you can install Jupyter with Python‘s pip package manager for more flexibility.
So based on your specific use case, pick the appropriate option below:
Option 1: Install Using Anaconda (Recommended)
Follow these simple steps to get up and running with Jupyter through Anaconda:
Step 1: Download latest Anaconda Installer
Visit https://www.anaconda.com/products/distribution and get the latest Anaconda installer for your operating system (Windows/Mac/Linux).
Step 2: Complete Anaconda Installation
Follow through the installation wizard to install Anaconda on your machine. Make sure to check the option to automatically append Anaconda to system PATH to avoid any environment issues afterwards.
This will install Python 3.x version along with Conda and Jupyter Notebook and other useful data science packages.
Step 3: Launch Jupyter Notebook
To open your Jupyter notebook and start coding, you can either:
- Open Anaconda Navigator GUI and Click Launch button under Jupyter Notebook.
OR
- Open terminal/command prompt and type:
jupyter notebookThis will start the Jupyter notebook server on your machine and automatically open up the Jupyter dashboard in your default browser.
And that‘s it! The Jupyter notebook dashboard allows you to start coding right away in a new Python notebook.
With Anaconda‘s packaging system Conda – installing additional data science libraries like Pandas, Scikit-Learn, Plotly etc for use in Jupyter notebook also becomes super convenient.
Option 2: Install Jupyter using Pip
If you already have Python setup on your machine and want to install Jupyter notebooks specifically, you can do so using pip which is Python‘s official package manager.
The instructions below show how to install Jupyter notebook using pip on both Windows and Linux:
On Windows
Make sure Python 3.x and pip are already available on your Windows machine, then:
Step 1: Upgrade pip package manager to latest version
python -m pip install --upgrade pipStep 2: Install Jupyter notebook using pip
pip install notebookStep 3: Launch Jupyter notebook server
jupyter notebookThis will open the notebook dashboard in your default browser.
On Linux (Ubuntu/Debian/Fedora)
If Python 3 is not installed, first install it using:
sudo apt install python3 #Debian/Ubuntu
sudo dnf install python3 #Fedora Then install Jupyter notebook using pip:
Step 1: Install pip for Python 3
sudo apt install python3-pip #Debian/UbuntuStep 2: Install Jupyter notebook
pip3 install notebookStep 3: Run Jupyter notebook server
jupyter notebookThe Jupyter dashboard will now open up in your browser locally.
And that‘s it! Whether you chose the Anaconda route or direct pip install, you now have Jupyter notebook available to start coding in Python for your data science projects! 🥳✨
Real-World Usage of Jupyter Notebooks
Now that you have installed Jupyter notebook, let‘s look some examples of it being used across various domains and industries to understand it‘s widespread adoption better:
Academic Research
Domains like physics, astronomy, social sciences that involve statistical modeling and simulations leverage custom notebooks for analysis.
Data Journalism
Journalists use notebooks to gather, clean, analyze public datasets and then create data-driven stories customizable using Voila.
Machine Learning
Notebooks allow iterative development process for ML models integrating code, visualizations and text narratives.
Financial Modeling
Analysts in finance create customizable notebooks for quantitative modeling incorporating company data.
The above examples highlight the broad applicability of Jupyter notebooks across domains. Companies like Netflix, NASA, Bloomberg use Jupyter Extensions like JupyterHub to build scaleable Enterprise notebook platforms.
Additional Customization and Resources
With Jupyter Notebook installed, you can further customize your environment using Jupyter Notebook Extensions.
Some useful extensions include:
- Table of Contents – Generate TOC from markdown headers
- Code Prettify – Display code line numbers, themes etc
- Variable Inspector – Explore variable contents Visually
- Spell Checker – Typo corrections
Refer the documentation on NBextensions to install and manage these.
For those looking for a more modern UI, you should check out JupyterLab which offers a rich extensions ecosystem of its own.
Additionally, for bundling notebooks to share analyses with folks lacking Python environments refer this guide on converting notebooks.
Some fantastic public repositories of Jupyter notebooks demonstrating real-world projects include:
For additional content, I highly recommend checking out the official Jupyter Notebook tutorials.
I hope you enjoyed this introduction guide on understanding Jupyter Notebooks, their importance for data scientists and steps to get started with using notebooks for your own projects.
Feel free to reach out if you have any questions!
