Jupyter has become an essential tool for data scientists, developers, and researchers worldwide, thanks to its interactive computing environment. It allows users to create and share documents containing live code, equations, visualizations, and narrative text.
In this tutorial, we’ll walk you through a step-by-step guide on how to master Jupyter. We will explore about how to install Jupyter Notebook and JupyterLab on your computer, so you can boost your productivity and create powerful data-driven projects.
Overview of Jupyter
- Jupyter Notebook: An interactive web-based application that allows users to create and share documents containing live code, equations, visualizations, and narrative text.
- JupyterLab: An extensible environment that provides all the features of Jupyter Notebook, plus additional tools for interactive computing and data science.
Python Vs Jupyter
Jupyter and Python are distinct but complementary tools in the world of programming and data science. Python is a high-level, versatile programming language known for its readability and extensive library support. It is widely used in various fields, including data analysis, web development, and artificial intelligence.
On the other hand, Jupyter is an interactive computing environment that allows users to create and share documents containing live code, equations, visualizations, and narrative text. While Jupyter supports multiple programming languages (referred to as “kernels”), Python is one of the most popular choices due to its power and ease of use.
In essence, Python is the programming language, while Jupyter provides a user-friendly platform for writing, running, and sharing Python code.
Supply Chain Relevance
Jupyter’s relevance to supply chain management is undeniable, as it empowers professionals to streamline their analytical workflows and gain insights into the intricacies of their supply chains. If you master Jupyter interactive computing environment, supply chain experts can quickly create, test, and refine data models, optimize logistics, and analyze complex data sets. These capabilities enable them to identify bottlenecks, forecast demand, and make data-driven decisions that improve efficiency and reduce costs.
With Jupyter Notebook and JupyterLab, supply chain managers have the perfect tools to stay ahead of the competition and drive their businesses toward success.
Prerequisites for Installing Jupyter
Before you start installing Jupyter, ensure you have the following software on your computer:
- A compatible operating system (Windows, macOS, or Linux)
- Python (version 3.3 or later)
Installing Jupyter Notebook
Install Python and Pip
First, you need to install Python and Pip (Python’s package manager) on your computer. You can download Python from the official website (https://www.python.org/downloads/) and follow the installation instructions for your operating system. Make sure to check the option “Add Python to PATH” during the installation process.
Install Jupyter Notebook
Open a terminal or command prompt and run the following command to install Jupyter Notebook:
pip install notebook
This command installs Jupyter Notebook and its dependencies on your computer.
To install JupyterLab, open a terminal or command prompt and run the following command
pip install jupyterlab
This command installs JupyterLab and its dependencies on your computer.
Running Jupyter Notebook and JupyterLab
To launch Jupyter Notebook, open a terminal or command prompt, navigate to your desired working directory, and run the following command
Alternatively you can type the below into your browser:
To launch JupyterLab, open a terminal or command prompt, navigate to your desired working directory, and run the following command
Both Jupyter Notebook and JupyterLab will open in your default web browser, providing an interactive computing environment for your projects.
Congratulations! You have successfully mastered Jupyter and installed Notebook and JupyterLab on your computer. Now you can enjoy the powerful interactive computing and data science features provided by these tools. Start exploring, creating, and sharing your projects with ease using the Jupyter ecosystem. Happy coding!