Intro to Jupyter

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Jupyter Lab

Documentation User guide

JupyterLab is an open-source, web-based interactive development environment (IDE) that provides a powerful interface for working with Jupyter notebooks, code, and data. It extends the classic Jupyter Notebook by offering a more flexible and modular user experience.

  • Interactive Notebooks
  • Integrated Development Environment
  • Support for Multiple Languages
  • Extensibility
  • Interactive Data Visualization
  • Version Control Integration

Setting up JupyterLab on a local machine can be done in several ways depending on your environment and preferences. Each method offers a slightly different approach, depending on your needs for flexibility, simplicity, and environment management. Here are the different options:

Make sure the version of Python used by the sample code is supported in the option you are using. Check the specific version in the

1. Using Anaconda Distribution

This is the recommended method if you don’t have any preference.

Install Anaconda: Download and install the Anaconda distribution, which includes JupyterLab, Python, and many common data science libraries.

Launch JupyterLab: Open Anaconda Navigator and launch JupyterLab from there, or run jupyter lab in the Anaconda Prompt. Pros: Easy installation, comes with many pre-installed libraries, environment management is built-in.

2. Using pip (Python Package Installer)

Install JupyterLab: If you have Python installed, you can install JupyterLab via pip by running:


pip install jupyterlab

# Launch JupyterLab: Start JupyterLab by running:

jupyter lab

Pros: Lightweight, allows for greater customization and control over the Python environment.

3. Using Docker

Install Docker: Download and install Docker on your machine. Run JupyterLab Container: Use the official Jupyter Docker image to start a container with JupyterLab:

docker run -p 8888:8888 jupyter/base-notebook

# Access JupyterLab: Open your browser and go to http://localhost:8888.

Pros: Isolated environment, easy to set up and tear down, avoids dependency conflicts.

4. Using Virtual Environments

Create a Virtual Environment:

python -m venv myenv
Activate the Environment:

On Windows:

myenv\Scripts\activate

On macOS/Linux:

source myenv/bin/activate

Install JupyterLab:

pip install jupyterlab

Launch JupyterLab:


jupyter lab

Pros: Keeps your JupyterLab environment isolated, preventing package conflicts.

5. Using conda (without Anaconda)

Install Miniconda: Miniconda is a lightweight version of Anaconda. Create and Activate Environment:

conda create -n myenv jupyterlab
conda activate myenv

Launch JupyterLab:

jupyter lab

Pros: Similar to Anaconda but more lightweight, with better control over environment size.

6. Installing JupyterLab via Homebrew (macOS)

Install Homebrew: If not already installed, you can get Homebrew from brew.sh. Install JupyterLab:

brew install jupyterlab

Launch JupyterLab:

jupyter lab

Pros: Quick and easy setup for macOS users.