How to Install Jupyter Notebook on macOS

Runcell Team,9/24/2025

Install Jupyter Notebook on macOS

Jupyter Notebook is one of the fastest ways to experiment with Python code, share interactive documents, and prototype data workflows. macOS gives you several ways to install it, so you can choose the workflow that best fits your toolchain. This guide compares the most common options, highlights when to reach for each one, and walks through the exact commands to get everything running smoothly.

Before you start

Make sure you have a recent version of Python. macOS Sonoma and newer include Python 3.11, but many developers prefer to install the latest Python from python.org or via Homebrew (brew install python).
Open the Terminal application. All commands below are intended to run there.
If you already installed Jupyter through one method, uninstall it before trying another to avoid confusing PATH entries.

Quick install with `pip`

If you only need Notebook occasionally and do not mind installing packages globally, use the pip command that ships with Python:


python3 -m pip install --upgrade pip
python3 -m pip install jupyter

Launch Notebook once the install finishes:


jupyter notebook

This approach works for quick experiments, but global installs can accumulate conflicting packages over time. Consider using a virtual environment for anything more than a throwaway session.

Recommended: `pip` inside a virtual environment

Virtual environments isolate dependencies for each project and prevent package conflicts. They are lightweight, standard, and easy to automate. To install Jupyter inside a venv:

Create and activate a virtual environment.


python3 -m venv ~/.venvs/jupyter
source ~/.venvs/jupyter/bin/activate

Upgrade pip and install Jupyter.


python -m pip install --upgrade pip
python -m pip install jupyterlab notebook

Launch Notebook (the jupyter command resolves to the copy inside the virtual environment).
```
jupyter notebook
```
When you are done, deactivate the environment with deactivate. Reactivate it any time you need Notebook again by running source ~/.venvs/jupyter/bin/activate.

💡 Tip: Add python -m pip install ipykernel to make the environment available as a kernel in other editors like VS Code.

Install with Conda (Anaconda or Miniconda)

Conda bundles Python, package management, and environment isolation. It is ideal if you work with data science libraries that ship Conda packages or if you prefer graphical installers.

Install Miniconda or the full Anaconda distribution for macOS (Intel or Apple Silicon).
Open a new Terminal window so that the conda command is on your PATH.

Create an environment and add Jupyter:


conda create --name notebooks python=3.11
conda activate notebooks
conda install jupyterlab notebook

Start Notebook with jupyter notebook. Because Conda environments are isolated, each environment can hold a different Python version or set of packages without clashing.

Install with Homebrew

Homebrew is the most popular package manager for macOS. Use it if you already rely on Brew to manage developer tools:


brew update
brew install jupyterlab

Homebrew installs the jupyter command to /opt/homebrew/bin (Apple Silicon) or /usr/local/bin (Intel). If you prefer the classic Notebook interface instead of JupyterLab, you can still launch it with jupyter notebook.

⚠️ Homebrew installs Jupyter system-wide. If you need multiple versions of Python or isolated dependencies, pair Brew with virtual environments or Conda instead.

JupyterLab Desktop for macOS

Project Jupyter maintains a desktop application that bundles JupyterLab with its own Python runtime. Download it from the JupyterLab Desktop release page and drag the app into your Applications folder. After launching the app you can open or create notebooks without touching the command line. It still respects Conda environments or virtual environments if you connect them via the kernel selector.

This option is perfect for new users who prefer a GUI or teams that need a self-contained installation.

Choosing the right path

Method	When to use it	Notes
`pip` (global)	Quick experiments, short-lived scripts	Fastest setup, but shares packages across projects.
`pip` + `venv`	Everyday development, reproducible environments	Recommended for most Python workflows; isolate dependencies per project.
Conda	Data science stacks, multiple Python versions	Excellent package availability and environment management.
Homebrew	Brew-based toolchains, system administrators	Installs once for the whole machine; combine with `venv` for isolation.
JupyterLab Desktop	GUI-first users, sandboxed installs	Drag-and-drop app with bundled Python runtime.

Whatever you choose, keep your environments tidy by periodically removing unused packages and pinning versions in a requirements.txt or environment.yml. Jupyter Notebook will feel right at home on your Mac once you do.