Installing Jupyter Notebook on macOS

Jupyter Notebook is one of the most popular tools for writing and running Python code interactively. It’s especially great for data analysis, prototyping, and teaching. If you're on macOS and want to get started, you've come to the right place. I’ll walk you through a few different ways to install Jupyter Notebook, along with tips for setting up a clean and manageable Python environment.

Before we start, it’s important to have a working Python installation. macOS comes with a system version of Python, but it’s often outdated and tied to system operations—so we generally avoid using it directly. Instead, we’ll use a version manager or a package and environment manager to keep things tidy.

Installing Python Using Homebrew

One of the easiest and most recommended ways to install Python on macOS is through Homebrew. If you don't have Homebrew installed yet, open your Terminal and run:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Once Homebrew is ready, you can install the latest version of Python by typing:

brew install python

This will install both Python and pip, the Python package installer. To verify the installation, run:

python3 --version
pip3 --version

You should see version numbers for both. Now you’re ready to install Jupyter Notebook using pip:

pip3 install notebook

That’s it! You can now start Jupyter Notebook by running:

jupyter notebook

This will open a new tab in your default web browser where you can create new notebooks and start coding.

Using Virtual Environments

It’s a best practice to use virtual environments to isolate your project dependencies. This helps you avoid version conflicts between packages used in different projects. Here's how you can create a virtual environment and install Jupyter inside it.

First, install virtualenv if you haven’t already:

pip3 install virtualenv

Now, navigate to your project directory and create a new virtual environment:

virtualenv my_jupyter_env

Activate the environment:

source my_jupyter_env/bin/activate

Your command prompt should now show the environment name. Install Jupyter Notebook within this environment:

pip install notebook

Now when you run jupyter notebook, it will use this isolated environment. To deactivate the environment later, just type deactivate.

Installing with Anaconda

If you prefer an all-in-one distribution that includes Python, Jupyter, and many data science libraries pre-installed, Anaconda is a great choice.

• Visit the Anaconda download page
• Download the macOS installer
• Open the .pkg file and follow the installation instructions

Once installed, you can launch Jupyter Notebook from the Anaconda Navigator app or directly from the terminal with:

jupyter notebook

Anaconda also comes with conda, a powerful package and environment manager. You can create a new environment with:

conda create --name my_env jupyter

And activate it with:

conda activate my_env

Installing with Miniconda

If you want the environment management capabilities of conda without the large download size of Anaconda, try Miniconda. You can install it via Homebrew:

brew install miniconda

After installation, initialize conda for your shell (usually bash or zsh):

conda init "$(basename "$SHELL")"

Close and reopen your terminal. Now create a new environment and install Jupyter:

conda create --name jupyter_env python=3.9
conda activate jupyter_env
conda install jupyter

This gives you a minimal installation that you can customize with only the packages you need.

Verifying Your Installation

No matter which method you choose, it's good to verify that everything is working correctly. Start Jupyter Notebook:

jupyter notebook

Your browser should open to the Jupyter dashboard. Create a new notebook by clicking New > Python 3. In the first cell, type:

import sys
print(sys.version)

Run the cell with Shift+Enter. You should see your Python version printed. Try another test:

print("Hello, Jupyter!")

If that works, congratulations—you’re all set!

Troubleshooting Common Issues

Sometimes things don’t go as smoothly as planned. Here are a few common issues and how to resolve them:

• Command not found: jupyter – This usually means the installation path isn’t in your PATH. Try restarting your terminal or specifying the full path (e.g., ~/.local/bin/jupyter notebook).
• Port already in use – Jupyter defaults to port 8888. If that’s taken, you can specify a different port: jupyter notebook --port 8889.
• Browser doesn’t open automatically – You can copy the URL from the terminal and open it manually.

If you installed via pip and are having permission issues, you may want to use the --user flag:

pip3 install --user notebook

For conda users, make sure you’ve activated the correct environment before installing or running Jupyter.

Customizing Jupyter Notebook

Jupyter is highly customizable. You can change the theme, install extensions, and modify settings to suit your workflow. One popular extension is jupyter_contrib_nbextensions, which adds useful features like table of contents, code folding, and more.

Install it with:

pip install jupyter_contrib_nbextensions && jupyter contrib nbextension install --user

You can then enable extensions from the Nbextensions tab in the Jupyter dashboard.

Another way to personalize your notebooks is by using different kernels. For example, if you work with R or Julia, you can install their kernels and switch between them within Jupyter.

Keeping Jupyter Updated

It’s a good idea to keep your software up to date. To update Jupyter Notebook installed via pip, use:

pip3 install --upgrade notebook

For conda users, update with:

conda update jupyter

Regular updates ensure you have the latest features and security patches.

Using Jupyter Notebook Effectively

Now that you have Jupyter up and running, let’s go over a few tips to use it effectively:

• Use keyboard shortcuts – They can significantly speed up your workflow. Press H in command mode to see all available shortcuts.
• Markdown cells – Use these for documentation, explanations, and headings. They make your notebooks more readable and shareable.
• Restart and run all – If you change a variable or function early in the notebook, use Kernel > Restart & Run All to re-execute all cells in order.

Jupyter also supports magic commands—special commands prefixed with % or %% that provide extra functionality. For example, %timeit lets you measure the execution time of a single line, and %%writefile writes the cell content to a file.

%timeit [x**2 for x in range(1000)]

%%writefile hello.py
print("Hello from a file!")

Exploring these features will help you get the most out of Jupyter Notebook.

Alternative: JupyterLab

If you like Jupyter Notebook but want a more integrated and powerful interface, try JupyterLab. It includes notebooks, text editors, terminals, and other components in a single tabbed interface. You can install it with:

pip3 install jupyterlab

Then launch it with:

jupyter lab

Many users find JupyterLab to be a natural upgrade once they're comfortable with the classic notebook interface.

Final Thoughts

Installing Jupyter Notebook on macOS is straightforward, especially with tools like Homebrew, pip, and conda. Whether you choose a minimal setup or a full Anaconda installation, you now have a powerful environment for interactive Python programming.

Remember to use virtual environments or conda environments to keep your projects organized. Explore extensions and magic commands to enhance your productivity. And most importantly—have fun creating and sharing your notebooks!

If you run into any issues, the Jupyter community is very active and helpful. Check out the Jupyter documentation or forums for more guidance.

Happy coding