Using Napari through Jupyter

Last updated on 2026-09-22 | Edit this page

Overview

Questions

  • How can you interact with Napari in a notebook?
  • How are images represented in the computer?
  • What are the main structures that Napari uses to represent imaging data?
  • How can we get basic measurements out from this

Objectives

  • Understand how to open and interact with Napari via JupyterLab notebooks

Instead of entering Python commands in Napari’s built‑in console, we will write and run our code in a computational notebook using JupyterLab. This allows us to build a reusable workflow that is easy to repeat and adapt.

We have kept the level of programming knowledge required to the minimum possible and all code can be run by copy and pasting, so don’t worry if you don’t understand it all yet.

Most, if not all, of the functions we will use in this lesson are also accessible via various Napari plugins, so the analysis pipeline could also be assembled within Napari if you prefer.

Creating a notebook in JupyterLab

1. Activate your Napari environment

Open the terminal (the same one you used for Napari installation: see ‘Opening a terminal’ section of the setup instructions), and activate the environment you created for Napari:

BASH 

cd napari-ai-workshop
source .venv/bin/activate

2. Launch JupyterLab

Start JupyterLab:

BASH 

jupyter lab

The JupyterLab interface should appear in a browser window.

3. Create and navigate to your workshop folder

It is best practice to keep all your project files together in a dedicated folder.

  • Use the file browser on the left-hand side
  • Navigate to a location that is easy to find again (like your Desktop)
  • Right-click to create a new folder and name it workshop-notebooks

4. Create a new notebook

Once you are inside the workshop-notebooks folder, create a new notebook.

For example by using the JupyterLab menu bar to select: File > New > Notebook > Python 3 (ipykernel)

This will open a new Python notebook.

5. Name your notebook

Renaming your notebook immediately helps keep your workflow tidy and makes it easier to find later.

Right click the default name at the top of the notebook tab (e.g., Untitled.ipynb), and select Rename Notebook....

Enter a meaningful name, for example: image_display.ipynb.

About notebooks

A notebook is made up of building blocks called cells.

For this workshop, we will only use Code Cells.

When you run a Code Cell, the output typically appears underneath it. This could be a number, text, a table, or an error message.

By splitting code up into cells, you can run one specific part of your code without having to re-run the whole file and get instant feedback.

Be careful about the order of your notebook cells. Running them out of sequence can leave variables outdated or missing, which can lead to confusing results.

Using Python inside a notebook

Run each of the following examples in separate notebook cells so you can clearly see the output after each step.

PYTHON 

# Everything after a hash (#) is a comment and is ignored by Python.
# Use comments to explain what you're doing.

If you want to create another cell, click the + button in the toolbar or use the Insert Cell Below button on the right side of the cell.

PYTHON 

# Python can do basic calculations
1 + 1
1 + 2
# and will display the last output

OUTPUT 

3

PYTHON 

# Python can assign values to variables
one = 1
two = one + one

Notice that there is no output.

PYTHON 

# Variables store values rather than return them
# To see their value write the variable name
two

OUTPUT 

2

Note: In a standard Python script, writing a variable name on its own does nothing and you must use print() to show output.

PYTHON 

# Python's print function
print("one plus one is", two)
print("one plus two is", one + two)

OUTPUT 

one plus one is 2
one plus two is 3

Using Napari from within a notebook

First import napari

PYTHON 

# Import napari package
import napari

Then open it from the notebook

PYTHON 

# Open Napari from the notebook
viewer = napari.Viewer()

Finally open a sample image.

PYTHON 

# Open Cells (3D + 2Ch) sample image in napari's viewer
viewer.open_sample("napari", "cells3d")

The output should look like this:

OUTPUT 

[<Image layer 'membrane' at 0x1853b7738c0>,
 <Image layer 'nuclei' at 0x1853c844710>]

The memory addresses (0x1853b7738c0 and 0x1853c844710) will be different for you. They indicate the locations in memory where Python happened to store those layer objects.

Napari’s viewer should open in a separate window, preloaded with the cells3D sample image.

Now that we are able to interact with Napari via a lab-book, let’s now look more into the structure of imaging data.

Key Points
  • You can interact with Napari both through the interface, it’s built-in console or through a Jupyter lab notebook
  • Napari represents all elements for viewing (Images, Points, etc) as layers