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napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. It’s built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy). It includes critical viewer features out-of-the-box, such as support for large multi-dimensional data, and layering and annotation. By integrating closely with the Python ecosystem, napari can be easily coupled to leading machine learning and image analysis tools (e.g. scikit-image, scikit-learn, TensorFlow, PyTorch), enabling more user-friendly automated analysis.


We’re developing napari in the open! But the project is in an alpha stage, and there will still likely be breaking changes with each release. You can follow progress on our GitHub repository, test out new versions as we release them, and contribute ideas and code.


Which distribution to install

If you want to contribute back to the napari codebase, you should install from source code: see the from source section.

If you only wish to use napari as GUI app (and not from python code), the bundled app is the easiest way to install, it does not require having python pre-installed.

If you are using napari from Python to programmatically interact with the app, you can install via pip, conda-forge, or from source.

From pip, with “batteries included”

napari can be installed on most macOS, Linux, and Windows systems with Python 3.8-3.10 using pip:

pip install "napari[all]"

(See Specifying a GUI Backend below for an explanation of the [all] notation.)


While not strictly required, it is highly recommended to install napari into a clean virtual environment using an environment manager like conda or venv. For example, with conda:

conda create -y -n napari-env -c conda-forge python=3.9
conda activate napari-env
pip install "napari[all]"

From conda

conda install -c conda-forge napari

Current development branch from github

To install the current main branch on github (which will usually be ahead of the latest release on pypi)

pip install "git+[all]"

For more information or troubleshooting see our installation tutorial

Specifying a GUI Backend

napari needs a library called Qt to run its user interface (UI). In Python, there are two alternative libraries to run this, called PyQt5 and PySide2. By default, we don’t choose for you, and simply running pip install napari will not install either. You might already have one of them installed in your environment, thanks to other scientific packages such as Spyder or matplotlib. If neither is available, running napari will result in an error message asking you to install one of them.

Running pip install "napari[all]" will install the default framework – currently PyQt5, but this could change in the future.

To install napari with a specific framework, you can use:

pip install "napari[pyqt5]"    # for PyQt5

# OR
pip install "napari[pyside2]"  # for PySide2

Getting started

If you are new to napari, check out our getting started guide.


(The example below requires the scikit-image package to run. We just use data samples from this package for demonstration purposes. If you change the example to use your own dataset, you may not need to install this package.)

From inside an IPython shell or jupyter notebook you can open up an interactive viewer by calling:

from skimage import data
import napari

viewer = napari.view_image(data.astronaut(), rgb=True)


To do the same thing inside a script call (note the call to at the end)

from skimage import data
import napari

viewer = napari.view_image(data.astronaut(), rgb=True)  # start the event loop and show viewer


Check out the scripts in our examples folder to see some of the functionality we’re developing!

napari supports six main different layer types, Image, Labels, Points, Vectors, Shapes, and Surface, each corresponding to a different data type, visualization, and interactivity. You can add multiple layers of different types into the viewer and then start working with them, adjusting their properties.

All our layer types support n-dimensional data and the viewer provides the ability to quickly browse and visualize either 2D or 3D slices of the data.

napari also supports bidirectional communication between the viewer and the Python kernel, which is especially useful when launching from jupyter notebooks or when using our built-in console. Using the console allows you to interactively load and save data from the viewer and control all the features of the viewer programmatically.

You can extend napari using custom shortcuts, key bindings, and mouse functions.


For more details on how to use napari checkout our tutorials. These are still a work in progress, but we’ll be updating them regularly.

Mission, values, and roadmap

For more information about our plans for napari you can read our mission and values statement, which includes more details on our vision for supporting a plugin ecosystem around napari.


Contributions are encouraged! Please read our contributing guide to get started. Given that we’re in an early stage, you may want to reach out on our Github Issues before jumping in.

Code of conduct

napari has a Code of Conduct that should be honored by everyone who participates in the napari community.


You can learn more about how the napari project is organized and managed from our governance model, which includes information about, and ways to contact, the @napari/steering-council and @napari/core-devs.

Citing napari

If you find napari useful please cite this repository using its DOI as follows:

napari contributors (2019). napari: a multi-dimensional image viewer for python. doi:10.5281/zenodo.3555620

Note this DOI will resolve to all versions of napari. To cite a specific version please find the DOI of that version on our zenodo page. The DOI of the latest version is in the badge at the top of this page.


We’re a community partner on the forum and all help and support requests should be posted on the forum with the tag napari. We look forward to interacting with you there.

Bug reports should be made on our github issues using the bug report template. If you think something isn’t working, don’t hesitate to reach out - it is probably us and not you!