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Jupyter kernel for the Python programming language
C++ CMake Jupyter Notebook Python
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xeus-python is a Jupyter kernel for Python based on the native implementation of the Jupyter protocol xeus.


Launch the Jupyter notebook with jupyter notebook or Jupyter lab with jupyter lab and launch a new Python notebook by selecting the xpython kernel.

Code execution and variable display:

Basic code execution

Output streams:


Input streams:


Error handling:

Erro handling



Code completion:


Rich display:

Rich display

And of course widgets:

Widgets Widgets binary


xeus-python has been packaged for the conda package manager.

To ensure that the installation works, it is preferable to install xeus-python in a fresh conda environment. It is also needed to use a miniconda installation because with the full anaconda you may have a conflict with the zeromq library which is already installed in the anaconda distribution.

The safest usage is to create an environment named xeus-python with your miniconda installation

conda create -n xeus-python
conda activate xeus-python # Or `source activate xeus-python` for conda < 4.6

Installation directly from conda

Then you can install in this environment xeus-python and its dependencies

conda install xeus-python notebook -c conda-forge

Installation from source

Or you can install it from the sources, you will first need to install dependencies

conda install cmake xeus nlohmann_json cppzmq xtl pybind11 pybind11_json jedi pygments notebook -c conda-forge

Then you can compile the sources

cmake -D CMAKE_PREFIX_PATH=your_conda_path -D CMAKE_INSTALL_PREFIX=your_conda_path -D PYTHON_EXECUTABLE=`which python`
make && make install

Trying it online

To try out xeus-python interactively in your web browser, just click on the binder link:



To get started with using xeus-python, check out the full documentation

What are the advantages of using xeus-python over ipykernel (IPython kernel)?

Check-out this blog post for the answer: Long story short: xeus-python does not cover 100% of the features of ipykernel. For examples, IPython magics are not supported yet by xeus-python. However:

  • xeus-python is a lot lighter than ipykernel and IPython combined, which makes it a lot easier to implement new features on top of it. Our next goal is to augment the protocol to implement a Python debugger in JupyterLab.
  • xeus-based kernels are more versatile in that one can overload e.g. the concurrency model. This is something that Kitware’s SlicerJupyter project takes advantage of to integrate with the Qt event loop of their Qt-based desktop application.


xeus-python depends on

xeus-python xeus xtl cppzmq nlohmann_json pybind11 pybind11_json jedi pygments
master >=0.23.2,<0.24 >=0.6.8,<0.7 ~4.3.0 >=3.6.1,<4.0 >=2.2.4,<3.0 >=0.2.2,<0.3 >=0.15.1,<0.16.0 >=2.3.1,<3.0.0
0.6.11 >=0.23.2,<0.24 >=0.6.8,<0.7 ~4.3.0 >=3.6.1,<4.0 >=2.2.4,<3.0 >=0.2.2,<0.3 >=0.15.1,<0.16.0 >=2.3.1,<3.0.0
0.6.10 >=0.23.2,<0.24 >=0.6.8,<0.7 ~4.3.0 >=3.6.1,<4.0 >=2.2.4,<3.0 >=0.2.2,<0.3 >=0.15.1,<0.16.0 >=2.3.1,<3.0.0
0.6.9 >=0.23.2,<0.24 >=0.6.8,<0.7 ~4.3.0 >=3.6.1,<4.0 >=2.2.4,<3.0 >=0.2.2,<0.3 >=0.15.1,<0.16.0 >=2.3.1,<3.0.0
0.6.8 >=0.23.2,<0.24 >=0.6.8,<0.7 ~4.3.0 >=3.6.1,<4.0 >=2.2.4,<3.0 >=0.2.2,<0.3 >=0.15.1,<0.16.0 >=2.3.1,<3.0.0
0.6.7 >=0.23.2,<0.24 >=0.6.8,<0.7 ~4.3.0 >=3.6.1,<4.0 >=2.2.4,<3.0 >=0.2.2,<0.3 >=0.15.1,<0.16.0 >=2.3.1,<3.0.0


See to know how to contribute and set up a development environment.


We use a shared copyright model that enables all contributors to maintain the copyright on their contributions.

This software is licensed under the BSD-3-Clause license. See the LICENSE file for details.

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