Important

This documentation covers IPython versions 6.0 and higher. Beginning with version 6.0, IPython stopped supporting compatibility with Python versions lower than 3.3 including all versions of Python 2.7.

If you are looking for an IPython version compatible with Python 2.7, please use the IPython 5.x LTS release and refer to its documentation (LTS is the long term support release).

# Installing the IPython kernel

See also

Installing Jupyter
  The IPython kernel is the Python execution backend for Jupyter.

The Jupyter Notebook and other frontends automatically ensure that the IPython kernel is available. However, if you want to use a kernel with a different version of Python, or in a virtualenv or conda environment, you’ll need to install that manually.

## Kernels for Python 2 and 3

If you’re running Jupyter on Python 3, you can set up a Python 2 kernel after checking your version of pip is greater than 9.0:

    python2 -m pip --version

Then install with

    python2 -m pip install ipykernel
    python2 -m ipykernel install --user

Or using conda, create a Python 2 environment:

    conda create -n ipykernel_py2 python=2 ipykernel
    source activate ipykernel_py2    # On Windows, remove the word 'source'
    python -m ipykernel install --user

Note

IPython 6.0 stopped support for Python 2, so installing IPython on Python 2 will give you an older version (5.x series).

If you’re running Jupyter on Python 2 and want to set up a Python 3 kernel, follow the same steps, replacing `2` with `3`.

The last command installs a kernel spec file for the current python installation. Kernel spec files are JSON files, which can be viewed and changed with a normal text editor.

## Kernels for different environments

If you want to have multiple IPython kernels for different virtualenvs or conda environments, you will need to specify unique names for the kernelspecs.

Make sure you have ipykernel installed in your environment. If you are using `pip` to install `ipykernel` in a conda env, make sure `pip` is installed:

    source activate myenv
    conda install pip
    conda install ipykernel # or pip install ipykernel

For example, using conda environments, install a `Python (myenv)` Kernel in a first environment:

    source activate myenv
    python -m ipykernel install --user --name myenv --display-name "Python (myenv)"

And in a second environment, after making sure ipykernel is installed in it:

    source activate other-env
    python -m ipykernel install --user --name other-env --display-name "Python (other-env)"

The `--name` value is used by Jupyter internally. These commands will overwrite any existing kernel with the same name. `--display-name` is what you see in the notebook menus.

Using virtualenv or conda envs, you can make your IPython kernel in one env available to Jupyter in a different env. To do so, run ipykernel install from the kernel’s env, with –prefix pointing to the Jupyter env:

    /path/to/kernel/env/bin/python -m ipykernel install --prefix=/path/to/jupyter/env --name 'python-my-env'

Note that this command will create a new configuration for the kernel in one of the preferred location (see `jupyter --paths` command for more details):

- system-wide (e.g. /usr/local/share),
- in Jupyter’s env (sys.prefix/share),
- per-user (~/.local/share or ~/Library/share)

If you want to edit the kernelspec before installing it, you can do so in two steps. First, ask IPython to write its spec to a temporary location:

    ipython kernel install --prefix /tmp

edit the files in /tmp/share/jupyter/kernels/python3 to your liking, then when you are ready, tell Jupyter to install it (this will copy the files into a place Jupyter will look):

    jupyter kernelspec install /tmp/share/jupyter/kernels/python3