This documentation covers a development version of IPython. The development version may differ significantly from the latest stable release.
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¶
- 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
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
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
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
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)"
--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