Try it out on Binder!
In its current form, this tutorial is meant to be executed with Jupyter notebook 5.0, using IPython 6.0 or newer on Python 3, the latest IPython version compatible with Python 2 is IPython 5.x that may not have the exact same behavior and all the features presented in this tutorial.
You can find our installation instructions for IPython and Jupyter notebook
To get the tutorial, checkout the ipython-in-depth repo:
git clone https://github.com/ipython/ipython-in-depth
Or download current master and unzip it.
At the command line, you can do this with (depending on whether your system uses wget or curl):
wget https://github.com/ipython/ipython-in-depth/zipball/master -O ipython-in-depth.zip
or
curl -L https://github.com/ipython/ipython-in-depth/zipball/master -o ipython-in-depth.zip
And then:
unzip ipython-in-depth.zip
Change directory inside the directory newly created:
cd ipython-in-depth
You can then start the Jupyter notebook server at a terminal with:
jupyter notebook
The tutorial do reference a couple of docker images that are quite heavy (several GB). Please do not download them on conference wifi. You may want to populate the Docker Cache you may want to use the following command ahead of time:
$ docker pull jupyter/datascience-notebook
The image contains a installation of the Jupyter notebook with R, Julia, Python2, Python3 and a couple of libraries for each language.
