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IPython and Jupyter in-depth Tutorial, first presented at PyCon 2012
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PyCon 2019 Jupyter tutorial.


This repository contain material and instructions to follow the "IPython and Jupyter in Depth: High productivity, interactive Python" tutorial during PyCon 2019.


Please read the following section and install the required software ahead of time. We may ask you to update versions of the software more closely to the tutorial date.

Please do not rely on cloud hosting to follow this tutorial, as the network connection may be unreliable. If possible, come to the tutorial with a computer where you have administrative privileges.

For this tutorial, we are standardizing on a conda-based python distribution (miniconda or Anaconda). We may not be able to help with installation issues if you are using a different python distribution.

Software installation

  1. Install either the full anaconda distribution (very large, includes lots of conda packages by default) or miniconda (much smaller, with only essential packages by default, but any conda package can be installed).

  2. To get the tutorial materials, clone this repository. Please plan to update the materials shortly before the tutorial.

    git clone

    To update the materials:

    cd ipython-in-depth
    git pull

    Feel free to open an issue or send a pull request to update these materials if things are unclear.

  3. Set up your environment.

    Create a conda environment:

    conda create -n pycon2019 -c conda-forge --yes python=3.7 pip cookiecutter=1.6 'notebook=5.7' pandas=0.24 nodejs=9.11 jupyterlab bqplot ipyvolume pythreejs aiohttp line_profiler matplotlib rpy2 simplegeneric trio cython pillow

    (You could instead create the environment from the supplied environment file with conda env create -f pycon2019-jupyterlab-tutorial/environment.yml)

    Activate the conda environment:

    conda activate pycon2019

    Install extra JupyterLab extensions:

    jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-threejs ipyvolume bqplot @jupyterlab/geojson-extension @jupyterlab/fasta-extension

If you open multiple terminal windows make sure to activate the environment in each of them. Your terminal prompt should be preceded by the name of the current environment, for example:

(pycon2019) ~/ipython-in-depth $

Starting JupyterLab

Enter the following command in a new terminal window to start JupyterLab.

$ jupyter lab

Removing environment

You can delete the environment by using the following in a terminal prompt.

conda env remove --name pycon2019 --yes

This will not delete any data, but only the conda environement named pycon2019 .


If you experience an out-of-memory error, you can increase the memory available:

NODE_OPTIONS=--max_old_space_size=4096 jupyter lab build


NODE_OPTIONS=--max_old_space_size=4096 jupyter labextension install ...

This increases the available memory for the build process to 4Gb.

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