Usage

This tutorial is based on Jupyter Notebooks. Jupyter Notebook is a web-based Python development environment allowing you to combine documentation (markdown), code, and their results) into a single document. This follows a similar idea as Mathematica.

JupyterLab is a web-based interactive development environment for Jupyter notebooks that adds a number of quality of life additions to working with notebooks.

Binder

Deploying Jupyter Notebooks is easy. mybinder.org provides you with a free service that turns a Github repository into a collection of interactive notebooks that are accessible online. By default, Binder will start a Jupyter Notebook environment. You can switch to JuypterLab by appending ?urlpath=lab to the URL.

To launch a JupyterLab environment for this tutorial, go to: https://mybinder.org/v2/gh/JKU-ICG/python-visualization-tutorial/master?urlpath=lab

MyBinder installs the dependencies specified inside of the environment.yml for you. We also add an extension to JupyterLab in the postBuild script file.

Locally

Setup

In this example, we use Anaconda and Python 3.

Install Anaconda

Download the Individual/Open Source Edition of Anaconda from: https://www.anaconda.com/download

Anaconda is a package distribution that contains the Conda package manager, but also a bunch of other frequently used packages,.

We use different frameworks/libraries in this tutorial:

• Numpy and Pandas for data manipulation
• Matplotlib, Seaborn, and Altair for visualization
• Scikit-learn for simple machine learning

Conda Environments
Best practice is to create an environment per project.
Some commands to manage environments from: https://conda.io/docs/user-guide/tasks/manage-environments.html

conda create --name test_env python                 ... create a python environment named test_env
conda create --name jupyter_env jupyter python   ... create a python environment named jupyter_env with package jupyter

conda activate test_env                             ... activate environment test_env
conda env list                                      ... list all environments
conda list                                          ... list all packages available in environment (!= packages shipped with anaconda)
conda install numpy                                 ... install an additional package
conda install --yes --file requirements.txt         ... install packages listed in a requirements file
python -m pip install <package>                     ... install a package not aviailable via conda, also see (†)
conda deactivate                                    ... leave conda enviroment
conda env remove --name test_env                    ... remove an environment from disk

† See this article on why you should prefer python -m pip over pip.

Start

Checkout this repo and change into the folder:

git clone https://github.com/JKU-ICG/python-visualization-tutorial.git
cd python-visualization-tutorial/

Load the conda environment from the shared environment.yml file:

conda env create -f environment.yml

Activate the loaded conda environment:

conda activate python-tutorial

Install Jupyter Lab extension for ipywidgets:

jupyter labextension install @jupyter-widgets/jupyterlab-manager

Launch JupyterLab:

jupyter lab

Jupyter should open a new tab with url http://localhost:8888/ and display the tutorial files.