fdoperezi / examples

Visualization-focused examples of using Python for specific topics

PyViz Topics Examples

This project contains self-contained, typically domain-specific examples illustrating how to use one or more open-source Python visualization tools to explore data or understand a topic. Each project is fully reproducible by downloading and running it locally, and can also be deployed automatically using an Anaconda Enterprise server.

Running Locally

To run an example locally first download it from https://examples.pyviz.org, unzip it, and cd into it. Then install anaconda-project and run the command defined in the anaconda-project.yml file:

conda install anaconda-project=0.8.3
anaconda-project run

Don't want to use anaconda-project?

anaconda-project is a handy way to automate a project, but if you don't want to use it, you can create a regular conda environment using:

conda env create --file anaconda-project.yml

Then activate the environment (be sure to replace env-name with the real name of the environment you created):

conda activate <env-name>

Then start a jupyter notebook as usual:

jupyter notebook

NOTE: If the notebook depends on data files, you will need to download them explicitly if you don't use anaconda-project, by extracting the URLs defined in anaconda-project.yml and saving the file(s) to the appropriate location in this directory.

Uploading to AE

In addition to running examples locally you can upload and share them using Anaconda Enterprise, which is the platform we use for publishing the public deployments. If you've already installed anaconda-project, then for an example named "bears" just do:

cd bears
anaconda-project archive bears.zip

Then in the AE interface select "Create", "Upload Project" and navigate to the zip file. Once your project has been created, you can deploy it.

Running on Binder

To experiment in a running environment, you can use binder:

Since the data involved is sometimes rather large, full datasets are not available on binder, but small versions of the datasets are included in the environment so that you can test things out.