The goal of this article/notebook is to approach the visualization of graphs using ipycytoscape assuming you are given the data in a tabular form (excel, CSV, google sheet etc) which actually might be relatively frequent. This article/notebook follows part 1 and part 2. The example I am basing this article is built upon the previous ones. We are rendering a small European rail network.
You are expected to have basic notions of python programming. You should have read the previous two notebooks/articles in the series (part 1 and part 2). You should have notions of pandas.
Let’s face it, most…
The objective of this article that follows a first one is to learn graphs from scratch using a visualizing tool called
ipycytoscape. In order to get into graphs I had made the point that it is better starting by visualizing because an image is worth a million words and also a million equations.
A small graph was built in the first part, and we made little by little some modifications to the graph showing how ipycytoscape can be used to get an idea of how data can be presented in a graph.
ipycytoscape project: https://github.com/QuantStack/ipycytoscape
The first medium article is already…
This article is for total beginners in graph theory and graph visualization in python.
This article’s goal is making accessible and understandable to total beginners the visualisation of graphs in the ipywidgets ecosystem (notebooks) and at the same time showing the internals of ipycytoscape, a great library for graph visualizations in jupyter notebooks based on cytoscape.js.
The reader might also refer to the medium original article of the authorof ipycytoscape Mariana Meireles.
Graphs are mathematical structures used to model pairwise relations between objects.
Examples can be thought as:
Python coder. NLP. Pandas. Currently heavily involved with ipywidgets (ipysheet, ipyvuetify, ipycytoscape)