Learning MDAnalysis

Once you had a look at the basic example you might want to learn more about how to use MDAnalysis. MDAnalysis is primarily a library that helps you to build your own tools but it also works very well for interactive data exploration of MD data in IPython, in particular within Jupyter notebooks and in conjunction with pandas. MDAnalysis is well suited for a rapid development approach.

The resources below should help you to quickly find out to best use MDAnalysis for your own specific uses.


Start with the Quick Start Guide when you are new MDAnalysis.

Then browse the User Guide, which contains detailed documentation for all the important parts of MDAnalysis and many self-contained tutorials.

There are a number of older tutorials available, too, although we recommend new users start with Quick Start Guide and then start reading the User Guide.


The User Guide contains installation instructions, the Quick Start Guide, and comprehensive description of the functionality of MDAnalysis from a user’s perspective. New users should start here!

The Online Documentation contains technical information on how to use MDAnalysis.

The paper on MDAnalysis contains a high-level description of the structure and philosophy of the library together with examples of its use.

The FAQ contains a growing list of specific (frequently asked) questions and answers.

GitHub Discussions

You can ask for advice or help on [GitHub Discussions] (https://github.com/MDAnalysis/mdanalysis/discussions). If you find bugs or want to request enhancements please file a report in the Issue Tracker.


The videos listed below were given by core developers at conferences. They highlight various aspects of MDAnalysis and show how to use it in a research context.


The universe as balls and springs: molecular dynamics in Python

@lilyminium’s talk at PyCon AU 2019 The universe as balls and springs: molecular dynamics in Python gives a general introduction to molecular dynamics and shows how to use MDAnalysis (and other tools such as OpenMM, nglviewer, pandas, plotly). If you want to better understand what MD simulations are and how scientists can make use of the vast Python eco-system to analyze (and run) MD simulations, start here:

MDAnalysis: A Python package for the rapid analysis of molecular dynamics simulations

@orbeckst’s talk at SciPy 2016 provides an introduction to the MDAnalysis library, its uses, and underlying philosophy:

Also read the paper MDAnalysis: A Python package for the rapid analysis of molecular dynamics simulations which adds detail to the concepts outlined in this talk.


Looking at molecules using Python

@jbarnoud presented at the PyGrunn 2017 conference Looking at molecules using Python where he shows how to use a whole range of MDAnalysis from the simple to the advanced in Jupyter notebooks (he also shows off nglview for visualization and datreant for organizing his data):

BioExcel Webinar: MDAnalysis: Interoperable analysis of biomolecular simulations in Python

In this BioExcel webinar, three of the MDAnalysis Core Developers (@orbeckst, @lilyminium, @IAlibay) summarize the basics of MDAnalysis, show more advanced ways to hack MDAnalysis and outline future developments.