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.


The MDAnalysis Tutorial serves as an introduction to the library and there are other tutorials available, too.

Interactive Jupyter notebooks show how to accomplish specific tasks (including visualizing trajectories with nglview); these notebooks can be run in the cloud on Binder (click the “launch binder” button to start a notebook server).



See the Online Documentation for more information on how to use MDAnalysis and the available documentation on the Wiki. 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.

Mailing list

Finally, you can also ask for advice or help on the mdnalysis-discussion mailing list. If you find bugs or want to request enhancements please file a report in the Issue Tracker.



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


@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):