Users should have basic knowledge of the Python programming language and the terminal/command prompt.
During the tutorial, attendees are strongly encouraged to follow along with the content on their own laptops. To do this, they will need to have IPython and all of its dependencies installed. The easiest way of getting all of these things is using the Anaconda Python distribution that can be downloaded for free here:
This distribution includes IPython 1.1 (with the Notebook and Parallel computing tools) along with 100+ other popular Python packages for data science and visualization. This download is large (275MB) so attendees should download+install it before coming. It is installed in a single directory that can be deleted after the tutorial if desired.
It is important that attendees have IPython 1.1 and not our latest pre-release of 2.0, which has many changes that will not be covered in this tutorial.
The tutorial content itself can be found in this git repo on GitHub:
This contains all of the lectures/examples in the form of IPython Notebooks. We will continue to update this repo as the tutorial approaches.
We will spend a few minutes at the beginning of the tutorial making sure that everyone has IPython installed and working.
IPython started in 2001 simply as a better interactive Python shell. Over the last decade it has grown into a powerful set of interlocking components that maximize developer productivity while working interactively with code and data. These components include:
In this hands-on, in-depth tutorial, we will briefly describe IPython’s architecture and will then show how to use the above components for a highly productive interactive computing workflow in Python.
An outline of the tutorial follows:
This tutorial will be very hands-on, with students encouraged to install IPython on their laptops and follow along with examples and exercises. It will be presented by two IPython core developers and project leaders.
Brian Granger is an Assistant Professor of Physics at Cal Poly State
University in San Luis Obispo, CA. He has a background in theoretical
atomic, molecular and optical physics, with a Ph.D from the University of Colorado. His current research interests include quantum computing, parallel and distributed computing and interactive computing environments for scientific and technical computing. He is a core developer of the IPython project and is an active contributor to a number of other open source projects focused on scientific computing in Python. He is @ellisonbg on Twitter and GitHub.
Fernando Pérez is a research scientist at UC Berkeley, working at the
intersection of brain imaging and open tools for scientific computing. He
created IPython while a PhD student in Physics at the University of Colorado in
Boulder. Today, with all the hard work done by a talented team, he continues
to lead IPython’s development as the interface between the humans at the
keyboard and the bits in the machine.
He is a founding member of NumFOCUS, a PSF member, and received the 2012 Award for the Advancement of Free Software for IPython and contributions to
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