# Python in the Scientific World

DevFeed: [Python in the Scientific World](<https://devfeed.tech/articles/python-in-the-scientific-world-38886.md>)

Original publisher: [Read original article](<http://neopythonic.blogspot.com/2009/11/python-in-scientific-world.html>)

Author: Guido van Rossum (noreply@blogger.com)

Published: 2009-11-05T18:12:00Z

Content type: article

Language: en

Sources: [Guido van Rossum](<https://devfeed.tech/sources/guido-van-rossum.md>)

Topics: [Python](<https://devfeed.tech/topics/python.md>), [Computing](<https://devfeed.tech/topics/computing.md>), [NumPy](<https://devfeed.tech/topics/numpy.md>), [data](<https://devfeed.tech/topics/data.md>), [IPython](<https://devfeed.tech/topics/ipython.md>)

Tags: [computing](<https://devfeed.tech/tags/computing.md>), [data](<https://devfeed.tech/tags/data.md>), [library](<https://devfeed.tech/tags/library.md>), [python](<https://devfeed.tech/tags/python.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [time-series](<https://devfeed.tech/tags/time-series.md>)

## AI overview

The article reports on a Py4Science meeting at UC Berkeley featuring lightning talks about projects using Python for scientific work. It describes Python and extensions such as NumPy being used across fields including neuroimaging, astronomy, genomics, and symbolic mathematics.

## Source excerpt

Yesterday I attended a biweekly meeting of an informal a UC Berkeley group devoted to Python in science (Py4Science), organized by Fernando Perez. The format (in honor of my visit) was a series of 4-minute lightning talks about various projects using Python in the scientific world (at Berkeley and elsewhere) followed by an hourlong Q&A session. This meant I didn't have to do a presentation and still got to interact with the audience for an hour -- my ideal format. I was blown away by the wide variety of Python use for scientific work. It looks like Python (with extensions like numpy) is becoming a standard tool for many sciences that need to process large amounts of data, from neuroimaging to astronomy. Here is a list of the topics presented (though not in the order presented). All these describing Python software; I've added names and affiliations insofar I managed to get them. (Thanks to Jarrod Millman for providing me with a complete list.) Most projects are easily found by Googling for them, so I have not included hyperlinks except in some cases where the slides emphasized them. (See also the blog comments.) Fernando gave an overview of the core Python software used throughout scientific computing: NumPy, Matplotlib, IPython (by Fernando), Mayavi, Sympy (about which more later), Cython, and lots more. On behalf of Andrew Straw (Caltech), Fernando showed a video of an experimental setup where a firefly is tracked in real time by 8 camaras spewing 100 images per second, using Python software. Nitimes, a time-series analysis tool for neuroimaging, by Ariel Rokern (UCB). A comparative genomics tool by Brent Pedersen of the Freeling Lab / Plant Biology (UCB). Copperhead: Data-Parallel Python, by Bryan Catanzaro (working with Armando Fox) and others. Nipype: Neuroimaging analysis pipeline and interfaces in Python, by Chris Burns (http://nipy.sourceforge.net/nipype/). SymPy -- a library for symbolic mathematics in Pure Python, by Ondrej Certik (runs on Google App Engin