Python in Academia by Marco Bardoscia

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Python is useful for academics working in various fields like physics, economics, social systems, and biology. It has many advantages over other languages like MATLAB, including having a large number of packages, being easily extensible, and its interactive nature. However, some academics care more about factors like speed, compatibility with legacy code, and reproducibility. Improving Python's performance relative to MATLAB, simplifying integration with compiled code, and enhancing default plotting options in Matplotlib could increase its adoption in academic settings.

Python in Academia
Marco Bardoscia
International Centre forTheoretical Physics

Background
> Theoretical (statistical) physicist
> Economic systems (ﬁnancial markets, economies)
> Social systems (social networks)
> Computational problems (SAT extensions)
> Biological systems (metabolic networks)

Tools in Science
“Using regular adhesive tape they managed to obtain
a ﬂake of carbon with a thickness of just one atom.”
nobelprize.org

ComputationalTools
> MATLAB
> A single FORTRAN ﬁle (5000 lines of code)
> C++ for data analysis
> R

ComputationalTools
> MATLAB
> A single FORTRAN ﬁle (5000 lines of code)
> C++ for data analysis
> R
… mostly true for small groups.

> General-purpose
> Plenty of packages
> Nicely extendable
> Interactive
But Python is:

> General-purpose
> Plenty of packages
> Nicely extendable
> Interactive
But Python is:
… but many people in academia care about different things!

What people care
about:
> Is it fast (enough)?
> Is it at least always faster than MATLAB?
> What about my legacy code?
(wrapping in Cython is hardly a choice)
> Can I get nice publication-ready plots?
(using defaults?)

What about
reproducibility?
If what I have done is interesting, someone else will do
it from scratch. If they ﬁnd the same results (modulo
some tiny details) using an entirely different code,
then we didn’t do anything terribly wrong…

> Up-to-date comparison with Matlab for the most
relevant functions
> Nice(r) defaults for Matplotlib plots
> Simpler integration with compiled code
What can be done?

Slide deck for the 45-60-minute introduction to Python session talk delivered at Silicon Valley CodeCamp 2015: https://www.siliconvalley-codecamp.com/Session/2015/what-is-python ABSTRACT Python is an agile object-oriented programming language that continues to build momentum. It can do everything Java, C/C++/C#, Ruby, PHP, and Perl can do, but it's also fun & intuitive! Enjoy coding as fast as you think with a simple yet robust syntax that encourages group collaboration. It is known for several popular web frameworks, including Django (Python's equivalent to Ruby on Rails), Pyramid, and web2py. There is also Google App Engine, where Python was the first supported runtime. Users supporting Zope, Plone, Trac, and Mailman will also benefit from knowing some Python. Python can do XML/ReST/XSLT, multithreading, SQL/databases, GUIs, hardcore math/science, Internet client/server systems & networking (heard of Twisted?), GIS/ESRI, QA/test, automation frameworks, plus system administration tasks too! On the education front, it's a great tool to teach programming with (especially those who have done Scratch or Tynker already) as well as a solid (first) language to learn for non-programmers and other technical staff. Finally, if Python doesn't do what you want, you can extend it in C/C++, Java, or C# (even VB.NET)! Have you noticed the huge growth in the number of jobs on Monster & Dice that list Python as a desired skill? Come find out why Google, Yahoo!, Disney, Cisco, YouTube, LinkedIn, Yelp, LucasFilm/ILM, Pixar, NASA, Ubuntu, Bank of America, and Red Hat all use Python!

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The slides shown here have been used for talks given to scientists in informal contexts. Python is introduced as a valuable tool for both producing and evaluating data. The talk is essentially a guided tour of the author's favourite parts of the Python ecosystem. Besides the Python language itself, NumPy and SciPy as well as Matplotlib are mentioned. A last part of the talk concerns itself with code execution speed. With this problem in sight, Cython and f2py are introduced as means of glueing different languages together and speeding Python up. The source code for the slides, code snippets and further links are available in a git repository at https://github.com/aeberspaecher/PythonForScientists

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