R vs Python performance-wise
Hi, Has anyone run any R vs Python (numpy) tests? I'd love to see what the differences performance-wise are, specially handling large sparse matrices. Since both rely on external C code, there might not be much of a difference. If you know and use both languages, what are the main differences and what made you stick to one over another? I also noticed that there are strong libraries for social networks on both. python: networkX: https://networkx.lanl.gov/wiki pySNA: http://www.menslibera.com.tr/pysna/ R: sna, network etc. see: http://www.jstatsoft.org/v24 Has anyone run a bechmark of the two systems doing the same operation? Which is the right environment for large social networks? Some packages have bindings for both languages, and of course, there's a reliable way to bind the two languages together, Rpy: http://rpy.sourceforge.net/ So this may not be a big deal which one to pick. Thanks, -Jose
Jose Quesada, PhD. Max Planck Institute, Human Development, Berlin http://www.andrew.cmu.edu/~jquesada