R and Python, memory differences yielding performance differences I think there are numerous posts regarding which one to use: R or Python. However, I'm curious about how their architecture differences yield differences in speed performance, not which one to use.

This blog post performs a small test between R and python to show that the (optimized) python code was 2x faster than R code.* And I've read in this post that R tends to put everything in memory, which is why computations on large datasets is generally slow.

But what makes python's low level memory management so much different than R, which helps it yield these benchmarks?

*Though python was 2x faster in this test than R, I'm not saying that python is generally 2x faster than R.
It depends on the usage of various packages (Numpy/Scipy etc), which are written in C and be incredibly fast also Python can be complied using JIT.  Here is an excellent comparison between R and Python : https://learnanalyticshere.wordpress.com/2015/05/14/clash-of-the-titans-r-vs-python/