Time to learn Python
Tags: #Python #R #scientific computing
Apparently Python is taking over the world (from a post by Tal Yarkoni):
In 2013, my toolbox looks like this:
- Python for text processing and miscellaneous scripting;
- Python (NumPy/SciPy) for numerical computing;
- Python (Neurosynth, NiPy etc.) for neuroimaging data analysis;
- Python (NumPy/SciPy/pandas/statsmodels) for statistical analysis;
- Python (scikit-learn) for machine learning;
- Excursions into other languages have dropped markedly.
I can’t speak on the relative merits of Python over R, other than a general impression that R has stronger stats but some quirks as a language (pdf), while Python is generally more powerful, but less capable beyond basic statistical tools. I did spend some time trying to learn Python during my last year in graduate school, but it was while I was really still becoming comfortable with R and so I didn’t put much effort into it. Seems like it’s time to head back in that direction again.