Tal Yarkoni, R-bloggers, The homogenization of scientific computing, or why Python is steadily eating other languages’ lunch, here.
Over the past two years, my scientific computing toolbox been steadily homogenizing. Around 2010 or 2011, my toolbox looked something like this:
- Ruby for text processing and miscellaneous scripting;
- Python/Numpy (mostly) and MATLAB (occasionally) for numerical computing;
- MATLAB for neuroimaging data analysis;
- R for statistical analysis;
- R for plotting and visualization;
- Occasional excursions into other languages/environments for other stuff.
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.
You may notice a theme here.