Python and R for code development

The previous post glossed about why I now prefer python to write code, including for a module like logopt. This post explains in more details some specific differences where I prefer one of these two languages:

0-based indexing in python versus 1-based indexing in R. This may seem a small difference but for me, 0-based indexing is more natural and results in less off by one errors. No less than Dijkstra opines with me on 0-based indexing . = versus <- for assignment. I like R approach here, and I would like to see more languages doing the same. I still sometimes end up using = where I wanted ==. If only R would allow <- in call arguments. CRAN versus pypi CRAN is much better for the user, the CRAN Task Views is a gold mine, and in general CRAN is a better repository, with higher quality packages. But publishing one CRAN is simply daunting, and the reason logopt remained in R-Forge only. The manual explaining how to write extensions is 178 pages long. Python has better data structures, especially the Python dictionary is something I miss whenever I write in R. Python has no native dataframe, but this is easily taken care of by importing pandas. Object orientation is conceptually clean and almost easy to use in Python, less so in R. Plotting is better in R. There are some effort to make Python better in that area, especially for ease of use. Matplotlib is powerful but difficult to master. lm is a gem in R, the simplicity with which you can express the expressions you want to model is incredible

All in all, I prefer coding in Python. This is a personal opinion of course, and R remains important because of some packages, but for more general purpose tasks, Python is simpler to use, and that translates in being more productive.