How Python became the language of choice for data science
I started out with MATLAB, played around enough with Python to realize that it wasn't enough better than MATLAB to be worth switching. (My impression was that Python was <i>almost</i> flexible enough as a language to make writing code that uses NumPy/SciPy feel natural, but it didn't quite achieve that.) I finally switched to Julia, which is both faster and (IMO) more enjoyable to write than either MATLAB or Python, although the current package ecosystem is pretty small. I think the main difficulty in creating a new programming language for numerical computing is that you need knowledgeable people to do it, and most of those people are more interested in analyzing their data than building a programming language (or core packages for a programming language) to do it better.<p>My impression on two small bits of this article:<p><i>Python is also somewhat restrictive with what you can say on a single line. In Matlab you would often load some data, start editing the functions and build you data analysis step by step, while in Python you tend to have files which you start from the command line (or at least that’s how I tend to do it).</i><p>I actually find that this kind of analysis is more conveniently done in IPython (or IJulia) Notebook than in MATLAB.<p><i>I still have dreams of a plotting library where the type of visualization is decoupled from the data (such that you can say “plot this matrix as a scatter plot, or as an image”).</i><p>This is the design goal of both R's ggplot2 and Julia's Gadfly.jl.