I too am a Python-preferring machine learning engineer, but my reasons are almost precisely the opposite of the post's. 1. No matter how much you ever think, a

I too am a Python-preferring machine learning engineer, but my reasons are almost precisely the opposite of the post's. 1. No matter how much you ever think, as a scientist, that you "only do an analysis one time" it is false 99.99999% of the time. You will always want to run it multiple times. Other people will want help modifying and running variations of it. Employers will need you, the scientist, to "productionize" it and make it suitable for automated deployment, probably cross-platform. 2. Your analysis will have to adapt to changing data inputs, which means you invariably have to create a (well-designed, unit-tested, and best-practices compliant) tool kit for custom data cleaning, pre-processing, database I/O, file system I/O, and visualization. 3. You will inevitably need to be concerned with raw-metal performance, but generally in isolated pockets of your code, so you'll need a language like Python that supports targeted performance optimization with tools like Cython. 4. Code is read (especially by newbies who need your help) much more than it is written, so you need a language that is easy to explain and reason about, with very few syntactical tricks and complicated conceptual nuances. Overall, Python suits this niche very well. It is a full-service object-oriented language with a huge and well-maintained standard library. The third party tools for machine learning and general numeric computing are by far the best in the open source world (apart from a handful of boutique R libraries, which can use via rpy2 anyway), and Python is a simple language that is easy to teach and explain but also supports lots of targeted optimization in the CPython layer. From the very first line of code you write, when you still naively believe "I will only run this once and I just need to crank it out," you need to be obsessed with writing well-designed, extensible, unit-tested code that is only a short distance from already being "production ready" -- and Python is a great language choice for this.