Article is well written but fails to address its own thesis by postponing it to a sequel article. At its current state only alludes that Python is not great because requires specialized packages. (And counterexample is R for which also used a package.)
Thanks! In such serial articles usually there's link to the end pointing to the next one so, since there wasn't any, thought next one hadn't been written. This one indeed addresses the thesis. The TL;DR, taken directly from the article,
>The core problems I see with Python as a language for data science are call-by-reference semantics, lack of built-in concepts of missing values, lack of built-in vectorization, and lack of non-standard evaluation.
