I recently started using python packages for some statistical work.

I then discovered that there are often bugs with many of the python stats packages. Many python numerical packages also have the reputation of changing how things work "under the hood" from version to version. This means that you can't trust your output after a version change.

Given all of the above, I can see why "serious" data scientists stick with R and this article is just another reason why.