The list is very long.
Oh, don't forget COBOL.
My team spent a whole quarter on converting R code into Python, because we wanted to use Tensorflow for machine learning(). When they finally got the thing running, they found out it wasn't performing as fast as R. I thought, that couldn't be possible, they use pretty much the same linear algebra libraries. So I peeked into the code to see what went wrong and found out that they writing it the wrong way: (1) calculated on Pandas dataframe directly instead of extracting the values first when doing matrix calculation, (2) instead of plain ndarray, were using matrix instead, which is slower. Both of which someone without experience in Python wouldn't have known.
() On a hindsight, did it have to be Tensorflow? Besides, there's already an interface for R. Maybe the team decided on Python anyway in case they want to try out the plethora of ML libraries available for that language.