Re:Welcome to the Python Engineering blog
by dr_zoidberg on Sunday February 21, 2016 @07:06PM
(#11147392)
a) Having "beautiful code" helps keep focus where it matters: the "difficult" sutff (be data data science, machine learning, math problems, physics simulations, etc).
b) True, there are beautiful libraries all around, and easy to use. In particular to data science, I think NumPy+SciPy+matplotlib are comparable to MATLAB. With beautiful code, portability and no need to pay an expensive license.
c) Totally agree. Though there are small nitpicks in different platforms, most of the times they can be overcome.
d) Totally agree.
Also: the Anaconda distribution solves most of the problems that anyone could have with (b), (c) and (d).
a) Somehow the fact that indentation is meaningful forces my code to be neat automatically without having to run any beautifier through it. I consider myself a bit OCD on how the code looks (can't explain it) and I like how Python code finally looks. I like code to be maintainable and self-explanatory without leaving puzzles for my successors to solve.
b) Batteries included. Granted there are libraries for most things in other languages, but nothing with the ease of access as Python.
c) Portability without having to know the compiler intricacies of each Port/Platform/OS
d) Being able to deliver solutions on time and within budget without putzing around with irrelevant stuff that the customer never cares about.
Not being a Data Scientist, I don't know if any of this is relevant. But being educated in CS and responsible for delivering on promises I care about some of this a lot.