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Comment by vanderZwan | Hacker News Reader

I work at Karolinska as the sole developer on a data browsing platform, inside a neurobiology research group. The entire department is either using or learning Python. Of course, the work environment itself is a severe form of selection bias, but there definitely has been an uptake in Python among scientists.

(Which leaves me kinda sad, because I like Julia more - I'm sure it will keep growing though)

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Julia is definitely a step up in data science programming. It's purpose built for data science. I guess that's also the reason why Python has grown more than Julia. The likelihood of someone having worked with Python in an unrelated field means that when starting out in another field, if the Python option exists, they are probably going to take it.

Apart from that, if one is just getting started out in data science and has never really handled a programming language before, Julia is very nice really. And their documentation is pretty damn awesome. And once you get past the performance recommendations I think it gets pretty fast (?). I'm out of my depth beyond a certain point since I've only ever experimented with it.

The problem is that data science, often, isn't just data science. It might involve doing a bunch of activities that are strictly not data science - scraping data off the web (Python has requests), visualization for some understanding (Python has dozens of nifty libraries), store data in a small db (Python works nicely with sqllite, among others), convert all of this into an app (Python has Flask, again among others).

R's data frame concept was fantastic for those transitioning from 'spreadsheet' analysis but with Pandas, Python has improved and enhanced this concept even further.

This is just one example but I think it is this versatility that makes it more attractive than R or Julia.

Yes, in principle Julia should be able to do all of these things too, but it simply doesn't have the decades-old history of libraries to stand on yet.

Yeah I love Julia but it's hard to argue with Python's data science stack. Sometimes it's almost to easy to get things done.

Julia is really fast but the DataFrames/DataTables package was kind of slow and feature-incomplete. Because Pandas is so optimized, Julia wasn't winning out over Python on the speed side of things, and was definitely losing on the ergonimics, so for data science it wasn't the best choice (unlike say numerical simulation).

I'm sure this will change if it hasn't already, especially with the better parallelization that Julia is capable of.

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