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.