UPDATED!!
NB This walkthrough requires a bit of experience with both PyCharm and Docker, plus a *NIX dev env.
At the moment of writing, PyCharm doesn't support development process with a remote Docker host, which is quite an obstacle for serious and comfortable data-analysis work. Fortunately, present-day PyCharm does support "remote interpreters" via SSH, so here I describe how to setup a containerised project appropriately. This takes a little more than 5 minutes, but in the end you will have the comfort of the local PyCharm plus security and performance of a remote host (plus the evident interoperability of Docker).