Opinions:
1) mypy is the best type checker, you could look into pylance or pyre though
2) Django's ORM is kinda bad vs. sqlalchemy, but this isn't new. I'd consider any framework where you can use sqlalchemy (most everything)
3) No improvements to interpreter really. In python3 you now have to import reload though :(
4) Celery still exists, it's inside of Airflow. In general "scheduling asynchronous jobs" is a thing that is ubiquitous and celery is a bit limiting with its queue model. Many people use things like kubernetes to let a more advanced scheduler find space & time to run your async jobs, vs forcing them to be evaluated by a fixed set of workers. That being said, celery is fine for simple stuff.
5) Deploying python: everything is docker + kubernetes these days. There are the serverless true believers out there, so AWS lambda might be up your alley if you like that sort of thing. Definitely wouldn't invest time in building on app engine, and I'd just use ec2 as nodes for your docker deploys. Immutable deployment artifacts are great
6) APIs: use REST/json, it's fine. Everything speaks it. If you have a big team where front end and back end can't work in lockstep, or you have multiple clients with diverse needs, consider GraphQL, but if you're doing a web frontend... might not bother? GraphiQL is really nice, but if you generate OpenAPI definitions the tooling for REST is acceptable.
7) Parallelism: still use multiprocessing. GIL is alive and kicking, no change there. I'd say more parallelism is going into horizontally scaling containers vs. trying to run a lot of processesin a single server. Have nginx (or your favorite kubernetes ingress) multiplex your containers
8) I've ignored async mostly, so take this with a grain of salt: you probably don't need async. Tornado is a good bet if you want async though for things like websockets etc. You can do sync or async with tornado, which is nice.
9) Numpy and scipy still alive and kicking. Not sure what your use case is, but the projects are very much active. There are obviously a lot of deep learning libraries etc in python now, so depending on what your needs are there is a lot of statistics / ML you can do in python.