Best Python resources for 2019

After listening to Talk Python to Me 194, I started thinking about what I would recommend to people who are getting started with python. It's a complicated question, actually, because it depends on what this hypothetical neophyte intends to do, and more importantly, whether she/he is interested in getting nerdy about python or just wants to know enough to get it done. 
In any case, it doesn't really matter, because lists like this aren't really for this hypothetical person. This is a list of stuff I like, and that I recommend. So let's not get caught up in some conceit, and just get to a list of good python resources.
First, I find a lot of utility in the posts at RealPython.com. Each post is based on a python package or a feature and gives a fairly detailed walk through of that topic. They aren't always relevant for my own uses, but I even read some of those to get a better idea of how the other half live. Other good sites that are worth visiting include FullStackPython.comPython.org, and SciPy.org. The latter two lead to the official documentation, which is sometimes (not always) useful for learning about the details. I've also got a news feed set up for python-related content (using Feedly), and much of the content actually comes through PlanetPython.org; it's more of a mixed bag but I find and read enough content through it that it is worth mentioning.
I listen to two podcasts focused on python, both hosted by Michael Kennedy: Talk Python To Me and Python Bytes. The first is an interview show, usually with a developer or someone who works in some corner of the python world. The second is a weekly python news show, and they really do a great job of finding the breaking python news and reporting on new and interesting projects. I recommend both, but Python Bytes seems nearly essential at this point. There are other python podcasts, but I haven't gotten into regularly listening to any of them. I think that's because the ones I've heard get a little too into details that I don't care about, especially about web development (a topic that I know nothing about and I get lost very quickly).
On the video front, there are a couple of content providers worth subscribing to. First is Dan Bader's videos; he's the now runs RealPython.com, too, and his videos are well produced, reasonable length tutorials that are usually on some feature of standard python. I think his publishing frequency as decreased since moving to RealPython.com, but some of his videos are worth revisiting. I also highly recommend the videos by Corey Schafer. He provides more step-by-step tutorials, and does a great job of stepping through and building up lessons. I also keep an eye on Coding Tech, which often has videos on programming that are interesting. I also watch a lot of the recorded presentations from python conferences: PyCon, PyData, SciPy especially, but others as well (listPyVideo.org). I also think the Socratica python videos are good, if a bit hoakey. Finally, Enthought publishes a lot of useful videos, and seem to be responsible for the SciPy conference talks.
Finally, tools for leveling up your python skills. There are tons of resources out there, and I have not tried all of them. I have experimented, and I've returned to a few of them. First is ProjectEuler.net. It isn't a python site; it really isn't even a coding site, so much as an algorithm and math site. It's just a list of problems, you have to solve the problem in input the answer; that unlocks a discussion board about that problem where you can see other solutions and sometimes detailed "official" solutions with explanation. I haven't done very many of the problems still, but I find them to be more fun to do than other similar sites. A more sophisticated version of the same concept is CodeSgnal, which used to be called CodeFights. The problems are much more computer science focused, and geared toward developers and want-to-be developers getting ready for interviews. The fun thing is that the site provides a full programming environment and your whole code is supplied and run to get the solutions. It is well designed and many of the problems that I've done have been fun. You can choose from many languages, so it is not a python-specific resource. CodeSignal seems to be nearly the same idea as CodeWars, which I have not tried. A new one to keep an eye on is Coder.com; this is VSCode in the browser with access to computational resources. I'm not sure where this is going, but it seems well-made and interesting. 
Okay, finally, finally is the omnipresent internet resource for coding things: stackoverflow. I don't really like SO in many ways. The main ones are (a) I don't think people do a good job of asking their questions, and (b) people get pedantic (or just jerky) about answering questions. That said, a lot of the answers are very useful, and the best ones provide great guidance and insight about how things work.


In-place upgrade from python 3.6 to 3.7

Based on reports Python Bytes and from [here] and [there] it seems like 3.7 is generally faster than 3.6. So, I decided to try it. On one machine, I set up a fresh conda environment with 3.7 and installed all the packages I typically use. The first time I did that, which was months ago, not everything was working, and I put this upgrade plan on hold. Later, I re-tested, and all my packages seemed to be playing nicely with 3.7. I worked in that environment for a while with no problems.

During down time today, I thought it might be good to move another machine to 3.7. This time I decided to take the leap and move my base environment to python 3.7 from 3.6.6. Why not?

There is one step:
$ conda install python=3.7

This takes some time for conda to "solve" the environment. I'm not sure what this actually does, but since it checks for dependencies, it is no wonder that it will take a while because essentially every installed package will need to be removed and reinstalled.

One potential gotcha with this approach is that anything that was pip installed will need to be reinstalled. I think there are a couple of these, but I don't know how to tell which are which. Oh well, I guess I'll find out when something breaks. 

Eventually the environment does get solved, and a plan is constructed. Answer 'y' and conda dutifully downloads and extracts many packages.

Conda does all the work:
Preparing transaction step happens with the spinning slash, and finishes.
Verification step happens with the spinning slash, and finishes.
Removing some deprecated stuff (jupyter js widgets) ...  And then enabling notebook extensions and validating. Give the OK.
Prints done, returns the prompt.

Did it work?

$which python

$python --version
Python 3.7.1

Python 3.7.1 | packaged by conda-forge | (default, Nov 13 2018, 09:50:42)
[Clang 9.0.0 (clang-900.0.37)] :: Anaconda custom (64-bit) on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> print(2.**8)
>>> import numpy as np
>>> import pandas as pd
>>> import matplotlib as mpl
>>> import xarray as xr
>>> xr.DataArray(np.random.randn(2, 3))

array([[-0.355778,  0.836539,  0.210377],
       [ 0.480935,  0.469618, -0.101545]])
Dimensions without coordinates: dim_0, dim_1
>>> data = xr.DataArray(np.random.randn(2, 3), coords={'x': ['a', 'b']}, dims=('x', 'y'))
>>> xr.DataArray(pd.Series(range(3), index=list('abc'), name='foo'))

array([0, 1, 2])
  * dim_0    (dim_0) object 'a' 'b' 'c'

Okay, this seems to be working. Repeated similar interactive test with ipython. So far, so good.

Lesson: conda is kind of amazing.