I was told last year I can be way too optimistic / silver-lining-finding in negative situations. I used to always look for the solution to a problem as opposed to providing whatever the sharer of that problem wanted (usually validation/assurance).

It has been somewhat difficult to turn off that "always helping" part of me - and to be frank it will probably never go away completely which I don't think is a bad thing - but substantial progress has been made.

Agreed on learning blackjack. I went to Las Vegas about a month ago and spent the four-hour plane ride trying to memorize some of the pattern in a blackjack strategy chart. I didn't memorize all of the actions for every possible hand combination, but I learned enough to be dangerous (read: enough not to make dumb/obvious mistakes). I ended up quadrupling my money (only a few hundred dollars) and have been playing really well on a blackjack app on my phone.

Practice makes perfect is really the maxim here.

Python dev for 5 years: personally I like `[1 for _ in range(10)]` and `[1] * 10`. The `for _ in` syntax denotes the variable is basically a throwaway, whereas the `for n in` construct is a bit confusing - almost like we're supposed to be doing something with the `n` in the comprehension.

A few years ago, I began to grow old of working with dataset pre-processing scripts/"libraries" for machine learning and began to create my own sort-of "pipelines." Last year, I stumbled upon `sklearn.compose`, a relatively newer module within the scikit-learn ecosystem.

I have had a lot of success with this module since then, and wanted to share a tutorial I put together which touches on the idea of managing your machine learning dataset creation steps completely via a configuration.