The suggestion to use
split is helpful, and could look something like this:
words = [word for word in top_level_comment.body.split()]
split splits on spaces by default.
Looks like you’d still have to sort out all the punctuation though, which is a common problem in natural…
Fun project! I’d be interested to see how the data looks if some of the frequent words got rolled up into “topic”-level categories…maybe
nltk or something could help with that.
Note that you can view documentation in the plain Python interpreter (and with about the same level of ease as IPython) using the built-in
>>> x = 'python' >>> help(x.capitalize)Help on built-in function capitalize:capitalize() method of builtins.str…
This was informative—I always create my Django projects from the CLI and then open them in PyCharm later, partly due to my own paranoia. I might start using this approach now though 😄
It would be fantastic if there were a built-in way to accomplish this, or have it called out with the other hints in the UI. I often resort to using asterisks because I either forget real bullets are available or don’t feel like typing option-8 all the time when I do remember! My two cents :)
Cheers to all who worked on this and its supporting projects. Design considerations like this really inspire me because the final outcome is something that feels so natural that one wonders why it hasn’t always been that way. Thanks for making things better! Note that I’m eagerly anticipating this landing in Trello soon after those last few GIFs ;)