Notices tagged with pandas
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LinuxWalt (@lnxw48a1) {3EB165E0-5BB1-45D2-9E7D-93B31821F864} (lnxw48a1@nu.federati.net)'s status on Thursday, 21-Mar-2024 02:01:33 UTC 
LinuxWalt (@lnxw48a1) {3EB165E0-5BB1-45D2-9E7D-93B31821F864}
Weird feeling for me: In a few of these #DataCamp courses, I'm now feeling lost when I'm doing the exercises. I think I need to spin up a separate data analysis project using #R-Lang, or #Python + #numpy + #pandas or #SQL ... or maybe do the same project three ways.
With SQL, at least, it seems to be an artifact of the way their hands-on code runner works (Displays a short `head` of the relevant tables ... so when you're working on queries, you may not have a direct way to see whether your query does specifically what you expected and intended.)
With R-Lang, it is just that it isn't always apparent what the language will do. Some things are inexplicably backwards compared to most other languages I've seen, so mentally I tend to go with the wrong choice. Also, the practice question set is too small. I've reached the point where some of the practice exercises are familiar enough that I know which answer to choose immediately without having any understanding of why that is the correct choice. -
Stephen Sekula (steve@chirp.cooleysekula.net)'s status on Thursday, 21-Mar-2019 21:00:51 UTC 
Stephen Sekula
From ben_j_lindsay https://twitter.com/ben_j_lindsay on Twitter: One of the most underrated #Pandas functions in #Python is `.query()`. I use it all the time. data = data.query('age==42') looks so much nicer than: data = data[data['age'] == 42] And it allows chaining like: data = data.query('age >18').query('age < 32')
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