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That sounds incredible to me as using pandas without falling into the traps of chained indexing seems so much harder than 90% of Python.

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I've been planning to go from "can work it out with copious examples" to "knows when to use apply, transform, etc. off the cuff" level of knowledge in Pandas - do you have any suggestions on good resources on this?

I'd like to understand better why the data structures work the way they do and thus have an intuition on what operations to use when. The O'Reilly Python Data Science Handbook[0] seems like it might be useful here, but I'm not sure if it is still up to date.

[0] https://www.oreilly.com/library/view/python-data-science/978...

I find the pandas documentation pretty good [0]. Actually, I find the documentation, official tutorials of pandas, numpy, matplotlib all pretty good. Each package has their own idiosyncrasies but I think the documentation and code examples cover them well enough.

The OReily book should not be that outdated if at all. It also covers other essential tools.

[0] https://pandas.pydata.org/pandas-docs/stable/getting_started...

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