It's not really a question of the "correct" data. To evaluate whether an observation is anomalous, you typically define a threshold for what's considered not-normal, e.g. an observation 2 or 3 standard deviations above the mean.
This may leave you with some false positives (like any system of this nature would). Of course, you could go the route of actually defining an anomaly yourself and building a more sophisticated model (i.e. one not using z-scores as the measure of anomaly), but that's obviously a different scope.
Why not learn R? In the last year I've spent around 80% of my time working with R, coming from the last five years almost exclusively with Python, and there are some great reasons to use R. Although if the tidyverse didn't exist I'm not sure I'd be saying that. I find that suite of packages together to be a very cohesive set of tools for doing data science.
Not the person to whom you asked this question, but I'm also a user of TabNine. In my experience, the "recommendations" / autocompletions provided by the tool are usually very short (probably less than 20 characters on average), and I don't use it for terribly complex chunks of code. Where I like it most is in the initialization of common code chunks like `if` and `for` loops, using variables instantiated in nearby preceding lines. It figures out things like `for(customer in customers)` as I'm writing `for`.
This may leave you with some false positives (like any system of this nature would). Of course, you could go the route of actually defining an anomaly yourself and building a more sophisticated model (i.e. one not using z-scores as the measure of anomaly), but that's obviously a different scope.