What's your definition of an outlier? Explicitly? You first need to determine your logical criteria and then implementation is easy. The code is the easy part of this work, deciding what/how to do things is the hard part.
An explicit definition is something like:
Any values in the top and bottom 1 percentile is considered an outlier.
Now for percentiles, you also need to consider how you want to handle tied values.
Note that you would make a rule for each variable, not a broad rule as above for all variables. That's a very broad approach, kinda like trying to sharpen a pencil with an axe.
Now what are you planning to do once you've identified these outliers? Remove them?
You may actually get better answers if you explain what you're trying to do overall. What's the context of the analysis problem you're facing that makes you think you have outliers in your data? Are outliers good or bad or both? Do you want to identify them for what reason?
Randomly happened to walk through a review of call center analysis earlier this morning with my analysts. We found calls that were less than a minute which are likely disconnects/wrong numbers so we decided to remove those for example. It usually takes a few minutes to authenticate and verify a user so those are not valid for the analysis we're doing (first call resolution, how many customer service calls were resolved on the first call). Longer calls, like 5+ are our complex calls s those are filtered out for separate analysis and further text analytics.