I got into Python because I outgrew Excel (and Origin) and started with Jupyter notebooks and Pandas. The Pandas functions DataFrame.from_excel and DataFrame.to

I got into Python because I outgrew Excel (and Origin) and started with Jupyter notebooks and Pandas. The Pandas functions DataFrame.from_excel and DataFrame.to_excel combined with how visual a notebook is made the transition very easy for me. I do come across some perl every now and then but it looks like Bash on steriods to me (perhaps because of the $variables), and I never see any DataFrames or similar structure that looks familiar to me. Idk, Perl never occurred to me and nobody ever recommended it. Is Perl good for data science? Do you have any examples? I never ran into "efficiency problems" with Python btw, I'm not using it at that scale, at what scale would I notice this? I have run out of ram at times, but that's usually when a dataset on disk is already larger than my ram. But then I usually find a tool to deal with the data anyway (for example pyosmium for super large osm/o5m fles). Edit: I feel that Python also gives me other nice things to get started with, things like Django and Snakemake. This also leads me to recommend Python to other people, it's a broad basis for a lot of stuff. That's why even though some people recommended R when I got started, I choose Python anyway. I have no regrets, unless you are going to blow my mind with some examples...