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Python proper is terrible for analyzing data at scale. It's the numpy, scipy, jupyter tools, as well as Anaconda, that make the sale. Too, it's not just the a
by smitty1e 5y ago
Python proper is terrible for analyzing data at scale.
It's the numpy, scipy, jupyter tools, as well as Anaconda, that make the sale.
Too, it's not just the analysis, but the extract/transform/load (ETL) beforehand. Python's general purpose wealth of tools had me transforming the FCC antenna coverage .zip[1] to shapefile and parking it in a spatialite file for QGIS in a relative jiffy.
Doubtless I could do all that in Julia or R, but I get to hone my python-fu at the office doing cloud stuff by day--another argument in favor of the general-purpose tool.
[1] ftp://ftp.fcc.gov/pub/Bureaus/MB/Databases/fm_service_contour_data/FM_service_contour_current.zip
- taeric 5y agoI argue that what makes the sale, is that that is what so many in the field are using. Not much inherent to the technologies, but you are taking on a daunting task if you want to compete with pytorch, instead of using it.
- smitty1e 5y agoNetwork effects: we got 'em.
- agumonkey 5y agoBut we can assume people got on python/numpy stack because it brought some value compared to R. I'm not a big fan of numpy but I find it a bit shallow to think it's mostly follower effect at play. It might be though.. but that would be sad. Maybe Julia will bring balance back in the universe.
- taeric 5y agoWell, R is a rather awkward programming environment. And many folks learned on Mathematica and Matlab. What python brought to the table, was that it was freely installed on most machines.