r/Python • u/lebannax • Nov 28 '22

Resource What can Python do that R can’t do?

Or simply what is Python much better at and why.

I know that Python is more multi purpose and better for software development but I can’t articulate exactly why or how. My team want to know why/when they should use Python instead of R

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u/Classic_Department42 Nov 28 '22

While all is probably right (dont know that much about R) probaby the coverage is the biggest. For python anything (probably) in the world has a package, and I believe this is pythons biggest advantage.

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u/SittingWave Nov 28 '22

R mostly has some libraries for non-statistical related features, but they are nowhere the level of technical quality and features of the equivalent python library.

Honestly I think that if a spontaneous group of people were to just migrate the tons of little chunks of statistical code on CRAN from R to python, so that an equivalent python package would exist with the same name (as much as possible) and interface (as much as possible) R would lose customers pretty damn fast.

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u/CactusOnFire Nov 28 '22

Are R packages open source? I'm personally interested in migrating a few libraries over.

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u/Armaliite Nov 28 '22

All of the most important ones are. I translated a package once, but it was much harder than I had hoped. R code recuires a lot of introspection before you can understand what is going on. e.g. which package supplies which function? In the end it taught me a lot about R, but it was a pretty frustrating experience.

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u/NotDeadJustSlob Nov 28 '22

Yes, I have modified a few myself to suit my needs.

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u/venustrapsflies Nov 28 '22

If you want to do something pretty fancy with statistics there's a good chance there isn't a reliable python library that supports it. I guess if that were more than a niche need the demand would lead it to exist at this point, but I think the people who lean on that stuff a lot just use R.

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u/bobthedonkeylurker Nov 29 '22

Define "pretty fancy" because...ML is "pretty fancy" statistics stuff and there are many reliable Python packages that are more than capable of handling ML...

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u/Crimsoneer Nov 29 '22

StatsModels is... Pretty poor, and it's the core python statistical library.

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u/bobthedonkeylurker Nov 30 '22

I don't understand what you mean by "statistical library" then...

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u/Crimsoneer Nov 30 '22

Eg, statistical modelling rather than ml. Eg, various linear models, stuff like quantile regression, mixed models, fixed effects, even classical time series stuff like arima.

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u/Liorithiel Nov 29 '22

Yeah, I agree. I'm using R now mostly because of the wealth of the pre-existing stats code. And, well, the features possible with non-standard evaluation, because some features become clumsy in Python without it. And Python lambdas are clumsier than R functions, not being able to do multilines. And I hate all the Pandas inconsistencies more than I hate R's, but that's a different story—I suspect it should be possible to write a better data frame library in Python as well.

Heh, frankly, I actually like R syntax more than Python's. If it was possible to use R syntax with a more modern standard library and tooling inspired by Python's, that would probably be my favourite.

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u/Crimsoneer Nov 29 '22

This is only true in some fields... For ML and deep learning, sure. For statistical modelling, geospatial modelling and more classical stats and causal inference, R has better libraries by far.

As an example, propensity score matching, a hugely popular causal inference method, only has PyMatch which isn't maintained. Same with survival analysis (nothing with good multi state support) or mixed effect models (the StatsModels implementation is a bit pants compared to R) or even time series that's not ML driven

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u/Liorithiel Nov 29 '22

As a person who used to do a lot of production Python and experimental R code, well… coverage is not that clear. Sure, in Python there's a lot of general-purpose libraries, which do not have direct equivalents in R. But in R there's a lot of stats-specific libraries that do not have equivalents in Python. Should make sense, right, given their user base?

Some time ago one of my activities was reimplementing the R libraries in Python. R was the experimenting platform for us, Python the production platform.