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I still have to finish reading it, but I wonder how python will ultimately stack up against R?

by jeo1234 11y ago

bsg75 11y ago
Depends on what you want. R has arguably better coverage for packages that provide various statistical tools, but Python is arguably a better general purpose language.
Lofkin 11y ago
Python has more consistent stats, time series and programming syntax (for the packages it does have), and better bayesian inference package (pymc 3 > stan). Python is also better than R for ad hoc statistical modeling and algorithim development (you can write python code on the order of C fast with numba) , general programming, scraping, natural language processing, agent based modeling etc. Python is also better for GIS, optimization, symbolic math and larger datasets with blaze and dask and pyspark. R right now is a bit better for visualization, reporting and exploratory data analysis (I think this will change soon though with Bokeh and blaze) and has many more esoteric stats packages. With statsmodels, pymc3, pandas and scikitlearn etc you can probably do 98% of everything you need in python without needing to dip into the more esoteric packages of R (with some exceptions). For everything else, you can call R packages with Rpy2. With this you get all the advantages of working in one language (not spread too thin) and the advantages that python offers while leveraging R's wealth of packages. The latter is a bit more difficult through python, but not as hard as trying to remember the syntax of and gluing together a two language workflow. That is why I chose python... Also I can write excel addins with python (xlwings) instead of using VBA
- mikecb 11y ago
  Just to note, if you like Stan it has a python lib just as much as an R one, and Julia, MATLAB, etc (Stan itself is written in c++). As for vis, yhat wrote a python version of ggplot:http://ggplot.yhathq.com/ http://ggplot.yhathq.com/
- graffitici 11y ago
  xlwings looks absolutely amazing. Thanks for pointing it out!
- data_scientist 11y ago
  Genuine question, why do you find Pymc3 better than stan? Pymc3 is still in alpha, while Stan is stable since many years. They both implements the same algorithm (NUTS), so is it just the nicer syntax or is it anything else?
  - peatmoss 11y ago
    I've made minimal use of Stan, and not really used Pymc3, but from a quick look, it seems Pymc3 is a bit more integrated than RStan. In RStan, you end up writing Stan code as an alien, wrapping the foreign syntax in quotes, and then shoveling the code as a string into Stan. There isn't really an R grammar for Stan as near as I can tell.
  - Lofkin 11y ago
    Aside from the written in python making it more natural and extensible and amenable to messing around with the models, pymc 3 can sample directly from discrete parameters and STAN cannot. Pymc3 has more sampling routines.
baldfat 11y ago
This is an unnecessary fight. They both are great. I personally strongly prefer R over Python due to libraries and the flexibility. Others hate the flexibility of R and like Python's only one way to correctly do something. It comes down to preferences. I like the R is for statistical specifically. There are people who call R an aweful horrible language and it isn't. It has grown incredibly over the last 5 years and has major funding from many corporations for a reason. If it seems passionate for or against either language it is more an emotional response to the question. R works best for me and Python might be someone else's prefer solution.
- Lofkin 11y ago
  Ultimately Julia combines the strengths of both and much more. It is the future IMO, not R or python.
  - hueving 11y ago
    way to miss the point. Part of a language selection for a real -world project is based on the libraries available for it. If I'm doing systems administration programming, there is a much higher probability of there being a python library to interact with BGP or namespaces than there is for Julia. Selecting Julia blindly is just as stupid as selecting R or python blindly. Language features are only a small part of the reason you choose a language for a large project.
    - apl 11y ago
      The Julia FFI for Python is absolutely excellent -- calling a particular Python library from Julia takes very, very little effort. As a language for scientific programming, Julia is way ahead of Python. So I'm not sure if this particular argument holds much water. https://github.com/stevengj/PyCall.jl https://github.com/stevengj/PyCall.jl
      - baldfat 11y ago
        I think it is a chicken or the egg problem with Julia right now. Julia is very much a positively viewed language that isn't being used much. There are not enough libraries (I looked 3 months ago) for me to do my work in Julia. There isn't enough users to make the libraries for my use case.
hessenwolf 11y ago
Python is better for programming, R is better for statistics. A lot of data analysis packages require a bit of both, so I usually use a bit of both, and C++ underneath when something has to run faster. To expand, R has every cool statistical model (outside finance) to hand. It's deadly buzz. It's finicky as hell to program in though. Classes are a bit crap. Brackets are all over the shop. Arrows? Really? Meh. Statistical models take ages to program and test, though, and the academic paper that was published last week has had the model as an R package for 6 months. Python is a beautiful programming language, especially for anything involving transferring data from here to there and possibly back again. I particularly like it when working with a diverse team of context experts, who would think nothing of laying a faecal nugget in your code-base, to save the time it takes to drink a cup of coffee. It's a lot harder to write crap code in Python than in many other languages. So, in our local technical landscape, R is used for prototyping, and is used in a slave fashion for running complex stats models. Python is used as the "Controller" of the program, and also for database type operations, web-scraping, calling QuantLib, ...

I still have to finish reading it, but I wonder how python will ultimately stack up against R?

by jeo1234 11y ago

I still have to finish reading it, but I wonder how python will ultimately stack up against R?

bsg75 11y ago

Depends on what you want. R has arguably better coverage for packages that provide various statistical tools, but Python is arguably a better general purpose language.

Lofkin 11y ago

Python has more consistent stats, time series and programming syntax (for the packages it does have), and better bayesian inference package (pymc 3 > stan). Python is also better than R for ad hoc statistical modeling and algorithim development (you can write python code on the order of C fast with numba) , general programming, scraping, natural language processing, agent based modeling etc. Python is also better for GIS, optimization, symbolic math and larger datasets with blaze and dask and pyspark. R right now is a bit better for visualization, reporting and exploratory data analysis (I think this will change soon though with Bokeh and blaze) and has many more esoteric stats packages. With statsmodels, pymc3, pandas and scikitlearn etc you can probably do 98% of everything you need in python without needing to dip into the more esoteric packages of R (with some exceptions). For everything else, you can call R packages with Rpy2. With this you get all the advantages of working in one language (not spread too thin) and the advantages that python offers while leveraging R's wealth of packages. The latter is a bit more difficult through python, but not as hard as trying to remember the syntax of and gluing together a two language workflow. That is why I chose python... Also I can write excel addins with python (xlwings) instead of using VBA

mikecb 11y ago
Just to note, if you like Stan it has a python lib just as much as an R one, and Julia, MATLAB, etc (Stan itself is written in c++). As for vis, yhat wrote a python version of ggplot:http://ggplot.yhathq.com/ http://ggplot.yhathq.com/
graffitici 11y ago
xlwings looks absolutely amazing. Thanks for pointing it out!
data_scientist 11y ago
Genuine question, why do you find Pymc3 better than stan? Pymc3 is still in alpha, while Stan is stable since many years. They both implements the same algorithm (NUTS), so is it just the nicer syntax or is it anything else?
- peatmoss 11y ago
  I've made minimal use of Stan, and not really used Pymc3, but from a quick look, it seems Pymc3 is a bit more integrated than RStan. In RStan, you end up writing Stan code as an alien, wrapping the foreign syntax in quotes, and then shoveling the code as a string into Stan. There isn't really an R grammar for Stan as near as I can tell.
- Lofkin 11y ago
  Aside from the written in python making it more natural and extensible and amenable to messing around with the models, pymc 3 can sample directly from discrete parameters and STAN cannot. Pymc3 has more sampling routines.

baldfat 11y ago

This is an unnecessary fight. They both are great. I personally strongly prefer R over Python due to libraries and the flexibility. Others hate the flexibility of R and like Python's only one way to correctly do something. It comes down to preferences. I like the R is for statistical specifically. There are people who call R an aweful horrible language and it isn't. It has grown incredibly over the last 5 years and has major funding from many corporations for a reason. If it seems passionate for or against either language it is more an emotional response to the question. R works best for me and Python might be someone else's prefer solution.

Lofkin 11y ago
Ultimately Julia combines the strengths of both and much more. It is the future IMO, not R or python.
- hueving 11y ago
  way to miss the point. Part of a language selection for a real -world project is based on the libraries available for it. If I'm doing systems administration programming, there is a much higher probability of there being a python library to interact with BGP or namespaces than there is for Julia. Selecting Julia blindly is just as stupid as selecting R or python blindly. Language features are only a small part of the reason you choose a language for a large project.
  - apl 11y ago
    The Julia FFI for Python is absolutely excellent -- calling a particular Python library from Julia takes very, very little effort. As a language for scientific programming, Julia is way ahead of Python. So I'm not sure if this particular argument holds much water. https://github.com/stevengj/PyCall.jl https://github.com/stevengj/PyCall.jl
    - baldfat 11y ago
      I think it is a chicken or the egg problem with Julia right now. Julia is very much a positively viewed language that isn't being used much. There are not enough libraries (I looked 3 months ago) for me to do my work in Julia. There isn't enough users to make the libraries for my use case.

hessenwolf 11y ago

Python is better for programming, R is better for statistics. A lot of data analysis packages require a bit of both, so I usually use a bit of both, and C++ underneath when something has to run faster. To expand, R has every cool statistical model (outside finance) to hand. It's deadly buzz. It's finicky as hell to program in though. Classes are a bit crap. Brackets are all over the shop. Arrows? Really? Meh. Statistical models take ages to program and test, though, and the academic paper that was published last week has had the model as an R package for 6 months. Python is a beautiful programming language, especially for anything involving transferring data from here to there and possibly back again. I particularly like it when working with a diverse team of context experts, who would think nothing of laying a faecal nugget in your code-base, to save the time it takes to drink a cup of coffee. It's a lot harder to write crap code in Python than in many other languages. So, in our local technical landscape, R is used for prototyping, and is used in a slave fashion for running complex stats models. Python is used as the "Controller" of the program, and also for database type operations, web-scraping, calling QuantLib, ...