Python vs R for Machine Learning



Ok yes, the open debate about which programming language, Python or R, is better for Machine Learning? In this topic, I am thinking about machine learning and Artificial Intelligence as incorporated into the term data science. This is practically the data science likeness tabs versus spaces for programming engineers, in any event at the season of this written work.

Some genuine critical contrasts to consider when you are picking R or Python more than each other:


Robustness & Production Ready: The genuine distinction amongst Python and R comes to being production ready. Python, accordingly is an undeniable programming language and numerous companies utilize it in their production systems. R is a statistical programming software favored by numerous scholarly world and because of the ascent in data science and accessibility of libraries and being open source, the industry has begun utilizing R. Huge numbers of these companies have their production systems either in Java, C++, C#, Python and so forth. 

Assuming you would need to integrate your machine learning algorithms into some sort of interface that is communicating with other code, written by different software engineers, Python may be the better decision. R can be utilized for fast prototyping or to take care of a particular issue, yet Python will be simpler to keep up and scale over the long haul. So Python fits all the more normally into a mind boggling coding condition.

Python 2 Points, R 1 Points. Winner Here is : Python


Libraries: Both the languages have colossal and dependable libraries. R has around approx. 5K libraries taking into account numerous domains while Python has some amazing packages like Pandas, NumPy, SciPy, Scikit Learn, Matplotlib.

  • theano > Lasagne > nolearn: Libraries for neural networks – they can be trained on GPU (nvidia, CUDA is required) without any adjustment this is a Big plus in Python.

  • sklearn: General learning algorithms

So Python gets 3 Points , R gets 2 points. Winner Here is : Python


Development : Both the languages are interpreted languages . Many say that python is anything but difficult to take in, it’s practically similar to perusing english (like a kid reading English) yet R requires more introductory studying exertion.

Python 4 Points, R 3 Points. Winner Here is : Python


Integrated Development Environment (IDE):

Both Python and R have extensive command line interfaces (CLIs) which can be utilized through a terminal running the language’s read– eval– print loop (REPL) interactive environment.

Unlike R, Python doesn’t generally have a to some degree “official” IDE like R Studio. Three of the most widely recognized Python IDEs I have made a case study for Top 3 Free Best Python IDEs I listed, albeit any content tool can be viewed as an IDE similarly as with R. Python code and contents can likewise be run through the Python REPL condition and CLI.

Python 2 Points, R 3 Points. Winner Here is : R


Speed: R programming at first had issues with massive calculations (say, as nxn matrix multiplications ). In any case, this issue is tended to with the presentation of R by Revolution Analytics . They have re-composed calculation serious operations in C which is amazingly very quick. Python being a high level language is moderately moderate. But I would also like to add that python is one of the top and best 3 languages supported by apache spark, which has blazing fast speeds.

Python 3 Points, R 2.9 Points. Winner Here is : Python


Visualizations : In data science, we much of the time tend to plot data to exhibit patterns to clients. Hence, visualisations turn into an essential criteria in picking a product and R totally murders Python in such manner. On account of Hadley Wickham for a mind blowing ggplot2 bundle. R wins undoubtedly.

Correspondingly python have ggplot (which I don’t utilize myself, since there are more pythonic choices, like seaborn), it can even do interactive visualization in the browser with bundles like bokeh

But R has extensive visualization capabilities when compared to python.

Python 2 Points, R 3 Points. Winner Here is : R


Managing with Enormous Data: One of the constraints of R is it stores the information in system memory (RAM). In this way, RAM limit turns into a constraint when you are taking care of Enormous Data. Python does well, however I would state, as both R and Python have HDFS connectors, utilizing Hadoop infrastructure would give generous performance improvement .

Along these lines I give , Python 5 Points, R 4 Points. Winner Here is : Python


Good community:

  • Python has 448,000+ questions on SO (R has 99,000+)

    The python community is really active and have a few great frameworks for ML and data mining etc.

Winner Here is : Python



Good support for testing your code in Python when compared to R.

Python has Very intuitive syntax to learn for anyone.

We can do end to end machine learning tasks such as gather data using a HTTP server written in Python

Licensing: Most of the aforementioned wonderful R libraries are GPL (e.g. ggplot2data.table). This prevents you from distributing your software in a proprietary form. Unlike in Python most libraries have business-friendly distribution licenses, such as BSD or MIT.


Finally look at the below Google Trends data from 2012-2017 Python is Ahead of R for ML:


Credit: kdnuggets



In view of 954 voters, demonstrate that in 2017 Python ecosystem system overwhelmed R as the main stage for Analytics, Data Science, Machine Learning.

While in 2016 Python was in second place (“Mainly Python” had 34% share versus 42% “For the most part R”), in 2017 Python had 41% share versus 36% for R.

Python in huge ways likewise expanded from 8.5% to 12% out of 2017, while the share who for the most part utilized different tools dropped from 16% to 11%.

In this way, both the languages  are similarly great. Therefore, depending on your area and the place you work, you need to smartly pick the correct language . The innovation world as a rule lean towards utilizing a solitary language.

Business clients (marketing analytics, retail analytics ) normally run with statistical programming languages like R, since they every now and again do brisk prototyping and build visualisations (which is quicker done in R than Python).


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