0Most of the students usually ask me this question before they join the classes, whether to go with Python or R. Here is my short analysis on this very common topic.
If you have interest/or having a job requirement of data analysis and visual presentation of data using open source languages, then the search become so narrow and you have the question whether to learn Python or R.
Here is my first thought:
To start data analysis projects, both Python and R are easy-to-use, free and does not need any heavy expertise to implement.
The installation, configuration and package management for both languages is simple and efficient. In general, for a newbie in data science development, it makes sense to be unsure whether to learn R or Python first.
In this article, I will highlight some of the differences between R and Python, and how they both have a place in the data science and statistics world.
- Python is a general purpose interpreted programming language, which can be used for normal software development,web programming ,data mining application development,statistical data analysis,data visualization and ETL programming.But R has been developed by keeping the needs of statisticians in mind, thereby has limited area of development.
- R can be difficult to get into if you have experience with a previous programming language: it isn’t constructed by computer scientists for computer scientists. Unlike Python which is built to have a simple syntax, R has a tricky syntax with a bit of a steep learning curve.
- R is mainly used when the data analysis task requires standalone computing or analysis on individual servers, but Python supports web programming, distributed data system management, support for the most popular Block Chain database management and many free python packages designed for specific needs like: posting daily status updates in Facebook, Data file version management, Wikipedia content viewer package etc. It means, you can code the complex requirements in just few lines, just by importing appropriate package.
- R has some specific IDE for program development. The most popular one is the RStudio.Python is not bound to a particular IDE.Dozens of Python IDEs are available like: Anaconda, PyScriptor, WingIDE, Spyder, GlueViz, PyCharm, PyDev, IDLE,Komodo edit. Python has been used to write all, or parts of, popular software projects like dnf/yum, OpenStack, OpenShot, Blender, Calibre, and even the original BitTorrentclient, Youtube is the best example of python’s capability.
- In data science point of view,both R and Python are powerful.Below is the statistical figures of the comparison.Despite the above figures, most people prefer Python instead of R, due to its flexibility and simplicity.
- R programming is basically meant for statistical programmers,who deal with high level of statistical data analysis.But Python is general-purpose programming language that can also be used as statistical data analyzer.Just use some packages !
- The programming construct of R is based on “Vector Concept”.R treats each and every entity as a vector.Hence, some hands-on for vectors needs to be done to understand the variable control strategy in R.Python is a free form language which completely based on OOP concepts. Each entity is considered either as a variable with some valid values assigned or an object which is instantiated from a class.
- R programming construct is a little bit complicated as compared to Python.Let’s take an example: Finding mean in R:- sapply(nba, mean, na.rm=TRUE)
- Finding mean in Python:- np.mean() The built-in functions in Python are easy-to-use and simple. No need to pass too many parameters.
- R programs run slower than Python. When a sensitive production environment like Telecom Billing Engine scenario is considered,it adds a negative feedback. Python is faster and fault-tolerant. The Python apps are light-weight,though they have the capacity to process Gigabytes of data.
- Python supports Hadoop programming construct i.e. you can write a python program which can use the virtues of HadoopMapReduce construct for exploratory data analysis. However, R does not support Hadoop.
- You can link any open source ETL tool like Pentaho, Kettle, CloverETLetc.If you are an expert in the ETL concepts, you can use the “PyGramETL” package of Python for building your own ETL tools in Python !!
- Conclusion:- Python is versatile, simple, easier to learn, and powerful because of its usefulness in a variety of contexts, some of which have nothing to do with data science. R is a specialized environment that looks to optimize for data analysis, but which is harder to learn.
