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Comparing R Language vs Python

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  • Last Updated September 21, 2017

Comparing R language vs Python

Introduction:

Now a day’s analytics market has grown at a double speed. Today, the analytics software market is having numerous players and ranging from billion-dollar companies to a single person operated shops and businesses offering sophisticated and custom-based solutions to various communities as per their requirements. The analytics field is continuously growing, new tools and technologies have started coming on the market, causing the business analysts to master them for better career options.

Today, a number of analytical tools are available in the market, but the major competition is observed between R and Python.

Before diving into the main agenda of this article, let us first understand what R, Python is?

R:

R is the lingua franca of statistics. It is a procedural language that depends on a series of sequential subroutines. R is open source and cost-effective approach and the highly preferred programming language among many data Science. With its built-in packages and library functions. R is the most preferable language in the case of data and plot visualization which are crucial in the context of data analysis.

Python:

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Python is an open-source and multi-purpose programming language, gained immense popularity because of its data mining libraries, functions and its active community that can be widely used to perform any kind of statistical operations.

R Vs Python:

Comparing attributes:

  1. Availability / Cost

  2. Data handling and management

  3. Graphical capabilities

  4. Big Data Applications

  5. Deep Learning Support

  6. Learning curve

  7. Career Opportunities

  8. Customer service support and Community

1. Availability / Cost:

R and Python both are open source tools and accessed for free of cost and having a number of open community forums and support development teams. Which makes R and Python widely popular among many startups and well-established company

2. Data handling & management:

R: R computes everything in RAM (Random access memory), which makes it difficult to run even a small task.

Python:  Packages like Plyr, DPlyr, etc. and extensions like NumPy, Panda, etc. helping python to a smoother approach for data handling & management.

They no longer have differentiation in data management. Hadoop and Spark Integrations are added and supporting Cloudera and Apache Pig.

Graphical Capabilities:

R: It steals the limelight for making data visualization and graphics more appealing with packages like RGIS, Lattice, GGPlot, etc.

Python: Python having decent visualization feature but as compare to R Its bit less.

Big Data Applications:

R: R integrates well with Hadoop, they lack the feasibility required for machine learning analytics.

Python: Python having the best-suited capabilities required to integrate well with Hadoop, and enables to design various machine learning algorithms.

Deep Learning support:

R: R has recently added support for kerasR and keras packages. In R act as an interface to the original Python package and Keras.

Python: Python has had great advancements in the Deep learning field with his its numerous packages like Tensorflow and Keras.

Learning Curve:

R: It’s a low-level programming language designed to perform data analysis with more user-friendly behavior.

Python: Python is a scripting language. It is highly known for its simplicity in the programming environment. The syntax and analytics-friendly libraries make ‘Python’ more choice for the data scientists. 

Frequently Asked Python Interview Questions & Answers

Career Opportunities:

R: R is highly famous across startups and middle-level organizations and MNCs. Which shows a better opportunity.

Python: Python also highly famous across startups and middle-level organizations and MNCs. Which shows a better opportunity.

Customer Service support & community:

R & Python: R & Python does not have customer service support but having many online communities. So, in R if you have any problem you will solver on your own or you take a help of online community people.

Parameter R Python
Cost More preferable More preferable
Ease of learning Good Good
Data handling capacities Preferable Preferable
Graphical capabilities Preferable Preferable
Advancement in tools Preferable Preferable
Job opportunity Preferable Preferable
Customer Service Support & community Good Good
Deep Learning Support Good Preferable

Advantages

R Python
Big communities who creates libraries Scalability
Free to use General Purpose Language
Early Adopter in Explanatory and Predictive modeling Easy to learn
Easy to connect to data sources, including no SQL and web scraping Good in Machine Language
  Big community
  Free to use

Disadvantages:

R Python
Can be slow with big Datasets Not as strong in explanatory modeling
Steep learning Curve No user interface
No official support No official Support
No user interface  

Conclusion:

To Sustain in analytic market mandatory to expertise in the high-level coding and programming. With their open community forums and regular updates R and Python trending all the business in the market.

Fresher:

  • Fresher having good knowledge of math and statics R is preferable to learn.
  • Fresher good on programming and coding Python is the best option.

Experienced:

  • If you are experienced in analytics, already spent time in the industry, you should try and diversify your expertise be learning a new tool.
  • If you want to an expert in the analytical field you should know any two-analytical technology.

Industry:

  • For Startup, freelancing R/Python are Recommendable.
  • Researchers and statistician’s R is recommendable because it helps in heavy calculations.
 
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