A comparison of the advantages and disadvantages of Python and R for Machine Learning.

1. Comparing Python & R

2. Cited from “Python vs R For Machine Learning”
authored by Vinita Silaparasetty

3. Advantages of Python & R
•Great for Visualisation: One of the most
prominent features of R is its beautiful
visuals.
•Portable: It can be run on any operating
system.
•Does not require a programming
background: Unlike Python, there is no
major coding involved.
•Open source: IT is free to use and anyone
can contribute to its development.
• High-level : It is is user-friendly and lets the
user focus on logic.
• Simplicity: Less lines of code are required
to complete a task.
• Easy to understand syntax: The syntax or
‘formatting’ of the programme is not very
complex.
• Intuitive: Most Python commands are easy
to remember.
• Multi-purpose: Python can be used to
perform machine learning operations on
data as well as to build applications.
• Flexibility: In Python, the objects have a
type but the variables do not.
Python R

4. Advantages of Python & R
Python
• Powerful Extensive Libraries: A vast collection of powerful computing libraries such as
lumpy, script and scikit-learn are available that make machine learning easy.
• Reusable: Previously written code is easily accessible via the ‘import’ command.
• Open source: It is free to use and anyone can make contributions to improve it.
• Interpreted: The source code can be directly run on the computer without compilation.
• Embeddable: Python code can be embedded within the code of other programming
languages such as C.
• Portable: It can be run on any operating system.

5. Disadvantages of Python & R
• Obscure Syntax: R does not have a well
defined ecosystem as compared to
Python. (Analytics Trainings, 2012)
• Steep Learning Curve (as compared to
Python): Since R is designed mostly for
statisticians with no programming
experience, it may take a while for the
user to remember the libraries. (Analytics
Trainings, 2012)
• Limited capacity: It is unable to handle
very large datasets.
• Speed Limitations: It is slower than
Python. (Analytics Trainings, 2012)
• Lack of security: It does not have built in
security features.
• Not as good for statistical
visuals: While Python has
libraries such as Seaborn to
create beautiful visuals, they
are not print ready.
• Memory Intensive: Python
consumes a lot of memory
space in order to work on
large datasets.
Python R

6. V I N I T A S I L A P A R A S E T T Y
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