At
present, big data is one of the hottest trends in enterprise
application development. Most organizations nowadays need custom
applications to collect, store, analyze and exchange huge volumes of
data in a fast, efficient and secure way. The software developers
have option to write these applications in a number of high-level
programming languages including Ruby and Python. Both Ruby and
Python are object-oriented, dynamic, and general-purpose programming
languages.
In
addition to supporting functional programming, Ruby allows developers
to take advantage of features like blocks, mutable strings, and
hashable/unhashable types. Likewise, Python also comes with several
useful features including internal functions, modules, and rich set
of data structures. Also, it handles namespaces in a more efficient
way. But a number of surveys indicate that a large percentage of data
scientists prefer Python
to Ruby.
Why
Data Scientists Prefer Python to Ruby?
Simple
Syntax Rules
In
addition to being easy
is python to learn for a first time developer, Python also has
simple, precise and efficient syntax. So it becomes easier for users
to express concepts without writing longer lines of code. Also,
Python, unlike Ruby, requires developers to follow guidelines related
to layout, indentation and whitespace usage strictly. So it makes it
easier for data scientists to build and manage a variety of custom
applications without putting extra time and effort.
Faster
than Other Programming Languages
Earlier,
programming languages like Matlab, Octave and Stata were used
widely by data scientists. These programming languages provide
features for text filing, data visualizations and file parsing. But
Python is much faster and more scalable than these conventional
programming languages. Also, it helps data scientists to keep project
overheads under control as an open
source programming language.
Option
to Include Graphics
Often
data scientists are required to present the data analysis in a clear
and easy-to-understand way. So these professionals explore ways to
boost data visualization by using a variety of graphics. Python
enables developers to include graphics in data analysis and reports
through various data visualization libraries and application programming interfaces (APIs). At the same time, the data scientists
can also use Python for connecting different units of a business, and
make the data accessible throughout the organization.
Availability
of Many Data Analysis Libraries
The
users can further simplify data analysis using Python libraries like
SciPy, NumPy, SciKit, Pandas and Matplotlib. SciPy is designed with
features to simplify technical and scientific computing, while NumPy
makes it easier for data scientists to integrate and use other Python
libraries. Likewise, Panda facilitates data munging by
providing features like support for automatic data alignment and
option to handle missing data. Also, it helps users to work
efficiently with data collected from various sources and indexed in a
number of ways.
As
a machine learning library, SciKit provides a variety of algorithm
related to regression, classification and clustering. At the same
time, Matplotlib is designed as a 2D
plotting library with interactive features. Its features enable users
to publish quality figures in different formats and across multiple
platforms. The data scientists can further integrate these Python
libraries seamlessly, and use them together to collect, manage
and analyze huge volumes of data more efficiently and quickly. These
data analysis libraries make many data scientists to prefer Python
over Ruby.
Large
and Active Community
The
members of the large community also contribute immensely towards
making Python the language of choice for data scientists. The
thriving Python community
includes many data scientists and data analysts. Such members have
been continuously developing new data analysis library for the
programming language. At present, the data scientists can take
advantage of several data science or data analytics libraries
including NumPy, SciPy, Statsmodels, Pandas and SciKit learn.
The
data scientists still have option to use Ruby
for specific purposes. But the features provided by Ruby enable
developers to build a variety of modern websites and web application
rapidly. On the other hand, Python provides specific features to
effectuate collection, storage, analysis and exchange of large chunks
of structured and unstructured data more efficiently and securely.
