Why do Many Data Scientists love using Python over Ruby?

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.