Why Python has become the favorite programming language for data scientists

The high-level general purpose programming language, Python, has emerged as a superb programming language, all because of its data science pipeline execution, data focused libraries and rich data structures. Being able to be applied for several classes of problems, this programming language has become the most favored for data scientists for executing comprehensive and qualitative data analysis.

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We take a look at 5 most pivotal reasons why it is the best language for data analysis:

  1. Enhanced Readability

The ease of readability in Python makes it a better language to work upon, in comparison to other scripting languages. The whole syntax architecture is very well thought-out and makes for great collaborative implementation. The basic principality of the Python is to make its code easily reusable, which has been well admired by the developers. The use of indentation also makes it highly beneficial in terms of readability factor.

  1. Data Structures

Python has built-in correct data structures for the algorithm, like sets, lists, tuples, strings, dictionaries and more, which holds data objects and items in an ordered manner, essential for optimal development implementation. These features make it Python highly scalable for research-oriented coding.

  1. Extensive range of libraries

One of the aspects which is highly admired by the developers is the availability of a wide range of libraries in Python, like IPython, NumPy/SciPy, Cython, PyTables, PyQt, TreeDict, and many more, enabling data scientists to do more and better functions. This built-in database functionality lets the data scientist to explore and make use of rich features for executing different tasks, without having to switch to another language.

  1. Language Incorporation

The ease of calling other language functions from Python (through the MATLAB engine) provides high-end functionality. This interface universality lets the scientist to code and develop required programs in an advanced and efficient manner.

  1. In-Built testing framework

The low hurdle-to-entry, built-in testing framework in Python makes the whole workflow process faster, with implementation of test cases writing. This encouraged process for good tests enhances the overall experience to develop efficient and advanced programs.

Thus, these functionalities, among others, make this highly advanced and efficient programming language as the favorite for data scientists. Anyone willing to incorporate high-end programming in data science and research should undertake a quality Python programming training, so as to build their knowledge in Python applications, which is becoming highly favored by industries for data science application.

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