Python, originated in 1989, is a high-level, general purpose and interpreted dynamic programming language that focuses mainly on readability of code and has gained immense popularity right from the day of its launch. With growing popularity, Python now consists of multiple implementations including scripted in Java language for Java Virtual Machine, Jython and many more. 

Today, Python is extensively used by research scholars across the globe to perform research especially in computing and numeric domain. But with so many options out there such as MATLAB, GNU Octave etc, why use Python for conducting research in scientific computing at all? To know the reason behind this, we have conducted a survey among the research consultants offering the best PhD research guidance in Chennai and have enlisted few causes for the same. 

• Python has built-in that supports scientific computing -  SciPy ecosystem, a Python distribution, includes SciPy, a numerical computation package (NumPy) and  multiple independent toolkits which help in performing various scientific operations. SciPy ecosystem also includes Matplotlib for plotting graphs and 2D plots. 

• Python has bridges to Octave & MATLAB  -  Python programs enable you to call MATLAB as a computational engine by installing the MATLAB Engine API. Python uses Python distributions use packages like Pymatbridge, which supports MATLAB as well as Octave.

• Python is an extensible language -  For years, researchers have used Python wrappers for C/C++ programs. Python makes use of the C Foreign Function Interface for Python to interact with C code without any intermediate. Tools like SWIG makes this process an easier one to accomplish. Not just this, by using the Fortran to Python interface generator package, F2Py (now a part of NumPy), researchers can also call Fortran subroutines from Python.

• Python has an excellent input/output (I/O) options - Python has prolonged aided multiple options for I/O and several other additional packages that supports all kinds of I/O formats, including the streaming formats and the real-time ones.

• Python has powerful support for task automation - Python has in-built scripting features and several multiple packages that provide strong support for task automation. Tasks like automation of repetitive processes and conducting data logging are made easy with Python and consume less or no effort.

• Python can utilise a web front end -  Python packages like Flask and Django enables the researcher to generate and use this software as an API with a web front end. This functionality is useful while using a cloud-based infrastructure as a medium to access the high-performance computing (HPC) back end.

If you are willing to conduct research in scientific computation but do not have access to advanced features of this software, then visit some PhD guidance center consisting of this software and conduct your research. Further, if you need any guidance regarding the research or the software, consider taking help from research consultants providing PhD research guidance.