Python in Data Science
The programming requirements of data science demands a very versatile yet flexible language which is simple to write the code but can handle highly complex mathematical processing. Python is most suited for such requirements as it has already established itself both as a language for general computing as well as scientific computing. More over it is being continuously upgraded in form of new addition to its plethora of libraries aimed at different programming requirements. Below we will discuss such features of python which makes it the preferred language for data science.
- A simple and easy to learn language which achieves result in fewer lines of code than other similar languages like R. Its simplicity also makes it robust to handle complex scenarios with minimal code and much less confusion on the general flow of the program.
- It is cross platform, so the same code works in multiple environments without needing any change. That makes it perfect to be used in a multi-environment setup easily.
- It executes faster than other similar languages used for data analysis like R and MATLAB.
- Its excellent memory management capability, especially garbage collection makes it versatile in gracefully managing very large volume of data transformation, slicing, dicing and visualization.
- Most importantly Python has got a very large collection of libraries which serve as special purpose analysis tools. For example – the NumPy package deals with scientific computing and its array needs much less memory than the conventional python list for managing numeric data. And the number of such packages is continuously growing.
- Python has packages which can directly use the code from other languages like Java or C. This helps in optimizing the code performance by using existing code of other languages, whenever it gives a better result.
Our aim is here to learn some basics of Python before going into complex coding for data science and statistics. Let’s look at some basic concept in python.