Python is an open source, interpreted, high-level programming language that offers an excellent approach to object-oriented programming. It is one of the most used languages among data scientists for various data science projects and applications. Python has a lot of capability for dealing with arithmetic, statistics, and scientific functions. It offers excellent libraries for dealing with data science applications.
Python's simplicity of use and simple syntax make it easier to adapt for those without a technical experience, which is one of the key reasons why it is extensively utilized in the scientific and research community. It is also more suited for rapid prototyping. Python is also preferred among ML scientists in terms of application domains. When it comes to areas such as building fraud detection algorithms and network security, developers leaned toward Java, whereas for applications such as natural language processing (NLP) and sentiment analysis, developers leaned toward Python, because it provides a large collection of libraries that help to solve complex business problems easily, as well as build strong system and data applications.
It employs beautiful syntax, making the applications easier to read.
It is an easy-to-use language, making it straightforward to get the application to operate.
The big standard library, as well as the community support.
Python's interactive mode makes it easy to test codes.
Python also makes it simple to modify the code by adding additional modules written in other programming languages such as C++ or C.
Python is an expressive language that can be embedded into programs to provide a customizable interface.
Allows developers to run their programs on platforms such as Windows, Mac OS X, UNIX, and Linux.
Most Commonly used libraries for data science :
Numpy: Numpy is Python library that provides mathematical function to handle large dimension array. It provides various method/function for Array, Metrics, and linear algebra. NumPy stands for Numerical Python. It provides lots of useful features for operations on n-arrays and matrices in Python. The library provides vectorization of mathematical operations on the NumPy array type, which enhance performance and speeds up the execution. It’s very easy to work with large multidimensional arrays and matrices using NumPy.
Pandas: Pandas is one of the most popular Python library for data manipulation and analysis. Pandas provide useful functions to manipulate large amount of structured data. Pandas provide easiest method to perform analysis. It provide large data structures and manipulating numerical tables and time series data. Pandas is a perfect tool for data wrangling. Pandas is designed for quick and easy data manipulation, aggregation, and visualization. There two data structures in Pandas – Series – It Handle and store data in one-dimensional data. DataFrame – It Handle and store Two dimensional data.
Matplotlib: Matplotlib is another useful Python library for Data Visualization. Descriptive analysis and visualizing data is very important for any organization. Matplotlib provides various method to Visualize data in more effective way. Matplotlib allows to quickly make line graphs, pie charts, histograms, and other professional grade figures. Using Matplotlib, one can customize every aspect of a figure. Matplotlib has interactive features like zooming and planning and saving the Graph in graphics format.
Scipy: Scipy is another popular Python library for data science and scientific computing. Scipy provides great functionality to scientific mathematics and computing programming. SciPy contains sub-modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, Statmodel and other tasks common in science and engineering.
Scikit – learn: Sklearn is Python library for machine learning. Sklearn provides various algorithms and functions that are used in machine learning. Sklearn is built on NumPy, SciPy, and matplotlib. Sklearn provides easy and simple tools for data mining and data analysis. It provides a set of common machine learning algorithms to users through a consistent interface. Scikit-Learn helps to quickly implement popular algorithms on datasets and solve real-world problems.