Table of Contents
What is Python ?
Python is a high-level, interpreted programming language, designed and created by Guido van Rossum in 1991. Python has an object-oriented approach aimed to help programmers to write clear and logical code. The language is dynamically typed and garbage collected in a general-purpose programming language. The language supports multiple programming paradigms, including object-oriented, structured, and functional programming. Python was conceived in the late 1980s as a successor of the ABC language. By 2000, advanced features like list comprehensions and garbage collection systems with reference counting were added to Python.
Why Do I Learn Python?
Python is an easy language with simple syntax. Python is becoming popular day by day as it is the major backbone of new technologies like data science, machine learning, and artificial intelligence. So several educational websites are offering the best Python courses.
What is Python Used For?
With a vast number of advanced features that Python offers a huge number of applications. Google, NASA, CERN, Yahoo, Wikipedia, are the major platforms that use Python for their development.
Let’s have a look at the major use of python:
- Web Development: Python offers pre-built libraries and frameworks such as Pyramid, Django, and Flask; web applications can be developed rapidly. A framework is made with common backend logic & several libraries help integrate protocol such as FTP, HTTP, SSL, and processing of XML, JSON, and much more. Python Frameworks provides unparalleled security, scalability, and convenience as compared to building websites from scratch
- Scientific Computing: Python has a list of science-friendly & science-specific libraries & is thus used for scientific research and computing.
Some popular Python libraries for Scientific Computing
- Astropy for Astronomy
- Biopython for biology & bioinformatics
- Graph-tool for static analysis of graphs
- Psychology for neuroscience & experimental psychology
- Data Science & Visualization: Data is a treasure in this era of technology. You can earn a lottery if you know how to extract relevant information from the data and calculate risks to increase profits. Python libraries like Panda, Numpy help in extracting information from the data. Also, Matplotlib, Seaborn, is a data visualization library that helps visualize data like plotting graphs.
- Machine Learning: Machine Learning is different from data science & so are the Python libraries for ML. Machine Learning is about training the computer & making it learn through past experiences or patterns using the data stored or creating algorithms through which the computer learns itself. ML offers applications like recommendation systems on Netflix or Amazon & speed recognition. Python plays a vital role in providing ML libraries & frameworks like Sci-kit learn, TensorFlow & more.
- Financial Industry: Python is much in demand all over the financial world. Python offers features like it is fast, robust, and secure, making its fastest-growing language in finance. Finance technologies in big banks like BOA are working towards transforming their legacy code to Python.
- Making Bots
- Python-rtmbot: popular bot framework for constructing slack bots with real-time messaging (RTM) API over WebSockets.
- GitHub provides resources for creating bots, including code snippets and useful tips.
- Errbot: is a chatbot for creating bots for Slack, Discord, Hipchat. Errbot aims to allow people to generate their programs by manipulating the provided Python source code.
- Data Mining: The process of analyzing large databases to construct tendency predictions refers to DM. It is a complex process and involves an analysis of social networks, crime imaging, etcetera. Python is considered one of the best languages to organize and clean data. Also, Python simplifies data analysis with the use of frameworks and algorithms. Popular frameworks for data mining include NumPy, SciPy, Sci-kit learn, Dask.
- GUI Based Desktop Applications: Graphical User Interface allows users to interact with computers using visuals like icons and images instead of text-based commands. Python enables us to design desktop applications by providing useful toolkits and libraries.
- Tkinter: a built-in Python interface that runs on all of the most popular platforms like Microsoft, Linux, and Mac OS X.
- PyGTK: a free toolkit to create graphical interfaces.
- WxPython: is a binder for the cross-platform wxWidgets and GUI toolkits.
- Kivy: is a Python library for generating mobile apps and multi-touch application software.
- Game Development and 3D Graphics
- Interactive games can be built with Python; it provides functionality and libraries for game development. Some of the Python libraries and frameworks for game development are:
- PyOpenGl: The library provides modules for producing fully featured games and multimedia programs.
- Panda3D: It is a wrapper for OpenGL programming.
- Blender: It is a tool for creating 3D graphic models.
- Arcade: It is a Python library for introducing 2D games into the world.
- Web Scraping Application: The scrapping of a large amount of data from the website proves to be useful later in several real-life processes like job listing, price comparisons, research and development, and much more. Python helps in this web scrapping of data with the library called BeautifulSoup.
- Business Applications: Business applications involve domains such as e-commerce, ERP, and many more, and hence require scalable, extensible, easy readable applications. Python fits well with such requirements. Platforms like Tryton are used to develop such business applications.
- CAD Applications: Computer-Aided Designing is a complex application and involves several objects and their representation functions to be taken care of. Python makes this complex application simple. Fandango is a popular CAD application.
- Embedded Applications: Python is C-based and can be used to create Embedded C software for embedded applications. RaspberryPi is a well-known application that uses Python for its computing as Python helps perform higher-level applications on smaller devices. The device can be used as a computer or like a simple embedded board to perform high-level computations.