Introduction & Features of Python

History of Python

• Python was developed by Guido van Rossum, and released in Feb. 1991 as Version 0.9.0., at Centrum Wiskunde & Informatica (CWI) in the Netherlands as a successor to the ABC language. He named this language after a popular comedy show called ‘Monty Python’s Flying Circus’ (and not after Python-the snake).
• Python was developed & copyrighted by the Python Software Foundation (PSF), a non-profit organization.
• The latest version of Python is Python 3.9 released in Oct. 2020.
• The official website of Python is: https://www.python.org

Introduction of Python

Definition of Python

• Python is an advanced, free, pure object-oriented, high-level, cross-platform, general-purpose, and open-source programming language.

Characteristics of Python

• Python has a simple syntax.
• Python provides enhanced readability.
• Python is an interpreter-based programming language.
• Python has rich basic data types such as numbers (floating point, complex, and unlimited-length long integers), strings (both ASCII and Unicode), arrays & lists, and dictionaries.
• Variables in Python are strongly typed as well as dynamically typed.
• Python supports full object-oriented programming concepts.
• Python has automatic memory management.
• Various built-in and third-party modules can be used & available independently in the Python application, which can be imported as needed in the program. 
• Python can be treated procedurally, in an object-oriented way, or in a functional way.

Advantages of Python

• Python is an extensible language i.e., additional functionalities(other than what is provided in their core part) can be made available through modules and packages written in other languages (such as C, C++, Java, etc.)
• Python is a cross-platform language. It works equally on different OS platforms like Windows, Linux, Mac OSX, Raspberry Pi, etc. Hence Python applications can be easily ported across OS platforms.
• Python supports multiple programming paradigms including imperative, procedural, object-oriented, and functional programming styles.
• Python has a standard DB-API for database connectivity. It can be enabled using any data source (Oracle, MySQL, SQLite, etc.) as a backend to the Python program for storage, retrieval, and processing of data.
• Python has rich GUI toolkits i.e., The standard distribution of Python contains the Tkinter GUI toolkit, which is the implementation of a popular GUI library called Tcl/Tk. An attractive GUI can be constructed using Tkinter. Many other GUI libraries like Qt, GTK, WxWidgets, etc. are also ported to Python.
• Python can be integrated with other popular programming technologies like C, C++, Java, ActiveX, and CORBA.

Use/Applications of Python

• Python is a versatile and powerful programming language used in various domains due to its simplicity, readability, and extensive libraries.
• Python’s versatility and extensive ecosystem make it a go-to language for various applications, ranging from web development and data science to automation and machine learning.
• Its ease of use, combined with powerful libraries and frameworks, enables developers to build robust applications across different domains.
• The following are some of the key applications/uses of Python:-
  • Web Development
    • Python is used to create a commercial level of web applications and hence is widely used for web development through frameworks like Django, Flask, and Pyramid.
    • It helps in building scalable and secure web applications, content management systems, and web services.
    • Examples : Instagram, Pinterest, and Spotify use Python for their web backends.

  • Data Science and Analytics

    • Python is used to handle and analyze big data and perform complex mathematics(=Data science).
    • Python’s rich ecosystem of libraries like Pandas, NumPy, Matplotlib, and SciPy make it a popular choice for data analysis and visualization.
    • It is used for data manipulation, statistical analysis, data visualization, and even building complex models for predictive analytics.
    • Examples : Companies like Netflix and Airbnb use Python for data analysis to improve their recommendation systems and business insights.

  • Machine Learning and Artificial Intelligence

    • Frameworks/Libraries such as TensorFlow, Keras, Scikit-learn, and PyTorch enable the development of machine-learning models.
    • Python is extensively used in building neural networks, deep learning models, and AI applications such as image recognition, natural language processing, and autonomous systems.
    • Examples : Google’s TensorFlow and OpenAI’s GPT models are developed using Python.

  • Automation and Scripting

    • Python’s simplicity makes it ideal for automating repetitive tasks such as file manipulation, web scraping, and batch processing.
    • Libraries/Tools like Selenium, Beautiful Soup, and PyAutoGUI are commonly used for automation scripts.
    • Examples : Automating data entry tasks, sending automated emails, or creating bots for web scraping.
  • Scientific Computing
    • Python is used in scientific computing with rich libraries like SciPy, SymPy, and BioPython.
    • It is used in fields like bioinformatics, computational biology, physics, and chemistry for simulations, calculations, and modeling.
    • Examples : Python is used in the Large Hadron Collider(LHC, a circular tunnel that’s 27 kilometers long and 50–175 meters underground on the border between France and Switzerland. The LHC is the world’s most powerful particle accelerator and is operated by the CERN )Tunnel project at CERN for data analysis and simulation.
  • Game Development
    • Pygame is a popular library for game development in Python.
    • Python is used for developing simple 2D games and even as a scripting language for game engines.
    • Examples : Games like “Eve Online” and “Civilization IV” use Python for scripting purposes.
  • Network Programming

• • • Python provides libraries like Twisted and Asyncio for building networked applications.
    • It is used for developing network tools, building network servers, and handling network protocols.
    • Examples : Python is used in building network security tools, monitoring systems, and handling communication between distributed systems.
  • Desktop GUI Applications
    • Python supports GUI development through frameworks like Tkinter, PyQt, and Kivy.
    • It is used to create cross-platform desktop applications with graphical interfaces.
    • Examples : Applications like Dropbox and BitTorrent have been developed using Python.
  • Embedded Systems
    • Python is used in embedded systems to control hardware, especially with microcontrollers like the Raspberry Pi.
    • Examples  : Python is often used in robotics, IoT (Internet of Things) projects, and for creating prototypes for hardware devices.
  • Education and Training
    • Python’s simplicity and readability make it a preferred language for teaching programming and computational thinking.
    • Examples : Many universities and online courses use Python as the introductory programming language in their curriculum.
  • Cybersecurity
    • Python is used in cybersecurity for writing scripts to detect vulnerabilities, automate tasks, and create security tools using related rich libraries.
    • It is used in penetration testing, malware analysis, and developing security automation scripts.
    • Examples : Tools like “Scapy” for network packet manipulation and “SQLMap” for SQL injection testing are written in Python.
  • Software Development
    • Python is used for developing software tools, build systems, and even IDEs (Integrated Development Environments).
    • Examples : Python itself is used in the development of popular IDEs like PyCharm and Spyder, and tools like SCons for build automation.
  • Blockchain and Cryptography
    • Python has rich libraries like PyCryptodome and Blockchain for developing cryptographic applications.
    • It is used to create and test blockchain applications, develop smart contracts, and implement cryptographic algorithms.
    • Examples : Python is used for creating blockchain prototypes and implementing cryptocurrency-related applications.
  • DevOps and Cloud Computing
    • Python is widely used in DevOps for automation, configuration management, and cloud computing.
    • Python-based tools like Ansible for configuration management and Boto3 for interacting with AWS services are commonly used.
    • Examples : Python scripts are used to automate cloud resource management and deploy applications in cloud environments like AWS and Google Cloud.

Common Python Tools & Frameworks

• Python IDE Tools: Pycharm, IDLE (Integrated Development and Learning Environment), Visual Studio Code, Sublime Text 3, Atom, Jupyter, Spyder, PyDev(IDE for Eclipse), Thonny, Wing, PyLin etc.
  • IDE for Beginner users – IDLE & Thonny are good options.
  • IDE for Intermediate users – PyCharm, VS Code, Atom, and Sublime Text 3 are good options.
  • IDE for Data Science Work – Jupyter Notebook, Spyder, PyCharm professional(Paid).
  • IDE for Web Development – VS Code, PyCharm professional(Paid).
  • IDE for Scripting work – PyCharm Community(Free), Atom, PyDev, Sublime Text 3. 
• Python Web Development Framework: Django, Pyramid, Flask, Bottle, Tornado, web2py etc.
• Python Automation Testing Python tools: Selenium, Robot Framework, TestComplete etc.
• Python Web Scraping Python tools: Beautiful Soup, LXML, Scrapy, etc.
• Python GUI Development Tools: Tkinter, PyGObject, PyQt5, PySide, Kivy, wxPython, Libavg, PySimpleGUI, PyForms, Wax, etc.
• Python Scientific and Numeric(Data Science & Machine Learning) Tools: Theano, Scikit-Learn, SciPy, Pandas, IPython, Keras, etc. 
• Python Software Development Tools: Buildbot, Trac, Roundup, etc.
• Python System Administration Tools: Ansible, Salt, OpenStack, etc.