Overview of the programming and coding environments
Introduction to Integrated Development Environments (IDEs) available for Python
History of Python
Developed by Guido van Rossum in the late 1980s
Developed at the National Research Institute for Mathematics and Computer Science, Netherlands
Major version changes: 3 versions with Python 3 being the latest; current version is 3.8
Characteristics of Python
Programming Paradigms
Supports multiple paradigms:
Functional
Structural
Object-Oriented Programming (OOP)
Typing and Error Handling
Dynamically typed: Type safety checks are done at runtime
Handles type-based errors effectively
Automatically deallocates unused objects
Late binding: Methods are looked up by name at runtime
Design Philosophy
Guided by the 20 aphorisms known as The Zen of Python by Tim Peters
Examples of aphorisms: "Simple is better than complex" and "Complex is better than complicated"
The 20th aphorism is left for Guido van Rossum to define
Interpreters and Libraries
Standard interpreter: CPython, managed by the Python Software Foundation
Other interpreters: JPython (Java), IronPython (C#)
Standard libraries written in Python; emphasizes high readability
Supported across platforms: Linux, Windows, Mac
Comparison with Java
Java: Statically typed - type safety checks at compile time (static compilation)
Python: Dynamically typed - checks at runtime, resulting in less verbose code and more readability
Python is more suited for data science due to libraries and tools availability
Installation and Coding Environment
Python can be used in:
Terminal
Command prompt
Text editor
Integrated Development Environment (IDE)
Python version 2 support ended in 2020; support for version 3.6 and onwards continues
Basic Python can be downloaded from the official website; offers a terminal for basic commands
Integrated Development Environments (IDEs)
Purpose of IDEs
Tools to support software development for various languages
Simplifies software development process
Common IDE features:
Source code editor
Compiler
Debugger
Syntax and error highlighting
Code completion
Version control features
Popular Python IDEs
General IDEs: Sublime Text, Atom
Python-specific IDEs:
PyCharm: Professional and community versions; suitable for data science; complex interface
Spyder: Open-source; similar to MATLAB; good for data science; features include syntax highlighting, code completion, and debugging
Jupyter Notebook: Web app for creating and manipulating documents (notebooks) with code, text, and plots; ideal for educational and presentation purposes; lacks IDE features but provides descriptive text capabilities
Choosing the Right IDE
Selection depends on individual requirements and comfort level
Explore various IDEs to find the best fit for your needs
No definitive good or bad IDE; each serves different purposes
Conclusion
Importance of understanding Python's history, features, and available tools
Encouragement to experiment with different coding environments and IDEs in future practices.